Introduction Imagine a world where knowledge isn’t limited to humans!!! A world in which computers will think and collaborate with humans to create a more exciting universe. Although this future is still a long way off, Artificial Intelligence has made significant progress in recent years. In almost every area of AI, such as quantum computing, healthcare, autonomous vehicles, the internet of things, robotics, and so on, there is a lot of research going on. So much so that the number of annual Published Research Papers on Artificial Intelligence has increased by 90% since 1996.
Keeping this in mind, there are several sub-topics on which you can concentrate if you want to study and write a thesis on Artificial Intelligence. This article covers a few of these subjects and provides a short overview. Here some of the recent Research Topics, Artificial Intelligence and Machine learning – Recent Trands How AI and ML can aid healthcare systems in their response to COVID-19 Machine learning and artificial intelligence in haematology Tackling the risk of stranded electricity assets with machine learning and artificial intelligence
Deep Learning Deep Learning is a type of machine learning that learns by simulating the internal workings of the human brain in order to process data and make decisions.Deep Learning is a form of machine learning that employs artificial neural networks. These neural networks are linked in a web-like structure, similar to the human brain’s networks (basically a condensed version of our brain!). Artificial neural networks have a web-like structure that allows them to process data in a nonlinear manner, which is a major advantage over conventional algorithms that can only process data in a linear manner. Rank Brain, one of the variables in the Google Search algorithm, is an example of a deep neural network.
Recent research topics Artificial intelligence & deep learning : PET and SPECT imaging Hierarchical Deep Learning Neural Network (HiDeNN): A computational science and engineering in AI architecture. AI for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using Deep Learning Deep learning-enabled medical computer vision
Reinforcement Learning Reinforcing Learning is an aspect of Artificial Intelligence in which a computer learns something in the same way as humans do. Assume the computer is a student, for example. Over time, the hypothetical student learns from its errors. As a outcome of trial and error, Reinforcement Machine Learning Algorithms learn optimal behaviour. This means that the algorithm determines the next way to proceed by learning behaviours based on its current state that will increase the reward in the future. This also works for robots, just as it does for humans! Google’s AlphaGo Computer Programme, for example, used Reinforcement Learning to defeat the world champion in the game of Go (a human!) in 2017.
Recent research topics Experimental quantum speed-up in reinforcement learning agents Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety
Robotics Robotics is an area concerned with the creation of humanoid robots that can assist humans and perform several acts. In certain cases, robots can behave like humans, but can they think like humans as well? Kismet, a social interaction robot developed at M.I.T.’s Artificial Intelligence Lab, is an example of this. It understands human body language as well as our voice and responds to them appropriately. Another example is NASA’s Robonaut, which was designed to assist astronauts in space.
Recent research topics Regulating artificial intelligence and robotics: ethics by design in a digital society Regional anaesthesia :usages of artificial intelligence and robotics in Third Millennium Life Saving Smart Cyberspace Driven by AI and Robotics
Natural Language Processing
Humans can obviously communicate with each other by speech, but now machines can as well! This is known as Natural Language Processing, and it involves machines analysing and understanding language and expression as it is spoken (which means that if you speak to a computer, it might only respond!). Speech recognition, natural language production, natural language translation, and other aspects of NLP are all concerned with language.
NLP is recently very important in customer service applications, particularly chatbots. These chatbots use machine learning and natural language processing to communicate with users in textual form and respond to their questions. As a result, you get a personal touch in your customer service experiences without actually speaking with a human. Here are several research papers in the field of Natural Language Processing that have been published. You can look at them to get more ideas for research and thesis topics on this subject.
Recent research topics Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study Sympathetic the temporal evolution of COVID-19 Research Through machine learning and natural language processing
Computer Vision The internet is full of images! This is the selfie age, and taking and posting a photo has never been easier. Each day, millions of images are uploaded to the internet and viewed. It’s important for computers to be able to see and understand images in order to make the most of the vast amount of images available online.
To extract information from images, Computer Vision utilizes Artificial Intelligence. This knowledge may include object detection in the image, image content recognition to group images together, and so on. Navigation for autonomous vehicles using images of the surroundings is one use of computer vision, such as AutoNav, which was used in the Spirit and Opportunity rovers that landed on Mars.
Recent research topics Deep learning-enabled medical computer vision Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning An Open‐Source Computer Vision Tool for Automated Vocal Fold Tracking From Video endoscopy
Recommender Systems Do you get movie and series recommendations from Netflix based on your previous choices or favourite genres? This is achieved by Recommender Systems, which offer you advice about what to do next from the vast array of options available online. Content-based Recommendation or even Collaborative Filtering may be used in a Recommender System.
The content of all the products is analysed in Content-Based Recommendation. For example, based on Natural Language Processing performed on the books, you might be recommended books that you may enjoy. Collaborative Filtering, on the other hand, analyses your past reading behaviour and then recommends books based on it.
Recent research topics Artificial intelligence in recommender systems Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems. Recommender systems for configuration knowledge engineering
Internet Of Things Artificial intelligence is concerned with the creation of systems that can learn to perform human-like tasks based on prior experience and without the need for human interaction. The Internet of Things, on the other hand, is a network of different devices linked to the internet and capable of collecting and exchanging data.
All of these IoT devices now generate a large amount of data, which must be collected and mined in order to produce actionable results. Artificial Intelligence enters the picture at this stage. The Internet of Things is used to collect and manage the massive amounts of data that Artificial Intelligence algorithms need. As a consequence, these algorithms transform the data into useful actionable results that IoT devices can use.
Recent research topics Enhanced Medical Systems by using Artificial Intelligence and Internet of Things Artificial Intelligence and Internet of Things in Instrumentation and Control in Waste Biodegradation Plants: Recent Developments AIoT-Artificial Intelligence of Things
Conclusion In this blog discussed the recent enhancement for artificial intelligences and their sub field. This will help to the PhD scholar who are interested to research in artificial intelligences domain.
References 1. Shouval, R., Fein, J. A., Savani, B., Mohty, M., & Nagler, A. (2021). Machine learning and artificial intelligence in haematology. British journal of haematology, 192(2), 239-250. 2. van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., … & Ercole, A. (2021). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110(1), 1-14. 3. Nyangon, J. (2021). Tackling the risk of stranded electricity assets with machine learning and artificial intelligence. In Sustainable Energy Investment-Technical, Market and Policy Innovations to Address Risk. IntechOpen. 4. Saha, S., Gan, Z., Cheng, L., Gao, J., Kafka, O. L., Xie, X., … & Liu, W. K. (2021). Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering. Computer Methods in Applied Mechanics and Engineering, 373, 113452. 5. Mascagni, P., Vardazaryan, A., Alapatt, D., Urade, T., Emre, T., Fiorillo, C., … & Padoy, N. (2021). Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Annals of Surgery. 6. Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., … & Socher, R. (2021). Deep learningenabled medical computer vision. npj Digital Medicine, 4(1), 1-9.
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