Premium AI News

Aalto University researchers have developed a method to execute AI tensor operations using just one pass of light. By encoding data directly into light waves, they enable calculations to occur naturally and simultaneously. The approach works passively, without electronics, and could soon be integrated into photonic chips. If adopted, it promises dramatically faster and more energy-efficient AI systems.

One of the Mac’s most popular productivity apps is incorporating generative artificial intelligence in a way that keeps it offline, private, and customizable.

The European Union is set next week to kickstart a rollback of landmark rules on artificial intelligence and data protection that face powerful pushback on both sides of the Atlantic.

Deals between media conglomerates and tech companies serve both sets of interests, while leaving artists by the waysideThe world’s biggest music company is now in the AI business. Last year, Universal Music Group (UMG), alongside labels including Warner Records and Sony Music Entertainment sued two AI music startups for allegedly using their recordings to train text-to-music models without permission.But last month, UMG announced a deal with one of the defendants, Udio, to create an AI music platform. Their joint press release offered assurances that the label will commit to “do what’s right by [UMG’s] artists”. However, one advocacy group, the Music Artists Coalition, responded with the statement: “We’ve seen this before – everyone talks about ‘partnership’, but artists end up on the sidelines with scraps.”Alexander Avila is a video essayist, writer and researcher Continue reading...

As America's aging roads fall further behind on much-needed repairs, cities and states are turning to artificial intelligence to spot the worst hazards and decide which fixes should come first.

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning tasks. Supervised Reinforcement Learning (SRL) reformulates problem-solving as a sequence of logical “actions,” providing rich learning signals during the training process.This approach enables smaller models to learn complex problems that were previously out of reach for other common training techniques. Experiments show that SRL not only excels on math reasoning benchmarks but also generalizes effectively to agentic software engineering tasks.SRL is a versatile training framework that can elevate smaller and less expensive models to higher reasoning abilities.The limits of current LLM reasoning trainingRecent advances in training large language models (LLMs) for reasoning have largely been driven by reinforcement learning with verifiable rewards (RLVR), a method where a model is rewarded based on the correctness of its final answer. By repeatedly trying to solve problems and getting feedback on the final outcome, the model gradually learns effective problem-solving strategies. However, the success of this outcome-based approach depends on the model's ability to discover a correct solution within a limited number of attempts, or "rollouts." Since each rollout is computationally expensive, models can't try indefinitely. This method hits a wall when problems are so difficult that the model rarely, if ever, finds the right answer within its budget.This creates a critical learning bottleneck. In many multi-step reasoning problems, a model might correctly solve several steps but get derailed by a single mistake, leading to an incorrect answer. With RLVR, this entire effort receives a negative reward, and the model learns nothing from its partially correct work. It’s an all-or-nothing approach that fails to provide granular feedback and provides sparse rewards.An alternative method is supervised fine-tuning (SFT), where the model learns from examples containing the full reasoning process laid out by experts. While SFT can instill reasoning abilities, it often leads to overfitting (the model simply learns to imitate the trajectories in the training data instead of learning to generalize to problems beyond the examples it has seen). This issue is made worse by the fact that high-quality, human-created training data is both scarce and expensive to produce.As the paper notes, these limitations leave "a critical gap for training small open-source models to effectively learn difficult problems."How supervised reinforcement learning worksSRL introduces a framework that reformulates problem-solving as a "sequential decision-making process," striking a balance between pure outcome-based RL and pure imitation learning. Instead of optimizing only for the final answer or forcing the model to imitate an expert's entire thought process, SRL teaches the model to reproduce a sequence of key actions that form the backbone of expert reasoning. This allows the model to learn to take actions similar to an expert while developing its own internal reasoning style.In the SRL framework, expert demonstrations are broken down into a series of intermediate, concrete actions, each representing a meaningful step. For a math problem, an action might be an algebraic manipulation. For a software engineering agent, it could be a command executed in a code repository. To generate training data, SRL uses a powerful teacher model to create solution trajectories, which are then used to train a smaller model.According to I-Hung Hsu, a research scientist at Google and co-author of the paper, this middle-ground approach is key to its effectiveness in real-world scenarios. "SRL sits in the middle: It captures the structured flexibility of real-world problem solving, where there are multiple valid strategies but also clear notions of what ‘good reasoning’ looks like at each step," Hsu told VentureBeat. "This makes SRL suitable for domains like data science automation or probably supply chain optimization — tasks that reward sound intermediate reasoning rather than mere final answers."During training, the model first generates an "inner monologue" (its internal reasoning process, enclosed in <think> tags) before committing to an action. At each step, SRL provides a reward based on the similarity between the model's predicted action and the expert's action. This step-wise reward system provides dense, fine-grained feedback, allowing the model to learn and improve even if its overall solution isn't perfect. This solves the sparse reward problem RLVR faces.SRL in actionThe researchers' experiments show that SRL significantly outperforms strong baselines in both challenging mathematical reasoning and agentic software engineering benchmarks. They also observed that SRL encourages more flexible and sophisticated reasoning patterns in models, such as interleaved planning and self-verification, which improve solution quality without just making the outputs longer.For enterprise leaders, performance gains are only valuable if they don't come with runaway costs. Hsu clarifies that SRL-trained models are more efficient in their reasoning. "The gains come from better reasoning quality and structure, not from verbosity," he said. "In terms of efficiency, SRL-trained models are roughly on par with the base model in token usage... while SRL isn’t designed to reduce inference cost, it achieves stronger reasoning performance without increasing it."For the math tests, the team fine-tuned Qwen2.5-7B-Instruct on a dataset of 1,000 difficult math questions. They compared its performance against models trained with SFT and RLVR (using the GRPO algorithm common in models like DeepSeek-R1) on four competition-level math benchmarks. The SRL-trained model achieved a substantial 3.0% average performance boost over other methods. The team extended SRL to agentic software engineering, a domain critical for enterprise automation. They trained a coding-specialized model, Qwen2.5-Coder-7B-Instruct, on 5,000 expert trajectories of agents interacting with a coding environment. The SRL-trained model was benchmarked against the original base model and SWE-Gym-7B, a strong baseline fine-tuned with SFT. SRL achieved a 14.8% task resolve rate, representing a 74% relative improvement over the SFT-based model. This shows SRL's ability to train more competent AI agents for complex, real-world programming tasks.A new standard for high-stakes AI?The paper's strongest results came from combining methods: First, using SRL to teach foundational reasoning, then using RLVR to refine that skill. In their experiments, when the researchers used SRL as a pre-training and applied RLVR in post-training, they observed a 3.7% average increase, demonstrating a powerful curriculum learning strategy.This raises the question of whether this could become a new blueprint for building specialized AI."We view SRL as a strong foundation," Hsu said. "In a sense, SRL provides a curriculum — teaching models to think and act step by step — before we refine those behaviors with outcome-based reinforcement learning. This SRL-first approach not only stabilizes the later RL stage but also makes reasoning more interpretable and generalizable, which is critical for high-stakes applications."Looking ahead, Hsu acknowledges that scaling this pipeline still faces challenges, particularly the high cost and complexity of end-to-end RLVR for agentic tasks. However, he is optimistic about the path forward. "While high-quality expert trajectories remain important," he concluded, "we think the next big leap will come from automating their generation and filtering — leveraging strong teacher models or even self-improving student models to bootstrap new data."

Reaction follows Wall Street’s worst day in a month and unprecedented slump in investment in ChinaBusiness live – latest updatesGlobal markets suffered another day of volatile trading after a tech sell-off that fuelled Wall Street’s worst day in a month and weak economic data from China showed an unprecedented slump in investment.The FTSE 100 fell by 1.1% in London, closing down about 100 points at 9,698, as bellwether banking stocks tumbled. Barclays, Lloyds and NatWest slumped between 2.7% and 3.6%. Continue reading...

New research from the University of Waterloo is making inroads on one of the biggest problems in theoretical computer science. But the way to do it, according to Cameron Seth, a Ph.D. researcher working in the field of algorithmic approximation, is by breaking the problem down into smaller pieces.

A new report finds that local opposition to data centers skyrocketed in the second quarter of this year.

Anthropic says financial firms and government agencies were attacked ‘largely without human intervention’A leading artificial intelligence company claims to have stopped a China-backed “cyber espionage” campaign that was able to infiltrate financial firms and government agencies with almost no human oversight.The US-based Anthropic said its coding tool, Claude Code, was “manipulated” by a Chinese state-sponsored group to attack 30 entities around the world in September, achieving a “handful of successful intrusions”. Continue reading...

Wall Street endured its worst day in a month on Thursday as fears that tech companies are now overvalued loom largeWall Street came under pressure on Thursday, enduring its worst day in a month as a sell-off of technology stocks intensified.After an extraordinary rally around hopes for artificial intelligence that propelled global stock markets to record highs, fears that tech firms are now overvalued loom large. Continue reading...

It was originally found in leaked code and publicized by AI influencers on X, but OpenAI has made it official: ChatGPT now offers Group Chats, allowing multiple users to join the same, single ChatGPT conversation and send messages to each other and the underlying large language model (LLM), online and via its mobile apps. Imagine adding ChatGPT as another member of your existing group chats, allowing you to text it as you would one of your friends or family members and have them respond as well, and you'll have an idea of the intriguing power and potential of this feature.However, the feature is only available as a limited pilot for now to ChatGPT users in Japan, New Zealand, South Korea, and Taiwan (all tiers, including free usage).“Group chats are just the beginning of ChatGPT becoming a shared space to collaborate and interact with others,” OpenAI wrote in its announcement.This development builds on internal experimentation at OpenAI, where technical staffer Keyan Zhang said in a post on X that OpenAI's team initially considered multiplayer ChatGPT to be “a wild, out-of-distribution idea.”According to Zhang, the model’s performance in those early tests demonstrated far more potential than existing interfaces typically allow.The move follows OpenAI investor yet competitor Microsoft's update of its Copilot AI assistant to allow group chats last month, as well as Anthropic's introduction of shareable context and chat histories from its Claude AI models through its Projects feature introduced summer 2024, though this is not a simultaneous, realtime group chat in the same way. Collaborative functionality integrated into ChatGPTGroup chats function as shared conversational spaces where users can plan events, brainstorm ideas, or collaborate on projects with the added support of ChatGPT. These conversations are distinct from individual chats and are excluded from ChatGPT’s memory system—meaning no data from these group threads is used to train or personalize future interactions.Users can initiate a group chat by selecting the people icon in a new or existing conversation. Adding others creates a copy of the original thread, preserving the source dialogue. Participants can join via a shareable link and are prompted to create a profile with a name, username, and photo. The feature supports 1 to 20 participants per group.Each group chat is listed in a new section of the ChatGPT interface, and users can manage settings like naming the group, adding or removing participants, or muting notifications.Powered by GPT-5.1 with expanded toolsThe new group chat feature runs on GPT-5.1 Auto, a backend setting that chooses the optimal model based on the user’s subscription tier and the prompt. Functionality such as search, image generation, file upload, and dictation is available inside group conversations.Importantly, the system applies rate limits only when ChatGPT is producing responses. Direct messages between human users in the group do not count toward any plan’s message cap.OpenAI has added new social features to ChatGPT in support of this group dynamic. The model can react with emojis, interpret conversational context to decide when to respond, and personalize generated content using members’ profile photos—such as inserting user likenesses into images when asked.Privacy by default, controls for younger usersOpenAI emphasized that privacy and user control are integral to group chat design. The feature operates independently of the user’s personalized ChatGPT memory, and no new memories are created from these interactions. Participation requires an invitation link, and members are always able to see who is in a chat or leave at any time.Users under the age of 18 are automatically shielded from sensitive content in group chats. Parents or guardians can disable group chat access altogether via built-in parental controls.Group creators retain special permissions, including immunity from being removed by others. All other participants can be added or removed by group members.A testbed for shared AI experiencesOpenAI frames group chats as an early step toward richer, multi-user applications of AI, hinting at broader ambitions for ChatGPT as a shared workspace. The company expects to expand access over time and refine the feature based on how early users engage with it.Keyan Zhang’s post suggests that the underlying model capabilities are far ahead of the interfaces users currently interact with. This pilot, in OpenAI’s view, offers a new “container” where more of the model’s latent capacity can be surfaced.“Our models have a lot more room to shine than today’s experiences show, and the current containers only use a fraction of their capabilities,” Zhang said.With this initial pilot focused on a limited set of markets, OpenAI is likely monitoring both usage patterns and cultural fit as it plans for broader deployment. For now, the group chat experiment offers a new way for users to interact with ChatGPT—and with each other—in real time, using a conversational interface that blends productivity and personalization.Developer access: Still unclearOpenAI has not provided any indication that Group Chats will be accessible via the API or SDK. The current rollout is framed strictly within the ChatGPT product environment, with no mention of tool calls, developer hooks, or integration support for programmatic use. This absence of signaling leaves it unclear whether the company views group interaction as a future developer primitive or as a contained UX feature for end users only.For enterprise teams exploring how to replicate multi-user collaboration with generative models, any current implementation would require custom orchestration—such as managing multi-party context and prompts across separate API calls, and handling session state and response merging externally. Until OpenAI provides formal support, Group Chats remain a closed interface feature rather than a developer-accessible capability.Here is a standalone concluding subsection tailored for the article, focusing on what the ChatGPT Group Chat rollout means for enterprise decision makers in both pilot regions and globally:Implications for enterprise AI and data leadersFor enterprise teams already leveraging AI platforms—or preparing to—OpenAI’s group chat feature introduces a new layer of multi-user collaboration that could shift how generative models are deployed across workflows. While the pilot is limited to users in Japan, New Zealand, South Korea, and Taiwan, its design and roadmap offer key signals for AI engineers, orchestration specialists, and data leads globally.AI engineers managing large language model (LLM) deployments can now begin to conceptualize real-time, multi-user interfaces not just as support tools, but as collaborative environments for research, content generation, and ideation. This adds another front in model tuning: not just how models respond to individuals, but how they behave in live group settings with context shifts and varied user intentions.For AI orchestration leads, the ability to integrate ChatGPT into collaborative flows without exposing private memory or requiring custom builds may reduce friction in piloting generative AI in cross-functional teams. These group sessions could serve as lightweight alternatives to internal tools for brainstorming, prototyping, or knowledge sharing—useful for teams constrained by infrastructure, budget, or time.Enterprise data managers may also find use cases in structured group chat sessions for data annotation, taxonomy validation, or internal training support. The system’s lack of memory persistence adds a level of data isolation that aligns with standard security and compliance practices—though global rollout will be key to validating regional data handling standards.As group chat capabilities evolve, decision makers should monitor how shared usage patterns might inform future model behaviors, auditing needs, and governance structures. In the long term, features like these will influence not just how organizations interact with generative AI, but how they design team-level interfaces around it.