AI technology can be divided into 3 layers. While we will likely see incremental improvements in algorithm design and application of those algorithms to specific domains, step change is possible in computing if breakthroughs can be achieved in quantum computing.
- Algorithms that enable machine decision making including artificial neural networks (ANN), Bayesian inference and evolutionary computing. ANNs can be categorized by their depth (i.e. number of layers) and structure (i.e. how nodes are connected). Most recent progress in AI was due to deep neural networks (also called deep learning).
- Computing technology to run those algorithms: Computing is key in AI, advances in computing power enabled the wave of AI commercialization thanks to deep learning since the 2010s.
- Application of those algorithms to specific domains including reinforcement learning, computer vision, machine vision, natural language processing (NLP), recommendation systems.
Algorithms are like recipes for AI systems to learn from data or to make decisions. Advances in algorithm design are key for advancement for AI.
Artificial Neural Networks (ANNs)
The neural network is a popular machine learning technique that is inspired by the human brain and the neural network in our brains.