Desmond  Gerber

Desmond Gerber


Agent-LLM: An Artificial Intelligence Automation Platform

Agent-LLM (Large Language Model)

Agent-LLM is an Artificial Intelligence Automation Platform designed to power efficient AI instruction management across multiple providers. Our agents are equipped with adaptive memory, and this versatile solution offers a powerful plugin system that supports a wide range of commands, including web browsing. With growing support for numerous AI providers and models, Agent-LLM is constantly evolving to empower diverse applications.


Media Coverage ⏯️


Key Features 🗝️

Adaptive long-term and short-term memory management

Versatile plugin system with extensible commands for various AI models

Wide compatibility with multiple AI providers, including:

OpenAI GPT-3.5, GPT-4

Oobabooga Text Generation Web UI




Google Bard

And More!

Web browsing and command execution capabilities

Code evaluation support

Seamless Docker deployment

Integration with Hugging Face for audio-to-text conversion

Interoperability with platforms like Twitter, GitHub, Google, DALL-E, and more

Text-to-speech options featuring Brian TTS, Mac OS TTS, and ElevenLabs

Continuously expanding support for new AI providers and services

Web Application Features

The frontend web application of Agent-LLM provides an intuitive and interactive user interface for users to:

  • Manage agents: View the list of available agents, add new agents, delete agents, and switch between agents.
  • Set objectives: Input objectives for the selected agent to accomplish.
  • Start tasks: Initiate the task manager to execute tasks based on the set objective.
  • Instruct agents: Interact with agents by sending instructions and receiving responses in a chat-like interface.
  • Available commands: View the list of available commands and click on a command to insert it into the objective or instruction input boxes.
  • Dark mode: Toggle between light and dark themes for the frontend.
  • Built using NextJS and Material-UI
  • Communicates with the backend through API endpoints

Quick Start

  1. Obtain an OpenAI API key from OpenAI and add it to your .env file.
  2. Set the OPENAI_API_KEY in your .env file using the provided .env.example as a template.
mv .env.example .env
  • Run the following Docker command in the folder with your .env file:
docker compose up -d

Running a Mac?

You'll need to run docker compose to build if the command above does not work.

docker compose -f docker-compose-mac.yml up -d

Not using OpenAI? No problem!

We are constantly trying to expand our AI provider support. Take a look at our Jupyter Notebooks for Quick starts for these:

Reminder: ⚠️ Run this in Docker or a Virtual Machine!

  1. OpenAI
  2. llamacpp
  3. Oobabooga Text Generation Web UI
  4. ChatGPT
  5. Google Bard

For more detailed setup and configuration instructions, refer to the sections below.


Agent-LLM utilizes a .env configuration file to store AI language model settings, API keys, and other options. Use the supplied .env.example as a template to create your personalized .env file. Configuration settings include:

  • INSTANCE CONFIG: Set the agent name, objective, and initial task.
  • AI_PROVIDER: Choose between OpenAI, llama.cpp, or Oobabooga for your AI provider.
  • AI_PROVIDER_URI: Set the URI for custom AI providers such as Oobabooga Text Generation Web UI (default is
  • MODEL_PATH: Set the path to the AI model if using llama.cpp or other custom providers.
  • COMMANDS_ENABLED: Enable or disable command extensions.
  • MEMORY SETTINGS: Configure short-term and long-term memory settings.
  • AI_MODEL: Specify the AI model to be used (e.g., gpt-3.5-turbo, gpt-4, text-davinci-003, Vicuna, etc.).
  • AI_TEMPERATURE: Set the AI temperature (leave default if unsure).
  • MAX_TOKENS: Set the maximum number of tokens for AI responses (default is 2000).
  • WORKING_DIRECTORY: Set the agent's working directory.
  • EXTENSIONS_SETTINGS: Configure settings for OpenAI, Hugging Face, Selenium, Twitter, and GitHub.
  • VOICE_OPTIONS: Choose between Brian TTS, Mac OS TTS, or ElevenLabs for text-to-speech.

For a detailed explanation of each setting, refer to the .env.example file provided in the repository.

API Endpoints

Agent-LLM provides several API endpoints for managing agents, managing tasks, and managing chains.

To learn more about the API endpoints and their usage, visit the API documentation at http://localhost:7437/docs (Swagger) or http://localhost:7437/redoc (Redoc).

Extending Functionality


To introduce new commands, generate a new Python file in the commands folder and define a class inheriting from the Commands class. Implement the desired functionality as methods within the class and incorporate them into the commands dictionary.

AI Providers

To switch AI providers, adjust the AI_PROVIDER setting in the .env file. The application is compatible with OpenAI, Oobabooga Text Generation Web UI, and llama.cpp. To support additional providers, create a new Python file in the provider folder and implement the required functionality.

Coming Soon: Any providers defined in the .env file will be usable on different agents in the application and will not need to be manually switched.


We welcome contributions to Agent-LLM! If you're interested in contributing, please check out the open issues, submit pull requests, or suggest new features. To stay updated on the project's progress, follow @AgentLLM, @Josh_XT and @JamesonRGrieve on Twitter, and join our Discord.

Donations and Sponsorships

We appreciate any support for Agent-LLM's development, including donations, sponsorships, and any other kind of assistance. If you would like to support us, please contact us through our Outreach Email, Discord server or Twitter @AgentLMM.

We're always looking for ways to improve Agent-LLM and make it more useful for our users. Your support will help us continue to develop and enhance the application. Thank you for considering to support us!


This project was inspired by and utilizes code from the following repositories:

Please consider exploring and contributing to these projects as well.

⚠️ Run this in Docker or a Virtual Machine!

You're welcome to disregard this message, but if you do and the AI decides that the best course of action for its task is to build a command to format your entire computer, that is on you. Understand that this is given full unrestricted terminal access by design and that we have no intentions of building any safeguards. This project intends to stay light weight and versatile for the best possible research outcomes.

⚠️ Monitor Your Usage!

Please note that using some AI providers (such as OpenAI's GPT-4 API) can be expensive! Monitor your usage carefully to avoid incurring unexpected costs. We're NOT responsible for your usage under any circumstance.

⚠️ Under Development!

This project is under active development and may still have issues. We appreciate your understanding and patience. If you encounter any problems, please first check the open issues. If your issue is not listed, kindly create a new issue detailing the error or problem you experienced. Thank you for your support!

Download Details:

Author: Josh-XT
Source Code: 

#python #intelligence #automation #ai #openai 

Agent-LLM: An Artificial Intelligence Automation Platform
Royce  Reinger

Royce Reinger


AI2thor: An open-source platform for Visual AI


A Near Photo-Realistic Interactable Framework for Embodied AI Agents

🏡 Environments

A high-level interaction framework that facilitates research in embodied common sense reasoning.A mid-level interaction framework that facilitates visual manipulation of objects using a robotic arm.A framework that facilitates Sim2Real research with a collection of simlated scene counterparts in the physical world.

🌍 Features

🏡 Scenes. 200+ custom built high-quality scenes. The scenes can be explored on our demo page. We are working on rapidly expanding the number of available scenes and domain randomization within each scene.

🪑 Objects. 2600+ custom designed household objects across 100+ object types. Each object is heavily annotated, which allows for near-realistic physics interaction.

🤖 Agent Types. Multi-agent support, a custom built LoCoBot agent, a Kinova 3 inspired robotic manipulation agent, and a drone agent.

🦾 Actions. 200+ actions that facilitate research in a wide range of interaction and navigation based embodied AI tasks.

🖼 Images. First-class support for many image modalities and camera adjustments. Some modalities include ego-centric RGB images, instance segmentation, semantic segmentation, depth frames, normals frames, top-down frames, orthographic projections, and third-person camera frames. User's can also easily change camera properties, such as the size of the images and field of view.

🗺 Metadata. After each step in the environment, there is a large amount of sensory data available about the state of the environment. This information can be used to build highly complex custom reward functions.

📰 Latest Announcements

5/2021 RandomizeMaterials is now supported! It enables a massive amount of realistic looking domain randomization within each scene. Try it out on the demo
4/2021We are excited to release ManipulaTHOR, an environment within the AI2-THOR framework that facilitates visual manipulation of objects using a robotic arm. Please see the full 3.0.0 release notes here.
4/2021 RandomizeLighting is now supported! It includes many tunable parameters to allow for vast control over its effects. Try it out on the demo!

2/2021We are excited to host the AI2-THOR Rearrangement Challenge, RoboTHOR ObjectNav Challenge, and ALFRED Challenge, held in conjunction with the Embodied AI Workshop at CVPR 2021.
2/2021AI2-THOR v2.7.0 announces several massive speedups to AI2-THOR! Read more about it here.
6/2020We've released 🐳 AI2-THOR Docker a mini-framework to simplify running AI2-THOR in Docker.
4/2020Version 2.4.0 update of the framework is here. All sim objects that aren't explicitly part of the environmental structure are now moveable with physics interactions. New object types have been added, and many new actions have been added. Please see the full 2.4.0 release notes here.
2/2020AI2-THOR now includes two frameworks: iTHOR and RoboTHOR. iTHOR includes interactive objects and scenes and RoboTHOR consists of simulated scenes and their corresponding real world counterparts.
9/2019Version 2.1.0 update of the framework has been added. New object types have been added. New Initialization actions have been added. Segmentation image generation has been improved in all scenes.
6/2019Version 2.0 update of the AI2-THOR framework is now live! We have over quadrupled our action and object states, adding new actions that allow visually distinct state changes such as broken screens on electronics, shattered windows, breakable dishware, liquid fillable containers, cleanable dishware, messy and made beds and more! Along with these new state changes, objects have more physical properties like Temperature, Mass, and Salient Materials that are all reported back in object metadata. To combine all of these new properties and actions, new context sensitive interactions can now automatically change object states. This includes interactions like placing a dirty bowl under running sink water to clean it, placing a mug in a coffee machine to automatically fill it with coffee, putting out a lit candle by placing it in water, or placing an object over an active stove burner or in the fridge to change its temperature. Please see the full 2.0 release notes here to view details on all the changes and new features.

💻 Installation

With Google Colab

AI2-THOR Colab can be used to run AI2-THOR freely in the cloud with Google Colab. Running AI2-THOR in Google Colab makes it extremely easy to explore functionality without having to set AI2-THOR up locally.

With pip

pip install ai2thor

With conda

conda install -c conda-forge ai2thor

With Docker

🐳 AI2-THOR Docker can be used, which adds the configuration for running a X server to be used by Unity 3D to render scenes.

Minimal Example

Once you've installed AI2-THOR, you can verify that everything is working correctly by running the following minimal example:

from ai2thor.controller import Controller
controller = Controller(scene="FloorPlan10")
event = controller.step(action="RotateRight")
metadata = event.metadata
print(event, event.metadata.keys())


OSMac OS X 10.9+, Ubuntu 14.04+
Graphics CardDX9 (shader model 3.0) or DX11 with feature level 9.3 capabilities.
CPUSSE2 instruction set support.
PythonVersions 3.5+
LinuxX server with GLX module enabled

💬 Support

Questions. If you have any questions on AI2-THOR, please ask them on our GitHub Discussions Page.

Issues. If you encounter any issues while using AI2-THOR, please open an Issue on GitHub.

🏫 Learn more

DemoInteract and play with AI2-THOR live in the browser.
iTHOR DocumentationDocumentation for the iTHOR environment.
ManipulaTHOR DocumentationDocumentation for the ManipulaTHOR environment.
RoboTHOR DocumentationDocumentation for the RoboTHOR environment.
AI2-THOR ColabA way to run AI2-THOR freely on the cloud using Google Colab.
AllenActAn Embodied AI Framework build at AI2 that provides first-class support for AI2-THOR.
AI2-THOR Unity DevelopmentA (sparse) collection of notes that may be useful if editing on the AI2-THOR backend.
AI2-THOR WebGL DevelopmentDocumentation on packaging AI2-THOR for the web, which might be useful for annotation based tasks.

📒 Citation

If you use AI2-THOR or iTHOR scenes, please cite the original AI2-THOR paper:

  author={Eric Kolve and Roozbeh Mottaghi and Winson Han and
          Eli VanderBilt and Luca Weihs and Alvaro Herrasti and
          Daniel Gordon and Yuke Zhu and Abhinav Gupta and
          Ali Farhadi},
  title={{AI2-THOR: An Interactive 3D Environment for Visual AI}},

If you use 🏘️ ProcTHOR or procedurally generated scenes, please cite the following paper:

  author={Matt Deitke and Eli VanderBilt and Alvaro Herrasti and
          Luca Weihs and Jordi Salvador and Kiana Ehsani and
          Winson Han and Eric Kolve and Ali Farhadi and
          Aniruddha Kembhavi and Roozbeh Mottaghi},
  title={{ProcTHOR: Large-Scale Embodied AI Using Procedural Generation}},
  note={Outstanding Paper Award}

If you use ManipulaTHOR agent, please cite the following paper:

  title={{ManipulaTHOR: A Framework for Visual Object Manipulation}},
  author={Kiana Ehsani and Winson Han and Alvaro Herrasti and
          Eli VanderBilt and Luca Weihs and Eric Kolve and
          Aniruddha Kembhavi and Roozbeh Mottaghi},

If you use RoboTHOR scenes, please cite the following paper:

  author={Matt Deitke and Winson Han and Alvaro Herrasti and
          Aniruddha Kembhavi and Eric Kolve and Roozbeh Mottaghi and
          Jordi Salvador and Dustin Schwenk and Eli VanderBilt and
          Matthew Wallingford and Luca Weihs and Mark Yatskar and
          Ali Farhadi},
  title={{RoboTHOR: An Open Simulation-to-Real Embodied AI Platform}},

👋 Our Team

AI2-THOR is an open-source project built by the PRIOR team at the Allen Institute for AI (AI2). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.

Download Details:

Author: allenai
Source Code: 
License: Apache-2.0 license

#machinelearning #python #computervision #physics #engine #artificial #intelligence

AI2thor: An open-source platform for Visual AI
Monty  Boehm

Monty Boehm


Learn to Data Intelligence Benefits and its Use Cases

What is Data Intelligence?

The world is leading towards data-driven intelligence. Organizations must make data and AI-based decisions to stand in the world of evolving technology and the competition phase. It becomes difficult for the organizations that are not working on those aspects and data to know the facts and insights while making decisions.

Data Intelligence enables the process of multisource data and generates meaningful insights that would help to make valuable decisions. It allows combining unstructured data and text analytics results with structured data for predictive analytics. It can give a real-time statistical analysis of structured or unstructured data to understand data patterns and dependencies.

Why do we need Data Intelligence?

Data intelligence is required to process and understand the data. Data intelligence is rapidly becoming one of the most important elements of big data. Data intelligence has progressed from the infantile stage to a point where it can handle vast amounts of data with intelligence. It will not fold its wings either; the immediate positive results have attracted many organizations' attention. Various entrepreneurs have expressed interest in using and developing data intelligence to make intelligent decisions in driving their business. There are multiple cases where we may need it. Some instances have been discussed that helps to know why we require data intelligence:

  • Artificial Intelligence: Using a machine learning algorithm helps find the predictive analysis and recognize correlation. It helps to find domain-specific custom entities and word usage.
  • Intuitive Visualization: It allows us to understand data effectively in less time using informative intuitive charts and graphs. Visualization helps to understand complex data within seconds rather than reading and understanding an excel or any other data file. Visualization also generates insights and clear data patterns that are difficult to find in tables or datasets. It enables to easily filter and drill down the reports according to the requirements.
  • Insight Generation: Based on the collected data, it allows generating or taking insight from the visualization that helps understand business progress and customer needs.
  • Data-driven decision-making: To make better and data-driven decisions so that correct decision can be taken to gain customer satisfaction and revenue.

The Base Foundation For Data Intelligence

Data intelligence is an optimized way that provides an unconventional 360 view of the business environment. It helps to understand the customer requirements better and monitors the organization's performance. Based on the data or insights, make decisions according to customer preferences and improve revenue and benefits. Data intelligence is based on several techniques accordingly to enrich business decisions and processes. These are:

  • Descriptive("What happened" ): It is used to review and examine the historical and real-time data to understand business performance and customer behavior. It detects a particular occurrence of a situation.
  • Diagnostic("How it happened" ): To know the reason for the occurrence of a particular instance or situation.
  • Predictive("What could happen" ): It uses historical and, based on that, predicts future occurrences using some ML algorithm.
  • Prescriptive("What Should We Do" ): To develop and analyze the alternative knowledge that can be applied in the course of action. It helps us to understand what to do in the future.
  • Decisive: Decisive analytics helps to measure data suitability. It chooses the recommended action to implement in the environment and real-time process with multiple possibilities.

How can we use Data Intelligence?

Data intelligence performs the following steps to identify relations and mentions of unstructured or structured data.

  • Data Ingestion: It collects structured and unstructured data from different sources such as documents, emails, databases, websites, and data repositories. Data can be inserted into the application or platform manually or scheduled at fixed intervals. Data can be processed and used by that application to perform tasks.
  • Data Processing: Now, data collected from sources can be processed and generated insights. It makes it possible to find a relation between data. Several tools provide an easy-to-use interface for creating custom models to train and test the model to find entities and data relations. It allows using models for future predictive analytics.
  • Reporting and Visualizations: Reporting and Visualization is the final step that analyzes the data using charts and graphs. Visualization makes it easy to understand large and complex data effectively.

What are the benefits of Data Intelligence?

Data intelligence gives wings to the technology by providing intelligence in their daily tasks and decisions. Let's discuss the benefits of data intelligence that why organizations should embrace them:

  • Changing demands: Data intelligence makes the organization adopt the dynamic changes of the industries. The business nowadays is continuously evolving. To stand in the competition and reduce the chances of failure, organizations must accept and update the newly emerging trends. For example, the adoption of selfie cameras in smartphones was increasing. Mobile businesses that do not capitalize on the trend are doomed to fail.
    Data intelligence helps organizations to understand customer behavior and change. Firms are informed about repeated changes and the pattern of occurrence by smart adaptive dynamics. It allows the company to make informed decisions based on the analysis.
  • Strong Foundation of Data: Data intelligence makes big data more strong and strengthened by restructuring the process of data arrangements. It allows to gather insights from big data and then render optimized engagement capability.
  • Useful Data: No doubt the world is generating a large volume of data every day that can change the world and improve the services according to customer demands and preferences. But most of the data is not in the form of use. It is not possible to directly use that data.
    Data intelligence cleans and transforms data into smart capsules of ready-to-use data that can be used in the company to assess results. It is required to transform it into a helpful form to use data. Data intelligence is also in charge of converting raw data into cumulative information. Data intelligence makes it possible not to worry about defining particular cases to computers.
  • Augmented Analytics: Advanced statistical approaches are used in data intelligence to advance visualized predictive and prescriptive analytics. Advanced simulations enable businesses to predict potential outcomes and make changes to prescriptions as needed. Instead of building a complete application every time, it automates the data processing that can be completed just by simple steps. If required, further changes may be recommended based on the results. There is no way for business plans to fail with such extensive planning for a real-life scenario.
  • Accelerate Innovation: Data intelligence makes it possible to accelerate innovations by making smart use of data. It allows using data insights to drive business innovations and develop their services by considering customer preferences and requirements.

What is the difference between data information and intelligence?

  • Data: It is the raw form of data recorded truth at a time. It might be a conversation, a purchase, or an interaction with your company's website. Data is the compilation of results from those incidents that are then quantifiably recorded so that companies can review them easily.
  • Information: It is a collection of data or a way of bringing data together. When data is picked from an event and put into narrative forms, it helps to answer the following questions:
    -What is the churn rate of employees?
    -How long is the sales cycle of an organization?
    The information helps to answer these questions that move the business.
  • Intelligence is a group of information to derive intelligence or decisions in their application or tasks. For instance, suppose you are selling more in southern regions, then the smart and intelligent answer will be why that might be. To get a response, it will look at the number of events, amount spent on advertisements, marketing campaigns southern region clients receive. After that, it can be compared with the other region(North region).

Through this analysis, we know that there are more client interactions in the southern region, so to increase sales in the North region, it is necessary to do the same.

Data Intelligence Use Cases

Data Intelligence helping the various industries as below mentioned


Rapid digitalization of healthcare systems are adopting technologies to create a connected healthcare environment. Hospitals need to synchronize with the technology to become smart, advanced, and accurate. Hospitals use various types of sensors, apps, and digital equipment that regularly generate a large volume of data. This data can be used to automate several administrative, treatment, and clinical processes. Data intelligence capabilities allow ML, AI, and Deep learning to make healthcare processes more accurate and fast and help practitioners handle the increasing number of cases and methods. These advanced technologies allow extract real-time intelligence and make decisions regarding the diagnosis process, prescribing medicines, hospital management, laboratory, patient care, etc., leading to high operational efficiency and care delivery.

Supply Chain Management

Supply chain software generates and collects a vast amount of data. But they are not aware of how they can best use it to make their operations more effective. The supply chain management network data intelligence predicts business risk, minimizes loss, and makes automated self-learning supply chains. As a result, it drives real-time coordination and innovations.

Human Resource

Organizations are using HR software to manage internal HR functions such as payroll, employee benefits, recruitment, training, talent management, attendance management, employee engagement, etc., to enhance their features and capabilities. They always have to do many tasks to understand employees better, attract top talent, and initiate programs to retain them and analyze their performance. They have a lot of data generated from their HRMS(Human Resource Management System) software. Data Intelligence can help them analyze and understand the data, gather insights, and make a precise decision that can make their organization drive healthier and faster.


One of the success secrets of an e-commerce website is using customer reviews to know their experience, preference and then use them to make profitable decisions. Using ML and Natural Language Processing techniques to interact with their customers, get data from them, and use it to drive performance, improve Customer Engagement, Service Quality, Support Quality, and ultimately Sales.

Data Intelligence makes it possible to accomplish these tasks, recommend products, understand customer preferences, solve their queries, improve quality and services, etc.

Harnessing this information can give you a treasure trove of insights that can power your products and processes, improve customer experience, marketing, manage store operation, etc.


Akira AI is a data intelligence platform that provides intelligence using analysis and learning by processing various sources.

Original article source at:

#data #intelligence #dataintelligence 

Learn to Data Intelligence Benefits and its Use Cases
Nat  Grady

Nat Grady


Learn Customer intelligence Benefits and Its Use Cases

Introduction to Customer Intelligence

Businesses can not exist without their customers. The customers are essential for every business as they bring revenues. Every business is in the race to attract more customers than other businesses either by lowering the prices of their products or services, providing offers, advertising, or developing unique and loved products.
Every person is a customer of one business or another. If anybody has a bad experience with a company, they may lose trust in the company and lose its customer.

Businesses need to understand their customers and engage them. It helps the businesses to acquire new customers and keep the old ones. Happy customers are more likely to repeat business with the companies that fulfill their needs and expectations and provide good services.

What are the challenges of Customer Intelligence?

Market changes very rapidly, and it is the need of the hour for companies to convert and retain loyal customers. A company needs to understand its customer’s interests and preferences. The main challenges that a company faces while understanding their customers are:

  • How does a company know what its customers want?
  • How does a company provide the best services and products to its customers?

It explores how the customers interact with the company and its website. The company must track its customer’s purchase history, behavior, time spent on particular pages to get an idea of improving their products and services.

Customer Intelligence in Business and Marketing

Customer intelligence is the analysis and collection of customer data to understand the customer needs and interests, provide the best services and make informed decisions.

In every sector, businesses can benefit from customer intelligence. The more a company knows its customers, the better it can interact with them. It allows the companies better to understand their customers’ preferences, motivations, patterns, wants, needs by combining demographic data, transactions, second-and third-party data, channel activity, and sales and marketing history. It also enables the companies to build more profound and more effective customer relationships. It is becoming a critical ingredient in making effective strategic decisions, and it’s the foundation of building future business intelligence capabilities.

Customer intelligence collects data from multiple sources and uses artificial intelligence, machine learning, business intelligence, data visualization, and predictive analytics. It helps the business develop insights around hyper-segmentation, personalization, next best action, and forecasting. These insights lead to reduced customer churn and improved customer experiences.

What is the use of Customer Intelligence?

Fig 2: Uses of Customer Intelligence

Behavioral Segmentation

Behavioral segmentation divides the whole population into segments based on the same pattern followed by customers. Customers may have the same previously purchased products, similar reactions to messages, and similar feedback.


Apps like online food delivery use customers' locations to offer the closest restaurant to the customer. It is the easiest and effective way to customize messaging and offers.


The company will do personalized messaging and provide offers accordingly based on the customers' behavioral segments, known preferences, or buying patterns.

Modeling User Flows

User Flow is a path the user takes on a website or an app while completing the task. Businesses can monitor users’ movements through their journey with the help of customer intelligence and enable businesses to model user flows on-site and identify improvements to optimize the user flows. For example, when a person arrives at an online store, the products he searches, products added to the cart, and finally, the purchase is a user flow.

What are the benefits of Customer Intelligence?

Using customer intelligence will benefit the company from any sector. Some of the advantages of customer intelligence are:

  • Data-driven decisions: Collecting and analyzing customer data in detail will help the companies make data-informed decisions. These decisions will lead the company to take steps that will benefit its customers the most.
  • Personalized Marketing: A customer intelligence system enables highly personalized customer interactions.
  • Customer Satisfaction: The personalized interaction achieved from customer intelligence will help in better customer satisfaction, which helps to increase the Net Promoter Score and other attributes.
  • Customer Retention: Customer intelligence will help reduce the organization's customer retention challenges.
  • Keeping up with Market Changes: E-commerce and retail industries are changing very fast. It is not affordable for any company to be behind the market. Customer intelligence will make a company aware of the latest trends and people's interests.

A good customer intelligence approach will give an organization a clear view of its marketing efforts. It focuses on the customer journey, which can help the company keep track of marketing activities bringing in better communication.

What is the Intelligent Approach to Customer Intelligence?

An intelligent approach is needed to achieve customer intelligence.

Data Collection

The first step in the customer intelligence process is to collect data. Various types of data are collected for customer intelligence.

  • Demographic: The company can collect demographic data from surveys, statistics, records, and accounts, which will give information about who the customer is.
  • Psychographic: The psychographic data is needed to know the customer’s personality and attitude. This data type can be collected from customer interviews, reviews, questionnaires, and surveys.
  • Behavioral: This data will give customers how they behave when they interact with its products and services. This data can be collected from the company’s website by monitoring the customer’s activity, comments, and mobile browsing.
  • Transactional: The data describes how the customer spends on the company’s products and services. It can be collected from payment methods, transactions data, order information, etc.

Evaluate the data / Analyze the data

The next step in the customer intelligence process is to analyze the collected customer data. Businesses can use various analytics tools to analyze the data and segment their customers based on their behavior and feedback. The companies can also pick up metrics that matter to their business and give a 360-degree view of their customers.

Share Insights

After analyzing the data, the next step is to share the insights obtained with the organization. It can be achieved using dashboards, reports, and customer journey maps.

Customer Intelligence By Customer Journey Mapping

This will help the companies to understand how, where, and when the customers have experienced the brand, creating a proper channel for customer intelligence through data collection and communication.

To achieve a successful customer experience, the company needs to measure the customer’s perception of the company from time to time. Businesses use some platforms to gather insights from customer journey mapping.

  • Physical Location: When a customer comes to the store, restaurant or hotel, etc., the company can collect feedback from customers at the location itself.
  • Emails: It is the easiest way to collect feedback from customers. Whenever a customer completes a purchase, the system automatically sends a message to give feedback.
  • Website: If the company has an online retail store and customers visit the website more often, they can communicate with their customers and gather feedback from the website.

Use Cases of Customer Intelligence

The below highlighted are the Use Cases of Customer Intelligence

Financial Services

Customer Intelligence helps a bank to:

  • Identify patterns
  • Identify any unusual suspicious activity
  • Determine the risks such as bad debt or fraud depending on the information about the customer.
  • Predict Churning.
  • It helps banks limit their offers to only those likely to leave and make cost-effective decisions.


Personalized Discounts

Retailers can reward customers for their loyalty using customer intelligence. They install sensors all around the store, and these sensors will send messages via email or app notification on their smartphones when the customer is near a particular product. One condition for the reward is that the customer must opt for themselves in the loyalty program.

Also, retailers can make online driven offlines sales from websites. Retailers can track what customers are looking for from their website and when the next time a customer enters the store, the retailer will send a personalized discount for the product.


Adopting technologies for providing improved customer experiences is a key to making more profits and customer retention in an organization. If the company wants to stand out from the competition, it should start using customer intelligence seriously to make informed data-driven decisions.
The insights organization will get from customer intelligence will increase brand loyalty and make the business ready to face any change in the industry.

Original article source at:

#intelligence #benefits #customerintelligence 

Learn Customer intelligence Benefits and Its Use Cases
Rupert  Beatty

Rupert Beatty


Best 10 Real World Artificial intelligence Applications

ust 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 marketing, finance, banking and so on.

If you’re new to AI make sure to check out this blog on what is AI

What is Artificial Intelligence Used For?

  1. AI In Marketing
  2. AI In Banking
  3. AI In Finance
  4. AI In Agriculture
  5. AI In HealthCare
  6. AI In Gaming
  7. AI In Space Exploration
  8. AI In Autonomous Vehicles
  9. AI In Chatbots
  10. AI In Artificial Creativity

Artificial Intelligence Applications: 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 there 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 are reading 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.

AI Applications - AI in Marketing

Artificial Intelligence Applications – AI in Marketing

One reason why we’re all obsessed with Netflix and chill is because, Netflix 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, this technology is getting smarter and smarter every day.

With the growing advancement in AI, in the near future, it may be possible for consumers on the web to buy products by snapping a photo of it. Companies like CamFind and their competitors are experimenting this already.

Artificial Intelligence Applications: Banking

AI in banking is growing faster than you thought! A lot of banks have already adopted AI-based systems to provide customer support, detect anomalies and credit card frauds. An example of this is 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.

Artificial Intelligence Applications - AI in BankingArtificial Intelligence Applications – AI in Banking

The use of AI for fraud prevention is not a new concept. In fact, AI solutions are there 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.

Artificial Intelligence Applications: 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.

Artificial Intelligence Applications - AI in Finance

Artificial Intelligence Applications – AI in Finance

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.

Artificial Intelligence Applications: 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.

Artificial Intelligence Applications - AI in Agriculture

Artificial Intelligence Applications – AI in Agriculture

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%.

Artificial Intelligence Applications: Health Care

When it comes to saving our 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 heart stroke.

Artificial Intelligence Applications - AI in Health Care

Artificial Intelligence Applications – AI in Health Care

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. Just by using an existing medication in the right way, you can also save lives.

Artificial Intelligence Applications: 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 famous for defeating Lee Sedol, the world champion in the game of GO, is  one of the most significant accomplishment 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, AlphaGo Zero 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.

F.E.A.R. - Artificial Intelligence Applications - Edureka

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.

Artificial Intelligence Applications: 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.

Mars Rover - Artificial Intelligence Applications - Edureka

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 autonomous targeting of cameras in order to perform investigations on Mars.

Artificial Intelligence Applications: Autonomous Vehicles

For the longest time, self-driving cars have been a buzzword in the AI industry. The development of autonomous vehicles will definitely revolutionaries the transport system.
Companies like Waymo conducted several test drives in Phoenix before deploying their first AI-based public ride-hailing service. The AI system collects data from the vehicles radar, cameras, GPS, and cloud services to produce control signals that operate the vehicle.

Waymo - Artificial Intelligence Applications - Edureka

Advanced Deep Learning algorithms can accurately predict what objects in the vehicle’s vicinity are likely to do. This makes Waymo cars more effective and safer.

Another famous example of an autonomous vehicle is Tesla’s self-driving car. Artificial Intelligence implements computer vision, image detection and deep learning to build cars that can automatically detect objects and drive around without human intervention.

Elon Musk talks a ton about how AI is implemented in tesla’s self-driving cars and autopilot features. He quoted that,

“Tesla will have fully self-driving cars ready by the end of the year and a “robotaxi” version – one that can ferry passengers without anyone behind the wheel – ready for the streets next year”.

Artificial Intelligence Applications: Chatbots

These days Virtual assistants have become a very common technology. Almost every household has a virtual assistant that controls the appliances at home. A few examples include Siri, Cortana, 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.

Amazon Echo - Artificial Intelligence Applications - Edureka

Another example is the Google’s virtual assistant called Google Duplex, that has astonished millions of people. Not only can it respond to calls and book appointments for you, but it also adds a human touch.

Google Assistant - Artificial Intelligence Applications - Edureka

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 Intelligence Applications: 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 present.

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.

Face Recognition - Artificial Intelligence Applications - Edureka

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.

If you want to learn Artificial Intelligence and Machine Learning in-depth, come to us and sign up for this Post Graduate Diploma AI and Machine Learning courses at Edureka.

Artificial Intelligence Applications: 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 - Artificial Intelligence Applications - Edureka

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 – Wordsmith. Wordsmith is a natural language generation platform that can transform your data into insightful narratives.

Wordsmith - Artificial Intelligence Applications - Edureka

Tech giants such as Yahoo, Microsoft, Tableau, are using WordSmith to generate around 1.5 billion pieces of content every year.

Introduction to Artificial Intelligence | Edureka

This Edureka video on Artificial Intelligence gives you a brief introduction to AI and how AI can change the world.

I’d like to conclude by asking you, how you think AI will benefit us in the future? 

Original article source at:

#ai #intelligence #applications 

Best 10 Real World Artificial intelligence Applications

Jim Walsh



Did you know that the global #business #intelligence market is predicted to grow from $21.06 billion in 2019 to $44.16 billion by 2027? Watch this video to learn the responsibilities and skills required to become an experienced specialist in the #BI domain.

Jack Forbes

Jack Forbes


How Artificial Intelligence is Transforming Digital Marketing

Artificial intelligence (AI) is one of the most interesting business innovations. Many companies are turning to artificial intelligence (AI) to help them make crucial business decisions, and it can do a lot of things. However, since the technology is fresh, it has a lot of unanswered questions. One of the most frequently asked questions is about AI’s position in digital marketing and whether it is changing the industry’s landscape.

Artificial intelligence has the ability to completely disrupt and improve the digital marketing industry. Digital marketing is already a huge industry, and the advancement of AI can only make it bigger.

A Better Consumer and User Experience

You can gain continuous access to new or updated insights using AI, which can help you develop your marketing campaigns and how you reach people. You can understand how people want to be sold to, what works best, and use analytics to help fine-tune the marketing.

Marketers may use AI to obtain a better understanding of their customers’ desires, habits, and journeys. It may also aid in the prediction of various buying motivations and activities. The more information you have, the more the journeys will be personalized. It can be extremely helpful if what you give matches up well with what your audience or potential customers expect.

Increased Personalization in Marketing

Personalization must be considered in every aspect of the company, from goods to marketing. You will basically have built-in tools for making choices and choosing the best marketing approach to use with AI and machine learning. The more data you have, the more effective machine learning becomes.
Some customers may not be open to your message if they don’t feel like you’re reaching out to them directly. The use of artificial intelligence (AI) in digital marketing allows for even more personalization and customization.

AI Can Save Companies a Ton of Money and Time

In the past, a lot of marketing was done by guessing, checking, and changing items until they were just right. Even today, several businesses will put hundreds of new tactics to the test to see which one works best. By using artificial intelligence, many of these systems will learn as they go and will not need any human interaction.

They will be able to decide which approaches are most likely to be successful and which are not based on previous data. As you would expect, this frees up a significant amount of time for employees to work on more pressing issues.
Although artificially intelligent tools and software are not always inexpensive, they are much less expensive than paying a team to test various techniques and see what works.

Discover how marketers are using artificial intelligence (AI) to streamline and optimize digital marketing campaigns and increase client satisfaction in the digital age.

**How Artificial Intelligence is Transforming Digital Marketing

#artificial #intelligence #transforming #digital #marketing #industry

How Artificial Intelligence is Transforming Digital Marketing
Queenie  Davis

Queenie Davis


Understand Artificial Intelligence (AI)

You were dreaming about understanding AI and machine learning? Well, this article is made for you. We are going to demystify AI.

What is machine learning?

Before we go further in this article, we do need to define what is machine learning.

To summarize, machine learning is the fact of solving a problem without telling a computer how to solve it. What I mean by that is that in classic programming you would write code to explain to the computer how to solve a problem and explain to him what are the _different step_s to do it. With machine learning, the computer is using statistical algorithms to solve a problem by itself thanks to input data. It does this, by finding the patterns between the input and the output of the problem.

What do I need to do to do machine learning?

To do machine learning, you will need data, a lot of data.

When you do have these data, what you will need to do is to split your data into two datasets:

  • The test dataset: the data you will use for testing if your machine learning model (algorithm) is properly working.
  • The** training dataset:** the data you will use for training your machine learning model.

So remember, data is key, you need to have a proper amount of data, and clean these data (we will talk about how to clean your data for your dataset in another article).

What can I use machine learning for?

You can actually use machine learning for solving a lot of problems. Here are a few examples:

  • Recommendations of products on e-commerce website (Amazon, eBay, …).
  • Recommendations for a search engine website (Google, Facebook search, …).
  • Netflix uses it as well to recommend movies and TV series depending on what you actually like.
  • Youtube to put the subtitles under your videos, …

How can I teach machines to learn?

There are different ways for machines to learn, here are the four most popular ways:

  • Supervised learning: your model will learn thanks to input labeled data that you provide to it (your data are already tagged with the correct labels). Which means that we show the correct answers to the machine. It can be used for classifying data, for example, classify cats by breeds.
  • Unsupervised learning: your model will learn by observing. Which means that it will learn and improve by trial and error. In that case, we are not working with labeled data, so we don’t show the machine the correct answer. It can be used for clustering data, for example, group the loyal customers.
  • Semi-supervised learning: your model starts with a small dataset and applies supervised learning (labeled data). Then we will feed the rest of the data to our model and observe them by applying unsupervised learning (non-labeled data). This will allow the computer to expand its vocabulary based on what it learned and classified during the supervised learning stage.
  • Reinforcement learning: we train our model by rewarding it every time it has the correct output. Then the computer will try to get as many rewards as possible and will learn by itself. It can be used to create an AI for video games.

#data-science #supervised-learning #understand #artificial #intelligence #ai

Understand Artificial Intelligence (AI)
Lina  Biyinzika

Lina Biyinzika


5 Significant Benefits of Artificial Intelligence [Deep Analysis]

Artificial Intelligence (AI) has come a long way from being the subject matter of science fiction to be the living and breathing reality of the 21st century. It is the fuel that runs the business machinery today. Thanks to the rapid advancements in technology, the applications of Artificial Intelligence have manifested before us in myriad forms. From smart virtual assistants like Siri and Alexa to highly advanced self-driving cars and IoT devices, AI never ceases to amaze us. Read how AI has startled the world in the recent past.

  1. Automation takes the load off of human employees.

Companies across all sectors of the industry are now using AI to take over all the routine and monotonous operations. This is allowing their employees to focus on the more critical aspects of the business that require human cognitive and creative abilities. By automating the routine processes, AI is freeing up the time of the employees who can spend it on performing value-added services for the company and even upskilling.

2.Machines remain at your beck and call, 24×7.

Unlike humans who require breaks between tasks, machines have no such requirement. Machines do not get tired, or bored, or distracted, making them all the more perfect for accomplishing tasks. They can be at your service 24×7, all year round. By programming machines to perform automated tasks that require minimal human intervention, companies can build a smooth operational and delivery system.

3. AI leaves zero scope for error.

Even though the AI at our disposal today is primarily narrow AI, it is capable of performing an array of tasks successfully without human intervention. Such is the power of ML algorithms – if you design them correctly to perform specific functions and to learn from experience, these algorithms leave no room for error. However, with humans, it is as the saying goes, “to err is human.”

4.Smart digital assistants are reshaping customer-brand interaction.

Virtual digital assistants have already become a big part of our lives. Think Siri, Alexa, Cortana, and Google Assistant – we’ve all used the assistance of these smart assistants at some point or the other. Digital assistants as these are designed to interact with customers just like another human being would. Thanks to these advanced AI applications, surfing the online domain has become much more smoothe for us.

5. AI applications are enhancing healthcare.

One of the most significant contributions of AI has been in the field of healthcare. For instance, IBM’s Watson for Health is enabling healthcare organizations to leverage advanced cognitive technology to unlock vast amounts of healthcare data.

#significant #intelligence

5 Significant Benefits of Artificial Intelligence [Deep Analysis]

Why Should I Care About How Humans Think?

This is how we, as humans, think.

I wonder sometimes whether someone who defends their own brand of AI with that argument actually gets what they are really saying. What does that mean?It is especially irritating considering that:

  1. Not it is not: More often than not the claim made is something, at best, out of pseudoscience.
  2. I’m not trying to make a human here: Planes don’t fly the same way birds do.
  3. More generally, why should I care? Humans are terrible at many things, and especially at what we want AI to be good at. We make bad decisions, biased decisions, uninformed decisions. We are cruel, selfish and most of the time, wrong.

#neural-networks #intelligence #artificial-intelligence #research #symbolic-ai

Why Should I Care About How Humans Think?
Jack Forbes

Jack Forbes


How AI will change Your IAM Initiatives

This trend is not limited to identity and access management (IAM). As vendors employ robust artificial intelligence approaches to counter existing identity risks, IAM has become a focal point within enterprise security.
Artificial Intelligence (AI) isn’t a modern concept, but it’s quickly changing many different technologies and processes.

This whitepaper provides a concise summary of artificial intelligence (AI) for an audience of thought leaders and interested analysts, demonstrating how the technology is already transforming the environment and addressing critical customer identity management issues.

You’ll discover:

  • What are the benefits of having a mature IAM for businesses?
  • What are the problems of managing digital identities?
  • What does a gradual transition toward AI entail?
  • What are the similarities and differences between customer and business identity services?
  • What does a great CIAM platform look like?
  • How can AI help you build a frictionless, user-centric CIAM?

Artificial intelligence is no longer a hazy, far-fetched idea that no one can apply in a practical way. The more you know about an AI-enabled world, the less you can disrupt your customers’ experience.

Download this whitepaper to learn why the LoginRadius all-in-one CIAM framework should be considered for managing individuals, processes, and devices across the organization.

How AI is Going to Change Your IAM Initiatives

#iam #ai #artificial #intelligence #whitepaper

How AI will change Your IAM Initiatives
Jack Forbes

Jack Forbes


Steps To Grow Emotional Intelligence | LoginRadius

Emotional intelligence aids communication and conflict resolution. It decides how well teams work and how they remain motivated to improve their performance. After Daniel Goleman’s book Emotional Intelligence was released in the 1990s, the term gained popularity.
Self-awareness, self-regulation, inspiration, empathy, and social skills — the top five components of EI — seemed to have an effect on business communication.

Why Emotional Intelligence Improves Consumer Relations

Consumer service agents’ emotional intelligence provides insight into issues because their conflict management and communication skills allow customers to share feedback. Better individual experiences with customers have a long-term impact on their purchasing decisions. Empathy, adaptability, self-control, teamwork, and a desire to learn are all qualities that emotional intelligence helps agents acquire.

How to Assist Agents in Developing Emotional Intelligence

Begin by cultivating an emotional intelligence culture inside your business. Follow a study guide to learn why agents need EI and how it will help them improve customer relations.

  • Introduce the core principles of EI and discuss them during business meetups or team building events.
  • Suggest books or critical essays on the topic.
  • Visit seminars and workshops with your support team or organize your own in the company.
  • Recommend TED talks or educational podcasts about EI.

Consumer service agents must remain involved and respond rapidly when speaking with customers over the phone or in live chats. To ensure emotional intelligence success, use simple strategies.

The ability to control emotions and relate well to others is referred to as emotional intelligence. Incorporating EI training into management systems and empowering employees to expand their EI creates a stronger company. Grow your agents’ emotional intelligence, become emotionally intelligent yourself, and integrate it into your business communication systems.

Discover some steps to grow your emotional intelligence for better consumer relations.

**Steps to Grow Your Emotional Intelligence

#grow #emotional #intelligence #steps

Steps To Grow Emotional Intelligence | LoginRadius
Otho  Hagenes

Otho Hagenes


A Simple Approach to Define Human and Artificial Intelligence

I recently started to follow an exciting and mind-bending philosophy online course at MIT called Minds and Machines.
The course is a thorough, rigorous 12 Weeks Learning Path introduction to contemporary philosophy of mind, exploring consciousness, reality, artificial intelligence (AI), and more. It is definitively one of the most in-depth philosophy courses available online that I ever frequented.
The first effect of starting study philosophy at Massachusetts Institute of Technology is that I’m asking more challenging questions… the second effect is that I’m writing more about those questions.
I‘m in this moment, exploring the relationship between the mind and the body, the capacity of computers to think, the way we perceive reality, and the perspective of the existence of a science of consciousness.
As a first result, I’ve started to pay particular attention to one specific question that definitively has a lot to relate to my daily work as an AI expert: what is intelligence?
In this article, I will explore human and artificial intelligence concepts to find relevant similarities and differences.

#ai #artificial-intelligence #intelligence #philosophy #humans

A Simple Approach to Define Human and Artificial Intelligence

Krystin Woods


How to Start a Career in Artifical Intelligence

Artificial Intelligence, also known as AI, is a field of study concerned with making machines and computers behave in ways that have so far only been attributed to humans. Some of the things that AI systems can do include image recognition, speech and language processing, pattern recognition, learning from experience, and moving and manipulating objects.


Required Skills

For those interested in a career in AI, it’s important to know what skills you need. These skills include mathematics, programming, statistics and problem-solving. The specific skills required will depend on whether the professional is focusing on software or hardware or if he/she intends to pursue a career in academia.

Programming Languages
Professionals looking at AI scopes in the software development sector must know at least one programming language like Java, Python or Perl. The high-level languages that are used for AI development include C++, Prolog, Lisp and Smalltalk. It is imperative that professionals not only have good knowledge of these languages but also know how to use them with applications like MATLAB and S-PLUS.

Statistics and Mathematics
Professionals in AI also need to have significant knowledge of statistics and mathematics. Statistics will enable professionals to develop models as well as track how well a model is working or not working. Professionals are also required to make statistical inferences from their data. An ability to apply mathematical concepts like linear algebra, discrete mathematics, and probability theory will help professionals with AI development.

Communication skills
Professionals should be able to communicate effectively both verbally and in writing to coordinate their efforts for better results. In addition, they should be able to give training and presentations to their clients or other professionals.

Problem Solving Skills
AI developers need problem-solving skills so that they can come up with a solution to a problem presented. They should be able to convert the problem into a mathematical model in the form of an equation or algorithms. Professionals should also have the ability to try out various solutions by using them with their mathematical models.

Artificial Intelligence

Required Qualifications

The qualifications needed to start a career in AI are usually a technical degree or a degree in a hard science such as physics or mathematics with AI as one of its subjects.

One of these degrees will provide you with the required knowledge and give you industrial experience so that you are more easily able to find work after graduation. In the field of AI, it is often said that experience is worth its weight in gold. To access the most advanced roles, you will commonly require a PhD. A PhD in artificial intelligence is a rigorous three-year course of study that will teach you everything you need to know about the field.

The first year of a PhD in AI will typically cover topics such as numerical algorithms, optimisation and computational theory. The second-year will cover topics such as machine learning, computer vision and natural language processing. The third-year will be devoted to writing a dissertation on your own research into AI.

How to gain experience?

As mentioned previously, experience is invaluable in the field of AI. No matter how good your degree was, if you don’t have any practical experience, it will be difficult to get a job in AI when you graduate. The best way to gain experience in AI is to find a research position in a university or company that works on AI.

Research assistant positions are available for those who already have a PhD and are therefore familiar with the rigorous work requirements of running an academic study or research project from start to finish. Research assistant positions can be found advertised by universities and companies, and you can apply directly through their websites.

Top 4 career paths in Artificial Intelligence

1. Data Analytics
Data Analytics is a broad term used to encompass many different jobs. The most common job title is Data Scientist. These people are required to have the skills of a computer programmer, mathematician and statistician. Their primary concern is the extraction of information from large data sets (known as Big Data) for use by other departments within an organisation or by customers.
Typical salary: £45,000 - £65,000 ($60,000 - $90,000) per year.

2. Natural Language Processing
With the rise of the Internet and social media, the ability to capture and analyse people’s language has grown exponentially. Natural Language Processing (NLP) is a subset of AI concerned with developing computer programs that can understand natural human language. This includes both written and spoken languages. It’s common for these types of systems to be implemented across areas such as voice recognition, chatbots, spam filtering, translation services and customer service.
Typical salary: £65,000 - £90,000 ($90,000 - $125,000) per year.

3. Machine Learning Engineer
Machine Learning is a subset of AI concerned with giving computers the ability to learn for themselves. An example of this is a machine learning classifier that can sort new data based on its similarity to existing data. This is interesting because it means that the system has learned from previous experiences and can make more accurate decisions in future.
Typical salary: £70,000 - £90,000 ($95,000 - $125,000) per year.

4. Research Scientist
Research Scientists are required to have an in-depth knowledge of the subject they are working on. For example, a Research Scientist working in machine learning will need to have an in-depth understanding of mathematical algorithms and programming languages such as MATLAB, Scilab and Python.
Typical salary: £50,000 - £80,000 ($70,000 - $110,000) per year.

Careers in AI


In conclusion, AI is an extensive and highly lucrative industry with a massive potential for growth in the next few years. Whether you decide to become a Machine Learning Engineer or Data Analytics, it is sure to be a lucrative career path that will be in demand well into the future.

#artificial #intelligence #ai

How to Start a Career in Artifical Intelligence

Top 10 Artificial Intelligence And Machine Learning Trends In 2021

While the COVID-19 pandemic affected numerous aspects of how we do business, it didn’t reduce the impact of Artificial Intelligence(AI) on our day to day lives. It is turning out to be obvious that self-teaching algorithms and intelligent machines will play an important role in the ongoing battle against pandemic and others that we face in the near future. It is obvious that AI remains in trend and will change how we live, work and play. Artificial intelligence is a reason behind many technological comforts that are our daily lives, like smart gadgets, netflix recommendations, amazon’s alexa and Google Home. With continuous research, technology has made huge developments in fields like retail, healthcare, automotive, manufacturing and finance. Artificial Intelligence will keep on serving as a central technological innovation in 2021 and in the future. Let u see what we can expect in years to come.

Top 10 Artificial Intelligence And Machine Learning Trends-

1. Artificial Intelligence And Machine Learning In Hyperautomation-
Hyperautomation is a tech trend popular with other names as “Digital process Automation” and “Intelligent Process Automation”. It combines the appropriate technologies for automating, simplifying, discovering, designing, measuring, and managing workflows and processing across the organization. COVID-19 pandemic has rapidly increased the adoption of this concept where AI and ML are key components and its significant drivers. It includes some other technologies like robotic automation tools, but successful initiatives can’t depend on static packaged software. It is necessary for automated business processes to adapt changing circumstances and respond proactively to unexpected situations. Here comes the role of AI, ML models and deep learning. Adopting these algorithms and models with automated system’s data to make enhancements timely and respond to changing process and requirements of the business.

**2. Augmented Analytics Will Transform Business Intelligence – **
Augmented analytics uses ML and AI technologies for data preparation, insight generation, explanation to expand the way of exploration, data analysis in analytics and BI platforms. Artificial intelligence is proving to be a critical enabling technology, and enterprises need an efficient way to scale their AI practices and implement AI in business. Most of the times, organizations face high pressure to optimize their workflows, many companies will approach to BI teams to develop and manage AI/ML models. This empowers a new class of BI-based “AI developers” that will be driven by two factors-

  1. empowering BI teams with AutoML platforms is more sustainable and scalable than hiring dedicated data scientists.

  2. Because BI teams are closer to business use-cases than data scientist, the life-cycle from “requirement” to the work model will be accelerated.

Many BI vendors will offer AI capabilities like natural language processing, text analytics, predictive dashboards and so on.

3. Conversational Artificial Intelligence Bots-
The most creative approach to deal with client queries is using Chatbots. Conversational AI bots bring the power of AI through Natural Language Processing (NLP) and Natural Language Understanding (NLU). It allows the functionality of buttons and facilitates customer queries. Conversely, conversational AI Bots provides unlimited scalability with the help of machine learning. Natural language processing provides human experience to customers. With the help of conversational AI bots, users can file insurance claims, book healthcare appointments, apply for jobs, block their financial cards and so on. This helps businesses to automate their customer support, sales and knowledge support. For instance, companies can help employees not answer repetitive questions from clients or employees by automating the process. Conversational AI bots will manage all incoming queries through Automatic Semantic Understanding.

Know more at-

#artificial #intelligence #machine-learning #technology #tech

Top 10 Artificial Intelligence And Machine Learning Trends In 2021