Currently, Neural Networks are the most ubiquitous artificial intelligence method. Artificial intelligence (AI) has had massive breakthroughs over the last 15 years. From the year 2000 until 2008, computers were not able to get near human level performance in most pattern recognition tasks. For example, they had about 75% accuracy when converting spoken words to text, maybe a 30% chance of recognizing things in images (like people, planes, or mountains), and maybe a 90% accuracy rate on recognizing hand written text. Big data helped and fast computers helped, but they still were not getting human level performance. Different methods were used for each pattern recognition system. Now in 2023, neural nets seem are about as good as humans in many pattern recognition tasks and, in almost all cases, the computers are using neural nets to achieve human level performance.
Neural Nets were first discovered in the 1960s, but they were not very useful. They were used for some tasks in AI, but they were not the best tool for anything except maybe hand written digit recognition until about 2008. In about 2007 (or maybe 2006?), Geoffrey Hinton discovered how to train “deep” neural nets. Neural Nets have layers and it was very difficult to teach any neural net with more than three layers. Hinton found a way to train neural nets with up to 15 layers. (I attended the lecture at the NIPS conference in 2007 when Hinton introduced this new method–it was very exciting.) This method massively improved the pattern recognition abilities of neural nets and by 2010 neural nets were one of the best pattern recognition systems. In 2012, Hinton discovered the “Drop Out” training method. After Drop Out was introduced, neural nets became better than humans in many pattern recognition tasks like image recognition.
Over the last 10 years, three other big break throughs occurred: GANs (generative adversarial networks), transducers, and diffusion models. These breakthroughs have created neural nets that can create photo realistic faces, digital image art, and intelligently answer almost almost any question (the answer is correct only around 70% of the time). The biggest recent breakthrough is the GPT3 neural net which can pass final exams in several college subjects, write good essay on just about any topic, write short computer programs, and it can carry on a short conversation.
For further information, I am adding links for an introduction to neural nets that actually describes what they are, and a 17 page history of AI.