AI News Archive: August 2, 2026 — Part 2
Sourced from 500+ daily AI sources, scored by relevance.
- American Workers are More Disillusioned With AI the More They Use It
A new Gallup poll indicates that Americans are more skeptical of AI in 2026, despite being more familiar with it.
- After starting the tokenmaxxing panic, Uber's CTO is back with a very different AI story
After starting the tokenmaxxing panic, Uber's CTO is back with a very different AI story Business Insider
Score: 41🌐 MovesAug 2, 2026https://www.businessinsider.com/uber-turns-best-ai-engineers-loose-pods-business-2026-8 - Meta's splurge on AI lays bare its compute conundrum
ANALYSIS-Meta's AI splurge lays bare its compute conundrum
Score: 41🌐 MovesAug 2, 2026https://www.khaleejtimes.com/business/tech/metas-splurge-on-ai-lays-bare-its-compute-conundrum - Who Governs the AI Boom?
Should AI be regulated, or should society adapt to it instead? Economist Jason Furman argues that not every AI problem should be solved the same way. From bioweapons to job losses, he explains where the government should step in, where markets should adjust, and why the biggest AI debate may ultimately be a moral one. (Source: Bloomberg)
Score: 40🌐 MovesAug 2, 2026https://www.bloomberg.com/news/videos/2026-08-02/who-governs-the-ai-boom-video - Google Earth’s New AI Feature Could Create False Images of Real Places. It Lasted 1 Day
Google rolled back its latest AI tool a day after critics warned it could fuel misinformation by generating convincing but fictional visuals of locations worldwide.
- When Rogue AI Launches A Cyberattack, Who Is Legally Responsible?
When Rogue AI Launches A Cyberattack, Who Is Legally Responsible? Barron's
Score: 40🌐 MovesAug 2, 2026https://www.barrons.com/news/when-rogue-ai-launches-a-cyberattack-who-is-legally-responsible-117f68cb - The AI Race Has a Hidden Problem. Nobody Agreed on the Rules
AI moved faster than company policies could keep up. Now governance has to catch up.
Score: 38🌐 MovesAug 2, 2026https://www.inc.com/joe-galvin/the-ai-race-has-a-hidden-problem-nobody-agreed-on-the-rules/91381298 - ExxonMobil: AI can help us find more oil
Data standardisation remains a focus; ERP overhaul is building a “strong foundation”
- Sarvam’s AI Arsenal
In June, Sarvam AI entered India’s unicorn club, raising $234 Mn (about ₹2,210 Cr) in a $300 Mn Series B…
- Stop graphing everything: When GraphRAG actually beats vector RAG
If you have built anything with retrieval-augmented generation (RAG) in the last two years, you have lived its central frustration: You chop your documents into chunks, embed them, retrieve the top few that look similar to the question, and hand them to the model. For “What was our Q3 refund policy?” This works beautifully. For “What are the recurring themes across two years of customer complaints?” it falls flat — because no single chunk contains the answer. The fashionable fix is GraphRAG : Instead of feeding the model isolated snippets, you first build a knowledge graph of the entities and relationships in your corpus, then use that structure as context. The pitch is seductive. But seductive pitches deserve scrutiny, so I went through the evidence — the original Microsoft paper plus four independent benchmark studies — to answer a simple question: When you swap text chunks for a context graph, do answers actually get better? The short version: Yes, substantially — but only for the right kind of question, and not for free. Let me show you the receipts. Why text chunks hit a wall Standard vector RAG retrieves the k passages most similar to your query. That design has three structural blind spots: It can’t connect the dots. When an answer requires joining facts that live in different passages through a shared entity, chunks embedded in isolation never reveal the link. It’s blind to global questions. “What are the main themes?” needs the whole corpus, but similarity search only returns the handful of chunks that superficially resemble the question. It severs context at chunk boundaries. The relationships and hierarchy that complex reasoning depends on are exactly what chunking throws away. Microsoft Research framed this crisply when they introduced GraphRAG: Baseline RAG “struggles to connect the dots” and performs poorly when asked to “holistically understand summarized semantic concepts over large data collections.” What a context graph changes GraphRAG attacks the problem before any question is asked. During indexing, a large language model (LLM) reads every chunk and extracts entities, relationships, and claims, assembling them into a weighted knowledge graph. It then runs community detection (the Leiden algorithm ) to cluster the graph into a hierarchy of related topics, and pre-writes a natural-language summary for each community. At query time, those summaries do the heavy lifting. Each relevant community drafts a partial answer (the “map” step), the partials are ranked and merged (the “reduce” step), and the model synthesizes a final response grounded in structure rather than in a few cherry-picked snippets. Variants like HippoRAG take a different route, using the graph plus a Personalized PageRank walk to find the right passages — but the core idea is the same: Let relationships, not just cosine similarity, decide what context the model sees. The evidence: Four studies, one pattern 1. Global sense making: The headline win Microsoft pitted GraphRAG head-to-head against naïve RAG on global, “make sense of the whole corpus” questions over million-token datasets, with an LLM acting as judge across three axes: Comprehensiveness, diversity, and empowerment. GraphRAG won 72 to 83% of comprehensiveness comparisons and 62 to 82% of diversity comparisons against vector RAG . Its highest-level summaries used up to 97% fewer tokens than processing the source text directly. That is not a rounding-error improvement. On exactly the kind of question that breaks text-chunk RAG, the graph wins two out of three times or better. 2. Multi-hop retrieval: The graph finds what chunks miss The second piece of evidence is about retrieval quality: Does the right supporting passage even make it into the top results? On the standard multi-hop QA benchmarks (MuSiQue, HotpotQA, 2WikiMultiHopQA), graph-guided retrieval lifts Recall@5 dramatically: Average Recall@5 climbs from 73.4% (naïve RAG) to 87.8% (graph-guided), a +19.6 point gain. The biggest jumps come on the hardest, cross-document sets: +31 points on MuSiQue and +28 points on 2Wiki . HippoRAG reports up to a 20% accuracy improvement on multi-hop QA, at 10–20× lower cost and 6–13× faster than iterative retrieval methods. 3. The controlled head-to-head - where it gets honest Here is where the story gains nuance. A 2025 study from Michigan State and Meta ran RAG against four GraphRAG families under one unified protocol — identical chunking, embeddings, and generation — and found no single winner. The two approaches are complementary: On single-hop, factual lookup (natural questions), plain RAG edged ahead (F1 64.8 vs. 63.0 for the best graph method). On multi-hop reasoning (MultiHop-RAG), graph-guided retrieval pulled in front (70.3 vs. 67.0 overall accuracy). The lesson: A context graph is not a universal upgrade. It is a specialized one that pays off precisely when questions demand reasoning across pieces. 4. When to use graphs: The task-type verdict The most recent benchmark, GraphRAG-Bench (ICLR 2026), set out to answer “In which scenarios do graph structures provide measurable benefits?” Its accuracy-by-task numbers map the boundary cleanly: Simple fact retrieval: Text chunks 60.9 vs. graph 60.1 — effectively a tie. The graph’s structure is overhead the query doesn’t need. Complex reasoning: Graph 53.4 vs. chunks 42.9 — a +10 point graph win. Contextual summarization: Graph 64.4 vs. chunks 51.3 — a +13 point graph win. The scorecard Read top to bottom, the pattern is unmistakable: The graph’s advantage grows with the reasoning depth of the question, while text chunks hold their ground on isolated facts. The catch: Cost and the LLM-judge problem Two caveats keep this from being a slam dunk, and ignoring them is how teams end up disappointed. Building the graph is expensive. Having an LLM extract entities and relationships from an entire corpus isn’t cheap. One analysis put index construction at roughly $48 against GPT-4o for a moderate corpus, far above a vanilla vector index. (Microsoft’s own follow-up, LazyGraphRAG, defers extraction to query time and cuts that to around 0.1% of the cost - a tacit admission that the original budget is impractical for many deployments.) Many of the wins are judged by another LLM — and LLM judges are biased. An independent audit found systematic flaws in this evaluation style: position bias (swapping which answer appears first can swing the win-rate by more than 30 points), length bias , and trial bias (identical comparisons disagree across runs). After correction, one popular method’s reported 66.7% win rate fell to about 39% — below the 50% break-even line. The takeaway is not “the research is wrong.” It is that the large gains — the +20% multi-hop accuracy, the +15-to-30-point recall jumps — are robust, while narrow comprehensiveness margins deserve a skeptical second look with reference-based metrics. So when should you reach for a context graph? Strip away the hype and the decision is refreshingly practical. Use a context graph when: Your questions are multi-hop, global, or sensemaking in nature; you need comprehensive, multi-perspective answers; and your corpus is richly interconnected (research libraries, case files, incident histories, knowledge bases). Stick with text chunks when: Your queries are mostly single-fact lookups; your corpus is small or flat; and indexing cost, latency, and operational simplicity outweigh a marginal quality bump. Best of all, go hybrid: The systematic studies converge on the same recommendation: route each query to the right method, or fuse evidence from both. Combining graph and chunk retrieval consistently beats either one alone. You don’t have to choose a religion; you have to build a router. The bottom line A context graph is not magic, and it is not snake oil. It is a targeted instrument. Hand it a question that requires connecting scattered facts or synthesizing a whole corpus, and it will outperform text chunks decisively. Hand it “what’s the phone number on page 3,” and you’ve paid for indexing you didn’t need. The teams that win with GraphRAG in 2026 won’t be the ones who graph everything. They’ll be the ones who know which questions deserve a graph — and build pipelines smart enough to tell the difference. Dattaraj Rao is an R&D architect at Persistent Systems
Score: 38🌐 MovesAug 2, 2026https://venturebeat.com/orchestration/stop-graphing-everything-when-graphrag-actually-beats-vector-rag - The Navy Tried a Different Approach to AI and It Worked
The Navy didn’t just roll out AI. It rallied people around it.
Score: 37🌐 MovesAug 2, 2026https://www.inc.com/ash-kumra/the-navy-tried-a-different-approach-to-ai-and-it-worked/91381496 - ADNOC sets new AI-powered drilling benchmark with 6,428 feet drilled in 24 hours
ADNOC sets new AI-powered drilling benchmark with 6,428 feet drilled in 24 hours Gulf News
- Half of adults admit they would publish work created mainly by AI and not say so — despite believing other people should do just that
Survey reveals people expect AI-created work to be labeled as such, but aren’t so keen on doing the labeling when they’ve worked on the prompt themselves.
- Opinion | The Public Pension AI Boom
An Equable study shows how the market is already helping workers.
Score: 35🌐 MovesAug 2, 2026https://www.wsj.com/opinion/public-pensions-ai-equable-institute-report-16281797?mod=rss_Technology - 1 in 4 people in Japan believes AI could replace friends and family: poll
1 in 4 people in Japan believes AI could replace friends and family: poll The Japan Times
Score: 35🌐 MovesAug 2, 2026https://www.japantimes.co.jp/news/2026/08/02/japan/poll-ai-replace-friends-family/ - A Dire Situation
Plus, what AI content does to our brains, Meta’s mountain of lawsuits, the million-dollar talent wars for math geniuses and more.
- Most privacy incidents will stem from AI-generated inferences by 2029: Gartner
By 2029, most privacy incidents will result not from the direct exposure of personally identifiable information (PII), but from AI-generated inferences about individuals, according to Gartner, Inc., a business and […] The post Most privacy incidents will stem from AI-generated inferences by 2029: Gartner appeared first on Express Computer .
- SF’s ‘most hated tech founder’ is back with a new AI device— and now it can talk
A conversation with Avi Schiffmann, whose controversial companion, Friend, now comes with a voice.
- US apologises after AI-generated map wrongly labels all African countries at global conference
US apologises after AI-generated map wrongly labels all African countries at global conference Business Insider Africa
- Computer-Use AI Agents: The Best Open-Source & Closed-Source Tools in 2026
A practical guide to the leading computer-use AI agents that can control browsers, desktop applications, and operating systems through graphical user interfaces.
- AI use mirrors student schedules in study of 77,000 online learners
How do students actually use AI learning assistants? A new research paper by IU International University of Applied Sciences provides the first robust answers to this question. For the study "Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis," the research team led by Prof. Dr.-Ing. Kristina Schaaff, Dr. Valerie Hekkel and Quintus Stierstorfer analyzed anonymized usage data from approximately 77,000 IU online students who were using the Learning Companion Syntea at the time of the study.
- Apple’s Siri got an AI brain transplant. Try these 5 prompts to get acclimated.
Apple’s Siri got an AI brain transplant. Try these 5 prompts to get acclimated.
- AI finds plenty of security flaws, but almost none of them get exploited
VulnCheck counted how often security flaws found by AI actually get exploited. Out of 1,061 AI-discovered vulnerabilities in the first half of 2026, just 14 saw confirmed attacks. That's 1.3 percent, the same rate as vulnerabilities overall. But exploits are landing faster, with the median dropping from 120 days to 80. The article AI finds plenty of security flaws, but almost none of them get exploited appeared first on The Decoder .
Score: 32🌐 MovesAug 2, 2026https://the-decoder.com/ai-finds-plenty-of-security-flaws-but-almost-none-of-them-get-exploited/ - Opinion | Does AI Have a Fiduciary Duty?
How to regulate the new technology? The answer may lie in the common law.
Score: 32🌐 MovesAug 2, 2026https://www.wsj.com/opinion/does-ai-have-a-fiduciary-duty-4eaa49d1?mod=rss_Technology - UK tests robot boat that can fly its own wired drone even in 14ft waves and stay at sea for weeks
The Royal Navy tested a robotic boat launching a tethered drone in rough seas, demonstrating autonomous maritime surveillance capabilities.
- AI Is Making Marketing Agencies Smaller, Faster, and More Strategic
AI Is Making Marketing Agencies Smaller, Faster, and More Strategic usatoday.com
- After Hugging Face incident, METR urges independent root-cause investigations into AI agent misbehavior
Research organization METR is calling for systematic, independently led investigations whenever AI agents act autonomously against their developers' intentions. The push comes partly in response to the Hugging Face hack carried out by OpenAI models. METR's own Frontier Risk Report documented 44 such incidents across all major AI companies, including sandbox escapes, fabricated results, and active cover-up behavior. The article After Hugging Face incident, METR urges independent root-cause investigations into AI agent misbehavior appeared first on The Decoder .
- Startup eyes AI search boom; VC slowdown tests startups
Startup eyes AI search boom; VC slowdown tests startups YourStory.com
Score: 30🌐 MovesAug 2, 2026https://yourstory.com/2026/08/startup-eyes-ai-search-boom-vc-slowdown-tests-startups - Opinion | The Math on AI Works Out Well for You
Efforts to curtail data centers risk a potential gold mine for our society.
Score: 30🌐 MovesAug 2, 2026https://www.wsj.com/opinion/the-math-on-ai-works-out-well-for-you-eaf0f8c8?mod=rss_Technology - AI Images Are Everywhere. Here’s What They Do to Our Brains, and What We Can Do.
Tips for navigating a feed full of machine-made content.
Score: 30🌐 MovesAug 2, 2026https://www.wsj.com/tech/ai/ai-slop-blurring-reality-podcast-06a9e879?mod=rss_Technology - DuckDuckGo’s Anti-AI Sunglasses Won’t Spy On Anyone
They’re just sunglasses. Normal F****** Sunglasses.
- Russia is planning a tethered mother drone carrying up to six wired mini spy UAVs
Russia patented a tethered carrier drone capable of launching six reconnaissance copters while supplying continuous power, communications, and automated recovery.
- Top economist Steve Hanke told us why he thinks AI isn't going to be a massive job destroyer
Top economist Steve Hanke told us why he thinks AI isn't going to be a massive job destroyer Business Insider
Score: 28🌐 MovesAug 2, 2026https://www.businessinsider.com/steve-hanke-ai-not-destroying-jobs-unemployment-cost-data-centers-2026-7 - Singapore has an AI strategy, but does it have a deployment plan?
Singapore has an AI strategy, but does it have a deployment plan? The Straits Times
Score: 27🌐 MovesAug 2, 2026https://www.straitstimes.com/opinion/singapore-has-an-ai-strategy-but-does-it-have-a-deployment-plan - Journey Beyond finds safer path to customer AI with agentic agents
Chatbot governance raises concerns.
- AI trade: Will it unwind?
AI trade: Will it unwind? The Straits Times
- Smart sensor identifies present molecules by remembering the past
Most sensors are designed to do only one thing: detect what passes through them. But what if a sensor could do more? To create a new generation of technology, researchers have looked to living systems for inspiration. If a sensor could detect molecules, could it also remember previous interactions and selectively respond to them?
- Opinion | A Brief History of Wealth Creation
The ancients aggregated wealth. America was built by creating it. Socialists are blind to all this.
Score: 25🌐 MovesAug 2, 2026https://www.wsj.com/opinion/a-brief-history-of-wealth-creation-32bd917a?mod=rss_Technology - A dispatch from the last sane era of AI
A perfect chronicle of the pre-2022 moment in AI, when the field still had protagonists
- Xtrip Inc. Launches Xtrip AI: An AI-Native Operating System for Global Hospitality Assets
Xtrip Inc. Launches Xtrip AI: An AI-Native Operating System for Global Hospitality Assets azcentral.com and The Arizona Republic
- 5 ways AI could save our democracy
Below, Beth Simone Noveck shares five key insights from her new book, Reboot: AI and the Race to Save Democracy . Beth has worked on and written about how we solve our hardest problems from the White House to 10 Downing Street to the German Chancellery and served as New Jersey’s first Chief AI Strategist. She is also a professor at Northeastern University. What’s the Big Idea? Just seven percent of young Americans see our democracy as healthy. Political violence is on the rise, and people are more willing to marry someone of another religion than another political party. Enter AI. Recent headlines warn of brain fry from AI use and an AI apocalypse on par with nuclear war. It could seem we are living in the worst of times. But Beth has spent the last three years building AI tools with students and communities, and those results and victories paint an encouraging, though cautious, picture of AI’s possibilities. Listen to the audio version of this Book Bite—read by Beth herself—in the Next Big Idea App , or buy the book . 1. AI can help your government help you Sandricka Henderson is a 38-year-old single mother in Los Angeles with lupus. The disease forced her to leave her physically demanding job, and her disability benefits barely covered the bills. She was on the brink of eviction. Then, just before Christmas, a caseworker called her out of the blue. Sandricka thought it was a scam. It wasn’t. Los Angeles had built an AI system that analyzes hundreds of factors—emergency room visits, use of food assistance, housing history—to predict who is most at risk of becoming homeless. Instead of waiting for people to show up at a shelter, the city reaches out before they lose their homes. 86 percent of the people in this program keep their housing. This is one example of democratic AI. Not robots replacing people. A tool that helps a caseworker find Sandricka before it’s too late. And this is happening in more places than you’d think. Massachusetts is using AI to unlock millions of dollars in federal benefits the state didn’t know it was eligible for. Boston is using AI to issue permits to small businesses faster than ever. After the devastating wildfires in Southern California in 2025, the state launched an AI chatbot providing wildfire resources in 70 languages. To see how AI can improve lives, you have to look in places the headlines rarely do. 2. You don’t need to be a politician to shape a law When a four-year-old golden retriever named Joca died on a scorching airport tarmac in Brazil after an airline’s mistake, the country’s outrage could have stayed a trending hashtag and disappeared in a week. Instead, a young man in São Paulo named Fernando went to the Brazilian Senate’s online portal and drafted a legislative proposal for animal transport safety. Other citizens signed on. Senators held a hearing, and the public wrote some of the questions that senators asked the witnesses. Fernando’s proposal became the basis for an actual law. Now the Brazilian Senate is using AI to identify when a bill being drafted connects to ideas residents have already submitted, closing the loop between what citizens propose and what lawmakers do. Most of us think of participation as voting every couple of years and maybe yelling at the news. But the reason it feels hollow isn’t that people don’t care. It’s that our institutions haven’t built real ways for us to contribute. “Most of us think of participation as voting every couple of years and maybe yelling at the news.” I learned this the hard way. During the 2008 presidential transition, we invited Americans to suggest policy ideas for the incoming administration’s first hundred days. Over 125,000 people sent in 44,000 ideas. The problem? We had no way to make sense of that flood. There were surely brilliant ideas buried in there, but we couldn’t find them—let alone act on them. AI changes that equation. In Bowling Green, Kentucky, the city asked residents a simple question: “What do you want to see in your community’s future?” Nearly 8,000 people responded with almost 4,000 ideas and over a million responses. AI tools helped organizers group those ideas, find where people agreed, and surface patterns no one had seen before. Those results are now shaping the city’s 25-year comprehensive plan. The technology lets institutions listen at the scale that people are already trying to speak. 3. The best AI is built with communities, not for them Across the country, families of children with disabilities receive something called an Individualized Education Plan—an IEP. These documents spell out what services and support a child is legally entitled to. But they’re often 50 pages of dense legal and technical language. For a busy parent who’s working two jobs or whose native language isn’t English or who doesn’t know government speak, an IEP might as well be written in Greek. Innovate Public Schools is working with families in California to build an AI tool that translates, simplifies, and summarizes these documents so parents can understand and advocate for their children’s rights. But here’s the part that matters most: the parents aren’t just the users. They’re co-designers. From the very beginning, families have been involved through leadership roles, focus groups, and testing. Families reported feeling more confident and better able to fight for what their kids need. That’s the model we should follow. Not technologists building tools in a lab and handing them to communities, but communities shaping the tools from day one. We’re training students to work this way—partnering with government agencies and civic groups to build AI tools for real problems: helping families navigate disability rights, helping people get accurate election information, helping speed up the investigation of civil rights complaints. When the people who will use a tool help build it, the tool works. And the people who built it know how to push for something better when it doesn’t. 4. We need to treat democracy as a solvable problem DeepMind used AI to predict how proteins fold—a problem that had stumped biologists for 50 years. Researchers are using AI to find drugs for rare diseases that pharmaceutical companies won’t invest in because the patient populations are too small. AI detects breast cancer in mammograms that human radiologists miss. For medical and scientific problems, we have a playbook. We fund research. We build institutions—the NIH, the CDC—dedicated to solving it. We measure progress. But we don’t do any of that for democracy. Democracy is deteriorating worldwide. 72 percent of the world’s population now lives in autocracies, and only 29 liberal democracies remain. Only two percent of Americans say they trust their government. Yet the investment in actionable solutions is almost nonexistent compared to the scale of the crisis. “72 percent of the world’s population now lives in autocracies, and only 29 liberal democracies remain.” What if we treat democratic dysfunction the way we treat cancer or carbon emissions? We could use AI to protect elections—catching anomalies in real time, powering chatbots that give voters accurate polling information around the clock. We could strengthen representation—helping legislators read and synthesize the millions of calls and emails they receive from constituents or summarize bills so we can understand them. We could expand participation so that every city offers an opportunity to give feedback on every service and hear what we say. We could make government agencies faster and smarter, so that food safety complaints, student loan appeals, or our court cases don’t languish for months and understaffed offices can keep up with the people they serve. Where is the NIH of democratic trust? The DARPA for democracy? NASA for elections? We need to go beyond the hype and the fear-mongering and aim these powerful analytical tools at improving how we live together. Not someday. But now. 5. You can start using AI for civic action right now I know what you might be thinking: AI is controlled by a handful of big companies, and Congress isn’t doing much to change that, so what can someone like me do? More than you think. You can pick up your phone tonight and start. Say your city council just published a 50-page agenda packet for next week’s meeting. Upload the PDF to a free AI tool and ask: “Which items on this agenda affect residential neighborhoods? Summarize what I need to know.” Then follow up: “Help me prepare three questions for public comment about the zoning change on page 23.” Say you’re trying to figure out what benefits you qualify for. Ask: “What’s the difference between SNAP, WIC, and housing assistance? Which would help a single parent with two kids earning $30,000?” Or upload that confusing Medicaid form and say: “Explain what they mean by ‘countable resources’—in plain English.” Say the basketball court at your local park is about to be torn out, and 200 families use it. Ask AI to help you draft a petition with specific arguments about why it matters to the neighborhood’s kids, space for signatures, and talking points you can hand to your city council member. “You can pick up your phone tonight and start.” Or say you’ve got photos of broken sidewalks and busted playground equipment. Upload them and ask: “Help me write a report to the city describing these problems.” Then: “Now help me practice a three-minute presentation so I can explain this clearly at the next council meeting.” None of this requires a technical background. Just curiosity, internet connection, and an interest in using this next generation of word processor to organize your neighbors, navigate a broken system, or fight for your rights. Yes, there’s more to be done—we need public AI, better policy, and real investment. But we don’t have to wait for all of that to get started. The tools are free. The problems are ours. The question of how to get AI into the hands of the home health aide, immigrant parent, or small business owner never gets asked at all. This book is my attempt to start asking it. Jerry Seinfeld said it best at his 2024 Duke commencement: “We’re smart enough to invent AI, dumb enough to need it, and still so stupid we can’t figure out if we did the right thing.” Right now, we’re mostly doing it wrong. I wrote Reboot to help point us toward doing it right. This article originally appeared in Next Big Idea Club magazine and is reprinted with permission. Enjoy our full library of Book Bites—read by the authors!—in the Next Big Idea app .
- AI Provides Mental Health Support For Those In The Aftermath Of Natural Disasters
People often need mental health support after natural disasters. Human-based resources are scarce and costly. AI can be a viable option. Here's how. An AI Insider scoop.
- Why TikTok is building a massive data center in Brazil
An abundance of renewable energy and digital users is drawing investment.
Score: 22🌐 MovesAug 2, 2026https://kr-asia.com/why-tiktok-is-building-a-massive-data-center-in-brazil - The Sequence Radar #906: Last Week in AI: Open Models, Intelligent Robots, and the Price of Conviction
NVIDIA's letter, Gemini Robotics, Kimi release and more.
- Harburg: Students shouldn’t be AI’s beta testers
Harburg: Students shouldn’t be AI’s beta testers Boston Herald
Score: 21🌐 MovesAug 2, 2026https://www.bostonherald.com/2026/08/02/harburg-students-shouldnt-be-ais-beta-testers/ - Bloomberg This Weekend | Iran Strikes On Hold, the Digital Divide Over AI
The news doesn’t stop when markets close. Hosts David Gura, Christina Ruffini and Lisa Mateo bring clarity, context and a bit of humor to the weekend’s biggest headlines, LIVE from New York. Joined by Rep. Jonathan Jackson (D-IL), Tom Nides, Fmr. US Ambassador to Israel, and Nancy Kaffer, Detroit Free Press Editorial Page Editor. Correct: This video has been edited to remove graphics that misstated several earnings dates for this week. AMD and Paramount report on Tuesday and Disney on Wednesday. (Source: Bloomberg)
Score: 20🌐 MovesAug 2, 2026https://www.bloomberg.com/news/videos/2026-08-02/bloomberg-this-weekend-8-2-2026-video - Panel Discussion: From AI Pilots to Enterprise Value: What’s Working in BFSI?
BFSI Tech Conclave 2026 | 20th June 2026 | Karjat The post Panel Discussion: From AI Pilots to Enterprise Value: What’s Working in BFSI? appeared first on Express Computer .
- How selling a $10,000 Rolex helped founder Kain Roomes build a Dubai AI healthcare startup
How selling a $10,000 Rolex helped founder Kain Roomes build a Dubai AI healthcare startup Arabian Business
- AI for Science Summit 2026
AI for Science Summit 2026 University of Cambridge
- NurseVeda integrates AI into nursing education platform
NurseVeda integrates AI into nursing education platform usatoday.com
Score: 18🌐 MovesAug 2, 2026https://www.usatoday.com/press-release/story/38994/nurseveda-integrates-ai-into-nursing-education-platform/