Monty  Boehm

Monty Boehm


Artificial Intelligence in Cybersecurity

Overview of AI in Cybersecurity

Current Technologies put the organization's cyber security at risk. Even with the new advancements in the defense strategies, security professional fails at some point. Combining the strength of Artificial Intelligence in cyber security with the skills of security professionals from vulnerability checks to defense becomes very effective. Organizations get instant insights, in turn, get reduced response time. The type of attacks we are prone to currently being -

The primary targets of listed cyber attackers or threats are enterprises, government, military, or other infrastructural assets of a nation or its citizens. The volume and advanced cyber-attacks have increased as mentioned earlier. These reasons require the incorporation of Artificial Intelligence with existing methods of cybersecurity to appropriately analyze and reduce the occurrence of cyber-attacks.

Why do we need AI Cybersecurity Detection systems?

  • The Rule-based detection systems for the handling of false positives results while handling attacks.
  • Hunting of threats efficiently.
  • Complete analysis of threat incidents and investigation.
  • Threat forecasting
  • Retrieve the affected systems, examine the root causes of the attack, and improving the security system.
  • Monitoring of security.

What are the core capabilities of the AI based Cybersecurity System?

Please make sure your AI cyber security Tools for your organization have the below-defined core capabilities;

System Security

Data Security

  • Security Analytics
  • Threat Prediction
  • ML for Cyber
  • Social Network Security
  • Insider Attack Detection

Application Security

  • FinTech and Blockchain
  • Risk and Decision making
  • Trustworthiness
  • Data Privacy
  • Spam Detection

AI Cyber Security Analytics Solutions for Enterprises

Below defined are the advanced Artificial Intelligence cyber security Analytics for the Enterprises

  1. Perspective Analytics: Determination of the actions required for analysis or response.
  2. Diagnostic Analytics: Evaluation of root cause analysis and modus operandi of the incidents and attacks.
  3. Predictive Analytics: Determination of higher risk users and assets in the future and the likelihood of upcoming threats.
  4. Detective Analytics: Recognition of hidden, unknown threats, bypassed threats, advanced malware, and lateral movement.
  5. Descriptive Analytics: For obtaining the current status and performance of the metrics and trends.

AI-powered Risk Management Approach to Cybersecurity

  • Right Collection of Data.
  • Representation Learning Application.
  • Machine Learning Customization.
  • Cyber Threat Analysis.
  • Model Security Problem.

How Machine Learning and Deep Learning is helping in Cybersecurity?

ClassificationFor determining whether the security event is reliable or not and belongs to the group or not.Probabilistic Algorithms such as Naive Bayesian and HMM Instance-based algorithms such as KNN, SVM, and SOM. Neural Networks Decision Trees
Pattern MatchingDetection of malicious patterns and indicators in large datasets.Boyer Moore KMP Entropy Function
RegressionDetermination of trends in security events as well as prediction of the behavior of machines and usersLinear Regression Logistic Regression Multivariate Regression
Deep LearningCreating automated playbooks based on past actions for hunting attacks.Deep Boltzmann Machine Deep Belief Networks
Association RulesAlerting after detecting similar attackers and attacks.Apriori Eclat
ClusteringDetermination of outlier and anomaly. Creation of peer groups of machines and users.K-means Clustering Hierarchical Clustering
AI using NeuroscienceAugmentation of human intelligence, learning with each interaction to proactively detect, analyze, and provide actionable insights into threats.Cognitive security

The algorithms mentioned above have some limitations due to this, they are not able to work appropriately for security analytics. Therefore, some primary techniques need to be implemented for performing security analytics.

Specialized Knowledge

Security analytics is a complex task that requires specialized knowledge of risk management systems, log files, network systems, and analytics techniques.


Statistics, machine learning, and mathematics behind every technique and the reasons for choosing a specific technology over others are lost or forgotten once a choice is made. With rules-based systems, the sheer quantity of rules generates a cognitive burden that makes blocks comprehensive understanding. Finally, these outputs in systems are hard to capture and improve only incrementally over time.

How is Analytics with Artificial Intelligence supporting Cybersecurity?

Analytics of any kind starts with Data collection. Below are the various data sources from where data is collected and then analyzed.

Type of DataCategoryDescription
User DataUBA ProductsCollection and analyzing user access and activities from AD, Proxy, VPN, and applications.
Application DataRASP ProductsCollection and analysis of calls, data exchange, commands along with the WAF data for installing the agents on the application.
Endpoint DataEDR ProductsAnalyzing the internal endpoints such as files, processes, memory, registry, connections, and many more by installing agents.
Network DataNetwork Forensics and Analytics ProductsCollecting and analyzing the packets, net flows, DNS, and IPS data by installing the network appliance.


Performance Attributes Solutions for Cyber Security

Cyber Security Analytics Solutions - XenonstackIt relates to the performance quality attributes

Unnecessary Data Removal

The subset of event data that is not useful for the detection process is taken as redundant data. Therefore, data is removed so that performance could be increased.Data Cleaning Process - XenonStackAs shown in the figure, after the removal of unnecessary data, the data is forwarded to the data analytics component to detect cyber attacks. Finally, the results are visualized using visualization components.

Feature Extraction and Selection

The feature extraction and feature selection processes allow parallel processing abilities to increase the speed of the selection and extraction process. Then, the extracted feature dataset is forwarded onto the data analysis module that performs a different operation to analyzes the decrease in the size of the dataset to identify cyber-attacks. In the situation of an attack, alerts are provoked that can be visualized by the user (e.g., network administrator or security expert) using the visualization component. Once these attack alerts come under notice, an enterprise or user can take significant steps to mitigate or prevent the effects of the attack.Feature Extraction and Selection Process - XenonStackData Cutoff

The data cutoff component imposes the cutoff by neglecting security events that emerge after the connection of a network or process has reached its already defined limit. Any security event that emerges after the predefined limit does not contribute undoubtedly to the attack detection process, therefore, analyzing these types of security events implies an extra burden on data processing resources without any recognizable gain. The data storage entity can store the security event data left after cutoff. The data analysis module reads the stored data to analyze it for detecting cyber attacks. In the end, the results of the analysis are visualized to a user through a visualization entity, which allows a user to take vital action upon the arrival of every outstanding alert.Securing the data -XenonStackParallel Processing

The data collector entity captures security event data from different resources depending on the different types of security analytics and security requirements of a specific enterprise. The data collector delivers the captured data to a data storage entity, which stores the data. There are many ways to store data such as Hadoop Distributed File System ( HDFS), Relational Database Management System (RDBMS), and HBase. To apply parallel processing, the stored data needs to be distributed into fixed-size blocks (e.g., 128MB or 64 MB). After partitioning, data is imported in the data analysis component through different nodes working in parallel based on the guidelines of a distributed framework such as Spark or Hadoop. The result received by the analysis is shared with the user through the visualization component.

Parallel Processing Solutions - XenonStack

ML and DL algorithms for Enabling Artificial Intelligence Cybersecurity

The data collection entity captures security event data for the training process of a security analytics system. The training data can be grabbed from sources within the enterprise where an order is supposed to be deployed.

Today’s customer wants to get detailed insights about the sectors with the most number of attacks, their cost, yearly analysis of security incidents. Taken From Article, Automating AI and ML models in Cyber Security

After gathering the data for training, the data preparation component starts the process of preparing the data for model training by applying various filters. After that, the selected ML algorithm is implemented in the prepared training data to train an attack detection model. The time which is taken by the algorithm to train a model (i.e., training time) alters from algorithm to algorithm.Overview of ML and DL Algorithms - XenonStackAfter the training of the model, it is tested to investigate whether the model can detect cyber attacks. For model testing, data is collected from the enterprise. The data which is for testing is filtered through the data preparation module and imported into the attack detection model, which is used to analyze the data for identifying the attacks on the basis of the rules which are learned during the phase of the training. The time is taken by an attack detecting model to conclude whether a specific stream of data relates to an attack (i.e., decision time) depends upon the implemented algorithm. The result received by the data analysis is visualized to the user through a visualization component.

Accuracy in Security Models

This section includes accuracy quality attribute:

Alert Correlation

The data collection component grabs security event data from different resources after that; collected data is then stored in the data storage and copied to the data per-processor module for applying pre-processing techniques on the raw data. The data which is pre-processed is ingested into the alert analysis module, which performs analysis on the data for identifying attacks. It is necessary to signify here that the Alert analysis module analyzes the data in a deserted fashion (without seeing any contextual information) anomaly-based or either using misuse-based analysis or both. The generated alerts are forwarded to the alert verification module, which uses different techniques to identify whether an alert is falsely positive. The warnings identified as false positives are neglected at this level.

The bright and well-arranged alerts are then forwarded to the alert correlation module for further analysis. After that, the alerts are correlated (i.e., logically linked) using different techniques and algorithms such as rule-based correlation, scenario-based correlation, temporal correlation, and statistical correlation. The Alert correlation module synchronizes with data storage for taking the required contextual information about alerts. The results of the correlation are liberated through the visualization module. Finally, either an automated response is developed, or a security administrator performs the analysis of the threat and responds accordingly.

Attacks can originate internally due to malicious intent or negligent actions or externally by malware, target attacks, and APT. Taken From Article, Anomaly Detection for Cyber Network Security

Signature Based Anomaly Detection 

The data collection component collects security-relevant data from different resources. After that, the collected data is stored by the data storage module. Next, data is imported into the signature-based detection component that performs the analysis of the data to detect patterns of the attack. For such analysis, this component provides the advantage of the pre-designed rules from the database of the states that identify patterns of the attack. If any match is detected, an alert is directly generated through a visualization module.Signature Based Anomaly Detection - XenonStackIf the signature-based detection component does not identify any pattern of attack in the data, the data is passed to the anomaly-based detection component for detecting unknown attacks that cannot be identified by the signature-based detection component. An anomaly is defined as the unusual behavior or pattern of the data. This particular indicates the presence of the error in the system. Taken from Article, Log Analytics, Log Mining and Anomaly Detection with Deep Learning

The anomaly-based detection module analyzes the data using algorithms of machine learning to identify deviations from normal behavior. When an anomaly (deviation) is identified, an alert is produced through the visualization module. At the same instance of time, the anomaly is defined in the form of an attack pattern or rule and forwarded to the database of the rules. Using this way, the rules database is continuously updated to enable the signature-based detection component to detect a variety of attacks.

Attack Detection Algorithm

The data collection module grabs security event data for training the security analytic system for detecting cyber attacks. The training data can be collected from different resources within an enterprise where an order is supposed to be deployed. After the process of data collection related to the training data, the data preparation module prepares the data for training the model by implying different filters and techniques of feature extraction.Attack Detection Algorithm - XenonStackNext, the prepared training data initialize training the attack detection module. Once the module is prepared, it is validated to investigate whether the model can identify cyber-attacks. For validating the model, the data is collected from an enterprise. The test data is prepared for forwarding into the attack detection module. The prepared test data is imported to the attack detection model, which performs the analysis based on the rules learned during the phase of the training. Here, the imported test data instances are classified as either malicious or legitimate. The analysis results are visualized to a user through the visualization module. In the situation of malicious or attack situation, a user can take immediate required actions that may include blocking a few ports or slicing off the affected components from the network to stop further damage.

Combining Multiple Detection Methods

Security event data is grabbed from different resources. It is important to note that the resources from where security event data can be grabbed are not limited to what is demonstrated in the image.

Attack Detection Techniques - XenonStack

The choice of data resources differentiates from organization to organization relies upon their exact security requirements. After completing the process of the collection, the resulting data is stored in a data storage component. Then the data is passed to the data analysis component where different attack detection methods and techniques are implemented to analyze the data. The choices and number of attack detection methods and techniques rely upon some factors.

These factors comprise the processing ability of an organization, the data resources, security requirements, and finally, the security expertise of the organization. For example, an immensely security-sensitive organization (for example, National Security Agency) having a high budget as well as the tools of high computational power may incorporate several attack detection methods and techniques to secure their data and infrastructure from attacks related to cyber technologies. The attack detection methods and techniques are imposed on the whole dataset in a parallel manner. The visualization component immediately informs about any outstanding anomalies to users or administrators, who are expected to respond to security alerts.

Artificial Intelligence Cybersecurity Solutions for Scalability

This section relates to the Reliability quality attribute

Dropped Netflow Detection

The network traffic is fleeting through the router demonstrated in the figure. A NetFlow grabber is attached to the router, which grabs the NetFlow and stores them into the NetFlow storage. During the NetFlow collection procedure, the NetFlow sequence monitor module is monitoring the sequence numbers which are embedded (by design) into the NetFlow.Netflow Detection and Security - XenonStackIn the condition of sequence numbers are found out of order at any stage, the NetFlow sequence monitor sends a warning message representing the missing flow in the particular stream of NetFlow. The warning message is then logged alongside the exact stream in the NetFlow storage module to point out that the stream of NetFlow has some flows missing that might be crucial for identifying an attack. At the same instance time, a warning is visualized to a security administrator through the visualization module. Then a security administrator may take immediate action for solving the issue due to which some NetFlows may get dropped.

What are the measures of Artificial Intelligence in Cybersecurity?

The nodes are used for collecting security event data are placed in different sectors for collecting different types of data. Some collect data related to network traffic, and others collect database access information, and so on. Security measures are implemented to the data which is collected to ensure its secure transfer process from the data collection module to the data storage and analysis module. The security measures incorporated differentiate from system to system.Data Security Process - XenonStackSome systems give preference to encrypt the collected data and then perform the transfer process of the data in encrypted form. Other systems prefer to use Public Key Infrastructure (PKI) to ensure a secure transfer process of data and verification of the party transferring the data. As soon as the data is received by the data storage module and analysis module in a secure mode, the data analytic operations are applied to perform analysis processes on the data for detecting attacks. The results which are generated from the analysis are presented to users through the visualization component.

Artificial Intelligence Cybersecurity Alert Ranking Modules

The data collection module grabs security event data from different resources, which is then pre-processed by the pre-processing data module. The pre-processed security event data is passed to the data analysis component, which performs different analytical procedures on the data for identifying cyber attacks. The results exported from the analysis (i.e., alerts) are passed to the alert ranking module, which ranks the alerts based on predefined rules to assess the impact of the alert on the whole organization’s infrastructure. The criterion for ranking the alerts relies on the organization.Alert Ranking Process - XenonStackFor example, the ranking rules for an organization vulnerable to DoS attacks will rely on an organization vulnerable to brute force attacks. Finally, the ranked list of easy-to-interpret and straightforward alerts is shared with security administrators using the visualization module, which eases the task of a security administrator to first give a response to the alerts on the utmost of the rank list as these alerts are foreseen to be more consequential and dangerous.

What are the Best Tools for Artificial Intelligence in Cybersecurity?

These are some of the tools that are using the various algorithm of AI to get the best security to organizations. 

  1. Symantec's Targeted Attack Analytics - This tool is used to uncover private and targeted attacks. It applies Artificial intelligence and machine learning to the processes, knowledge, and capabilities of Symantec's security experts and researchers. The Targeted Attack analytics tool was used by Symantec to counter the Dragonfly 2.0 attack. This attack targeted multiple energy companies in The USA and tried to gain access to operational networks.
  2. Sophos' Intercept X tool - Sophos is a British software and hardware security company. Intercept X uses a deep learning neural network that functions like a human brain. Before a file is performed, Intercept X will retrieve millions of features from a file, perform an in-depth review and decide whether a file is benevolent or harmful within 20 milliseconds
  3. IBM QRadar Advisor - IBM's QRadar Advisor is utilizing IBM Watson technologies to counter cyber-attacks. This utilizes AI to auto-examine signs of any vulnerability or exploitation. QRadar Advisor utilizes cognitive reasoning to provide valuable feedback and speeds up the response process.
  4. Vectra's Cognito - Vectra's Cognito detects attackers in real-time using AI. Threat detection and identification of attackers are automated in this tool. Cognito collects logs, cloud events, network usage data, and behavioral detection algorithms to reveal hidden attackers in workloads and IOT devices.   
  5. Darktrace Antigena - Darktrace is the effective method of self-defense. Antigena extends the critical functionality of Darktrace to recognize and duplicate the role of digital antibodies that recognize and neutralize threats and viruses. Antigena utilizes the Enterprise Immune System of Darktrace to recognize and react to malicious behavior in real-time, based on the nature of the danger.

Artificial Intelligence Cybersecurity Strategy

Effective network security analytics is not a function of applying just one technique. To stay ahead of evolving threats, a network visibility and analytics solution needs to be able to use a combination of methods. This begins by collecting the right data for comprehensive visibility and using analytical techniques such as behavioral modeling and machine learning. All this is supplemented by global threat intelligence that is aware of the malicious campaigns and maps the suspicious behavior to an identified threat for increased fidelity of detection.

Original article source at:

#cybersecurity #AI #artificialintelligence 

Artificial Intelligence in Cybersecurity
Eran Feit

Eran Feit


What actually sees a deep neural network model ?



How to visualize CNN Deep neural network model ? 

What is actually sees during the training ? 

What are the chosen filters , and what is the outcome of each neuron .

This is part no. 5 of Tensorflow tutorial for classify monkey’s species images using CNN and transfer learning 

In this part we will focus of showing the outcome of the layers.

Very interesting !!

I also shared the Python code in the video description.


Oreily  come up with this book . the best book for learning Deep learning based on Tensorflow-Keras. This is the link :


You can find the link for the video tutorial here :




#Python #Cnn #TensorFlow #Deeplearning #AI

What actually sees a deep neural network model ?

Artificial intelligence & MarkLogic


Automation is a crucial part of the Artificial Intelligence cycle. It allows organizations to perform tasks that require human input and improves tradecraft. This also increases efficiency in order to keep pace with changing technologies and requirements. And MarkLogic is providing some extra benefits in implementing AI processes. MarkLogic has its own optimized algorithms and implementations of some AI methodologies. Achieved due to its internal Data storing mechanism and Implementation. We see how Artificial Intelligence & MarkLogic are Wonderful Architecture together.


This is the use of software to perform tasks that would otherwise be done by humans. The benefits include:

  • Increased efficiency and productivity
  • Increased quality, safety, and security
  • Reduced costs through reduced error rates, decreased training time, and fewer errors made during production or delivery cycle times. Sometimes automation involves human-machine interaction, as in a robot arm that picks up parts from a factory floor; other times it may involve no direct human control at all (e.g., automatic checkout systems).

Improved Tradecraft

  • Increased time to focus on the mission.
  • More time to focus on the customer.
  • More time to focus on people, including your team members, fellow analysts, and partners.
  • Increased efficiency and accuracy of the intelligence cycle by having a better knowledge of what’s happening in your environment so you can make faster decisions about how best to proceed with your workflows or activities (e.g., whether or not it’s worth investing more resources into a particular project).

Mission Focus

MarkLogic is a proven technology that has been used in mission-critical environments for many years. It’s also one of the most widely deployed and trusted enterprise data management products on the market.

MarkLogic provides an open, secure platform for integrating, managing, and analyzing large amounts of structured data — including unstructured information such as text files, documents, emails, and images – across heterogeneous systems and environments. MarkLogic’s flexible design allows you to easily build your own applications (such as CRM applications) on top of our platform — without having to worry about writing code!

Increased Efficiency

The increased speed of the intelligence cycle allows you to be more efficient, especially in regard to training. This can allow you to increase your training budget and spend more time on analysis and research instead of man hours!

The ability to analyze large amounts of data has also led MarkLogic developers down a path where they now have an increased focus on mission planning and execution as well.

Proven Technology

MarkLogic is a proven technology for storing and querying unstructured data. MarkLogic has been used in many high-profile projects including the Olympics and the NFL, by government agencies like the FBI, CIA, and NASA.

MarkLogic’s proven record of success can be attributed to its ability to handle large volumes of structured or unstructured data with ease. It also benefits from a unique database architecture that supports high-performance queries on both structured and unstructured sources.


Artificial Intelligence and MarkLogic have been proven to provide:

  • Increased speed, quality, and accuracy of the intelligence cycle.
  • Improved tradecraft through automation of repetitive tasks, such as data preparation and reporting.
  • Mission focus on improved efficiency from a single source for all your data needs.

Positive of an Automation Infrastructure

An automation infrastructure is a foundation on which your organization can build an effective AI solution. It provides a framework that helps you automate processes, automate data, and integrate with other technologies.

It can help organizations save time and effort by automating repetitive tasks, thereby freeing up people to focus on more important work. You’ll also see improvements in accuracy as well as consistency across different systems—allowing for faster decision-making, increased efficiency, and improved customer service levels.

Difficulties of Performing Automation Tasks

  • Automation is not a silver bullet.
  • Automation can be difficult to implement and maintain, especially when it comes to integrating with other systems that have been developed using different technologies.

Increased speed, quality, and accuracy of the intelligence cycle.

AI can speed up the intelligence cycle, increase the quality of the intelligence cycle, and improve accuracy. AI can also increase efficiency within an organization by automating repetitive processes that would otherwise be handled by humans.


The benefit of automation is that it reduces the amount of manual work and makes your business more efficient. It also helps you to focus on what’s important, rather than spending time dealing with mundane tasks. In addition, automated processes are more reliable and have less risk than human ones as they don’t require any human interaction. Thus we can say that Artificial Intelligence & MarkLogic is a Wonderful Architecture to achieve great AI performance.

For more such blogs please visit:
References :
1) enduring intelligence automation
2) Sample read-only document

Original article source at:

#artificialintelligence #AI 

Artificial intelligence & MarkLogic

Who is an AI Engineer? | How to be an AI engineer

Artificial Intelligence was one of the growing subfields of the Information Technology field. Artificial Intelligence is needed in almost every organization. AI Engineers are in high demand. AI has seemingly endless potential to enhance and simplify tasks commonly done by humans, including speech recognition, image processing, business process management, and even the diagnosis of diseases. If you’re already technically inclined and have a background in software programming, you’ll want to think about a lucrative AI career and learn more about how to become an AI engineer. However, a background in AI is not essential as you can upgrade yourself with free online courses and learn the fundamentals required. 

This article will help you to understand how to become an AI expert, what are its roles and responsibilities, salary range, and many more.

What is Artificial Intelligence?

Artificial Intelligence (AI) may be a computer system’s ability to mimic human behavior. Machines demonstrate this type of intelligence, which may be compared to the natural intelligence that humans and animals demonstrate. 

In AI, machines learn from past data and actions that are positive or negative. With this new information, the machine is in a position to form corrections to itself in order that the issues don’t resurface, also as make any necessary adjustments to handle new inputs. Finally, the machine is in a position to perform human-like tasks.

Who is an AI Engineer?

An Artificial Intelligence Engineer is an IT expert whose mission is to develop intelligent algorithms capable of learning, analyzing, and predicting future events. Their role is to make machines capable of reasoning, just like the human brain. 

An AI engineer is therefore also a researcher: he or she analyses the functioning of the human brain so as to create computer programs with equivalent cognitive abilities as humans. 

An Artificial Intelligence Engineer can specialize in different areas like Machine Learning or Deep Learning, which are derived from AI. Machine Learning is predicated on algorithms and decision trees, while Deep Learning is predicated on neural networks.

Let’s take the instance of a corporation that sells beauty products. Having the ability to predict the trends and preferences of its customers would allow the corporate to focus on its expectations more effectively. The corporate would see its sales soar and its profitability increase. 

The mission of the Artificial Intelligence Engineer is to generate algorithms that take input data on the records of investments, sales, and products so as to foretell future customer action. 

AI engineer Job description

As an AI engineer, you play a lot of important roles for an organization, from developer to making strategies for future business plans, from analyzing data and employing Artificial intelligence accordingly to training AI based on the collected data for future uses. Let’s understand how AI is important for an organization irrespective of its field.

AI Application in E-Commerce

Personalized Shopping

Artificial Intelligence engineer makes technology easy to employ to make recommendation engines through which you’ll interlace better with your customers. These suggestions are delivered following their browsing history, preference, and interests. It helps in improving your relationship with your customers and their loyalty to your brand.

AI-powered Assistants

AI engineer empowers us with Virtual shopping assistants and chatbots, which assists us in improving the user experience while shopping online. Language Processing is employed to form the conversation sound as human and private as possible. Moreover, these assistants can have real-time engagement together with your customers. Chatbots are being used by various companies. 

Fraud Prevention

Credit card frauds and faux reviews are two of the foremost significant issues that E-Commerce companies are affected by. By considering the usage patterns, AI can help reduce the likelihood of Credit Card fraud. Many purchasers like to buy a product or service supported by good customer reviews. AI can assist in recognizing and handling fake reviews. 

AI Applications in Navigation

According to research from MIT, GPS technology can equip operators with objective, up-to-date, and complete information to intensify safety. The technology utilizes composite applications of Convolutional Neural Network and Graph Neural Network, which advances life easier for users by automatically identifying the number of roads and road examples behind barriers on the roads. AI is gradually applied by Uber and lots of logistics organizations to improve operational performance, investigate road traffic, and optimize plans.

AI Applications in Robotics

Robotics is another field where AI applications are commonly used. Robots powered by Artificial Intelligence utilize time to time updates to understand the barriers in their path and pre-plan their journey immediately. 

They are often used for-

  • Carrying goods in hospitals, factories, and warehouses
  • Cleaning offices and enormous equipment
  • Inventory management
  • AI Applications in Human Resource

Did you recognize that companies use intelligent software to ease the hiring process?

Artificial Intelligence helps with blind hiring. Using machine learning software, you’ll examine applications that support specific parameters. AI drive systems can scan job candidates’ profiles and resumes to supply recruiters with an understanding of the talent pool they need to choose between. 

AI Applications in Healthcare

Artificial Intelligence engineer finds ways to implement AI and make diverse applications within the healthcare sector. AI applications are utilized in healthcare to create sophisticated machines which will detect diseases and identify cancer cells. AI can help analyze chronic conditions with lab and other medical data to make sure early diagnosis. AI uses the mixture of historical data and medical intelligence for the invention of the latest drugs.

There are more uses of AI in an organization or daily life, so AI engineers do a lot of things according to their demands. But as a fundamental definition, they perform some specific tasks.

An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which might be accustomed make business decisions that affect the whole organization. These engineers also create weak or strong AIs, counting on what goals they require to attain. 

AI engineers have an intellectual opinion of programming, software engineering, and data science. They use different tools and techniques so that they can process data, additionally as develop and maintain AI systems.

Roles and Responsibilities of an AI Engineer

As an AI engineer, you would perform certain tasks like developing, testing and deploying AI models through programming algorithms such as random forest, logistic regression, rectilinear regression, and so on.

Responsibilities include: 

  • Convert the machine learning models into application interfaces (APIs) in order that other applications can use it.
  • Develop AI models from scratch and assist the several elements of the company(for instance product managers and stakeholders) know what results they gain from the model.
  • Build data ingestion and data transformation infrastructure.
  • Automate infrastructure that the info science team uses.
  • Perform statistical analysis and tune the results in order that the organization can make better-informed decisions.
  • Set up and manage AI development and merchandise infrastructure.
  • Be an honest team player, as coordinating with others may be a must.


Let’s say an IT company runs a successful business, providing a service to a good audience online. The requirements of the business are such they require constant prototyping and mocking from various website layouts. This is often thanks to the corporate employing solid UI and UX practices and learning from continuously running various A/B tests on their website’s pages.

The company is additionally tracking user behavior with a tracking tool like HotJar (which records users’ mouse clicks and scroll events on the online page, in order that the possible points of user confusion are often analyzed by observing user behavior on the website).

An AI Engineer’s responsibility might contain the following:

  • Construct a machine learning algorithm that will get photos of whiteboard drawings of website designs suggested by the UX team and deliver finished website designs– which can then be used by the Software developing team. If this workflow is successfully implemented, it could save countless man-hours and speed up the feedback loops associated with attempted improvement within the website’s UX.
  • Collect info from numerous of HotJar operators and operate it on machine learning algorithms to endeavor out common deadfalls and sources of user interference. Analyze the info and find patterns in how, when, and why user confusion occurs.
  • Develop a model connecting HotJar and A/B testing data, including the Google Analytics information and data on cart abandonment to offer enhanced designs that strength end in extended time used on the location, higher customer purchase, or extra goal, as specified by the department.
  • Try to predict the success of varied layouts proposed by the UX team.

As we will see, it’s tough to explain the role of an AI Engineer, partially due to the sector being very young, and since each business will have its own specific implementations of artistic automation methods.

Also Read: Career Opportunities in AI

AI Engineer Salary Trends in India/US

AI Engineer gets really a good package whether it’s India, USA or some other country they are offered really good package. To be honest, AI Engineers are the one who gets the most decent salary. The national average salary for AI Engineers in India is about ₹8,70,030 per year in India.

AI Engineer Salary: Based on Company

CompanySalary in ₹
Google₹1.5 cr/yr

AI Engineer Salary: Based on Experience

ExperienceSalary in $
0-2 Years $20,000- $50,000
3-7 Years $78,000-100,000
7-10 years $100,000-150,000

AI Engineer salary based on Skills 

Big data $65k
Computer vision $65k
Deep learning$75k

Skills Required to be an AI Engineer

There are several skills that are essential for an AI engineer.

Technical skills

Two of the leading essential technical skills for an AI engineer to learn are programming and math/statistics. 

Programming: Software developers shifting into an AI role or developers with a degree in computer or any technology probable have now got a hold on a couple of programming languages. Two of the foremost commonly used languages in AI, and specifically machine learning, are Python and R. Any aspiring AI engineer should have a minimum of being conversant in these two languages and their most ordinarily used libraries and packages. An AI Engineer should be good with Big data, Algorithms, Computer vision, Deep learning, etc.

Math/statistics: AI engineering is quite just coding. Machine learning models are supported mathematical concepts like statistics and probability. You’ll also get to have a firm grasp on concepts like statistical significance once you are determining the validity and accuracy of your models.

Soft skills

AI engineers don’t add a vacuum. So while technical skills are going to be what you would like for modeling, you’ll also need the subsequent soft skills to urge your ideas across to the whole organization. 

Creativity – AI engineers should grow on the sentinel for duties that individuals do ineffectively, and machines could do properly. You ought to stay au courant new AI applications within and out of the doors of your industry and consider if they might be utilized in your company. Additionally, you shouldn’t be afraid to undertake out-of-the-box ideas. 

Business knowledge – It’s essential to recall that your position as an AI engineer is assumed to furnish value to your organization. You can’t provide value if you don’t really understand your company’s interests and wishes from a strategic and tactical level. 

A great AI application doesn’t expect much if it isn’t appropriate to your organization or can’t promote business services in any way. You’ll get to understand your company’s business model, who the target customers and targets are, and if it’s any long- or short-term product plans. 

Communication – within the role of an AI engineer, you’ll have the chance to figure with groups everywhere in your organization, and you’ll get to be ready to speak their language. For instance, for one project, you’ll have to: 

Discuss your needs with data engineers in order that they can deliver the proper data sources to you.

Explain to finance/operations how the AI application you’re developing will save costs by the end of the day or usher in more revenue.

Work with marketing to develop customer-focused collateral explaining the worth of a replacement application.

Prototyping – Your ideas aren’t necessarily getting to be perfect on the primary attempt. Success will depend upon your ability to quickly test and modify models until you discover something that works.

How to be an AI engineer?

To become an ai engineer, you need to learn several skills from various fields. An AI Engineer should get a technical degree, for example, B.e or  in Computer science or IT. Other than those skills, you should also have some skills. Below is the list of those skills.

Mathematical skills

AI engineers build AI models using algorithms, which rely heavily on statistics, algebra, and calculus. Additionally, you’ll attempt to be conversant in probability to interact with a number of artificial intelligence’s commonest machine learning models. This includes Hidden Markov, Gaussian mixture, and Naive Bayes models.

Programming skills

To gain accomplishment as an AI engineer, you’ll need a strong experience of traditional programming languages like C++, Java, and Python. You’ll frequently employ these programming languages to develop and deploy your AI models. AI engineers may use these skills to write down programs that allow them to analyze various factors, make decisions and solve problems.

Analytical skills

AI engineers are regularly asked to review and evaluate important information. To try to do this adequately, you’ll necessitate to analyze information, develop insights and brainstorm for possible solutions. Engineers during this field could also be liable for drawing insights from large volumes of knowledge by breaking them down into smaller parts.

Business intelligence

Successful AI projects can solve most issues associated with an organization’s operation. Maintaining business intelligence permits you to transform your technical concepts into practical business projects. No matter the industry you’re working in, you’ll attempt to develop a basic understanding of how businesses operate, their target audiences, and competition within the market.

Communication skills

Artificial intelligence developers are often required to speak technical information to a good sort of individuals with varying degrees of technical expertise. For instance, you’ll be asked to implement and present a replacement AI model to each department in an organization. You’ll require good written and verbal communication skills to speak complex ideas and ideas in ways in which everyone can understand.

Collaboration skills

Individuals in this field often work within a team of other AI developers and IT professionals. It’s beneficial to develop the power to figure efficiently and effectively within a team. You’ll need to integrate with small and enormous teams to figure towards achieving complex goals. Considering inputs given by others and contributing your ideas through effective communication can cause you to be an honest team player.

Critical Thinking Capability

To come up with innovative AI models and technological solutions, you’ll need to generate a spread of possible solutions to one problem. You’ll even have to analyze available data quickly to draw plausible conclusions from them. Though you’ll learn and develop many of those skills while pursuing your undergraduate degree, you’ll search for additional experiences and opportunities to develop your abilities in this area.

What are the Advantages of a AI Engineer Course?

AI technologies are ready to bring a revolution in human lives, transforming the economy globally, creating a high demand for related jobs. 

A career in AI is the future proof meaning the roles are likely to survive well into the longer term. AI is outperforming humans in sectors like marketing, communication, automation, finance, sales, analysis, and more. 

AI is predicted to extend the typical income between 20-30% by 2030, as per reports. Another report by Gartner suggests that by 2020, there’ll be 25 billion connected devices, indicating the long term of AI.


1. What is the salary of an AI Engineer?

As per glass door, the AI engineer’s average salary is often $116,549 per year.

2. What is the salary of an AI Machine Learning Engineer?

As per Indeed, the AI machine learning engineer salary is often $150,300 per annum on average.

3. What is the hourly salary of an AI Engineer?

The common hourly payroll of AI engineers varies between $42 to $50 per hour.

4. What is the salary of an AI engineer in the US?

The AI engineer salary within the US is $111,187 on an average a year and $53 per hour just in case you choose to freelance or on a contract basis.

5. What is the educational qualification required to be an AI Engineer?

AI engineering may be a developing career field that will provide an abundance of opportunities in the future. The essential qualification to become an AI engineer may be a baccalaureate in a related field, like information technology, computing, statistics, or data science. After gaining a baccalaureate, you’ll also pursue a postgraduate degree specializing within the specific field of AI. Earning data science, machine learning, and deep learning certifications are often very beneficial during a job search and may offer you a comprehensive understanding of relevant concepts. You can take up various Artificial Intelligence Courses offered online and upskill today.

6. Which engineering degree is best for artificial intelligence?

There are graduate, post-graduate, and certification courses in engineering that permit you to specialize in AI, machine learning, and deep learning. These include engineering degrees in IT, computing, and data science. Popular degrees and diplomas include B.Tech (Bachelor of Technology) in AI, B.Tech in computing Engineering, B.E. (Bachelor of Engineering) in AI, and M.Tech (Master of Technology) in computing Engineering.

7. How do I start a career in artificial intelligence?

AI engineers work closely with machine learning algorithms and other AI tools for the development of AI. To become successful at their job, they’ll get to have good programming and software development skills. Consider developing these early at college or with the assistance of online resources and forums.

Once you’ve got gained the required educational qualifications, you’ll start applying to entry-level jobs. This might provide you with the experience you would like to advance into higher positions. Periodically pursue online certification courses to stay up-to-date with changes within the AI industry.

8. What am I able to do as an AI engineer?

Al engineers are problem solvers who connect the passage between human understanding and computer-generated outcomes. They’ll perform a spread of job roles concerning data processing, data analysis, research, design, and programming. AI is dynamic technology with applications in almost every field. The utilization of AI may grow exponentially in the future, and AI engineers may have ample career opportunities. Machine learning and AI are developing specialities that will have an outsized impact on the general success of companies and organizations.


If you are someone who is interested in technology and good with mathematics and good with computers, then you can good to go for AI Engineer. You have to get a proper degree to be an AI Engineer, but still, you can learn the basics from online courses.

Original article source at:

#artificial-intelligence #AI 

Who is an AI Engineer? | How to be an AI engineer

AI, NLP, and ML: Game Changers Of The Business World

In this article, let's learn about AI, NLP and ML: Game Changers Of The Business World. 

Artificial Intelligence, Natural Language Processing, and Machine Learning have been in talks since the 1950s. With today’s advancements in technology, they are accepted with vast expectations. It is also pivotal to note that we live in an age where industries are transforming and readily accepting newer productive technologies. With digitization in pictures, AI, NLP, and ML are the building blocks of the tech world. It is seen that they rule current industries by providing them with various beneficial ways of achieving their goals.

Kimberly Silva, CEO of FindPeopleFirst, agrees with us and adds:

AI, ML, and NLP are drastically changing the way businesses operate. Every kind of business, from agriculture to marketing, benefits from these tools. NLP is mostly being used for customer service, content moderation, and translation. AI and ML are helping businesses in fraud detection, predictive analytics, image recognition, and natural language translation.

The advances in these skills have brought about many changes in the way businesses operate. These skills are now being used for inventory management, data analysis, and marketing. They are also being used to optimize the customer experience by analyzing past customer behaviors and providing personal recommendations.

For artificial intelligence, future developments are becoming more and more advanced as time goes on. Computers are starting to recognize objects in images, comprehend speech and translate it, and learn from their mistakes. For natural language processing, computers are starting to understand human speech better and are able to process a much wider range of words than before. Lastly, for machine learning, computers are getting smarter with the help of algorithms that allow them to learn information more quickly with each round of training.

These technologies are giving computers the ability to do tasks that traditionally require human intelligence. They can help transform the way we interact with technology, help us understand new and complicated topics, and make life easier for the people who use them.

Let us look into what AI, NLP, and ML are and their role in the business world.

AI – Artificial Intelligence

It is well known that Artificial Intelligence is in constant development. It develops smart machines that usually perform tasks that require human intelligence. Hence, the name Artificial Intelligence. The impact of Artificial Intelligence on businesses is difficult to quantify, as its value depends on the specific business and its needs. However, AI has the potential to dramatically improve efficiency and productivity in several areas, including marketing, sales, and operations. In some cases, AI can even be used to create new products and services. Ultimately, the value of AI depends on how well it can address the specific needs of a business.

There are many businesses out there that are getting tremendously benefited by making use of AI in the right places. Take the example of Siri, Alexa, and other smart assistants that are vastly used by many of us. Self-driving cars are fascinating to experience. You may be fed up with the number of spam emails you get by the end of the day, and to solve this issue, you can go for email spam filters that are developed based on AI techniques. Hence, AI is an integral part of businesses, and you can become a successful AI leader with the right AI Specialist Course.

Scott Winstead, Founder | Editor in Chief of MyElearningWorld, states,


AI has the ability to process large amounts of data and make decisions quickly, which is why it’s so valuable for businesses. With AI, businesses can automate processes and make better decisions based on data. AI has brought a number of positive changes to the business world. Some of the most notable changes include:

  • Increased efficiency: With AI, businesses can automate processes and speed up decision-making. This leads to increased efficiency and faster turnaround times.
  • Improved accuracy: With AI automating processes, businesses are able to make more accurate decisions based on data. This leads to better outcomes for businesses and their customers.
  • Greater competitiveness: With AI providing businesses with a competitive edge, they are able to stay ahead of the competition and maintain their position in the market.

Marcin Stoll, Head of Product at Tidio, continues,


Usually, when people think about such innovative topics as AI, ML, and NLP, they imagine spectacular technologies like talking humanoids (e.g., Sophie), flying drone swarms, or jumping robot dogs from Boston Dynamics. All these remarkable examples are powered by artificial intelligence, but many other mundane AI innovations change the lives of millions of people.

AI, ML, and NLP technologies are widely used in customer service; robocalls, chatbots, and other tools use them to contact customers and manage their interactions with the business. AI-powered software may step in and works for all repetitive processes, preventing excessive employees rotation in these positions.

Artificial intelligence is a game-changer for businesses because of its ability to learn and adapt to new conditions; it offers flexibility that is not guaranteed by traditional software. This cutting-edge technology allows enterprises to scale up their economic activities.

Case Study on How AI is Affecting Business

Ben Richardson, a Senior Software Developer at Cloud Radius, shares his experience on how AI technologies are changing businesses and states,


Expertise in using artificial intelligence software is among the most in-demand skills currently ruling the business world. Companies are consistently looking to automate their business processes to increase efficiency, productivity, and competitiveness. At the same time, they are looking for impactful ways of gathering, analyzing, and using data to improve customer satisfaction while bringing in more sales and revenue for the business. Here is where AI software tools come into play.

These tools rely on AI, ML, & NLP technologies as well as highly advanced, specially formulated algorithms to automate selected business tasks. Companies with personnel skilled in the use and application of these tools will be able to develop and deploy a variety of AI-based applications such as recommendation systems, AI-powered sentiment analysis, intuitive and conversational live chat systems, and AI-powered content marketing. All of these applications of AI software will benefit the businesses that are first adopters the most while disrupting entire industries and revolutionizing how business is conducted globally.

Christian Velitchkov, Co-Founder of Twiz LLC, shares his views on how AI is beneficial for sales and marketing teams,


Marketing is an area of business that has changed a lot over the years. It has become a lot digitally, and people are influenced by so many types of media like TV Commercials, social media, google ads, etc.

AI will effectively handle CRM and will enhance marketing. Sales and marketing teams can apply AI to get to know their customers more and offer an enhanced customer experience. AI will also enable providing tailored services on the basis of the customer’s preference.

E-tailers are now using AI for asking several crucial questions to their customers about their preferences, and on the basis of that, they provide a customized experience by showing the products or services that match their preferences.

This will also gather data about the customer choices and preferences, and it will allow the marketing team to develop products and marketing strategies that will work better. The data generated will help in increasing sales.


Pro Tip: There is a high demand for AI skills in the job market. Some of the most in-demand skills in AI include machine learning, natural language processing (NLP), computer vision, and deep learning. With the right set of AI skills, you can create a bright Career in Artificial Intelligence.

NLP – Natural Language Processing

Neuro-Linguistic Programming (NLP) is considered a model of communication and change developed in the 1970s by Richard Bandler and John Grinder. It is based on the objective that all human behavior can be explained in terms of three elements: neurology, language, and programming. NLP is considered a system for understanding how people think, communicate, and behave. It is a tool for change and a model for personal growth and development.

NLP has been shown to be productive in many business applications, including Leadership Management, Sales and Marketing, Customer Service, Team Building, Change Management, and more. In simple terms, NLP helps businesses in a variety of ways. One of the most important ways is to help businesses understand their customers’ betterments. It can help businesses better understand what customers want and need, and it can help them understand how customers think and feel. This understanding helps them serve their customers better and meet their needs. NLP can also help businesses understand their own strengths and weaknesses, and it can hint them how they can improve. Finally, NLP can help businesses better understand how they can change and grow. Check out this NLP Language Detection course.

Brent Hale, Chief Content Strategist at Tech Guided, agrees with us and states,


After AI and AI acquired conspicuousness, there has been a flood in organizations that needed to send an NLP-based chatbot that surveys, breaks down, and speaks with clients, very much like a human, for an unmatched encounter. Allow us now to comprehend what NLP chatbots are to know then the benefits they deal with organizations. We will likewise figure out how they help in defeating client assistance problem areas in correspondence.

Looking to improve your Natural Language Processing skills? Check out these NLP courses today!

Case Study on How NLP is Affecting Businesses

Brian Penny, Founder & CEO of Thought for Your Penny – a full-service media and content agency, shares how NLP is affecting their business,


We provide and negotiate written, audio, and video content across podcasts, social media, and tier 1 media outlets reaching large audiences. NLP and GPT-3 play a large role in my business.

We use NLP transcription programs to transcribe every media interview, podcast, and social audio room we do. This is crucial because an hour-long read on a written blog is not easy (nor cheap) to accomplish. But these transcripts lay a crucial foundation for creating stellar written content. A researcher is hired to turn it into actual blog posts formatted for the internet public.

Also, GPT-3 is great for social media posts, summaries, and pitches. They take away the workload to ensure we can submit quality metadata and posts with our quality content mined from other formats. This repurposing puts my agency in a place where we’re doing more consulting, research, and high-level work. AI is also used for our SEO efforts and social media scraping, and it’s a vital part of our overall tech stack.

Ross Spark, an SEO Specialist at Business Online Canada, shares his experience on how an SEO specialist can effectively utilize NLP for their aid,


SEO specialists that understand NLP can create more effective content and analyze competitor strategies. Additionally, NLP can help identify patterns in customer behavior that can be used to improve marketing campaigns.

Machine learning and AI specialists are needed to develop algorithms and models for decision support systems, predictive analytics, and natural language processing. Businesses are also looking for experts in big data management and data mining to uncover trends and insights.

SEO specialists using NLP techniques can increase website traffic by creating more effective and keyword-rich content. Additionally, machine learning can be used to predict customer behavior, preferences, and purchasing patterns. This information can then be used to create targeted marketing campaigns that are more likely to result in conversions.

SEO and NLP will continue to be in high demand as businesses work to improve their online presence and reach more customers. Additionally, machine learning and AI will continue to develop and become more widely used in business decision-making. This will result in increased efficiency and accuracy in areas such as marketing, sales, and customer service.


Pro Tip – Beginners interested in NLP can start learning it with the help of this Introduction to NLP free course offered by Great Learning.

ML – Machine Learning

Machine learning is known as a subset of artificial intelligence that enables computers to learn from data without being explicitly programmed. It is a powerful tool for businesses as it can help them make better decisions by understanding patterns in data. Machine learning can be used to predict customer behavior, forecast demand for products and services, and identify opportunities and threats. It can also improve the efficiency of business processes by automating tasks that computers can do.

Machine learning has been used for many years in various applications, including speech recognition, natural language processing, and computer vision. More recently, machine learning has been used to create predictive models for various tasks, including predicting customer behavior, forecasting financial markets, and detecting fraudulent credit card transactions. With this confrontation on its importance, it is better to gain advanced knowledge on Machine Learning through a Machine Learning Course. The novice who wants some basics can learn the Basics of Machine Learning from Great Learning Academy.

Case Study on How ML Affects Businesses

Jon Torres, a Digital Marketing Consultant & founder of Jon Torres, shares his experience based on his background in AI and ML for Digital Marketing,


The digital transformation happening right now means proactive digital marketers need to use fast, accurate, and up-to-date website analytics to move forward at a modern pace. Keeping up with those standards is a significant challenge.

Machine learning presents a great way to improve digital marketing automation by using modernized tools such as voice assistants that boast of specialized roles, CoBots that can easily interrelate with humans in streamlining processes as well as advanced behavior tracking for flawless marketing campaigns.

In-demand Skills of AI, NLP, and ML Ruling the Business World

Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) are in high demand in the business world. They are used to make processes more efficient and effective, and AI is used to automate decision-making processes. NLP is used to understand and interpret human language, and ML trains computers to learn from data and make predictions. All three of these skills make business processes more efficient and effective. They are also used to make businesses more profitable. In addition, they make businesses more competitive. Hence, businesses that want to stay competitive need to use AI, NLP, and ML as they are the in-demand skills of the business world.

Steven Walker, CEO of Spylix, adds,


The in-demand skills of AI, ML, & NLP are more complicated than before in the present business world. But on the other hand, these skills got us a lot of development in these sectors. With the help of these skills, we have done that could never be possible for a human being. These skills are: –

  1. Technical Knowledge of the Field.
  2. Emotional Intelligence & Communication Skills to transfer those thoughts.
  3. An effective way of critical thinking.
  4. Curiosity to find & grow more.
  5. Quick decision-making ability.

Lyle Florez, Founder of EasyPeopleSearch, further adds,


These days, AI limits are pursued across gaming experiences, progressed mechanics, face confirmation programming, weaponry, talk declaration, vision demand, star plans, and web crawlers. Modernized thinking and AI occupations have wound by close to 75% all through late years and are prepared to keep on making. Below are some in-demand skills of Ai, ML, and NLP ruling the business world:

Data orchestrating: The essential stage in AI progress is pre-overseeing and managing unrefined data made by your structures. For example, we should imagine an electronic store that offers a blend of things to clients from one side of the world to the next. This online store will make loads of data related to certain events.

Security: As in everything diagram, organizing security for AWS AI procedures is a critical task, especially considering how AI models need a huge load of data to be ready, and approval for that data should be given to maintain people and applications in a way.

Future Trends and Positive Changes brought by these skills into Businesses

Artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) have been transforming businesses for the past few years. The impact of these technologies will only increase in the years to come. Here are some of the most critical ways in which AI, NLP, and ML are benefiting businesses:

AI can help businesses automate routine tasks.

AI can be utilized to automate repetitive and time-consuming tasks, which can help businesses save time and money. For example, AI can be used to automate the task of sorting through data.

AI can help businesses make better decisions.

AI can be utilized to make sense of large amounts of data, which can help businesses make better decisions regarding how to run their businesses. For example, AI can be used to predict customer behavior.

NLP can help businesses communicate with customers.

NLP can be used to understand natural language, which can help businesses communicate with customers more effectively. For example, NLP can be used to provide customer support.

ML can help businesses improve their operations.

ML can be used to improve the accuracy of predictions. This can help businesses improve their operations. For example, ML can be used to improve the accuracy of forecasts.

Geoff Cudd, Consumer Advocate & Owner of FindTheBestCarPrice, agrees with us and continues,


NLP, AI & ML possess the future developments & benefits are:

Natural language processing will be integrated into practically every part of life as we know it if there is one thing we can count on in the future. Because of integration across a wide range of products, from computers and refrigerators to speakers and automobiles, the past 5 years have been a steady burn of what NLP can achieve.

Humans, for example, have expressed more enthusiasm than disdain for the process of human-machine contact. In such a short time, NLP-powered tools have also demonstrated their worth.

These causes will lead to increased NLP integration: ever-increasing amounts of data generated in business dealings around the world, increased smart device use, and increased customer desire for better service.

The sky’s the limit when it comes to natural language processing. As technology becomes more ubiquitous and greater advancements in capability are explored, the future will see significant changes. Natural language processing, as a major component of artificial intelligence, will also contribute to the figurative invasion of robots in the workplace; thus, enterprises everywhere must start preparing.

AI will keep driving significant innovation, which will drive numerous existing industries and may have the ability to spawn a slew of new ones, all of which will result in more employment being created. Humans, on the other hand, have generalized intelligence, which includes problem-solving, abstract thinking, and critical judgment skills that will continue to be valuable in the commercial world. Human judgment would be important, if not in every work, then at least at every level and across all industries.

We constructed energy consumption forecast models for a cement mill at the plant using data science approaches and machine learning (ML) technology, which outperformed previous models. Based on the baseline method, the models use past sensor readings and operational data to estimate energy consumption, yielding a one-order-of-magnitude accuracy improvement for energy consumption prediction.

So far, the models developed have relied on data streams rather than first principles. Future research will use these concepts to increase performance in the development of hybrid process engineering models, generalize the technique, and enable analysis that can extract knowledge from data and assist decisions.

Hilda Wong, Founder of Content Dog, continues,


  1. Process automation is going to be one of the most basic and biggest changes in the business world caused by AI. Automation will enable reducing the time and effort required to perform routine tasks. This will allow employees to take up more productive work.
  2. AI will make marketing a more customized experience for the customers. With the help of AI, businesses will be able to understand the preferences of their customers, and it will enable them to provide a tailor-made experience to all the customers. Thus it will increase the effectiveness of marketing.
  3. AI and ML will help in gathering data, processing it, and analysis of data. The structured data generated will help business leaders understand their businesses better and make better decisions.
  4. AI and ML will help in customer interaction and engagement online. AI items like chat boxes are now used to handle basic customer interactions. We see them commonly on websites. AI Chatbots engage with customers.


Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) are three of the most talked-about technologies in the business world today. They have the potential to change the way businesses operate and the way employees work. We hope, through this article, you are now aware of the importance of AI, NLP, and ML in the Business World and gained enormous knowledge through the experiences shared by the specialists mentioned above. If you wish to dig deep into AI, NLP, and ML skills, take a look at the highly appreciated Online Artificial Intelligence Course Great Learning offers, and earn a course completion certificate.

Original article source at:

#AI #artificial-intelligence #machine-learning 

AI, NLP, and ML: Game Changers Of The Business World
Jillian  Corwin

Jillian Corwin


How is AI without coding applied in industries?

In this article let's learn together No-code AI: Applications across industries. No-code AI technologies are considered the next big thing in the digital world. It brings sustainability and affordability to the expensive AI tools that, to date, were accessible by big companies. The no-code AI approach eliminates the need for coding knowledge to implement artificial intelligence in businesses. These platforms enable non-engineers to apply AI and ML to classify and analyze data, streamlining the decision-making process. 

AI has been changing the technology landscape for over a decade now. AI is everywhere, improving customer relationships using virtual assistants to predict global weather conditions. The no-code AI expands its applicability, bringing AI to small enterprises that were earlier finding it expensive to use.   

Businesses across industries have started adopting no-code AI tools. Healthcare, finance, and marketing are some sectors that are reaping the benefits of no-code AI tools. Let’s dive deep into each industry and discover what they gain from no-code AI tools.

Financial sector

Finance is a data-driven industry that churns out tons of data every day. AI has helped streamline many processes like loan decisions and customer experience for banks and financial institutions. The no-code AI platform will further simplify these procedures and can be applied to other purposes like improving the loan portfolio, reducing risk for lenders, and automating the registration process. It can be used to predict financial risks, customer churn prediction, and plan a better customer experience, bringing efficiency to the system.

Different teams at banks and financial institutions can implement no-code AI to create unique platforms per their needs. For example, no-code AI can be used to build a model to screen loan applications and sort them into categories meeting the eligibility criteria. This will enable the underwriting team to focus on the approved applicants instead of scanning through all applications.


With the no-code AI, marketers can amp up their marketing efforts, tailoring them to the customer’s needs. They can build models to craftily sort and analyze data in meaningful ways to make informed decisions. They will be able to segregate the customer base and align targeted marketing campaigns accordingly. For example, marketers can segregate data about customer activities and lifetime value using no-code AI to tailor a Facebook ad to find a potential customer. No-code AI can also be used in marketing to better inform existing and potential clients about solutions they want and need. Check out Ai landscape.


From encouraging new collaboration between doctors and patients to giving unprecedented insight into patient health, AI has made significant advancements in the healthcare industry. No-code AI tools can empower healthcare professionals to build customized solutions like healthcare apps without a single line of code. Virtual visits – a key feature of telehealth will be improved to a great extent with the help of no-code AI, offering more transparent communication between patients and physicians. Health practitioners can also build intuitive platforms enabling patients to update their medical histories remotely before virtual check-ups. Do not forget to check out applications of ai.


A Gartner report shows that technology expansion reached 270% in the past 4 years, but only 40% of organizations have adopted AI. The no-code AI tools aim to democratize the implementation of AI by making it more accessible and affordable for small and medium businesses.

You can leverage the power of AI and Machine Learning to build on top of existing applications, create smart solutions and improve your data science capabilities without having to write a single line of code. Start your learning journey today with the No Code AI and Machine Learning Program by MIT Professional Education – Digital Plus Programs. The 12-week program is designed and taught by renowned MIT faculty and experts in advanced technologies.

Original article source at:

#AI #artificial-intelligence 

How is AI without coding applied in industries?
Royce  Reinger

Royce Reinger


Photoprism: AI-Powered Photos App for The Decentralized Web

PhotoPrism: Browse Your Life in Pictures

PhotoPrism® is an AI-Powered Photos App for the Decentralized Web. It makes use of the latest technologies to tag and find pictures automatically without getting in your way. You can run it at home, on a private server, or in the cloud.

To get a first impression, you are welcome to play with our public demo. Be careful not to upload any private pictures.

Feature Overview

Our mission is to provide the most user- and privacy-friendly solution to keep your pictures organized and accessible. That's why PhotoPrism was built from the ground up to run wherever you need it, without compromising freedom, privacy, or functionality:

Because we are 100% self-funded and independent, we can promise you that we will never sell your data and that we will always be transparent about our software and services. Your data will never be shared with Google, Amazon, Microsoft or Apple unless you intentionally upload files to one of their services. 🔒

Getting Started

Step-by-step installation instructions for our self-hosted community edition can be found on - all you need is a Web browser and Docker to run the server. It is available for Mac, Linux, and Windows.

The stable version and development preview have been built into a single multi-arch image for 64-bit AMD, Intel, and ARM processors. That means, Raspberry Pi 3 / 4 owners can pull from the same repository, enjoy the exact same functionality, and can follow the regular installation instructions after going through a short list of requirements.

Existing users are advised to update their docker-compose.yml config based on our examples available at

Support Our Mission 💎

We encourage all of our users to become a sponsor, as this allows us to make more features available to the public and remain independent.

Sponsors enjoy additional features, including access to interactive world maps, and can join our private chat room to connect with our team and other sponsors. We currently have the following sponsorship options:

You are welcome to contact us for crypto donations, bank account details, and business partnerships. Why your support matters:

  • Your continued support helps us provide regular updates and remain independent, so we can fulfill our mission and protect your privacy
  • Sustained funding is key to quickly releasing new features requested by you and other community members
  • Being 100% self-funded and independent, we can personally promise you that we will never sell your data and that we will always be transparent about our software and services

Visit to learn more. Also, please leave a star on GitHub if you like this project. It provides additional motivation to keep going.

Getting Support

Visit to learn how to sync, organize, and share your pictures. If you need help installing our software at home, you can join us on Reddit, ask in our Community Chat, or post your question in GitHub Discussions.

Common problems can be quickly diagnosed and solved using the Troubleshooting Checklists in Getting Started. Eligible sponsors are also welcome to email us for technical support and personalized advice.

Upcoming Features and Enhancements

Our Project Roadmap shows what tasks are in progress and what features will be implemented next. You are invited to give ideas you like a thumbs-up, so we know what's most popular.

Be aware that we have a zero-bug policy and do our best to help users when they need support or have other questions. This comes at a price though, as we can't give exact release dates for new features. Our team receives many more requests than can be implemented, so we want to emphasize that we are in no way obligated to implement the features, enhancements, or other changes you request. We do, however, appreciate your feedback and carefully consider all requests.

Because sustained funding is key to quickly releasing new features, we encourage you to support our mission by signing up as a sponsor or purchasing a commercial license. Ultimately, that's what's best for the product and the community.

GitHub Issues ⚠️

We kindly ask you not to report bugs via GitHub Issues unless you are certain to have found a fully reproducible and previously unreported issue that must be fixed directly in the app. Thank you for your careful consideration!

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Download Details:

Author: Photoprism
Source Code: 
License: View license

#machinelearning #AI #golang #photography #tensorflow 

Photoprism: AI-Powered Photos App for The Decentralized Web
Chloe  Butler

Chloe Butler


Pdf2gerb: Perl Script Converts PDF Files to Gerber format


Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

  1. Design the PCB using your favorite CAD or drawing software.
  2. Print the top and bottom copper and top silk screen layers to a PDF file.
  3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
  4. Use a Gerber viewer to double-check the output against the original PCB design.
  5. Make adjustments as needed.
  6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

#Pdf2Gerb config settings:
#Put this file in same folder/directory as itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)

#configurable settings:
#change values here instead of in main file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)} ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .030,  #heavy-current traces; be careful with these ones!
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
#number of elements in each shape type:
use constant
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,

#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#use Package::Constants;
#use Exporter qw(import); #

#my $caller = "pdf2gerb::";

#sub cfg
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code:

License: GPL-3.0 license


Pdf2gerb: Perl Script Converts PDF Files to Gerber format

Top 20 Artificial Intelligence (AI) Tokens

Artificial intelligence is already used for multiple purposes from self-driving cars to even beating chess players. Experts suggest that future crypto coins will focus on virtual reality and robotics.

Crypto coins are attracting everyone today. From investors to next-gen millennials, people are rushing to invest in cryptocurrency.

But did you know, there are crypto AI coins? Crypto coins that revolve around artificial technology are set to take the center stage in the future. As a matter of fact, there are plenty of AI crypto coins in the market.

In this article you will see Top 20 Artificial Intelligence (AI) Tokens by Volume, date: 5/10/2022. Let’s check.

1. Fetch - FET

DescribePrice overviewWebsite

Fetch.AI is an artificial intelligence (AI) lab building an open, permissionless, decentralized machine learning network with a crypto economy. democratizes access to AI technology with a permissionless network upon which anyone can connect and access secure datasets by using autonomous AI to execute tasks that leverage its global network of data.

The Fetch.AI model is rooted in use cases like optimizing DeFi trading services, transportation networks (parking, micromobility), smart energy grids, travel — essentially any complex digital system that relies on large-scale datasets.

Through the usage of FET, users can build and deploy their own digital twins on the network. Developers, by paying with FET tokens, can access machine-learning-based utilities to train autonomous digital twin and deploy collective intelligence on the network.


2. PlatON - LAT

DescribePrice overviewWebsite

PlatON, initiated and driven by the LatticeX Foundation, is a next-generation Internet infrastructure protocol based on the fundamental properties of blockchain and supported by the privacy-preserving computation network. “Computing interoperability” is its core feature.

By building a computing system assembled by Verifiable Computation, Secure Multi-Party Computation, Zero-Knowledge Proof, Homomorphic Encryption and other cryptographic algorithms and blockchain technology, PlatON provides a public infrastructure in open source architecture for global artificial intelligence, distributed application developers, data providers and various organizations, communities and individuals with computing needs.


3. Velas - VLX

DescribePrice overviewWebsite

Velas is the world's fastest EVM Blockchain enabling up to 75 000 tx/s, processed instantly, with highest security out there, at almost for free.

Velas Ecosystem consists of decentralised products built on top of its chain to present the ease of user experience of decentralised, open-source products.

Makes Velas Unique

EVM — supports all smart contracts and dApps built on the Ethereum stack.

Instant, low-cost transactions - extremely efficient performance at a fraction of the cost.

Velas Account - passwordless solution that facilitates interaction with blockchain apps to the level of Google account and PayPal-like convenience, without sacrificing our user's security.

Access Management - a decentralized access management system, which allows users to control access to files on IPFS using multiple encryption types.

Velas Vault - a novel solution to store secrets and private keys. This enables various use cases, such as decentralized custody solutions of assets native to other blockchains (BTC, ETH, ERC-20, etc).

Velas Wallet - multi-currency wallet with staking functionality.

MicroApps - Velas will soon support cross-platform decentralized applications where not only the code (logic) is stored on-chain, but also its user interface.


4. Ocean Protocol - OCEAN

DescribePrice overviewWebsite

Ocean Protocol is a blockchain-based ecosystem that allows individuals and businesses to easily unlock the value of their data and monetize it through the use of ERC-20 based datatokens.

Through Ocean Protocol, publishers can monetize their data while preserving privacy and control, whereas consumers can now access datasets that were previously unavailable or difficult to find. These datasets can be discovered on the Ocean Market, where they can be purchased and later consumed or sold.

On Ocean Protocol, each data service is represented by a unique datatoken, which is used to wrap a dataset or compute-to-data service — this essentially allows third-parties to perform operations on the data without it ever leaving the secure enclave of the publisher.

OCEAN is a utility token that is used for community governance and staking on data, in addition to buying and selling data as the basic unit of exchange on the Ocean Market. The price of these datatokens is set by an OCEAN-datatoken AMM pool, which adjusts the price of the datatoken as it is bought and sold based on supply and demand.


5. Numeraire - NMR

DescribePrice overviewWebsite

Numerai is an Ethereum-based platform allowing developers and data scientists to experiment and create machine learning models with improved reliability. The platform’s main goal is to bring decentralization to the data science field and allow developers to compete in creating effective machine learning prediction models.

Numerai and the Numeraire token are unique in terms of the idea behind their creation. This is reportedly the first cryptocurrency to be created and released by a hedge fund. One of the main benefits of the NMR token is that it is awarded to data scientists whose models perform well in the Numerai tournament. This means that the token becomes more valuable as more people enter the tournament and start competing.

Not only that, but the models ventured for the tournament allow Numerai to actively participate in stock market trading based on the results revealed by participating projects. This innovative approach to stock trading makes Numerai one of the few hedge funds to rely significantly on AI-generated data predictions.


6. AllianceBlock - ALBT

DescribePrice overviewWebsite

AllianceBlock is described to provide the bridge between traditional and digital capital markets for all participants, , reflecting how traditional finance would be designed today with current technology.

AllianceBlock is creating an ecosystem of stakeholders across the full spectrum of traditional and decentralised finance with a vision to create a fully decentralised and globally compliant capital market.

Industry stakeholders and service providers can become a ‘node’ in the AllianceBlock Ecosystem and propose their services while being compliant with multi-jurisdictional regulations and also seamlessly plugging into legacy TradFi systems.


7. SingularityNET - AGIX

DescribePrice overviewWebsite

SingularityNET is a blockchain-powered platform that allows anybody to easily "create, share, and monetize" AI services, thanks to its globally-accessible AI marketplace.

Through the SingularityNET marketplace, users can browse, test and purchase a huge variety of AI services using the platform’s native utility token — AGIX. Moreover, the marketplace represents an outlet AI developers can use to publish and sell their AI tools, and easily track their performance.

The team behind SingularityNET pioneered the development of an AI known as Sophia, which is described as the "world's most expressive robot". SingularityNET’s goal is to enable Sophia to be able to fully understand human language, and continue developing “OpenCog” — an AI framework that is hoped to eventually achieve a state known as “advanced general intelligence,” i.e. human-level artificial intelligence.


8. Sentinel Protocol - UPP

DescribePrice overviewWebsite

Sentinel Protocol is a blockchain-based, threat intelligence platform that defends against hacks, scams, and fraud using crowdsourced threat data collected by security experts and artificial Intelligence.

The goal is to significantly enhance crypto asset security by making a decentralized Threat Reputation Database (TRDB) available to crypto exchanges, wallets and payment services.


9. HackenAI - HAI

DescribePrice overviewWebsite

Hacken Token (HAI) is a cybersecurity coin underlying the rapidly growing Hacken Foundation. Hacken Foundation is a fully fledged organization that unites cybersecurity products and companies developing secure Web 3.0 infrastructure.

Hacken Foundation is trusted by both crypto industry leaders (Vechain, Ava Labs, FTX, and 300+ other entities) and traditional IT and product companies (Namecheap, AirAsia) while being an official partner of the government of Ukraine. Native token HAI serves as a utility for hundreds of different B2C, B2B, and B2G cybersecurity products and gives birth to new cybersecurity start-ups like (HAPI), Disbalancer (DDOS), and many more.

HAI token may be referred to as the index of crypto industry cybersecurity. The original HKN ERC-20 token was swapped into HAI and is no longer tradable at cryptocurrency exchanges. Please read detailed instruction below how to swap ERC20 HKN into HAI.


10. Cortex - CTXC

DescribePrice overviewWebsite

Cortex is an open-source, peer-to-peer, decentralized blockchain platform that supports Artificial Intelligence (AI) models to be uploaded and executed on the distributed network.

Cortex provides an open-source AI platform to achieve AI democratization where models can be integrated easily in smart contracts and create AI-enable decentralized applications (DApps).


11. Vectorspace AI - VXV

DescribePrice overviewWebsite

Vectorspace AI provides high-value correlation matrix datasets to give researchers the ability to accelerate their data-driven innovation and discoveries using patent-protected NLP/NLU.

Clients save time in the research loop by quickly testing hypotheses and running experiments with higher throughput.

Vectorspace AI originated in the Life Sciences dept. of Lawrence Berkeley National Laboratory (LBNL) where the founders developed the patents that drive the company’s innovation for a variety of academic institutions


12. GoCrypto Token - GOC

DescribePrice overviewWebsite
GoCrypto operates as a global payment scheme connecting all the stakeholders interested in crypto – crypto users, crypto wallets, crypto exchanges, cashier system providers, payment solution providers and merchants.

13. TrustVerse - TRV

DescribePrice overviewWebsite

TrustVerse aims to become the universe of trust. It describes itself as a blockchain-based AI-wealth management and digital asset planning protocol.

TrustVerse enables client to navigate the complex landscape of digital asset with a connected, manageable secure & protect suite of solutions.

Even works for DeFi, NFT asset with your full ownership and control


14. Cirus Foundation - CIRUS

DescribePrice overviewWebsite

Cirus Foundation (CIRUS) is a multi-layered blockchain-powered ecosystem with a global goal of accelerating web 3.0 adoption and building data economics. A powerful and easy-to-use platform, where users have complete control over their data and can successfully monetize it when needed.

Cirus consists of Cirus Device, Cirus Core Platform, and Cirus Confluence Network. Thus, the ecosystem combines Hardware, Traditional Software, Blockchain Technology and a Tokenized Ecosystem, where all elements work in concert, giving users ownership of the generated data streams, converting data into cryptocurrency and leveraging the data value in DeFi and web 3.0 protocols.

At the heart of everything is the Cirus hardware solution - a Wi-Fi router for collecting data from all connected devices. It also performs security functions and shares data across ecosystems. According to the developers, there will be two types of devices available. By eliminating intermediaries, Cirus aims to reduce losses and empower their users, who are in full control of their own data. Cirus’ users can decide how they wish to monetize their data and, in doing so, generate passive income.

The Cirus economy is supported by the CIRUS token, which is responsible for rewarding contributors, data providers and stakers. All income is paid in the form of CIRUS tokens.


15. Effect.AI - EFX

DescribePrice overviewWebsite

Effect Network (EFX) is a cryptocurrency and operates on the BSC and EOS platform.

Controlled by the EffectDAO, their main product is Effect Force: The first Blockchain-based framework for the Future-of-Work. A global, on-demand WorkForce is submitting microtasks on the blockchain to earn EFX. 


16. Connectome - CNTM

DescribePrice overviewWebsite

Connectome is a technology platform to realize human-like AI assistant, “Virtual Human Agent” (VHA). By harnessing the Artificial Intelligence (AI), Game AI (human-like intelligent NPC technology), blockchain and human sciences, VHAs can, amongst other things, be personal assistants, the cornerstone of productive organizations and companies, assist in healthcare, and be the future of human-technology interaction. The goal is to create a future where humans can trust in and live alongside AI technologies that will increase the quality of communication between humans, as well as between humans and technology.

The Connectome platform employs AI technology to provide a software development kit (SDK) which can be used by individual users to create human-like decision-making AI. The uses cases are many and varied. For example, game creators can design algorithms around situational understanding AI and use it to design their own characters. Individuals and companies can create VHAs that can function as tailor-made personal assistants or virtual, in-office co-workers.


article data source: coinmarketcap

How and Where to Buy AI Tokens?

You will have to first buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance (BNB)…

We will use Binance Exchange here as it is one of the largest crypto exchanges that accept fiat deposits.

Once you finished the KYC process. You will be asked to add a payment method. Here you can either choose to provide a credit/debit card or use a bank transfer, and buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance (BNB)…


Once finished you will then need to make a BTC/ETH/USDT/BNB deposit to the exchange from Binance depending on the available market pairs. After the deposit is confirmed you may then purchase Coin/Token from the Binance exchange.

Read more: How to Earn with Crypto Exchange Affiliate Programs

Thank you for reading !

#blockchain #bitcoin #cryptocurrency #token #AI #artificial-intelligence 

Top 20 Artificial Intelligence (AI) Tokens
Aketch  Rachel

Aketch Rachel


Libs for Swift AI-based Projects

In this Swift article, we learn about Libs for Swift AI-based Projects.. Libs for AI based projects (Machine Learning, Neural Networks etc)

Table of contents:

  • CoreML-Models - A collection of unique Core ML Models.
  • DL4S - Automatic differentiation, fast tensor operations and dynamic neural networks from CNNs and RNNs to transformers.


Libs for Swift AI-based Projects

  1. CoreML-Models

Since iOS 11, Apple released Core ML framework to help developers integrate machine learning models into applications. The official documentation

We've put up the largest collection of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques.

If you've converted a Core ML model, feel free to submit a pull request.

Recently, we've included visualization tools. And here's one Netron.

View on GitHub

2.  DL4S

DL4S provides a high-level API for many accelerated operations common in neural networks and deep learning. It furthermore has automatic differentiation builtin, which allows you to create and train neural networks without needing to manually implement backpropagation - without needing a special Swift toolchain.

Features include implementations for many basic binary and unary operators, broadcasting, matrix operations, convolutional and recurrent neural networks, commonly used optimizers, second derivatives and much more. DL4S provides implementations for common network architectures, such as VGG, AlexNet, ResNet and Transformers.

While its primary purpose is deep learning and optimization, DL4S can be used as a library for vectorized mathematical operations like numpy.


iOS / tvOS / macOS

  1. In Xcode, select "File" > "Swift Packages" > "Add Package Dependency"
  2. Enter into the Package URL field and click "Next".
  3. Select "Branch", "master" and click "Next".
  4. Enable the Package Product DL4S, your app in the "Add to Target" column and click "Next".

Note: Installation via CocoaPods is no longer supported for newer versions.

Swift Package

Add the dependency to your Package.swift file:

.package(url: "", .branch("master"))

Then add DL4S as a dependency to your target:

.target(name: "MyPackage", dependencies: ["DL4S"])

View on GitHub

Related videos:

Easy Machine Learning on iOS - Recognise foods | 100 Lines of Swift

Related posts:

#swift #machine-learning #AI 

Libs for Swift AI-based Projects
Python  Library

Python Library


Automate The Mass Creation Of AI-powered Artwork with Python

AI Art Generator

For automating the creation of large batches of AI-generated artwork locally. Put your GPU(s) to work cranking out AI-generated artwork 24/7 with the ability to automate large prompt queues combining user-selected subjects, styles/artists, and more! More info on which models are available after the sample pics.
Some example images that I've created via this process (these are cherry-picked and sharpened):
sample image 1 sample image 2 sample image 3 sample image 4 sample image 5 sample image 6  
Note that I did not create or train the models used in this project, nor was I involved in the original coding. I've simply modified the original colab versions so they'll run locally and added some support for automation. Models currently supported, with links to their original implementations:


You'll need an Nvidia GPU, preferably with a decent amount of VRAM. 12GB of VRAM is sufficient for 512x512 output images depending on model and settings, and 8GB should be enough for 384x384 (8GB should be considered a reasonable minimum!). To generate 1024x1024 images, you'll need ~24GB of VRAM or more. Generating small images and then upscaling via ESRGAN or some other package provides very good results as well.

It should be possible to run on an AMD GPU, but you'll need to be on Linux to install the ROCm version of Pytorch. I don't have an AMD GPU to throw into a Linux machine so I haven't tested this myself.


These instructions were tested on a Windows 10 desktop with an Nvidia 3080 Ti GPU (12GB VRAM), and also on an Ubuntu Server 20.04.3 system with an old Nvidia Tesla M40 GPU (24GB VRAM).

[1] Install Anaconda, open the root terminal, and create a new environment (and activate it):

conda create --name ai-art python=3.9
conda activate ai-art

[2] Install Pytorch:

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

Note that you can customize your Pytorch installation by using the online tool located here.

[3] Install other required Python packages:

conda install -c anaconda git urllib3
pip install transformers keyboard pillow ftfy regex tqdm omegaconf pytorch-lightning IPython kornia imageio imageio-ffmpeg einops torch_optimizer

[4] Clone this repository and switch to its directory:

git clone
cd ai-art-generator

Note that Linux users may need single quotes around the URL in the clone command.

[5] Clone additional required repositories:

git clone
git clone

[6] Download the default VQGAN pre-trained model checkpoint files:

mkdir checkpoints
curl -L -o checkpoints/vqgan_imagenet_f16_16384.yaml -C - ""
curl -L -o checkpoints/vqgan_imagenet_f16_16384.ckpt -C - ""

Note that Linux users should replace the double quotes in the curl commands with single quotes.

[7] (Optional) Download additional pre-trained models:
Additional models are not necessary, but provide you with more options. Here is a good list of available pre-trained models.
For example, if you also wanted the FFHQ model (trained on faces):

curl -L -o checkpoints/ffhq.yaml -C - ""
curl -L -o checkpoints/ffhq.ckpt -C - ""

[8] (Optional) Test VQGAN+CLIP:

python -s 128 128 -i 200 -p "a red apple" -o output/output.png

You should see output.png created in the output directory, which should loosely resemble an apple.

[9] Install packages for CLIP-guided diffusion (if you're only interested in VQGAN+CLIP, you can skip everything from here to the end):

pip install ipywidgets omegaconf torch-fidelity einops wandb opencv-python matplotlib lpips datetime timm
conda install pandas

[10] Clone repositories for CLIP-guided diffusion:

git clone
git clone
git clone

[11] Download models needed for CLIP-guided diffusion:

mkdir content\models
curl -L -o content/models/ -C - ""
curl -L -o content/models/ -C - ""
curl -L -o content/models/secondary_model_imagenet_2.pth -C - ""
mkdir content\models\superres
curl -L -o content/models/superres/project.yaml -C - ""
curl -L -o content/models/superres/last.ckpt -C - ""

Note that Linux users should again replace the double quotes in the curl commands with single quotes, and replace the mkdir backslashes with forward slashes.

[12] (Optional) Test CLIP-guided diffusion:

python -s 128 128 -i 200 -p "a red apple" -o output.png

You should see output.png created in the output directory, which should loosely resemble an apple.

[13] Clone Stable Diffusion repository (if you're not interested in SD, you can skip everything from here to the end):

git clone

[14] Install additional dependancies required by Stable Diffusion:

pip install diffusers

[15] Download the Stable Diffusion pre-trained checkpoint file:

mkdir stable-diffusion\models\ldm\stable-diffusion-v1
curl -L -o stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt -C - ""

If the curl command doesn't download the checkpoint, it's gated behind a login. You'll need to register here (only requires email and name) and then you can download the checkpoint file here.
After downloading, you'll need to place the .ckpt file in the directory created above and name it model.ckpt.

[16] (Optional) Test Stable Diffusion:
The easiest way to test SD is to create a simple prompt file with !PROCESS = stablediff and a single subject. See example-prompts.txt and the next section for more information. Assuming you create a simple prompt file called test.txt first, you can test by running:

python test.txt

Images should be saved to the output directory if successful (organized into subdirectories named for the date and prompt file).

[17] Setup ESRGAN/GFPGAN (if you're not planning to upscale images, you can skip this and everything else):

git clone
pip install basicsr facexlib gfpgan
cd Real-ESRGAN
curl -L -o experiments/pretrained_models/RealESRGAN_x4plus.pth -C - ""
python develop
cd ..

You're done!

If you're getting errors outside of insufficient GPU VRAM while running and haven't updated your installation in awhile, try updating some of the more important packages, for example:

pip install transformers -U


Essentially, you just need to create a text file containing the subjects and styles you want to use to generate images. If you have 5 subjects and 20 styles in your prompt file, then a total of 100 output images will be created (20 style images for each subject).

Take a look at example-prompts.txt to see how prompt files should look. You can ignore everything except the [subjects] and [styles] areas for now. Lines beginning with a '#' are comments and will be ignored, and lines beginning with a '!' are settings directives and are explained in the next section. For now, just modify the example subjects and styles with whatever you'd like to use.

After you've populated example-prompts.txt to your liking, you can simply run:

python example-prompts.txt

Depending on your hardware and settings, each image will take anywhere from a few seconds to a few hours (on older hardware) to create. If you can run Stable Diffusion, I strongly recommend it for the best results - both in speed and image quality.

Output images are created in the output/[current date]-[prompt file name]/ directory by default. The output directory will contain a JPG file for each image named for the subject & style used to create it. So for example, if you have "a monkey on a motorcycle" as one of your subjects, and "by Picasso" as a style, the output image will be created as output/[current date]-[prompt file name]/a-monkey-on-a-motorcycle-by-picasso.jpg (filenames will vary a bit depending on process used).

You can press CTRL+SHIFT+P any time to pause execution (the pause will take effect when the current image is finished rendering). Press CTRL+SHIFT+P again to unpause. Useful if you're running this on your primary computer and need to use your GPU for something else for awhile. You can also press CTRL+SHIFT+R to reload the prompt file if you've changed it (the current work queue will be discarded, and a new one will be built from the contents of your prompt file). Note that keyboard input only works on Windows.

The settings used to create each image are saved as metadata in each output JPG file by default. You can read the metadata info back by using any EXIF utility, or by simply right-clicking the image file in Windows Explorer and selecting "properties", then clicking the "details" pane. The "comments" field holds the command used to create the image.

Advanced Usage

Directives can be included in your prompt file to modify settings for all prompts that follow it. These settings directives are specified by putting them on their own line inside of the [subject] area of the prompt file, in the following format:

![setting to change] = [new value]

For [setting to change], valid directives are:

  • ITERATIONS (vqgan/diffusion only)
  • CUTS (vqgan/diffusion only)
  • SEED
  • LEARNING_RATE (vqgan only)
  • TRANSFORMER (vqgan only)
  • OPTIMISER (vqgan only)
  • CLIP_MODEL (vqgan only)
  • D_VITB16, D_VITB32, D_RN101, D_RN50, D_RN50x4, D_RN50x16 (diffusion only)
  • STEPS (stablediff only)
  • CHANNELS (stablediff only)
  • SAMPLES (stablediff only)
  • STRENGTH (stablediff only)
  • SD_LOW_MEMORY (stablediff only)
  • USE_UPSCALE (stablediff only)
  • UPSCALE_AMOUNT (stablediff only)
  • UPSCALE_FACE_ENH (stablediff only)
  • UPSCALE_KEEP_ORG (stablediff only)

Some examples:

!PROCESS = vqgan

This will set the current AI image-generation process. Valid options are vqgan for VQGAN+CLIP, diffusion for CLIP-guided diffusion (Disco Diffusion), or stablediff for Stable Diffusion.


This will force GPU 0 be to used (the default). Useful if you have multiple GPUs - you can run multiple instances, each with it's own prompt file specifying a unique GPU ID.

!WIDTH = 384
!HEIGHT = 384

This will set the output image size to 384x384. A larger output size requires more GPU VRAM. Note that for Stable Diffusion these values should be multiples of 64.


This will tell VQGAN to use the FFHQ transformer (somewhat better at faces), instead of the default (vqgan_imagenet_f16_16384). You can follow step 7 in the setup instructions above to get the ffhq transformer, along with a link to several others.

Whatever you specify here MUST exist in the checkpoints directory as a .ckpt and .yaml file.

!INPUT_IMAGE = samples/face-input.jpg

This will use samples/face-input.jpg (or whatever image you specify) as the starting image, instead of the default random noise. Input images must be the same aspect ratio as your output images for good results. Note that when using with Stable Diffusion the output image size will be the same as your input image (your height/width settings will be ignored).

!SEED = 42

This will use 42 as the input seed value, instead of a random number (the default). Useful for reproducibility - when all other parameters are identical, using the same seed value should produce an identical image across multiple runs. Set to nothing or -1 to reset to using a random value.


Setting any of these values to nothing will return it to its default. So in this example, no starting image will be used.

!STEPS = 50

Sets the number of steps (simliar to iterations) when using Stable Diffusion to 50 (the default). Higher values take more time and may improve image quality. Values over 100 rarely produce noticeable differences compared to lower values.

!SCALE = 7.5

Sets the guidance scale when using Stable Diffusion to 7.5 (the default). Higher values (to a point, beyond ~25 results may be strange) will cause the the output to more closely adhere to your prompt.


Sets the number of times to sample when using Stable Diffusion to 1 (the default). Values over 1 will cause multiple output images to be created for each prompt at a slight time savings per image. There is no cost in GPU VRAM required for incrementing this.

!STRENGTH = 0.75

Sets the influence of the starting image to 0.75 (the default). Only relevant when using Stable Diffusion with an input image. Valid values are between 0-1, with 1 corresponding to complete destruction of the input image, and 0 corresponding to leaving the starting image completely intact. Values between 0.25 and 0.75 tend to give interesting results.


Use a forked repo with much lower GPU memory requirements when using Stable Diffusion (yes/no)? Setting this to yes will switch over to using a memory-optimized version of SD that will allow you to create higher resolution images with far less GPU memory (512x512 images should only require around 4GB of VRAM). The trade-off is that inference is much slower compared to the default official repo. For comparison: on a RTX 3060, a 512x512 image at default settings takes around 12 seconds to create; with !SD_LOW_MEMORY = yes, the same image takes over a minute. Recommend keeping this off unless you have under 8GB GPU VRAM, or want to experiment with creating larger images before upscaling.


Automatically upscale images created with Stable Diffusion (yes/no)? Uses ESRGAN/GFPGAN (see additional settings below).


How much to scale when !USE_UPSCALE = yes. Default is 2.0x; higher values require more VRAM and time.


Whether or not to use GFPGAN (vs default ESRGAN) when upscaling. GFPGAN provides the best results with faces, but may provide slightly worse results if used on non-face subjects.


Keep the original unmodified image when upscaling (yes/no)? If set to no (the default), the original image will be deleted. If set to yes, the original image will be saved in an /original subdirectory of the image output folder.

!REPEAT = no

When all jobs in the prompt file are finished, restart back at the top of the file (yes/no)? Default is no, which will simply terminate execution when all jobs are complete.

TODO: finish settings examples & add usage tips/examples, document

Download details:

Author: rbbrdckybk
Source code: 
License: View license

#python #artificialintelligence #AI 

Automate The Mass Creation Of AI-powered Artwork with Python
Emmy  Monahan

Emmy Monahan


Artificial Intelligence Skills To Learn In 2022

Undoubtedly, AI is rapidly evolving and escalating more sophisticated every day. Amid the rapid expansion of AI capabilities, businesses in every industry are looking for ways to incorporate AI into their operations. As businesses strive to stay ahead of the curve, those with the right skills will be in high demand.

Without further ado, let’s delve into the most sought-after Artificial Intelligence skills employers seek in their employees and organizations.


#AI #artificial-intelligence 

Artificial Intelligence Skills To Learn In 2022
Hoang Tran

Hoang Tran


Những Kỹ Năng Trí Tuệ Nhân Tạo Cần Học Vào Năm 2022

Trí tuệ nhân tạo (AI) là quá trình lập trình một máy tính có thể suy luận và học hỏi giống như con người và đưa ra quyết định cho chính nó.

Những lời bàn tán xung quanh Trí tuệ nhân tạo (AI) đã phát triển đều đặn trong nhiều năm. Tuy nhiên, nó đã bùng nổ trong những tháng gần đây khi những gã khổng lồ công nghệ cũng như các công ty khởi nghiệp đua nhau phát triển các ứng dụng và khả năng AI mới.

Trong Trí tuệ nhân tạo, một cỗ máy có khả năng tự học và làm việc, đưa ra quyết định dựa trên dữ liệu mà nó được cung cấp. Mặc dù AI có nhiều định nghĩa khác nhau, nhưng nhìn chung, nó có thể được tóm tắt là một quá trình làm cho một hệ thống máy tính trở nên "thông minh" - có thể hiểu được các nhiệm vụ khó và thực hiện các lệnh phức tạp.

Một trong những lý do chính khiến AI ngày càng trở nên phổ biến là khả năng tự động hóa các tác vụ tiêu tốn nhiều thời gian hoặc mệt mỏi của con người. Ví dụ, trong bán lẻ, AI có thể theo dõi mức tồn kho và dự đoán nhu cầu của khách hàng, và thông tin này sau đó có thể được sử dụng để hợp lý hóa chuỗi cung ứng và cải thiện quản lý kho hàng. Trong lĩnh vực chăm sóc sức khỏe, AI có thể xử lý và giải thích các hình ảnh y tế, có thể giúp chẩn đoán bệnh và lập kế hoạch điều trị.

Do đó, có nhu cầu đáng kể về các kỹ năng AI trong nhiều doanh nghiệp và ngành công nghiệp. Doanh thu toàn cầu cho AI đã tăng 14,1% từ năm 2020 lên 51,5 tỷ USD vào năm 2021, theo dự báo từ Gartner. Theo Fortune Business Insights, trong giai đoạn dự báo 2021–2028, ngành công nghiệp AI trên toàn thế giới được dự đoán sẽ phát triển với tốc độ CAGR là 33,6%, đạt 360 tỷ USD vào năm 2028.

Không còn nghi ngờ gì nữa, AI đang phát triển nhanh chóng và ngày càng tinh vi hơn mỗi ngày. Giữa sự mở rộng nhanh chóng của các khả năng AI, các doanh nghiệp trong mọi ngành đang tìm cách kết hợp AI vào hoạt động của họ. Khi các doanh nghiệp cố gắng đi trước xu hướng, những người có kỹ năng phù hợp sẽ có nhu cầu cao.

Không cần giải thích thêm, chúng ta hãy đi sâu vào các kỹ năng Trí tuệ nhân tạo được tìm kiếm nhiều nhất mà các nhà tuyển dụng tìm kiếm ở nhân viên và tổ chức của họ.

Kỹ năng trí tuệ nhân tạo hàng đầu

Sự nhiệt tình xung quanh việc nắm bắt các kỹ năng AI là khá cao trong giới sinh viên, các chuyên gia đang làm việc và các nhà lãnh đạo doanh nghiệp. Vậy thì những khả năng quan trọng nào cần thiết để xây dựng sự nghiệp thành công với tư cách là Kỹ sư AI? Chúng được nêu dưới đây:

Kĩ năng lập trình

Bất kể bạn đang ở lĩnh vực nào, ngôn ngữ lập trình máy tính vẫn rất cần thiết vì chúng là nền tảng của các chương trình máy tính mà chúng ta thực thi hàng ngày. Chúng cho phép chúng ta giao tiếp với máy tính và tạo ra các chương trình làm cho chúng hoạt động. Thật khó, nếu không muốn nói là không thể, để tưởng tượng một thế giới không có ngôn ngữ lập trình máy tính.

Một người khao khát AI cần phải quen thuộc với bất kỳ ngôn ngữ lập trình nào được triển khai rộng rãi nhất, bao gồm Python, R, Java và C ++, trong số những ngôn ngữ lập trình khác. Mỗi ngôn ngữ lập trình đều có các thông số kỹ thuật riêng có thể được sử dụng trong Trí tuệ nhân tạo cũng như Học máy. 


Do tính đơn giản, độ tin cậy của mã và tốc độ thực thi, Python được sử dụng rộng rãi trong AI và Machine Learning. Nó yêu cầu rất ít mã và sẽ hỗ trợ bạn viết các thuật toán phức tạp, và nó bao gồm nhiều thư viện nâng cao để tính toán khoa học và phức tạp.


Đối với phân tích số, tính toán thống kê, Học máy, mạng nơ-ron và các tác vụ khác, R là bắt buộc. R cung cấp cho bạn khả năng thu thập và sắp xếp các tập dữ liệu, áp dụng Học máy và các hàm thống kê cũng như xử lý dữ liệu bằng cách sử dụng các phép biến đổi ma trận và đại số tuyến tính.


Trong AI, bộ lập bản đồ, bộ giảm, lập trình trí tuệ, thuật toán tìm kiếm, lập trình di truyền, phương pháp tiếp cận ML, mạng nơ-ron, v.v., đều được thực hiện bằng Java.

C ++

AI tận dụng lợi thế của ngôn ngữ C ++ để tạo điều kiện thuận lợi cho việc lập trình thủ tục và thao tác tài nguyên phần cứng. Nó có thể được sử dụng để tạo trình duyệt, trò chơi điện tử và hệ điều hành. Nó khá hữu ích trong AI, nhờ vào khả năng thích ứng và các tính năng hướng đối tượng.

Thư viện và Khung công tác

Khi tạo các ứng dụng AI, các nhà phát triển có quyền truy cập vào nhiều loại thư viện và khuôn khổ. Các khung và thư viện phổ biến bao gồm Seaborn, Matplotlib, TensorFlow, NumPy, Keras, Apache Spark và nhiều thư viện khác. Chúng được sử dụng cho các hoạt động về số, tính toán khoa học và nghiên cứu các tập dữ liệu lớn, cùng những thứ khác. Các thư viện này cũng có thể được sử dụng để viết mã một cách chính xác với tốc độ nhanh chóng.

Toán học và Thống kê

Chúng ta phải lập trình máy móc với sự hiểu biết và logic để làm cho chúng có khả năng học hỏi từ kinh nghiệm. Đây là lúc toán học và thống kê phát huy tác dụng. Thống kê là nghiên cứu về cách thu thập, phân tích và giải thích số liệu thống kê, trong khi toán học là nghiên cứu về các mẫu và mối tương quan trong các con số. Nhờ toán học và thống kê, chúng ta có các công cụ cần thiết để đánh giá và hiểu dữ liệu.

Các khái niệm toán học và thống kê bao gồm đại số tuyến tính, thống kê, xác suất, đồ thị, phương pháp tối ưu hóa, v.v. Những khả năng này có thể được sử dụng để giải quyết các vấn đề và phát triển các thuật toán dựa trên các đặc tả.

Học máy và Học sâu

Hai lĩnh vực Khoa học Máy tính đang mở rộng với tốc độ chóng mặt là Học máy và Học sâu. Cả hai đều liên quan đến việc đào tạo máy tính để lấy kiến ​​thức từ dữ liệu mà không cần lập trình rõ ràng. Máy học có thể được sử dụng để cải thiện độ chính xác của các dự đoán được thực hiện bởi phần mềm. Đồng thời, Học sâu có thể được sử dụng để cải thiện hiệu suất của hệ thống Học máy bằng cách cung cấp thêm dữ liệu để hệ thống học hỏi từ đó. 

Nhìn chung, Học máy và Học sâu ngày càng trở nên quan trọng khi chúng ta tiến tới một xã hội dựa trên dữ liệu nhiều hơn. Nhờ Machine Learning, máy tính có thể học hỏi kinh nghiệm và thích ứng với các tình huống mới. Học sâu, miền phụ của Học máy, sử dụng Mạng thần kinh để học ở cấp độ sâu hơn. Mạng nơ ron là một mạng lưới các nút xử lý được kết nối với nhau có thể học cách xác định các mẫu dữ liệu đầu vào.

Xử lý ngôn ngữ tự nhiên và thị giác máy tính

Nghiên cứu về cách máy tính có thể giải thích và xử lý ngôn ngữ của con người được gọi là Xử lý ngôn ngữ tự nhiên (NLP). Nó bao gồm các hoạt động như hiểu nghĩa của từ, phân tích các cụm từ thành các bộ phận cấu thành của chúng và hiểu các mối quan hệ từ. NLP có thể được sử dụng cho nhiều hoạt động, bao gồm hiểu máy, tóm tắt văn bản và dịch tự động.

Computer Vision (CV) tập trung vào việc giải thích và hiểu các hình ảnh kỹ thuật số bằng máy tính. Nó bao gồm các hoạt động như nhận dạng khuôn mặt, xác định các mục và đối tượng trong ảnh và dự đoán hình học 3D của các đối tượng trong ảnh.

NLP rất quan trọng đối với AI vì nó cho phép máy tính hiểu được ngôn ngữ của con người, điều này rất cần thiết cho các nhiệm vụ như tạo chatbot hoặc trợ lý giọng nói. CV rất quan trọng đối với AI vì nó cho phép máy tính giải thích và hiểu hình ảnh, điều này rất cần thiết cho các tác vụ như nhận dạng đối tượng hoặc nhận dạng khuôn mặt.

Khoa học dữ liệu và phân tích dữ liệu

Trong thế giới ngày càng dựa vào dữ liệu của chúng ta, việc có thể hiểu và phân tích dữ liệu quan trọng hơn bao giờ hết. Khoa học dữ liệu và Phân tích dữ liệu là những kỹ năng quan trọng cho phép chúng ta hiểu được núi dữ liệu ngày càng tăng bao quanh chúng ta.

Khoa học dữ liệu là quá trình rút ra ý nghĩa từ dữ liệu và nó liên quan đến mọi thứ, từ việc dọn dẹp và tổ chức dữ liệu đến thực hiện các phân tích phức tạp và tạo ra các mô hình dự đoán. Các nhà khoa học dữ liệu rất giỏi trong việc tìm kiếm các mẫu và thông tin chi tiết về dữ liệu, sau đó có thể được sử dụng để phát triển các thuật toán AI và đưa ra các quyết định tốt hơn.

Phân tích dữ liệu là một phần quan trọng của Khoa học dữ liệu. Nó liên quan đến việc lấy một tập dữ liệu lớn và trích xuất những thông tin chi tiết có thể hành động từ nó. Các nhà phân tích dữ liệu có kỹ năng xác định xu hướng, phát hiện các điểm bất thường và xác định mối quan hệ giữa các biến, điều này có thể cải thiện độ chính xác của các ứng dụng AI.

Các kĩ năng mềm

Bạn đang thắc mắc tại sao kỹ năng mềm lại cần thiết cho một lĩnh vực liên quan đến công nghệ như Trí tuệ nhân tạo? Câu trả lời cho những nghi ngờ của bạn là một câu đơn giản! Đúng vậy, kỹ năng mềm cũng là một phần quan trọng của thế giới công nghệ. 

Giá trị của các kỹ năng mềm trong kỷ nguyên AI đã bắt đầu được các nhà tuyển dụng hiểu rõ. Nhân viên sẽ cần phải có khả năng sử dụng những kỹ năng này để cộng tác với các nhân viên khác nhằm thành công trong thời đại của Trí tuệ nhân tạo.

Một số kỹ năng mềm cần thiết bao gồm hợp tác, giao tiếp, tư duy phản biện và giải quyết vấn đề. 

Sự hợp tác

Sự hợp tác giữa nhân viên và các đơn vị khác là rất quan trọng vì nó có thể giúp hiểu rõ vấn đề hơn, giải pháp nhanh hơn, ra quyết định tốt hơn và sản phẩm cuối cùng được cải thiện.

Liên lạc

Sở hữu kỹ năng giao tiếp tốt sẽ giúp mọi người xây dựng lòng tin và mối quan hệ với đồng nghiệp, ngăn ngừa và giải quyết xung đột, đồng thời biến họ thành những thành viên trong nhóm hiệu quả hơn. Những kỹ năng này cũng tạo điều kiện cho mọi người hiểu rõ hơn và thực hiện các hướng dẫn từ người giám sát của họ.

Tư duy phản biện và giải quyết vấn đề

Tư duy phản biện cho phép nhân viên nhìn thấy tất cả các mặt của một vấn đề và đưa ra quyết định tốt nhất cho công ty. Kỹ năng giải quyết vấn đề rất cần thiết vì chúng cho phép nhân viên tìm ra các giải pháp sáng tạo cho các vấn đề phức tạp. Những kỹ năng này làm cho nhân viên hiệu quả hơn và hiệu quả hơn trong công việc của họ.

Bước tiếp theo: Làm thế nào bạn có thể nâng cao?

Vì vậy, nếu bạn đang cân nhắc về một sự nghiệp trong lĩnh vực AI, thì bây giờ là lúc bạn nên thực hiện. Với việc các doanh nghiệp đầu tư mạnh vào công nghệ này và nhu cầu về nhân tài lớn hơn nguồn cung, chưa bao giờ có thời điểm tốt hơn để phát triển các kỹ năng AI của bạn.

Thuật ngữ “nâng cao kỹ năng” đã xuất hiện trong một thời gian, nhưng chỉ gần đây nó mới được sử dụng rộng rãi. Ở dạng đơn giản nhất, nâng cao kỹ năng là hành động học hỏi các kỹ năng mới hoặc cải thiện những kỹ năng hiện có. Chúng ta đang sống trong một thế giới không ngừng phát triển, nơi các công nghệ và xu hướng mới liên tục xuất hiện. Để đi trước những khúc quanh, chúng ta cần học hỏi và thích nghi liên tục.

Vì vậy, một số tùy chọn nâng cao kỹ năng là gì? 

Có nhiều cách khác nhau để học các kỹ năng mới, nhưng một số cách phổ biến nhất bao gồm học truyền thống, các khóa học trực tuyến, hội thảo, hội thảo và tự học.

Học tập truyền thống: Cao đẳng và Đại học

Cách học truyền thống thường là học trên lớp, đã có từ nhiều thế kỷ trước. Giả sử bạn đang tìm kiếm một chương trình lớp học truyền thống để học Trí tuệ nhân tạo. Trong trường hợp đó, bạn có thể lựa chọn một số tùy chọn, bao gồm các trường đại học tốt nhất thế giới, chẳng hạn như Viện Công nghệ Massachusetts (MIT) và Đại học Stanford. Cuối cùng, bạn sẽ nhận được chứng chỉ hoàn thành từ tổ chức hoặc trường đại học tương ứng.

Các khóa học trực tuyến: Nền tảng học tập điện tử

Trong những năm gần đây, các khóa học trực tuyến đang bùng nổ với tốc độ đáng kinh ngạc. Hiện nay có một số nền tảng khác nhau cung cấp các khóa học trực tuyến và bản thân các khóa học bao gồm một loạt các chủ đề.

Hội thảo và Hội thảo

Hội thảo và hội thảo là những cách tuyệt vời để học các kỹ năng mới hoặc đạt được kiến ​​thức mới, có thể mang tính giáo dục hoặc cung cấp thông tin và thường kéo dài từ vài giờ đến vài ngày. Nhiều người tham gia các hội thảo và hội thảo để nâng cao kỹ năng kinh doanh của họ hoặc tìm hiểu về các lĩnh vực hoặc ngành công nghiệp mới.

Lợi ích của việc tham dự các hội thảo và hội thảo bao gồm có được những hiểu biết mới, kết nối với các chuyên gia khác và có cơ hội học hỏi và tiếp thu kiến ​​thức từ các chuyên gia trong lĩnh vực này. Ngoài ra, hội thảo và hội thảo có thể là một cách tuyệt vời để xây dựng sơ yếu lý lịch và thúc đẩy sự nghiệp của bạn.

Tự học: YouTube & Sách

Nếu bạn quan tâm đến việc tự học, cách tốt nhất để bắt đầu là bắt đầu học! Chọn một chủ đề bạn quan tâm và tìm các tài nguyên để giúp bạn bắt đầu; các tài nguyên tốt nhất bao gồm YouTube và Sách.

YouTube là nền tảng video hàng đầu thế giới do Google sở hữu, bao gồm một số video hữu ích về AI, chẳng hạn như giới thiệu về AI, cách lập trình AI và cách áp dụng AI. Một số tài nguyên hàng đầu bao gồm Springboard, Arxiv Insights, và Edureka, cùng một số tài nguyên khác. 

Một số người thích hành động đọc sách do bản chất xúc giác của họ - cảm giác của giấy và khả năng làm nổi bật và chú thích vật lý khi họ đọc. Đây là hai cuốn sách mà tôi đã xem qua rất hấp dẫn và thú vị:

  1. Trí tuệ nhân tạo: Học các kỹ năng tự động hóa với Python
  2. Trí tuệ nhân tạo: Phương pháp tiếp cận hiện đại

Tổng hợp

Nhu cầu về các kỹ năng AI đang tăng lên khi các doanh nghiệp nhận thức rõ hơn về cách công nghệ này có thể nâng cao quy trình làm việc của họ. Các chuyên gia AI hiệu quả sẽ có nhu cầu cao và có thể tìm được việc làm trong nhiều ngành công nghiệp. Ngoài ra, những người thành thạo AI sẽ có thể mở ra những cánh cửa mới cho cả bản thân và công ty của họ.


#AI #artificial-intelligence 

Những Kỹ Năng Trí Tuệ Nhân Tạo Cần Học Vào Năm 2022

Навыки искусственного интеллекта, которым нужно научиться в 2022 году

Искусственный интеллект (ИИ) — это процесс программирования компьютера, который может рассуждать и учиться, как человек, и самостоятельно принимать решения.

Ажиотаж вокруг искусственного интеллекта (ИИ) неуклонно растет уже много лет. Тем не менее, в последние месяцы он взорвался, поскольку технологические гиганты и стартапы стремились разработать новые приложения и возможности ИИ.

В искусственном интеллекте машине дается возможность учиться и работать самостоятельно, принимая решения на основе предоставленных ей данных. Хотя ИИ имеет множество различных определений, в целом его можно охарактеризовать как процесс превращения компьютерной системы в «умную» — способную понимать сложные задачи и выполнять сложные команды.

Одной из основных причин чрезвычайно растущей популярности ИИ является его способность автоматизировать задачи, которые отнимают много времени или утомительны для людей. Например, в розничной торговле ИИ может отслеживать уровень запасов и прогнозировать потребительский спрос, а затем эту информацию можно использовать для оптимизации цепочки поставок и улучшения управления запасами. В здравоохранении искусственный интеллект может обрабатывать и интерпретировать медицинские изображения, что может помочь в диагностике заболеваний и планировании лечения.

Следовательно, существует значительный спрос на навыки ИИ во многих компаниях и отраслях. Согласно прогнозам Gartner, глобальный доход от ИИ увеличился на 14,1% с 2020 года до 51,5 млрд долларов в 2021 году. Согласно прогнозу Fortune Business Insights, в течение прогнозируемого периода 2021–2028 годов мировая индустрия искусственного интеллекта, как ожидается, будет расти в среднем на 33,6%, достигнув 360 миллиардов долларов к 2028 году.

Несомненно, ИИ стремительно развивается и с каждым днем ​​становится все более изощренным. В условиях быстрого расширения возможностей ИИ компании в каждой отрасли ищут способы внедрить ИИ в свою деятельность. Поскольку предприятия стремятся оставаться на шаг впереди, люди с нужными навыками будут пользоваться большим спросом.

Без лишних слов давайте углубимся в наиболее востребованные навыки искусственного интеллекта, которые работодатели ищут в своих сотрудниках и организациях.

Лучшие навыки искусственного интеллекта

Энтузиазм в отношении освоения навыков ИИ среди студентов, работающих профессионалов и руководителей бизнеса довольно высок. Какие важные способности необходимы для построения успешной карьеры инженера ИИ? Они указаны ниже:

Навыки программирования

Независимо от того, в какой области вы работаете, языки программирования необходимы, поскольку они являются основой компьютерных программ, которые мы выполняем каждый день. Они позволяют нам общаться с компьютерами и создавать программы, которые заставляют их работать. Трудно, если вообще возможно, представить мир без языков программирования.

Претендент на ИИ должен быть знаком с любым из наиболее широко используемых языков программирования, включая Python, R, Java и C++, среди прочих. У каждого языка программирования есть свои спецификации, которые можно использовать как в искусственном интеллекте, так и в машинном обучении. 


Благодаря своей простоте, надежности кода и скорости выполнения Python широко используется в ИИ и машинном обучении. Он требует очень мало кода и поможет вам в написании сложных алгоритмов, а также включает множество расширенных библиотек для сложных и научных вычислений.


Для численного анализа, статистических вычислений, машинного обучения, нейронных сетей и других задач требуется R. R предоставляет вам возможность собирать и упорядочивать наборы данных, применять машинное обучение и статистические функции, а также обрабатывать данные с помощью матричных преобразований и линейной алгебры.


В ИИ картографы, редукторы, интеллектуальное программирование, алгоритмы поиска, генетическое программирование, подходы машинного обучения, нейронные сети и т. д. — все это реализовано с использованием Java.


ИИ использует язык C++ для облегчения процедурного программирования и управления аппаратными ресурсами. Его можно использовать для создания браузеров, видеоигр и операционных систем. Это очень полезно в ИИ благодаря своей адаптивности и объектно-ориентированным функциям.

Библиотеки и фреймворки

При создании приложений ИИ разработчики имеют доступ к широкому спектру библиотек и фреймворков. Популярные фреймворки и библиотеки включают Seaborn, Matplotlib, TensorFlow, NumPy, Keras, Apache Spark и многие другие. Среди прочего, они используются для операций с числами, научных вычислений и изучения больших наборов данных. Эти библиотеки также можно использовать для точного написания кода в быстром темпе.

Математика и статистика

Мы должны программировать машины с пониманием и логикой, чтобы сделать их способными учиться на собственном опыте. Вот когда в игру вступают математика и статистика. Статистика — это наука о том, как собирать, анализировать и интерпретировать статистику, тогда как математика — это изучение закономерностей и корреляций в числах. Благодаря математике и статистике у нас есть инструменты, необходимые для оценки и понимания данных.

Математические и статистические концепции включают линейную алгебру, статистику, вероятность, графики, методы оптимизации и т. д. Эти возможности можно использовать для решения проблем и разработки алгоритмов на основе спецификаций.

Машинное обучение и глубокое обучение

Две области компьютерных наук, которые развиваются огромными темпами, — это машинное обучение и глубокое обучение. Оба предполагают обучение компьютеров извлечению знаний из данных без явного программирования. Машинное обучение можно использовать для повышения точности прогнозов, сделанных программным обеспечением. В то же время глубокое обучение можно использовать для повышения производительности системы машинного обучения, предоставляя системе больше данных для обучения. 

В целом машинное обучение и глубокое обучение приобретают все большее значение по мере того, как мы движемся к обществу, в большей степени управляемому данными. Благодаря машинному обучению компьютеры могут учиться на собственном опыте и адаптироваться к новым ситуациям. Глубокое обучение, подобласть машинного обучения, использует нейронные сети для обучения на более глубоком уровне. Нейронные сети представляют собой сеть взаимосвязанных узлов обработки, которые могут научиться идентифицировать шаблоны входных данных.

Обработка естественного языка и компьютерное зрение

Изучение того, как компьютеры могут интерпретировать и обрабатывать человеческий язык, известно как обработка естественного языка (NLP). Он включает в себя такие действия, как понимание значений слов, разбор фраз на их составные части и понимание отношений слов. НЛП можно использовать для широкого спектра действий, включая машинное понимание, обобщение текста и автоматический перевод.

Компьютерное зрение (CV) фокусируется на интерпретации и понимании цифровых изображений компьютерами. Он охватывает такие действия, как распознавание лиц, идентификация предметов и объектов на фотографиях и прогнозирование трехмерной геометрии объектов на изображениях.

NLP имеет решающее значение для ИИ, поскольку позволяет компьютерам понимать человеческий язык, что необходимо для таких задач, как создание чат-ботов или голосовых помощников. CV жизненно важен для ИИ, потому что он позволяет компьютерам интерпретировать и понимать изображения, что необходимо для таких задач, как распознавание объектов или лиц.

Наука о данных и анализ данных

В нашем мире, который все больше зависит от данных, как никогда важно иметь возможность понимать и анализировать данные. Наука о данных и анализ данных — это важнейшие навыки, которые позволяют нам разобраться в постоянно растущем массиве данных, который нас окружает.

Наука о данных — это процесс извлечения смысла из данных, и он включает в себя все: от очистки и организации данных до выполнения сложного анализа и создания прогностических моделей. Специалисты по данным умеют находить закономерности и идеи в данных, которые затем можно использовать для разработки алгоритмов ИИ и принятия более эффективных решений.

Анализ данных является важной частью науки о данных. Он включает в себя сбор большого набора данных и извлечение из него полезных идей. Аналитики данных умеют выявлять тенденции, выявлять аномалии и определять взаимосвязи между переменными, что может повысить точность приложений ИИ.

Мягкие навыки

Вы задаетесь вопросом, почему навыки межличностного общения необходимы для такой технологической области, как искусственный интеллект? Ответ на ваши сомнения прост! Да, навыки межличностного общения также являются жизненно важной частью мира технологий. 

Ценность soft skills в эпоху ИИ уже начинают понимать работодатели. Сотрудники должны будут иметь возможность использовать эти навыки для сотрудничества с другими сотрудниками, чтобы добиться успеха в эпоху искусственного интеллекта.

Несколько важных социальных навыков включают сотрудничество, общение, критическое мышление и решение проблем. 


Сотрудничество между сотрудниками и другими организациями имеет решающее значение, поскольку оно может привести к лучшему пониманию проблемы, более быстрому решению, более эффективному принятию решений и улучшению конечных продуктов.


Обладание сильными коммуникативными навыками поможет людям построить доверие и взаимопонимание с коллегами, предотвратить и разрешить конфликты и сделать их более эффективными членами команды. Эти навыки также помогают людям лучше понимать и выполнять инструкции своих начальников.

Критическое мышление и решение проблем

Критическое мышление позволяет сотрудникам увидеть проблему со всех сторон и принять оптимальное для компании решение. Навыки решения проблем необходимы, потому что они позволяют сотрудникам находить творческие решения сложных проблем. Эти навыки делают сотрудников более эффективными и результативными в своей работе.

Следующий шаг: как повысить квалификацию?

Так что, если вы подумываете о карьере в области искусственного интеллекта, сейчас самое время сделать свой шаг. Поскольку компании вкладывают значительные средства в эту технологию, а спрос на таланты превышает предложение, сейчас самое подходящее время для развития навыков ИИ.

Термин «повышение квалификации» существует уже некоторое время, но только недавно он стал широко использоваться. В своей простейшей форме повышение квалификации — это процесс изучения новых навыков или совершенствования существующих. Мы живем в постоянно развивающемся мире, где постоянно появляются новые технологии и тренды. Чтобы оставаться на шаг впереди, нам необходимо постоянно учиться и адаптироваться.

Итак, какие есть варианты повышения квалификации? 

Существуют различные способы изучения новых навыков, но некоторые из самых популярных включают традиционное обучение, онлайн-курсы, мастер-классы, семинары и самообучение.

Традиционное обучение: колледжи и университеты

Традиционное обучение обычно представляет собой обучение в классе, которое существует уже несколько столетий. Предположим, вы ищете традиционную классную программу для изучения искусственного интеллекта. В этом случае доступно несколько вариантов, из которых вы можете выбрать, в том числе лучшие университеты мира, например, Массачусетский технологический институт (MIT) и Стэнфордский университет. В конце концов, вы получите сертификат об окончании соответствующего учреждения или университета.

Онлайн-курсы: платформы электронного обучения

В последние годы онлайн-курсы процветают невероятными темпами. В настоящее время существует несколько различных платформ, предлагающих онлайн-курсы, а сами курсы охватывают широкий круг тем.

Мастер-классы и семинары

Мастер-классы и семинары — это отличный способ освоить новые навыки или получить новые знания, которые могут быть образовательными или информативными и обычно длятся от нескольких часов до нескольких дней. Многие люди участвуют в мастер-классах и семинарах, чтобы улучшить свои деловые навыки или узнать о новых областях или отраслях.

Преимущества посещения мастер-классов и семинаров включают в себя получение новых идей, общение с другими профессионалами и возможность учиться и приобретать знания у экспертов в этой области. Кроме того, мастер-классы и семинары могут стать невероятным способом составить ваше резюме и повысить вашу карьеру.

Самостоятельное изучение: YouTube и книги

Если вы заинтересованы в самообучении, лучший способ начать — просто начать учиться! Выберите интересующую вас тему и найдите ресурсы, которые помогут вам начать работу; лучшие ресурсы включают YouTube и книги.

YouTube — это ведущая в мире видеоплатформа, принадлежащая Google, которая содержит несколько полезных видеороликов об ИИ, таких как введение в ИИ, как программировать ИИ и как применять ИИ. Несколько первоклассных ресурсов включают Springboard, Arxiv Insights,, Edureka и ряд других. 

Некоторые люди предпочитают читать книги из-за их тактильной природы — ощущения бумаги и способности физически выделять и комментировать во время чтения. Вот две книги, которые я наткнулся на увлекательные и захватывающие:

  1. Искусственный интеллект: обучение навыкам автоматизации с помощью Python
  2. Искусственный интеллект: современный подход

Подводя итоги

Потребность в навыках ИИ возрастает по мере того, как компании все больше осознают, как эта технология может улучшить их рабочие процессы. Эффективные специалисты в области искусственного интеллекта будут пользоваться большим спросом и смогут найти работу в самых разных отраслях. Кроме того, те, кто освоит ИИ, смогут открыть новые двери как для себя, так и для своих компаний.


#AI #artificial-intelligence 

Навыки искусственного интеллекта, которым нужно научиться в 2022 году
鈴木  治

鈴木 治


2022 年要學習的人工智能技能

人工智能 (AI) 是對計算機進行編程的過程,該計算機可以像人類一樣推理和學習,並為自己做出決定。

多年來,圍繞人工智能 (AI) 的討論一直在穩步增長。儘管如此,隨著科技巨頭和初創公司等競相開發新的人工智能應用程序和功能,它在最近幾個月內呈爆炸式增長。



因此,眾多企業和行業對人工智能技能的需求很大。根據 Gartner 的預測,人工智能的全球收入從 2020 年增長 14.1% 至 2021 年的 515 億美元。根據財富商業洞察,在 2021-2028 年的預測期內,全球人工智能行業預計將以 33.6% 的複合年增長率增長,到 2028 年將達到 3600 億美元。




學生、專業人士和商業領袖對掌握 AI 技能的熱情很高。那麼,作為一名成功的人工智能工程師職業需要哪些關鍵能力呢?它們如下所述:



有志於 AI 的人需要熟悉任何最廣泛實施的編程語言,包括 Python、R、Java 和 C++ 等。每種編程語言都有自己的規範,可用於人工智能和機器學習。 


由於其簡單性、代碼可靠性和執行速度,Python 被廣泛用於人工智能和機器學習。它只需要很少的代碼,可以幫助您編寫複雜的算法,並且它包括各種用於復雜和科學計算的高級庫。


對於數值分析、統計計算、機器學習、神經網絡和其他任務,需要 R。R 為您提供了收集和排列數據集、應用機器學習和統計函數以及使用矩陣變換和線性代數處理數據的能力。


在 AI 中,映射器、reducers、智能編程、搜索算法、遺傳編程、ML 方法、神經網絡等都是使用 Java 實現的。


AI 利用 C++ 語言來促進過程編程和硬件資源操作。它可用於創建瀏覽器、視頻遊戲和操作系統。由於它的適應性和麵向對象的特性,它在人工智能中非常有用。


在創建 AI 應用程序時,開發人員可以訪問各種庫和框架。流行的框架和庫包括 Seaborn、Matplotlib、TensorFlow、NumPy、Keras、Apache Spark 等。它們用於數字運算、科學計算和研究大數據集等。這些庫也可用於快速準確地編寫代碼。








對計算機如何解釋和處理人類語言的研究被稱為自然語言處理 (NLP)。它包括諸如理解單詞含義、將短語解析為其組成部分以及理解單詞關係等活動。NLP 可用於廣泛的活動,包括機器理解、文本摘要和自動翻譯。

計算機視覺 (CV) 側重於計算機對數字圖像的解釋和理解。它涵蓋了諸如識別人臉、識別照片中的物品和對像以及預測圖像中對象的 3D 幾何形狀等活動。

NLP 對 AI 至關重要,因為它允許計算機理解人類語言,這對於創建聊天機器人或語音助手等任務至關重要。CV 對 AI 至關重要,因為它允許計算機解釋和理解圖像,這對於對象識別或面部識別等任務至關重要。




數據分析是數據科學的重要組成部分。它涉及獲取大型數據集並從中提取可操作的見解。數據分析師擅長識別趨勢、發現異常和確定變量之間的關係,這可以提高 AI 應用程序的準確性。












因此,如果您正在考慮從事 AI 行業,那麼現在是採取行動的時候了。隨著企業對這項技術的大量投資以及對人才的需求超過供應,現在是發展您的 AI 技能的最佳時機。





傳統學習通常是基於課堂的學習,已經存在了幾個世紀。假設您正在尋找一個傳統的課堂程序來學習人工智能。在這種情況下,您可以選擇多個選項,包括世界上最好的大學,例如麻省理工學院 (MIT) 和斯坦福大學。最後,您將收到相應機構或大學的結業證書。






自學:YouTube 和書籍

如果您對自學感興趣,最好的入門方式就是開始學習!選擇一個您感興趣的主題並找到幫助您入門的資源;最好的資源包括 YouTube 和圖書。

YouTube 是谷歌旗下的全球領先的視頻平台,包含了一些關於 AI 的有用視頻,例如 AI 介紹、如何編程 AI、如何應用 AI。一些一流的資源包括 Springboard、Arxiv Insights、 和 Edureka 等。 


  1. 人工智能:用 Python 學習自動化技能
  2. 人工智能:一種現代方法



來源:  https ://

#AI #artificial-intelligence 

2022 年要學習的人工智能技能