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UK Announces £1.6 Billion National AI Strategy to Boost Research and Innovation The UK’s primary public research funder, UK Research and Innovation (UKRI), has launched its first national strategy to integrate artificial intelligence into the nation’s science and research sectors. This landmark initiative is backed by a record £1.6 billion in funding for the AI sector, allocated between now and 2030. The strategy is designed to leverage AI for driving innovations across diverse fields, including healthcare, public services, and clean energy. The investment will concentrate on six key areas: advancing AI technology, developing skills and talent, and promoting responsible and trustworthy AI. UKRI-funded AI projects are already demonstrating significant impact, such as a system for detecting faults on the railway network and another that aids in analyzing brain scans for diseases like Alzheimer’s. ...

AI and CRISPR Identify New Kidney Cancer Drug Targets Researchers in the UK and Korea have developed a new machine learning pipeline to identify novel drug targets for clear cell renal cell carcinoma (ccRCC), a common type of kidney cancer. The innovative framework integrates single-cell RNA sequencing with CRISPR screens and protein network analysis to pinpoint tumor-specific vulnerabilities. This AI-driven approach identified 96 potential drug targets and 39 existing FDA-approved compounds for repurposing. Preclinical tests revealed that several of these drugs, including Ribociclib and Dasatinib, were significantly more effective than current standard treatments across all tested renal cancer cell lines. This research, published in npj Drug Discovery, focuses on targeting the tumor’s intrinsic weaknesses. ...

Meta Challenges OpenAI & Google with New Open-Source AI Model In a move to democratize artificial intelligence, Mark Zuckerberg announced Meta will provide full open-source access to a powerful new AI model, directly challenging closed systems from competitors like OpenAI and Google. This initiative aims to increase transparency and foster greater inclusivity in AI research and innovation. The announcement has generated excitement within the global scientific community, as it could reshape AI standards and ethics. The open-source model is expected to be a significant benefit for independent developers, academic institutions, and researchers in underrepresented regions. While promoting collaboration, the move also raises concerns about potential misuse for cyber-attacks and disinformation, highlighting the need for clear regulations. Meta has confirmed that safety tools and responsible-use guidelines will be released alongside the model. ...

AI Creativity Now Matches Average Humans, But Lags Behind Top Innovators, Study Reveals A landmark study from the Université de Montréal, co-authored by AI pioneer Yoshua Bengio, reveals that leading AI models now match or exceed the average human’s creative abilities in linguistic tasks. Published in Scientific Reports, the research is the largest comparative study of its kind, testing large language models like GPT-4, Claude, and Gemini against 100,000 people. While models like GPT-4 have achieved a significant milestone by reaching average human creativity, the study underscores that the most creative individuals still significantly outperform the most advanced AI systems. ...

Weekly Tech News: AI, Robotics, Autonomous Vehicles & Cloud Innovations Google’s Gemini 2.5 AI Wins Gold Medal in Global Programming Contest A specialized version of Google DeepMind’s Gemini 2.5 AI model, known as Deep Think, has achieved a gold medal-level ranking at the prestigious International Collegiate Programming Contest (ICPC). Held in Baku, Azerbaijan, the competition featured 139 of the world’s top university programming teams. Gemini 2.5 Deep Think successfully solved 10 of the 12 complex algorithmic problems, placing it second overall compared to the human teams. ...

Tech News Roundup: Anthropic, Quantinuum, and Kubernetes Updates This week’s technology digest highlights major developments across the industry, from a landmark legal settlement in AI that could reshape how models are trained, to massive funding in quantum computing, and crucial updates for cloud-native infrastructure and open-source software. Anthropic’s $1.5B Settlement Over AI Training Data Sets Industry Precedent AI company Anthropic has agreed to a $1.5 billion settlement in a major lawsuit concerning the use of pirated books to train its AI models. The lawsuit, filed by authors, alleged that the company used their copyrighted works without permission to develop its chatbots. This settlement represents a significant moment for the AI industry and could establish new standards regarding the use of copyrighted materials for training large language models (LLMs). ...

Liquid AI Releases LFM2-VL: Open-Weight Vision-Language Models for On-Device AI Liquid AI has released LFM2-VL, a new family of open-weight vision-language foundation models created for low-latency, on-device deployment. The new models are designed to run efficiently on smartphones, laptops, wearables, and other embedded systems without needing to rely on cloud infrastructure. LFM2-VL is available in two versions: LFM2-VL-450M for highly resource-constrained devices and the more powerful LFM2-VL-1.6B. These models can process both text and images, offering up to twice the inference speed on a GPU compared to similar existing models. The models are available on Hugging Face under a license based on Apache 2.0, allowing for free academic and research use, as well as commercial use for smaller companies. ...

MIT Report: 95% of Enterprise GenAI Projects Fail to Deliver ROI A new report from MIT’s NANDA initiative reveals that a staggering 95% of enterprise generative AI pilot programs are failing to produce a significant return on investment. Despite investments totaling between $35 and $40 billion in the US, only 5% of organizations have successfully deployed AI tools into production at scale. The report, titled “The GenAI Divide: State of AI in Business 2025,” is based on interviews with enterprise leaders, analysis of public AI initiatives, and a survey of business professionals. The primary reasons for failure are not a lack of infrastructure or talent, but rather the inability of AI systems to retain data, adapt, and learn over time. The study also found that purchasing AI tools from vendors and forming partnerships has a significantly higher success rate than in-house development. ...

How Google AI’s New Method Reduces LLM Training Data by 10,000x Google Research has unveiled a groundbreaking method for fine-tuning large language models (LLMs) that reduces required training data by up to 10,000 times. This innovative approach utilizes active learning to focus expert labeling on the most informative examples, especially “boundary cases” where model uncertainty is highest. In experiments with Gemini Nano models, this technique matched or surpassed the quality of models trained on 100,000 random labels with as few as 250 to 450 targeted examples. This development promises to make AI model development significantly leaner, more agile, and more cost-effective. ...

OpenAI Reverses Course on Searchable ChatGPT Conversations OpenAI has disabled a feature that allowed users to make their ChatGPT conversations discoverable by search engines. The move comes after reports revealed that private and sensitive user chats were appearing in Google search results. OpenAI’s CISO, Dane Stuckey, called the feature a “short-lived experiment” that created too many risks of accidental sharing. While the feature was opt-in, the incident has sparked significant concern over user privacy and data protection in AI applications. ...