“Our intelligence is what makes us human, and AI is an extension of that quality.” — Yann LeCun Professor, New York University.
Hope you guys have gone through my previous blog on “Introduction to Artificial Intelligence” which gives a basic idea about Artificial Intelligence (AI).
In this article, let’s further discuss some areas in AI so that you can dive into this oncoming industrial revolution:
- Hot topics in AI research
- Use cases of AI
AI is one of the most promising disciplines in Computer Science. There is a big demand for AI in the development process. The investment rate to work with it has increased tremendously as many companies want to stay ahead of the digital transformation as they were attracted by the versatility of AI. Following are some of the most prominent areas of development that have already been triggered.
- Natural Language Processing (NLP)
- Recommender System
- Speech Recognition
- Virtual Agents
- Internet of Things (IoT)
- Reinforcement Learning
1. Natural Language Processing (NLP)
NLP is the ability of a computer system to perceive and understand spoken human language to perform useful tasks. In other words, NLP research attempts to recognise how humans understand languages and how to apply this knowledge to train AI systems to perform desired tasks. It consists of sub-fields like speech recognition, natural language understanding, generation and translation.
The topmost useful application of NLP is Sentiment Analysis. Sentiment analysis is based on natural language processing, statistics, and text analysis to cite, and distinguish the emotions from text into positive, negative or neutral. This technique is widely used in identifying the sentiments from Tweets and Facebook newsfeeds. If you need further clarification on this, you can follow one of my previous posts.
2. Recommender System
A recommendation engine is a system that replaces the salesman in the virtual world and suggests products, services, information to users based on analysis of data, such as the records of the user and the behaviour/pattern of similar users.
There are three types of techniques for Recommender systems.
- content-based filtering
- collaborative filtering
- knowledge-based system
Content-based filtering is based on interactions and preference of a single user. Recommendations are based on the metadata gathered from a user’s history.
Collaborative filtering generates a much wider network, collecting information from the interactions of many other users to derive suggestions for you. This strategy makes recommendations based on other users with similar judgements or situations.
Knowledge-based systems are systems where suggestions are based on the influence on the needs of a user and a degree of experience and knowledge of the domain. The rules are defined to define the context of each recommendation.
3. Speech Recognition
Speech recognition enables a computer (or any other device) to identify spoken words. Speech recognition allows users to control digital devices through voice commands instead of using traditional tools such as a keyboard, buttons, or mouse. Speech recognition permits you to provide input by simply talking.
Large investments are currently made to formulate an artificial intelligence to transcribe and transform human speech into a useful format for computer applications to expand the business.
4. Virtual Agents
This type of AI goes from conversational bots to advanced operations that can build contacts with humans. It is widely used in customer service and support, and as smart home managers. The most frequent model of bots is a Chatbot, a software capable of simulating a conversation with a person. Chatbots are able to communicate with people, they are becoming increasingly present in messaging applications, as of today you can find them across messaging platforms, performing a variety of tasks.
Some virtual agents :
5. Internet of Things (IoT)
Internet of Things (IoT) is a concept that daily use of physical devices is connected to the internet and interrelated with each other via exchange of data without requiring human-to-human or human-to-computer interaction. The data collected is processed intelligently to make the devices smarter.
Robotics is a totally different aspect of its own but it does overlap with AI. AI has made robot exploration in dynamic environment possible. The robots are defined first of all by the ability to detect the input by sensing the environment, then calculating the inputs of these sensors and, finally, to act according to the results of these calculations.
7. Reinforcement Learning
Reinforcement Learning (RL) is the closed-form of learning about how humans learn. It is a smart agent that interacts intelligently with its environment to gain a numerical reward. The agent is intended to learn consecutive actions to maximize the long-time reward. The agent will update his or her values and beliefs by continuing to explore new things as a human being who learns from his or her experience with the real world.