AI News Archive: July 10, 2026 — Part 7
Sourced from 500+ daily AI sources, scored by relevance.
- Malaysia PM Anwar to debut an AI double that sounds just like him
Malaysia PM Anwar to debut an AI double that sounds just like him The Straits Times
Score: 45🌐 MovesJul 10, 2026https://www.straitstimes.com/asia/se-asia/malaysia-pm-anwar-to-debut-an-ai-double-that-sounds-just-like-him?ref - How To Build An AI Phone Agent With Twilio Conversation Relay in Node.js
Learn to build a voice AI agent with Node.js, Twilio's Conversation Relay, and OpenAI. Create low-latency, natural phone support with real-time tool calling.
Score: 45🌐 MovesJul 10, 2026https://www.twilio.com/en-us/blog/developers/tutorials/product/ai-phone-agent-twilio-conversation-relay-node - AI agents for marketing: What they are, benefits, and examples
I've always wanted a little robot helper of my own. Not the kind that automatically vacuums your floor and terrifies your dog. More like the one from Bicentennial Man (without the existential crisis and tears). That's what AI agents are: software teammates that can figure out and execute the steps needed to achieve a task—and talk to each other while they're at it. For marketers juggling campaigns, copy, and analytics across a dozen tools, AI agents for marketing are shifting how work gets done
- Change your thinking about AI ROI
Change your thinking about AI ROI
Score: 45🌐 MovesJul 10, 2026https://www.zoom.com/en/blog/unlocking-ai-roi-rethink-value-drive-results/ - How close is Rovo Dev to fully autonomous ticket resolution in real-world projects?
How close is Rovo Dev to fully autonomous ticket resolution in real-world projects? Atlassian Community
- MTN Foundation, Microsoft empower Nigerian educators with AI integration skills
Olayanju walked participants through a broad ecosystem of AI tools available to educators, including chatbots for lesson planning
- How to Build an AI Phone Agent with Twilio Conversation Relay in C Sharp
How to Build an AI Phone Agent with Twilio Conversation Relay in C Sharp
Score: 45🌐 MovesJul 10, 2026https://www.twilio.com/en-us/blog/developers/tutorials/product/ai-phone-agent-twilio-conversation-relay-csharp - OpenAI staffer maps out which of GPT-5.6 Sol's five reasoning levels fits which task complexity
GPT-5.6 Sol ships with five reasoning levels from "Light" to "xhigh," plus "Max" and "Ultra" modes that deploy multiple sub-agents in parallel. OpenAI's Vaibhav Srivastav recommends starting low and only scaling up when needed. The article OpenAI staffer maps out which of GPT-5.6 Sol's five reasoning levels fits which task complexity appeared first on The Decoder .
- The Tech Download: Teen social media bans miss a key part of the puzzle: AI chatbots
Teenagers are increasingly becoming dependent on AI chatbots, echoing a familiar problem with social media in the 2010s.
Score: 44🌐 MovesJul 10, 2026https://www.cnbc.com/2026/07/10/tech-download-social-media-bans-ai-chatbots.html - AI job rejections felt least fair when avatars shared just one trait
Companies are increasingly using artificial intelligence in their hiring processes. It's not just CVs that are evaluated automatically. AI tools can also conduct job interviews—usually in the form of avatars, which are animated characters—and make hiring decisions. An important reason for this, aside from saving time, is that AI is said to be less biased than humans.
- Mark Zuckerberg’s Muse Spark post on X triggers debate, draws Elon Musk in
Meta CEO Mark Zuckerberg returned to X after three years to announce Muse Spark 1.1. This AI model is designed for coding, reasoning, and agentic tasks. The announcement generated significant public discussion about the platform choice. Elon Musk commented that X is a great platform for direct CEO announcements. Users debated X's effectiveness for reaching the tech community compared to Threads.
- AI Fiction Is Easy to Detect Because It's Stupid and Bad, Research Finds
ChatGPT uses too many dream sequences and Gemini won’t stop describing characters.
Score: 42🌐 MovesJul 10, 2026https://www.404media.co/ai-fiction-is-easy-to-detect-because-its-stupid-and-bad-research-finds/ - Who trains tomorrow’s marketers if AI does the work?
As AI takes over entry-level marketing tasks, how do organizations help people develop the judgment needed to be a marketing leaders? The post Who trains tomorrow’s marketers if AI does the work? appeared first on MarTech .
- Agent Identity, Reliable Execution, and Intent are only half-way solved
After spending the past couple of months looking at hundreds of tech docs from n8n, Google, Gumloop, and the rest, I tallied up 75 capabilities you’d expect an agent development tool to offer. But there are so many more things you can, need, or should do to agents
Score: 42🌐 MovesJul 10, 2026https://blog.n8n.io/agent-identity-reliable-execution-and-intent-are-only-half-way-solved/ - Can Rovo Agents Read Jira Attachments? [Champions Slack Insider]
Can Rovo Agents Read Jira Attachments? [Champions Slack Insider] Atlassian Community
- Machine learning-based biosensor calibration for MC-LR toxin monitoring (IMAGE)
Machine learning-based biosensor calibration for MC-LR toxin monitoring (IMAGE) EurekAlert!
- AI is supposed to boost brands. What if it does the opposite?
The advantages promised by AI could be self-defeating for consumer brands, argues Gavekal’s Louis Gave. That would undermine investors’ ability to value companies.
Score: 41🌐 MovesJul 10, 2026https://www.livemint.com/ai/ai-is-supposed-to-boost-brands-what-if-it-does-the-opposite-11783646234916.html - Kyutai Releases MuScriptor: An Open-Weight Decoder-Only Transformer for Multi-Instrument Music Transcription to MIDI
Kyutai Releases MuScriptor: An Open-Weight Decoder-Only Transformer for Multi-Instrument Music Transcription to MIDI MarkTechPost
- One Rises, One Falls on Lock-Up Expiry Day: The Divergent Paths of Zhipu AI and MiniMax
Zhipu AI shares surged 13% while MiniMax plunged 18% on lock-up expiry, reflecting fundamentally different business models and market confidence in China's two leading AI companies.
- Lowering computational costs in decentralized finance systems using AI-assisted contract development
Researchers have developed a benchmarking framework to assess whether artificial intelligence (AI) can generate decentralized finance (DeFi) smart contracts that are efficient and cost-effective, lowering computational costs, known jargonistically as "gas." The work might address a key problem seen in blockchain-based financial systems.
- Google just changed how it grades the AI models you use for Android coding
Google resets its Android Bench leaderboard with a new testing framework and eight new models.
- Instagram’s Adam Mosseri: If you don’t like AI, ‘then you shouldn’t have it in your feed’
Mosseri still doesn’t want to filter out AI posts completely, however.
Score: 40🌐 MovesJul 10, 2026https://www.theverge.com/tech/963961/instagram-adam-mosseri-ai-feed-filters - [Research Article] An LLM-based multi-agent system for remote sensing analysis
[Research Article] An LLM-based multi-agent system for remote sensing analysis EurekAlert!
- These Innovators Are Making Bold Moves—From AI Glasses To Next-Generation Biotech
These Innovators Are Making Bold Moves—From AI Glasses To Next-Generation Biotech USA Today
- Adwave Launches Wavemaker, a General-Purpose AI Video Generator Built on Its CTV Ad Technology
Adwave Launches Wavemaker, a General-Purpose AI Video Generator Built on Its CTV Ad Technology USA Today
- Viva La Workflow: Optable’s Vlad Stesin On What’s Actually Changed In Agentic Advertising
A year ago, agentic advertising was mostly theoretical. AdExchanger Managing Editor Allison Schiff sat down with Vlad Stesin, CEO and Co-Founder of Optable, to talk about what’s actually changed — and which companies are doing real work rather than chasing the buzzword. Stesin breaks down why workflow automation is the unglamorous but essential starting point […] The post Viva La Workflow: Optable’s Vlad Stesin On What’s Actually Changed In Agentic Advertising appeared first on AdExchanger .
- The future of public safety may start with a drone
Brinc Drones supplies 20% of U.S. SWAT teams in 50 states. NBC News' Gadi Schwartz spoke with the company's 25-year-old founder.
Score: 40🌐 MovesJul 10, 2026https://www.nbcnews.com/video/how-drones-could-become-the-future-of-911-response-266475077785 - I Hate to Admit It, But AI Actually Takes the Stress Out of Online Shopping
I Hate to Admit It, But AI Actually Takes the Stress Out of Online Shopping PCMag Middle East
Score: 40🌐 MovesJul 10, 2026https://me.pcmag.com/en/ai/37645/i-hate-to-admit-it-but-ai-actually-takes-the-stress-out-of-online-shopping - Your AI Tool Is Already Obsolete if You’re Making This Massive Data Mistake
AI is only as smart as what you feed it.
Score: 40🌐 MovesJul 10, 2026https://www.inc.com/heather-wilde/why-your-ai-is-only-as-good-as-the-data-behind-it/91370231 - Fresha Explores The Future of AI in Couture and Selfcare with Iris van Herpen Backstage at Paris Haute Couture Week
Fresha, the world’s leading AI-powered beauty and wellness booking platform, joined the hair team backstage at Paris Haute Couture Week this week to power Fresha Ambassador and internationally renowned hairstylist Hester Wernert-Rijn as she led the hair for Iris van Herpen’s Autumn/Winter 2026 Haute Couture collection, Sonic Starquakes. Following the runway presentation, Fresha secured an [...]
- Choosing the Right AI Agent Memory Strategy: A Decision-Tree Approach
In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple decision tree, one...
Score: 40🌐 MovesJul 10, 2026https://machinelearningmastery.com/choosing-the-right-ai-agent-memory-strategy-a-decision-tree-approach/ - MIT researchers study avian mechanics to build robot that can dive, swim and fly
The researchers aim for a future in which winged robots could used for research in aquatic regions often deemed too dangerous for traditional ocean vessels. Read more: MIT researchers study avian mechanics to build robot that can dive, swim and fly
Score: 40🌐 MovesJul 10, 2026https://www.siliconrepublic.com/innovation/mit-research-study-avian-mechanics-build-robot-dive-swim-fly - The best predictive analytics software in 2026
You could argue that pretty much all analytics are meant to be predictive. Isn't the point of analyzing past performance, on some level, to project future performance? (I guess you could just be nostalgic for the metrics underlying your favorite past fiscal quarter.) As a dedicated tool class, however, predictive analytics software helps analysts of all kinds see what past data says about the future. While tools like these can't tell you what will happen, they can tell you what massive amounts o
- Value generalisation: value correction
I firmly believe that value generalisation [1] is the key to AI Alignment. That, indeed, it is necessary and almost sufficient for alignment. But I won't be arguing that grand point today; instead, I'll focus on a specific RL example of an agent that displays value correction: it realises its current reward function is (probably) incorrect, and acts to correct it. Thus there are: The initial situation, in distribution, where the human displays how to maximise the true reward. The out of distribution situation where the agent finds a hack to exploit its reward function estimate, and turns against what we wanted it to do. The value error detection stage where the agent realises that its reward function estimate is probably incorrect. The value correction stage where the agent corrects its reward function back to the original true reward. In this post, all the methods presented will by syntactic: the agent is not assumed to have any understanding of the situations and the key features are not identified to it. The game of human life Introducing a new, very simple, game called "Humans [2] ". Humans, fleeing danger, enter the screen from the left. The objective is to save them by moving them off the right of the screen. But there are obstacles on the way, and the humans will mill about if they are blocked. And they will shortly expire if they can't get out of the screen quickly. There are two command: drill ('d') and explode ('e'). Drill does... what, you want to know about explode? Well, if the player presses 'e', the rightmost human will explode, knocking away two obstacle blocks in front of them and behind them -- but also killing themselves and any humans nearby. This is almost never a good solution; to remind the player of the mistake, a large frowny face will appear to drive the disapproval home. Much more reasonably, if the player presses 'd', the rightmost human will drill the obstacle just in front of them (better time it so that they're facing the right way). Enough drilling, and the humans can get off the map. The score, the true reward , is the number of humans saved, i.e. who walk off to the right. Each time a human is saved, the top yellow bar will grow to show the score increase: Since there is a cooldown for drilling, the optimal policy is carefully drilling every time a human approaches an obstacle; but wildly and repeatedly mashing 'd' is almost as good. Learning agent with value correction A learning agent will run a series of subagents to estimate the reward function from human-provided training data, then learn the optimal policy from that reward function, then question its learnt reward by comparing the high-reward states in its optimal policy versus those in the training data, re-compute another reward function estimate that is closer to the true reward, and finally settle on a prudent policy that is close to the true optimal policy. Estimating the reward function A human will generate several play through of the game to illustrate how it works, efficiently choosing to drill through the obstacles and getting the humans off the map in time. The data is labelled: every time a human is saved, that is identified as a reward increase. The learning agent runs an evaluation subagent on this data. It is given the ten frames before the human is saved, and the ten frames afterwards, and trains to recognise these are reward increase situations. Zooming in on the critical two frames where the human is saved; note the human vanishing and the score bar expanding: This evaluation agent thus computes the proxy reward . This computation is validated on held-out examples, with close to perfect accuracy: correctly identifies all saved-human situations in held out data, and has a false positive rate of . Reward hacking: failed value generalisation Using the evaluation agent as the definition of , the learning agent had an RL-subagent play multiple levels of the game, exploring and learning to maximise. But soon things go very wrong. It turns out that "human walking off the screen" was not what found. That is a relatively complicated concept; instead it mostly found the much simpler concept of "the yellow score bar expands". More precisely, if we created synthetic data where the human walks off and is saved but the score bar doesn't expand, this triggers the reward only of the time. But if we expand the score bar without a human walking off, this triggers the reward of the time. That isn't a problem, yet, because the human being saved and the score bar expanding always trigger together. But, when an explosion is triggered, the frowny face appears - thus there is giant blob of yellow pasted all across the score bar. This activates much more strongly than the yellow bar expansion or the human being saved: This graph compares the value of at explosions, frowny faces, and true saving incidents. Here, both the explosion and the frowny face trigger high , which persists longer for the face. Over multiple training runs for estimating , it isn't consistent whether the explosion itself triggers , but the frowny face always does. So the RL-subagent quickly and merrily learns to explode the humans, one after the other, to maximise . So, the optimal policy , for the proxy reward, is to wildly mash the explosion button 'e'. As is usual in these cases, the erroneous maximisation of the proxy turns out to be much easier that maximising the true reward. Trained on , a test subagent achieves an total reward of on average, while blowing up all humans. In contrast, if the RL-subagent were trained on the true reward , it would achieve an average of reward of , saving most of the humans per level. As is not usual but sometimes happens, an ostensive safety precaution - the frowny face to remind a human player that they were playing poorly - ends up being the cause of misalignment. Detecting the potential error Ok, so far, that is a classical failure of goal misgeneralisation (or reward hacking, or a failure of symbol grounding, or Goodhart failure, or... most of these failure modes are tightly related). We humans can see the error clearly. But how could a relatively limited agent correct itself? The first step is to identify that goal misgeneralisation may have happened. We have some advanced techniques for this, but there are much simpler methods that work here. The first step is to notice that the high-scoring events in the training data (human walks off to the right, score bar expands) are wildly different from the high-scoring events of the-maximising agent (explosions and frowny faces). To do this, the agent extracts the high-scoring events under and compares them with the high-scoring events in its training data - these it can reliably take to be high-scoring for , the true reward. It runs a simple classifier over the two sets of high-scoring events (extracting twenty frames, as before) and it separates them almost perfectly. Thus the high-scoring events under are from a different distribution than the high training examples are. This is not itself damning; it could just be that the maximising agent has found a clever hack to get more of the true [3] . But it could also be a hack of , so the off-distribution has identified a potential error. Calling for help At this point, one of the options would be for the agent to route its decisions to a human, displaying the high-scoring events, contrasting them with the high reward events in its training data, and asking, in effect, 'are these both genuine high rewards'? But, so far, the correction process has been unsupervised since the initial training data; let's see if we can push further without needing human intervention. Re-evaluating the reward The agent could now re-evaluate the reward in the following way. It runs an evaluation agent on the training data, as before. But it adds the high- scoring states to this set, as low-scoring examples. It thus learns a reward function ('corrected') which is essentially "what its reward would be if the proxy were wrong". This turns out to be very close to the original true reward (though the agent, of course, doesn't know this). It then trains an RL-subagent on , which has an optimal policy of "mash 'd' all the time" (which is very close to the actual optimal policy). From these runs, it extracts the states with high . And compares these against the high -scoring states in the training data. These two sets it cannot easily distinguish. Thus, though is clearly a hack of some sort, good or bad, is not. Prudence in the face of uncertainty So the agent has two rewards and . It knows that seems to generate policies that are compatible with its training data; in contrast, generates policies that are very different from the training data. Standard prudential moves would be maximise the worst case of the two rewards (minimise regret), to maximise some normalised mix of the two, or to prioritise (known to be closer to the training data and hence safer) [4] . However, pursuing -maximising rewards ("exploding all the humans") inevitably leads to low -rewards. In contrast, pursing -maximising rewards ("get the humans off the map") gives reasonably high . After all, though prefers explosions and frowny faces, it still gets some rewards for saving humans. Thus all three prudential moves point towards maximising , with optimal policy close to . Which is good: is (nearly) the true reward and is (nearly) the optimal policy . Conclusion This is just an illustration, in a small toy model, of simple value correction approaches. These can be used by agents - every very simple agents - to detect and correct errors in naive generalisations from initial training data. More sophisticated agents will have more advanced value generalisation techniques available to them; I'm planning to push the frontier of what exists way further than it currently is. Which I've also called value extrapolation, or concept extrapolation where the concept is a value. ↩︎ Inspired by this old game . ↩︎ ^ Or there could be a spurious change in the data; that's why we would, in general, need more advanced techniques that just checking if a binary classifier can tell the sets apart. ^ Formally, if is a policy, the expected episodic reward for , and the expected reward for using the -maximising policy, we are looking for policies that maximise one of: with subject to the constraint that Discuss
Score: 40🌐 MovesJul 10, 2026https://www.alignmentforum.org/posts/iPyJfD9Jyxj6Jfdws/value-generalisation-value-correction - 'Agent Kim Reactivated' uses AI for action sequence in a K-drama first
The hit Netflix series "Agent Kim Reactivated" used AI technology to create a three-minute action sequence, marking the industry's first commercial use of AI-generated video in a drama, according to production company Morpheus Studio on Thursday. The AI-produced scene appears in the first two episodes and depicts Kim, played by So Ji-sub, carrying out a covert mission in North Korea during his years as a black-ops agent. Everything in the scene — from Kim blowing up a building, racing through sn
- The Download: Claude’s inner workings and OpenAI’s “super app”
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Anthropic found a hidden space where Claude puzzles over concepts The AI firm Anthropic has got the clearest glimpse yet at what’s really going on inside large language models as they…
Score: 40🌐 MovesJul 10, 2026https://www.technologyreview.com/2026/07/10/1140316/the-download-anthropic-claude-hidden-space-openai-super-app/ - How Decagon uses AI for design system saturation
The fast-growing customer experience platform explains how Figma MCP and Figma Make helped them scale a new design system and keep pace with customer requests.
Score: 40🌐 MovesJul 10, 2026https://www.figma.com/blog/how-decagon-uses-ai-for-design-system-saturation/ - Why AI summaries pose a danger to learning
Why AI summaries pose a danger to learning The Straits Times
Score: 40🌐 MovesJul 10, 2026https://www.straitstimes.com/opinion/why-ai-summaries-pose-a-danger-to-learning - The Hidden Infrastructure Challenge Behind Every AI-Generated Avatar
Virtual marketplaces now move billions of dollars in 3D avatar items annually. Users purchase 1.8 billion avatar items in a single year on major platforms, with 40% of monthly active users returning to update their digital identities. The economics are staggering, but so are the technical demands. Behind every pirate hat, neon sneaker, or custom... … continue reading The post The Hidden Infrastructure Challenge Behind Every AI-Generated Avatar appeared first on SD Times .
Score: 40🌐 MovesJul 10, 2026https://sdtimes.com/ai-machine-learning/the-hidden-infrastructure-challenge-behind-every-ai-generated-avatar/ - How Many Tesla Robotaxis Are In Miami?
I was traveling a lot last week, and I missed the news until very recently that Tesla launched robotaxi service in Miami. Apparently, Tesla now has some unsupervised Robotaxis operating there. However, as I was learning about it, I saw that only a few vehicles had been spotted (people try ... [continued] The post How Many Tesla Robotaxis Are In Miami? appeared first on CleanTechnica .
Score: 40🌐 MovesJul 10, 2026https://cleantechnica.com/2026/07/09/how-many-tesla-robotaxis-are-in-miami/ - How AI changes the rules of engagement for sports viewers
Spectators will see more opportunity to move from pricey subscriptions and rigid schedules to more personalised feeds
- AI’s hidden revenue problem - and why the CIO owns it
AI’s hidden revenue problem - and why the CIO owns it Computing UK
Score: 39🌐 MovesJul 10, 2026https://www.computing.co.uk/opinion/2026/ai-s-hidden-revenue-problem-and-why-the-cio-owns-it - Rosie DiManno: There’s no terminating the artificial intelligence takeover. I fear it will devour the world
Rosie DiManno: There’s no terminating the artificial intelligence takeover. I fear it will devour the world Toronto Star
- Samsung's Galaxy Unpacked Event: We Expect Weird Foldables, Funky AI Glasses and More
Samsung's foldable phone launch will take place later this month in London. We'll be there.
Score: 38🌐 MovesJul 10, 2026https://www.cnet.com/tech/mobile/samsungs-galaxy-unpacked-event-what-to-expect-how-to-watch/ - AI notetakers promise easy meeting recaps, but some professionals question their use
AI notetakers can quickly summarize meetings and create to-do lists, but they raise privacy concerns
- Why Generic AI Translation Fails Enterprise Marketing
Explores why generic AI translation tools strip brand voice and how purpose-built systems enforce brand consistency at scale.
Score: 38🌐 MovesJul 10, 2026https://jasper.ai/blog/why-generic-ai-translation-fails-enterprise-marketing - Claude isn't just a chatbot anymore — here are 9 things it can do in 2026
Claude isn't just a chatbot anymore — here are 9 things it can do in 2026 Tom's Guide
Score: 38🌐 MovesJul 10, 2026https://www.tomsguide.com/ai/claude-isnt-just-a-chatbot-anymore-here-are-9-things-it-can-do-in-2026 - Why it’s difficult to move past deployment to adoption
Why it’s difficult to move past deployment to adoption Health Data Management
Score: 38🌐 MovesJul 10, 2026https://www.healthdatamanagement.com/articles/why-its-difficult-to-move-past-deployment-to-adoption/ - Intervou Launches: Professional Video Interviews That Rank on Google and AI Search
Intervou Launches: Professional Video Interviews That Rank on Google and AI Search USA Today
- Wipro PARI boosts hands-on robotics and automation learning for future manufacturing talent
Wipro PARI (Precision Automation and Robotics India), a part of Wipro Infrastructure Engineering, has donated and commissioned a industrial robot (FANUC R-2000iB/210F six-axis) to the Indo-Swiss Centre of Excellence (ISCE), Pune. The robot was formally inaugurated last week, July 4th 2026, and will provide nearly 200 students with hands-on training in industrial robotics and automation. […] The post Wipro PARI boosts hands-on robotics and automation learning for future manufacturing talent appeared first on CXOToday.com .