A Review of AI History Beyond Deep Learning
There are people who equate AI with Machine Learning (and to an extent, deep learning), this is a huge misunderstanding.
Related work on Artificial Intelligence can be traced back to the 1940s, when Warren McCulloch and Walter Pitts showed that computing could be done by a network of connected neurons, and Donald Hebb demonstrated Hebbian learning.
In 1956, when the term “Artificial Intelligence” (AI) was formally coined, corresponding research started to develop quickly. AI was mostly used for problem-solving at that time. Although there were criticisms during the 1960s and 1970s for the slow development speed and limited progress of the neural network, the emergence of expert systems still attracted the interest and growth of AI studies.
During the 1980s, AI academic studies stepped into a period called “AI Winter”, as most studies of AI failed to deliver their original extravagant promises, resulting in the funding for AI research being moved to other areas. Luckily, connectionists reinvented back-propagation and brought neural networks back onto the stage.
In the 1990s, more scientific methods such as probabilistic models started to be applied; at the same time, Support Vector Machines (SVM) outperformed and replaced neural networks in many areas. The era of big data came soon after in the 2000s, which helped the development of various learning algorithms and enabled deep learning to flourish in recent years.
Now, more and more groups are starting to pay attention to AI research, and the number of papers published in elite AI conferences such as AAAI, IJCAI, AUAI, and ECAI has increased dramatically.
To learn more, subscribe to our upcoming newsletter, and get a copy of the Trends of AI Technology Development Report!
The purpose of this report is to detail the major technological branches of Artificial Intelligence (AI). By identifying these technologies’ paths of development, readers will be able to accurately and comprehensively learn about the past, present, and future of all modern AI research fields. The report will help you sort out the basics and provide you with the necessary background to move forward.
We’ll review all the mainstream AI research fields, with a focus on:
- Development history of AI sub-fields
- Prospective frontiers of AI research
The report is organized based on:
- 4 interdisciplinary fields related to AI
- 23 technical subcategories (AI algorithms, models, applications etc.)
We hope you find the report useful, also, let us know your thoughts in the comment section!
