When you purchase through links in our articles, we may earn a small commission. Adobe unveiled new AI additions to its Firefly image generation tool, plus Photoshop, Premiere, and Lightroom. First, there was generative AI, allowing creators, editors and memelords to create artificial worlds with just a few words. Now, Adobe is offering the ability to edit those worlds with Prompt to Edit, a new feature within Firefly, plus audio capabilities. Adobe announced the new capabilities at its MAX conference, where it typically rolls out new capabilities within its Creative Cloud suite as well as Firefly, its AI image generator -- which now includes soundtracks and AI voiceovers.

When you purchase through links in our articles, we may earn a small commission. If you chat with ChatGPT via WhatsApp, you only have a few months left before you'll have to find an alternative. Uh oh, Meta is implementing a change that some users aren't going to like (and others are going to applaud). Due to new guidelines for WhatsApp business accounts, soon AI chatbots will no longer be allowed when they're used as the main purpose of the messenger app. Providers and developers of artificial intelligence or machine learning technologies, including but not limited to large language models, generative artificial intelligence platforms, general-purpose artificial intelligence assistants are strictly prohibited from accessing or using the WhatsApp Business Solution, whether directly or indirectly, for the purposes of providing, delivering, offering, selling, or otherwise making available such technologies when such technologies are the primary (rather than incidental or ancillary) functionality being made available for use, as determined by Meta in its sole discretion.

Microsoft and OpenAI have reached a deal to allow the ChatGPT maker to restructure itself into a public-benefit corporation, valuing OpenAI at $500bn and giving it more freedom in its business operations. The deal, unveiled on Tuesday, removes a major constraint on raising capital for OpenAI that has existed since 2019. As its ChatGPT service exploded in popularity, those limitations had become a notable source of tension between the two companies. Microsoft will still hold a stake of about $135bn, or 27 percent, in OpenAI Group PBC, which will be controlled by the OpenAI Foundation, a nonprofit, the companies said. Microsoft, based in Redmond, Washington in the United States, has invested $13.8bn in OpenAI, with Tuesday's deal implying that the firm had generated a return of nearly 10 times its investment.

Sam Altman speaks in San Francisco on 2 June 2025. Sam Altman speaks in San Francisco on 2 June 2025. OpenAI said on Tuesday it had converted its main business into a for-profit corporation, the conclusion of a lengthy and fraught legal saga. A crucial regulator, Kathy Jennings, the Delaware attorney general, said she approved the plan for the startup, which began as a non-profit in 2015, to change to a public benefit corporation, a type of for-profit entity that expresses commitment to bettering society. The company also said it had reorganized its ownership structure and signed a new agreement with its longtime backer Microsoft that gives the software giant a roughly 27% stake in OpenAI's new for-profit corporation, but changes some of the details of their close partnership.

Taking the time to make a use case for AI will propel companies further and improve the return on investment in this fast-changing technology. The market is officially three years post ChatGPT and many of the pundit bylines have shifted to using terms like "bubble" to suggest reasons behind generative AI not realizing material returns outside a handful of technology suppliers. In September, the MIT NANDA report made waves because the soundbite every author and influencer picked up on was that 95% of all AI pilots failed to scale or deliver clear and measurable ROI. McKinsey earlier published a similar trend indicating that agentic AI would be the way forward to achieve huge operational benefits for enterprises. At's Technology Council Summit, AI technology leaders recommended CIOs stop worrying about AI's return on investment because measuring gains is difficult and if they were to try, the measurements would be wrong. This places technology leaders in a precarious position-robust tech stacks already sustain their business operations, so what is the upside to introducing new technology?

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Plus: OpenAI has unveiled estimates of how many of ChatGPT's weekly users are experiencing severe mental health symptoms "We will never build a sex robot," says Mustafa Suleyman Mustafa Suleyman, CEO of Microsoft AI, is trying to walk a fine line. On the one hand, he thinks that the industry is taking AI in a dangerous direction by building chatbots that present as human: He worries that people will be tricked into seeing life instead of lifelike behavior. On the other hand, Suleyman runs a product shop that must compete with those peers. Last week, Microsoft announced a string of updates to its Copilot chatbot designed to make Copilot more expressive, engaging, and helpful. Will Douglas Heaven, our senior AI editor, talked to Suleyman about the tension at play when it comes to designing our interactions with chatbots and his ultimate vision for what this new technology should be. A few weeks ago, I set out on what I thought would be a straightforward reporting journey.

If AI's hype has been punctured, I couldn't find a company willing to talk about it. A few weeks ago, I set out on what I thought would be a straightforward reporting journey. After years of momentum for AI--even if you didn't think it would be good for the world, you probably thought it was powerful enough to take seriously--hype for the technology had been slightly punctured. First there was the underwhelming release of GPT-5 in August. Then a report released two weeks later found that 95% of generative AI pilots were failing, which caused a brief stock market panic. I wanted to know: Which companies are spooked enough to scale back their AI spending?

Enterprises need access decisions that satisfy least privilege, comply with regulations, and remain auditable. We present a policy aware controller that uses a large language model (LLM) to interpret natural language requests against written policies and metadata, not raw data. The system, implemented with Google Gemini~2.0 Flash, executes a six-stage reasoning framework (context interpretation, user validation, data classification, business purpose test, compliance mapping, and risk synthesis) with early hard policy gates and deny by default. It returns APPROVE, DENY, CONDITIONAL together with cited controls and a machine readable rationale. We evaluate on fourteen canonical cases across seven scenario families using a privacy preserving benchmark. Results show Exact Decision Match improving from 10/14 to 13/14 (92.9\%) after applying policy gates, DENY recall rising to 1.00, False Approval Rate on must-deny families dropping to 0, and Functional Appropriateness and Compliance Adherence at 14/14. Expert ratings of rationale quality are high, and median latency is under one minute. These findings indicate that policy constrained LLM reasoning, combined with explicit gates and audit trails, can translate human readable policies into safe, compliant, and traceable machine decisions.

Large Language Models (LLMs) like ChatGPT and LLaMA drive rapid progress in generative AI, yet their huge parameter scales create severe computational and environmental burdens. High training costs, energy use, and limited device deployment hinder accessibility. Existing compression - pruning, distillation, low-rank, and quantization - reduces size but ignores complex inter-layer correlations. We propose KARIPAP, a quantum-inspired tensor network compression using Infinite Projected Entangled Pair States (iPEPS) and Tensor Renormalization Group (TRG) contraction. Unlike 1D Matrix Product States, iPEPS captures multi-directional entanglement in attention and deep transformer layers. TRG ensures polynomial-time contraction, making tensorization feasible while preserving key correlation geometry. Experiments on LLaMA-2 7B show up to 93% memory and 70% parameter reduction, with 50% faster training, 25% faster inference, and only 2-3% accuracy loss. Layer-wise entanglement profiling reveals redundancy in deeper layers, confirming their suitability for tensor factorization. KARIPAP demonstrates that modern LLMs occupy low-dimensional entanglement manifolds, enabling scalable, energy-efficient, and quantum-aware AI architectures.