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Keyboards, with their combination of software and lots of moving parts, can be subject to problems. Here’s how to fix frequent issues that you may encounter.

Hubble recently identified water vapor in the atmosphere of a small, hot exoplanet.

Chelsea takes on Aston Villa in an FA Cup showdown on Friday, and we’ve got your guide to watching a live stream of the match.

Reenter the world of The Karate Kid as we tell you everything you need to know about Cobra Kai season 6, which will bring the hit series to an end.

Brazil takes on Colombia in the 2024 CONMEBOL Olympic Qualifying Tournament today. Here are several ways you can watch a live stream if you’re in the US.

Tottenham takes on Manchester City in FA Cup action on Friday, and here’s how to watch a live stream of the match if you live in the US.

If there’s one thing we can all agree upon, it’s that the 21st century’s captains of industry are trying to shoehorn AI into every corner of our world. But for all of the ways in which AI will be shoved into our faces and not prove very successful, it might actually have at least one useful purpose. For instance, by dramatically speeding up the often decades-long process of designing, finding and testing new drugs.

Risk mitigation isn’t a sexy notion but it’s worth understanding how common it is for a new drug project to fail. To set the scene, consider that each drug project takes between three and five years to form a hypothesis strong enough to start tests in a laboratory. A 2022 study from Professor Duxin Sun found that 90 percent of clinical drug development fails, with each project costing more than $2 billion. And that number doesn’t even include compounds found to be unworkable at the preclinical stage. Put simply, every successful drug has to prop up at least $18 billion waste generated by its unsuccessful siblings, which all but guarantees that less lucrative cures for rarer conditions aren’t given as much focus as they may need.

Dr. Nicola Richmond is VP of AI at Benevolent, a biotech company using AI in its drug discovery process. She explained the classical system tasks researchers to find, for example, a misbehaving protein – the cause of disease – and then find a molecule that could make it behave. Once they’ve found one, they need to get that molecule into a form a patient can take, and then test if it’s both safe and effective. The journey to clinical trials on a living human patient takes years, and it’s often only then researchers find out that what worked in theory does not work in practice.

The current process takes “more than a decade and multiple billions of dollars of research investment for every drug approved,” said Dr. Chris Gibson, co-founder of Recursion, another company in the AI drug discovery space. He says AI’s great skill may be to dodge the misses and help avoid researchers spending too long running down blind alleys. A software platform that can churn through hundreds of options at a time can, in Gibson’s words, “fail faster and earlier so you can move on to other targets.”

CellProfiler / Carpenter-Singh laboratory at the Broad Institute

Dr. Anne E. Carpenter is the founder of the Carpenter-Singh laboratory at the Broad Institute of MIT and Harvard. She has spent more than a decade developing techniques in Cell Painting, a way to highlight elements in cells, with dyes, to make them readable by a computer. She is also the co-developer of Cell Profiler, a platform enabling researchers to use AI to scrub through vast troves of images of those dyed cells. Combined, this work makes it easy for a machine to see how cells change when they are impacted by the presence of disease or a treatment. And by looking at every part of the cell holistically – a discipline known as “omics” – there are greater opportunities for making the sort of connections that AI systems excel at.

Using pictures as a way of identifying potential cures seems a little left-field, since how things look don’t always represent how things actually are, right? Carpenter said humans have always made subconscious assumptions about medical status from sight alone. She explained most people may conclude someone may have a chromosomal issue just by looking at their face. And professional clinicians can identify a number of disorders by sight alone purely as a consequence of their experience. She added that if you took a picture of everyone’s face in a given population, a computer would be able to identify patterns and sort them based on common features.

This logic applies to the pictures of cells, where it’s possible for a digital pathologist to compare images from healthy and diseased samples. If a human can do it, then it should be faster and easier to employ a computer to spot these differences in scale so long as it’s accurate. “You allow this data to self-assemble into groups and now [you’re] starting to see patterns,” she explained, “when we treat [cells] with 100,000 different compounds, one by one, we can say ‘here’s two chemicals that look really similar to each other.’” And this looking really similar to each other isn’t just coincidence, but seems to be indicative of how they behave.

In one example, Carpenter cited that two different compounds could produce similar effects in a cell, and by extension could be used to treat the same condition. If so, then it may be that one of the two – which may not have been intended for this purpose – has fewer harmful side effects. Then there’s the potential benefit of being able to identify something that we didn’t know was affected by disease. “It allows us to say, ‘hey, there’s this cluster of six genes, five of which are really well known to be part of this pathway, but the sixth one, we didn’t know what it did, but now we have a strong clue it’s involved in the same biological process.” “Maybe those other five genes, for whatever reason, aren’t great direct targets themselves, maybe the chemicals don’t bind,” she said, “but the sixth one [could be] really great for that.”

FatCamera via Getty Images

In this context, the startups using AI in their drug discovery processes are hoping that they can find the diamonds hiding in plain sight. Dr. Richmond said that Benevolent’s approach is for the team to pick a disease of interest and then formulate a biological question around it. So, at the start of one project, the team might wonder if there are ways to treat ALS by enhancing, or fixing, the way a cell’s own housekeeping system works. (To be clear, this is a purely hypothetical example supplied by Dr. Richmond.)

That question is then run through Benevolent’s AI models, which pull together data from a wide variety of sources. They then produce a ranked list of potential answers to the question, which can include novel compounds, or existing drugs that could be adapted to suit. The data then goes to a researcher, who can examine what, if any, weight to give to its findings. Dr. Richmond added that the model has to provide evidence from existing literature or sources to support its findings even if its picks are out of left-field. And that, at all times, a human has the final say on what of its results should be pursued and how vigorously.

It’s a similar situation at Recursion, with Dr. Gibson claiming that its model is now capable of predicting “how any drug will interact with any disease without having to physically test it.” The model has now formed around three trillion predictions connecting potential problems to their potential solutions based on the data it has already absorbed and simulated. Gibson said that the process at the company now resembles a web search: Researchers sit down at a terminal, “type in a gene associated with breast cancer and [the system] populates all the other genes and compounds that [it believes are] related.”

“What gets exciting,” said Dr. Gibson, “is when [we] see a gene nobody has ever heard of in the list, which feels like novel biology because the world has no idea it exists.” Once a target has been identified and the findings checked by a human, the data will be passed to Recursion’s in-house scientific laboratory. Here, researchers will run initial experiments to see if what was found in the simulation can be replicated in the real world. Dr. Gibson said that Recursion’s wet lab, which uses large-scale automation, is capable of running more than two million experiments in a working week.

“About six weeks later, with very little human intervention, we’ll get the results,” said Dr. Gibson and, if successful, it’s then the team will “really start investing.” Because, until this point, the short period of validation work has cost the company “very little money and time to get.” The promise is that, rather than a three-year preclinical phase, that whole process can be crunched down to a few database searches, some oversight and then a few weeks of ex vivo testing to confirm if the system’s hunches are worth making a real effort to interrogate. Dr. Gibson said that it believes it has taken a “year’s worth of animal model work and [compressed] it, in many cases, to two months.”

Of course, there is not yet a concrete success story, no wonder cure that any company in this space can point to as a validation of the approach. But Recursion can cite one real-world example of how close its platform came to matching the success of a critical study. In April 2020, Recursion ran the COVID-19 sequence through its system to look at potential treatments. It examined both FDA-approved drugs and candidates in late-stage clinical trials. The system produced a list of nine potential candidates which would need further analysis, eight of which it would later be proved to be correct. It also said that Hydroxychloroquine and Ivermectin, both much-ballyhooed in the earliest days of the pandemic, would flop.

And there are AI-informed drugs that are currently undergoing real-world clinical trials right now. Recursion is pointing to five projects currently finishing their stage one (tests in healthy patients), or entering stage two (trials in people with the rare diseases in question) clinical testing right now. Benevolent has started a stage one trial of BEN-8744, a treatment for ulcerative colitis that may help with other inflammatory bowel disorders. And BEN-8744 is targeting an inhibitor that has no prior associations in the existing research which, if successful, will add weight to the idea that AIs can spot the connections humans have missed. Of course, we can’t make any conclusions until at least early next year when the results of those initial tests will be released.

Yuichiro Chino via Getty Images

There are plenty of unanswered questions, including how much we should rely upon AI as the sole arbiter of the drug discovery pipeline. There are also questions around the quality of the training data and the biases in the wider sources more generally. Dr. Richmond highlighted the issues around biases in genetic data sources both in terms of the homogeneity of cell cultures and how those tests are carried out. Similarly, Dr. Carpenter said the results of her most recent project, the publicly available JUMP-Cell Painting project, were based on cells from a single participant. “We picked it with good reason, but it’s still one human and one cell type from that one human.” In an ideal world, she’d have a far broader range of participants and cell types, but the issues right now center on funding and time, or more appropriately, their absence.

But, for now, all we can do is await the results of these early trials and hope that they bear fruit. Like every other potential application of AI, its value will rest largely in its ability to improve the quality of the work – or, more likely, improve the bottom line for the business in question. If AI can make the savings attractive enough, however, then maybe those diseases which are not likely to make back the investment demands under the current system may stand a chance. It could all collapse in a puff of hype, or it may offer real hope to families struggling for help while dealing with a rare disorder.

This article originally appeared on Engadget at https://www.engadget.com/ai-is-coming-for-big-pharma-150045224.html?src=rss

Apple’s Mac just turned 40 years old! This week, Devindra chats with Deputy Editor Nathan Ingraham about his Mac retrospective. We focus on how much has changed since Apple’s disastrous 2016 lineup, why the Apple Silicon chips feel so revolutionary, and look back at our earliest Mac experiences. Also, we review the Framework Laptop 16, a wonderfully modular miracle of a laptop, but one that we wish had more graphics power for gaming. (But hey, at least you can replace the GPU eventually!).

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Listen below or subscribe on your podcast app of choice. If you’ve got suggestions or topics you’d like covered on the show, be sure to email us or drop a note in the comments! And be sure to check out our other podcast, Engadget News!

Topics

• Framework Laptop 16 review: Amazingly modular, but not so great at gaming – 1:17

• The Mac turns 40 – 19:27

• More tech layoffs at Blizzard/Activision, Riot, eBay and others – 49:58

• Apple’s Car concept is allegedly still alive – 52:44

• Apple overhauls App Store rules in response to European Union regulation – 58:25

• Working on – 1:09:30

• Pop culture picks – 1:13:40

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Credits
Hosts: Devindra Hardawar and Nathan Ingraham
Producer: Ben Ellman
Music: Dale North and Terrence O’Brien

This article originally appeared on Engadget at https://www.engadget.com/engadget-podcast-the-mac-turns-40-141509644.html?src=rss

A large downside to Windows PCs with Arm64 processors like Microsoft’s own Surface Pro 9 5G has been a lack of native support for Chrome, the world’s most popular browser. Now, Google has finally released a Chrome Canary beta version that fully supports the Arm64 architecture, Windows Central has reported. 

The new version should significantly accelerate Chrome performance on Arm64 PCs, negating the need to run Chrome in emulation mode. The download can be installed on PCs running recent versions of Windows 11 for Arm processors, with one user confirming it runs on a seven-year-old Snapdragon 835 SoC. 

Chrome has been available for some time on Google’s Chromium on Arm64 and even Linux for Arm64, along with iOS and Mac. On top of that, Microsoft’s Edge browser (which is based on Chrome) has run natively on Arm64 for years. So why the delay for Windows on Arm64? It may be because there aren’t that many Arm64 Windows PCs and those that do exist are relatively expensive, especially compared to Chromebooks. 

Google might be reasoning that now is a good time to introduce the feature, since Qualcomm is set to release its Snapdragon X Elite chip, a successor to the Snapdragon 8cx Gen 3. Based on TSMC’s latest 4-nanometer tech, it’s promising performance double that of some 13th-gen Intel Core i7 CPUs with a third the power draw, allowing it to better compete with Apple’s latest M-series silicon. 

If Windows laptops using the chip can finally deliver performance that’s sadly been lacking in models to date, we may finally see them arrive in decent numbers. Snapdragon Elite X models are supposed to launch in mid-2024, so hopefully Google will be ready with a stable version of Chrome. If you have an Arm64 PC, you can download the Canary version here. 

This article originally appeared on Engadget at https://www.engadget.com/google-chrome-for-windows-is-finally-getting-native-arm-support-134832609.html?src=rss

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