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Stochastic parrot is now a neologism used by AI skeptics to refer to machines' lack of understanding of the meaning of their outputs and is sometimes interpreted as a "slur against AI". [6] Its use expanded further when Sam Altman, CEO of Open AI, used the term ironically when he tweeted, "i am a stochastic parrot and so r u."
For comparison, the report notes that the original 2017 Transformer model, which introduced the architecture underlying all of today’s LLMs, cost only around $900.
Advances in software and hardware have reduced the cost substantially since 2020, such that in 2023 training of a 12-billion-parameter LLM computational cost is 72,300 A100-GPU-hours, while in 2020 the cost of training a 1.5-billion-parameter LLM (which was two orders of magnitude smaller than the state of the art in 2020) was between $80,000 ...
English: The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size.
Timnit Gebru (Amharic and Tigrinya: ትምኒት ገብሩ; 1982/1983) is an Eritrean Ethiopian-born computer scientist who works in the fields of artificial intelligence (AI), algorithmic bias and data mining. [3]
The Stochastic Neural Analog Reinforcement Calculator (SNARC) is a neural-net machine designed by Marvin Lee Minsky. [ 1 ] [ 2 ] Prompted by a letter from Minsky, George Armitage Miller gathered the funding (a few thousand dollars) for the project from the Office of Naval Research in the summer of 1951 with the work to be carried out by Minsky ...
Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
Monte Carlo methods are used in corporate finance and mathematical finance to value and analyze (complex) instruments, portfolios and investments by simulating the various sources of uncertainty affecting their value, and then determining the distribution of their value over the range of resultant outcomes.