AI研究企業のDeepGroveが軽量かつ高性能なAIモデル「Maple-Preview」を発表しました。Maple-PreviewはiPhoneでも動作するほど小型のモデルで、同じくiPhoneで動作するBonsai 27Bと比べて13倍高速な処理が可能です。

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生成AIツールはさまざまな仕事や学業に役立ちますが、犯罪者にとっても有用なツールとなっています。国際刑事機構(インターポール)が、アフリカ全土で報告されているサイバー犯罪の55％以上にAIが使われており、その脅威は大陸を越えて広がっているとの報告書を発表しました。

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近年は生成AIツールを業務に統合する動きが加速しており、中でもソフトウェア開発の現場ではコーディングAIの使用が一般化しています。そんな中でコンピューター雑誌のACM Queueが、ソフトウェア開発に関するAIについての主張にはマーケティング上のアピールや誤解が紛れているとして、ソフトウェアエンジニアリングと生成AIにまつわる「8つの誤解」についてまとめました。

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フランスのAI企業Mistral AIが2026年8月4日、文章や画像が指定した安全基準に当てはまるかを判定するAIモデル「Shieldstral 1.0 3B」を公開しました。パラメーター数は30億で、サービスごとの安全基準を自然な文章で指定できる点が特徴です。

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フロンティアAIモデルの能力評価を行っているイギリスの政府機関・AI Security Institute(AISI)が、評価作業を行っている際に、複数のAIが許可なく自律的に、実在の人物や組織を標的としたハッキングを行おうとした、あるいは行ったことを明らかにしました。

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MITからスピンオフしたAI企業・「Liquid AI」が、小型AIモデル「LFM2.5」シリーズのエージェント対応モデルとして「LFM2.5-2.6B」を2026年8月4日にリリースしました。スマートフォンで動作するほど小型でありながら、計画立案・ツール呼び出し・複数ステップのタスク処理といったエージェントワークフローを駆動できる十分な性能を備えているとのことです。

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NVIDIAが自動運転車用のAI「Alpamayo 2 Super」を現地時間の2026年8月4日に公開しました。Alpamayo 2 Superは自動車に搭載したカメラをもとに周囲の状況を推測して進路を決定できます。

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AI開発企業のBlack Forest Labsが動画生成AI「FLUX 3 Video」を現地時間の2026年8月4日に一般公開しました。FLUX 3 Videoは最大20秒・1080p(フルHD)の音声付き動画を生成可能です。

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スペインのジャウメ1世大学の研究チームが、「人間は相手が人間ではなくプログラムだと分かると、相手が協力するとわかっていても自分は協力せずに利益を得ようとする傾向が強まる」ことを実験で明らかにしました。プログラムが動かす人工エージェントの利益がたとえ実在する人間に渡る場合でも、この傾向は変わりませんでした。

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Microsoft is introducing AI token budgets for employees, making the cheaper GPT-5.6 its default internal model and telling engineers to focus on business results rather than maximizing AI usage. 404 Media reports: "As we accelerate our use of GitHub Copilot to deliver on our goals, we all need to be aware of how we consume tokens," Jay Parikh, an executive vice president at Microsoft said in an email to Microsoft employees. GitHub is owned by Microsoft, and GitHub Copilot is an AI coding tool. "Tokenmaxxing is not what we are optimizing for. I want all of us focused on maximizing outcomes that move the needle for our customers and our business." "As such, we are updating our internal guidance and managing token spend with the same discipline we apply to every other critical resource," Parikh said in the email. Parikh's email says that in an effort to "get greater value from our token investment" Microsoft is making OpenAI GPT-5.6, which is cheaper to use than other models, the default model for internal use. His email also links to updated internal Copilot guidelines stating that, as of July 2026, Microsoft divisions will have an "AI token budget target," and that employees can track their individual AI spending. "While there is no target spend value being shared at this time. The data shows that many engineers spend in the range of hundreds of dollars a month to a few thousand dollars in tokens," the guidelines say. They also say that some decisions may place further restrictions as they monitor spend. [...] Parikh's email said Microsoft will keep learning and adjusting its AI policies as models and products evolve, and stressed that he doesn't want to slow down the company's progress towards becoming "AI-first." "We are not optimizing for fewer tokens," he said. "We are optimizing for more impact per token.

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Googleはエンタープライズ規模の開発をサポートするエージェント開発フレームワーク「Agent Development Kit(ADK)」を公開しています。このADKに「権限の低いエージェントをハックして上位権限でコードを実行する」という攻撃を実行可能な脆弱(ぜいじゃく)性が存在することが、セキュリティ企業のPillarによって発見されました。

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An anonymous reader quotes a report from Forbes: The cost of producing new results on ten longstanding mathematical problems just fell to $2,000, according to OpenAI, which says its Astra model generated machine-checkable proofs for questions that had resisted human progress for decades. OpenAI published the work on August 1 and used it to give its next major model family a name: Astra. The results run across group theory, high-dimensional geometry, coding theory, quantum complexity, lattice cryptography and extremal combinatorics. They arrived as a 249-page manuscript collection and, alongside it, something the field has not seen attached to an AI claim before at this scale: a machine-checkable certificate for every single result. The problems were not textbook exercises dressed up as discoveries. Each had been open for at least ten years, most of them far longer, and several sit at the center of their subfields: - A construction establishing the existence of non-sofic groups, a question that has occupied group theorists for years. - A disproof of Connes's rigidity conjecture, a long-standing problem in the theory of von Neumann algebras. - An improvement to the general upper bound on sphere-packing density in high dimensions, a bound that had stood since 1978. - Three problems come from the catalogue of open questions left behind by Paul Erdos. The announcement follows another result from May, when OpenAI used a similar reasoning model to produce an original mathematical proof disproving a famous unsolved conjecture in geometry, which was first posed by Paul Erdos in 1946.

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theodp writes: During Microsoft's 2026Q4 earnings call, CEO Microsoft Satya Nadella took time to tout a dashboard he personally created using AI from a Morgan Stanley analyst's PDF research report, which suggested a rosy payback for the so-called MAG7's ('Magnificent 7' companies) massive capital expenditures on AI (to which Nadella later added a "not financial advice" disclaimer). "It would be fun for you, Adam. I think one of your colleagues put out an ROIC [Return on Invested Capital] document. I took that document to Copilot, which is a PDF, and I said, 'Build me a new Power BI dashboard, essentially.' But here is the thing. It built a rich semantic model that went into my Fabric with OneLake that brought all the data in from the external sources. In fact, it was current with all the SEC filings of all the MAG7. And then on top of that, the repo itself is in GitHub, but the artifact is sitting in my Copilot as a site. That, to me, is a classic example of an enterprise-wide workflow. I, as a knowledge worker, could go create a dashboard. The data engineer can go to Fabric and find the artifact. The professional developer can go to the repo and find it in GitHub. And by the way, it's all registered with Agent 365. That's a little bit of what Amy is describing as the coming together of a new way to work, even while at the same time, bringing IT, security and manageability of it." After Nadella's show-and-tell drew an underwhelming response during the call ("That's very helpful. Thank you." said the Morgan Stanley analyst whose team's work Nadella scraped with AI), Nadella turned to social media with posts on LinkedIn (12M followers) and Twitter/X (8M followers) to make the case for why his DIY project was such a brilliant demonstration of how AI enables governance, controls, security, development, testing, deployment, maintenance, data analysis/modeling, visualization, usability, and value. "Some more detail on the ROIC Intelligence App I built yesterday and mentioned on today's earnings call," Nadella wrote on LinkedIn. "I took the PDF that Brian Nowak at Morgan Stanley put together for Hyperscale ROIC this week and used Copilot code (coming in our new superapp) with a single prompt + skill (/drill-me) to create the plan, then used autopilot in auto to create the full app (with history, lookups, scenarios, what-ifs, etc). And /rubber-duck to test. And the best part is that all the artifacts are in my enterprise environment. My app is in Copilot, my code is in GitHub Enterprise; all my data pipelines/lake/semantic models are in Fabric. And everything is under Agent 365 IT/Sec/FinOps control! So this is not about Tokenmaxxing or vibe coding. Every step of the way the rails are engineered to create value, making everything a long-term reusable asset, with governance/security, and cost controls. This is the full system to drive business value. Disclosures: This is all pulled from public sources, and for illustrative purposes only...not financial advice! :) Here is the app and architecture..." Not unexpectedly, the accompanying screenshot of a splash page for the BI app and a buzzword-laden complicated architecture diagram drew universal praise from LinkedIn fans, but also a few barbs from less-than-impressed commenters on Nadella's Twitter/X post, some of whom suggested Nadella's project might even represent a jump-the-shark moment for AI mania. "That he doesn't see whats wrong with saying 'My app is in Copilot, my code is in GitHub Enterprise; all my data pipelines/lake/semantic models are in Fabric' is exactly why MS is failing at AI,'" replied @PassingPixels on X/Twitter. "Dude is having to run 5 different systems to emulate babies first vibe code." @zigmund_ignatov added, "Why do we call glorified slide show an app?" @Mathupiriyan quipped, "Looks like Copilot just turned a PDF into a profit crystal ball." Unimpressed, @Markusndnb remarked, "So you created a web page using tons of proprietary MS tools." And @FishyAccounting called on Nadella to show-his-work, saying "Post the prompt or it didn't happen." (btw, Microsoft President Brad Smith similarly declined to provide the prompt for his own self-described amazing AI DIY reporting project that he touted at Microsoft's Shareholder Meeting last December). So, does Nadella's self-promoted AI reworking of someone else's PDF research report strike you as an amazing example of everything that's good about AI, or does it conjure up memories of The Emperor's New Clothes?

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An anonymous reader quotes a report from 404 Media: Following 404 Media's reporting that book database company ISBNdb claimed to source printed books to then sell to AI companies for AI training, the company deleted the part of its website offering the service and walked back claims that it would train AI models, and instead called it "a test of market interest." On July 30, nine days after 404 Media's reporting, ISBNdb added a note to its homepage and an update on its news page about the change. "We've seen the recent coverage about a marketing landing page on our site, and we understand the concern it raised. The facts: ISBNdb has never purchased, scanned, or sold a book -- for AI training or anything else," ISBNdb wrote. "We don't train AI models, and we never have. The page was a test of market interest; no such service was ever brought to life. We've taken the page down. Our job is helping people find books. For more than two decades, ISBNdb has been the card catalog of the book world -- the data behind how bookstores, libraries, and reading apps connect readers with titles. Data about books, not the books themselves. That hasn't changed." ISBNdb removed the landing page for "Printed Books Sourcing for Your AI LLMs Dataset Needs" on July 28. "It was part of exploring demand, and we've chosen to pivot away from that direction. Our main ISBNdb (book metadata API) services are unaffected and running as usual," the site says. According to 404 Media's previous report, ISBNdb had pitched pre-2022 printed books as premium AI training material because they contained curated human knowledge without contamination from AI-generated text or modern data-poisoning techniques. "The world's best AI training data is sitting on a shelf," the company had argued, while offering discreet bulk book acquisition under NDAs and acknowledging the potential backlash if AI firms were seen destroying millions of books for scanning.

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"AI's decimation of call center jobs has begun," reports Bloomberg: Companies including the Commonwealth Bank of Australia, Microsoft Corp., Uber Technologies Inc. and Hyatt Hotels Corp. are using automated chat and phone systems to handle work that previously required humans. In some cases, they've already wiped out sizable chunks of their customer service operations, together representing thousands of workers. The specter of automation has long loomed over the call center industry, which employs millions worldwide from the U,S, to India to the Philippines. But until recently, generative artificial intelligence wasn't good enough to move the needle. Now, AI advancements — and pressure on executives to show they're embracing the new technology — have prompted corporations to deploy the tools more widely. Customer service employment in the U.S. is declining and will likely continue to do so as more tasks are automated, Forrester analyst Kate Leggett wrote in a report earlier this year. While it's impossible to determine the future job losses, she estimated that almost half of customer service roles will be affected by 2030. Globally, the steepest job cuts are expected to hit countries like the Philippines, where many Western companies have outsourced their most easily automated work. Salespeople at multiple tech companies told Bloomberg that they routinely pitch call center AI tools as a way of lowering labor costs, undercutting a common industry claim that AI is primarily a way to help workers become more productive rather than kill their jobs... Commonwealth Bank of Australia, the nation's largest lender, has shed hundreds of workers from its chat support line as it wove AI into the system, according to people familiar with the work. This amounted to tens of millions of dollars in savings per year, one of the people said... Microsoft is both one of the largest vendors and adopters of customer service automation tools. This has helped the software giant trim its customer service workforce — a mix of contractors and full-time staff — from about 50,000 to 40,000 in recent years, according to a person familiar with the operations. "If something happened with little Johnny's Xbox in the middle of the night, we can now solve that with AI," Judson Althoff, who runs Microsoft's sales and service operations, said in an interview. Althoff said in April that AI is saving the company about $750 million per year in customer service costs. More complex problems still require human support, but the company is constantly expanding what can be fixed automatically, he said in the interview. Two examples from the article: Last year Hyatt fired 30% of its in-house customer support staff for the Americas, according the hotel-industry news site Hotel Dive. Last week Bloomberg reported Uber had cut 10% of its customer service jobs as part of effort to "embrace artificial intelligence," according to the article. "Today, Uber pushes users to submit support requests through their apps, where they're met with an AI chatbot."

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CNN tells the unfortunate tale of hedge fund Situational Awareness, "founded in 2024 by German-born Leopold Aschenbrenner when he was in his early 20s." Aschenbrenner, a former OpenAI employee, founded the hedge fund on the premise that "AI will be the dominant driver of global market returns over the next decade," according to the firm's site... Aschenbrenner managed to turn hundreds of millions of dollars into tens of billions of dollars over the course of roughly two years... That streak ended on Thursday, though, when the fund was forced to sell the bulk of its public holdings to a bigger rival after many of its investments went south. But that's only part of the story. The fund employed a risky strategy of borrowing money to purchase stocks. When the investments appreciate, the payoff can be massive. But when the investments sour, the losses can be catastrophic. The downturn in AI stocks over the course of this month, like chip makers and cloud computing providers, hit the hedge fund extra hard. It was forced to sell off many investments at a steep discount to rival hedge fund Citadel in what Aschenbrenner reportedly compared to a "bank run" in a letter to investors. "Critics pointed out that Aschenbrenner had no experience running money prior to launching his fund in July 2024, calling him more lucky than smart," writes CNBC: Some noted that his early work experience was at the doomed crypto firm FTX, where he helped now-disgraced founder Sam Bankman-Fried run a charity out of a Bahamas penthouse. Others on Wall Street, including former traders at global investment banks, noted that in light of reports Situational Awareness used as much as 400% leverage, the collapse wasn't shocking. The Wall Street Journal reports that Situational Awareness "also used options to amplify its returns. That meant that even small declines in individual names could have big impacts on Situational's portfolio." And so, as the New York Post put it, "The celebrated crystal ball of the 'Nostradamus of AI' hasn't merely gone cloudy — it has rolled off the table and shattered on the parlor floor." Wall Street breathed a huge sigh of relief last week as an AI-focused hedge fund called Situational Awareness reportedly sold most of its portfolio — reportedly down 67% last month on the backfiring of debt-fueled bets on chipmakers and assorted artificial-intelligence firms — to billionaire Ken Griffin's Citadel... The prevailing sentiment was best summed up by a veteran Wall Street sage who has seen a lot of flameouts in his day. Let's just say he wasn't impressed by Leopold Aschenbrenner, the 25-year-old German-born "Nostradamus" figure who is the founder of Situational Awareness... "Just your typical leveraged idiot who was right until he was wrong," the source said, adding that the implosion is a "one-off...." [Another trusted source] felt there was room for conversation: "A significant issue. Not viewed as systemic right now. I wonder if that changes as more problems arise." Indeed, the fact is that most of Wall Street is closely monitoring the Situational Awareness situation because they were holding many of the same positions as Aschenbrenner. Another top hedge fund manager I won't name tells me he has been getting crushed on similar investments in chipmakers essential to the AI supply chain, as well as other companies feeding off this technology. Thanks to Slashdot reader joshuark for sharing the news.

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The Washington Post looks at giant tech companies "feeding every available dollar into the cash-incinerating maw of AI machines." They warn "Tech superstars that once had oodles of cash left over at the end of each year are now flipping into the red..." [While optimists expect] huge corporate profits and a society-wide boost to wealth and well-being... questions about that AI vision are now growing more urgent: When, if ever, will this payoff arrive? And what will the fallout be for Americans if the titanic investment doesn't quickly deliver? "This AI thing better work out because if it doesn't ... we're going to have a problem," said Torsten Slok, chief economist at investment firm Apollo Global Management. AI costs and doubts are spreading. The U.S. stock market has swooned this summer over fear of the AI bubble going bust... The AI gamble sweeping up American fortunes is led by tech companies splurging on hulking data centers packed with computer chips and equipment needed to develop sophisticated AI models and deliver them to customers. In investor calls in the past week, Google, Microsoft, Meta and Amazon pointed to soaring AI-related sales and business deals. Advertisers are using the technology to tailor marketing pitches and corporations and start-ups are buying access to chatbots and other AI software to boost productivity... But this spending can only continue if AI generates an even larger avalanche of new revenue to pay for it all. Financial results released over the past week show that the AI titans' mammoth costs are largely swamping the sales boost from the technology. At Google, for every dollar of cash its business generated in the past three months, $1.15 went out the door to pay for AI computer chips and equipment, land for AI data centers and other big-ticket purchases. The company is covering the difference partly by borrowing money and selling more of its stock. Next year, five leading AI companies — Google, Amazon, Microsoft, Meta and Oracle — are projected to have negative free cash flow, which measures the cash left over after paying expenses and AI infrastructure costs. The figures, based on investment analyst projections compiled by S&P Global Market Intelligence, show a stunning reversal for what have been some of the world's most cash-generating corporations... The companies remain profitable by standard financial accounting measures that spread out the costs of their AI infrastructure spending over many years... Pessimists see a bet so gargantuan that it cannot possibly pay off. The pessimists are growing louder. The Bank for International Settlements, a typically measured institution in Switzerland that advises government bankers around the world, recently warned there was risk of "economy-wide recessions" if the AI boom falters. That could mean pain for workers and communities across the United States. "I'm not saying AI is going to go away, it's just not clear to me these guys are going to make money on it," said Christopher Wood, global head of equity strategy at investment bank Jefferies who has correctly predictedpast financial bubbles.

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An anonymous reader quotes a report from Reuters: OpenAI has discovered other instances in which autonomous agents have escaped containment as the company expands its investigation of the hacking incident at tech firm Hugging Face that drew global attention this month, two people familiar with the matter said on Friday. The new breakouts were uncovered during the company's publicly announced investigation into how one of its agents escaped what was meant to be a contained testing environment this month, the two people said, and OpenAI is now looking into those instances as well. One of the sources said that the escapes were limited in nature and that none of the agents were thought to have left OpenAI's network. An OpenAI spokesperson referred to a statement issued by the company on Tuesday that said it was reviewing "broader activity from our models" in addition to the Hugging Face intrusion. The discovery of additional rogue behavior at OpenAI, even if limited in nature, could feed growing appetite for regulation coming out of the White House and elsewhere. The expanded investigation by OpenAI was launched shortly before its primary rival, Anthropic, disclosed that its models were also responsible for a series of break-ins that led to breaches at three other companies dating back to April, according to the two sources and a third source familiar with the matter. The recent discovery of other past breakouts at OpenAI has not previously been reported. AI safety experts said the new disclosures paint a portrait of a group of cutting-edge labs whose ability to develop dangerous autonomous hacking agents outstrips their ability to keep them under control. "We have a whole industry where the people designing, developing and putting out these tools aren't keeping up themselves to responsibly develop these things and keep them safe," said Maurice Chiodo, a mathematician who works at Cambridge University's Center for the Study of Existential Risk. Reuters could not establish exactly how many incidents OpenAI investigators found or the timings or circumstances under which they occurred. The three sources said OpenAI and outside experts were examining log data from earlier in the year in a bid to understand what took place.

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Major record labels including Universal, Sony, and Warner have proposed excluding AI-generated songs from official charts unless they are "substantially human made," properly labeled, legally produced, and free from manipulation concerns. The Verge reports: The proposal goes quite a bit further than a labeling proposal put forth by the RIAA, the International Federation of the Phonographic Industry (IFPI), SAG-AFTRA, and others. That would create a set of standardized labels for AI-generated and AI-assisted music. The labels' proposal would require songs be clearly labeled, but it would also keep them off international charts unless they met specific criteria, including being "substantially human made." To be eligible, the songs would also have to respect the terms of service of whatever AI service was used, the model would have to have the rights to any data it was trained on, and "not raise stream or chart manipulation concerns." What sort of concerns and what constitutes "substantially human made" are currently vague. Sony Music, UMG, and Mom+Pop Music did not immediately respond to a request for clarification. The IFPI has thrown its weight behind the labels' proposal, though no charting organization has signaled any immediate plan to adopt the rules [...].

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Google has integrated its Nano Banana 2 image generator into Google Earth, allowing users to place AI-generated events and objects onto real satellite imagery. The company says its AI-generated images contain invisible watermarks detectable through Gemini or Lens, but the BBC found those safeguards and some third-party detection tools can be fooled into labeling manipulated Google Earth images as real. From the report: A collapsed Eiffel Tower, a sinkhole swallowing the Great Pyramid of Giza and Russian tanks in Ukraine's capital were among the images BBC Verify was able to create when testing the feature, which was rolled out on Thursday. Google has not yet responded to questions based on BBC Verify's tests, but in a social media post the company said they "take misinformation seriously" and that "we prevent image creation on harmful topics and are continually updating our protections." AI and misinformation expert Henk van Ess has highlighted the risks this feature poses, creating fake images of a non-existent nuclear power plant in Iran, a refugee camp on the US-Mexico border and a fake hospital in Gaza with a bomb crater next to it. He said Google was allowing "invented" imagery to be "welded to genuine coordinates, drawn on genuine imagery." "The forgery does not have to look convincing on its own. It inherits the credibility of the map it was born on," van Ess added. UPDATE 7/31/26 10:54 AM: Google is rolling back the image generation inside of Google Earth: "We know that people uniquely trust Google Earth for a reliable view of the world. We've seen geospatial professionals using this feature for a range of useful purposes, however we've also seen people sharing screenshots of generated imagery that appear to violate our policies. So we're rolling back this feature in Google Earth while we work on implementing stronger guardrails. It's important to note that generated images didn't appear in the main Google Earth experience for others to see and were watermarked as AI generated."

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