AI News Archive: June 12, 2026 — Part 14
Sourced from 500+ daily AI sources, scored by relevance.
- Cisco’s ‘all-in’ approach to AI for collaboration stands out at Cisco Live US 2026
Cisco’s ‘all-in’ approach to AI for collaboration stands out at Cisco Live US 2026 verdict.co.uk
- U.S. bank regulators increase oversight of AI use at financial companies
Regulators are assessing AI risks in lending, data access and third-party vendors
- Derbyshire police officer investigated over AI-generated ‘evidential material’
Unidentified officer removed from frontline duties in the first known case of its kind in the UK A police officer is under criminal investigation over the alleged use of artificial intelligence and has been removed from frontline duties in the first known case of its kind in the UK. The officer, who has not been named, is being investigated over allegations of using the technology to “create evidential material in a number of cases” and perverting the course of justice. Continue reading...
- A major KPMG report on AI was found to be chock-full of...AI hallucinations
GPTZero has been prompted to issue its second report revealing how a major report is full of fake, AI-generated citations.
- KPMG report on AI found riddled with AI hallucinations
A new probe into Big Four KPMG’s report on agentic AI found that the majority of its references were flawed, amid the latest news of AI-hallucinated reports published by professional services firms. The investigation, conducted by GPTZero, focused on KPMG’s October 2025 report, ‘Total Experience: Redefining Excellence in the Age of Agentic AI’, which summarises [...]
- KPMG report contained AI hallucinations on benefits of . . . AI
A KPMG report on how AI is being used by businesses across the world exaggerated adoption of the technology with bogus case studies that appear to have been based on AI hallucinations. The October report, “Redefining excellence in the age of agentic AI”, made numerous false claims about the use of AI by organisations including the Swiss bank UBS, the UK’s National Health Service and the public transit groups Swiss Federal Railways and Transport for London. The inaccuracies were identified as AI hallucinations by the research group GPTZero and verified by the FT. After being alerted to the issue, UBS said it would ask KPMG to remove the false claims, and the Big Four firm on Thursday pulled the report from some of its websites. The discovery is the latest in a number of apparent AI hallucinations in reports by professional services firms and follows EY’s retraction of a study last month over fake footnotes and other errors identified by GPTZero. The KPMG report claimed global wealth ...
- KPMG's AI report becomes an accidental demo of AI hallucinations
GPTZero claims only 5 of the report's 45 citations matched their sources, raising questions about how the Big Four's AI study was assembled
- TCS partners with Anthropic to deploy Claude across enterprise operations at scale
TCS and Anthropic have partnered to deploy Claude across enterprise operations, from software engineering to customer service, signalling a decisive shift from AI experimentation to large-scale adoption. As AI evolves into an agentic technology capable of executing complex, multi-step tasks, businesses are no longer asking whether to adopt AI, but how to embed it at the core of how they operate. For TCS, this collaboration is a strategic move to lead that transformation on behalf of enterprises worldwide.
- TCS forms partnership with Anthropic to scale enterprise AI
TCS forms partnership with Anthropic to scale enterprise AI YourStory.com
- Deeptech in focus during PM Modi’s France visit; TCS joins hands with Anthropic
Deeptech in focus during PM Modi’s France visit; TCS joins hands with Anthropic YourStory.com
- TCS partners with Anthropic to promote enterprise AI adoption
TCS partners with Anthropic to promote enterprise AI adoption verdict.co.uk
- Rekise Marine Bags $9.7 Mn To Build Autonomous Underwater Vessels
Marine robotics startup Rekise Marine has raised $9.7 Mn (₹92.5 Cr) in a seed funding round led by Accel and…
- Jeff Bezos-backed Prometheus raises $12B to build AI engineers
Jeff Bezos-backed Prometheus raises $12B to build AI engineers YourStory.com
- Jeff Bezos’s Prometheus raises $12bn to build AI ‘engineer’: Here’s what it means
Jeff Bezos’s Prometheus raises $12bn to build AI ‘engineer’: Here’s what it means
- STAT+: Prometheus raises $12 billion in capital for artificial engineers
Nonprofit Blood Cancer United acquires cancer drug, Lilly and Nvidia invest in Abridge, and more biotech news
- Apple isn't doing ‘AI for the sake of AI’, wants to give users 'superpowers'
Apple is not looking to give its users a free rope with its AI features and will limit their ability to use generative AI for image editing.
- Coinbase for Agents: Automating portfolio trading with AI
Coinbase for Agents connects AI to financial execution channels to automate trading and payments directly from user portfolios. Large language models process vast quantities of data but lack direct integration with active financial portfolios. Individuals frequently employ these models to evaluate market developments or research investment opportunities. These software tools possess the capacity for complex […] The post Coinbase for Agents: Automating portfolio trading with AI appeared first on AI News .
- Pine Labs Launches P3P Agentic Payment Protocol for Autonomous UPI Transactions in India
Pine Labs Payment Protocol (P3P) was launched in India on Thursday. The financial technology company said that it is a new payment infrastructure that enables AI agents to autonomously complete UPI transactions. P3P is claimed to eliminate the need for manual authentication during payments. It is built upon the existing UPI mandate framework. As per the company, users...
- Anthropic’s Claude Fable 5 adds hurdles for Chinese AI firms
Anthropic said the distillation filter was a response to earlier model-extraction attempts.
- Google unveils DiffusionGemma, an AI model that breaks free of left-to-right processing
Extremely powerful large language models (LLMs) still operate as though they’re typing on a keyboard, processing workloads in a simple left-to-right fashion. But in locally-run, single-user scenarios, this sequential processing can leave graphics processing units (GPUs) and tensor processing units (TPUs) underutilized. Google is betting that DiffusionGemma can get around this bottleneck. The new experimental open model generates text “exceptionally fast,” creating entire blocks of text simultaneously through diffusion techniques rather than through token-by-token processing. The company says this technique results in 4x faster inference compared to auto-regressive models that rely on sequential processing. It can also save users money. Technology analyst Carmi Levy noted that existing pay-per-token monetization models “penalize the use of less than optimally efficient AI solutions.” But DiffusionGemma “could herald a new generation of task-defined, efficient solutions that can enable expanded compute capacity without draining the operations budget,” he said. A contrast to left-to-right processing Built on Google’s Gemma 4 family and its Gemini Diffusion research, DiffusionGemma is a 26B mixture-of-experts (MoE) model designed to maximize text output generation. It essentially shifts how models use hardware , giving processors a larger hunk of work each cycle so it can draft full 256-token paragraphs in sequence. This allows the model to generate text up to 4x faster on GPUs, Google claims. It activates only 3.8B parameters during inference, and, when quantized, can fit within 18GB VRAM on high-end consumer GPUs like Nvidia RTX 5090. “It upgrades your model inference from a single, sequential typewriter to a massive printing press that stamps the entire block of text simultaneously,” Google research scientists Brendan O’Donoghue and Sebastian Flennerhag wrote in a blog post . AI image generators begin with pure, random ‘visual noise’ and iteratively refine that into a finalized picture (what’s known as ‘diffusion’); DiffusionGemma applies this same process to text. It does not generate tokens in order, but begins with a “canvas of random placeholder tokens” that it processes in multiple passes, identifying the context tokens it feels are most relevant and using those to refine the rest. The model has the ability to self-correct, using confidence scoring to re-evaluate tokens in the next pass. “The model iteratively refines its own output, allowing it to evaluate the entire text block at once to fix mistakes in real-time,” O’Donoghue and Flennerhag explained. DiffusionGemma also has bidirectional attention, they wrote. “Generating 256 tokens in parallel with each forward pass allows every token to attend to all others.” This can be particularly helpful in domains that are non-linear in nature, such as mathematical graphs, code infilling, and in-line editing, they said. DiffusionGemma is optimized across Nvidia’s hardware stack, making it compatible with consumer setups as well as with high-performance enterprise systems like Hopper and Blackwell. Because it is released under the Apache 2.0 license, developers can freely use, modify, distribute, and commercialize the software using their preferred tools. It can be run on GPUs or in the cloud through Google Cloud Model Garden or Nvidia NIM , and is available on Hugging Face , GitHub , and vLLM , with support for the open-source library llama.cpp coming soon. Key use cases The model is particularly useful in local workflows that are “speed critical,” such as generation of non-linear text structures, and unlocks what Google calls “new patterns of model behavior” like multimodal understanding and generating and rendering code in near real-time. Levy explained, “DiffusionGemma is particularly well suited for interactive coding and editing where its efficiency allows rapid processing and iterations,” noting that its ability to fit within 18GB of VRAM and its deployability on commonly available local GPUs can potentially benefit customer service-related workloads that lean heavily on real-time interaction and local processing. “DiffusionGemma also incorporates a thinking mode that is especially adept at problem solving,” he said. For instance, the model was fine-tuned to play Sudoku, a typically challenging task for autoregressive models because each token depends on future tokens. This “rather handily” illustrates the model’s capability to solve more complex problems, Levy noted. Limitations Google freely admits that DiffusionGemma is geared to specific workflows, and there are “key trade-offs.” The model is engineered for small batch size inferencing and low-latency, high-speed generation low-to-medium batch sizes on a “single capable accelerator.” In high-QPS cloud serving environments, (where infrastructure is designed to handle tens or hundreds of thousands of requests per second with ultra-low latency), DiffusionGemma’s parallel coding “offers diminishing returns,” and can even result in higher serving costs, Google conceded. In addition, its overall output quality is lower than that of standard Gemma 4, which is built for apps demanding maximum quality. However, Levy noted that while DiffusionGemma “can be less precise than other models in certain workloads,” subsequent refinement cycles could overcome this limitation. While Google isn’t sharing runtime costs, it’s clear that this is an efficiency play, he added. “When deployed across the kinds of workloads that would optimally benefit from its architecture, DiffusionGemma seems to have the potential to reduce processing overhead and related costs,” he said. This article originally appeared on InfoWorld .
- Microsoft president responds to students’ distrust for AI
Microsoft’s president, Brad Smith, has reacted to student discontent with AI, telling today’s graduates that there is still a place for human creativity. Students across the US have booed speakers who talked up AI at their graduation ceremonies in recent months, including Google’s former CEO Eric Schmidt , the CEO of a record label , and a real estate executive . Smith hasn’t ventured out onto a podium to share his views, but in a lengthy blog post, AI, Jobs and the Next Generation , acknowledged students’ concerns about their futures. He said that, just as painting survived the arrival of photography, so will the job market survive the arrival of AI. “While it may feel unfair that the job market is so uncertain, you were made for this moment. Technology is second nature to your generation. Constant change has taught you how to adapt quickly,” he wrote. He also used the blog to promote a book written by his colleagues Ryan Roslansky and Aneesh Raman on how to get ahead at work in the age of AI. The corporate world will see massive changes, he said: “This includes AI automation of tasks in current entry-level positions and, especially in the tech sector, corporate pressure to reduce headcount to help pay for AI’s enormous capital expenditures.” Some of those changes are already here. In the past six months, we have seen massive job losses at Oracle , at Meta and at AWS . There are no signs of any let-up: Last month saw the tech industry shed more than 38,000 jobs . Students contemplating their future will find little comfort in Smith’s optimistic words, particularly as his essay shows that Microsoft is not making any changes to its AI program going forward.