Latest news with #AlphaFold2
CNET
3 days ago
- Business
- CNET
Death to Gmail? Google DeepMind CEO Wants AI to Solve This One Annoying Problem
Google DeepMind CEO Demis Hassabis might have won a Nobel Prize for his work on AlphaFold 2, an AI model that can predict protein structures, but the solution to the problem he really wants to solve still evades him. The problem in question is infinitely easier to grasp and more relatable than Hassabis' work in the field of chemistry. "The thing I really want that we're working on is next-generation email," he said, speaking at SXSW London on Monday. "I would love to get rid of my email." Based on the crowd reaction, it was a popular sentiment in the room, where earlier that day, the former British Prime Minister Tony Blair admitted to sending only one email for the entire 10-year period he was in office. There is some irony to Hassabis' quest. The prize-winning scientist is responsible for developing some of the most complex and sophisticated AI models the world has ever seen, all in aid of working toward cures for diseases that are beyond anything we have access to today. His mission to render email (presumably Gmail?) – an annoyance of our own human invention – obsolete feels like small fry in comparison. But it also exposes the duality of Hassabis' responsibilities at Google. He is, and always has been, deeply committed to pursuing AI for the benefit of humankind. "My personal passion is applying [AI] to the frontiers of science and medicine," he said. At the same time he is beholden to the corporate interests of Google, which acquired DeepMind in 2014. Hassabis always imagined the development of AI to be more of a "scientific-led endeavor," spearheaded by a computer science equivalent to CERN, the famed particle physics lab in Switzerland. But the technology went a different way, becoming commercially viable much quicker than he anticipated. From there, he said, "the capitalist engine has done what it does best." Hassabis almost speaks as though he is separate from the "capitalist engine," but of course he is deeply embedded within it. DeepMind being owned by Google means that as well as pursuing his passion project of curing disease with AI – arguably the most noble use of AI – he must split his attention to ensure Google's AI products, from Gemini to Veo and everything else the company announced at I/O last month are up to scratch in a competitive market. In pursuit of AGI The competition is "ferocious" and it's a hefty work schedule for one man, who says he sleeps very little and doesn't expect to until "we get to AGI," or artificial general intelligence. Along with developing DeepMind's core AI models, and translating them into science, he continues to pursue the development of AGI, or AI that fully matches (or exceeds) human intellectual capabilities. "My feeling is that we're about five to 10 years away," he said. His vision for AGI is that it will unlock a world in which "we can cure many, many diseases – or maybe even all diseases," and "unlimited renewable energy." In some ways, the Google products are stopping off points on the way. One of the reasons DeepMind has built Veo 3, its latest video generation software, said Hassabis, is that AGI needs to have a physical understanding of the world around it. The world models built for Veo 3 are key to this understanding. In turn, these world models will be essential for a breakthrough in robotics, which Hassabis believes is due in the "next few years." While it's sometimes not clear where DeepMind's worthy mission ends and Google's commercial priorities kick in, it's clear that Hassabis is finding ways to make it work for him, and his long-term pursuit of an AGI breakthrough. In spite of the seismic shift he predicts this will cause, even he is skeptical of the hype around AI in the short term. "I mean, it couldn't be any more hyped," he said. "Therefore, it is a little bit overhyped."
Time of India
21-04-2025
- Business
- Time of India
Integrating AI into business operations for real-world impact
Embedding AI into business operations isn't about plugging in a tool—it's about transforming how decisions are made. Drawing parallels from scientific breakthroughs like AlphaFold, this article explores the five foundational elements that empower organizations to evolve into AI-native enterprises capable of intelligent, real-time action. From Data to Decisions In 2020, DeepMind stunned the scientific community with AlphaFold 2, solving the decades-old challenge of protein folding. This breakthrough wasn't merely about deep learning or compute power. It was made possible by a confluence of factors: vast public datasets, a clearly defined challenge, collaborative ecosystems like the Critical Assessment of Structure Prediction (CASP), and the foundational Protein Data Bank. This offers a compelling parallel for enterprises. Just as AlphaFold's success stemmed from shared frameworks, open collaboration, and a clear purpose, AI breakthroughs in business demand more than algorithms—they require foundational readiness across data, governance, systems, and teams. From Predictive to Prescriptive: The AI Advantage Predictive analytics laid the groundwork for data-driven strategies . But as data volumes explode to the tune of exceeding 180 zettabytes by 2025, organizations must evolve beyond prediction—toward systems that adapt, recommend, and act in real time. In manufacturing, for instance, AI goes beyond forecasting equipment failures—helping dynamically reschedule maintenance, triggering automated ordering of spare parts, and minimizing downtime. In retail, while predictive analytics help determine trends and assess customer behavior, AI enables intelligent pricing, localized inventory decisions, and hyper personalization. Building Blocks for AI Integration Data Readiness: From Silos to Systems AI is only as good as the data it learns from. Yet many enterprises operate in fragmented data environments. Transitioning to AI-ready platforms is essential—combining data lakehouses, real-time pipelines, and governance tools to democratize access and enable actionable intelligence. These architectures support multi-modal data, decouple storage and compute, and enable intelligent workflows across functions. Cross-functional Alignment and Talent Strategy Embedding AI isn't solely a technology initiative. It requires a well-coordinated effort between data scientists, domain experts, and process owners to convert models into meaningful outcomes. Organizations with centralized AI governance and strong executive sponsorship are more likely to scale AI successfully. Human-AI collaboration becomes essential, especially in high-stakes decisions where oversight, ethics, and context matter. Responsible Governance and Explainability With AI systems playing a key role in the core operations, trust and transparency become non-negotiable. AI systems should be auditable, explainable, and aligned with regulatory frameworks. When it comes to AI governance, data privacy is a critical element to consider, for it helps protect IP while ensuring compliance. Effective governance will not only help strengthen security but also build enterprise resilience. AI Architectures and Embedded Intelligence AI is shifting from being a bolt-on analytics layer to becoming an integral part of enterprise workflows. Modern enterprise platforms treat AI not as an add-on, but as foundational logic—driving decisions inside ERP, CRM, and SCM systems. Tech giants like Microsoft (Copilot), Salesforce (Einstein), and SAP (BTP), have set a benchmark for embedded intelligence. Intellectual Property and Competitive Edge As enterprises build and train proprietary models—especially domain-specific or task-specific models—they are creating strategic IP. This intellectual capital must be protected—via disciplined model lineage, data provenance, and secure deployment frameworks. It also opens doors for monetizing AI through platforms, APIs, and services that extend beyond internal efficiency to ecosystem leadership. Embedded AI in Action: Industry Snapshots HealthcareAutomotiveFinancial ServicesRetailAI helps optimize patient flow by reallocating resources dynamically across emergency rooms, labs, and diagnostics. Innovators like MDI, Activ Surgical, and Cala Health are embedding AI into surgical robotics, disease detection, and digital connected cars generating terabytes of data per hour, OEMs are embedding edge AI for local processing—powering safety features, predictive maintenance, and new business models like are moving beyond customer experience and fraud detection—embedding AI in real-time credit scoring, risk management, and personalized wealth management. Even regulators like RBI are leveraging AI to derive insights from supervised is powering micro-fulfilment centers, optimizing last-mile delivery, and enabling hyperlocal demand prediction. Walmart reportedly reduced stockouts by 30% using AI. Indian players like Flipkart, Myntra, and Blinkit are leveraging AI to redefine customer experience and last-mile delivery. The AI-Native Enterprise: A Strategic Imperative AI is not the end goal—it's a force multiplier. Enterprises that make AI integral to their DNA benefit from: Innovation & Agility with faster R&D cycles, scenario simulation, and market-fit experimentation Real-time Decisioning enabled by Intelligent automation and dynamic optimization across functionsCost Optimization through predictive maintenance and resource allocationRevenue Growth via hyper-personalization and next-best action recommendations and AI-native services AI success in the enterprise hinges on foundational readiness. Companies that embrace data as an asset, embed AI into operations, build responsible governance, and foster cross-disciplinary collaboration will thrive, becoming AI-native enterprises.
Associated Press
02-04-2025
- Science
- Associated Press
Collaborative Drug Discovery Integrates CDD Vault with NVIDIA BioNeMo NIM For AlphaFold2 and DiffDock Models
BURLINGAME, CA, UNITED STATES, April 2, 2025 / / -- Collaborative Drug Discovery, Inc. (CDD) announces the integration of NVIDIA BioNeMo NIM microservices for AlphaFold2 and DiffDock models into CDD Vault. This integration enriches experimental data within CDD Vault, a single, intuitive web-based platform, by incorporating predictive, industry-leading models. Researchers can manage, analyze, and securely collaborate on the integrated data with enhanced models, unlocking deeper insights and enabling more informed decision-making. Scientists engaged in both commercial and humanitarian drug discovery have demonstrated that the integration with BioNeMo NIM for AlphaFold2 and DiffDock significantly enhances CDD Vault's existing AI module. These models are a powerful addition to CDD's chemistry-aware bioisosteric generation for novel structures (IP) and ultrafast deep-learning similarity capabilities. Chemists and biologists can now combine the best of their intuition with generative capabilities for small molecules visualized together with biological proteins. NVIDIA NIM is a set of easy-to-use microservices designed to accelerate the deployment of generative AI models across the cloud, data center, and workstations. NIM microservices are categorized by model family and on a per-model basis. 'We wanted to give the 690+ research labs that are using CDD Vault effortless access to the powerful BioNeMo NIM tools, enabling them to seamlessly combine over 4 billion experimental data points and 80 million structures with AI-driven insights from these models,' said CDD Cheminformatician and Research Informatics Senior Scientist Dr. Peter Gedeck. Collaborative Drug Discovery empowers CDD Vault users with access to powerful NVIDIA AI tools, BioNeMo NIM for AlphaFold2, a deep learning model that accelerates protein structure determination, and DiffDock, which predicts the 3D orientation and docking interactions of small molecules with proteins. These advanced tools provide a comprehensive structure-based drug discovery (SBDD) extension, enhancing the traditional capabilities of the CDD Vault scientific informatics platform. 'CDD Vault's strength is biological and chemical activity data management,' said Barry Bunin, CEO and Founder of Collaborative Drug Discovery. 'With our ELN (electronic laboratory notebook), Inventory, Curves, Automation, and AI modules, it was natural for us to collaborate with NVIDIA, the leading company for biological modeling. Our customers, some of whom are already using AlphaFold2 and DiffDock, are excited to have access to the NIM for these models, to support seamlessly integrating with CDD Vault, and more quickly move to deployment.' 'The ability to model biology and generate new chemical structures with AI is a profound breakthrough transforming the healthcare and life sciences space,' said Janet Paulsen, Senior Alliance Manager, Drug Discovery, NVIDIA. 'The integration of NVIDIA BioNeMo NIM microservices into CDD's Vault platform equips researchers on the forefront of this innovation with the advanced AI tools needed to harness data that can unlock biological insights.' About Collaborative Drug Discovery (CDD) CDD's ( flagship product, CDD Vault®, is a premier hosted database solution for the secure management and sharing of biological and chemical research data. CDD Vault® provides tools for managing chemical and biological registrations, structure-activity relationships (SAR), and organizing experiments. The platform's available modules include Registration, Activity, Visualization, Assays, ELN, Inventory, Curves, AI, and Automation. Abraham Wang +1 650 242 5259 Legal Disclaimer:
Yahoo
07-03-2025
- Science
- Yahoo
AI is creating 'overly compliant helpers,' not revolutionaries, said the top scientist at Hugging Face
Thomas Wolf said AI excels at following instructions but struggles to create new knowledge. AI needs to question its training data and take counterintuitive approaches, the Hugging Face exec wrote on X. Wolf's comments come as tech focuses on agentic AI. AI excels at following instructions — but it's not pushing the boundaries of knowledge, said Thomas Wolf. The chief science officer and cofounder of Hugging Face, an open-source AI company backed by Amazon and Nvidia, analyzed the limits of large language models in a Thursday post on X. He wrote that the field produces "overly compliant helpers" rather than revolutionaries. Right now, AI isn't creating new knowledge, Wolf wrote. Instead, it's just filling in the blanks between existing facts — what he called "manifold filling." Wolf argues that for AI to drive real scientific breakthroughs, it needs to do more than retrieve and synthesize information. AI should question its own training data, take counterintuitive approaches, generate new ideas from minimal input, and ask unexpected questions that open new research paths. Wolf also weighed in on the idea of a "compressed 21st century"— a concept from an October essay by Anthropic's CEO Dario Amodei, "Machine of Loving Grace." Amodei wrote that AI could accelerate scientific progress so much that discoveries expected over the next 100 years could happen in just five to 10. "I read this essay twice. The first time I was totally amazed: AI will change everything in science in five years, I thought!" Wolf wrote on X. "Re-reading it, I realized that much of it seemed like wishful thinking at best." Unless AI research shifts gears, Wolf warned, we won't get a new Albert Einstein in a data center — just a future filled with "yes-men on servers." Wolf did not respond to a request for comment, sent outside standard business hours. Wolf's comments come as the AI world focuses on agentic AI. Sam Altman, the CEO of OpenAI, has predicted that this may be the year the first "agents" — a set of artificial intelligence tools that can perform tasks independently — "join the workforce." "If 2024 was the year of LLMs, we believe 2025 will be the year of agentic AI," Praveen Akkiraju told Business Insider in January. He's a managing director at Insight Partners, a VC firm whose agentic plays include Writer, Jasper, and Torq. Investors are betting big on this idea. According to PitchBook data, startups exploring the application of agents raised $8.2 billion last year. Unlike AI assistants, which mainly retrieve and summarize information, agents can break down complex tasks, make decisions, and refine their approach based on outcomes. Researchers have also used AI to achieve scientific breakthroughs. Oxford professor Matthew Higgins used AlphaFold2, an AI tool from Alphabet-owned DeepMind, to crack the shape of a key malaria protein — something his lab had struggled with for years. That breakthrough led to an experimental malaria vaccine being tested in people. Without AlphaFold, "we'd probably still be trying, to be honest," Higgins told BI in 2023. Read the original article on Business Insider
Yahoo
13-02-2025
- Business
- Yahoo
Latent Labs Secures $50M in Funding to Realize the Potential of AI-Powered, Programmable Biology
Financing was co-led by Radical Ventures and Sofinnova Partners, with participation of Flying Fish, Isomer, Google Chief Scientist Jeff Dean, and existing investors 8VC, Kindred Capital and Pillar VC. Founder, alumnus of DeepMind's Nobel Prize-winning AlphaFold team, will lead generative protein design effort. The frontier AI lab enables biotech and pharmaceutical companies to generate and optimize proteins. LONDON & SAN FRANCISCO, February 13, 2025--(BUSINESS WIRE)--Latent Labs, the company building AI foundation models to make biology programmable, today emerged from stealth with $50M in total funding to accelerate their progress and partnerships. The company was founded by Dr Simon Kohl, previously a co-lead of DeepMind's protein design team and a senior research scientist on DeepMind's AlphaFold2, the project which earned a Nobel Prize for Chemistry for Demis Hassabis and John Jumper. The funding includes a $40M Series A co-led by Radical Ventures and Sofinnova Partners, with the participation of Flying Fish, Isomer, as well as existing investors 8VC, Kindred Capital and Pillar VC. Notable angel investors include Google Chief Scientist Jeff Dean, Transformer architecture inventor and Cohere founder Aidan Gomez, and ElevenLabs founder Mati Staniszewski. DeepMind's AlphaFold solved the decades-old problems of protein structure prediction and showcased how machine learning can help us understand biology; now, the opportunity lies in advancing and applying the latest generative techniques to design proteins from scratch. Latent Labs' platform does just that: by empowering researchers to computationally create new therapeutic molecules, such as antibodies or enzymes, the AI lab will help partners unlock previously challenging targets and open new paths to personalized medicines. What's more, partners can leverage the platform to design proteins with improved molecular features (such as increased affinity and stability), expediting drug development timelines and raising success rates. Latent Labs CEO and founder, Simon Kohl, said: "Every biotechnology or pharmaceutical company wants to be at the forefront of technology to find the best therapeutic molecules, yet not all are in a position to develop the most advanced AI models for the job. That's where Latent Labs comes in. We push the frontiers of generative biology, giving our partners instant access to tools that accelerate their drug design programs." Radical Ventures partner, Aaron Rosenberg, the former Head of Strategy & Operations at DeepMind, where he contributed to spinning out Isomorphic Labs to build upon AlphaFold, said: "We've partnered with Latent Labs because we're confident that this team will realize the therapeutic and commercial potential of de novo protein design. Such a capability has never before been possible, one which can benefit humanity in such a profound way. Accelerating the development of more effective cures for disease, Latent is at the vanguard of innovation in computational biology, and we are excited to join them on this journey." Edward Kliphuis, partner at Sofinnova Partners, said: "Latent Labs transforms biology from an observational science into an engineering craft, granting us precise control over life's building blocks. In practical terms, it means crafting bespoke molecules that tackle challenges once thought insurmountable. It's a revolution in our ability to harness nature's building blocks to develop breakthrough treatments and transform our lives. With pharmaceutical companies overwhelmingly demanding agile, next-generation tools to accelerate discovery and improve patient outcomes, Latent Labs is at the forefront of this rapidly growing market need." Latent Labs has attracted world class talent, bringing experience from DeepMind, Microsoft, Google, Stability AI, Exscientia, Mammoth Bio, Altos Labs and Zymergen. The company is based in London and San Francisco, where it experimentally validates its AI platform in its lab facilities. Researchers can register interest at About Latent Labs Latent Labs is an AI-driven biotechnology company developing AI foundation models to make biology company innovates on and applies generative AI technologies to enable biotechnology and pharmaceutical companies to computationally create new molecules for a range of therapeutic, industrial, and environmental uses. Founded by Dr. Simon Kohl, a former DeepMind researcher and key contributor to the AlphaFold project, the company is backed by leading investors including Radical Ventures, Sofinnova Partners, 8VC, Kindred Capital, Pillar VC, Isomer and Flying Fish. Latent Labs operates globally, with offices in London and San Francisco. To join us visit: View source version on Contacts contact@
