Friday - AI Wrap-up #18

AuthorAuthor

Martin Musiol & Daniel Aguspina
August 23, 2024

Friday wrap-up! Read:

  • The Beginning of an AI Intelligence Explosion?

  • AI turns your ideas into code; no coding skills needed! ✨

  • Other news that have huge implications

Reading time is 148 sec. Let’s go! 

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The Beginning of an AI Intelligence Explosion - Sakana AI’s AI Scientist

Take a look at this 11-page paper. It is cutting-edge and provides value to AI research. Really!

This paper introduces an adaptive dual-scale denoising approach for low- dimensional diffusion models, addressing the challenge of balancing global struc- ture and local detail in generated samples. While diffusion models have shown re- markable success in high-dimensional spaces, their application to low-dimensional data remains crucial for understanding fundamental model behaviors and address- ing real-world applications with inherently low-dimensional data. However, in these spaces, traditional models often struggle to simultaneously capture both macro-level patterns and fine-grained features, leading to suboptimal sample qual- ity. We propose a novel architecture incorporating two parallel branches: a global branch processing the original input and a local branch handling an upscaled ver- sion, with a learnable, timestep-conditioned weighting mechanism dynamically balancing their contributions. We evaluate our method on four diverse 2D datasets: circle, dino, line, and moons. Our results demonstrate significant improvements in sample quality, with KL divergence reductions of up to 12.8% compared to the baseline model. The adaptive weighting successfully adjusts the focus be- tween global and local features across different datasets and denoising stages, as evidenced by our weight evolution analysis. This work not only enhances low-dimensional diffusion models but also provides insights that could inform improvements in higher-dimensional domains, opening new avenues for advancing generative modeling across various applications.

The twist? AI handled ideation, validation, experiments, writing, and review— all for just $15 in computing power.

Sakana’s AI Scientist is officially the first of its kind. And many more will follow.

How it works/ its workflow

Conceptual illustration of The AI Scientist. The AI Scientist first brainstorms a set of ideas and then evaluates their novelty. Next, it edits a codebase powered by recent advances in automated code generation to implement the novel algorithms. The Scientist then runs experiments to gather results consisting of both numerical data and visual summaries. It crafts a scientific report, explaining and contextualizing the results. Finally, the AI Scientist generates an automated peer review based on top-tier machine learning conference standards. This review helps refine the current project and informs future generations of open-ended ideation.

  • It brainstorms ideas and evaluates their novelty.

  • It edits a codebase using automated code generation to implement novel algorithms.

  • It run experiments to gather numerical data and visual summaries.

  • It crafts a scientific report explaining and contextualizing the results.

  • It generates an automated peer review based on top-tier ML conference standards.

  • It uses the review to refine the project and inform future ideation.

All of this results in AI being able to perform scientific research independently.

(Sure, it has some bugs and misinterpretations at this stage, but that will be reduced.)

It is the beginning of an intelligence explosion

Now, there is no reason not to make 1000s of AI Scientists run in parallel, 24-7 all year long.

The money will be there for it, as new scientific breakthroughs = opportunities to make money.

They can provide value to some fields already: healthcare, environmental science, computer science, and AI.

Scaling from 1,000 to 1,000,000 AI Scientists, continuously updating and building models, would accelerate AI advancements beyond our comprehension.

At this point it is not unthinkable any more, and I am curious to hear your thoughts around it.

What do you think will be the most significant impact of deploying AI Scientists at scale?

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If you can’t code but have ideas - now, you can code! ✨

Online course interface for learning AI-driven software development without coding, featuring progress tracker and lesson overview.

So excited about Everyone can Code!✨- doors for enrollment are open.

It is the world's most enabling AI course.

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- You'll learn to build anything (software or feature).
- You'll be able to build your version 1 in under 1 hour.
- Build anything: web or mobile apps, browser extensions, AI applications (from secure offline SLM to RAG with LLMs!), and all kinds of frontends are needed.
- Most powerful: have access to THE Exclusive Community of AI Builders (Discord) 🤝 We build projects together, fast.

A new product development era starts: Build fast (e.g. 3 features), get real user reaction (e.g. pixel), kill all features but the winner. Rinse. Repeat. A deep-dive.

Unhappy? -> Money back! (No cap! 🙂‍↔️)

The batch 1 enrollment window closes at the end of next week, permanently.

Other topics I will examine soon…

Streaming LLMs, meaning context windows are obsolete?

Software companies as multi-agent systems, fully managed by AI agents?

(Source)

"Diagram of MetaGPT's multi-agent system for software development, illustrating roles and processes including product managers, architects, project managers, engineers, and QA."

The new way of working, according to Andreessen Horowitz

(Source)

Evolution from physical files to digital files and AI automation, illustrating past, present, and future workflows in information processing and action determination.
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I didn’t post on Tuesday, because I was at a client in St. Louis.

Have a great weekend!

-Martin