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This page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. Overview.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
Timeline of natural language processing models. In 1990, the Elman network, using a recurrent neural network, encoded each word in a training set as a vector, called a word embedding, and the whole vocabulary as a vector database, allowing it to perform such tasks as sequence-predictions that are beyond the power of a simple multilayer perceptron.
That development led to the emergence of large language models such as BERT (2018) [28] which was a pre-trained transformer (PT) but not designed to be generative (BERT was an "encoder-only" model). Also in 2018, OpenAI published Improving Language Understanding by Generative Pre-Training, which introduced GPT-1, the first in its GPT series. [29]
Further LLM developments during what has been called an "AI boom" included: local or open source versions of LLaMA which was leaked in March, [39] [40] [41] news outlets reported on GPT4-based Auto-GPT that given natural language commands uses the Internet and other tools in attempts to understand and achieve its tasks with unclear or so-far ...
Claude is a family of large language models developed by Anthropic. [1] [2] The first model was released in March 2023.The Claude 3 family, released in March 2024, consists of three models: Haiku optimized for speed, Sonnet balancing capabilities and performance, and Opus designed for complex reasoning tasks.
Gemini's launch was preluded by months of intense speculation and anticipation, which MIT Technology Review described as "peak AI hype". [49] [20] In August 2023, Dylan Patel and Daniel Nishball of research firm SemiAnalysis penned a blog post declaring that the release of Gemini would "eat the world" and outclass GPT-4, prompting OpenAI CEO Sam Altman to ridicule the duo on X (formerly Twitter).
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1] [2] It learns to represent text as a sequence of vectors using self-supervised learning.