In this article, takes a look at 9 fields that AI applications have impacted and will dominate such as marketing, agriculture, gaming, healthcare... and more!
Just the mention of AI and the brain invokes pictures of Terminator machines destroying the world. Thankfully, the present picture is significantly more positive. So, let’s explore how AI is helping our planet and at last benefiting humankind. In this blog on Artificial Intelligence applications, I’ll be discussing how AI has impacted various fields like healthcare, finance, agriculture, and so on.Banking
AI in banking is growing faster than you think! A lot of banks have already adopted AI-based systems to provide customer support and detect anomalies and credit card fraud. An example of this is the HDFC Bank.
HDFC Bank has developed an AI-based chatbot called EVA (Electronic Virtual Assistant), built by Bengaluru-based Senseforth AI Research.
Since its launch, Eva has addressed over 3 million customer queries, interacted with over half a million unique users, and held over a million conversations. Eva can collect knowledge from thousands of sources and provide simple answers in less than 0.4 seconds.
The use of AI for fraud prevention is not a new concept. In fact, AI solutions can be used to enhance security across a number of business sectors, including retail and finance.
By tracing card usage and endpoint access, security specialists are more effectively preventing fraud. Organizations rely on AI to trace those steps by analyzing the behaviors of transactions.
Companies such as MasterCard and RBS WorldPay have relied on AI and deep learning to detect fraudulent transaction patterns and prevent card fraud for years now. This has saved millions of dollars.Marketing
Marketing is a way to sugar coat your products to attract more customers. We humans are pretty good at sugar coating, but what if an algorithm or a bot is built solely for the purpose of marketing a brand or a company? It would do a pretty awesome job!
In the early 2000s, if we searched an online store to find a product without knowing it’s exact name, it would become a nightmare to find the product. But now when we search for an item on any e-commerce store, we get all possible results related to the item. It’s like these search engines read our minds! In a matter of seconds, we get a list of all relevant items. An example of this is finding the right movies on Netflix.
One reason why we’re all obsessed with Netflix is that it provides highly accurate predictive technology based on customer’s reactions to films. It examines millions of records to suggest shows and films that you might like based on your previous actions and choices of films. As the data set grows, the technology gets smarter and smarter every day.
With the growing advancement in AI, it may be possible in the near future for consumers on the web to buy products by snapping a photo of it. Companies like CamFind and their competitors are experimenting with this already.Finance
Ventures have been relying on computers and data scientists to determine future patterns in the market. Trading mainly depends on the ability to predict the future accurately.
Machines are great at this because they can crunch a huge amount of data in a short span. Machines can also learn to observe patterns in past data and predict how these patterns might repeat in the future.
In the age of ultra-high-frequency trading, financial organizations are turning to AI to improve their stock trading performance and boost profit.
One such organization is Japan’s leading brokerage house, Nomura Securities. The company has been reluctantly pursuing one goal, i.e. to analyze the insights of experienced stock traders with the help of computers. After years of research, Nomura is set to introduce a new stock trading system.
The new system stores a vast amount of price and trading data in its computer. By tapping into this reservoir of information, it will make assessments. For example, it may determine that current market conditions are similar to the conditions two weeks ago and predict how share prices will be changing a few minutes down the line. This will help to take better trading decisions based on the predicted market prices.Healthcare
When it comes to saving lives, a lot of organizations and medical care centers are relying on AI. There are many examples of how AI in healthcare has helped patients all over the world.
An organization called Cambio Health Care developed a clinical decision support system for stroke prevention that can give the physician a warning when there’s a patient at risk of having a heat stroke.
Another such example is Coala Life, which is a company that has a digitalized device that can find cardiac diseases.
Similarly, Aifloo is developing a system for keeping track of how people are doing in nursing homes, home care, etc. The best thing about AI in healthcare is that you don’t even need to develop a new medication. By using an existing medication in the right way, you can also save lives.Agriculture
Here’s an alarming fact, the world will need to produce 50 percent more food by 2050 because we’re literally eating up everything! The only way this can be possible is if we use our resources more carefully. With that being said, AI can help farmers get more from the land while using resources more sustainably.
Issues such as climate change, population growth, and food security concerns have pushed the industry into seeking more innovative approaches to improve crop yield.
Organizations are using automation and robotics to help farmers find more efficient ways to protect their crops from weeds.
Blue River Technology has developed a robot called See & Spray, which uses computer vision technologies like object detection to monitor and precisely spray weedicide on cotton plants. Precision spraying can help prevent herbicide resistance.
Apart from this, Berlin-based agricultural tech start-up called PEAT has developed an application called Plantix that identifies potential defects and nutrient deficiencies in the soil through images.
The image recognition app identifies possible defects through images captured by the user’s smartphone camera. Users are then provided with soil restoration techniques, tips, and other possible solutions. The company claims that its software can achieve pattern detection with an estimated accuracy of up to 95%.Space Exploration
Space expeditions and discoveries always require analyzing vast amounts of data. Artificial intelligence and machine learning is the best way to handle and process data on this scale. After rigorous research, astronomers used Artificial Intelligence to sift through years of data obtained by the Kepler telescope in order to identify a distant eight-planet solar system.
Artificial intelligence is also being used for NASA’s next rover mission to Mars, the Mars 2020 Rover. The AEGIS, which is an AI-based Mars rover, is already on the red planet. The rover is responsible for the autonomous targeting of cameras in order to perform investigations on Mars.Gaming
Over the past few years, artificial intelligence has become an integral part of the gaming industry. In fact, one of the biggest accomplishments of AI is in the gaming industry.
DeepMind’s AI-based AlphaGo software, which is known for defeating Lee Sedol, the world champion in the game of GO, is considered to be one of the most significant accomplishments in the field of AI.
Shortly after the victory, DeepMind created an advanced version of AlphaGo called AlphaGo Zero, which defeated the predecessor in an AI-AI face off. Unlike the original AlphaGo, which DeepMind trained over time by using a large amount of data and supervision, the advanced system, taught itself to master the game.
Other examples of artificial intelligence in gaming include the First Encounter Assault Recon, popularly known as F.E.A.R, which is a first-person shooter video game.
But what makes this game so special?
The actions taken by the opponent AI are unpredictable because the game is designed in such a way that the opponents are trained throughout the game and never repeat the same mistakes. They get better as the game gets harder. This makes the game very challenging and prompts the players to constantly switch strategies and never sit in the same position.Chatbots
These days, virtual assistants are a very common technology. Almost every household has a virtual assistant that controls their appliances at home. A few examples include Siri, Cortana, and Alexa, which are gaining popularity because of the user experience they provide.
Amazon’s Echo is an example of how artificial intelligence can be used to translate human language into desirable actions. This device uses speech recognition and NLP to perform a wide range of tasks on your command. It can do more than just play your favorite songs. It can be used to control the devices at your house, book cabs, make phone calls, order your favorite food, check the weather conditions, and so on.
Another example is the newly released Google’s virtual assistant called Google Duplex, which has astonished millions of people. Not only can it respond to calls and book appointments for you, but it also adds a human touch.
The device uses Natural language processing and machine learning algorithms to process human language and perform tasks such as manage your schedule, control your smart home, make a reservation, and so on.Artificial Creativity
Have you ever wondered what would happen if an artificially intelligent machine tried to create music and art?
An AI-based system called MuseNet can now compose classical music that echoes the classical legends, Bach and Mozart.
MuseNet is a deep neural network that is capable of generating 4-minute musical compositions with 10 different instruments and can combine styles from country to Mozart to the Beatles.
MuseNet was not explicitly programmed with an understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning on its own.
Another creative product of artificial intelligence is a content automation tool called Wordsmith. Wordsmith is a natural language generation platform that can transform your data into insightful narratives.
Tech giants such as Yahoo, Microsoft, Tableau, are using WordSmith to generate around 1.5 billion pieces of content every year.Social Media
Ever since social media has become our identity, we’ve been generating an immeasurable amount of data through chats, tweets, posts, and so on. And wherever there is an abundance of data, AI and machine learning are always involved.
In social media platforms like Facebook, AI is used for face verification wherein machine learning and deep learning concepts are used to detect facial features and tag your friends. Deep learning is used to extract every minute detail from an image by using a bunch of deep neural networks. On the other hand, machine learning algorithms are used to design your feed based on your interests.
Another such example is Twitter’s AI, which is being used to identify hate speech and terroristic language in tweets. It makes use of machine learning, deep learning, and natural language processing to filter out offensive content. The company discovered and banned 300,000 terrorist-linked accounts, 95% of which were found by non-human, artificially intelligent machines.Conclusion:
Artificial Intelligence is shaping today and tomorrow. The technology has benefited the modern society with an outlook of a better world that not only peep out of the curtain at present but give a significant and clear picture of an improved and happy world.
I’d like to conclude by asking you how you think AI will benefit us in the future?
Thanks for reading. Keep Visiting
Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning - Learn about each concept and relation between them for their ...
Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning - Learn about each concept and relation between them for their ...What is Data Science?
Data Science is an interdisciplinary field whose primary objective is the extraction of meaningful knowledge and insights from data. These insights are extracted with the help of various mathematical and Machine Learning-based algorithms. Hence, Machine Learning is a key element of Data Science.
Alongside Machine Learning, as the name suggests, “data” itself is the fuel for Data Science. Without the availability of appropriate data, key insights cannot be extracted from it. Both the volume and accuracy of data matters in this field, since the algorithms are designed to “learn” with “experience”, which comes through the data provided. Data Science involves the use of various types of data, from multiple sources. Some of the types of data are image data, text data, video data, time-dependent data, time-independent data, audio data, etc.
Data Science requires knowledge of multiple disciplines. As shown in the figure, it is a combination of Mathematics and Statistics, Computer Science skills and Domain Specific Knowledge. Without a mastery of all these sub-domains, the grasp on Data Science will be incomplete.What is Machine Learning?
Machine Learning is a subset or a part of Artificial Intelligence. It primarily involves the scientific study of algorithmic, mathematical, and statistical models which performs a specific task by analyzing data, without any explicit step-by-step instructions, by relying on patterns and inference, which is drawn from the data. This also contributes to its alias, Pattern Recognition.
Its objective is to recognize patterns in a given data and draw inferences, which allows it to perform a similar task on similar but unseen data. These two separate sets of data are known as the “Training Set” and “Testing Set” respectively.
Machine Learning primarily finds its applications in solving complex problems, which, a normal procedure oriented program cannot solve, or in places where there are too many variables that need to be explicitly programmed, which is not feasible.
As shown in the figure, Machine Learning is primarily of three types, namely: Supervised Learning, Unsupervised Learning and Reinforcement Learning.
Artificial Intelligence is a vast field made up of multidisciplinary subjects, which aims to artificially create “intelligence” to machines, similar to that displayed by humans and animals. The term is used to describe machines that mimic cognitive functions such as learning and problem-solving.
Artificial Intelligence can be broadly classified into three parts: Analytical AI, Human-Inspired AI, and Humanized AI.
From the above introductions, it may seem that these fields are not related to each other. However, that is not the case. Each of these three fields is quite closely related to each other than it may seem.
If we look at Venn Diagrams, Artificial Intelligence, Machine Learning and Data Science are overlapping sets, with Machine Learning being a subset or a part of Artificial Intelligence, and Data Science having a significant chunk of it under Artificial Intelligence and Machine Learning.
Artificial Intelligence is a much broader field and it incorporates most of the other intelligence-related fields of study. Machine Learning, being a part of AI, deals with the algorithmic learning and inference based on data, and finally, Data Science is primarily based on statistics, probability theory, and has significant contribution of Machine Learning to it; of course, AI also being a part of it, since Machine Learning is indeed a subset of Artificial Intelligence.
Similarities: All of the three fields have one thing in common, Machine Learning. Each of these is heavily dependent on Machine Learning Algorithms.
In Data Science, the statistical algorithms that are used are limited to certain applications. In most cases, Data Scientists rely on Machine Learning techniques to extract inferences from data.
The current technological advancement in Artificial Intelligence is heavily based on Machine Learning. The part of AI without Machine Learning is like a car without an engine. However, without the “learning” part, Artificial Intelligence is basically Expert Systems, Search and Optimization algorithms.
Difference between the three
Even though they are significantly similar to each other, there are still a few key differences that are to be noted.
Since all the three domains are interrelated, they have some common applications and some unique to each of them. Most applications involve the use of Machine Learning in some form or the other. Even then, there are certain applications of each domain, which are unique. A few of them are listed below:
Since the fields are interrelated by a significant degree, the skill-set required to master each of these fields is nearly the same and overlapping. However, there are a few skill-sets that are uniquely associated with each of them. The same has been discussed further.
The Job Market for each of these fields is in very high demand. As a direct quote from Andrew Ng says, “AI is the new Electricity”. This is quite true as the extended field of Artificial Intelligence is at the verge of revolutionizing every industry in ways that could not be anticipated earlier.
Hence, the demand for jobs in the field of Data Science and Machine Learning is quite high. There are more job openings worldwide than the number of qualified Engineers who are eligible to fill that position. Hence, due to supply-demand constraints, the amount of compensation offered by companies for such roles exceeds any other domain.
The job scenario for each of the different domains are discussed further:
Data Science, Machine Learning and Artificial Intelligence are like the different branches of the same tree. They are highly overlapping and there is no clear boundary amongst them. They have common skill set requirements and common applications as well. They are just different names given to slightly different versions of AI.
Finally, it is worth mentioning that since there is high overlap in required skill-set, an optimally skilled Engineer is eligible to work in either of the three domains and switch domains without any major changes.
Learn the Difference between the most popular Buzzwords in today's tech. World — AI, Machine Learning and Deep Learning
In this article, we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning.
Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning.
Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays.
But still, there is a big misconception among many people about the meaning of these terms.
In the worst case, one may think that these terms describe the same thing — which is simply false.
A large number of companies claim nowadays to incorporate some kind of “ Artificial Intelligence” (AI) in their applications or services.
But artificial intelligence is only a broader term that describes applications when a machine mimics “ cognitive “ functions that humans associate with other human minds, such as “learning” and “problem-solving”.
On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. So basically Artificial Intelligence can be nothing more than just a bunch of if-else statements.
An if-else statement is a simple rule explicitly programmed by a human. Consider a very abstract, simple example of a robot who is moving on a road. A possible programmed rule for that robot could look as follows:
Instead, when speaking of Artificial Intelligence it's only worthwhile to consider two different approaches: Machine Learning and Deep Learning. Both are subfields of Artificial IntelligenceMachine Learning vs Deep Learning
Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.
Machine Learning incorporates “ classical” algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data. The more data you provide to your algorithm, the better it gets.
The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.
For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.
When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?
At this point, you may ask: How do we minimize the error?
One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.
Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.
In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.
Machine Learning is a pretty old field and incorporates methods and algorithms that have been around for dozens of years, some of them since as early as the sixties.
Some known methods of classification and prediction are the Naive Bayes Classifier and the Support Vector Machines. In addition to the classification, there are also clustering algorithms such as the well-known K-Means and tree-based clustering. To reduce the dimensionality of data to gain more insights about it’ nature methods such as Principal component analysis and tSNE are used.Deep Learning — The next big Thing
Deep Learning, on the other hand, is a very young field of Artificial Intelligence that is powered by artificial neural networks.
It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.
Artificial neural networks have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve.
All recent advances in intelligence are due to Deep Learning. Without Deep Learning we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate app would remain primitive and Netflix would have no idea which movies or TV series we like or dislike.
We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and Deep Learning. This is the best and closest approach to true machine intelligence we have so far. The reason is that Deep Learning has two major advantages over Machine Learning.Why is Deep Learning better than Machine Learning?
The first advantage is the needlessness of Feature Extraction. What do I mean by this?
Well if you want to use a Machine Learning model to determine whether a given picture shows a car or not, we as humans, must first program the unique features of a car (shape, size, windows, wheels etc.) into the algorithm. This way the algorithm would know what to look after in the given pictures.
In the case of a Deep Learning model, is step is completely unnecessary. The model would recognize all the unique characteristics of a car by itself and make correct predictions.
In fact, the needlessness of feature extraction applies to any other task for a deep learning model. You simply give the neural network the raw data, the rest is done by the model. While for a machine learning model, you would need to perform additional steps, such as the already mentioned extraction of the features of the given data.
The second huge advantage of Deep Learning and a key part in understanding why it’s becoming so popular is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. To quote Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project:
“ The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms. “
Deep Learning models tend to increase their accuracy with the increasing amount of training data, where’s traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.
When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them
When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them
We live in a data-driven world. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Now that Hadoop and other frameworks have resolved the problem of storage, the main focus on data has shifted to processing this huge amount of data. When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them.
In this article on Data Science vs Data Analytics vs Big Data, I will be covering the following topics in order to make you understand the similarities and differences between them.
Introduction to Data Science, Big Data & Data AnalyticsWhat does Data Scientist, Big Data Professional & Data Analyst do?Skill-set required to become Data Scientist, Big Data Professional & Data AnalystWhat is a Salary Prospect?Real time Use-case## Introduction to Data Science, Big Data, & Data Analytics
Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics.
It also involves solving a problem in various ways to arrive at the solution and on the other hand, it involves to design and construct new processes for data modeling and production using various prototypes, algorithms, predictive models, and custom analysis.
Big Data refers to the large amounts of data which is pouring in from various data sources and has different formats. It is something that can be used to analyze the insights which can lead to better decisions and strategic business moves.
Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It is all about discovering useful information from the data to support decision-making. This process involves inspecting, cleansing, transforming & modeling data.
[Source: ibm.com]What Does Data Scientist, Big Data Professional & Data Analyst Do?
Data Scientists perform an exploratory analysis to discover insights from the data. They also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. This involves identifying hidden patterns, unknown correlations, market trends and other useful business information.
Roles of Data Scientist
The responsibilities of big data professional lies around dealing with huge amount of heterogeneous data, which is gathered from various sources coming in at a high velocity.
Roles of Big Data Professiona
Data analysts translate numbers into plain English. Every business collects data, like sales figures, market research, logistics, or transportation costs. A data analyst’s job is to take that data and use it to help companies to make better business decisions.
Roles of Data AnalystSkill-Set Required To Become Data Scientist, Big Data Professional, & Data Analyst What Is The Salary Prospect?
The below figure shows the average salary structure of **Data Scientist, Big Data Specialist, **and Data Analyst.A Scenario Illustrating The Use Of Data Science vs Big Data vs Data Analytics.
Now, let’s try to understand how can we garner benefits by combining all three of them together.
Let’s take an example of Netflix and see how they join forces in achieving the goal.
First, let’s understand the role of* Big Data Professional* in Netflix example.
Netflix generates a huge amount of unstructured data in forms of text, audio, video files and many more. If we try to process this dark (unstructured) data using the traditional approach, it becomes a complicated task.
Approach in Netflix
Traditional Data Processing
Hence a Big Data Professional designs and creates an environment using Big Data tools to ease the processing of Netflix Data.
Big Data approach to process Netflix data
Now, let’s see how Data Scientist Optimizes the Netflix Streaming experience.
Role of Data Scientist in Optimizing the Netflix streaming experience
User behavior refers to the way how a user interacts with the Netflix service, and data scientists use the data to both understand and predict behavior. For example, how would a change to the Netflix product affect the number of hours that members watch? To improve the streaming experience, Data Scientists look at QoE metrics that are likely to have an impact on user behavior. One metric of interest is the rebuffer rate, which is a measure of how often playback is temporarily interrupted. Another metric is bitrate, that refers to the quality of the picture that is served/seen — a very low bitrate corresponds to a fuzzy picture.
How do Data Scientists use data to provide the best user experience once a member hits “play” on Netflix?
One approach is to look at the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.
For example, a member with a high-bandwidth connection on a home network could have very different expectations and experience compared to a member with low bandwidth on a mobile device on a cellular network.
By determining all these factors one can improve the streaming experience.
A set of big data problems also exists on the content delivery side.
The key idea here is to locate the content closer (in terms of network hops) to Netflix members to provide a great experience. By viewing the behavior of the members being served and the experience, one can optimize the decisions around content caching.
Another approach to improving user experience involves looking at the quality of content, i.e. the video, audio, subtitles, closed captions, etc. that are part of the movie or show. Netflix receives content from the studios in the form of digital assets that are then encoded and quality checked before they go live on the content servers.
In addition to the internal quality checks, Data scientists also receive feedback from our members when they discover issues while viewing.
By combining member feedback with intrinsic factors related to viewing behavior, they build the models to predict whether a particular piece of content has a quality issue. Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by the Netflix users to close the loop on quality and replace content that does not meet the expectations of the users.
So this is how Data Scientist optimizes the Netflix streaming experience.
Now let’s understand how Data Analytics is used to drive the Netflix success.
Role of Data Analyst in Netflix
The above figure shows the different types of users who watch the video/play on Netflix. Each of them has their own choices and preferences.
So what does a Data Analyst do?
Data Analyst creates a user stream based on the preferences of users. For example, if user 1 and user 2 have the same preference or a choice of video, then data analyst creates a user stream for those choices. And also –
Orders the Netflix collection for each member profile in a personalized way.We know that the same genre row for each member has an entirely different selection of videos.Picks out the top personalized recommendations from the entire catalog, focusing on the titles that are top on ranking.By capturing all events and user activities on Netflix, data analyst pops out the trending video.Sorts the recently watched titles and estimates whether the member will continue to watch or rewatch or stop watching etc.
I hope you have *understood *the *differences *& *similarities *between Data Science vs Big Data vs Data Analytics.