AI News Archive: June 30, 2026 — Part 6
Sourced from 500+ daily AI sources, scored by relevance.
- Threads adds translation to Live Chats and expands chat creation to more users
Threads is rolling out several updates to Live Chats, including translation support, new moderation tools, and expanded access for Community Champions. Here are the details.
Score: 55🌐 MovesJun 30, 2026https://9to5mac.com/2026/06/30/threads-adds-translation-to-live-chats-and-expands-chat-creation-to-more-users/ - Finding photos is so much easier with Siri AI in iOS 27 that I no longer scroll
iOS 27 lets you describe a photo in plain English, and Siri will find it. I tested it extensively on the developer beta, and it saves more time than you'd expect.
- iOS 27 takes huge leaps with AI, but it's still missing this key feature Android has had for years
iOS 27 takes huge leaps with AI, but it's still missing this key feature Android has had for years Tom's Guide
- AI agents: Cause of database sprawl. And also the proposed solution
DB wrangling tech needs to meet demands of AI agents, Cockroach Labs CEO Spencer Kimball tells El Reg
- How AI Is Replacing Children’s Ability to Think (with Randi Weingarten)
How AI Is Replacing Children’s Ability to Think (with Randi Weingarten)
Score: 55🌐 MovesJun 30, 2026https://futureoflife.org/podcast/how-ai-is-replacing-childrens-ability-to-think-with-randi-weingarten/ - Visual Studio 2026 Brings AI Deeper Into Development and It’s 93% Off Right Now
Microsoft's latest 64-bit IDE adds AI-assisted coding, faster performance, and advanced collaboration tools. The post Visual Studio 2026 Brings AI Deeper Into Development and It’s 93% Off Right Now appeared first on TechRepublic .
Score: 55🌐 MovesJun 30, 2026https://www.techrepublic.com/article/microsoft-visual-studio-professional-2026/ - SpaceX’s Cursor: New iPhone App Brings AI Coding Agents to Mobile
Cursor launched a public beta for iPhone and iPad that lets paid subscribers run, monitor, and review AI coding agents on mobile devices. The post SpaceX’s Cursor: New iPhone App Brings AI Coding Agents to Mobile appeared first on TechRepublic .
Score: 55🌐 MovesJun 30, 2026https://www.techrepublic.com/article/news-spacex-cursor-ai-coding-agents-iphone/ - Will tech giants ever let us opt out of AI search features?
The internet is moving from being search-driven to answer-driven. In the current era of AI -powered search, publishers are watching while their content is scraped and paraphrased by large language models, users hold concerns about not being able to easily opt out of AI features on large platforms, and a couple of Big Tech companies stand to benefit the most from this new paradigm. A common search on Google means coming face-to-face with its AI Overviews, which populate the top of the search page with a summary of key information about the topic searched and links to where the answers came from. A year after the initial launch of AI Overviews in May 2024, Google said the feature drove a 10%-plus increase in usage of the search engine in its biggest markets, like the U.S. and India, for queries that show AI Overviews. This is good news for the company. Google claims that thanks to its generative AI features, people are more satisfied with search and using it more often. Along with fellow tech giants such as Microsoft and Meta Platforms, the company has spent the past few years since the public launch of OpenAI’s ChatGPT aggressively embedding artificial intelligence features across most of its services. However, these same platforms have faced criticism from users and industry peers alike for what many say has felt like a heavy-handed approach. “Forcing a publisher to consent or forcing a user to consent was not how we wanted to implement AI,” says Kamyl Bazbaz, chief communications and policy officer for DuckDuckGo, one of Google’s biggest search competitors, which stands apart from many of today’s tech platforms by offering an AI-free version. In May, a week after Google announced an upgrade to its search with Gemini 3.5 Flash, DuckDuckGo received an increase in U.S. installs by 30% week over week. Bazbaz says DuckDuckGo’s install levels are still about 30% above where they were before Google’s announcement. “Some folks sort of thought, This is enough for me. I don’t like the direction this is going. It’s making me uncomfortable. I feel like I have no control ,” he says. Bazbaz believes what prompted people to switch to DuckDuckGo is its headline approach of being private, useful, and AI optional. Fast Company reached out to Google, which shared information about its generative AI search features and its updates. Google said the decisions that websites make about participating in its AI features will be respected, but it also noted that people are increasingly gravitating to generative AI tools to aid them in finding and understanding information. The company did not provide specifics about what opt-out features might be available for users of its AI tools in the future. According to Bazbaz, user surveys show that a large percentage of people who switched over to DuckDuckGo in the last month or so are mostly dissatisfied with Google’s search results. Another large percentage are dissatisfied with the lack of control in AI. These dissatisfied users are joined by another critic of Google’s AI search features—regulators in the U.K. On June 3, in what it calls a “world first,” the country’s Competition and Markets Authority (CMA) required Google to allow U.K. publishers to opt out of their content being used to power its search engine’s AI features, and to make sure all content is properly attributed. “With features like AI Overviews rapidly reshaping online search, it is crucial that content publishers, including news organisations, have appropriate bargaining power over how their content is used,” Sarah Cardell, chief executive of the CMA, said in a statement. It remains to be seen how effective the CMA’s regulations will end up being. So far, Google has complied, saying it will engage with the CMA “to ensure website owners have the right tools as user preferences evolve.” Further action was taken by the CMA on June 17, when it introduced two new conduct requirements for Google Search: Google must improve fairness in how search results are ranked and allow users to port their search data to authorized third parties. What do people actually want in regulations? While moves for regulation are being made in the U.K., on the other side of the pond, the U.S. has yet to see federal AI regulations. But as AI usage grows daily, more and more Americans say they want it. A new survey by researchers at Johns Hopkins University found that most U.S. adults strongly support regulations on the technology. In April and May 2026, more than 2,000 U.S. adults were polled—with those who trust Al “a great deal,” “not at all,” or “somewhere in between” all in support of Al regulation at approximately the same level. U.S. adults are also concerned with how true the information is that AI provides them, and many hold skepticism toward what AI serves up. When asked how much they would trust AI to look up factual information, 41% of U.S. adults said “somewhat,” and 18% said “not at all.” These feelings are not new. Concerns over AI in daily life have increased among U.S. adults, based on a Pew Research survey conducted in June 2025, in which 50% of those polled said they were “more concerned than excited.” That number grew from a 2021 poll, when only 37% were “more concerned than excited” about AI use in daily life. It’s fair to say that U.S. adults would like to see AI regulations applied to search engines. But what would the path to federal regulations on AI search look like? Pete Pachal, founder and editor-in-chief of The Media Copilot , a publication that reports on AI, is very cautious about attempts to regulate an economy of AI consumption. Pachal (whose work is also featured on Fast Compan y) believes overregulation can sometimes do more harm than good, particularly in terms of competition. Burdensome rules would pose a particular threat to smaller AI startups that might be innovating in the space, he notes, while Big Tech companies like Google and Meta would have the compliance resources to pull out even further ahead. “It’s true of a lot of regulations,” Pachal says. “They will favor incumbents and larger players. So I think the right balance here is to try to get what is the simplest thing you could do that would help everybody that doesn’t put a bunch of clamps on a system that makes it immobile.” Pachal likes the idea of requiring AI bots to identify themselves and be as transparent as possible. Identifying bots, he says, might keep the government away from making value judgments on content more broadly. According to Pachal, online traffic will be primarily AI agent-driven in 5 to 10 years, with people most often talking to machines for answers—their phones, computers, or whatever else is cooking up in labs in Silicon Valley. “We’re just talking to them, and they’re just giving us answers, and it’s intuiting things from whatever data we give it, and that just seems kind of obvious to me,” Pachal says. As a self-described “power user of AI,” Pachal is already personally moving this way, talking more to his computer these days than typing. Tech giants have more data than regulators Back at DuckDuckGo, Bazbaz calls the CMA’s ruling the tip of the spear for Google. Defining the company as a “monopoly,” he sees the ruling leveling the playing field. He says it’s always a good thing to see regulators take action with specificity and conviction. “A federal judge in the United States and many regulators all around the world, including those in Europe, have found Google to have engaged in anti-competitive behavior, and they are using a lot of the same tactics and tricks that they did in this last era to maintain this dominance in the AI era,” Bazbaz says. Still, he understands that regulations can be tricky, particularly as regulators often don’t have the full picture of a given situation. “Google will always have more information than the regulator. They’ll always know more because it’s their stuff,” Bazbaz says. “That power imbalance even exists in the regulatory process.” His solution begins with a ban on surveillance or behavioral advertising in the context of AI. Referencing philosopher Shoshana Zuboff’s work on surveillance capitalism, Bazbaz touches on surveillance advertising being the original sin of this tech era. He believes part of the problem is the extent to which people naturally share more with AI than they do with a regular search. “If you think about the surveillance economy and the extraction of your data that mines what you might do in the future and is able to sell you something in the future, [that’s] a cornerstone of all the problems,” Bazbaz says. “It’s sort of the original catalyst.” A feasible solution Senior attorney Jason Henderson says regulation is going to move at the pace that it tends to move, which is slow. Henderson, a lawyer with an expertise in sports, entertainment, and streaming media, says requirements like the CMA’s are going to happen everywhere at some point, but it may not matter because publishers are going to do deals. He believes the open web is dying before our eyes, because when people use AI search—across Google and Microsoft’s Bing—and receive an answer, they don’t click through to the original article. Henderson notes that publishers are losing money as 68% of searches now end without a single click to the original source. “They take the benefit of that content, but they don’t actually look at the original, so they don’t see any of those ads,” he says, referring to the common AI search user. When publishers look to the government for what they should do to address their content being scraped, Henderson says there are a couple of approaches to take. Publishers can ask for a chance to opt out of being scraped, allowing users to visit their sites directly. Or they can pursue licensing deals with AI companies, allowing them to scrape their content in exchange for paying them a license. “The last [option] is you can sue,” Henderson says. “And all of those are happening, and they’re all happening simultaneously.” He cites The New York Times establishing a licensing deal with Amazon versus filing lawsuits against OpenAI and Perplexity. At the end of the day, he says it’s just about the market. While publishers still own their content, they need to figure out by any means necessary how to get paid for it. “Ultimately, what this means is the gold rush period where people were just scraping data willy-nilly and not having to pay for it is coming to an end,” Henderson says, “because a small provider might not have the heft to be able to demand to get paid, but a large provider absolutely would.” The next big trend for publishing, Henderson says, will include licenses for data training. He cites the American Society of Composers, Authors and Publishers’ music licensing as an example. A business pays an annual license fee to ASCAP to receive access to its expansive music catalog. This license agreement gives a business permission to play music from any ASCAP member. Part of the license fee goes back to the ASCAP member as royalties, thereby paying them for their content to be used. “I think that there will be something like that, but it will not be for reads or listens,” Henderson says. “It’ll be for scrapes.”
- ZenaTech Advances IQ Nano Indoor Drone with New AI-Enabled LED Camera for Commercial and Defense Applications
ZenaTech Advances IQ Nano Indoor Drone with New AI-Enabled LED Camera for Commercial and Defense Applications azcentral.com and The Arizona Republic
- FoodChain ID Evolves AI Strategy to Transform Decision-Making Across Food & Beverage
FoodChain ID Evolves AI Strategy to Transform Decision-Making Across Food & Beverage azcentral.com and The Arizona Republic
- 1mind Ride-Along Shows How AI Sales Engineers Are Moving From Copilot to Live Call Participant
1mind Ride-Along Shows How AI Sales Engineers Are Moving From Copilot to Live Call Participant USA Today
- Saha Group taps global AI partners for digital push
E-commerce Digital Thai Holding Plc (EDTH), the digital investment arm of Saha Group, has forged an artificial intelligence (AI) alliance with TrueBusiness, EGG Digital and Japan's SoftBank Corp to accelerate the adoption of AI, big data and digital technologies across one of Thailand's largest industrial conglomerates.
Score: 55🌐 MovesJun 30, 2026https://www.bangkokpost.com/business/general/3279182/saha-group-taps-global-ai-partners-for-digital-push - HelloFresh boosts chilled fulfillment capacity via robotics
The meal kit company can now fulfill a greater variety of SKUs after deploying Locus Origin robots at its Phoenix facility.
Score: 55🌐 MovesJun 30, 2026https://www.supplychaindive.com/news/hellofresh-boosts-chilled-fulfillment-capacity-via-robotics/823994/ - Hyro rolls out analytics platform to glean insights from AI agent interactions
The solution—dubbed Care Intelligence—allows organizations to measure, benchmark and optimize care access through patients’ AI agent conversations.
- When cheap AI becomes a secret weapon
When cheap AI becomes a secret weapon
- FactSet Welcomes Google's Agents Deeper Within Its Gates
FactSet and Google say they will co-develop finance agents and weave "agentic experiences across the investment and dealmaking life cycles." The post FactSet Welcomes Google's Agents Deeper Within Its Gates appeared first on Investor's Business Daily .
Score: 55🌐 MovesJun 30, 2026https://www.investors.com/news/factset-google-new-generation-of-ai-agents-finance/ - Google's Gmail Live AI feature is now available in beta
You can use Gemini to quickly search your Gmail inbox with natural language.
Score: 55🌐 MovesJun 30, 2026https://www.engadget.com/2204880/google-gmail-live-ai-feature-is-now-available-in-beta/ - 52% of UAE, Saudi employees admit they could be tricked to deepfake scam at work
52% of UAE, Saudi employees admit they could be tricked to deepfake scam at work
- German-listed DDB acquires Singapore’s Infinium Robotics in US$24M share deal
Singapore-founded Infinium Robotics has been acquired by German-listed Deutsche Defence Beteiligungen (DDB) in a share-based transaction valued at up to about US$24 million, giving the autonomous warehouse drone company a public-market platform in Europe as demand for industrial automation continues to rise. The deal involves DDB acquiring 100 per cent of Infinium Robotiics’s 2,395,455 shares […] The post German-listed DDB acquires Singapore’s Infinium Robotics in US$24M share deal appeared first on e27 .
Score: 55🌐 MovesJun 30, 2026https://e27.co/german-listed-ddb-acquires-singapores-infinium-robotics-in-us24m-share-deal-20260630/ - Autonomous Ops & Observability: Watching Systems That Increasingly Watch Themselves: SD Times 100
Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. Operations and observability have always been about answering one question fast: what’s happening in our systems right now, and what do we do about it? What’s changed in 2026 is who’s doing the answering. … continue reading The post Autonomous Ops & Observability: Watching Systems That Increasingly Watch Themselves: SD Times 100 appeared first on SD Times .
- DeepSeek V4 Official Version Due Mid-July with Peak-Hour API Pricing Doubling
DeepSeek announces the official V4 release for mid-July, introducing peak-hour API pricing at 2x standard rates between 9am-12pm and 2pm-6pm, drawing comparisons to time-of-day electricity pricing.
- For Europe to lead in AI, sovereignty must mean choice
For Europe to lead in AI, sovereignty must mean choice
- Europe’s AI opportunity is not where everyone is looking
For the past two years, Europe has been asking itself a question that sounds strategic but may be profoundly misleading: how can we compete in artificial intelligence if we do not control the largest frontier models ? The question is understandable. The most visible AI companies are American . The most powerful models are trained by companies with enormous access to capital, compute, talent , and energy. The public imagination has been captured by the model race: who has the biggest model , the longest context window, the best benchmark score, the most impressive demo, the most persuasive chatbot. From that perspective, Europe looks late. Too slow, too fragmented, too regulated, too cautious, too short of hyperscalers, and too short of trillion-dollar technology companies willing to spend tens of billions on GPUs. The Stanford AI Index 2025 makes the gap brutally visible: US private AI investment in 2024 was vastly higher than that of China, the UK or Europe, and the gap is even sharper in generative AI. But what if the question itself is wrong? What if the future of enterprise AI is not decided by who owns the biggest model, but by who owns the architecture that turns models into corporate intelligence? That distinction matters enormously. A model is a source of cognitive capability. It can write, summarize, classify, reason, code, translate, search, retrieve, plan and increasingly act. But a company is not a model and it does not operate like one . A company is a system of processes, permissions, workflows, constraints, institutional memory, incentives, decisions, exceptions, relationships and measurable outcomes. The model can be brilliant and the company can still fail to transform This is exactly what we have seen. Generative AI has been extraordinary for individuals. For a person at a keyboard, the value is immediate: write this, summarize that, explain this, draft that, think through this problem with me. The interaction is conversational, bounded and personal. The model fits the problem. The enterprise is different. The enterprise does not need a clever assistant that answers questions in isolation. It needs systems that know the state of work, understand which constraints apply, act inside permission boundaries, learn from outcomes, remember what happened, and improve the next iteration. It needs continuity. It needs accountability. It needs feedback loops. It needs a way to convert operational experience into accumulated intelligence. That is not a bigger chatbot: it is a different layer This is where Europe should pay attention, because the model race and the enterprise architecture race are not the same race. The first rewards scale, capital concentration and compute. The second rewards formalization, governance, industrial discipline, trust, interoperability, domain knowledge and the ability to represent complex organizations without reducing them to conversations. Europe may not be naturally positioned to dominate the first race. It is much better positioned than it thinks for the second. The current AI debate is still too obsessed with models. That is not surprising: models are visible, spectacular and easy to compare. Benchmarks create rankings. Demos create headlines. New releases create market drama. But enterprise value rarely settles permanently at the most visible layer. In technology, value tends to move toward the abstraction that makes everything beneath it usable, repeatable and governable. Enterprise AI is waiting for the same thing Today’s agent systems are transitional. They are useful, but most of them still orbit the model. They assemble prompts, tools, memory, retrieval, APIs, evaluators and orchestration. They can produce impressive results, but when they enter a real company, someone still has to reconstruct the organization around them: what the process is, which data source is authoritative, who has permission to do what, which outcome matters, what exceptions are allowed, how feedback should be interpreted, and how improvement should propagate. That reconstruction is still largely manual. It is why so much enterprise AI feels like consulting with a model attached. It is why forward-deployed engineers have become such a revealing feature of the market. If an AI system requires experts to embed inside each customer to define workflows, map constraints and translate organizational reality into something the system can use, then the product is not yet a platform. The missing layer is being supplied by humans. McKinsey’s State of AI 2025 points in the same direction: AI use is widespread, but most organizations have not embedded it deeply enough into workflows and processes to realize material enterprise-level benefits. That is the key phrase: not enough into workflows and processes. Not enough into the company itself. A mature enterprise AI architecture would make that layer explicit. It would represent the company not as a pile of documents or chat histories, but as a living system of objects, states, workflows, permissions, constraints and outcomes. It would record what happens as structured traces. It would connect those traces to business results. It would allow each process to define what success means. It would make institutional memory queryable. It would let the organization learn from its own activity. Most importantly, it would be model-independent That is the point Europe should not miss. If the model becomes the sovereign layer, European companies will remain dependent on whoever owns the largest models. Their knowledge will be mediated by external systems, their workflows wrapped around rented intelligence, their accumulated expertise increasingly exposed to platforms whose incentives may not align with theirs. But if models are components inside a higher corporate intelligence architecture, the strategic picture changes. A company can use American models, European models, open-source models, specialized models or several at once. It can replace one with another as technology improves. The durable asset is not the model. The durable asset is the company-owned learning loop: the structured memory, the operational traces, the reward functions, the process intelligence, the governance layer and the accumulated judgment of the firm. This is not a minor technical distinction. It is the difference between renting intelligence and compounding it. Europe’s opportunity is to define and own that higher layer. Not because Europe should reject frontier models, but because it should refuse to confuse them with the whole architecture. Models are engines. Companies need vehicles. Engines matter enormously, but no one confuses an engine with a transport system, a logistics network or an industrial economy. This also fits Europe’s strengths far better than the current debate suggests. Europe understands regulated industries. It understands complex industrial systems. It understands process, compliance, institutional trust, privacy, auditability and long-term organizational relationships. It has deep expertise in enterprise software, manufacturing, finance, healthcare, logistics, energy, public administration and cross-border governance. These are not weaknesses in corporate AI. They are precisely the terrain on which corporate AI must eventually work. The European Commission appears to understand part of this. Its AI Continent Action Plan explicitly tries to turn Europe’s strengths in talent and traditional industries into AI accelerators, while InvestAI aims to mobilize €200 billion for AI investment, including AI gigafactories. The AI Act gives Europe a horizontal framework for trustworthy AI, rooted in the functioning of the internal market, fundamental rights and safety. And the Draghi report on European competitiveness has made the broader point unavoidable: Europe needs a new strategy for innovation, productivity and industrial competitiveness. But Europe should be careful not to translate all of this into a single obsession with compute and frontier models. Compute matters. Sovereign models matter. AI factories matter. But they are not enough. A country or continent can own a model and still fail to transform its companies. Conversely, if Europe develops the architecture that allows organizations to own their learning loops, it can turn every European company into a system that becomes more intelligent through use, regardless of which model sits underneath. That is a much more powerful version of sovereignty The corporate intelligence layer would also change the economics of AI. In the current model-centric world, intelligence concentrates. A small number of frontier model companies absorb data, talent, capital and strategic leverage. Companies become customers of intelligence. In a learning-loop architecture, intelligence distributes. Each organization becomes a site of compounding capability. The model providers remain important, but they are no longer the only place where value accumulates. For Europe, that matters politically as much as economically. A continent made of thousands of specialized firms, industrial champions, public institutions, mid-sized companies and regulated sectors does not need an AI economy in which all roads lead to a handful of external model providers. It needs an AI economy in which its own organizations become more capable, more adaptive and more productive while retaining control over their knowledge. The next stage of enterprise AI will therefore not be defined by whether a company has “an AI strategy” in the superficial sense. It will be defined by whether it has an architecture for learning. Can it observe its own activity? Can it encode outcomes? Can it preserve context? Can it operate within constraints? Can it improve workflows through feedback? Can it use different models without losing its own accumulated expertise? Can it turn daily operations into institutional intelligence? Those are the questions that matter Europe should stop apologizing for not being Silicon Valley. The next AI opportunity may not require Europe to imitate Silicon Valley at all. It may require Europe to do what it has often done best: formalize complex systems, make them trustworthy, industrialize them, and embed them in institutions. The frontier model race is important. But it is not the whole game. The real corporate AI revolution will happen one layer above the models, where intelligence becomes organizational, persistent, governed and cumulative. That layer is still open. Europe should build it.
- Apple Creator Studio adds AI tools across Final Cut Pro, Logic Pro and Pixelmator Pro
Apple Creator Studio has received a major AI update, adding automatic captions, edit detection, Auto Mask, image generation, and tighter workflows between Apple’s creative apps.
- Companies Are Making Claude and Codex Talk Like Cavemen to Stop AI’s Soaring Costs
A senior OpenAI employee has contributed code to the project, simply called 'caveman.'
Score: 52🌐 MovesJun 30, 2026https://www.404media.co/companies-are-making-claude-and-codex-talk-like-cavemen-to-stop-ais-soaring-costs/ - How is AI changing datacenter network fabrics?
EXPLAINER AI workloads are overwhelming the traditional datacenter fabrics they run on. Here's what's replacing them.
Score: 52🌐 MovesJun 30, 2026https://www.theregister.com/networks/2026/06/30/how-is-ai-changing-datacenter-network-fabrics/5262667 - Unlocking AI's value through 'phygital' care
Unlocking AI's value through 'phygital' care Healthcare IT News
Score: 52🌐 MovesJun 30, 2026https://www.healthcareitnews.com/news/asia/unlocking-ais-value-through-phygital-care - Tokenmaxing is out — Frugal AI is the new trend
Dutch startup Tokenmaxing shifts focus to frugal AI, highlighting cost-effective AI solutions for businesses.
Score: 52🌐 MovesJun 30, 2026https://ioplus.nl/en/posts/tokenmaxing-is-out--frugal-ai-is-the-new-trend - The Human Cost of AI Acceleration
The companies that endure in the era of AI will be the ones with systems designed to help humans thrive.
Score: 52🌐 MovesJun 30, 2026https://www.inc.com/maria-fernanda-levis/the-human-cost-of-ai-acceleration/91366618 - US DRAM lawsuit unlikely to derail AI memory boom
The world's three largest memory chipmakers have been named in a proposed US class-action lawsuit accusing them of restricting DRAM supply during the industry's shift toward artificial intelligence memory products. Analysts, however, expect little immediate impact on Samsung Electronics, SK hynix and Micron Technology, or the memory market. According to industry sources Tuesday, 17 plaintiffs comprising individual consumers and small businesses filed the complaint in the US District Court for th
- Securing AI agents: When AI tools move from reading to acting
The post Securing AI agents: When AI tools move from reading to acting appeared first on Source .
Score: 52🌐 MovesJun 30, 2026https://www.microsoft.com/en-us/security/blog/2026/06/30/securing-ai-agents-ai-tools-move-from-reading-acting/ - Pickle Robot partners with Ambi as Fortune 500 clients demand more scale
Pickle Robot will unload packages from trailers while Ambi's system scans and stacks them. The companies plan to remain financially independent.
- Libby will filter out AI content, kind of
The ebook-lending app’s filters will rely on AI content being self-labeled.
- ‘There’s this deep mystery of what, actually, is this thing?’: the philosopher inside Google DeepMind AI
Since 2017, Iason Gabriel has worked at the tech giant, trying to anticipate – and think through – the impact of AI. But as commercial and geopolitical pressures escalate, can ethicists make any difference? In 2017, a 33-year-old political philosopher named Iason Gabriel was told by a friend that he ought to apply for a job at DeepMind, the London-based subsidiary of Google where much of its AI research was concentrated. The suggestion was not an obvious one. Gabriel was a cheerful but intense junior academic with a passion for Vipassana meditation and what his brother calls “enthusiastic” rock climbing. The eldest son of a Greek management professor and a British documentary maker, Gabriel split his time between teaching and international development work. At the University of Oxford, where he was a fellow at St John’s College, Gabriel taught courses on political theory and wrote papers on the moral contortions of “yuppie ethics” and the ethical blind spots of effective altruism. When he wasn’t there, he did crisis work for the United Nations Development Programme in Sudan and Lebanon. Continue reading...
- 'Building AI Apps is Not Easy': Stretch, ISTE's Chatbot, Is Finally Ready to Go
ISTE spent the past three years testing and refining the tool, which is now ready for a wider audience.
- AI is making creative the new targeting
As Google, Meta, and TikTok automate audience targeting, your headlines, images, and videos are becoming the strongest signals for who sees your ads. The post AI is making creative the new targeting appeared first on MarTech .
- THINKCAR Unveils Tyler as the Industry’s First AI Diagnostic Agent at Global Distributors Conference
THINKCAR Unveils Tyler as the Industry’s First AI Diagnostic Agent at Global Distributors Conference USA Today
- When AI meets supercomputing: A new era of scientific computing
When AI meets supercomputing: A new era of scientific computing
- Everyone Thought AI Was Killing Entry-Level Jobs. They Were Wrong
A massive new study reveals the entry-level roles automated by AI aren’t disappearing—they’re getting a major promotion.
Score: 50🌐 MovesJun 30, 2026https://www.inc.com/bruce-crumley/everyone-thought-ai-was-killing-entry-level-jobs-they-were-wrong/91367551 - Healthcare's AI problem isn't the model – it's the data
Healthcare's AI problem isn't the model – it's the data Healthcare IT News
Score: 50🌐 MovesJun 30, 2026https://www.healthcareitnews.com/news/healthcares-ai-problem-isnt-model-its-data - MongoDB embeds reranking into Atlas as enterprises look to simplify AI stacks for scale
MongoDB embeds reranking into Atlas as enterprises look to simplify AI stacks for scale InfoWorld
- Q&A: Nvidia exec on how ‘confidential computing’ can secure AI agents
There are a variety of security concerns about artificial intelligence (AI), especially when it comes to the behavior of agentic AI . But until recently, the concept of locking down the models to prevent tampering hasn’t gotten a lot of attention. Now, a security technology called “confidential computing” has emerged that could help solve that problem: it protects AI models from hackers by restricting models to authorized users. (It also protects data wherever it is — in storage, when moving between systems, and when it is accessed.) With many top cloud and hardware providers championing confidential computing for AI, Computerworld talked with Dion Harris, Nvidia’s senior director of high-performance computing and AI factory solutions, about what the technology does and how it works. width="1024" height="722" sizes="auto, (max-width: 1024px) 100vw, 1024px"> Dion Harris, Nvidia’s senior director of high-performance computing and AI factory solutions. Nvidia Why should organizations care about confidential computing now? “Seventy percent of data exists outside the cloud, in on-premise data centers and data lakes. To deliver AI on this data while maintaining security, you need confidential computing to unlock that use case for enterprise AI.” Why is confidential computing suddenly important in the AI age? “Enterprises want AI on sensitive data — customer records, medical information, financial data — without exposing it in cloud environments where they lose control. “Traditional encryption protects data at rest and in transit, but not during computation. When you run an AI model, you must decrypt data. It sits in plain text in memory, accessible to administrators and cloud operators. Confidential computing creates a hardware-rooted trusted zone where data is decrypted only when computation needs it, then immediately re-encrypted. “This lets enterprises get AI value without compromising security. Financial services, healthcare, government, and regulated industries are adopting it.” How is confidential computing changing with agentic AI? “We’ve gone from generative to agentic AI being deployed and used to solve real business problems. To deliver agentic capabilities with required privacy, security and performance in enterprise, confidential computing provides the unlock. “We can spawn agents, access data, leverage tools and generate real useful work. With agentic AI, confidential computing helps in two ways: protecting the data and helping design implementation and workloads. It’s deployment, implementation, and use combined.” How does confidential computing work? “ Encryption at rest protects stored data. Encryption in transit protects data moving over networks. Encryption in use is the problem — when you compute on data, you must decrypt it in memory. “Confidential computing encrypts data in memory and between CPUs and GPUs. A dedicated element in the GPU decrypts information only when needed for computation, 100% inline, with minimal performance impact.” Can you walk through a real-world example? “ Apple’s standard operating policy for years has been to keep private information on device to avoid having access to private data. However, to leverage advanced AI models, they no longer fit on the device. Apple instituted their Private Compute Cloud, and now they’re extending that to Google Cloud. “Let me give a hypothetical and hopefully describe how it works. If you have a user who wants to upload a medical transcript from their doctor, their system creates a secure, attested environment. It sends a request to the server: validate yourself. That attestation says: ‘I am a Nvidia GPU. This environment is secure. It hasn’t been tampered with.’ Now, it’s okay for you to send your information over that secure line. Remote attestation allows the edge device to ensure it’s sending to a trusted environment. “The medical data comes over encrypted and remains encrypted until it lands in the GPU memory. Specific compute engines in the processor decrypt that information only to use it. The LLM dissects and summarizes it. Then it re-encrypts and sends it back over the wire. “They get the capabilities of data center AI mode — larger, more advanced, delivering more services. But they also get the security and privacy from Apple’s [Private Cloud Compute] platform. It’s the best of both worlds: efficiencies and intelligence from data center AI with Apple’s PCC security. What prevented confidential computing adoption? “ The main challenge in the past was significant performance impact. When you adopted confidential computing, you had to trade off performance for privacy. A 30% to 40% throughput reduction undermined the economic viability of the entire solution. If you sacrifice that much performance, it reduces the ability to get full utilization out of the hardware you’re deploying to deliver tokens or deliver a service in an economical fashion.” How was that solved? “With Blackwell and newer GPU architecture, you can deploy confidential computing without impact to performance. You get both privacy and performance, a win-win scenario. “Performance now translates directly into economics. You get full utilization of the hardware, which matters for delivering tokens or services at scale. When we built Blackwell, we made confidential computing a first-class system feature to deliver not just the security, but also the performance the market requires.” Where is adoption accelerating? “Companies are thinking about the cloud. Model builders can’t expose APIs, enterprises customize for their business. Hybrid on-prem and cloud models — where builders deliver services behind enterprise walls — require zero trust. We’re no longer in fully owned infrastructure. “By 2030, [billions of dollars] is expected in confidential computing use cases. It’s emerging as essential infrastructure for AI adoption across the industry. For organizations using cloud infrastructure, deploying AI on sensitive data, or operating under regulatory requirements, confidential computing is becoming essential.” What should organizations do? ”The performance problem is solved. The technology is ready, ecosystems are built, partners enable it. If you want to deploy agentic AI on sensitive data, protect customer privacy, meet regulatory requirements, and maintain security across hybrid environments, you need confidential computing in your strategy. “Most customers start with a developer license, validate performance and security, then migrate to a commercial license. Partners like Red Hat and Fortanix integrate these mechanisms within their platforms. Google Cloud offers it through their services. “Start with a proof of concept. Validate it works. Plan deployment. Organizations gain competitive advantage securing AI on sensitive data.”
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