AI News Archive: June 8, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- Carney government testing use of AI in prisons to create profile reports of offenders
Carney government testing use of AI in prisons to create profile reports of offenders Toronto Star
- Behind-the-meter data center gas plants will raise US energy bills
Counterintuitively, it is data centers’ independence from the grid and use of natural gas that will hike energy costs for homes and businesses, write experts from Energy Innovation.
Score: 42🌐 MovesJun 8, 2026https://www.utilitydive.com/news/data-centers-raise-energy-bills-not-for-reason-you-think/822205/ - C.H. Robinson’s next AI step: adding Engineer to the Planner
C.H. Robinson launched its Planner last year in Managed Solutions; Engineer will be its next step. The post C.H. Robinson’s next AI step: adding Engineer to the Planner appeared first on FreightWaves .
Score: 42🌐 MovesJun 8, 2026https://www.freightwaves.com/news/c-h-robinsons-next-ai-step-adding-engineer-to-the-planner - The future of defense manufacturing will be distributed, autonomous and software-defined
The future of defense manufacturing will be distributed, autonomous and software-defined Breaking Defense
- Apple’s new AI photo tool can literally change where the camera was standing
Apple has unveiled Spatial Reframing for the Photos app, an Apple Intelligence feature that can virtually reposition the camera and intelligently generate missing parts of an image.
- Oracle Earnings On Deck As Rally For AI Stocks Wobbles. Here's What To Watch.
Oracle stock has bounced back from a steep slump but will face a big test with its fiscal Q4 results due on Wednesday. The post Oracle Earnings On Deck As Rally For AI Stocks Wobbles. Here's What To Watch. appeared first on Investor's Business Daily .
Score: 42🌐 MovesJun 8, 2026https://www.investors.com/news/technology/oracle-stock-earnings-preview-may-2026-ai-orcl/ - Agentic commerce: AI is now your shopper’s first stop. Showing up is just the beginning.
Retailers who dismiss product visibility on AI assistants as a future problem are already behind.
- SA emerging as global AI inference powerhouse
Local organisations rapidly adopt AI inference to move beyond pilots, driving real-time insights and global innovation leadership, says Lenovo.
Score: 42🌐 MovesJun 8, 2026https://www.itweb.co.za/article/sa-emerging-as-global-ai-inference-powerhouse/nWJadMbNE2wMbjO1 - Romain Huet: OpenAI's View on Agents and Infrastructure - Ctrl Alt Lead podcast
Romain Huet: OpenAI's View on Agents and Infrastructure - Ctrl Alt Lead podcast Computing UK
Score: 42🌐 MovesJun 8, 2026https://www.computing.co.uk/podcasts/2026/romain-huet-openai-on-agents-and-infrastructure-ctrl-alt-lead - GS E&C, Daedong Robotics team up on AI construction robots
GS E&C said Monday it had signed a partnership with Daedong Robotics to develop AI-powered autonomous robots for construction sites, as builders increasingly look to automation to improve safety and productivity. Under the agreement, signed June 5 at GS E&C's R&D Center in Seoul, the two companies will begin field trials of Daedong Robotics' autonomous robots and jointly develop models tailored to construction environments. Established in 2024, Daedong Robotics is expanding beyond agricultural d
- Dublin-based AI IP start-up Midnight Labs backed by Sony
Expansion in Japan will enable Midnight to operate in a country that is 'uniquely vulnerable to AI-generated copyright infringement' due to 'sophisticated digital piracy syndicates' operating at 'unprecedented scale'. Read more: Dublin-based AI IP start-up Midnight Labs backed by Sony
Score: 42💰 MoneyJun 8, 2026https://www.siliconrepublic.com/start-ups/dublin-based-ai-ip-start-up-midnight-labs-backed-by-sony - JD.com, Tencent reportedly team up on AI agent collaboration
JD.com and Tencent have reportedly teamed up to collaborate on AI agents, integrating JD.com’s e-commerce and fulfillment capabilities with Tencent’s vast user-facing platforms. According to the report, JD.com’s AI agent has been connected with devices from major manufacturers including Huawei, Oppo, and Honor. Through Agent-to-Agent (A2A) integration, users can make shopping requests and access product […]
Score: 42🌐 MovesJun 8, 2026https://technode.com/2026/06/08/jd-com-tencent-reportedly-team-up-on-ai-agent-collaboration/ - Train Models Faster with JAX and MaxText Using NVFP4 on NVIDIA Blackwell
Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step...
Score: 42🌐 MovesJun 8, 2026https://developer.nvidia.com/blog/train-models-faster-with-jax-and-maxtext-using-nvfp4-on-nvidia-blackwell/ - Frontier Radar #3: How agentic AI is turning tokens into a business metric
Monthly subscription, open chat, ask question: This is how generative AI used to work. Agentic workflows go beyond this model. They consume many times more tokens, run autonomously for hours and make flat rates unaffordable for providers. At the same time, token prices vary according to speed, specialization and the economic value of the result. The third edition of the AI radar categorizes the emerging token economy: How is billing shifting from subscription to consumption? Why does a favorable token price reveal little about the actual costs? And why is token consumption alone the wrong measure of AI value creation? The article Frontier Radar #3: How agentic AI is turning tokens into a business metric appeared first on The Decoder .
Score: 42🌐 MovesJun 8, 2026https://the-decoder.com/frontier-radar-3-how-agentic-ai-is-turning-tokens-into-a-business-metric/ - Microsoft Research's Lens proves detailed captions matter more than raw scale for training efficient image generators
Microsoft Research presents Lens, a text-to-image model with just 3.8 billion parameters that matches much larger rivals on benchmarks, at a fraction of the training cost. The secret sauce: 800 million detailed image captions generated by GPT-4.1 instead of vague web alt-text. Code and weights are openly available under an open-source license. The article Microsoft Research's Lens proves detailed captions matter more than raw scale for training efficient image generators appeared first on The Decoder .
- AI data center expansion to drive combined monthly capacity of EML and CW-DFB LDs to 50.7 million units in 2026
The rapid expansion of AI data centers and the intensifying race for AI computing power are accelerating the transition toward transmission speeds above 1.6 Tbps, according to TrendForce’s latest research. Major players such as NVIDIA, Google, and Meta are securing a stable supply by strategically locking in production capacity from electro-absorption modulated laser (EML) and continuous-wave distributed feedback laser diode (CW-DFB LD) suppliers. This has prompted suppliers to aggressively expand capacity in response to customer demand, doubling the combined monthly production capacity for EML and CW-DFB LDs to around 50.7 million units in 2026. The top three suppliers, Broadcom, Lumentum, and Sumitomo Electric, are expected to collectively account for 55% of the market. EMLs are high-performance optical communication components that integrate a laser source and an electro-absorption modulation (EAM) onto a single chip. Since the laser diodes remain continuously active, EMLs offer ultra-low noise interference and extremely narrow spectral characteristics. This makes them the ideal solution for data transmission exceeding 800 Gbps and for medium-to-long-distance transmission exceeding 2 kilometers. Given the high technical barriers, the top three suppliers—Lumentum, Broadcom, and Mitsubishi Electric—collectively hold approximately 72% market share. Lumentum is not only aggressively expanding production capacity for 100/200 Gbps EML products, but also successfully demonstrated 400 Gbps-per-lane EML technology at OFC 2026 to address future 3.2 Tbps market demand. Additionally, while NVIDIA-led EML solutions focus on maximizing signal integrity and transmission performance, other CSPs are actively developing CW-DFB LDs for optical circuit switches (OCS) and silicon photonics co-packaged optics (SiPh CPO) solutions. Broadcom and Sumitomo Electric currently lead the industry in CW-DFB LD production capacity, followed by Coherent and LandMark/LuxNet. Together, these suppliers account for approximately 74% of total market capacity. Meanwhile, Coherent is accelerating the transition toward 6-inch InP epi-wafer production to support large-scale manufacturing, while also developing 400 mW CW-DFB LDs for silicon photonics pluggable transceivers and co-packaged optics applications. Source: TrendForce, Taiwan.
- Is AI riding a bubble?
SpaceX, Anthropic, Open AI, Google are mopping up huge sums from the market riding on the AI promise
Score: 42🌐 MovesJun 8, 2026https://www.thehindubusinessline.com/opinion/is-ai-riding-a-bubble/article71077614.ece - Semi-automated offside is coming for the World Cup. Here’s how one referee uses it
Micheal Barwegan is part of an all-Canadian crew at this World Cup, and says the new offside system makes his job easier in some ways The 2026 World Cup will be the first edition of the tournament to feature semi-automated offside technology, utilizing a dozen cameras to track player movement at a rate of 50 stills per second. In theory, it sounds like an effective, if dizzying, way to cut down on delays and better aid the officials. One of those officials is Micheal Barwegan, who is part of the first all-Canadian officiating team in men’s World Cup history. He has worked with referee Drew Fischer and fellow assistant referee Lyes Arfa increasingly often over the past two years. The team worked in-tandem at the 2024 Olympics and last summer’s Club World Cup along with their more regular work in club soccer. Continue reading...
Score: 42🌐 MovesJun 8, 2026https://www.theguardian.com/football/2026/jun/08/semi-automated-offside-world-cup - PointFive co-founder Gal Ben David reveals plans after $60 million Series B
PointFive co-founder Gal Ben David reveals plans after $60 million Series B
- Anthropic’s Claude Code creator says there are days he manages tens of thousands of AI agents at once
Anthropic’s Claude Code creator says there are days he manages tens of thousands of AI agents at once Fortune
- Opinion | Anthropic might be the most powerful company in the world
Opinion | Anthropic might be the most powerful company in the world The Washington Post
Score: 42🌐 MovesJun 8, 2026https://www.washingtonpost.com/opinions/2026/06/08/anthropic-ai-powerful-company/ - Panasonic looks to quadruple AI-related sales with data center batteries
Panasonic looks to quadruple AI-related sales with data center batteries Nikkei Asia
- Apple Downplays Concerns That Its Use of Google AI Models Will Undermine Privacy
Apple Inc., which just unveiled a revamped artificial intelligence platform built in part with Google technology, said the new approach will still preserve the company’s privacy safeguards.
- Researchers trained an open source AI search agent, Harness-1, that outperforms GPT-5.4 on recalling relevant information
A joint research collaboration between researchers at the University of Illinois at Urbana-Champaign (UIUC), UC Berkeley, and the open source AI-native vector database platform Chroma unveiled Harness-1 , a 20-billion parameter open-source search agent built atop OpenAI's gpt-oss-20B open source model that fundamentally redesigns how AI executes complex retrieval tasks. Harness-1 achieves a massive leap in performance, scoring 73% average on its ability to recall relevant information correctly from a curated dataset, outperforming even GPT-5.4 (70.9%) and the next, most accurate open source search agent, Tongyi DeepResearch 30B , by 11.4 percentage points. (While GPT-5.5 has also been out for more than a month, the researchers didn't test against this model as it wasn't available when they were building theirs.) Crucially for developers, the model and its environment are available immediately under the highly permissive Apache 2.0 license and model code/weights on Hugging Face . Harness-1 also serves as proof-of-efficacy of another effort, Tinker , the distributed, web-based AI model training and fine-tuning API developed by Thinking Machines. Tinker was used specifically to train and run inference for Harness-1, highlighting how interactive infrastructure is actively enabling the next generation of autonomous models. So how did the researchers do it? Benchmarks Decoded (and Why Harness-1 Could Help Enterprises Tremendously) To actually put these models to the test, the researchers evaluated Harness-1 and its competitors across eight highly complex search benchmarks. Rather than asking simple trivia questions, these tests required the AI to act like a real researcher sifting through diverse, dense data sources. The benchmarks spanned several different domains, including open web searches, complex financial filings from the SEC, technical patent databases from the USPTO, and "multi-hop" question-answering tasks where the AI had to logically piece together scattered clues from multiple different documents to arrive at the correct answer. When the results came in, Harness-1 dominated the open-source competition in its ability to successfully find and curate the right facts. Even more impressively, this relatively small 20-billion parameter model went toe-to-toe with massive, expensive proprietary AI systems. It actually outperformed heavyweights like GPT-5.4, Sonnet-4.6, and Kimi-K2.5 — thought to be the hundreds of billions or trillions of parameters. Only one giant frontier model—Opus-4.6 — managed to narrowly edge it out in overall average performance. Harness-1 achieves its performance gains by offloading the exhaustive "bookkeeping" of a search session out of the model's working memory and into a structured software environment. As enterprise use cases grow more sophisticated, demanding that models autonomously sift through thousands of corporate documents or financial filings, these systems frequently succumb to "search amnesia"—forgetting their original queries, looping over rejected documents, or losing track of the specific claims they are trying to verify. Until now, the prevailing solution to this amnesia has been brute force. Engineers typically force models to constantly reread an ever-expanding, append-only transcript of their own actions, piling every search, read, and thought back into a massive context window. Harness-1 introduces a paradigm shift away from this method, proving that the bottleneck for true artificial autonomy isn't necessarily the size of the model, but how efficiently its working environment manages state. It highlights once more, as Anthropic's Claude Code has also done, that the raw model is arguably less important than the harness — or set of conditions — through which it runs. Technology: Doing the Paperwork in the Environment To understand the technical leap of Harness-1, consider a real-world analogy. Imagine hiring a brilliant research assistant and placing them in an empty room without a desk, notepads, or filing cabinets. You ask them to write a comprehensive report on a highly complex topic, which requires them to read dozens of books while keeping every single quote, citation, and dead-end search perfectly memorized in their own head. Eventually, no matter how intelligent the assistant is, their cognitive load will max out, and they will start dropping facts or losing the thread of the assignment. This is exactly how traditional search agents operate today. They are trained as policies over growing transcripts, meaning the model searches, reads, searches again, and appends everything into its own context window. As lead researcher Patrick (Pengcheng) Jiang of the University of Illinois noted on X : "At some point the model is not just 'searching' anymore. It is also being asked to be a memory system, a note taker, a verifier, and a librarian." Harness-1 solves this by giving the AI a desk and a filing cabinet—what the research team calls a "state-externalizing harness." This harness is an active, surrounding environment that takes over the routine bookkeeping, maintaining a recoverable working memory that includes a candidate pool of documents, an importance-tagged curated evidence set, compact evidence links, and verification records. By separating semantic choices from structural state management, the AI is freed up to do what it does best. The policy still decides what to search, determines which documents to keep, and knows when to stop, while the environment simply holds the state. Here is a subsection breaking down the training methodology and how it differs from prior agentic search models: Training Harness-1: A Masterclass in Data Efficiency The training pipeline for Harness-1 represents a fundamental shift in how the AI industry approaches agentic learning. Historically, developers have treated search agents as policies operating over massive, ever-growing transcripts, forcing reinforcement learning (RL) algorithms to simultaneously optimize both semantic reasoning and the raw memorization of a search state. Harness-1’s creators took a radically different approach: because their custom "harness" handles all the routine bookkeeping—like maintaining evidence links, candidate pools, and verification records—the training process only needed to teach the model how to operate this structured interface. This division of labor drastically simplified what the underlying 20-billion parameter model actually needed to learn. The process began with a remarkably narrow Supervised Fine-Tuning (SFT) stage. Rather than scraping petabytes of new behavioral data, the team generated just 899 filtered trajectories using a GPT-5.4 teacher agent that was plugged into the exact same harness environment the student model would eventually use. The goal of this SFT phase was not to inject vast amounts of domain knowledge into the model, but simply to teach it the mechanical rhythms of a good researcher: how to format tool calls, how to tag documents by importance, and the discipline of verifying a claim before promoting it to the final curated set. Following SFT, the model underwent Reinforcement Learning (RL) using an algorithm called CISPO, applied over full search episodes capping at 40 turns. The team designed a highly specific terminal reward function that explicitly separated discovery from selection . The model was rewarded not just for finding a relevant document, but for successfully promoting it into the final answer set, while being penalized if it found the answer but failed to curate it. The researchers also instituted a "tool diversity" bonus; without this specific incentive, they found the policy would quickly collapse into a lazy, search-heavy strategy where it spammed queries but bypassed the harder work of reading and verifying the text. What makes Harness-1 truly innovative compared to prior work is its unprecedented data efficiency. The entire model was trained on roughly 4,400 unique items—899 SFT trajectories and 3,453 RL queries. In stark contrast, competing open-source models required vastly larger datasets to achieve worse results: Context-1 utilized over 17,200 training items, while Search-R1 relied on a staggering 221,300 items to learn search behaviors. By proving that a smarter external cognitive architecture can replace brute-force data scaling, Harness-1 suggests that the future of agentic AI lies in building better environments for models to work within, rather than just training larger models on more data. Product: Enterprise Applicability and Generalization From a product perspective, Harness-1 is delivered as a highly capable 20B agent merged into the openai/gpt-oss-20b base architecture. For enterprise tech stacks, the applicability is massive because businesses need AI to execute multi-step research across proprietary databases without hallucinating or running up exorbitant compute bills. Harness-1 manages its frontier-level performance at what the creators describe as "Context-1-level cost and latency." Because the context window is strictly managed by the budget-aware harness rather than continuously expanding, enterprises can deploy this agent autonomously without incurring the exponential token costs typically associated with long-horizon AI tasks. Even more impressively, Harness-1 proves it can generalize well beyond its training data. According to the research team, it was incredibly cheap to train, utilizing just 899 filtered supervised fine-tuning (SFT) trajectories and a mere 3,453 reinforcement learning (RL) queries. "Instead of training the model to survive a giant append-only transcript, we train it to use a structured search interface: search, curate, revisit, verify, and submit," Jiang explained. This leanness proves a critical point for the AI industry: developers do not necessarily need petabytes of new behavioral data if they build a better cognitive framework for the model to operate within. Licensing: The Power of Apache 2.0 One of the most significant aspects of the Harness-1 release is its licensing. In plain language, Apache 2.0 is a highly permissive, enterprise-friendly software license that fundamentally enables commercialization. Unlike "copyleft" licenses (such as the GPL) that can force companies to open-source their own proprietary software if they integrate the code, or "research-only" licenses that ban commercial use entirely, Apache 2.0 gives businesses the green light to freely build, modify, and monetize the technology. For developers and startups, this means Harness-1 can be seamlessly integrated into commercial enterprise search products, internal data retrieval tools, or customer-facing AI applications without fear of legal reprisal. The only major requirement is that users must include the original copyright notice and explicitly state any significant modifications they make to the source code, positioning Harness-1 as a highly viable foundational building block for the enterprise. Community Reactions: A Resounding Validation The announcement has clearly struck a nerve within the developer community, validating the very real pain points engineers face when building agentic systems. Jiang’s multi-part announcement thread on X quickly garnered massive traction, pulling in over 256.1K views, 3.7K likes, 2.9K bookmarks, and nearly 300 reposts within a matter of days. This high engagement underscores a growing consensus in the AI space that brute-forcing context windows is a losing battle. When Jiang posted on X, "I’ve been wondering: maybe search agents are bad at search partly because we make them do all the paperwork in their head," the resonance was immediate. For developers who have spent the last year wrestling with AI agents that confidently forget their primary instructions halfway through a database search, the Harness-1 approach feels like a desperately needed course correction. Ultimately, the community sentiment highlights a shift in industry priorities. Developers are moving away from asking how large an AI model's context window can get, and instead asking how efficiently an AI model's environment can manage that context for it. By offloading the paperwork, Harness-1 is proving that smaller, smarter systems can outmaneuver the giants—provided they have the right desk to work at.
- Apple is using AI to fix Safari’s extension problem
Apple is trying to solve one of Safari's biggest weaknesses with AI. Safari has long lacked the robust library of extensions that its rivals have, mainly due to the stringent development requirements from Apple. But now, Apple is inviting users to essentially vibe-code their own extensions. In a demo shared by Apple, the company showed […]
- Google AI Plus gets price drop to $4.99 and storage bump
Google announced today that its AI Plus subscription is getting a price drop to $4.99 per month and now includes 400 GB of storage. more…
- Gemini 3.5 and Antigravity come to Google NotebookLM
NotebookLM is getting a big upgrade, but it's only for AI Ultra and enterprise accounts right now.
Score: 42🌐 MovesJun 8, 2026https://arstechnica.com/ai/2026/06/gemini-3-5-and-antigravity-come-to-google-notebooklm/ - How ChatGPT's new Lockdown mode protects you from data theft (and what else it does)
The goal is to protect you against attackers who try to steal your personal data through prompt injection. But it does limit your ability to access the web.
Score: 40🌐 MovesJun 8, 2026https://www.zdnet.com/article/chatgpt-lockdown-mode-data-theft-prompt-injection/ - Huge Acquires Rotate°, Adding Composable Commerce Expertise to Its AI-Native Design and Technology Practice
London-based Shopify Plus specialists join Huge as the company formalizes composable commerce as a core capability
- Finding hidden catalytic knowledge from literature data
Finding hidden catalytic knowledge from literature data EurekAlert!
- Apple’s new Siri camera trick is giving strong Google Lens vibes
Siri mode in Camera has a familiar Android flavor.
- 56% of new US data centers could be built in "high risk" disaster-prone states with insurers warning of $800 billion investment exposed
Over half of planned US data centers are in high-risk, disaster-prone states, but the weather is only one consideration.
- Do better research with NotebookLM
Better research with NotebookLM
Score: 40🌐 MovesJun 8, 2026https://blog.google/innovation-and-ai/products/notebooklm/better-research-notebooklm/ - OpenAI rolls out Lockdown Mode to curb prompt-injection data theft risks
OpenAI's new Lockdown Mode restricts web-connected features like Deep Research and Agent Mode to protect sensitive data from prompt injection attacks, though the company admits it's not a complete solution. The post OpenAI rolls out Lockdown Mode to curb prompt-injection data theft risks appeared first on MEDIANAMA .
Score: 40🌐 MovesJun 8, 2026https://www.medianama.com/2026/06/223-openai-lockdown-mode-curb-prompt-injection-data-theft-risks/ - ‘We may be flying blind’: AWS wants to fix the problem of AI agents straying off task
‘We may be flying blind’: AWS wants to fix the problem of AI agents straying off task Fortune
Score: 40🌐 MovesJun 8, 2026https://fortune.com/2026/06/08/aws-amazon-ai-agents-flying-blind-benchmaxing-sandbox-research/ - Manitoba plans to ban AI chatbots for those under 16. This school uses them as an educational tool
Manitoba plans to ban AI chatbots for those under 16. This school uses them as an educational tool CBC
Score: 40🌐 MovesJun 8, 2026http://www.cbc.ca/news/canada/manitoba/manitoba-ai-chatbots-education-9.7223822 - Bots Now Outnumber Humans Online. Here’s What It Means for Your Business
Here’s how some companies are starting to optimize their websites for AI agents.
- OpenAI, Anthropic, and Nvidia ramped up H-1B filings before a judge blocked Trump's $100,000 fee
OpenAI, Anthropic, and Nvidia ramped up H-1B filings before a judge blocked Trump's $100,000 fee Business Insider
Score: 38🌐 MovesJun 8, 2026https://www.businessinsider.com/h-1b-visa-filings-rise-for-anthropic-openai-nvidia-2026-6 - The AI trade’s worst day in a year became a buying opportunity by Monday
The AI trade’s worst day in a year became a buying opportunity by Monday Fortune
Score: 38🌐 MovesJun 8, 2026https://fortune.com/2026/06/08/ai-rally-chipmakers-treasury-yields-spacex-ipo/ - AI Made Building Software Free And Selling It Brutally Expensive
AI tools have made software development nearly free; but enterprise GTM costs are rising, not falling. Box CEO Aaron Levie explains why the moat in software has moved from code to trust, distribution, and consultative selling, and what that means for the next wave of venture bets.
- How a few AI chip giants warped Asia's stock picking game
How a few AI chip giants warped Asia's stock picking game Reuters
Score: 38🌐 MovesJun 8, 2026https://www.reuters.com/business/finance/how-few-ai-chip-giants-warped-asias-stock-picking-game-2026-06-08/ - Next-gen AI can learn continuously while consuming a fraction of the computing energy required by today’s AI systems
Next-gen AI can learn continuously while consuming a fraction of the computing energy required by today’s AI systems EurekAlert!
- The OpenAI Cloud Deal Powering Cerebras Stock Higher
The OpenAI Cloud Deal Powering Cerebras Stock Higher Barron's
- Apple will let you build workflows using AI in its new Shortcuts app
Shortcuts gets an AI upgrade, letting you describe the workflow you want in a prompt.
Score: 38🌐 MovesJun 8, 2026https://techcrunch.com/2026/06/08/apple-will-let-you-build-workflows-using-ai-in-its-new-shortcuts-app/ - Reading FC launches AI Centre of Excellence with Stelia AI, NVIDIA and Lenovo
Reading Football Club today announced a major strategic AI initiative with Stelia AI, powered by NVIDIA and Lenovo, bringing together professional sport and advanced computing to accelerate the practi...
Score: 38🌐 MovesJun 8, 2026https://tech.eu/2026/06/08/reading-fc-launches-ai-centre-of-excellence-with-stelia-ai-nvidia-and-lenovo/ - How to get Apple's new Siri AI on your iPhone with the iOS 27 developer beta
Apple has launched the developer beta for iOS 27 and related OS. Here's how you can try out the latest version of iOS.
- Apna eyes ₹150 crore revenue from AI-led push for India’s 300 million blue-collar workers
Multilingual interview bots and employability tools are helping first-time job seekers cross the formal-sector divide
- Driverless cars are a solution looking for a problem
Driverless cars are a solution looking for a problem The Telegraph
Score: 38🌐 MovesJun 8, 2026https://www.telegraph.co.uk/business/2026/06/08/driverless-cars-are-a-solution-looking-for-a-problem/ - Telangana plans ‘Unified Card’: An AI-based 360-degree beneficiary profiling system
This AI profiling system of 360-degree beneficiary data will collect "individual and family-oriented data from all citizens" in Telangana, and the Unified Cards will have data points across welfare departments. The post Telangana plans ‘Unified Card’: An AI-based 360-degree beneficiary profiling system appeared first on MEDIANAMA .
Score: 38🌐 MovesJun 8, 2026https://www.medianama.com/2026/06/223-telangana-unified-card-ai-360-degree-beneficiary-profiling-system/ - Google quietly installs 4GB AI model through Chrome; here’s how to remove it
Google quietly installs 4GB AI model through Chrome; here’s how to remove it