Browsing: AI News & Trends

Anthropic launched inference hooks on August 5, 2026, a beta feature for Claude Enterprise that routes every employee prompt through the organization’s own security server for an allow-or-deny verdict before the model ever sees it. The system, described in Anthropic’s announcement, extends the kind of inline data loss prevention that security teams already run on email and web traffic to chat, Claude Code, and Claude Cowork sessions, with a single organization-level configuration.Until now, Anthropic’s only native inline enforcement lived in Claude Code’s client-side hooks, which run on the user’s machine. Inference hooks moves the checkpoint onto Anthropic’s servers, after a…

Inna Braverman, Founder and CEO of Eco Wave Power – is a technology entrepreneur working to establish wave energy as a commercially viable source of renewable electricity. She founded Eco Wave Power in 2011 at the age of 24, inspired in part by her personal connection to the Chernobyl nuclear disaster and a commitment to reducing pollution. Under her leadership, the company has developed grid-connected wave energy installations, built an international project pipeline and gained recognition from organisations including the United Nations, the European Union and Israel’s Ministry of Energy. Braverman has also delivered several TEDx talks and has been…

TIER IV, the Tokyo-based company behind the open-source autonomous driving software Autoware, has signed a memorandum of understanding with automotive supplier Astemo to jointly build a next-generation development platform for end-to-end autonomous driving AI, the companies announced on August 5, 2026. The platform is targeted for commercialization around 2030, with Astemo aiming to put end-to-end AI models into passenger vehicles in the early 2030s.The deal centers on TIER IV’s Co-MLOps, a collaborative data-sharing system for autonomous driving development. Under the agreement, Astemo will license the Co-MLOps architecture and related technologies to build a platform covering the full loop of AI…

Lithium-ion batteries are the leading choice in today’s electric vehicle and battery energy storage system industries, but they contain a number of critical minerals — including lithium, cobalt, nickel, and graphite — that are considered essential for economic and national security reasons, and therefore vulnerable to supply chain disruptions. As renewable energy, electrified infrastructure, and high-power digital technologies continue to grow, there is an increasing need for energy storage systems that are low-cost, resource-abundant, and capable of fast charging and discharging. That need, among other reasons, has motivated a group of researchers — based at MIT and led by Ju Li,…

Phylo, Inc. announced on August 4, 2026 that Chugai Pharmaceutical Co., Ltd. will deploy Biomni Lab, Phylo’s agentic AI platform for biomedical research, across Chugai’s drug discovery workflows, covering four areas: single-cell analysis, human genetics, disease biology, and target evaluation. The Tokyo-based drugmaker, a Roche Group member listed on the Tokyo Stock Exchange’s Prime Market, joins what Phylo calls its founding group of customers for the platform.Under the collaboration, Biomni Lab connects with Chugai’s proprietary research data inside the company’s secure environment, so its agents work over internal data alongside the platform’s integrated biomedical resources. Phylo is pitching the setup…

A one-size-fits-all approach likely isn’t the best strategy when designing artificial intelligence systems that assist users in disease diagnosis.A new study by researchers at MIT and elsewhere found that, while AI assistance generally improved the accuracy of non-experts and clinicians in diagnosing skin diseases, AI explainability methods had different impacts depending on the users’ knowledge level. Explainable AI methods help users know when to trust a model’s predictions by describing or validating the model’s decision-making. For instance, a model might use a heat map to highlight image regions that were most important in its diagnosis or a large language model (LLM)…

ExlService Holdings (EXLS ), better known as EXL, has completed its acquisition of AI data specialist iMerit, bringing together two companies focused on different but closely connected parts of the enterprise artificial intelligence stack.The transaction, originally announced in June, was valued at up to $310 million. It includes $170 million in upfront consideration and up to $140 million in incentives and earnouts tied to milestones over the next two years. iMerit founder and CEO Radha Ramaswami Basu will join EXL as Executive Vice President and Head of iMerit, while also becoming a member of EXL’s executive committee.Addressing the Gap Between…

Alexander “Sasha” Rakhlin PhD ’06, the Distinguished Professor in Data, Systems, and Society at the MIT Institute for Data, Systems, and Society (IDSS); and a professor of brain and cognitive sciences at MIT, has been named the next director of the MIT Statistics and Data Science Center (SDSC). Rakhlin succeeds Ankur Moitra, the Norbert Wiener Professor of Mathematics, associate director of the IDSS, and a faculty member in the MIT Department of Electrical Engineering and Computer Science (EECS) who has been SDSC director since 2021. Philippe Rigollet, the Cecil and Ida Green Distinguished Professor of Mathematics and a core faculty member in IDSS, also…

I’ve noticed a consistent pattern across financial institutions that have tested general-purpose AI tools but struggle to identify and measure impact. Leadership makes the decision to invest, then implements across teams and waits for the impact. After a few months, the confusion hits. Results aren’t clear, processes aren’t improving, adoption is lagging, and the numbers remain unchanged. The ROI is not evident, and they point to the tool as the problem, but the reality is that when you implement a general tool for general productivity gains, there isn’t a meaningful way to determine ROI.When I talk with banks and credit…

Everyone is talking about the cost of AI. Usually they’re talking about GPUs, model licensing, or token consumption. I think they’re looking in the wrong place.The biggest cost of enterprise AI may turn out to be the people needed to supervise it.A recent study found employees save about 11 hours a week using AI, but spend more than six hours checking outputs, fixing mistakes, adding missing context and making sure the results are actually usable. Someone coined the term “botsitting” for it. It’s a catchy name, but it points to a much bigger issue.AI was supposed to reduce work. Instead,…