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DeepMind’s New AI made a Breakthrough in Computer Science!

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Mentioned Videos:
AI designing Computer Chips: https://youtu.be/NeHgMaIkPuY
Deepmind AI made a Breakthrough in Math: https://youtu.be/DU6WINoehrg.

Deepmind Paper “Faster sorting algorithms discovered using deep reinforcement learning”:
https://www.nature.com/articles/s41586-023-06004-9

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AI predicts multiple sclerosis & flags contaminated samples

A new artificial intelligence model can predict people’s risk of multiple sclerosis years before diagnosis, potentially enabling quicker treatment, according to research revealed at the 2023 AACC Annual Scientific Meeting & Clinical Lab Expo. Another breaking study demonstrates how machine learning can help detect lab samples contaminated with intravenous fluids—a finding that could reduce lab errors that delay diagnosis and raise healthcare costs.

Taken together, the results highlight key advances in the use of artificial intelligence and machine learning to improve patient care.

AI model harnesses patient data to predict multiple sclerosis risk.

AI Unlocks Olive Oil’s Potential in Alzheimer’s Battle

This is a good use of AI. Definitely regular it but I can see it’s contributing to medical research.


Summary: Researchers have utilized artificial intelligence to uncover the promising potential of extra virgin olive oil (EVOO) in combating Alzheimer’s disease (AD).

By integrating AI, chemistry, and omics research, the study identified specific bioactive compounds in EVOO that could contribute to the treatment and prevention of AD. Ten phytochemicals within EVOO, such as quercetin, genistein, luteolin, and kaempferol, were found to exhibit potential impacts on AD protein networks.

The study adds to the growing evidence for the neuroprotective effects of a Mediterranean diet, rich in EVOO, in mitigating dementia and cognitive decline.

ChatGPT Is Replacing Humans in Studies on Human Behavior—and It Works Surprisingly Well

The show provides a glimpse into humanity’s astonishing diversity. Social scientists have a similar goal—understanding the behavior of different people, groups, and cultures—but use a variety of methods in controlled situations. For both, the stars of these pursuits are the subjects: humans.

But what if you replaced humans with AI chatbots?

The idea sounds preposterous. Yet thanks to the advent of ChatGPT and other large language models (LLMs), social scientists are flirting with the idea of using these tools to rapidly construct diverse groups of “simulated humans” and run experiments to probe their behavior and values as a proxy to their biological counterparts.

ChatGPT broke the Turing test — the race is on for new ways to assess AI

The world’s best artificial intelligence (AI) systems can pass tough exams, write convincingly human essays and chat so fluently that many find their output indistinguishable from people’s. What can’t they do? Solve simple visual logic puzzles.

In a test consisting of a series of brightly coloured blocks arranged on a screen, most people can spot the connecting patterns. But GPT-4, the most advanced version of the AI system behind the chatbot ChatGPT and the search engine Bing, gets barely one-third of the puzzles right in one category of patterns and as little as 3% correct in another, according to a report by researchers this May1.

The team behind the logic puzzles aims to provide a better benchmark for testing the capabilities of AI systems — and to help address a conundrum about large language models (LLMs) such as GPT-4. Tested in one way, they breeze through what once were considered landmark feats of machine intelligence. Tested another way, they seem less impressive, exhibiting glaring blind spots and an inability to reason about abstract concepts.