GENERATIVE AI

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Understanding Diffusion Models: A Unified Perspective Tutorial

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@jeande_d

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13 April 2023 22h12

Understanding Diffusion Models: A Unified Perspective Diffusion models are the engine behind novel image generative systems. This tutorial provides an intuitive and comprehensive understanding of diffusion models. Paper: https://
arxiv.org/abs/2208.11970
Blog: https://
calvinyluo.com/2022/08/26/dif
fusion-tutorial.html
…

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AI21 Labs Partners with Amazon for Bedrock Service Launch

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@ai21labs

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13 April 2023 21h23

We're thrilled to announce our partnership with Amazon for their latest service, Bedrock. As our Co-CEO, Ori Goshen, stated, "With Jurassic-2 models and Bedrock, developers can maximize the performance of language tasks while optimizing the cost." https://
ai21.com/blog/announcin
g-amazon-partnership
…

Chain-of-Thought Prompting Enables Multi-Step Reasoning in Language Models

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13 April 2023 20h32

3 (cont). One way to elicit reasoning is via "chain-of-thought (CoT) prompting", which gives examples of intermediate reasoning steps in-context. CoT prompting enables large LMs to do multi-step reasoning tasks, increasing the range of tasks that LMs can do.

Untested Abilities and Emergent Phenomena in Scaling Large Language Models

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13 April 2023 20h32

2C. Since we haven't tested all possible abilities, we don't know the full range of abilities that have emerged in large language models.
2D. We're likely to see more emergent phenomena as we continue to scale up models (and implicit argument for more scaling).

Unpredictable Emergence in Language Models: Key Implications

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13 April 2023 20h32

There are at least four profound implications of emergence:
2A. Emergence cannot be predicted simply by extrapolating the scaling curves from smaller models.
2B. Emergent abilities are not explicitly specified by the trainer of the language model.

Emergence: Large Language Models Gaining Unexpected Complex Abilities

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13 April 2023 20h32

2. Emergence is a phenomenon where large language models gain abilities that are not present in smaller language models. An example of an emergent ability is doing complex math questions.

Scaling Laws: Model Size, Data, and Compute for LM Improvement

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13 April 2023 20h32

Key takeaways: 1. Scaling involves increasing model size, data, and compute. Scaling is challenging (cost, infra, etc), but important, since "scaling laws" tell us that scaling predictably makes LMs better.

Three Ideas Driving the LLM Revolution: Scaling, Emergence, and Reasoning

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13 April 2023 20h32

I gave an invited lecture at New York University for @hhexiy
's class! I covered three ideas driving the LLM revolution: scaling, emergence, and reasoning. I tried to frame them in a way that reveals why large LMs are special in the history of AI. Slides: