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Last year DeepMind’s breakthrough AI system AlphaFold2 was recognised as a solution to the 50-year-old grand challenge of protein folding, capable of predicting the 3D structure of a protein directly from its amino acid sequence to atomic-level accuracy. This has been a watershed moment for computational and AI methods for biology.

Building on this advance, today, I’m thrilled to announce the creation of a new Alphabet company – Isomorphic Labs – a commercial venture with the mission to reimagine the entire drug discovery process from the ground up with an AI-first approach and, ultimately, to model and understand some of the fundamental mechanisms of life.

For over a decade DeepMind has been in the vanguard of advancing the state-of-the-art in AI, often using games as a proving ground for developing general purpose learning systems, like AlphaGo, our program that beat the world champion at the complex game of Go. We are at an exciting moment in history now where these techniques and methods are becoming powerful and sophisticated enough to be applied to real-world problems including scientific discovery itself. One of the most important applications of AI that I can think of is in the field of biological and medical research, and it is an area I have been passionate about addressing for many years. Now the time is right to push this forward at pace, and with the dedicated focus and resources that Isomorphic Labs will bring.

Many people develop Alzheimer’s or other forms of dementia as they get older. However, others remain sharp well into old age, even if their brains show underlying signs of neurodegeneration.

Among these cognitively resilient people, researchers have identified and amount of time spent on intellectually stimulating activities as factors that help prevent dementia. A new study by MIT researchers shows that this kind of enrichment appears to activate a called MEF2, which controls a genetic program in the brain that promotes resistance to cognitive decline.

The researchers observed this link between MEF2 and cognitive resilience in both humans and mice. The findings suggest that enhancing the activity of MEF2 or its targets might protect against age-related dementia.

Billionaire Elon Musk and his Twitter account have had some adventures.

In 2,018 Musk’s tweet that he was “considering taking Tesla private” invited charges from the Securities and Exchange Commission and a $20 million fine.

As the country was locked down in April 2020 amid the coronavirus pandemic, Musk tweeted: “FREE AMERICA NOW.”

The Rise of actually real and useful Nanobots making use of the rapidly advancing miniaturization of robotics and microchips through companies such as TSMC, Intel and Samsung. These nanobots are soon going to enable things such as full dive virtual reality, healing diseases such as cancer and potentially even increasing the longevity up to 200 years. These tiny computer/robots will enter our bloodstream and cross the blood brain barrier to read and write similar to how Brain Computer Interfaces such as Neuralink currently work. The future of technology is looking really exciting.

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TIMESTAMPS:
00:00 Have we reached the Nanobot-Era?
02:51 The Applications of Nanobots.
04:26 All the types of BCI’s.
06:44 So, when will there be Nanobots?
09:13 Last Words.

#nanobots #ai #nanotechnology

Digital Storage Capabilities are about to increase 100x in the very near future with the biggest tech companies like Microsoft or Intel working on making DNA Storage a possibility and bringing it to consumers. This is being accomplished with advances in Artificial Intelligence models in combination with better understanding of Biotechnology. This is the future of storage technology and we may see petabytes of cheap storage in the form of AI very soon.
DNA digital data storage is the process of encoding and decoding binary data to and from synthesized strands of DNA Storage Technology.

If you enjoyed this video, please consider rating this video and subscribing to our channel for more frequent uploads. Thank you! smile

TIMESTAMPS:
00:00 Storage Today.
01:08 A new approach to Storage.
02:06 The difference between DNA and Storage.
04:30 When is the Future of Storage?
07:01 Microsoft’s Goal.
08:31 Last Words.

#dna #ai #storage

“We think between now and the end of 2,022 this is the point where we get control over this virus … where we can significantly reduce severe disease and death,” Maria Van Kerkhove, an epidemiologist leading the World Health Organization’s (WHO) COVID-19 response, told Reuters.


CHICAGO, Nov 3 (Reuters) — As the devastating Delta variant surge eases in many regions of the world, scientists are charting when, and where, COVID-19 will transition to an endemic disease in 2022 and beyond, according to Reuters interviews with over a dozen leading disease experts.

They expect that the first countries to emerge from the pandemic will have had some combination of high rates of vaccination and natural immunity among people who were infected with the coronavirus, such as the United States, the UK, Portugal and India. But they warn that SARS-CoV-2 remains an unpredictable virus that is mutating as it spreads through unvaccinated populations.

None would completely rule out what some called a “doomsday scenario,” in which the virus mutates to the point that it evades hard-won immunity. Yet they expressed increasing confidence that many countries will have put the worst of the pandemic behind them in the coming year.

Trust in AI. If you’re a clinician or a physician, would you trust this AI?

Clearly, sepsis treatment deserves to be focused on, which is what Epic did. But in doing so, they raised several thorny questions. Should the model be recalibrated for each discrete implementation? Are its workings transparent? Should such algorithms publish confidence along with its prediction? Are humans sufficiently in the loop to ensure that the algorithm outputs are being interpreted and implem… See more.


Earlier this year, I wrote about fatal flaws in algorithms that were developed to mitigate the COVID-19 pandemic. Researchers found two general types of flaws. The first is that model makers used small data sets that didn’t represent the universe of patients which the models were intended to represent leading to sample selection bias. The second is that modelers failed to disclose data sources, data-modeling techniques and the potential for bias in either the input data or the algorithms used to train their models leading to design related bias. As a result of these fatal flaws, such algorithms were inarguably less effective than their developers had promised.

Now comes a flurry of articles on an algorithm developed by Epic to provide an early warning tool for sepsis. According to the CDC, “sepsis is the body’s extreme response to an infection. It is a life-threatening medical emergency and happens when an infection you already have triggers a chain reaction throughout your body. Without timely treatment, sepsis can rapidly lead to tissue damage, organ failure, and death. Nearly 270,000 Americans die as a result of sepsis.”