Artificial Intelligence

Recent advancements in the sophistication of and capacity of artificial intelligence (AI) platforms as well as the public release of interactive generative AI tools has renewed public interest in the field. The current generation of AI algorithms and tools are descendants of pioneering work on cognitive science, computer science, economics, game theory, and mathematics going back to the 1950s. 

Early Developments in Artificial Intelligence

• In 1944, Warren McCulloch and Walter Pitts publish the first mathematical model of a neural network, arguing that complex intelligence through many simple interconnected nodes (neurons) was possible.
   
• In 1950, Alan Turing publishes “Computing Machinery and Intelligence” in the journal Mind in 1950.
   
• In 1959, Arthur Samuel publishes an article coining the term “machine learning” to describe an algorithm that is designed to play the game of checkers.

Modern AI platforms are composed of multiple groups of algorithms with different goals. At their simplest, these platforms take training data, use machine learning algorithms to "learn" from this data, and then pass on what it has learned to a model which uses this knowledge to generate some output. Below are some simple definitions for key ideas related to modern AI platforms.  See the IBM link below for more information.

• Artificial intelligence (AI) is a field of study dedicated to creating computer programs or other machine-driven forms of intelligence. 
• Deep Neural Networks employ many layers of neural networks to deal with complex subjects. 
• Generative AI is a type of AI system that generates text, images, or other media in response to user prompts.
• Large Language Models such as chatGPT apply deep neural networks to text data and generate output from prompts.
• Machine learning is a sub field of AI focused on the problems of designing recursive algorithms capable of learning.
• Natural Language Processing refers to a branch of artificial intelligence concerned with giving computers the ability to understand text and spoken word in the same way humans can.
• Neural Networks are an approach to machine learning using many simple, but densely connected algorithms to solve complex problems.
• Supervised learning is a machine learning technique where the authors of the model tell the machine learning algorithm how to handle the training data in order to generate the desired output.
• Training data are the information that is digested by a machine learning algorithm. 
• Unsupervised learning is a machine learning technique where the machine learning algorithm creates its own labels for variables within the training data.