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Google AI wins another Nobel Prize, this time in Chemistry

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Google AI wins another Nobel Prize, this time in Chemistry

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A trio of scientists consisting of Demis Hassabis, co-founder and CEO of Google’s AI division DeepMind, as well as John Jumper, Senior Research Scientist at Google DeepMind and David Baker of the University of Washington have been awarded the 2024 Nobel Prize in Chemistry for their groundbreaking work in predicting and developing new proteins.

The DeepMinders won for AlphaFold 2, an AI system capable of predicting the 3D structure of proteins from their amino acid sequences. Meanwhile, Baker won for leading a laboratory where the 20 amino acids that form proteins were used to design new ones, including proteins for “pharmaceuticals, vaccines, nanomaterials and tiny sensors,” according to the Nobel committee’s announcement.

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The award highlights how artificial intelligence is revolutionizing biological science — and comes just one day after what I believe to be the first Nobel Prize awarded to an AI technology, that one for Physics to fellow Google DeepMinder Geoffrey Hinton and Princeton professor John J. Hopfield, for their work in artificial neural networks.

The Royal Swedish Academy of Sciences announced the prize as it did with the Physics one, valued at 11 million Swedish kronor (around $1 million USD), split among the laureates — half will go to Baker and the other half divided again in fourths of the total to Hassabis and Jumper.

The committee emphasized the unprecedented impact of AlphaFold, describing it as a breakthrough that solved a 50-year-old problem in biology: protein structure prediction, or how to predict the three-dimensional structure of a protein from its amino acid sequence.

For decades, scientists knew that a protein’s function is determined by its 3D shape, but predicting how the string of amino acids folds into that shape was incredibly complex. Researchers had attempted to solve this since the 1970s, but due to the vast number of possible folding configurations (known as Levinthal’s paradox), accurate predictions remained elusive.

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AlphaFold, developed by Google DeepMind, made a breakthrough by using AI to predict the 3D structures of proteins with near-experimental accuracy, meaning that the predictions made by AlphaFold for a protein’s 3D structure are so close to the results obtained from traditional experimental methods—like X-ray crystallography, cryo-electron microscopy, or nuclear magnetic resonance (NMR) spectroscopy—that they are almost indistinguishable.

When AlphaFold achieved “near-experimental accuracy,” it was able to predict protein structures with a level of precision that rivaled these methods, typically within an error margin of around 1 Ångström (0.1 nanometers) for most proteins. This means the model’s predictions closely matched the actual structures determined by experimental means, making it a transformative tool for biologists.

Hassabis and Jumper’s work, developed at DeepMind’s London laboratory, has transformed the fields of structural biology and drug discovery, offering a powerful tool to scientists worldwide.

“AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery,” Hassabis said in a statement. “I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery.”

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AlphaFold’s Global Impact

AlphaFold’s predictions are freely accessible via the AlphaFold Protein Structure Database, making it one of the most significant open-access scientific tools available. Over two million researchers from 190 countries have used the tool, democratizing access to cutting-edge AI and enabling breakthroughs in fields as varied as molecular biology, drug development, and even climate science.

By predicting the 3D structure of proteins in minutes—tasks that previously took years—AlphaFold is accelerating scientific progress. The system has been used to tackle antibiotic resistance, design enzymes that degrade plastic, and aid in vaccine development, marking its utility in both healthcare and sustainability.

John Jumper, co-lead of AlphaFold’s development, reflected on its significance, stating, “We are honored to be recognized for delivering on the long promise of computational biology to help us understand the protein world and to inform the incredible work of experimental biologists.” He emphasized that AlphaFold is a tool for discovery, helping scientists understand diseases and develop new therapeutics at an unprecedented pace.

The Origins of AlphaFold

The roots of AlphaFold can be traced back to DeepMind’s broader exploration of AI.

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Hassabis, a chess prodigy, began his career in 1994 at the age of 17, co-developing the hit video game Theme Park, which was released on June 15 that year.

After studying computer science at Cambridge University and completing a PhD in cognitive neuroscience, he co-founded DeepMind in 2010, using his understanding of chess to raise funding from famed contrarian venture capitalist Peter Thiel. The company, which specializes in artificial intelligence, was acquired by Google in 2014 for around $500 million USD.

As CEO of Google DeepMind, Hassabis has led breakthroughs in AI, including creating systems that excel at games like Go and chess.

By 2016, DeepMind had achieved global recognition for developing AI systems that could master the ancient game of Go, beating world champions. It was this expertise in AI that DeepMind began applying to science, aiming to solve more meaningful challenges, including protein folding.

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The AlphaFold project formally launched in 2018, entering the Critical Assessment of protein Structure Prediction (CASP) competition—a biannual global challenge to predict protein structures. That year, AlphaFold won the competition, outperforming other teams and heralding a new era in structural biology. But the real breakthrough came in 2020, when AlphaFold2 was unveiled, solving many of the most difficult protein folding problems with an accuracy previously thought unattainable.

AlphaFold 2’s success marked the culmination of years of research into neural networks and machine learning, areas in which DeepMind has become a global leader.

The system is trained on vast datasets of known protein structures and amino acid sequences, allowing it to generalize predictions for proteins it has never encountered—a feat that was previously unimaginable.

Earlier this year, Google DeepMind and Isomorphic Labs unveiled AlphaFold 3, the third generation of the model, which the creators say uses an improved version of the Evoformer module, a deep learning architecture that was key to AlphaFold 2’s remarkable performance.

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The new model also incorporates a diffusion network, similar to those used in AI image generators, which iteratively refines the predicted molecular structures from a cloud of atoms to a highly accurate final configuration.

David Baker’s Contribution to Protein Design

While Hassabis and Jumper solved the prediction problem, David Baker’s work in de novo protein design offers an equally transformative approach: the creation of entirely new proteins that do not exist in nature.

Based at the University of Washington’s Institute for Protein Design, Baker’s lab developed Rosetta, a computational tool used to design synthetic proteins.

Baker’s work has led to the development of proteins that could be used to create novel therapeutics, including custom-designed enzymes and virus-like particles that may serve as vaccines. His group has even designed proteins to detect fentanyl, an opioid at the center of a global health crisis.

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By designing new proteins from scratch, Baker’s research expands the boundaries of what proteins can do, complementing the predictive power of AlphaFold by enabling the creation of molecules tailored to specific functions.

The Future of AI in Science

The Nobel Prize recognition of AlphaFold and Baker’s work underscores a broader trend: AI is rapidly becoming an indispensable tool in scientific research. AlphaFold’s success has sparked new interest in the potential of AI to solve complex problems across various fields, including climate change, agriculture, and materials science.

The Nobel Committee highlighted the transformative potential of these discoveries, emphasizing that they “open up vast possibilities” for the future of biology and chemistry. Hassabis has long been vocal about AI’s potential to drive innovation, but he is also clear-eyed about the risks. “AI has the potential to accelerate scientific discovery at a rate we’ve never seen before, but it’s crucial that we use it responsibly,” he said in a recent interview.

As AI systems like AlphaFold continue to evolve, their ability to simulate biological processes and predict outcomes could revolutionize healthcare, sustainability efforts, and beyond. Jumper and Hassabis’ Nobel Prize is a recognition of their work’s enormous impact, but it also signals the dawn of a new era in science—one where AI plays a central role in unlocking the mysteries of life.

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What’s next?

The 2024 Nobel Prize in Chemistry recognizes the profound contributions of Demis Hassabis, John Jumper, and David Baker, whose pioneering work has reshaped the landscape of protein science. AlphaFold, now a cornerstone tool for researchers worldwide, has accelerated discovery in ways previously unimaginable.

David Baker’s work in computational protein design further expands the possibilities for biological innovation, offering new solutions to global challenges.

Together, these advancements mark the beginning of a new era for artificial intelligence in science—one where the possibilities are just beginning to unfold (pun intended).

While he remains optimistic about AI’s positive impact, Hassabis warns that the risks, including the potential for societal-scale disasters, must be taken as seriously as the climate crisis.

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MiLaboratories gets $10M for a platform play to accelerate genomic research

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MiLaboratories computational biology platform for analyzing genomic data-sets

Advances in DNA sequencing and the vast amounts of genomic data being produced by next-generation sequencing (NGS) technology have created a startup opportunity to build software for biologists so they can more easily analyze this big data and take the next leap. It could help when it comes to developing new vaccines, cancer treatments and so on.

For the last four years, MiLaboratories, a San Francisco-based startup with an R&D facility in Bilbao, Spain, has been building a computational biology platform to make it easier for biologists to process, analyze and aggregate their data. It incorporates features like data visualization and generative AI to boost usability.

Its platform is also designed to be a marketplace for other scientists so that they can distribute more specialized computation tools in the form of apps to keep expanding the utility for the genomics research community. MiLaboratories target scientists whose skillsets span biology, computer science and math — so-called bioinformaticians.

“It’s a ‘no code’ style approach for biologists and we also release an [open source] SDK — software development kit — allowing bioinformaticians to build real applications,” CEO Stan Poslavsky tells TechCrunch.

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“During my and our founders’ scientific career, we saw a huge inefficiency . . . in how modern therapies, how modern drugs, are developed,” he explains. “Because of this friction between the data — the big data, generated by the biologists, the sequencing data — and the data analysis which is not available for them.”

While there are “thousands” of software programs and tools that can do analysis of NGS data, he says most have been developed within academia, where the focus tends to be on utility rather than usability.

There’s also a need for biologists to aggregate and integrate results from multiple analyses, he says. “In a unified picture, allowing you to understand what’s going on. And that’s the place where our platform helps dramatically,” he suggests.

The startup hopes its platform will free up bioinformaticians from being called upon to deal with the grunt work of genomic data processing so these multidisciplinary scientists can apply their skillset to the more complex tasks of building algorithms that might help advance cutting-edge science.

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“Bioinformaticians are actually spending a lot of time just doing a monkey job of running the software for biologists,” says Poslavsky. “To process this data, you need to have Linux machines, go over SSH, run complicated software tools to get the analysis done and get the insight from the data.”

“[A doctor] has no skills to do this on Linux, on HPC [high performance computing] cluster, because he has other things to do. And that’s what most bioinformaticians in the companies and academia are doing, actually, just this monthly job of running the tools.”

MiLaboratories founding team, with Stan Poslavsky second from leftImage Credits:MiLaboratories

On Thursday, MiLaboratories officially took the wraps off its SDK, Platforma.bio, which lets third-party developers contribute apps — although it’s been in alpha and beta testing for several years. (Poslavsky says “around 300 labs” have been using the beta, and “around 20” apps have been made available through the platform, so far.)

“The first applications that are available in the platform are built around our biological and bioinformatic applications, which are very popular . . . [with] companies and people involved in immune therapy developments. But we already have . . . a good selection of collaborations and people willing to bring their applications on the platform, both from academia and from the industry,” he adds.

The 2021-founded startup is also announcing a $10 million Series A funding round to continue development, with a focus on investing in community building.

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“The key reason for raising money is just to plug more hands into the development of our platform. We are hiring more engineers. We are hiring what is called developer advocates, who are propagating the technology around — primarily — the academic community, because most bioinformatics software is developed in academia.”

“For the upcoming year [we will] focus on the propagation of the technology around the community, and engaging community to build their apps, to wrap their existing software, to deliver them through the platform,” he adds.

MiLaboratories’ Series A is led by Madrid-based Kfund, with participation from Acrobator Ventures, EGB Capital, Courtyard Ventures, Somersault Ventures, Speedinvest and Ten13.

Commenting in a statement, Miguel Arias, general partner of Kfund, said: “Investing in platforms that bridge the gap between developers (in this case bioinformaticians) and business users (in this case biologists) is at the core of what we want to do in our fund. There is tremendous potential in democratizing access to complex data enabling the delivery of immunological insights.”

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MiLaboratories offers its software for free to academics but it’s also taking revenue via a paid model for commercial users. Per Poslavsky the startup is approaching 100 paying customers at this stage.

“Many of the big pharma companies — like Moderna, Bristol-Myers Squibb — they are our customers,” he notes, adding: “We have revenue — good revenue — allowing us to not be so dependent on venture money.”

At the start of 2022, the startup raised a $2.5 million seed round. It also previously took in a small pre-seed from a few angels.

Discussing the challenges of developing the computational biology platform, Poslavsky says the staggering amount of data being generated by NGS meant startup had to pay very careful attention to ensuring processing efficiency to avoid generating “crazy costs”.

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“The amount of data generated in the space are actually, well, crazy,” he emphasizes. “Big pharma companies, our customers . . . they have petabytes of genetic data generated so far. So that’s huge scale.”

MiLaboratories has developed what Poslavsky couches as a “very sophisticated” and “mathematically proven” technology which allows for many sorts of calculations to be performed in “a very optimized way.” He suggests this tech — which it has patented — enables the platform to reach 10x efficiency compared to some other types of computational workflow.

“That’s a very important thing. It’s hidden from the eyes of the biologist — because the valuable proposition for the biologist is ‘I want to click buttons and get insight’ — but it’s very important for the business owners.”

Competition wise, Poslavsky names Seqera (and its Nextflow software) as the closest rival — in terms of popularity and value proposition. There are also open source tools for NGS processing, such as Galaxy, but MiLaboratories reckons its platform offers researchers a more accessible route to data insights.

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Network server rack 15U management patch panel installation & network cable management installation

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Network server rack 15U management patch panel installation & network cable management installation



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The best Prime Day deals you can get on some of our home office go-tos

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The best Prime Day deals you can get on some of our home office go-tos

One of the favorite series on this site — at least, as far as many of our staff are concerned — is “What’s on your desk?” where we look at the home workspace of some of the people here at The Verge. (For one thing, it’s reassuring to know that most of us share the same clutter syndrome, and that’s after cleaning the space up for the photos!)

We checked to see if any of the interesting stuff we have found in the home office setups of our staff — office chairs, string lights, drawing tablets, etc. — was on sale during this fall’s Amazon Prime Day event, and we came up with these.

I just had to look up the name on Amazon: it’s the “HON Office Chair Black | Ignition 2.0 | Ergonomic | Adjustable Tilt, Swivel Wheels, Comfortable for Long Hours,” which should tell you basically everything you need to know about how good and fancy it is. I bought it in 2021 for $332, after a huge amount of research because I just couldn’t splash out on a properly high-end desk chair. I’ve had back problems forever, and this one has actually served me quite well — it’s starting to tatter a bit, and one of the arms just split open a few weeks ago, but I sit in this thing for too many hours a week, and I’m living to tell the tale.

I have an Amazon Echo Dot smart speaker. We have several around the flat to control various smart home settings, but having one in my office also keeps me alerted to any deliveries. I even use it as a speaker output for my PC when I can’t be bothered to wear headphones.

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I’ve got a Logitech MX Vertical ergonomic mouse. I’ve been a vertical mouse evangelist for years, and of all the ones I’ve tried, I like this one best. It’s not overly complicated, and it dramatically reduced my wrist pain.

I’ve also got the Insta360 Link webcam because I was tired of looking like a blurry potato on calls. This one is neat because it tracks your position, though sometimes it doesn’t always work the way it’s supposed to and my coworkers get to look at a close-up of my forehead.

I almost always have either Apple’s AirPods Pro or Max headphones on throughout the day, either to take calls or listen to music.

Update, October 9th: Adjusted prices and added new deals, including those for the AirPods Max.

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US Department of Justice wants to break up Google over monopoly concerns — and Google Chrome and Android are on the chopping block

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Person using Google Search on laptop

The US Department of Justice has released its proposals for breaking up Google’s alleged monopoly, and Google is not happy with the suggestions.

Among the proposed changes offered by the Department of Justice (DOJ), as reported by Ars Technica, is forcing Google to share its search data with rivals, blocking existing distribution agreements with browser developers like Mozilla Firefox and Apple Safari, and even forcing the spinning off of Google Chrome and Google Android into separate companies.

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19" Rackmount Servers Gotta Go!

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19" Rackmount Servers Gotta Go!



Check out our other videos here — http://vid.io/xL #winning
So my old server equipment i used for clanlife and gameserver hosting is now up for sale, i wanted to shop around first to find cheaper co-location but it looks like everywhere is expensive to co-locate now. My equipment is therefore up for sale. ————— i’m using —————-

– apogee mic 96k professional microphone (£199) – http://amzn.to/25nGQbt
– logitech c920 web camera (£50) – http://amzn.to/25nPOFE

i love and i want (for bella, videodirect and me!)

– amazon fire tv game controller (£40) – http://amzn.to/25nJr5g (remote game playing)
– amazon fire tv 4k (£80) – http://amzn.to/1TCV2I2 (static for videodirect/ella)
– mis gs60 6qe ghost pro 4k – http://amzn.to/25nJIFs (need for mobile gaming!)
– nintendo handheld console 3ds XL (£170) – http://amzn.to/25nKkuF (she lost last one!)
– pokemon alpha sapphire (£40) – http://amzn.to/25nKfXL (new pokemon game!)
– panasonic dmc-g7 camera (£500) – http://amzn.to/1TCXQoC (4k camera for nomad pics)
– canon powershot g7x mark II (£620) – http://amzn.to/1TCYT7W (daily vlogging camera 60fps)
– canon xa xc10 full hd (£800-£1600) – http://amzn.to/1TCYCSo (4k static shots for nomad.video)
– lexar professional 64gb 3400x (£160) – http://amzn.to/1TD8snw (card for the camera above)
– samsung galaxy s7 32gb (£465) – http://amzn.to/1TD2Bi1 (that android life)

stuff you should check out

– anker powercore 1000 portable charger (£16) – http://amzn.to/25nJAWg
– anker powercore 20100 (£24) – http://amzn.to/1TCWrhR
– aukey bluetooth sport headphones (£13) – http://amzn.to/1TCYSRm (sound awesome too!)
– wakawaka base 5 (£99) – http://amzn.to/1XVtahS (great solar base for digital nomads)

things i’m getting soon and will review

– vanguard veo am-264tr (£80) – http://amzn.to/25nJNc5 (for vlogging/filming)
– apple iPhone SE 64gb (£465) – http://amzn.to/25nPs1L (4k, up to date, speedy, content making)
– uhuru rechargeable mouse, noiseless/silent click (£13) – http://amzn.to/1ZOyLG8 (course making)
– gopro hero session camera (£159) – http://amzn.to/1TD9qQv (time-lapse, b-roll)
– wakawaka base 10 (£140) – http://amzn.to/1XVsDga (solar charging on the road) .

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How to watch Crew-8 depart the space station on Sunday

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How to watch Crew-8 depart the space station on Sunday

NASA and SpaceX are preparing to bring home three American astronauts and a Russian cosmonaut from the International Space Station (ISS).

On Sunday, NASA’s Matthew Dominick, Michael Barratt, and Jeanette Epps, and Roscosmos cosmonaut Alexander Grebenkin, will fly home aboard the same Crew Dragon capsule that they arrived in back in March.

The four ISS inhabitants spent much of their time in orbit carrying out science research, including a number of studies aimed at improving human health.

The short video below offers a neat overview of Crew-8’s time aboard the station some 250 miles above Earth:

NASA’s SpaceX Crew-8: Science, Innovation, and Discovery

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One of the astronauts, Matthew Dominick, also shared a steady stream of impressive photos and time-lapse videos during his first orbital mission.

How to watch

NASA is currently targeting 3:05 a.m. ET (12:03 a.m. PT) on Sunday, October 13, for the undocking of the Crew-8 mission from the space station, though it is monitoring the effects of Hurricane Milton across the Florida peninsula and close to the splashdown zone and will reschedule the flight home if necessary. The space agency’s next weather briefing is planned for 11 a.m. on Friday.

NASA will live-stream the undocking on its website, or you can watch the same footage via the video player that will appear later at the top of this page.

You’ll also be able to listen in on communications between the crew and Mission Control, and NASA is likely to provide a commentary to offer more insight into the spacecraft’s departure.

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While the Crew Dragon will appear to edge away from the ISS at a very slow speed, keep in mind that both the spacecraft and the station are in fact orbiting Earth at a colossal 17,500 mph.

NASA will also live stream the splashdown off the coast of Florida, but the agency has yet to share a specific time for that.






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