/AI Weekly/Issue 54

Issue #54 14 stories

Global AI Weekly

Big tech has distracted world from existential risk of AI, says top scientist

Published Tuesday, May 28, 2024

In this issue

Highlights

4 stories
Big tech has distracted world from existential risk of AI, says top scientist
theguardian.com

Big tech has distracted world from existential risk of AI, says top scientist

Max Tegmark argues that the downplaying is not accidental and threatens to delay, until it’s too late, the strict regulations needed Big tech has succeeded in distracting the world from the existential risk to humanity that artificial intelligence still poses, a leading scientist and AI campaigner has warned. Speaking with the Guardian at the AI Summit in Seoul, South Korea, Max Tegmark said the shift in focus from the extinction of life to a broader conception of safety of artificial intelligence risked an unacceptable delay in imposing strict regulation on the creators of the most powerful programs.

Microsoft intros a Copilot for teams
techcrunch.com

Microsoft intros a Copilot for teams

Microsoft wants to make its brand of generative AI more useful for teams — specifically teams across corporations and large enterprise organizations. This morning at its annual Build dev conference, Microsoft announced Team Copilot, the latest expansion of its Copilot family of generative AI tech. Unlike Microsoft’s previous Copilot-branded products, Team Copilot isn’t so much […]

In this issue

Research

1 story
arxiv.org

Understanding Diffusion Models

Generative AI is a highly complex subject. LLMs are probably the best-understood models among all of the generative models in AI, and most of us will still have difficulty understanding the maths behind these models. This week's paper of the week tries to clear up another type of generative model, the diffusion model. It's used for image generation, but how does it work? Prepare to be amazed!

In this issue

Video

1 story
In this issue

Articles

8 stories
Profiling CUDA using Nsight Systems: A Numba Example
towardsdatascience.com

Profiling CUDA using Nsight Systems: A Numba Example

Following my initial series CUDA by Numba Examples (see parts 1, 2, 3, and 4 ), we will study a comparison between unoptimized, single-stream code and a slightly better version which uses stream concurrency and other optimizations. In this example we are following the "reduce" pattern introduced in article CUDA by Numba Examples Part 3: Streams and Events to compute the sum of an array.

An Introduction to Reinforcement Learning
towardsdatascience.com

An Introduction to Reinforcement Learning

Dynamical programming solves general stochastic optimal control problems (afflicted by the curse of dimensionality   — meaning that computational requirements grow exponentially with the number of state variables) by decomposing them into smaller sub-problems and computing the value function. As we demonstrate the rudiments of reinforcement learning, we will delve into the heart of dynamic programming: the recursive relationship between the state and value functions of the agent.

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