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Machine learning for materials discovery and optimization

This Collection supports and amplifies research related to SDG 9 — Industry, Innovation & Infrastructure.

Discovering new materials with customizable and optimized properties, driven either by specific application needs or by fundamental scientific interest, is a primary goal of materials science. Conventionally, the search for new materials is a lengthy and expensive manual process, frequently based on trial and error, requiring the synthesis and characterization of many compositions before a desired material can be found. In recent years this process has been greatly improved by a combination of artificial intelligence and high-throughput approaches. Advances in machine learning for materials science, data-driven materials prediction, autonomous synthesis and characterization, and data-guided high-throughput exploration, can now significantly accelerate materials discovery.

This Collection brings together the latest computational and experimental advances in artificial intelligence, machine learning and data-driven approaches to accelerate high-throughput prediction, synthesis, characterization, optimization, discovery, and understanding of new materials.

Cannabis use associated with quadrupled risk of developing type 2 diabetes, finds study of over 4 million adults

Cannabis use is linked to an almost quadrupling in the risk of developing diabetes, according to an analysis of real-world data from over 4 million adults, being presented at the Annual Meeting of the European Association for the Study of Diabetes (EASD) held in Vienna, Austria (15–19 Sept).

Cannabis use is increasing globally with an estimated 219 million users (4.3% of the global adult population) in 2021, but its long-term metabolic effects remain unknown. While some studies have suggested potential anti-inflammatory or weight management properties, others have raised concerns regarding glucose metabolism and , and the magnitude of the risk of developing diabetes hasn’t been clear.

To strengthen the evidence base, Dr. Ibrahim Kamel from the Boston Medical Center, Massachusetts, U.S. and colleagues analyzed from 54 health care organizations (TriNetX Research Network, with centers from across U.S. and Europe) to identify 96,795 outpatients (aged between 18 and 50 years, 52.5% female) with cannabis-related diagnoses (ranging from occasional use to dependence, including cases of intoxication and withdrawal) between 2010 and 2018.

Harvard scientists pinpoint how sleep stabilizes memory in fascinating neuroscience breakthrough

New research from Harvard scientists suggests that sleep helps the brain strengthen newly learned motor skills by boosting spindle activity in the exact regions involved during learning. The greater the increase in this activity, the more participants improved after napping.

Defeating Nondeterminism in LLM Inference

Reproducibility is a bedrock of scientific progress. However, it’s remarkably difficult to get reproducible results out of large language models.

For example, you might observe that asking ChatGPT the same question multiple times provides different results. This by itself is not surprising, since getting a result from a language model involves “sampling”, a process that converts the language model’s output into a probability distribution and probabilistically selects a token.

What might be more surprising is that even when we adjust the temperature down to 0This means that the LLM always chooses the highest probability token, which is called greedy sampling. (thus making the sampling theoretically deterministic), LLM APIs are still not deterministic in practice (see past discussions here, here, or here). Even when running inference on your own hardware with an OSS inference library like vLLM or SGLang, sampling still isn’t deterministic (see here or here).

Feudal Futures: Knights & Nobles in the Space Age

Feudalism once ruled Earth—could it rule the cosmos too? We dive into the strange but plausible world of space nobles, orbital dukes, and knights of the vacuum clad in power armor or piloting mecha.

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Credits:
Feudal Futures — Knights & Nobles in the Space Age.
Written, Produced & Narrated by: Isaac Arthur.
Select imagery/video supplied by Getty Images.
Music Courtesy of Epidemic Sound http://epidemicsound.com/creator.

Chapters.
0:00 Intro.
0:12 Feudalism… IN SPACE!!!? Knights, Mechs, and Space Lords.
5:04 Lords of the Vacuum – Whoever Builds the Habitat, Owns the Habitat.
9:10 Feudal Claims on Natural Resources.
14:31 Vassalage in Vacuum – How Loyalty Could Work in Space.
18:20 Breaking the Mold – Alternative Models to Feudal Ownership.
20:52 Natural Armor.
22:12 Would It Work? Or Collapse Gloriously?
23:38 The Knight Reforged – Power Armor, Mechs, and Weapons of Mass Respect.
25:12 A New Middle Ages Among the Stars?

Mo Gawdat on AI, ethics & machine mastery: How Artificial Intelligence will rule the world

Mo Gawdat warns that AI will soon surpass human intelligence, fundamentally changing society, but also believes that with collective action, ethical development, and altruistic leadership, humans can ensure a beneficial future and potentially avoid losing control to AI

## Questions to inspire discussion.

AI’s Impact on Humanity.

🤖 Q: How soon will AI surpass human intelligence? A: According to Mo Gawdat, AI will reach AGI by 2026, with intelligence measured in thousands compared to humans, making human intelligence irrelevant within 3 years.

🌍 Q: What potential benefits could AI bring to global issues? A: 12% of world military spending redirected to AI could solve world hunger, provide universal healthcare, and end extreme poverty, creating a potential utopia.

Preparing for an AI-Driven Future.

Continue reading “Mo Gawdat on AI, ethics & machine mastery: How Artificial Intelligence will rule the world” | >
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