8 Best Topics for Research in Artificial Intelligence

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8 Best Topics for Research and Thesis in Artificial Intelligence

Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancement in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, Internet of Things, Robotics, etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996.
Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

1. Machine Learning

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.
However, generally speaking, Machine Learning Algorithms are divided into 3 types i.e. Supervised Machine Learning Algorithms, Unsupervised Machine Learning Algorithms, and Reinforcement Machine Learning Algorithms.

2. Deep Learning

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!).
This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.

3. Reinforcement Learning

Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from its own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error.
This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

4. Robotics

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments.
An example of this is Kismet, which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut, which was developed by NASA to work alongside the astronauts in space.

5. Natural Language Processing

It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation, etc.
NLP is currently extremely popular for customer support applications, particularly the chatbot. These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.

Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.

6. Computer Vision

The internet is full of images! This is the selfie age, where taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in.
Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

7. Recommender Systems

When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering.
Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.

8. Internet of Things

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things, on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other.
Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by the Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices.

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If you are reading this article, you are already surrounded by AI-powered tech more than you can imagine. From the website in front of you to reading CT scans, AI applications are inevitable.

Generally when people hear about AI they often equate it to Machine Learning and Deep Learning, but they are just two of the many subtopics in AI research. These two are arguably most effective themes in today’s AI world but there are many other subtopics that have gained significant traction in the AI community for their applications and the future potential. In this article we will discuss some of the hot subtopics in the AI research, many of these topics are interlinked and come under broad umbrella of artificial intelligence:

Large scale Machine Learning

Machine Learning (ML) is concerned about developing systems that improve their performance with experience. In the last decade progress in AI can easily be attributed to the advances in ML. ML is so popular that it has become synonymous with AI. The researchers are now focusing on scaling the state-of-the-art ML algorithms to large datasets. For more information on ML read this introductory blog.

Deep Learning

A subset of ML, Deep Learning (DL) is re-branding of neural networks- a class of models inspired by biological neurons in our brain. DL has been driving force for lots of applications in AI like object recognition, speech, language translation, playing computer games and controlling self driving cars. For more information on DL read this introductory blog.

Reinforcement Learning

Reinforcement Learning (RL) is the closed form of learning to the way a human being learns. It consists of an intelligent agent that interacts with its environment smartly to reap a numerical reward. The goal of the agent is to learn sequential actions so as to maximize the long time reward. Like a human being who learns from his experience with the real world, keep exploring new things and updating his values and beliefs, the RL agents works on the similar principle to maximize his own rewards in the long run. In 2017, Google’s AlphaGo computer program used RL to beat the world champion in the game of Go. For more information on RL read this blog.

Robotics

Technically speaking, Robotics is a separate branch of its own but it do has some overlap with AI. AI has made robot navigation in dynamic environment possible. How do you make sure that a self driving car goes from point A to point B without harming itself and anyone else in the least time? Advances in DL, RL probably have answers to such questions in Robotics. For more information on robotics read this blog on AI powered Robotics and watch these demonstration videos: 1, 2, 3.

Computer Vision

If We Want Machines to Think, We Need to Teach Them to See. Fei-Fei Li, Director of Stanford AI labComputer vision (CV) is concerned with how the computer visually perceive the world around it. Ironically, computers are good at doing mammoth tasks like finding tenth-root of a 100 digit number but struggle in simple tasks like recognizing and differentiating objects. Recent advances in DL and availability of labelled datasets and high computing power have made possible for CV systems to outperform their human counterparts for some of the narrowly defined tasks like visual object classification. For more information on CV read this blog.

Natural Language Processing

Natural Language Processing (NLP) is concerned with systems that are able to perceive and understand spoken human language. It consists of sub tasks like speech recognition, natural language understanding, generation and translation. With multiple languages used across the globe, NLP systems could be a real changer. Current NLP research includes developing chat bots that can dynamically interact with humans. For more information on NLP read this introductory blog.

Recommender Systems

From what to read, what to buy, to whom to date, Recommender Systems (RS) are everywhere and have completely replaced the annoying salesman in the virtual world. Companies like Netflix and Amazon heavy rely on RS. A RS takes into consideration a user’s past preferences, preferences of its peers and trends to make an effective recommendation. For more information on RS read the following articles: 1, 2.

Algorithmic Game Theory and Computational Mechanism Design

Algorithmic game theory considers systems with multiple agents from economics and social science perspective. It sees how these agents make choices in a incentive-based environment. These multi-agent systems can include self-interested human members along with intelligent agents that compete together in a limited resource environment. For more information on this topic you can follow writings of Professor David Parkes. This link is also a good resource.

Internet of Things

Internet of Things (IoT) is a concept that daily use physical devices are connected to the internet and can communicate with each other via exchange of data. The data collected could be processed intelligently to make the devices smarter. This article explains how AI could be used to make smarter buildings.

Neuromorphic Computing

With rise of Deep Learning that relies on neurons based models, researchers have been developing hardware chips that can directly implement neural network architecture. These chips are designed to mimic the brain at the hardware level. In an ordinary chip, the data is required to be transferred between central processing unit and storage blocks, which results in time overheads and energy consumption. In an neuromorphic chip, data is both processed and stored in the chip in an analog manner and can generate synapses when required, saving time and energy. For more information on development of these brainy chips read these two articles: 1, 2.

Other articles detailing trends in AI research: 0, 1, 2, 3.

Note: The article was first published as “Hot subtopics in AI research” in publication AI Tale on medium on May 7, 2018.

References:
Stone, Peter, et al. “Artificial intelligence and life in 2030.” One Hundred Year Study on Artificial Intelligence: Report of the 2015–2016 Study Panel (2016)