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The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
IBM Granite is a series of decoder-only AI foundation models created by IBM. [3] It was announced on September 7, 2023, [4] [5] and an initial paper was published 4 days later. [6] Initially intended for use in the IBM's cloud-based data and generative AI platform Watsonx along with other models, [7] IBM opened the source code of some code models.
Paradigms of AI Programming: Case Studies in Common Lisp (ISBN 1-55860-191-0) is a well-known programming book by Peter Norvig about artificial intelligence programming using Common Lisp. History [ edit ]
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
Originally, Llama was only available as a foundation model. [6] Starting with Llama 2, Meta AI started releasing instruction fine-tuned versions alongside foundation models. [7] Model weights for the first version of Llama were made available to the research community under a non-commercial license, and access was granted on a case-by-case basis.
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Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process all the input tokens together one layer after another, while the decoder consists of decoding layers that iteratively process the encoder's output and the decoder's output tokens so far.