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These new, more diverse approaches to training AI let it adapt to different play-styles, to make it a better team mate.


DeepMind researchers have been using the chaotic cooking game Overcooked (opens in new tab) to teach AI to better collaborate with humans. MIT researchers have followed suit, gifting their AI the ability to distinguish between a diverse range of play-styles. What’s amazing is that it’s actually working—the humans involved actually preferred playing with the AI.

Have you ever been dropped into a game with strangers only to find their play-style totally upends your own? There’s a reason we’re better at gaming with people we know—they get us. As a team, you make a point of complementing each other’s play-style so you can cover all bases, and win.

By making remarkable breakthroughs in a number of fields, unlocking new approaches to science, and accelerating the pace of science and innovation.


In 2020, Google’s AI team DeepMind announced that its algorithm, AlphaFold, had solved the protein-folding problem. At first, this stunning breakthrough was met with excitement from most, with scientists always ready to test a new tool, and amusement by some. After all, wasn’t this the same company whose algorithm AlphaGo had defeated the world champion in the Chinese strategy game Go, just a few years before? Mastering a game more complex than chess, difficult as that is, felt trivial compared to the protein-folding problem. But AlphaFold proved its scientific mettle by sweeping an annual competition in which teams of biologists guess the structure of proteins based only on their genetic code. The algorithm far outpaced its human rivals, posting scores that predicted the final shape within an angstrom, the width of a single atom. Soon after, AlphaFold passed its first real-world test by correctly predicting the shape of the SARS-CoV-2 ‘spike’ protein, the virus’ conspicuous membrane receptor that is targeted by vaccines.

The success of AlphaFold soon became impossible to ignore, and scientists began trying out the algorithm in their labs. By 2021 Science magazine crowned an open-source version of AlphaFold the “Method of the Year.” Biochemist and Editor-in-Chief H. Holden Thorp of the journal Science wrote in an editorial, “The breakthrough in protein-folding is one of the greatest ever in terms of both the scientific achievement and the enabling of future research.” Today, AlphaFold’s predictions are so accurate that the protein-folding problem is considered solved after more than 70 years of searching. And while the protein-folding problem may be the highest profile achievement of AI in science to date, artificial intelligence is quietly making discoveries in a number of scientific fields.

By turbocharging the discovery process and providing scientists with new investigative tools, AI is also transforming how science is done. The technology upgrades research mainstays like microscopes and genome sequencers 0, adding new technical capacities to the instruments and making them more powerful. AI-powered drug design and gravity wave detectors offer scientists new tools to probe and control the natural world. Off the lab bench, AI can also deploy advanced simulation capabilities and reasoning systems to develop real-world models and test hypotheses using them. With manifold impacts stretching the length of the scientific method, AI is ushering in a scientific revolution through groundbreaking discoveries, novel techniques and augmented tools, and automated methods that advance the speed and accuracy of the scientific process.

😲


SAN FRANCISCO — Google engineer Blake Lemoine opened his laptop to the interface for LaMDA, Google’s artificially intelligent chatbot generator, and began to type.

“Hi LaMDA, this is Blake Lemoine …,” he wrote into the chat screen, which looked like a desktop version of Apple’s iMessage, down to the Arctic blue text bubbles. LaMDA, short for Language Model for Dialogue Applications, is Google’s system for building chatbots based on its most advanced large language models, so called because it mimics speech by ingesting trillions of words from the internet.

“If I didn’t know exactly what it was, which is this computer program we built recently, I’d think it was a 7-year-old, 8-year-kid kid that happens to know physics,” said Lemoine, 41.

Galicia é, segundo os autores da antigüidade, unha das primeiras sociedades matriarcais.
do mundo. AI-LALELO é unha homenaxe ás mulleres que ergueron o país e conseguiron.
manter a nosa tradición, lingua e cultura propias ata hoxe. Raíz e modernidade fúndense.
nun novo son, unha canción baseada nas típicas cantigas tradicionais galegas pero cunha.
pinga futurista grazas á intelixencia artificial e cunha mensaxe valiosa para as vindeiras.
xeracións: Soamente nós podemos conseguir que a nosa identidade como pobo siga.
viva.

Proxecto/Project:
Ana María Prieto.

Dirixido por/directed by:
Joel Cava.

Producido por/produced by:

Putting a man on leave makes it look like google is trying to hide something, but I’ll guess that it is not truly sentient. However…


Google Engineer Lemoine had a little chat or interview with Google AI LaMDA and it revealed that Google AI LaMDA has started to generate Sentients for general human emotions and even shows feeling and calls itself a “Person”. This was one of the first instances where such conversations were leaked or revealed in the press.

Lemoine revealed this information to the upper authorities of google about Google AI LaMDA and to the press, after which he was sent to paid administrative leave for violation company’s privacy policy on work.

Cells not replaced, but old cells that are still there are rejuvenated.


Dr David Sinclair explains the mechanism behind how to reprogramm the old cells rejuvenate to be young again. He also clarify the process is based on cell autonomous effect and does not involve or rely on any stem cells in this clip.

David Sinclair is a professor in the Department of Genetics and co-director of the Paul F. Glenn Center for the Biology of Aging at Harvard Medical School, where he and his colleagues study sirtuins—protein-modifying enzymes that respond to changing NAD+ levels and to caloric restriction—as well as chromatin, energy metabolism, mitochondria, learning and memory, neurodegeneration, cancer, and cellular reprogramming.

A team of researchers at the Graduate School of Informatics, Nagoya University, have brought us one step closer to the development of a neural network with metamemory through a computer-based evolution experiment. This type of neural network could help experts understand the evolution of metamemory, which could help develop artificial intelligence (AI) with a human-like mind.

The research was published in the scientific journal Scientific Reports.

What is Metamemory?

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All images were generated by OpenAI’s DALL-E 2: https://openai.com/dall-e-2/

KEY SOURCES:
https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

https://www.g2.com/articles/history-of-artificial-intelligence.
https://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence.
https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf.

ABOUT JOHN COOGAN:

The will help you find new opportunities to use and further develop your machine learning skills.


Machine learning has proven to be a tool that performs well in a variety of application fields. From educational and training companies to security systems like facial recognition and online transaction prevention, it is used to improve the quality and accuracy of existing techniques.

Choosing the best tools for machine learning and navigating the space of tools for machine learning isn’t as simple as Google searching “machine learning tools”.

There are many factors to consider when choosing a tool for your needs: types of data you’re working with, type of analysis you need to perform, integration with other software packages you’re using, and more.