There are four machine learning categories that these algorithms fall into: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning (RL). Supervised learning algorithms rely on large amounts of manually annotated data to learn patterns, in order to make predictions on new, unseen data. The most common supervised learning algorithms are classification and regression. Classification algorithms predict the output of discrete values, such as judging whether a photo depicts a dog or a cat, whereas regression algorithms predict the output of continuous values, for example, house prices based on historical data. On the other hand, unsupervised algorithms do not have a requirement for labelled data, given that data can be clustered based on their characteristics, for example, customer segmentation based on their preferences. Semi-supervised algorithms only need a few labelled training samples and they are currently a hot research area. Some examples include self-training, active learning, and graph-based semi-supervised learning. RL algorithms rely on a rewards hypothesis by selecting actions that maximize the total future reward. Those actions may have long-term consequences and the reward is not immediate. Some examples include financial investments and scheduling optimization of capital projects. OpenAI Gym is an open-source platform for developing RL algorithms.

One of the most prevalent sub-fields of AI is Natural Language Processing (NLP), where computers recognize and understand human text language. There are multiple NLP applications such as sentiment analysis, information extraction, and machine translation. Transformers (such as GPT-3 and BERT ) have been widely used. Transformers have a looping mechanism that acts as a highway to allow information to flow from one step to the next and retain sequential memory. This makes them ideal for speech recognition, language translation, and stock predictions. State-of-the-art (SOTA) transformers for NLP tasks can be found at multiple open-source Github repositories .

Another AI sub-field is Computer Vision - often referred to as Machine Perception. The goal of these algorithms is to enable computers to visualize the world as humans do. The most widely adopted Computer Vision problem is object recognition in 2D and 3D data. There are multiple tasks involved with image recognition such as image classification, segmentation, and object detection. Some of the most commonly used Convolutional Neural Networks for object detection are YOLOv3 and deep residual networks with the SOTA being Vision Transformers .

Decision-making is another complex task that involves data analysis and merging information from disparate sources while leveraging information importance. AI can solve competitive human-level tasks and even beat humans, for example, when AlphaGo defeated the world chess champion using RL algorithms.

Despite the advances in every AI sub-field, there are significant challenges to overcome. Some of these include explainability of the developed models (technical challenge), algorithmic bias (technical and societal challenge), and transparency in usage (societal, political, and legal challenge). Quantum computing can assist in mitigating some of these obstacles. It can be used to rapidly train and generate optimized ML algorithms (using superposition and entanglement). A recent open-source library for quantum ML is Tensorflow Quantum (TFQ) which combines a suite of quantum modelling and Machine Learning tools. Some contributions of Quantum AI are: quick and optimal weight selection of neural networks, faster encryption based on quantum search, and quantum algorithms based on the Hamiltonian time evolution that represent problems with optimal decision trees faster than random walks. A summary of AI models, papers, datasets, and open-source libraries can be found at Stateoftheart.ai .
The evolution of AI spanning across disciplines has been inspired by advances in biology, cognition, and system theories. The next breakthroughs will not only give machines more logical reasoning capabilities but also the possibility of surpassing human senses for the better of humanity.