Carbon Chemist

Tag: AI

  • Astronomers using AI to prepare for ton of data from new telescopes

    Astronomers using AI to prepare for ton of data from new telescopes

    [ad_1]

    It’s a problem that will be repeated in other places over the coming decade. As astronomers construct giant cameras to image the entire sky and launch infrared telescopes to hunt for distant planets, they will collect data on unprecedented scales. 

    “We really are not ready for that, and we should all be freaking out,” says Cecilia Garraffo, a computational astrophysicist at the Harvard-Smithsonian Center for Astrophysics. “When you have too much data and you don’t have the technology to process it, it’s like having no data.”

    In preparation for the information deluge, astronomers are turning to AI for assistance, optimizing algorithms to pick out patterns in large and notoriously finicky data sets. Some are now working to establish institutes dedicated to marrying the fields of computer science and astronomy—and grappling with the terms of the new partnership.

    In November 2022, Garraffo set up AstroAI as a pilot program at the Center for Astrophysics. Since then, she has put together an interdisciplinary team of over 50 members that has planned dozens of projects focusing on deep questions like how the universe began and whether we’re alone in it. Over the past few years, several similar coalitions have followed Garraffo’s lead and are now vying for funding to scale up to large institutions.

    Garraffo recognized the potential utility of AI models while bouncing between career stints in astronomy, physics, and computer science. Along the way, she also picked up on a major stumbling block for past collaboration efforts: the language barrier. Often, astronomers and computer scientists struggle to join forces because they use different words to describe similar concepts. Garraffo is no stranger to translation issues, having struggled to navigate an English-only school growing up in Argentina. Drawing from that experience, she has worked to put people from both communities under one roof so they can identify common goals and find a way to communicate. 

    Astronomers had already been using AI models for years, mainly to classify known objects such as supernovas in telescope data. This kind of image recognition will become increasingly vital when the Vera C. Rubin Observatory opens its eyes next year and the number of annual supernova detections quickly jumps from hundreds to millions. But the new wave of AI applications extends far beyond matching games. Algorithms have recently been optimized to perform “unsupervised clustering,” in which they pick out patterns in data without being told what specifically to look for. This opens the doors for models pointing astronomers toward effects and relationships they aren’t currently aware of. For the first time, these computational tools offer astronomers the faculty of “systematically searching for the unknown,” Garraffo says. In January, AstroAI researchers used this method to catalogue over 14,000 detections from x-ray sources, which are otherwise difficult to categorize.

    Another way AI is proving fruitful is by sniffing out the chemical composition of the skies on alien planets. Astronomers use telescopes to analyze the starlight that passes through planets’ atmospheres and gets soaked up at certain wavelengths by different molecules. To make sense of the leftover light spectrum, astronomers typically compare it with fake spectra they generate based on a handful of molecules they’re interested in finding—things like water and carbon dioxide. Exoplanet researchers dream of expanding their search to hundreds or thousands of compounds that could indicate life on the planet below, but it currently takes a few weeks to look for just four or five compounds. This bottleneck will become progressively more troublesome as the number of exoplanet detections rises from dozens to thousands, as is expected to happen thanks to the newly deployed James Webb Space Telescope and the European Space Agency’s Ariel Space Telescope, slated to launch in 2029. 

    Processing all those observations is “going to take us forever,” says Mercedes López-Morales, an astronomer at the Center for Astrophysics who studies exoplanet atmospheres. “Things like AstroAI are showing up at the right time, just before these faucets of data are coming toward us.”

    [ad_2]

    Source link

  • AI noise-cancelling headphones let you focus on just one voice

    AI noise-cancelling headphones let you focus on just one voice

    [ad_1]

    Young woman listening to music in headphones while walking in a city

    Selectively cutting out some external noises could leave you hearing only the sounds you want

    Cavan Images/Alamy

    Prototype noise-cancelling headphones allow you to select which background noises to drown out, letting you put an “audio spotlight” on one specific voice so you can concentrate on it.

    Conventional noise-cancelling headphones reduce unwanted sounds like the rumble of a bus engine, but because the technology cancels out certain frequencies entirely, it can also suppress sounds we want to hear.

    Now, Shyam Gollakota at the University of Washington in Seattle and his colleagues have created headphones that can remove any unwanted noises while leaving others intact, regardless of their frequencies. It can also be trained with the press of a button to home in on a specific person’s voice and exclude all other noise.

    The researchers are presenting their prototype at a joint meeting of the Acoustical Society of America and the Canadian Acoustical Association this week. The device uses an artificial intelligence system called a neural network that has been trained on many examples of 20 different types of sound, including alarm clocks, crying babies and birdsong. The user can choose to turn on or off each category of sound from an app, allowing it to pass through the headphones or be blocked.


    The prototype consists of commercially available headphones with a microphone attached on the outside of the housing that covers each ear. These microphones record ambient sound and pass it to either a small Orange Pi microcontroller or a smartphone on which the neural network runs. This AI then removes unnecessary sounds and transmits the edited audio feed to the headphones. Gollakota says the equipment could be built into a set of headphones.

    The technology works in the same way as the AI that was used to isolate individual instruments and voices amid a noisy jumble recorded during work on The Beatles’s 1970 album Let It Be, allowing director Peter Jackson to create the documentary series The Beatles: Get Back.

    That process took some time, but this prototype can process audio within just 8 milliseconds – because the team kept the neural network small and simple enough for a mobile device to run quickly – to avoid confusing delays between things happening and you hearing them.

    Gollakota says that the effect is like an “audio spotlight” being turned onto a noise source, allowing you to focus intently on it even in chaotic and loud environments.

    “This has new capabilities which give more control to the user. We’re taking the first steps of human acoustic perception augmentation right now,” says Gollakota.

    Topics:

    [ad_2]

    Source link

  • Surgeons can use AI chatbot to tell robots to help with suturing

    Surgeons can use AI chatbot to tell robots to help with suturing

    [ad_1]

    A virtual assistant for surgeons translates text prompts into commands for a robot, offering a simple way to instruct machines to carry out small tasks in operations

    [ad_2]

    Source link

  • Prepare to Get Manipulated by Emotionally Expressive Chatbots

    Prepare to Get Manipulated by Emotionally Expressive Chatbots

    [ad_1]

    The emotional mimicry of OpenAI’s new version of ChatGPT could lead AI assistants in some strange—even dangerous—directions.

    [ad_2]

    Source link

  • Generative AI Doesn’t Make Hardware Less Hard

    Generative AI Doesn’t Make Hardware Less Hard

    [ad_1]

    Things aren’t going so well for AI hardware startups.

    After years of development, startup Humane launched a $700 wearable in early April that leans heavily on artificial intelligence. The original pitch for the Ai Pin was that you no longer need to juggle different apps; its operating system can “search for the right AI at the right moment,” allowing it to play music, translate languages, and even tell you how much protein is in a palmful of almonds. And because it doesn’t have a traditional display, the Ai pin was supposed to be a tiny tincture for the disease of screentime; smartphones were on their way out.

    The pin has been panned. WIRED’s Julian Chokkattu scored the Ai Pin a 4 out of 10. Popular YouTuber Marques Brownlee complimented the device’s hardware design but still called it “The Worst Product I’ve Ever Reviewed … For Now.” The company has since massaged the message that it’s meant to replace your phone. Humane co-founder and chief executive Bethany Bongiorno has been fastidiously responding to displeased customers—and some fanboys—on Twitter, with apologies, assurances that improvements are coming, and video demos of the gadget’s UI, which replaces the smartphone in your palm by projecting lasers onto your palm.

    Humane appears to have lost the thread on its own product launch, and it’s not alone. The cheaper Rabbit R1, which was sold for $200 as a generative AI “pocket companion” and generated a lot of initial excitement, has now been labeled “underwhelming,” “half-baked,” “undercooked” and “unreliable.” WIRED’s Chokkattu gave it a 3 out of 10, while some people have questioned the way the device handles logins for outside apps such as Uber.

    These early hardware #fails aren’t unprecedented. Plenty of startups have overpromised in marketing and then built and shipped lackluster products. Competing in hardware is especially difficult in the age of Tech Giants, whose ecosystems rule over all. Developer Ben Sandofsky surmised that the Humane cofounders’ adherence to the “Apple Way,” or toiling in a secretive vacuum, is partly to blame. They spent years polishing that singular product the way a giant tech company would, he wrote in a blog post, but with $230 million in venture capital funding instead of billions in cash stores.

    But both Humane and Rabbit appear to have made another error in judgment: Both were banking on AI excitement in the ChatGPT era to capture early customers and keep themselves out of the gadget graveyard. Instead, they rode the AI hype train straight into a non-working brick wall. It turns out generative AI doesn’t make hardware any less hard.

    Expensive Flops

    “To really create a great new AI device you have to have both hardware and software figured out, and the question with some of these startups is how much of that software layer is just a skin,” says MG Siegler, a partner at GV, Alphabet’s venture capital firm.

    Sielger says that tech incumbents now have an even bigger advantage, because they can build using their own infrastructure and afford to lose money while they’re iterating on new versions of products. While startups are attempting to launch their scrappy AI products out of nothing, Meta, Google, Microsoft, and Apple can tap existing teams and services to put AI assistants into infinitely wearable sunglasses, churn out phones with built-in generative AI search, create designated keys for AI on their laptops, and pack their tablets with “outrageously powerful” AI chips.

    “Bigger tech companies are able to have five shots on a hardware product whereas a startup may only have one,” says Jacob Andreou, an investor at Greylock who spent several years growing products at Snap. “The odds of one of these smaller companies raising a future fundraising round after releasing an expensive flop are not good odds.”

    [ad_2]

    Source link

  • DeepMind AI can predict how drugs interact with proteins

    DeepMind AI can predict how drugs interact with proteins

    [ad_1]

    Visualisation of a protein binding to a DNA molecule

    Science Photo Library/Alamy

    An artificial intelligence system can now determine not only how proteins fold but also how they interact with other proteins, drug molecules or DNA. Biochemists and pharmaceutical researchers say the tool has the potential to vastly speed up their work, such as helping to discover new drugs.

    Proteins, which play many important roles in living things, are made up of chains of amino acids, but their complex 3D shapes are difficult to predict.

    The AI company DeepMind first announced in 2020 that its AlphaFold AI could accurately predict protein structure from amino acid sequences, solving one of the biggest challenges in biology. By the middle of 2021, the company said that it had mapped 98.5 per cent of the proteins in the human body.

    Now the latest version, AlphaFold 3, is able to model how proteins, including antibodies, interact with each other, as well as with other biomolecules such as DNA and RNA strands. DeepMind says the accuracy of its predictions is at least 50 per cent higher than existing methods.

    Most drug molecules function by binding to specific sites on proteins. AlphaFold 3 could rapidly speed up the development of new drugs by creating a fast way to test how candidate drug molecules interact with proteins in a computer before running lengthy and expensive laboratory tests.

    Like earlier versions of AlphaFold, models of proteins or their interactions generated by the latest update aren’t experimentally validated. DeepMind’s chief executive, Demis Hassabis, says AlphaFold 3 only offers predictions, so validation in the lab remains vital – but that research will now be “massively accelerated”.

    Julien Bergeron at King’s College London, who wasn’t involved in developing AlphaFold 3 but has been testing it for several months, says it has changed the way his experiments are run. “We can start testing hypotheses before we even go to the lab, and this will really be transformative. I’m pretty much certain that every single structural biology or protein biochemistry research group in the world will immediately adopt this system,” he says.

    Keith Willison at Imperial College London says the tool has the potential to streamline large portions of drug discovery and biological research, allowing researchers to focus in on useful molecules that they may never have been able to discover previously.

    “Organic chemists used to say the chemical space is larger than the number of atoms in the universe, and we’ll never be able to access even the remotest, tiniest portion of it. But I think these AI techniques are going to be able to access a huge amount of relevant chemical space,” he says.

    Matt Higgins at the University of Oxford says the new features in DeepMind’s AI will make a huge difference to biomedical researchers, including in his own work studying host-parasite interactions in malaria.

    “While AlphaFold transformed our ability to predict the structures of protein molecules, the protein machines used by our cells rarely work alone,” he says. “AlphaFold 3 brings the new and exciting ability to modify protein molecules with the most common additions or bind them to the most common binding partners found in our bodies and to see what happens.”

    Topics:

    [ad_2]

    Source link

  • China has a flourishing market for deepfakes that clone the dead

    China has a flourishing market for deepfakes that clone the dead

    [ad_1]

    Deepfake technologies have evolved to the point where it’s now easy and affordable to clone people’s looks and voices with AI. Meanwhile, large language models mean it’s more feasible than ever before to conduct full conversations with AI chatbots. 

    I just published a story today about the burgeoning market in China for applying these advances to re-create deceased family members. Thousands of grieving individuals have started turning to dead relatives’ digital avatars for conversations and comfort. 

    It’s a modern twist on a cultural tradition of talking to the dead, whether at their tombs, during funeral rituals, or in front of their memorial portraits. Chinese people have always liked to tell lost loved ones what has happened since they passed away. But what if the dead could talk back? This is the proposition of at least half a dozen Chinese companies offering “AI resurrection” services. The products, costing a few hundred to a few thousand dollars, are lifelike avatars, accessed in an app or on a tablet, that let people interact with the dead as if they were still alive.

    I talked to two Chinese companies that, combined, have provided this service for over 2,000 clients. They describe a growing market of people accepting the technology. Their customers usually look to the products to help them process their grief.

    To read more about how these products work and the potential implications of the technology, go here.

    However, what I didn’t get into in the story is that the same technology used to clone the dead has also been used in other interesting ways.

    For one, this process is being applied not just to private individuals, but also to public figures. Sima Huapeng, CEO and cofounder of the Chinese company Silicon Intelligence, tells me that about one-third of the “AI resurrection” cases he has worked on involve making avatars of dead Chinese writers, thinkers, celebrities, and religious leaders. The generated product is not intended for personal mourning but more for public education or memorial purposes.

    Last year, Silicon Intelligence replicated Mei Lanfang, a renowned Peking opera singer born in 1894. The avatar of Mei was commissioned to address a 2023 Peking opera festival held in his hometown, Taizhou. Mei talked about seeing how drastically Taizhou had changed through modern urban development, even though the real artist died in 1961.

    [ad_2]

    Source link

  • Deepfakes of the dead are a growing Chinese business

    Deepfakes of the dead are a growing Chinese business

    [ad_1]

    In Chinese homes, it’s common to put up a portrait of a deceased relative for a few years after the death. Zhang Zewei, founder of a Shanghai-based company called Super Brain, says he and his team wanted to revamp that tradition with an “AI photo frame.” They create avatars of deceased loved ones that are pre-loaded onto an Android tablet, which looks like a photo frame when standing up. Clients can choose a moving image that speaks words drawn from an offline database or from an LLM. 

    “In its essence, it’s not much different from a traditional portrait, except that it’s interactive,” Zhang says.

    Zhang says the company has made digital replicas for over 1,000 clients since March 2023 and charges $700 to $1,400, depending on the service purchased. The company plans to release an app-only product soon, so that users can access the avatars on their phones, and could further reduce the cost to around $140.

    The purpose of his products, Zhang says, is therapeutic. “When you really miss someone or need consolation during certain holidays, you can talk to the artificial living and heal your inner wounds,” he says.

    And even if that conversation is largely one-sided, that’s in keeping with a strong cultural tradition. Every April during the Qingming festival, Chinese people sweep the tombs of their ancestors, burn joss sticks and fake paper money, and tell them what has happened in the past year. Of course, those conversations have always been one-way. 

    But that’s not the case for all Super Brain services. The company also offers deepfaked video calls in which a company employee or a contract therapist pretends to be the relative who passed away. Using DeepFace, an open-source tool that analyzes facial features, the deceased person’s face is reconstructed in 3D and swapped in for the live person’s face with a real-time filter. 

    Example of a deepfake video call Super Brain did in July 2023. The face in the top right corner is from the deceased son of the woman.

    SUPER BRAIN

    At the other end of the call is usually an elderly family member who may not know that the relative has died—and whose family has arranged the conversation as a ruse. 

    Jonathan Yang, a Nanjing resident who works in the tech industry, paid for this service in September 2023. His uncle died in a construction accident, but the family hesitated to tell Yang’s grandmother, who is 93 and in poor health. They worried that she wouldn’t survive the devastating news.

    [ad_2]

    Source link

  • Roundtables: Inside the Next Era of AI and Hardware

    Roundtables: Inside the Next Era of AI and Hardware

    [ad_1]

    Recorded on April 30, 2024

    Inside the Next Era of AI and Hardware

    Speakers: James O’Donnell, AI reporter, and Charlotte Jee, News editor

    Hear first-hand from our AI reporter, James O’Donnell, as he walks our news editor Charlotte Jee through the latest goings-on in his beat, from rapid advances in robotics to autonomous military drones, wearable devices, and tools for AI-powered surgeries.

    Related Coverage

    • An OpenAI spinoff has built an AI model that helps robots learn tasks like humans
    • Watch this robot as it learns to stitch up wounds
    • A new satellite will use Google’s AI to map methane leaks from space

    [ad_2]

    Source link

  • Image-generating AI creates uncanny optical illusions

    Image-generating AI creates uncanny optical illusions

    [ad_1]

    Motion hybrid - ancient ruins and a teddybear

    An AI-generated image of ancient ruins looks like a teddy bear when motion blur is added

    Daniel Geng, Inbum Park, Andrew Owens

    AI models designed to create images from text descriptions can make optical illusions that look like different objects depending on how they are viewed.

    Daniel Geng at the University of Michigan and his colleagues made slight adaptions to an existing AI model to get it to make a range of illusions. Some appear to depict other things as you view them from further away; some become three different images depending on their rotation and…

    [ad_2]

    Source link