This article lists the top ten research and thesis topics for AI projects in 2022. Imagine a day in the future when intelligence is not limited to human beings, at a time when machines are intelligent enough to collaborate with people to create a fascinating universe.
Nearly all branches of AI are the subject of extensive research, including quantum computing, healthcare, autonomous driving, computer vision, the Internet of Things, robotics, and others. Due to its adaptability and rapid development, AI has many applications.
The number of research articles on artificial intelligence that are published each year has increased by 90% since 1996. There are numerous sub-topics you might concentrate on if you want to do research based on artificial intelligence.
Large scale Machine Learning
Without programming, machine learning enables machines to learn a task via experience. Our algorithms will depend on our data and the task we’re trying to automate. (In short, computers learn autonomously without human input!). This process starts with providing them with high-quality data, and then the machines are taught by building several machine-learning models using the data and different techniques.
However, there are three categories into which machine learning algorithms fall. There are three machine learning algorithms: supervised, unsupervised, and reinforcement learning. You can also learn more about it from best data science blogs. Numerous fundamental issues with machine learning(such as supervised and unsupervised learning) are well understood. Scaling up already-in-use algorithms to deal with big data sets is a crucial focus of current efforts.
Deep Learning (DL) is a subset of ML, a rebranding of neural networks, a class of models that take their signals from the biological neurons in our brains. Many AI applications, like speech recognition, language translation, playing video games, and driving self-driving cars, are possible due to Deep Learning (DL).
Like neural networks in the human brain, these networks connect in a web-like structure. This web-like network enables them to analyze data nonlinearly, giving them a considerable edge over conventional algorithms. The Google Search algorithm component RankBrain is an example of a deep neural network.
Reinforcement learning is a subset of AI that allows a machine to learn something like how people learn. Consider, for example, that the machine is a student. Here, the fictitious student gradually learns from its errors (just as we did!). Therefore, reinforcement learning algorithms discover the best course of action via trial and error.
To optimize the long-term benefit, the agent aims to learn sequential actions. The RL agents adopt a strategy like that of a person who learns from his encounters with the present, continues to learn new things, and modifies his values and beliefs to maximize his rewards over time. Google’s AlphaGo computer algorithm defeated the Go world champion in 2017 using RL.
Robotics is a field that deals with developing humanoid devices that can act like humans and carry out some human-like tasks. Robots may now mimic human behavior in some circumstances, but can they also think like us? Artificial intelligence can help in this situation.
AI enables robots to behave intelligently in specific circumstances. These robots might be able to learn in controlled contexts or solve issues in a specific domain.
Kismet is an example, a social interaction robot created at M.I.T.’s Artificial Intelligence Lab. It interacts with people in a way that considers our speech and body language. Another illustration is the Robonaut, which NASA created to assist astronauts in space.
The most common type of machine perception today is computer vision. The development of deep learning has significantly changed this branch of AI. Support vector machines were the go-to technique for the majority of visual classification jobs up until a few years ago.
However, with the development of neural network methods, and large-scale processing, especially on GPUs, access to enormous datasets, primarily via the internet. These factors together have led to much superior performance on benchmark tests.
Computer vision uses AI to extract data from images. AutoNav, installed on the Spirit and Opportunity rovers that touched down on Mars, is an example of computer vision navigating autonomous vehicles by evaluating photos of their surroundings.
Natural Language Processing
Humans can communicate verbally, but now machines can, too! Natural Language Processing is the process through which machines analyze and comprehend voice and language. If you speak to a machine, it might even respond. Speech recognition, natural language creation, natural language translation, and other language-related NLP subfields are only a few examples.
NLP is currently quite popular for chatbots and other customer service applications.These chatbots engage with people in textual form and respond to their questions using ML and NLP. So even though you never speak to a human directly, you still experience the human touch in your customer service encounters.
Recommender Systems (RS) replaced the salesman in the virtual world, giving users recommendations on everything from what to read to what to buy to whom to date. Companies like Netflix and Amazon heavily utilize Recommender System. Moreover, RS considers a user’s previous choices, the preferences of their peers, and trends to generate a practical suggestion.
Internet of Things
Artificial intelligence is the field that deals with building machines that can mimic human tasks using their prior experience and without any operator assistance. On the other hand, the Internet of Things is a network of various devices connected to the internet and capable of collecting and exchanging data
Since there are so many IoT devices on the market today, which collect and process data to provide helpful information. So, you can use artificial intelligence in this situation. The Internet of Things also collects and manages a huge quantity of AI algorithms. These algorithms transform the data into meaningful, employable outputs that IoT devices may use.
Algorithmic Game Theory and Computational Mechanism Design
AI’s social and economic aspects, such as incentive systems, are receiving more attention. The internet has accelerated the development of distributed AI and multi-agent systems, which have been studied since the early 1980s and gained popularity in the late 1990s. It only makes sense to handle possibly mismatched incentives, such as those from selfish businesses or human participants, as well as automated AI-based agents operating on their behalf.
From an economics and social science perspective, the algorithmic game theory looks at systems with numerous agents. It observes these agents’ decisions in a setting where incentives are present. In these multi-agent systems, intelligent agents and self-interested human competitors coexist in a resource-constrained environment.
As Deep Learning, which relies on neuron-based models, has gained popularity, researchers have been working on hardware circuits that can directly incorporate neural network design. In terms of hardware, these chips resemble the human brain.
Data transfers between the central processing unit and storage blocks are necessary for a typical chip, which uses energy and takes time. In a neuromorphic device, data is processed and stored analogically and used to create synapses.