AI News Archive: August 20, 2026 — Part 14
Sourced from 500+ daily AI sources, scored by relevance.
- Alibaba quarterly profit drops 75% as AI investment spending grows
China's Alibaba reports a 75% drop in profit for the latest quarter from the year before to roughly $1.6 billion as it invests heavily in AI infrastructure
- How a Texas student blew the whistle on a rogue AI hacking attempt
Incident occurred a fortnight ago.
- UK cinemas restricting Meta AI and other smart glasses over piracy concerns
UK cinemas restricting Meta AI and other smart glasses over piracy concerns The Straits Times
- Citi, HSBC, StanChart adopt Ant International's forex AI tool
The Singapore-based fintech giant rolled out its Falcon Time-Series Transformer Model 2.0 and has partnered with six large banks including Citi, HSBC, Deutsche Bank, Standard Chartered, and Barclays, according to Kelvin Li, the firm's general manager of platform tech.
- Anthropic plans to change enterprise data retention policy
For Claude use.
- Robots poised for 'ChatGPT moment,' Unitree CEO says
Robots poised for 'ChatGPT moment,' Unitree CEO says Reuters
- Robots poised for a 'ChatGPT moment': Unitree CEO
Robots poised for a 'ChatGPT moment': Unitree CEO The Straits Times
- Robots poised for ‘ChatGPT moment’, Unitree CEO says
Robots poised for ‘ChatGPT moment’, Unitree CEO says The Straits Times
- Robots poised for 'ChatGPT moment,' Unitree CEO says
UPDATE 2-Robots poised for 'ChatGPT moment,' Unitree CEO says
- Stripe Takes OpenRouter
Stripe Takes OpenRouter The Information
- Stripe beefs up AI as it encroaches on banking
The payments company has acquired artificial intelligence firm OpenRouter in a deal that comes as banks consider strategies for large language models and Stripe is rumored to be in talks to buy PayPal.
- Google's China shift and the battle over AI models
Google's China shift and the battle over AI models Nikkei Asia
- Amazon bundles free Alexa+ with Fire TV devices in select regions: Details
Amazon is rolling out Alexa+ on Fire TV at no additional cost in the US, while the AI assistant is yet to launch in India
- Alexa+ Is Now Free on Fire TV Devices: Here's What the AI Tool Can Do For You
Alexa+ Is Now Free on Fire TV Devices: Here's What the AI Tool Can Do For You PCMag
- Alexa+ Is Now Free on Fire TV Devices: Here's What the AI Tool Can Do For You
Alexa+ Is Now Free on Fire TV Devices: Here's What the AI Tool Can Do For You PCMag UK
- Google offers students in India one-year of free AI Plus plan: How to claim
Google is offering eligible college students in India a free Google AI Plus plan for 12 months, alongside discounted AI Pro plans and new Gemini study tools
- Google is giving 1-year Gemini AI plans free for students: What you need to know
Google is giving 1-year Gemini AI plans free for students: What you need to know
- How OpenAI plans to monitor for AI misuse without looking at your data
How OpenAI plans to monitor for AI misuse without looking at your data
- Adobe Firefly expands its creative AI studio: generate music, speech, and sound effects in one place
Adobe Firefly expands its creative AI studio: generate music, speech, and sound effects in one place Adobe
- Adobe expands generative AI audio with Firefly music, speech and sound effects
Adobe Inc. today announced the general availability of audio capabilities in Firefly, the company’s all-in-one creative AI tool suite, allowing users to produce music, speech, and sound effects for their projects. In June, the company unveiled big changes for Firefly, including a large number of skills for Firefly AI Assistant that built in agentic capabilities […] The post Adobe expands generative AI audio with Firefly music, speech and sound effects appeared first on SiliconANGLE .
- No Mic Needed: You Can Create Music and Speech With Adobe’s AI Audio Tools
Adobe’s instrumental AI soundtracks come with a universal license, meaning it’s safe to use for any project.
- Adobe Firefly’s music, voiceover, and sound effects generation tools now generally available
Users can now create fully licensed tracks, voiceover, and sound effects on Adobe’s Firefly AI assistant. Here are the details.
- Introducing Slack Code
Introducing Slack Code
- Slack Code taps into collective vibe, puts AI agents into the group chat
Developers can now invite the whole team along for their quality time with the coding bot
- When AI explains its decision, humans may stop thinking independently
AI is known to be confidently wrong, and now it’s influencing humans to be that way, too. In a new study, researchers tested AI’s influence on humans reviewing innovation proposals, and found that AI recommender tools were persuasive enough to convince the evaluators to reject decisions made by independent human experts, thus causing them to pass on promising innovations. Similarly, they went along with AI approval of ideas that the human experts found sub-par. Interestingly, reviewers were also more inclined to defer to an incorrect AI decision when the model explained itself. Narrative explanations degraded human judgment, rather than enhancing it. People did better when they weren’t given a reason for the AI’s decision. “Our findings reveal that LLM explanations do not necessarily improve decision-making,” the researchers, associated with Harvard Business School, MIT, and the University of Washington explained in their findings . “Effective human-AI collaboration requires designs that preserve rather than supplant independent human judgment.” AI rationale can undermine human judgment Every enterprise screens proposed projects before pursuing them, but there is always uncertainty, and the risk of trade-offs like false positives (going forward with projects that ultimately fail) or false negatives (rejecting ideas that might have succeeded). For an example of the former, the researchers point to Google Glass or Amazon’s Fire Phone; for the latter, Xerox terminating early Ethernet and PostScript projects. Because they have limited time and only basic information to go on, decision-makers are increasingly turning to LLMs that use predictive algorithms to generate recommendations and rationales based on context. The researchers set out to explore AI’s role in what they called “early-stage innovation screening.” They judged how human evaluators were influenced by LLM recommendations , both with and without explanations from the model on how and why it reached its decision. Their experiment asked 228 experienced evaluators to assess nearly 50 submissions to an MIT challenge. They tested three different scenarios: human-only proposals with no AI assistance; LLM evaluations with a written rationale for the decision; and black-box AI pass-fail recommendations with no accompanying explanation. Evaluators’ decisions were then compared to those made by four human experts. Those decisions were considered the ‘correct’ baseline. They were judged on whether they outright complied with the LLM’s recommendations , overrode them, or productively overrode them, meaning they independently verified persuasive model outputs before making a decision. Their decisions were classified as correct (agreeing with human experts’ positive/negative decisions), false positive (supporting submissions that experts would reject), and false negative (rejecting submissions experts would move forward with). Overall, the evaluators accepted LLM recommendations 67% of the time. They agreed with both black-box and narrative LLM decisions roughly 75% of the time, but only agreed with human decisions 54% of the time. Seemingly counterintuitively, black-box recommendations improved the quality of decisions (aligning them with human experts) but recommendations with narratives did not. When given an LLM recommendation to reject a submission and an accompanying reason why, evaluators disproportionately agreed, which reduced false positives, but “substantially” increased false negatives. The researchers posit that this is because narrative explanations “suppress” productive overrides; LLMs provide a convincing argument that is easy to accept, essentially discouraging independent human verification. This contradicts a common assumption that LLM explanations augment human decision-making. The researchers pointed out that people are cognitively predisposed to weigh negative information more heavily than positive information; the phenomenon is known as ‘negativity bias.’ “Rejection is an active, eliminative decision that feels more consequential and accountable than preserving optionality,” they wrote. It also maintains the status quo, avoids risk and bias, and requires no resource commitment. LLM explanations provide “ready-made justifications” for going along with rejection decisions without independently verifying them; humans effectively offload their thinking to AI, researchers explained. Evaluators often rely on surface cues such as fluency, coherence, and seeming credibility. LLMs are particularly well-suited to exploit this because they are linguistically fluent and expert-like, creating an “illusion of explanatory depth.” Thus, “individuals tend to overestimate their understanding of a decision despite limited insight into its reasoning,” the researchers wrote. Finding a balance in recommendation systems The researchers pointed out that their findings have “clear implications” for enterprises designing AI-assisted evaluation systems. Enterprises should be cautious with LLM explanations in high-stakes decision-making, they advised. AI recommendations should not be taken at face value; they should always be tested before any associated deployment. This helps improve accuracy and encourages human reviewers to detect errors and learn how models operate, or potentially can even increase human-AI agreement. In decision contexts such as quality control, compliance screening, or fraud detection, LLM explanations could support conservative human decision-making, the researchers noted. On the other hand, in tasks like early-stage screening, LLM narratives could undermine performance by “discouraging independent judgment and suppressing productive human override.” In this context, simpler or more opaque recommendations may preserve human discretion and verification. Future design of explanation systems should factor in the nature of the task and the potential cost of errors made by AI, the researchers advised. Enterprises could experiment with models that support contrasting narratives (reasons to reject an idea alongside reasons to accept it) or uncertainty disclosures based on a fixed threshold, rather than on purely binary decisions. Systems could also be structured to invite human disagreement. The researchers also noted that there is opportunity to test whether narrative explanations have different impacts at later stages of decision-making, when evaluators have fewer options, more information, and increased incentive to verify outputs and think the problem through. Ultimately, the researchers emphasized, “organizations should treat AI explanations not as universally beneficial transparency tools, but as behavioral interventions whose effects depend on how evaluators process information under uncertainty.” This article originally appeared on CIO.com .
- OpenAI ‘temporarily slows’ scaling efforts, also promises zero data retention for select frontier model customers
OpenAI this week announced multiple moves designed to counter negative perceptions of its security and privacy, saying it had slowed its pace of scaling, implemented a two-week pause in reinforcement learning, and will be offering zero data retention for “eligible API customers.” In its first announcement , issued Tuesday, OpenAI said it “temporarily” slowed the pace of its scaling, in addition to pausing reinforcement learning training. Those efforts occurred while OpenAI hardened and red-teamed its research environment and expanded monitoring, it said, adding, “our largest planned frontier RL run remains on hold while we conduct smaller-scale training and evaluations to assess model behavior, validate our safeguards, and establish more evidence of alignment before proceeding.” OpenAI’s statement said the company will “now require stronger evidence of aligned behavior throughout all of training, building on research and evaluations already underway. Keeping increasingly capable systems aligned is a challenge the whole field will need to address.” It also highlighted other recent efforts to improve its procedures, including workload isolation, network isolation and “continuous security testing.” However, the company noted that its newly proposed monitoring will generate overhead costs of “roughly 20% of the inference compute being monitored, though the cost varies substantially across training and evaluation workloads.” It promised to share more details about this system in a forthcoming blog post. Analysts and consultants said that the moves were likely announced to position OpenAI better for an imminent IPO . Carmi Levy , an independent technology analyst, viewed the statements as “a slickly conceived move to win PR points as safety concerns around agentic AI continue to mount. It signals that the company is doing something , even if that something is woefully inadequate. In the absence of explicit regulations forcing vendors like OpenAI to permanently prioritize safety above all other factors, a two-week pause is little more than window dressing designed to deflect criticism.” Jason Andersen , principal analyst at Moor Insights & Strategy, agreed, saying that he thought that “this is a little bit of pragmatic theater as they move into an IPO.” But he suggested that there also may be more going on. Enterprises will continue to spend on AI aggressively, and “it will be pedal to the metal until they get sued.” Then they’ll back away. However, he said, “the only way that these [large AI] companies are going to be successful post-IPO, the only way to scale, is to get much deeper into enterprises. And the only way to do that is to alleviate fear and risk.” Zero data retention? In Wednesday’s announcement , OpenAI didn’t say what constitutes eligibility for the zero data retention program, only that it would start in September, when the company would share details in a “technical white paper.” But Andersen said that this move has to be viewed in the context of today’s complicated vendor relationships. Much of OpenAI’s revenue is not direct from the enterprise, but through partners like Microsoft and AWS, he pointed out. “So let’s say I use a tool like Amazon Kiro, which can use OpenAPI via API to build my app without my knowledge of the model. It sounds like Amazon is the customer and you are Amazon’s customer. If you are an enterprise and want this [zero data retention] protection, you must provide your own API key to Kiro. The enterprise just becomes the direct customer and now gets the lockbox access. AWS no longer has access and loses out on revenue/margins.” Consultant Brian Levine , executive director of FormerGov, added that the data retention promise is also complicated by how processes tend to function. “OpenAI says it can now monitor for abuse across interactions without any staff ever reading the underlying content,” he said. “That is a strong technical promise, because watching for misuse and not being able to see the data have historically pulled in opposite directions. And the proof is a white paper that is still weeks away.” In addition, he noted, “Zero is never quite zero because CSAM-flagged content is still retained for legal reporting.” Flavio Villanustre , CISO for the LexisNexis Risk Solutions Group, saw the move somewhat differently, suggesting that it was designed to soften possible legislation. “It is likely that they are seeing the writing on the walls about upcoming regulations that could have a significant impact on them, and this could be their attempt at showing a desire to self-regulate to avoid a more draconian legislation in the future,” Villanustre said. Still, Mike Wilkes , enterprise CISO at Aikido Security, observed, “sincerity is not the same thing as permanence. In the old Norse/Scandinavian image of a giant sea monster waiting beneath the surface, you might call this ‘Pause the Kraken.’ The real question is what conditions have to be met before OpenAI decides to release it again.” Added Justin St-Maurice , technical counselor at Info-Tech Research Group, OpenAI seems to want credit for doing the bare minimum of what a major AI firm should have always done. “If a carmaker announced that it was going to take basic safety testing more seriously before production, it wouldn’t be to fanfare. Frankly, it would be embarrassing that something so fundamental needed clarifying to a skeptical public,” he said. “The question for me is why this needs to be an announcement now, and whether they hold the line once a competitor ships something that makes a pause expensive.” Thus, he advised, “stop treating these announcements as diligence. Ask for the evidence of what you’re actually getting, not what you’ve been promised. If a vendor can pause development for security reasons, and the way you found out was through a blog post, then you should be asking what your contract requires them to disclose to you.”
- OpenAI builds zero data retention system for frontier models
But the policy is still only available to "approved" users.
- Tech Mahindra, ServiceNow expand alliance to take enterprise AI deployments beyond pilots
Tech Mahindra, ServiceNow expand alliance to take enterprise AI deployments beyond pilots Techcircle
- AI bias isn't just an error in the algorithm, it's a chain of human decisions
In the United States, leading human resources software company Workday is facing a lawsuit over its use of AI-powered job screening tools that allegedly discriminated against applicants based on factors such as age, disability and race.
- SoftBank, Ericsson validate AI software on 5G
The companies said the AI-native scheduler adjusts downlink transmission settings in real time inside the radio access network, or RAN.
- MiniMax Design
Your own agent team for open-ended creation
- Neural network approach makes AI uncertainty checks far more efficient
McGill University researchers have developed a more energy-efficient method of building AI systems that are better at measuring—and indicating—their own uncertainty. This will help users determine when human oversight is needed, when additional data should be collected and when a model is being asked to work beyond the conditions it was trained for, the researchers said.
- McGill researchers develop a more efficient way to identify when AI responses may need human review
McGill researchers develop a more efficient way to identify when AI responses may need human review EurekAlert!
- ChatGPT mysteriously stops citing Reddit in many responses to users
Reddit citations by ChatGPT have took a sudden nosedive this month. What's going on?
- Apple Music will soon get visible labels for AI-generated content
Apple has shared more details about the AI labeling requirements it announced for Apple Music back in March. Here are the details.
- Amazon delivery drone dumps Texas woman's parcel straight into her swimming pool — viral video surfaces the same week the company announces 500-city Prime Air expansion
A Texas woman has filmed the moment an Amazon Prime delivery drone dumped her package straight into her swimming pool.
- Are You Breaking the Law? 7 Types of Photos You Should Never Edit With AI
Are You Breaking the Law? 7 Types of Photos You Should Never Edit With AI PCMag UK
- Are You Breaking the Law? 7 Types of Photos You Should Never Edit With AI
Are You Breaking the Law? 7 Types of Photos You Should Never Edit With AI PCMag Australia
- Your AI Prompts Aren't Private: The Most (and Least) Invasive Chatbots, Ranked
Your AI Prompts Aren't Private: The Most (and Least) Invasive Chatbots, Ranked PCMag UK
- NTT DATA and Palo Alto Networks Sign Global Strategic Alliance to Accelerate Secure AI Transformation
NTT DATA, a global leader in AI, digital business and technology services, and Palo Alto Networks (NASDAQ: PANW) today announced a multi-year strategic alliance designed to help organizations securely adopt AI, modernize cybersecurity, simplify complex technology environments and build cyber resilience for the AI era.
- Palo Alto Networks and NTT DATA team up on a $1 billion AI security push
An AI agent deployed inside a bank or hospital system does not sit still. It queries databases, moves files, and calls other software on its own schedule. To do any of that, it needs credentials, just like an employee does. In February, Palo Alto Networks put the ratio at more than 80 machine accounts for every human one inside a typical enterprise. Most security teams cannot produce a full list of them, so they cannot say who or what has access to their most sensitive systems. NTT DATA and Palo Alto Networks are offering a fix. The two companies announced on Thursday a multiyear global alliance targeting $1 billion in joint business by the end of 2029, pairing Palo Alto’s security platforms with NTT DATA’s consulting and managed services to help clients assess cyber risk, deploy AI securely, and manage the controls afterward. Asked directly, NTT DATA said the figure represents the total value of customer contracts and orders the two companies sell together over the period, blending product and subscription revenue with services, integration, and managed services. The company said it does not break out further details. Palo Alto Networks confirmed the definition and declined to provide additional financial specifics, citing the quiet period ahead of its fiscal fourth-quarter results, due September 1. That is a combined sales target across the two companies’ books rather than new revenue for either one, and neither would say what the joint business is worth today. The release separately describes the alliance as backed by “joint investments,” though neither company would say how much. Palo Alto Networks alone guided to roughly $11.4 billion in revenue for its 2026 fiscal year. Split across three years and two balance sheets, $1 billion tells you what the companies intend to chase, not what either one has won. WHAT CHANGED SINCE APRIL The two firms were already working together. NTT DATA was one of five launch partners in Palo Alto’s Frontier AI Alliance in April, alongside Accenture, Deloitte, IBM, and PwC. The group has since grown to more than a dozen firms, including Infosys, Wipro, TCS, and McKinsey. Palo Alto Networks said the earlier alliance was narrowly focused on securing AI deployments and frontier models, while this agreement spans its full portfolio globally. Chairman and CEO Nikesh Arora said the expanded alliance lets the company “operationalize platformization at true global scale,” industry shorthand for pushing customers off a patchwork of security tools and onto one vendor’s stack. NTT DATA described it as a step further, saying the Frontier AI Alliance created the ecosystem while this agreement creates a dedicated go-to-market and delivery model between the two companies. Being one of thirteen partners is not the same as having a joint engineering roadmap, early access to unreleased platform features, and a shared revenue target. THE IDENTITY PLAY The identity security work in the new joint business agreement runs on technology Palo Alto Networks acquired when it closed its $25 billion purchase of CyberArk in February and rebranded it in May as Idira . Palo Alto Networks said NTT DATA was a top-tier CyberArk partner before the acquisition and is already selling Idira at scale. That makes this as much a distribution decision as a partnership announcement. Palo Alto spent $25 billion to make identity a core pillar of its platform, and it needs large systems integrators to carry that product into regulated industries. NTT DATA, with more than 7,500 cybersecurity staff and 70 delivery centers, is one of the few firms positioned to do it. Notably, nothing here is exclusive. NTT DATA’s managed security services also run on CrowdStrike’s Falcon platform under a partnership expanded in March 2025, and the company said technology choices remain client-led. Enterprises should expect their integrator to keep selling more than one security platform. THE PEOPLE QUESTION The first of the six solution areas in the new alliance is an autonomous security operations center (SOC) , which raises an obvious question about what happens to the analysts. Asked whether that means fewer of them, NTT DATA said the idea of a SOC running without security professionals is a misconception. Automation handles alert triage and repetitive investigation, the company said, freeing analysts for threat hunting, incident command, and recovery work. It described the shift as workforce transformation rather than workforce reduction. Whether that holds is the thing to watch over the three years the alliance runs. NTT DATA’s own research, covering more than 2,300 business and IT decision-makers, found that fewer than half feel highly prepared to manage cloud and AI security risk, even where formal risk plans are in place. That unprepared majority is the demand both companies are counting on. It also explains why alliances like this keep appearing. Abhijit Dubey, NTT DATA’s CEO and chief AI officer, said organizations now need an approach to cyber resilience that combines “AI-driven security, deep industry expertise, and global scale.” Enterprises buying AI security are not simply buying a product. They need someone to assess the risk, install the technology, and then run it for them month after month. Palo Alto Networks builds the software but does not employ enough consultants to do that inside thousands of large companies. NTT DATA employs the consultants but does not build security platforms like Palo Alto Networks does. Neither can do the whole job alone, and $1 billion over three years is their estimate of what doing it together is worth.
- YouTubers worry that a new policy will lead to more AI slop
YouTubers worry that a new policy will lead to more AI slop azcentral.com and The Arizona Republic
- Why Amazon and Alphabet May Be the Best Way to Play the AI E-Commerce Shift
Why Amazon and Alphabet May Be the Best Way to Play the AI E-Commerce Shift Barron's
- Grok chat duped into swallowing injected instructions
A spoonful of encryption helps the malware go down
- Uber launches autonomous rides in Zagreb, its first in Europe
Currently, riders can book robotaxis in key areas in Zagreb, including the city center, with service availability and geographic coverage expected to expand over time, the companies said.
- Tesla's Robotaxi fleet might finally be driving around Austin unsupervised
It's been slow going, but it looks like Tesla's Robotaxi fleet in Austin is operating fully autonomously.
- Waymo details the custom chip in its autonomous driving system
Waymo LLC today shared new details about the computing module that powers its autonomous taxis. The Alphabet Inc. unit operates about 4,000 vehicles in 11 U.S. cities. Most are based on the Jaguar I-Pace crossover, while the rest are Zeeker minivans and Hyundai Ioniq 5 SUVs. Consumers order rides via a standalone app and Uber. […] The post Waymo details the custom chip in its autonomous driving system appeared first on SiliconANGLE .
- OpenAI Halts AI Training on Advanced Model as It Detects Dark Signs Emerging
"As models become more capable, the risks associated with developing and testing them internally also grow." The post OpenAI Halts AI Training on Advanced Model as It Detects Dark Signs Emerging appeared first on Futurism .
- PSA: ChatGPT outage is blocking users from logging in or creating new accounts [U: Fixed]
Update, 8:54 p.m. ET: OpenAI says the issue is now fully resolved. The original story follows below. OpenAI has confirmed that users are encountering errors when trying to log in to their accounts or create new ones. Here are the details.
- Rubric
UX expertise your agent can call