This morning, Co-founder and CEO of Google DeepMind and Isomorphic Labs Sir Demis Hassabis, and Google DeepMind Director Dr. John Jumper were co-awarded the 2024 Nobel Prize in Chemistry for their work developing AlphaFold, a groundbreaking AI system that predicts the 3D structure of proteins from their amino acid sequences. David Baker was also co-awarded for his work on computational protein design.
Before AlphaFold, predicting the structure of a protein was a complex and time-consuming process.
AlphaFold’s predictions, made freely available through the AlphaFold Protein Structure Database, have given more than 2 million scientists and researchers from 190 countries a powerful tool for making new discoveries. The AlphaFold 2 paper, published in 2021, remains one of the most-cited publications of all time.
Artificial intelligence (AI) has long shown incredible potential for use in scientific research, and AlphaFold was proof-of-concept. As more scientists adopt AI for use in everything from building data, to simulating experiments, drug design, modelling complexity, discovering novel solutions for extant problems, and building upon existing knowledge, we will continue to see foundational scientific breakthroughs in the years ahead.
In a statement released after informed of the news, Demis Hassabis said:
“Receiving the Nobel Prize is the honour of a lifetime. Thank you to the Royal Swedish Academy of Sciences, to John Jumper and the AlphaFold team, the wider DeepMind and Google teams, and to all my colleagues past and present that made this moment possible. I’ve dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people. AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery. I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery.”
After receiving the news that he won the Nobel Prize, John Jumper released the following statement:
“Thank you to the Royal Swedish Academy of Sciences for this extraordinary honor. We are so honored to be recognized for delivering on the long promise of computational biology to help us understand the protein world and to inform the incredible work of experimental biologists. It is a key demonstration that AI will make science faster and ultimately help to understand disease and develop therapeutics. This is the work of an exceptional team at Google DeepMind and this award recognizes their amazing work.
Computational biology has long held tremendous promise for creating practical insights that could be put to use in real-world experiments. AlphaFold delivered on this promise. Ahead of us are a universe of new insights and scientific discoveries made possible by the use of AI as a scientific tool. Thank you to my colleagues over the years, for making possible this moment of recognition, as well as the many moments of discovery that lie ahead.”
For media inquiries, please contact gdm-press@google.com
TransHumanity Ltd., a spinout from Loughborough University, has secured approximately £400,000 in pre-seed investment. The round was led by SFC Capital, the UK’s most active seed-stage investor, with additional investment from Silicon Valley-based Plug and Play.
TransHumanity’s vision is to empower faster, smarter human decisions by transforming data into accessible intelligence using large language model based agentic AI.
Agentic AI refers to artificial intelligence systems that collaborate with people to reach specific goals, understanding and responding in plain English. These systems use AI “agents” — models that can gather information, make suggestions, and carry out tasks in real time — helping people solve problems more quickly and effectively.
TransHumanity’s first product, AptIq, is designed to help transport authorities quickly analyse transport data and models, turning days of analysis into seconds.
By simply asking questions in plain English, users can gain instant insights to support key initiatives like congestion reduction, road safety, creation of business cases and net-zero targets.
Dr Haitao He, Co-founder and Director of TransHumanity, said: “I am proud to see my rigorous research translated into trusted real-world AI innovation for the transport sector. With this investment, we can now realise my Future Leaders Fellowship vision, scaling a technology that empowers authorities across the UK to deliver integrated, net-zero transport.”
Developed from rigorous research by Dr Haitao He, a UKRI Future Leaders Fellow in Transport AI at Loughborough University, AptIq, previously known as TraffEase, has already garnered significant recognition.
The technology was named a Top 10 finalist for the 2024 Manchester Prize for AI innovation and was recently highlighted as one of the Top 40 UK tech start-ups at London Tech Week by the UK Department for Business and Trade.
Adam Beveridge, Investment Principal at SFC Capital, said: “We are excited to back TransHumanity. The combination of cutting-edge research, a proven founding team, clear market demand, and positive societal impact makes this exactly the kind of high-growth venture we are committed to supporting.”
AptIq is currently in a test deployment with Nottingham City Council and Transport for Greater Manchester, with plans to expand to other city, regional, and national authorities across the UK within the next 12 months.
With a product roadmap that includes diverse data sources, advanced analytics and giving the user full control over the AI tool when required, interest from the transport sector is already high. Professor Nick Jennings, Vice-Chancellor and President of Loughborough University, noted: “I am delighted to see TransHumanity fast-tracked from lab to investment-ready spinout.
This journey was accelerated by TransHumanity’s selection as a finalist in the prestigious Manchester Prize and shows what’s possible when the University’s ambition aligns with national innovation policy.”
An oversimplified approach I have taken in the past to explain wisdom is to share that “We don’t know what we don’t know until we know it.” This absolutely applies to the fast-moving AI space, where unknowingly introducing legal and compliance risk through an organization’s use of AI is a top concern among IT leaders.
We’re now building systems that learn and evolve on their own, and that raises new questions along with new kinds of risk affecting contracts, compliance, and brand trust.
At Broadcom, we’ve adopted what I’d call a thoughtful ‘move smart and then fast’’ approach. Every AI use case requires sign-off from both our legal and information security teams. Some folks may complain, saying it slows them down. But if you’re moving fast with AI and putting sensitive data at risk, you’re also inviting trouble if you don’t also move smart.
Here are seven things I’ve learned about collaborating with legal teams on AI projects.
1. Partner with Legal Early On
Don’t wait until the AI service is built to bring legal in. There’s always the risk that choices you make about data, architecture, and system behavior can create regulatory headaches or break contracts later on.
Besides, legal doesn’t need every answer on day one. What they do need is visibility into the gray areas. What data are you using and producing? How does the model make decisions? Could those decisions shift over time? Walk them through what you’re building and flag the parts that still need figuring out.
2. Document Your Decisions as You Go
AI projects move fast with teams needing to make dozens of early decisions on everything from data sources to training logic. So, it’s only natural that a few months later, chances are no one remembers why those choices were made. Then someone from compliance shows up with questions about those choices, and you’ve got nothing to point to.
To avoid that situation, keep a simple log as you work. Then, should a subsequent audit or inquiry occur, you’ll have something solid to help answer any questions.
3. Build Systems You Can Explain
Legal teams need to understand your system so they can explain it to regulators, procurement officers, or internal risk reviewers. If they can’t, there’s the risk that your project could stall or even fail after it ships.
I’ve seen teams consume SaaS-based AI services without realizing the provider could swap out a backend AI model without their knowledge. If that leads to changes in the system’s behavior behind the scenes, it could redirect your data in ways you didn’t intend. That’s one reason why you’ve got to know your AI supply chain, top to bottom. Ensure that services you build or consume have end-to-end auditability of the AI software supply chain. Legal can’t defend a system if they don’t understand how it works.
4. Watch Out for Shadow AI
Any engineer can subscribe to an AI service and accept the provider’s terms without knowing they don’t have the authority to do that on behalf of the company.
That exposes the organization to major risk. An engineer might accidentally agree to data-sharing terms that violate regulatory restrictions or expose sensitive customer data to a third party.
And it’s not just deliberate use anymore. Run a search in Google and you’re already getting AI output. It’s everywhere. The best way to avoid this is by building a culture where employees are aware of the legal boundaries. You can give teams a safe place to experiment, but at the same time, make sure you know what tools they’re using and what data they’re touching.
5. Help Legal Navigate Contract Language
AI systems get tangled in contract language; there are ownership rights, retraining rules, model drift, and more. Most engineers aren’t trained to spot those issues, but we’re the ones who understand how the systems behave.
That’s another reason why you’ve got to know your AI supply chain, top to bottom. In this case, when legal needs our help in reviewing vendor or customer agreements to put the contractual language into the appropriate technical context. What happens when the model changes? How are sensitive data sets safeguarded from being indexed or accessed via AI agents such as those that use Model Context Protocol (MCP)? We can translate the technical behavior into simple English—and that goes a long way toward helping the lawyers write better contracts.
6. Design with Auditability in Mind
AI is developing rapidly, with legal frameworks, regulatory requirements, and customer expectations evolving to keep pace. You need to be prepared for what might come next.
Can you explain where your training data came from? Can you show how the model was tested for bias? Can you justify how it works? If someone from a regulatory body walked in tomorrow, would you be ready?
Design with auditability in mind. Especially when AI agents are chained together, you need to be able to prove that identity and access controls are enforced end-to-end.
7. Handle Customer Data with Care
We don’t get to make decisions on behalf of our customers about how their data gets used. It’s their data. And when it’s private, it shouldn’t be fed to a model. Period.
You’ve got to be disciplined about what data gets ingested. If your AI tool indexes everything by default, that can get messy fast. Are you touching private logs or passing anything to a hosted model without realizing it? Support teams might need access to diagnostic logs but that doesn’t mean third-party models should touch them. Tools are rapidly evolving that can generate comparable synthetic data devoid of any customer private data that could help with support use cases for example, but these tools and techniques should be fully vetted with your legal and CISO organizations prior to using them.
The Reality
The engineering ethos is to move fast. But since safety and trust are on the line, you need to move smart, which means it’s okay if things take a little longer. The extra steps are worth it when they help protect your customers and your company.
Nobody has this all figured out. So ask questions by talking to people who’ve handled this kind of work before. The goal isn’t perfection—it’s to make smart, careful progress. For enterprises, the AI race isn’t a question of “Who’s best?” but rather “Who’s leveraging AI safely to drive the best business outcomes.”
Progress Software, a company offering artificial intelligence-powered digital experience and infrastructure software, has launched Progress Federal Solutions, a wholly owned subsidiary that aims to deliver AI-powered technologies to the federal, defense and public sectors.
Progress Federal Solutions to Boost Digital Transformation
The company said Monday the new subsidiary, announced during the Progress Data Platform Summit at the International Spy Museum in Washington, D.C., is intended to fast-track federal agencies’ digital modernization efforts, meet compliance requirements, and advance AI and data initiatives. The subsidiary leverages MarkLogic’s data management and integration expertise, a platform that Progress Software acquired in 2023.
Progress Federal Solutions functions independently but will offer the company’s full technology portfolio, including Progress Data Platform, Progress Sitefinity, Progress Chef, Progress LoadMaster and Progress MOVEit. These will be available to the public sector through Carahsoft Technology‘s reseller partners and contract vehicles.
Remarks From Progress Federal Solutions, Carahsoft Executives
“Federal and defense agencies are embracing data-centric strategies and modernizing legacy systems at a faster pace than ever. That’s why we focus on enabling data-driven decision-making, faster time to value and measurable ROI,” said Cori Moore, president of Progress Federal Solutions.
“Progress is a trusted provider of AI-enabled solutions that address complex data, infrastructure and digital experience needs. Their technologies empower government agencies to build high-impact applications, automate operations and scale securely to meet program goals,” said Michael Shrader, vice president of intelligence and innovative solutions at Carahsoft.
