Latest news with #AlphaFold3
Vox
3 days ago
- Science
- Vox
These stories could change how you feel about AI
is an editorial director at Vox overseeing the climate, tech, and world teams, and is the editor of Vox's Future Perfect section. He worked at Time magazine for 15 years as a foreign correspondent in Asia, a climate writer, and an international editor, and he wrote a book on existential risk. Here's a selection of recent headlines about artificial intelligence, picked more or less at random: Okay, not exactly at random — I did look for more doomy-sounding headlines. But they weren't hard to find. That's because numerous studies indicate that negative or fear-framed coverage of AI in mainstream media tends to outnumber positive framings. But as in so many other areas, the emphasis on the negative in artificial intelligence risks overshadowing what could go right — both in the future as this technology continues to develop and right now. As a corrective (and maybe just to ingratiate myself to our potential future robot overlords), here's a roundup of one way in which AI is already making a positive difference in three important fields. Science Whenever anyone asks me about an unquestionably good use of AI, I point to one thing: AlphaFold. After all, how many other AI models have won their creators an actual Nobel Prize? AlphaFold, which was developed by the Google-owned AI company DeepMind, is an AI model that predicts the 3D structures of proteins based solely on their amino acid sequences. That's important because scientists need to predict the shape of protein to better understand how it might function and how it might be used in products like drugs. That's known as the 'protein-folding problem' — and it was a problem because while human researchers could eventually figure out the structure of a protein, it would often take them years of laborious work in the lab to do so. AlphaFold, through machine-learning methods I couldn't explain to you if I tried, can make predictions in as little as five seconds, with accuracy that is almost as good as gold-standard experimental methods. By speeding up a basic part of biomedical research, AlphaFold has already managed to meaningfully accelerate drug development in everything from Huntington's disease to antibiotic resistance. And Google DeepMind's decision last year to open source AlphaFold3, its most advanced model, for non-commercial academic use has greatly expanded the number of researchers who can take advantage of it. Medicine You wouldn't know it from watching medical dramas like The Pitt, but doctors spend a lot of time doing paperwork — two hours of it for every one hour they actually spend with a patient, by one count. Finding a way to cut down that time could free up doctors to do actual medicine and help stem the problem of burnout. That's where AI is already making a difference. As the Wall Street Journal reported this week, health care systems across the country are employing 'AI scribes' — systems that automatically capture doctor-patient discussions, update medical records, and generally automate as much as possible around the documentation of a medical interaction. In one pilot study employing AI scribes from Microsoft and a startup called Abridge, doctors cut back daily documentation time from 90 minutes to under 30 minutes. Not only do ambient-listening AI products free doctors from much of the need to make manual notes, but they can eventually connect new data from a doctor-patient interaction with existing medical records and ensure connections and insights on care don't fall between the cracks. 'I see it being able to provide insights about the patient that the human mind just can't do in a reasonable time,' Dr. Lance Owens, regional chief medical information officer at University of Michigan Health, told the Journal. Climate A timely warning about a natural disaster can mean the difference between life and death, especially in already vulnerable poor countries. That is why Google Flood Hub is so important. An open-access, AI-driven river-flood early warning system, Flood Hub provides seven-day flood forecasts for 700 million people in 100 countries. It works by marrying a global hydrology model that can forecast river levels even in basins that lack physical flood gauges with an inundation model that converts those predicted levels into high-resolution flood maps. This allows villagers to see exactly what roads or fields might end up underwater. Flood Hub, to my mind, is one of the clearest examples of how AI can be used for good for those who need it most. Though many rich countries like the US are included in Flood Hub, they mostly already have infrastructure in place to forecast the effects of extreme weather. (Unless, of course, we cut it all from the budget.) But many poor countries lack those capabilities. AI's ability to drastically reduce the labor and cost of such forecasts has made it possible to extend those lifesaving capabilities to those who need it most. One more cool thing: The NGO GiveDirectly — which provides direct cash payments to the global poor — has experimented with using Flood Hub warnings to send families hundreds of dollars in cash aid days before an expected flood to help themselves prepare for the worst. As the threat of extreme weather grows, thanks to climate change and population movement, this is the kind of cutting-edge philanthropy. AI for good Even what seems to be the best applications for AI can come with their drawbacks. The same kind of AI technology that allows AlphaFold to help speed drug development could conceivably be used one day to more rapidly design bioweapons. AI scribes in medicine raise questions about patient confidentiality and the risk of hacking. And while it's hard to find fault in an AI system that can help warn poor people about natural disasters, the lack of access to the internet in the poorest countries can limit the value of those warnings — and there's not much AI can do to change that. But with the headlines around AI leaning so apocalyptic, it's easy to overlook the tangible benefits AI already delivers. Ultimately AI is a tool. A powerful tool, but a tool nonetheless. And like any tool, what it will do — bad and good — will be determined by how we use it.
Associated Press
26-05-2025
- Science
- Associated Press
ASC25 Student Supercomputer Challenge: Shanghai Jiao Tong University Crowned Champion; Peking University Earns Silver
BEIJING--(BUSINESS WIRE)--May 25, 2025-- The finals of the ASC Student Supercomputer Challenge (ASC25) successfully concluded at Qinghai University in Xining, China. Following five days of rigorous competition, the team from Shanghai Jiao Tong University emerged as the champion, while the team from Peking University secured the silver prize, both demonstrating outstanding competence in high-performance computing and scientific problem-solving. More than 300 university teams from around the world registered for ASC25. Following a preliminary selection round, 25 teams advanced to the finals held at Qinghai University—marking the geographically highest-altitude venue in the history of the ASC competition. In the final round, participating teams were required to design and deploy small-scale supercomputing clusters, each constrained by a 4,000-watt power consumption limit. Within these clusters, teams executed and optimized internationally recognized benchmark tests, HPL and HPCG. Additionally, they applied performance optimization strategies to a suite of advanced scientific and engineering applications, including acceleration for AlphaFold3 inference and RNA 5-methylcytosine modification site detection, as well as DeepSeek inference and cosmic neutrino detection simulations. Teams were further assessed through English-language oral defenses presented before a jury composed of more than ten internationally renowned experts in high-performance computing and scientific applications. Furthermore, the 25 finalist teams were randomly assigned into five groups for the Group Competition, where they collaborated across institutions to address a complex scientific challenge: numerical simulation of the Qinghai-Tibet Plateau climate. As anticipated, the final round of the competition was marked by exceptional intensity and high-level performance. The 25 finalist teams demonstrated excellence across a range of evaluation criteria, including system architecture design, power efficiency, application performance optimization, collaborative problem-solving, and oral defense presentations. Their collective pursuit of innovation and technical advancement contributed to a truly remarkable and impactful event. The team from Shanghai Jiao Tong University demonstrated comprehensive capabilities, delivering strong performances in multiple tasks such as AlphaFold3 inference optimization, DeepSeek inference, and cosmic neutrino detection simulation. Their results reflected a deep understanding of artificial intelligence, high-performance computing systems, and interdisciplinary scientific applications, as well as superior optimization skills—culminating in their attainment of the overall championship. Meanwhile, the team from Peking University delivered outstanding results in RNA 5-methylcytosine modification site detection and DeepSeek optimization, earning them the silver prize. In the designated task for the e Prize Challenge—AlphaFold3 inference optimization—the team from Shanghai Jiao Tong University implemented comprehensive system-level and algorithmic optimizations. These included framework migration, refinement of matrix algorithms, task decomposition, parallel computing strategies, and communication acceleration. As a result of these efforts, they successfully achieved efficient multi-node inference of AlphaFold3 on a general-purpose CPU-based cluster. Their outstanding performance secured the highest ranking in this challenge and earned them the prestigious e Prize. A group comprising students from Sun Yat-sen University, Peking University, Beijing Normal University, Fuzhou University, and National Taiwan University demonstrated exceptional coordination and exemplary inter-institutional collaboration. Their joint efforts resulted in the highest score in the Qinghai-Tibet Plateau climate simulation challenge. In recognition of their outstanding performance, the team was collectively awarded the Group Competition Award. Teams from Zhejiang University, Fuzhou University, and Qilu University of Technology were awarded the Application Innovation Award in recognition of their outstanding performance in specific competition challenges and their innovative approaches to application-level problem-solving. Jack Dongarra, Chair of ASC Advisory Committee, Turing Award winner, and Emeritus Professor at the University of Tennessee, stated: 'The ASC Student Supercomputer Challenge is not just a competition, but a platform connecting global students, mentors, and industry leaders—where knowledge, creativity, and cutting-edge technologies ignite new possibilities. The fascination with emerging technologies, the fearless courage to tackle complex challenges, and the spirit of cross-border collaboration demonstrated by young participants all fuel students' drive to explore, innovate, and ultimately achieve personal breakthroughs.' Shi Yuanchun, President of Qinghai University, stated: 'The competition is a premier platform for young students around the world to push the boundaries of computing and explore the frontiers of science and technology. It also serves as a bridge for mutual learning among civilizations. By hosting the ASC25 finals, Qinghai University has delivered a powerful message to the world—vibrant, rigorous, pragmatic, and driven toward progress. Looking ahead, Qinghai University will collaborate with global partners to address major scientific challenges, drive breakthroughs in cutting-edge supercomputing technologies, and unleash innovative momentum on the Qinghai-Tibet Plateau.' View the ASC25 Video here About ASC The ASC Student Supercomputer Challenge serves as a platform to promote the exchange and development of young talent in supercomputing worldwide, with support from experts and institutions across Asia, Europe, and America. The competition aims to elevate the application and R&D capabilities in supercomputing, harness its technological driving force, and foster innovation across science, technology, and industry. Since its inception in 2012, ASC has attracted tens of thousands of university students from six continents, solidifying its status as the largest university-level supercomputing competition globally. To discover more about this impactful endeavor, visit the website View source version on CONTACT: Media contact [email protected] KEYWORD: CHINA ASIA PACIFIC INDUSTRY KEYWORD: TECHNOLOGY ENGINEERING OTHER TECHNOLOGY SOFTWARE MANUFACTURING HARDWARE UNIVERSITY SCIENCE EDUCATION OTHER SCIENCE SOURCE: ASC Student Supercomputer Challenge Copyright Business Wire 2025. PUB: 05/25/2025 09:03 PM/DISC: 05/25/2025 09:02 PM
Yahoo
06-05-2025
- Business
- Yahoo
FutureHouse previews an AI tool for 'data-driven' biology discovery
FutureHouse, an Eric Schmidt-backed nonprofit that aims to build an "AI scientist" within the next decade, has released a new tool that it claims can help support "data-driven discovery" in biology. The new tool comes just a week after FutureHouse launched its API and platform. The tool, called Finch, takes in biology data (primarily in the form of research papers) and a prompt (e.g. "What can you tell me about molecular drivers of cancer mataseses?") and runs code before generating figures and inspecting the results. In a series of posts on X, FutureHouse co-founder and CEO Sam Rodriques compared it to a "first-year grad student." "[B]eing able to [do all] this in minutes is a superpower," Rodriques wrote. "[Finch] actually ends up finding some really cool stuff [...] For our own projects internally, we have found it to be pretty awesome." FutureHouse's proposition, like that of many, many startups and tech giants, is that Finch and other AI tools will someday automate steps in the scientific process. In an essay earlier this year, OpenAI CEO Sam Altman said "superintelligent" AI tools could "massively accelerate scientific discovery and innovation." Similarly, the CEO of Anthropic, which just this week launched an "AI for science" program, has boldly predicted that AI could help formulate cures for most cancers. Yet evidence is lacking. Many researchers don't consider AI today to be especially useful in guiding the scientific process. Tellingly, FutureHouse has yet to achieve a scientific breakthrough or make a novel discovery with its AI tools. Biology, particularly on the drug discovery side, is an attractive target for AI companies. Precedence Research estimates the market was worth $65.88 billion in 2024 and could reach $160.31 billion by 2034. While there have been some successes, AI hasn't provided an immediate magical solution in the lab. Several firms employing AI for drug discovery, including Exscientia and BenevolentAI, have suffered high-profile clinical trial failures in recent years. Meanwhile, the accuracy of leading AI systems for drug discovery, like Google DeepMind's AlphaFold 3, tends to vary widely.
Forbes
25-04-2025
- Health
- Forbes
This Dataset can Ignite An AI Revolution In Cancer Research
Imagine accelerating the discovery of new therapeutics through the development of AI models for mining drug-cell interactions at unprecedented resolution. Tahoe Therapeutics (formerly Vevo) new release may have redefined the race to map the human cellular landscape in cancer. AI and data driven drug-discovery. getty In an unusual move, Tahoe Therapeutics has released 'Tahoe 100M', a massive open-source dataset encompassing 100 million single-cell data points and 60,000 experiments, mapping 1,100 drug treatments across 50 cancer types. Tahoe 100M brings a 50-fold increase in publicly available perturbational single-cell data, positioning itself in the world's largest single cell repository. Tahoe 100M includes what researchers call 'single cell transcriptomics profiles', i.e., a comprehensive list of gene expression data for each individual cell. These 'profiles' provide a snapshot of each cell and how it responds to drug perturbations, portraying a more accurate mosaic of tumor cell interactions. Thus, researchers can use the mosaic to understand the behavior of individual cells and define the impact of cancer heterogeneity on the development of effective treatments. Dr. Johnny Yu, co-founder and technology platform developer at Tahoe, describes the company's unique 'Mosaic Platform', used to generate the dataset, as 'a technology that creates a 'mosaic tumor' that allows testing drugs across multiple cancer types simultaneously and at high throughput'. The 'Mosaic Platform', combined with single-cell resolution, yields 'approximately 20,000 measurements across all protein-coding genes per assay" he continues, 'offering a unique level of cellular granularity'. Using this approach ensures the dataset's immediate practical value, making it a precious resource for AI modeling. Tahoe Therapeutics and the Arc Institute have recently partnered in the launch of the Arc Virtual Cell Atlas: the most comprehensive and diverse public database of single-cell level transcriptomic data across a wide range of perturbations. These data can be obtained for free and used for further analysis and AI modeling. Just in the last month, the dataset has been downloaded almost 11,000 times on Hugging Face, a data sharing platform. Dr. Hani Goodarzi, Tahoe's scientific co-founder, Core Investigator at the Arc Institute and UCSF Professor, puts the dataset into context: 'Tahoe's 'Mosaic Platform' helped minimize 'batch effects', which can make single cell data difficult to compare, offering a more consistent and reliable resource for modeling'. While recent technological advances in using AI, such as the AlphaFold 3 model, have fundamentally unlocked the ability to predict protein structures and drug interactions, understanding patient biology complexity remains a critical challenge. At this intersection, the potential impact of single-cell perturbation datasets on drug discovery can be profound. 'Tahoe 100M enables the building of comprehensive models that can predict drug interactions across diverse patient populations,' states Dr. Nima Alidoust, co-founder and CEO at Tahoe. To develop effective cancer treatments, we need to understand biological interactions beyond simple protein binding. Datasets such as Tahoe 100M account for patient complexity from the earliest stages of drug discovery, thus, having the potential to unlock novel 'AI-first' approaches to drug discovery. Dr. Bo Wang, chief AI scientist for the University Health Network in Canada and among the leading experts in AI for biology and healthcare, believes that the release of this dataset is 'a big deal for the field'. His lab developed the single-cell GPT model (scGPT), one of the first attempts to apply AI large language modeling to single-cell data. This model was trained using 33 million human cells from tissues such as heart, brain, blood, etc. and allows accurate cell type classification in single-cell studies. He believes that 'the Tahoe 100M dataset significantly extends our ability to train AI models to learn more nuanced, dosage-dependent cellular responses in perturbation studies across different cancer types, which help portray more generalizable AI models for drug development'. He is confident that such models will provide more accurate means for early patient stratification and for in silico screening of patient response for precise treatment selection. AI modeling of single cell networks. getty The generous release of Tahoe 100M is a potential turning point for deciphering cancer vulnerabilities at scale and can trigger an open-source data sharing momentum in cancer research. By providing unprecedented access to high-quality, large-scale single-cell data, Tahoe is promoting a more open, collaborative approach to scientific discovery. This is important as recent reports warn about thousands of 3D protein structures and other disease-relevant big datasets held within the vaults of private companies. The release of Tahoe 100M may represent a first step towards creating the 'internet of biology', laying the foundation for the development of truly transformative AI models to integrate and understand cellular biology and drug development at high speed.
Yahoo
01-03-2025
- Business
- Yahoo
Alphabet Inc. (NASDAQ:GOOG): Enroute to $10 Trillion?
We came across a bullish thesis on Alphabet Inc. (NASDAQ:GOOG) on ValueInvestorsClub by cuyler1903. In this article, we will summarize the bulls' thesis on GOOG. The company's shares were trading at $190.00 when this thesis was published, vs. the closing price of $170.21 on Feb 28. The search engine offered by GOOG will continue to remain a market leader and can generate bigger monetization potential with AI capabilities. Its mapping platform Google Maps and video channel YouTube offer a seamless integration with its search engine which none of its competitors have managed to replicate. Even with a large user base, the headroom for growth is evident due to a lack of competition in this space. GOOG's AlphaFold 3, an AI system capable of predicting protein structure with accuracy, is underestimated in its capability to revolutionize the drug industry. Partnerships with Eli Lilly and Novartis have already been initialized with AlphaFold expected to expedite drug discovery with a high degree of accuracy. GOOG is yet to monetize Android, with Microsoft receiving $25-50 for each Windows device. There are 3 billion Android devices currently, with 1.5 billion units sold annually. Even if an estimated $10-30 is charged, GOOG can generate annual revenues of $15-45 billion. Even YouTube commands a monopoly in mobile video consumption with a potential trillion-dollar-plus valuation on a standalone basis. Waymo, the autonomous driving arm of GOOG has a first-mover advantage with access to real driving data that could give it an edge over Tesla. The use cases include autonomous trucking, ride services and licensing fees. This segment could be worth a trillion dollars once the regulatory environment stabilizes with autonomous driving becoming a more viable transportation option. GOOG is also in the developing stage of creating an artificial general intelligence (AGI) application that has the potential to transform multiple industries. While it is still early to comment on the valuation, this technology can shape markets that are worth trillions of dollars. GOOG's access to mega computing infra and exceptional talent would enable it to be a forerunner in this space. Summing up the potential valuation of each segment, GOOG can become the first $10 trillion company, offering multi-bagger returns to the tune of 4-5x. While we acknowledge the potential of GOOG as an investment, our conviction lies in the belief that some AI stocks hold greater promise for delivering higher returns, and doing so within a shorter time frame. If you are looking for an AI stock that is more promising than GOOG but that trades at less than 5 times its earnings, check out our report about the cheapest AI stock. READ NEXT: 8 Best Wide Moat Stocks to Buy Now and 30 Most Important AI Stocks According to BlackRock. Disclosure: None. This article was originally published at Insider Monkey. Sign in to access your portfolio
