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Artificial Intelligence (AI) Resource Guide

Background + Definitions

DEFINITIONS WITHIN THE AI LEXICON

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. 

  • 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.
  • Training data are the information that is digested by a machine learning algorithm. 

(From University of South Florida's AI Tools and Resources Guide) 

HISTORY

Recent advancements in the sophistication and capacity of artificial intelligence (AI) platforms, coupled with the public release of interactive generative AI tools, have ignited a renewed interest in the field. The current generation of AI algorithms and tools not only has roots in cognitive science, computer science, economics, game theory, and mathematics dating back to the 1950s but also holds significant implications for artists and designers.

[Credit: Julien Simon, Hugging Face]

KEY MOMENTS IN THE EARLY DEVELOPMENT OF ARTIFICIAL INTELLIGENCE

  • 1950 - Turing Test: Alan Turing proposed the Turing Test, setting a criterion for evaluating a machine's ability to display intelligent behavior comparable to or indistinguishable from that of a human. *1956 - Dartmouth Conference: Organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, this conference marked the official birth of artificial intelligence as a distinct field of study, coining the term "artificial intelligence."

  • Late 1950s - Early AI Programs: The development of early AI programs, such as the Logic Theorist by Newell and Simon in 1956 and the General Problem Solver (GPS) in 1957, represented significant strides in the field.

  • 1966 - ELIZA: Joseph Weizenbaum developed ELIZA, an early natural language processing computer program that highlighted the superficiality of communication between humans and machines, representing a noteworthy step in conversational agent development.

  • 1980s - Machine Learning Emerges: The shift towards machine learning, characterized by algorithms capable of learning from and making predictions on data, represented a major advancement. This era also witnessed the ascent of neural networks.

  • 1997 - Deep Blue vs. Kasparov: IBM's Deep Blue defeating world chess champion Garry Kasparov showcased AI's potential in mastering complex games requiring strategic thinking.

  • 2006 - Renaissance of Neural Networks: The introduction of the term "deep learning" spurred a resurgence of interest in neural network research, fueled by enhanced computing power and substantial datasets.

  • 2012 - AlexNet and Deep Learning: The success of AlexNet, a deep convolutional neural network, in the ImageNet competition significantly propelled the fields of computer vision and deep learning.

  • 2016 - AlphaGo vs. Lee Sedol: Google DeepMind's AlphaGo triumphed over world champion Lee Sedol in the game of Go, achieving a feat thought to be at least a decade away due to the game's complexity.

  • 2020s - Generative AI and Large Language Models: The rise and widespread adoption of large language models like GPT-3, along with generative AI tools, have profoundly impacted various industries and daily life. For artists and designers, this means exploring new avenues of creativity, collaboration, and innovation through AI-driven tools in fields such as digital art, graphic design, and interactive installations.

These milestones illustrate the evolution of AI from theoretical concepts to practical applications, highlighting the rapid advancements and expanding capabilities of AI systems, particularly in the realm of artistic and design endeavors.