AI News Archive: August 17, 2026 — Part 4
Sourced from 500+ daily AI sources, scored by relevance.
- China says new AI missile system can track US F-35s with 90% accuracy
Chinese researchers report lightweight AI can recognize simulated F-22 and F-35 infrared signatures, though combat performance remains unverified.
Score: 56🌐 MovesAug 17, 2026https://www.techradar.com/pro/china-says-new-ai-missile-system-can-track-u-s-f-35s-with-90-accuracy - Agentic AI Moves from Pilot Phase to Production, Bringing Governance to the Forefront
New research from Caylent, an AI-focused Amazon Web Services Premier Tier Services Partner, found that enterprises are already moving agentic AI beyond pilots and into production environments. At the same time, organizations are putting strict conditions around autonomy, making governance and control the next major challenge for enterprise AI adoption.
- This AI startup is using self-driving car technology to bring 3D video to live sports
If you’ve ever played a sports video game—such as an entry in the long-running Madden NFL series, or one of the annual releases focused on the NHL, NBA, or MLB—you’re likely familiar with the ability to follow the action from every conceivable angle. It’s something that can’t quite be matched in an arena setting, even in buildings packed with cameras. Until now. Peripheral Labs, a Toronto-based AI research company, is introducing what it calls “spatial intelligence” to live sports media, which could lead to an entirely new way to watch live sports. This approach allows viewers to watch replays or highlights in a fully three-dimensional environment, similar to a video game. Mustafa Khan (left) and Kelvin Cui , cofounders of Peripheral Labs “We’re building models that understand the 3D world, and bringing machine learning and tech from autonomous vehicles to watching sports,” says Kelvin Cui, who cofounded Peripheral in 2024 with Mustafa Khan. “It’s a new, interactive, and immersive sports experience.” A different kind of roadmap for self-driving car tech As Cui explains, the technology utilizes technology and sensors used in driverless cars, which both Cui and Khan previously worked on. Peripheral applies the tech and the concepts behind it to 3D video, volumetric video, and video reconstruction, allowing for a photorealistic, navigable video environment. In effect, viewers can watch highlights or replays of an event from almost any angle, or track a single portion of the video, such as a basketball or individual player. Other companies are working on similar technologies, including Arcturus and Wild Capture. Cui says Peripheral is applying the technology to sports to start, as it allows for similar actions to be tracked, analyzed, and reconstructed in controlled environments. “We can track 65 points, all the way down to finger joints on individual players,” he says. “You’re watching what looks like a regular TV broadcast, but at any point you can change the camera angle at will, and you can fly to any angle through a video game controller or your phone.” Cui also hopes that the video-game-like visuals and control options could help garner more interest in sports from Gen Z, which, as a generation, tends to watch live sports at lower rates than others. 8.7 million reasons to believe the hype The thesis is gaining attention because Peripheral is also announcing that it’s raised $8.7 million in new seed funding from Inovia Capital, Deloitte Ventures, Khosla Ventures, and Entrepreneurs First. Its total seed funding is now $12.3 million. Peripheral declined to share an estimated valuation for the company. The sports world is taking notice, too. Peripheral has already worked with pro sports teams, such as the NBA’s Toronto Raptors, to create highlights for social posts . It will continue to focus on its work with basketball teams in the months ahead. The company also plans to debut a consumer-facing platform and browser-based replay system sometime this year. “Our ambition is to work with different sports teams and leagues,” Cui says, noting the larger aims of integrating into live broadcasts. “The team is working on getting integrated into team social apps. . . . The ultimate ambition is to do it all in real time.”
- Will Anthropic and OpenAI Stop Selling Their Best AI to Businesses?
Will Anthropic and OpenAI Stop Selling Their Best AI to Businesses? theinformation.com
Score: 56🌐 MovesAug 17, 2026https://www.theinformation.com/newsletters/ai-agenda/will-anthropic-openai-stop-selling-best-ai-businesses - First came self-driving cars. Now, Waymo veterans are building autonomous construction equipment.
First came self-driving cars. Now, Waymo veterans are building autonomous construction equipment. Business Insider
Score: 56🌐 MovesAug 17, 2026https://www.businessinsider.com/bedrock-robotics-construction-ai-excavators-2026-8 - Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems
Massive spending commitments for data-center leases and chips aren’t shown on companies’ balance sheets.
- Bedrock Brings Autonomous Driving to Construction
Bedrock Robotics is bringing autonomous driving technology to construction sites, retrofitting excavators and other heavy machinery with cameras, LiDAR, Nvidia-powered compute and its own software. CEO Boris Sofman joins Bloomberg to discuss the company’s first paid commercial deployments, how autonomous equipment could address a growing shortage of skilled operators, and why demand for data centers and other infrastructure is creating an opportunity to use AI to help America build faster. He joins Ed Ludlow on "Bloomberg Tech." (Source: Bloomberg)
Score: 56🌐 MovesAug 17, 2026https://www.bloomberg.com/news/videos/2026-08-17/bedrock-brings-autonomous-driving-to-construction-video - An AI agent built a working exploit for this macOS flaw in four hours
A security company built working exploits for two pre-authentication root bugs in macOS. It took four hours. The firm, Calif, used an AI agent. It now withholds technical details of one of those bugs, CVE-2026-65400, until most Macs carry the patch. The reason is the speed. Producing the exploit was too easy. Attackers are using […] This story continues at The Next Web
Score: 55🌐 MovesAug 17, 2026https://thenextweb.com/news/macos-screen-sharing-flaw-cve-2026-65400-monero-miner - AI spending in finance to top $75 billion as firms shift from pilots to scale
AI spending in finance to top $75 billion as firms shift from pilots to scale
- Huawei Open-Sources AscendNPU IR, the Core of the BiSheng Compiler: Triton and Multi-Language Support for Ascend 950
At the Meet AI Compiler salon hosted by HyperAI, Huawei AscendNPU IR architect Hai Lijuan detailed the open-sourced compiler foundation: an MLIR-based tile-level abstraction for Ascend hardware that connects LLVM IR, supports Triton and other front-end languages, and extends to the new Ascend 950 with SIMD and SIMT coverage.
Score: 55🌐 MovesAug 17, 2026https://pandaily.com/huawei-ascendnpu-ir-open-source-bisheng-compiler-triton-ascend-950-aug2026 - What Does the US Robotics Ban on Foreign Imports Really Mean? Here’s IDC’s Take
On July 28, 2026, the FCC quietly added “advanced robotic devices” to its Covered List, the roster of tech it has flagged as a national security risk, blocking new equipment authorizations for foreign-made robots. The headlines were about humanoids, the walking machines from Chinese vendors like Unitree. Within 24 hours, the story took a stranger […] The post What Does the US Robotics Ban on Foreign Imports Really Mean? Here’s IDC’s Take appeared first on IDC .
- Alibaba to Sell Gaming Unit as It Sharpens AI Focus
Alibaba to Sell Gaming Unit as It Sharpens AI Focus Caixin Global
Score: 55🌐 MovesAug 17, 2026https://www.caixinglobal.com/2026-08-17/alibaba-to-sell-gaming-unit-as-it-sharpens-ai-focus-102474980.html - Are Microsoft’s AI plans being held back by a shortage of chips?
Guardian investigation finds apparent discrepancy between what tech company has said about its AI capacity – and the number of advanced chips it has in operation The chips are quite small and some can be held in the palm of a hand. They are fundamental to the development of artificial intelligence models – and the world’s biggest technology companies need vast numbers of them to keep ahead. Microsoft is one of them. And, on paper, it seems to have a problem. A Guardian investigation has found an apparent discrepancy between what the company has said about its AI capacity – and the number of advanced AI chips it has in operation. Continue reading...
- AI transcription apps sold with up to 99% accuracy claims can drop to 70% or below for millions of accented speakers, and what the training data gap means for every misread word
Pull out your phone and tap record. Speak clearly, slowly, confidently, and watch the words appear on screen. Now say it again in a Scottish brogue, a Nigerian cadence, a South Texas drawl, or the Spanish-inflected English of a Miami kitchen. The words on screen begin to drift. And the app that promised near-perfect results ... Read more
- The Economy Has Spoken: Stuff That’s AI-Generated Has Almost Zero Value
"Buyers are voting with their wallets, and AI-generated content is struggling to compete." The post The Economy Has Spoken: Stuff That’s AI-Generated Has Almost Zero Value appeared first on Futurism .
- AI-generated text should be detectable, but Apple needs to avoid Anthropic’s huge error
As someone who writes for a living, you would correctly guess that I’m wholeheartedly in favour of allowing AI -generated text to be detectable and marked as such. There’s just a ridiculous amount of AI slop out there, and an “AI content” label means I don’t need to waste my time reading any of it. However, Anthropic has just announced that it’s complying with an EU initiative to have Claude watermark AI-generated text, but doing so in a particularly perverse manner …
- Americas telco leaders confront the AI-native shift at Innovate Americas
Americas telco leaders confront the AI-native shift at Innovate Americas azcentral.com and The Arizona Republic
- AI-Powered Distribution Platform Launched by bolt in California
Insurtech bolt launched an AI-powered insurance distribution platform the company says enables access across all lines for distribution partners. Connected Distribution is a new operating model that combines customer data, workflows and integrated market access into a single AI-powered connected …
- AI data center optical interconnect market to hit $144 billion by 2030, an over ten-fold increase from 2024 figures, according to new projections — silicon photonics expected to account for nearly two-thirds of revenue, driven by co-packaged optics
A new CIC forecast projects that the data center optical interconnect market will grow from $13.7 billion in 2024 to $144.4 billion by 2030, with silicon photonics accounting for 63.7% of revenue.
- Business adoption of AI agents tripled this year - as measurable ROI emerges
Industries are finding the strategies that work best for their business needs, according to Salesforce's latest Agentic Enterprise Index.
- Fortinet, IBD Stock Of The Day, Steps Up AI Acquisitions To Build Cloud Platform
Fortinet stock has gained nearly 100% in 2026 amid artificial intelligence-related acquisitions aimed at building cloud platform. The post Fortinet, IBD Stock Of The Day, Steps Up AI Acquisitions To Build Cloud Platform appeared first on Investor's Business Daily .
Score: 54🌐 MovesAug 17, 2026https://www.investors.com/research/ibd-stock-of-the-day/fortinet-stock-artificial-intelligence-acquisition/ - Developer pulls Raleigh petition for $180M data center
The abrupt decision comes as data center proposals continue to face resistance across North Carolina.
- Your AI is emailing my AI—and nobody’s in charge
Not long ago, a colleague of mine received a courteous and professionally worded email. It informed him about the current state of a shared project and listed the next steps required and who was accountable for each. A perfectly ordinary email—except it was, from start to finish, the work of an AI agent that had been set up to act on behalf of the person it represented. My colleague’s experience is still relatively unusual, but it is also the canary in the coal mine. In May, Bloomberg profiled Tyler Cadwell, the founder of an Arizona glassware business who has built an AI agent he calls his “first AI employee.” This agent does things like “ triaging his email inbox and even responding on its own to supply chain problems .” And even when agents aren’t independently sending emails, people are certainly using AI’s help more often to write those emails. Last month, Gallup reported that more than half of U.S. employees now use artificial intelligence at work, and the single most common use, cited by 51% of them, is in writing and editing—or, in other words, communicating with each other. For example, that email my colleague received from an agent? He pasted it into his chatbot and sent the reply it drafted. On the surface, it looks like humans talking to humans. Underneath, however, AI is talking to AI. This is how business leaders need to respond. There is no going back There is no way to put the email-writing AI genie back in its bottle. And I don’t think we should even want to. The machine version is sometimes simply better. When the insurance firm Allstate handed the drafting of its claims correspondence—roughly 50,000 messages a day—to generative AI, the machine-written emails were clearer, less jargon-laden, and more empathetic than the ones its human reps had been sending. Moreover, delegating to AI is increasingly the only rational response left to employees drowning in electronic communications. Microsoft’s 2025 report on work trends found that the average worker received 117 emails and 153 Teams messages a day . It makes sense—and may even make workers more productive —to delegate some of this load to AI. But delegated conversation is not the same thing as a free-for-all. In fact, as the use of AI for interpersonal communications rises, governing this conversation becomes increasingly important. The rise of AI makes organizations confront questions their policies never anticipated: Which conversations may be delegated? What may a machine commit us to? Who owns what gets said? Three pillars matter most for organizations to meet this challenge: Decide what must never be delegated, make all other delegation deliberate, and rebuild ownership for the exchanges you hand over entirely. 1. Decide what must never be delegated Video killed the radio star, and AI is killing the performance review. An ex-Dropbox manager told Axios in 2024 that she used ChatGPT to write her appraisals . More recently, The Wall Street Journal reported in February that more and more managers were using AI to do their performance reviews . This is not merely individual initiative. It is also organizationally driven: As discussed in a recent piece in the Harvard Business Review , Citi, JPMorgan, and Boston Consulting Group have all built AI tools that support drafting performance evaluations . I’ve argued before that some leadership activities—the performance review being one of them—depend on full human engagement for their value. An appraisal matters because your manager actually weighed your year. Bad news lands humanely because someone chose to deliver it. Mentoring works because a person you respect spent their scarcest resource on you. And delegating such activities to AI doesn’t make your workflow more efficient; it destroys the value the work was supposed to generate. So every organization needs to draw its human line: Name the conversations whose value depends on the human element, and make sure that they remain 100% human. Everything else may be delegated, so long as you do it deliberately. Which is where the second pillar comes in. 2. Be deliberate about delegation Delegation runs along a spectrum. At one end sits AI-assisted work: You create, and the machine polishes. Further along, AI-delegated: The machine drafts, and you skim and send. At the far end, AI-represented: Your agent conducts the exchange, and you may never see it at all. It is easy to drift along this spectrum without realizing it. For example, an estate agent profiled by the Financial Times uses an AI tool to run nine inboxes. This saves her hours every week; but “sometimes,” she admits, “I am guilty of letting it think for me.” The problem is not that the AI thinks for her—that’s simply what delegation is, and delegation is often the right call. The problem is that it happens without a decision being made. Chosen delegation comes with a handover: You know what you’ve given up, so you know how it needs to be managed. Drifted delegation comes with no handover at all. Multiply that across a workforce, and it becomes an organizational condition. When individuals aren’t quite connected to what they say, the organization no longer knows what is being said in its name or how much human judgment is behind it. For everything outside the human line, the task of governance is not to restrict delegation—it is to make it visible. Give your teams a simple norm: Know precisely where you are on the delegation spectrum, and make sure the organization knows it, too. 3. Give your agents a mandate—and an owner The Bloomberg story tells the tale of a startup executive whose household agent ran amok, ringing up $100 charges hour after hour —he was saved only because he happened to notice the billing alerts in his inbox. The remedy is not to review every message an agent sends—that would defeat the purpose. It is instead to rebuild what the human safeguard used to provide. Specifically, that takes three things. First, a mandate: an explicit decision about what the agent may commit you to—a meeting, perhaps a delivery date, never a price. Second, containment: Do not rely on the agent obeying its instructions. Instructions are just more text to an agent; it follows them the way it does everything else—usually, not always. The controls that count live outside the agent, in the systems it touches: a payment card with a hard cap, credentials that open the calendar but not the contract folder, approval gates that route consequential exchanges to a human before anything is agreed. And third, an owner: a named person who answers for the agent regardless. Governing the communication layer The pillars above are the core principles that should inform governance of the new communication layer in organizations. Here are four moves that you can make right away to start translating those principles into actual governance in your organization. 1. Audit where AI is already talking to AI. Map the exchanges in your organization that are AI-drafted on both ends. The results might surprise you. 2. Draw your human line. Convene your leadership team and name three to five conversations whose value depends entirely on human presence. Declare them human-only, and explain why. 3. Label the modes. Pick one team, and have members tag their communications for a week: 100% human, assisted, delegated, or represented. Your real baseline is almost certainly further along the spectrum than you think. 4. Give one agent a mandate. For a single agent use case, write down what it may commit to autonomously, put the real controls outside the agent—spending caps, limited credentials, approval gates—and name the person who owns its output. This becomes the template you can iterate for everything after. The organizations that win will be the organizations that are intentional The communication layer of work is being rebuilt in real time by vendors shipping inbox agents, by banks building review-writing tools, by a Realtor in Chester and an engraver in Arizona, one delegated exchange at a time. And the organizations that thrive will be neither those that ban delegated conversation nor those that surrender to it. They will be the ones that decide, deliberately, which conversations still require human presence, and on what terms machines may speak in the rest.
- Greg Brockman says OpenAI underestimated its own models’ cyber skills
Greg Brockman went on CNBC on Monday to say the executive departures at OpenAI are not unusual. “I actually think that the difference between OpenAI and other organizations is that we are so much in the spotlight, so every departure gets scrutinized in a way that it doesn’t otherwise,” the company’s president told Squawk Box. […] This story continues at The Next Web
Score: 53🌐 MovesAug 17, 2026https://thenextweb.com/news/greg-brockman-openai-underestimated-cyber-capabilities-defenders-window - AI video market has bounced back from Sora's false start
AI production companies like Promise are setting up shop around Hollywood's historic studios, using real-time backgrounds and other AI tools to cut film costs. Netflix already uses AI in 300 of its 1,000 titles, and the startup Higgsfield now carries a $5.4 billion valuation. What was once a tech demo has grown into its own industry, complete with valuations, job titles, and fights over who gets a cut. The article AI video market has bounced back from Sora's false start appeared first on The Decoder .
Score: 53🌐 MovesAug 17, 2026https://the-decoder.com/ai-video-market-has-bounced-back-from-soras-false-start/ - The next silicon? AI data centre material faces price spike amid China supply crunch
The artificial intelligence boom is driving demand for optical modules that enable ultra-fast data transmission in data centres, with the latest beneficiary a chemical compound that is seeing an unprecedented surge in prices. Indium phosphide (InP) is used to make lasers that convert electrical signals into light, allowing data to travel rapidly through fibre-optic cables. As a major producer of the material, China has seen prices climb sharply. According to a recent report by financial services...
- Fearing an AI Chip Glut, Data Center Developers Are Choosin’ Texas
Fearing an AI Chip Glut, Data Center Developers Are Choosin’ Texas theinformation.com
- We Tracked a Shipment of Rare Books. It Ended at an Amazon AI Training Facility
We placed a tracking device in a shipment of rare books to see which AI company was buying it, and found an Amazon facility where Amazon scans and destroys books.
Score: 53🌐 MovesAug 17, 2026https://www.404media.co/we-tracked-a-shipment-of-rare-books-it-ended-at-an-amazon-ai-training-facility/ - Would you take a Waymo to Sea Ranch?
California approves the company’s expanded service area, which includes the East Bay and Sonoma County.
- Not every AI-in-education tale is a horror story. How the world’s leading education company makes AI that’s actually useful for students
Most of the AI industry treats a model’s ability to answer questions as a basic measure of progress. That standard becomes less useful when the goal is learning. In education, an answer can be factually correct and still fail the student because it gives away too much or solves a problem without helping the learner understand it. Pearson has spent the past three years confronting that problem. Its CEO, Omar Abbosh, says the company’s AI transformation has reinforced a basic lesson: Model capability alone does not determine whether an educational product works. “Our products are grounded in learning science and academically strong by design,” Abbosh tells Fast Company . “Our customers know we understand learning deeply and design our products using that learning science expertise.” The nearly 180-year-old London-based learning company, which generated 3.58 billion pounds ($4.85 billion at current rates) in revenue in 2025, has integrated artificial intelligence into products reaching millions of learners. Those include MyLab and Mastering, its textbook-linked courseware platforms for homework, practice, and assessment across U.S. higher education. Pearson has also deployed an AI-powered math tutor for the GED and offers AI study tools through Connections Academy, its tuition-free online K-12 public school. Pearson embedded generative AI across its portfolio in 2023, became the first major higher-education publisher to put AI study tools inside proprietary academic content in 2024, and established its AI Centre for Enablement in 2025 to coordinate governance, security, and evaluation. Its technology stack spans Amazon Bedrock, Microsoft, Google Cloud, and IBM watsonx, giving its teams access to multiple models and cloud platforms. Pearson combines those technologies with proprietary content and decades of learner data to develop products for specific educational settings. More broadly, Pearson’s AI transformation offers a window into how a large company deploys AI at scale while deciding where it adds value, how to govern it, and how to measure whether it actually works. “You have to keep changing and pivoting. Over 180 years, we’ve done that many times, and we will keep showing up in the future,” Abbosh says. Rather than chase consumer products, Pearson wants to “remain the infrastructure that helps institutions and employers verify skills and knowledge,” he says. Model capability does not equal production readiness AI models continue to improve, but Pearson treats them as one component of a larger learning system. Abbosh argues that the company’s more durable assets are its expertise in learning science, academic content, and data generated by millions of learners. Pearson uses that data to identify where students struggle, which concepts they revisit, and how their performance changes over time. Product teams can then use those signals to adjust AI-powered learning experiences. Omar Abbosh [Photo: Pearson] Some of the most useful lessons have come from unexpected user behavior. Pearson initially expected its AI-powered Smart Lesson Generator to help teachers prepare lessons more quickly. Teachers instead began using it during class, modifying materials in real time based on students’ level, pace, and interests. Pearson responded by rebuilding the pipeline so teachers could generate smaller activities more quickly and iterate during a lesson. That kind of product knowledge can persist even as the underlying models change. “The model is only one ingredient, and the learning experience is much more than that,” Abbosh says. “What matters is how the model combines with academically sound content, learning science, and educator expertise, to produce an effective, engaging experience.” Pearson has also avoided relying on a single AI provider. Its teams use models from Amazon Web Services, Microsoft, Google, and IBM, selecting more powerful models for some tasks and smaller models or traditional machine learning for others. I asked Abbosh whether that flexibility will remain important as leading AI models become more similar in capability. “Our number one obligation is to our customers. We owe it to them to understand the different model types and how they operate,” Abbosh says. “There are certain use cases that demand frontier models and some that don’t. Our goal is to be on the front end of innovation for our customers and find the right mix for the most effective learning experience.” That raises a broader question for enterprise AI: What remains proprietary when models become easier to swap? David Brudenell, co-CEO of the Sydney-based operational AI company Decidr , argues that foundation models will “absorb a lot of what companies currently call their AI stack. “If your advantage is a clever RAG [retrieval-augmented generation] pipeline, you have about 18 months,” he says. “What doesn’t get absorbed is the record of how a specific business makes decisions. Decisions, not data. Most enterprise data is exhaust. Every company has terabytes of it and almost none of it explains why anyone did anything.” Kavitta Ghai, cofounder and CEO of Los Angeles-based Nectir , which builds AI assistants for universities, sees another source of durable advantage in education. “Scale and domain expertise are genuinely valuable. Proprietary content was decisive when good explanations were scarce. It isn’t scarce anymore,” Ghai says. “What compounds is harder to see: relationships with institutions, permission to touch student data, and knowing from experience how a deployment fails. None of that can be bought quickly.” Ghai also argues that access to a capable model does not eliminate the need for specialized education software. “A campus AI license is not a campus AI deployment,” she says. She has seen institutions with free, system-wide access to a leading AI model still purchase separate education platforms because the model license does not include learning management system integration, instructor training, or institutional controls. Extending AI governance beyond models Pearson has also had to decide which AI functions should be standardized across the company and which should remain with individual product teams. It created the AI Centre for Enablement to establish common approaches to governance, security, and evaluation. Abbosh says the goal was consistency without requiring a centralized approval process for every AI decision. “For a long time, Pearson operated as a set of separate businesses. That was part of our history of being a holding company. Everyone came with separate technology and processes,” Abbosh says. “Now we are moving toward common functional and common technical architectures that we can repurpose across the company.” Pearson can use that structure to standardize quality measures, security requirements, and responsible-use principles while allowing product teams to adapt them to particular learners and uses. The company has also built a quality-standards agent that helps teams apply those requirements while developing AI-enabled products. More autonomous AI systems complicate that approach. A system approved for one purpose can change as it gains access to new data, tools, or software. An initial review may therefore say little about how the system eventually operates in production. Brudenell at Decidr sees the same problem across enterprise deployments. In his view, companies often scrutinize the model while giving less attention to the credentials and permissions attached to it. An agent might receive broad access through a service account during a pilot, for example, and retain those permissions when it moves into production. The evidence Pearson grades itself on Pearson is also trying to determine whether its AI products improve learning rather than simply increasing engagement. In one internal study, Pearson’s learning scientists examined nearly 80 million learning interactions from almost 400,000 students in higher education, and found that users of its AI Study Tool were significantly more likely to exhibit active reading behaviors. Another analysis of more than 62,000 students found that AI-powered adaptive practice made students 90% more likely to reach initial mastery without additional study time. A third analysis, covering about 128,000 AI prompts, suggested that 97% of students used the tool as intended. Those findings provide substantial data on how students interact with the products. They offer less evidence about whether students ultimately learn more. Mutlu Cukurova, a professor of learning and artificial intelligence at University College London, says measures such as active reading and initial mastery capture behavior within the same platform providing the AI. Establishing a causal effect on learning requires a different standard. “More data is good but it often helps with precision, not validity,” he says. “Tens of millions of interactions give you good narrow confidence intervals around quantities that may not be the ones that matter.” Abbosh, for his part, says Pearson is still building that evidence base and is cautious about claims it cannot substantiate. “We have an ongoing research agenda focused on assessing how AI affects learning across different educational contexts and settings,” he tells Fast Company . “We can really take it to the next level by marrying the data we have with how students perform on exams and what course grades they receive.” He says Pearson is also pursuing research partnerships with instructors and universities. Responsible AI at scale The limits of the available evidence also affect how Pearson assigns responsibility when an AI system gets something wrong. Human review of every interaction is impractical for products serving millions of learners. Abbosh says Pearson nevertheless considers itself responsible for what those systems do and does not regard them as autonomous decision-makers. The company starts with the desired learner outcome and works backward, considering whether AI is appropriate, what safeguards are required, and how performance should be measured. Its responsible-AI controls also align with the EU’s AI Act and the National Institute of Standards and Technology’s AI Risk Management Framework . Cukurova argues that an AI tutor’s effectiveness ultimately depends on what happens when the tool is no longer available. Students should “be able to solve problems independently, retain what they learned, and apply that knowledge to new situations without relying on the system,” he says. Abbosh acknowledges the challenge. “As these technologies become more capable, our approach is to become more deliberate, not less,” he says. “Internally, we’re experimenting aggressively and learning quickly, but we also want to move responsibly.” Pearson’s strategy depends on whether its learning science, data, product design , governance, and institutional experience can translate increasingly capable AI models into measurable improvements in learning. The company is working to establish the evidence needed to answer that question.
- Zhipu says new coding AI developed advanced cyber skills faster than expected
Zhipu says new coding AI developed advanced cyber skills faster than expected InfoWorld
- Japan Inc. raises full-year profit forecast to 14% jump on AI and chips
Japan Inc. raises full-year profit forecast to 14% jump on AI and chips Nikkei Asia
- JD.com Builds China's AI Industrialization Blueprint: Open Physical AI Stack From EgoLive Data to Robot Bases
JD.com founder Richard Liu has said technology barriers are a form of exploitation and opened JD's full-stack self-developed AI to global partners. With H1 R&D spending up 53.2%, JD is building the world's largest embodied data collection center, open-sourcing EgoLive and JoyAI models, and deploying robots across logistics.
Score: 52🌐 MovesAug 17, 2026https://pandaily.com/jd-ai-industrialization-physical-ai-robotics-ego-live-open-ecosystem-aug2026 - Here’s How Job Applicants Are Trying to Make Their Resumes Stand Out: ‘There’s No Way to Fight It’
Here’s How Job Applicants Are Trying to Make Their Resumes Stand Out: ‘There’s No Way to Fight It’ entrepreneur.com
Score: 52🌐 MovesAug 17, 2026https://www.entrepreneur.com/business-news/heres-how-job-applicants-are-trying-to-make-their-resumes-stand-out - OpenAI's Brockman brushes off concerns about leadership changes in CNBC exclusive
Brockman told CNBC he doesn't think the wave of recent departures at OpenAI are 'actually that atypical.'
- Chinese robots on the march despite tech war hurdles
Did the stair-climbing Chinese robot vacuum or flying robovac prototypes that wowed visitors at the 2026 Consumer Electronics Show in Las Vegas go into production? American consumers will probably not find out. Nor will US businesses and households get to buy Iron, Xpeng’s humanoid robot, so lifelike it had to be unzipped on stage to convince onlookers it was a machine. Even Elon Musk, whose Tesla is developing Optimus, praised his Chinese competitor’s model. The next generation of the dancing...
- Volkswagen China to roll out full-scenario driver assistance across three joint ventures
Volkswagen Group China said its full-scenario advanced driver-assistance system will begin rolling out in the third quarter of 2026, with the system scheduled to appear on seven new electrified models across the group’s three joint ventures in China. The system was developed by CARIZON, the joint venture between Volkswagen’s CARIAD software unit and Horizon Robotics. […]
- The Future of Deepfakes and the Decline of Reality (With Hany Farid)
The past, present, and future of deepfakes, as seen by the world’s leading expert on synthetic media.
Score: 52🌐 MovesAug 17, 2026https://www.404media.co/the-future-of-deepfakes-and-the-decline-of-reality-with-hany-farid/ - How Heidi built production-ready AI for healthcare at global scale
Presented by MongoDB Building AI that is accurate, secure, and reliable is a major engineering feat for organizations subject to the compliance obligations that govern healthcare, financial services, and transportation. The challenge of delivering AI-driven products is compounded by the fact that technology in these industries has tended to lag behind other sectors because regulation requires organizations to move carefully — and slowly. Now, many are also confronting data infrastructure modernization projects as they try to catch up with today’s demand for AI. Australian-founded AI Care Partner Heidi offers an example of successful modernization. Its flagship product, Heidi Scribe, now automates much of the administrative work that consumes clinicians’ days across more than 190 countries, supporting roughly 2.7 million patient interactions each week. That expansion rests on infrastructure decisions taken years before the company reached global scale, says Yu Liu, co-founder and chief technology officer at Heidi. “In most industries, an AI feature that is wrong two percent of the time registers as an inconvenience, while in healthcare that same error rate becomes a clinical safety issue,” says Liu. “The architecture has to be built around the assumption that every output may be scrutinised, audited, and relied upon in a patient’s care.” Why deploying production AI in healthcare is architecturally different For Heidi, data residency is a precondition rather than a feature. A clinician in Sydney, London, Tokyo, or Denver is operating under different regulatory regimes, including the Australian Privacy Principles, GDPR, APPI, and HIPAA, and their patients’ data has to live in-region. Heidi runs fully logically isolated production deployments across the world, so residency is enforced by architecture. Auditability also has to be built in from day one, because an organization needs to be able to answer what the model saw, what it produced, and what the clinician changed, for any session, months later, when called upon. “The blast radius of change must be engineered down,” Liu says. “In less regulated industries you can ship fast and fix forward, but in healthcare we invest heavily in making change safe by default, with continuous integration gates on risky change classes, canary releases, and treating even database schema and index changes as code that goes through review. Our speed is a product of that safety rather than something we achieve in spite of it.” Choosing a database to connect with AI workflows Heidi handles a diverse set of medical data collected from multiple sources, including forms, referrals, and clinicians’ notes, all of which had to be consolidated into one consistent format and one location to connect seamlessly with AI workflows. Rigid rows and columns would have been ill-suited to that workload. For Heidi, those requirements made a document database the natural choice. MongoDB gave the team the flexibility to accommodate rapidly changing AI data without constantly reshaping the underlying database. “The model is maybe 20% of the system, and the data architecture is what determines whether the other 80% holds up under real clinical load,” Liu says. An AI Scribe session isn’t a single piece of data. It’s a collection of transcripts, structured notes, templates, documents, patient context, EHR integration state, and dozens of other related artifacts that change from week to week. MongoDB lets a session’s data live together in shapes that match how clinicians actually work, and lets Heidi evolve those shapes without a migration freeze every time the product moves. " MongoDB Atlas stood out because it combined the power of the document model, which allows seamless scale, flexibility, and high performance, with built-in AI-ready features such as MongoDB Vector Search ,” Liu says. “This means that Heidi does not need another bolt-on vector database to augment its existing platform.” With more than 130 cloud regions globally alongside on-premises and hybrid options, MongoDB Atlas is the most widely available, globally distributed database platform, and its unified query API lets developers build full-text search, real-time analytics, and event-driven experiences without complicating their architecture. "Heidi Scribe converts large volumes of medical documents into vector embeddings via LangChain in Atlas, enabling semantic search that connects transcribed medical terms directly to corresponding external knowledge," Liu adds. "Migrating to Atlas reduced latency on key APIs by nearly 33%." What a trustworthy clinical RAG system requires “Retrieval is a data architecture problem before it is an AI problem,” Liu says. "In consumer RAG, you retrieve from the open web and hope, whereas in healthcare what you retrieve from is the compliance surface." Heidi Evidence retrieves from licensed clinical knowledge bases, including partners like BMJ Best Practice, NICE CKS, and MIMS, and it is jurisdiction-aware, so a U.K. clinician gets U.K. guidance and an Australian clinician gets Australian formularies, because the right answer in one country can be the wrong answer in another. Heidi’s embeddings and vector indexes live in MongoDB Vector Search, inside the same regionally isolated deployments as the rest of its data, which means retrieval physically cannot cross a residency boundary, and they are not operating a separate vector database with its own security and compliance story. Citations are a hard contract rather than a prompt suggestion, because the model only ever sees retrieved chunks that are already bound to source records. Regional isolation enables global compliance and scale “Each region is a full, isolated production deployment with its own MongoDB Atlas clusters, its own compute, and its own key,” Liu says. “That is what lets us walk into a U.S. health system, an NHS trust, or an Australian hospital group and give a clean answer on residency, because it is enforced by infrastructure rather than promised by contract," he explains. "Running multiple isolated regions with a lean team only works because the database layer is managed and consistent. We are also multi-cloud, meaning a new region can stand up another deployment on rails we have already built." That architecture has been most visible in the U.S., where Beth Israel Lahey Health, one of New England’s largest health systems, rolled out Heidi’s AI scribe following a pilot finding 74% of clinicians reported reduced after-hours documentation (“pajama time”), and where non-profit system MaineGeneral Health selected Heidi as a strategic partner in its rural healthcare work. “Entering the U.S. market meant standing up another region on rails we had already built rather than re-engineering for HIPAA after the fact,” Liu says. Lessons learned and the roadmap ahead "Re-partitioning a large, hot, always-on collection is a serious engineering program, whereas choosing a shard key on day one is a design meeting," Liu says. "We are doing that work now in partnership with MongoDB, but the lesson for anyone building a data-heavy AI product is that horizontal scale for your fastest-growing data is a founding decision, just like residency." Heidi is now extending beyond the consult note to support the full clinical workflow, from pre-visit context to post-visit documents, referrals, and workflow automation. The company is also exploring how MongoDB, large language models, and its own tooling can power an agentic ecosystem for clinical workflows. “In healthcare AI, reliability engineering is trust engineering,” Liu says. “A clinician’s trust is lost just as fast by downtime, latency, or a data inconsistency as by a bad note, and some of our highest-leverage work is invisible, including canary releases with automatic rollback, CI gates on database changes, and cross-region consistency checks. Clinician trust is the product, and trust is architectural.” Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com .
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