AI/ML News & Innovations Hub

How Bayer Built an Enterprise-Scale Search Engine with Qdrant

Bayer is a global life sciences company operating at the intersection of two of the most consequential fields in human life: health and nutrition. Its pharmaceutical work supports drug discovery and patient care, while its crop science work supports food production at planetary scale. The company’s guiding ambition, “Health for all, hunger for none,” frames how it thinks about technology: AI is not a side project, but a lever applied across the entire organization, from improving the productivity of colleagues to accelerating yield prediction and drug discovery.

SAMRC and UCT Launch AI Unit Built Around African Health Data, Targeting Cancer, Diabetes, TB and HIV

The South African Medical Research Council and the University of Cape Town have launched an AI for African Population Health Unit that will build machine learning tools trained specifically on African biomedical and clinical datasets — a direct response to the problem that most medical AI is trained on populations it will never treat. The [...]

DeepMind founder ascends to singular AI role at Google

Demis Hassabis, the driving force behind Google DeepMind, is ascending to the role of chief scientist at Alphabet, Google’s parent company, replacing Jeff Dean who is leaving to work at a start-up. The role will enable Hassabis to “put his full attention on actively shaping the future of AGI,” or artificial general intelligence, Alphabet CEO Sundar Pichai wrote on the company’s Inside Google blog . Hassabis’ attention will still be divided, however: He will continue to lead research at Google spin-off Isomorphic Labs, which works on drug discovery, and although he will no longer be CEO of DeepMind, he will be its chair. Koray Kavukcuoglu will take over DeepMind, reporting directly to Pichai. He is currently its CTO. Hassabis has been a strong promoter of AGI, defined by Google as the “hypothetical intelligence of a machine that possesses the ability to understand or learn any intellectual task that a human being can.” He has a long career in AI, having helped found DeepMind in 2010. He has been a prominent figure in the AGI field, prophesying in May that it will be a viable technology within three years . He has been keen to tackle any barriers in the way of developing the technology; just last month, he called for greater self-regulation in the market, arguing that it would help drive the technology forward. Hassabis welcomed the chance to focus on AGI development. “We have arrived at a pivotal moment in human history. I’ve been working towards AGI my whole life, and now, I…

Plucky founders, not Indian pharma giants, could reshape drug discovery in its new ‘golden age’

The post Plucky founders, not Indian pharma giants, could reshape drug discovery in its new ‘golden age’ appeared first on The Ken .

Google’s Top AI Brains Are Leaving to Launch Discovery Loop

Jeff Dean and other high-profile Google executives have founded Discovery Loop, a startup that will seek AI-powered breakthroughs in everything from drug discovery to chip design.

Will AI healthcare be China’s new global growth engine?

Artificial intelligence (AI) has transformed healthcare for hundreds of millions of people in China. AI-powered apps help them manage their health and seek care, while others help doctors better diagnose and treat their patients. AI analysis of routine scans detects cancers and other conditions physicians might have missed. Personalised cancer vaccines built on AI analysis will soon begin rolling off production lines in China. The country’s leading tech companies have poured resources into...

Why haven't organoids solved all of drug discovery?

Thank you to Jimmy Sastra and Kitchener D. Wilson for discussions relating to this piece. All opinions are my own. What are organoids anyway? If someone placed a gun to the side of your temple and asked you to correctly define what exactly an “organoid” is, or else they’ll shoot, you may get a little nervous. The whole concept is something, you suddenly realize, you never quite understood. It’s more of a you-know-it-when-you-see-it thing, nobody in their right mind would ever ask for a clear definition, but you forgot to account for those who are not in their right mind. Desperately, you rack your brain for details. It’s an aggregate of cells, right? You stammer this out, tack on ‘ it’s three-dimensional too! ’, and stare at your antagonist expectantly. They consider your answer, and squeeze their hands. If we were to interview your terrorizer later on, they would sheepishly admit that your answer is not the worst one they’ve heard. It really does encapsulate the many forms that organoids may take. Unfortunately, your given definition would also welcome blood clots, teratomas, and biofilms, all of which are indeed aggregates of cells arranged in three dimensions, but none of which would be considered an organoid by anybody on the planet. You should not feel bad about this. In fact, you are in good company. The organoid field as a whole is not known to have particularly useful definitions. One attempt at a definition comes from a 2014 Science review titled ‘ Organogenesis in…

Closing the data loop in AI-driven drug discovery

Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug to market takes an average of 10-15 years and costs…

MedPerf Meets Google Cloud Confidential Computing: Secure AI Benchmarking for Brain Tumor Research

At Google Cloud Next 2026 in Las Vegas, MLCommons and Google Cloud demonstrated a powerful new capability for trustworthy medical AI - one that protects patient data, model IP, and benchmark integrity all at once. The post MedPerf Meets Google Cloud Confidential Computing: Secure AI Benchmarking for Brain Tumor Research appeared first on MLCommons .

🔬Causal Models Need Causal Data - Xaira’s X-Cell model for Drug Discovery (Bo Wang & Ci Chu, Chief Discovery Officer & Chief AI Scientist)

Xaira Therapeutics is all in on data generation for model building! We talk with Bo Wang and Ci Chu about how and why.

Johns Hopkins-FDA Team Builds Tool to Catch Hidden Bias in Medical AI Training Data

Researchers at Johns Hopkins University, working with the U.S. Food and Drug Administration, have built a tool designed to uncover hidden problems in the datasets used to train medical AI — a class of research whose implications land squarely in African healthcare, where the imaging systems, patient populations and clinical workflows behind training data often [...]

Sightera Biosciences closes €3M pre-seed to expand its patient-derived AI drug discovery platform

Sightera Biosciences, a Belgiantechbio company using generative AI to develop novel small-molecule therapies,has raised €3 million in a pre-seed funding round led by Entourage, Anacura andQBIC.A spin-...

Drug discovery Is changing. Drug development must change too.

In this New Scientist CoLab podcast, experts from global life sciences leader Cytiva explain the hidden, high-stakes science of purification that is required to close the gap between drug discovery and the pharmacy shelf.

OpenAI researcher Miles Wang in talks to launch AI drug discovery startup valued at $2B

The funding discussions point to investor interest in applying AI to make breakthroughs in life sciences.

Meet Biomni: the free powerful biomed AI agent turning data into hypotheses

A Stanford University-led team including two Chinese researchers said they built the first general-purpose biomedical AI agent capable of working alongside human scientists, taking on complex tasks that once required groups of specialists. Jure Leskovec, a Stanford computer science professor who supervised the work, said the agent had been released as an open-source system with a web interface so that biologists could use it without writing code. “We have over 10,000 scientists all over the...

Anthropic launches its own drug discovery programs to tackle diseases Big Pharma considers unprofitable

Anthropic is launching its own drug development program for neglected diseases that the pharmaceutical industry considers unprofitable. Novartis CEO Vas Narasimhan thinks AI could cut development time from twelve years to seven or eight and double the success rate from 8 to 16 percent. The article Anthropic launches its own drug discovery programs to tackle diseases Big Pharma considers unprofitable appeared first on The Decoder .

Empowering biomedical evidence exploration and synthesis with deep knowledge graph research

Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01266-0 Wang et al. develop DeepEvidence, a biomedical deep research agent for exploring and synthesizing evidence across various knowledge sources to support drug discovery, clinical trials and evidence-based medicine.

🔬 The Coolest Diffusion Research Isn't in LLMs — Evan Feinberg & Sergey Edunov, Genesis Molecular AI

Why the Llama lead left Meta for drug discovery, PEARL's zero-shot OpenBind win, and what becomes possible when co-folding finally crosses the accuracy threshold.

Build an agentic AI healthcare claims pipeline with Amazon Bedrock and AWS HealthLake

In this post, we show you how to build an automated claims processing pipeline using two key Amazon Bedrock capabilities: Amazon Bedrock Data Automation for intelligent document extraction from healthcare claim forms, and Amazon Bedrock AgentCore for hosting an AI agent that validates and transforms the extracted data into FHIR (Fast Healthcare Interoperable Resources) resources in AWS HealthLake. You will learn how to combine these services to create an end-to-end workflow that reduces manual processing while maintaining accuracy through automated validation checks.

A Soweto startup’s unlikely journey from gadgets to AI healthcare

Founded in Soweto and backed largely with internally generated revenue and founder reinvestment, Khoi Tech initially built its reputation through consumer devices such as the Khoi Afripods true wireless earphones and the Khoi Afriwatch1 smartwatch.

Exclusive: JiviAI shuts down; founder Ankur Jain may rejoin BharatPe

JiviAI, an AI healthcare startup founded by former BharatPe Chief Product Officer Ankur Jain, has shut down operations, according to multiple sources familiar with the matter. The development comes less than two years after the startup entered the crowded generative AI healthcare space. The company had bet on proprietary AI models to deliver medical assistance and healthcare related services. The startup also raised an undisclosed funding in late 2024. According to sources, the shutdown came amid rising infrastructure costs, funding challenges, and failed acquisition discussions. “Building and running proprietary AI models became increasingly expensive. When you’re up against companies like OpenAI and Google, it becomes very difficult to make the economics work,” said a person familiar with the matter, requesting anonymity. According to another source, investors who had initially shown interest in backing the company did not participate in its planned funding round, putting additional pressure on its finances. “There were a few acquisition discussions as well, but none of them materialised. Once those fell through, the company had very few options left,” the person said. Sources said employees have been informed about the shutdown and have been asked to leave as the company winds down operations. Industry sources also suggest that Jain is evaluating his next move. Some industry observers have speculated about a possible return to BharatPe following the recent departure of Gr…

Comment on Healthcare Benchmarks Are Only as Good as Their Assumptions by Healthcare Benchmarks Are Only As Good As Their Assumptions - Cosmic Meta Digital

[…] benchmarks for 2026, largely due to increased costs in traditional Medicare. At the same time, new AI benchmarks for evaluating medical AI are also being […]

How a Google DeepMind Spin-off Hunts Hidden Drug Targets

For more than a decade, artificial intelligence has been touted as a way to dramatically accelerate drug discovery . Yet despite billions of dollars in investment, relatively few AI-designed medicines have made it to patients. That’s partially because the timelines for careful drug testing can’t be easily compressed—and partially because drug development is just really hard. Isomorphic Labs , the Google DeepMind spin-off that’s building on DeepMind’s Nobel Prize-winning work on protein structure prediction , may be making the most progress. The company has signed major drug-discovery partnerships with Novartis and Eli Lilly and recently raised US $2.1 billion in funding . In February, it published a technical report describing its new Isomorphic Drug Design Engine, a system created to discover the “pockets” on proteins where drugs can bind and in general to predict how proteins and drug molecules interact. IEEE Spectrum spoke with Adrian Stecuła , a group leader in the machine learning organization at Isomorphic Labs, about how close AI may be to becoming a practical tool for designing new medicines. Going Beyond AlphaFold AlphaFold2 and AlphaFold3 were massive leaps forward for computational biology. Why weren’t those models sufficient for actually designing drugs? Adrian Stecuła: AlphaFold2 was eventually recognized with the Nobel Prize , because it arguably solved the problem of protein folding. But proteins don’t exist in a vacuum, right? They interact with a wide variet…

Demis Hassabis On What AI Will Do Next

Thank you to Google DeepMind for the invite. 🙏 ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: https://felicia.hu 00:00 Intro 00:40 Gemini Health Scans and Gemma 4 01:30 AI as a Brainstorming Partner 02:30 Second Order Nobel 03:15 DeepMind Co-Scientist 05:00 Curing All Diseases 06:30 Exponential Growth in Drug Discovery 07:45 Regulatory Bottlenecks 09:45 Accelerating Clinical Trials 11:15 EVE Online Partnership 13:15 The Einstein Test 15:30 Recursive Self-Improvement 18:15 Lightning Round 19:30 The Badge of Honor 20:10 Behind the Scenes

How Sunny Health Built an AI Healthcare Concierge with Qdrant

Most people don’t read their insurance pamphlet. The benefits are there: deductibles, copays, in-network providers, what dental covers, what dermatology covers, when an optometry visit is included in the medical plan. But the document is dense, the website is worse, and the result is that patients pay for plans they barely understand and delay care because finding an in-network provider with availability takes more energy than they have. Sunny Health is building a healthcare concierge that insurance companies and care providers offer to their members as part of the existing plan experience. When a member signs in (typically through SSO from their payer), Sunny Health already knows who they are and what their plan covers. They land in a chat experience where they can ask “show me dermatologists nearby,” get matched to in-network options, and have Sunny Health book the appointment on their behalf. Three things on one retrieval layer: benefits navigation, provider matching, and appointment booking.

Intelligence is collective, not artificial — Prof. Michael I. Jordan (UC Berkeley / Inria)

Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open. Apply now: https://cyber.fund --- Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence. We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem. ERRATA: Science magazine ranked him the most influential computer scientist, not Nature --- TIMESTAMPS: 00:00:00 Cold open: A demoralizing message to young builders 00:02:04 CyberFund sponsor read 00…

Innovative Solutions to Improve Health and Patient Outcomes Featured at Inaugural AI in Healthcare Technology Showcase

At the inaugural AI in Healthcare Technology Showcase today, AI Singapore exhibited 16 AI projects that are set to improve healthcare processes and patient outcomes in Singapore. The event...

OpenEvidence Powers Medical AI with Zilliz Cloud

The benefits of medical AI assistance vary based on user expertise

The benefits of medical AI assistance vary based on user expertise

The benefits of medical AI assistance vary based on user expertise

Anthropic: AI Drug Discovery and Medical Research

Capgemini: How AI is Transforming Pharma Drug Discovery

ByteDance spins out drug discovery unit to test AI4S commercialization

Bayer Deploys Iambic AI to Accelerate Drug Discovery