AI - AI Dynamics

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Becoming an AI Powerhouse Requires Complete Commitment

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@ronald_vanloon

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12 March 2023 15h11

Becoming an ‘#AI Powerhouse’ Means Going All In
by @tdav @randybeannvp @mitsmr Learn more: https://
buff.ly/3mSuMf3 #BigData #MachineLearning #ArtificialIntelligence #ML #MI #DataScience cc: @ravikikan @kuriharan @karpathy

GigaGAN: Scaling GANs for Fast High-Resolution Image Synthesis

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12 March 2023 15h08

10/ GigaGAN – enables scaling up GANs on large datasets for text-to-image synthesis; it’s found to be orders of magnitude faster at inference time, synthesizes high-resolution images, & supports various latent space editing applications.

OpenICL: Open-source toolkit for in-context learning and LLM evaluation

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12 March 2023 15h08

8/ OpenICL – a new open-source toolkit for in-context learning and LLM evaluation; supports various state-of-the-art retrieval and inference methods, tasks, and zero-/few-shot evaluation of LLMs.

MathPrompter Improves LLM Mathematical Reasoning Performance

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12 March 2023 15h08

9/ MathPrompter – a technique that improves LLM performance on mathematical reasoning problems; it uses zero-shot chain-of-thought prompting and verification to ensure generated answers are accurate.

Foundation Models for Decision Making: Tools and Methods

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12 March 2023 15h08

6/ Foundation Models for Decision Making – provides an overview of foundation models for decision making, including tools, methods, and new research directions.

Hyena Hierarchy: Subquadratic Attention Replacement for LLMs

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12 March 2023 15h08

7/ Hyena Hierarchy – a subquadratic drop-in replacement for attention; it interleaves implicit long convolutions and data-controlled gating and can learn on sequences 10x longer and up to 100x faster than optimized attention.

A History of Generative AI: From GAN to ChatGPT

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12 March 2023 15h08

4/ A History of Generative AI – an overview of generative AI – from GAN to ChatGPT.

LLMs Override Semantic Priors Through In-Context Learning at Scale

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12 March 2023 15h08

5/ LLMs do In-Context Learning Differently – shows that with scale, LLMs can override semantic priors when presented with enough flipped labels; these models can also perform well when replacing targets with semantically-unrelated targets.