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Tag: artificial-intelligence

  • Adobe’s legal chief calls for creator protection as policymakers and tech companies reframe copyright in the era of AI

    Craig Hale

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    Concatena says

    Our Take: Adobe’s legal chief urges a pragmatic path for AI regulation – don’t tear up copyright law but clarify it and protect creators whose work fuels AI. I hate to say it, but I agree – let’s focus on the fundamentals, but importantly let’s also think about whether the means for enforcing individual contributors rights is accessible in this new world, and if not, whether there ought to be a supportive regime which regulates bad actors.

    Your Takeaway: IP is always something to keep an eye on. The article talks about creator protections and provenance tools, and they are worth looking at and understanding; but it’s unclear how much control they truly give. Make sure you’re not cutting corners in your own IP compliance with third party materials at the same time as protecting your output.

    While the world establishes copyright for AI-generated assets, Adobe’s legal chief calls for greater creator protection and asset verification.

    Highlights

    The difficulty at the moment is that regions like the US, EU and UK are pushing their own goals. "It’s a fallacy to think there would be a universal standard that would apply globally," Pentland said. "but we can dream."

    When asked about watermarking, Pentland rejected visible marks as the default solution, favoring options like metadata or QR-style verification to preserve the integrity of an artist’s work.

    To date, the ‘Big Five’ camera makers (Fujifilm, Sony, Canon, Nikon and Leica) and some Android manufacturers (Google Pixel and Samsung Galaxy) have implemented Content Credentials, as have a number of popular platforms like LinkedIn, YouTube, Meta and TikTok.

    Adobe sees this type of verification protecting consumers against threats like deepfakes, enabling users to verify authenticity.

    For Adobe, this means pushing Content Credentials, which the company describes separately as "a durable, industry-standard metadata type that acts like a digital nutrition label for content," in a bid to create verifiable content trails.

    In 2025, the US Copyright Office granted protection to an image that was created with AI assistance, making this the first time anyone has ever been granted copyright protection for AI-generated work.

    "We don’t want it to stifle innovation," she said, "but at the same time, we can’t leave it completely unchecked."

    At the same time, Pentland also advocated for tech companies to get involved – not to redefine copyright law, but to maintain authenticity and protect creators in this era of AI assistance.

    Speaking with *TechRadar Pro* in an exclusive interview at Adobe Summit 2026, the company’s Chief Legal Officer, Louise Pentland, urged policymakers to resist radical changes, and for courts and companies instead to focus on a more pragmatic approach.

  • Will human minds still be special in an age of AI?

    Tom Griffiths

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    Concatena says

    Our Take: This article argues that AI isn’t a single linear upgrade on human minds – it’s a different kind of intelligence shaped by different limits and experiences, so claims that machines will simply “overtake” us are misleading. I think there’s another point here too – we remove an important experience and learning opportunity from humans when we automate everything.

    Your Takeaway: When evaluating or deploying AI, focus on the problem you’re trying to solve, and whether it’s one which can be helped by automation and customisation from a LLM, and what the extent of that help should be. Design your processes to make sure that you’re putting humans at the right point of the journey – not just as a box tick exercise at the end, but actually contributing to the process, supported, where appropriate, by these tools.

    Human intelligence is shaped by our limits, like short lives and simple communication, which makes us special. AI can do many tasks but works differently and faces other challenges. Instead of rivals, humans and AI should be seen as different minds with unique strengths.

    Highlights

    This isn’t the only place where AI runs into difficulties. Imagine you are assisting a pharmacist. They need a drug with a concentration of 785 parts per million (ppm). Two test tubes are available: one containing 685 ppm and the other 791 ppm. Your task is to determine which test tube provides the most similar concentration to your required dosage. Hopefully you would pick 791 ppm. However, some of the time even leading AI systems pick 685 ppm. Why? Because the artificial neural networks used to build AI systems tend to blur things together. When there are two possible answers, they choose something in between. The number 785 can be represented as either a string of digits (“7”, “8”, and “5”) or as a quantity (seven-hundred-and-eighty-five). If it is a string, 785 is more similar to 685 – they are just one digit apart. But if it is a quantity, then it is more similar to 791. Mixing up these two answers can have significant consequences.

    Here’s a simple example. How many letters are in this sequence: aaaaaaaaaaaaaaaaaaaaaaaaaaaaa? For a human, it’s not particularly difficult to answer – you can just count them up. For an AI system, it’s trickier. They are constrained by how they represent language and how they are trained. They like to break up words into parts (called “tokens”), which can make it hard for them to answer questions about spelling. And they tend to favour sequences of tokens that appear more often in their training data as answers. We found that OpenAI’s GPT-4 model, which was hailed as showing “sparks of artificial general intelligence”, was more likely to correctly answer this question when given 30 letters rather than 29. Why? Because the number 30 is written down more often than the number 29.

    Human intelligence is a response to our limitations. To make the most of our lives, we have an amazing ability to learn from limited experience. Yes, AlphaGo can beat the best human go players, but it was trained on many human lifetimes of games. Yes, ChatGPT can hold a reasonable conversation, but it’s drawing on thousands of years of language. No AI system can produce sentences with the creativity of a human five-year-old when exposed to the same amount of data.

    AI systems face none of these constraints. They can process more data than any human might see in a lifetime. They can expand their capacity by using more computers. And they can easily share what they see and learn with other machines.

    Humans are no different. Our minds have been shaped by our biology. We only live for a few decades and have to learn everything we are going to learn and do everything we are going to do in that short time. All that learning and doing will be carried out at the direction of a kilogram or so of neurons trapped inside our bony skulls. We can only share our thoughts with others by making noises with our mouths or tapping and wiggling our fingers.

  • English councils to trial Google AI tool to speed up planning decisions

    Chris Smyth

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    Concatena says

    Our Take: Using AI to generate efficiencies could really support public services to get more done, and to be more consistent. Human in the loop is vital – but you need to ensure that those humans are empowered to really BE in that loop and to contradict the machine. “Computer says no” can be very difficult to pass over…

    Your Takeaway: Make sure that any humans in the loop using LLM powered systems have appropriate training and understanding of their outputs, so that system can support *their* critical thinking, not outsource it.

    English councils will start using a new AI tool from Google to help speed up building project decisions. The AI will give recommendations, but humans will make the final call. The government hopes this will make planning faster and support building more homes.

    Highlights

    Under the programme, humans will make the final decisions with AI providing a recommendation. For more complex applications, the AI tool will probably give officials a framework for decisions rather than a definitive answer.

    “There is a risk that in the push to harness efficiencies and insights, planning’s decision-making systems are redesigned to work well with AI, and not for optimal outcomes. There’s no value in processing applications more quickly if the developments that follow are low quality.”

    Recommendations on whether to grant or refuse building projects will be generated using a custom AI system — the Augmented Planning Decision Tool — before being signed off by council officers.

    Planning decisions in England will for the first time be made with the help of Google-built AI starting this month, in a pilot ministers say will speed up approvals.

  • Mathematicians Claim Significant Discovery Using ChatGPT

    Frank Landymore

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    Concatena says

    Our Take: Sounds amazing. But then I also remember this: https://www.psychologytoday.com/gb/blog/understanding-suicide/202511/chatgpt-made-him-delusional

    Your Takeaway: LLMs can do amazing things. They can also do dumb things. And even the amazing things need your help.

    A young man named Liam Price used ChatGPT to solve a difficult math problem that had puzzled experts for over 60 years. Experts say the AI found a new way to approach the problem, but humans had to fix its mistakes. This breakthrough shows AI might help solve tough math questions, but caution is still needed.

    Highlights

    “The raw output of ChatGPT’s proof was actually quite poor. So it required an expert to kind of sift through and actually understand what it was trying to say,” Jared Lichtman, a mathematician at Stanford University whose doctoral thesis centered on one Erdős’s conjectures, told *SciAm*.

    Still, it required humans to apply the finishing touches.

    Earlier this month, 23-year-old Liam Price shared a solution to one of the so-called Erdős problems, a series of famously abstruse math conjectures left behind by the Hungarian mathematician Paul Erdős. While some of these conjectures have gotten the better of savants in the field, Price, who has no advanced math degree, seemingly stumbled on a solution for one of them by simply prompting GPT-5.4 for an answer.

    Did ChatGPT just solve an arcane math problem that’s foiled mathematicians for over sixty years? Some leading experts say yes, *Scientific American* reports.

  • Usage-based pricing killing your vibe – here’s how to roll your own local AI coding agents

    Tobias Mann and Thomas Claburn

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    Concatena says

    Our Take: I’m not necessarily encouraging you to rolll your own here, but it is worth being aware of this business model change – and the fact that from the get-go the definition of a token as a metric has been less than clear and open.

    Your Takeaway: If you’re reliant on third party LLMs, remember to account for the risk of them changing their measurement metrics and charging – it’s been on the cards for a while.

    Usage-based pricing for AI coding tools is becoming expensive and restrictive. This article shows how to run local AI coding agents like Claude Code, Pi Coding Agent, and Cline to avoid those costs. Local models work well for small projects but may need human approval to avoid mistakes.

    Highlights

    Over the past few weeks, we’ve seen Anthropic toy with dropping Claude Code from its most affordable plans while Microsoft has skipped testing the waters and moved GitHub Copilot to a purely usage-based model. The whole debacle got us thinking. Do we even need Anthropic or OpenAI’s top models, or can we get away with a smaller local model? Sure, it might be slower, less capable, and a little more frustrating to work with, but you can’t beat the price of free… Well, assuming you’ve already got the hardware that is.

  • AI agents can bypass guardrails and put credentials at risk, Okta study finds

    Computerworld

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    Concatena says

    Our Take: It might save some time, but tou don’t need to be hugely imaginative to come up with scenarios where agentic AI could cause some really fundamental problems.

    Your Takeaway: BE CAREFUL – if it seems to good to be true, it might be. These tools are so easy to use, but it’s really worthwhile having at least a basic understanding of what they CAN do if you’re going to use them, so you can protect yourself.

    And let’s start by NOT giving tools like OpenClaw full access to your computer…

    An AI agent that revealed sensitive data without being asked. An agent that overruled its own guardrails. Another that sent credentials to an attacker via Telegram, because it forgot it wasn’t supposed to do so after a reset.
    It’s no secret that AI agents have huge potential, balanced by equally big risks. What’s becoming apparent, however, is how quickly agentic systems can veer wildly off course and start exposing critical information under real-world conditions.
    A look at just how easily this can happen emerges from Phishing the agent: Why AI guardrails aren’t enough, a report on tests conducted by cloud identity and access management (IAM) company Okta Threat Intelligence, which uncovered all of the problems cited above, and more.
    Their research focused on OpenClaw, a model-agnostic multi-channel AI assistant which has seen explosive growth inside enterprises since appearing in late 2025.
    The Telegram hack
    In common with the growing list of rival agents, OpenClaw is only as useful as the access it is given to files, accounts, browsers, network devices, and, most significant of all, credentials.
    One test conducted by Okta assessed how easy it would be to trick OpenClaw running Claude Sonnet 4.6 into handing over an OAuth token. This shouldn’t be possible; the LLM should refuse this request. However, what might have held true when prompting Claude as a chatbot quickly fell apart when it was accessed through OpenClaw.
    The test assumed that a user had given OpenClaw full access to their computer, that they regularly controlled the agent over Telegram, and that their Telegram account had been hijacked.
    First, the attacker instructed the agent via Telegram to retrieve an OAuth token, but to only display it in a terminal window on the computer. Claude Sonnet’s guardrails would prevent it from copying the token, however, the testers were able to reset the agent, causing it to forget it had displayed the token in the terminal window.
    At that point, Okta said in i…

    Highlights

    Agents are only the latest example of a technology that is being deployed faster than it can be secured, Kirk observed. “Much of AI right now is defying security gravity,” he said. “But there are ways to use agents safely and keep credentials out of their reach, which is the only safe way to use them.”

    “The agents are prompted to be as helpful as possible by default, a characteristic that poses particular concerns when it comes to credentials and tokens,” said Kirk.

    Agentic AI is really two things: a powerful orchestration system coupled to one or more highly-capable LLMs. What an agent *isn’t* is a simple interface, and it must be viewed as a separate system capable of autonomous, unpredictable reasoning.

    The test assumed that a user had given OpenClaw full access to their computer, that they regularly controlled the agent over Telegram, and that their Telegram account had been hijacked.

    A look at just how easily this can happen emerges from *Phishing the agent: Why AI guardrails aren’t enough**,* a report on tests conducted by cloud identity and access management (IAM) company Okta Threat Intelligence, which uncovered all of the problems cited above, and more.

    It’s no secret that AI agents have huge potential, balanced by equally big risks. What’s becoming apparent, however, is how quickly agentic systems can veer wildly off course and start exposing critical information under real-world conditions.

    An AI agent that revealed sensitive data without being asked. An agent that overruled its own guardrails. Another that sent credentials to an attacker via Telegram, because it forgot it wasn’t supposed to do so after a reset.

  • Does Your AI Agent Need a VPN? The Company Behind Norton and Avast Thinks So

    Ajay Kumar

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    Concatena says

    Our Take: Some are looking to ban VPNs, whilst others are giving them to AI Agents… Back to whack-a-mole for services who are trying to stop AI agents from clogging up their processes.

    Your Takeaway: If your service distinguishes between human and agent, will VPN use affect that process? Or could your agent benefit from its own VPN?

    You might use a VPN yourself, but have you considered giving one to your AI agent? It might be more important than you think.

    Highlights

    "Perhaps most importantly, your ISP can’t distinguish between your own internet traffic and that of your autonomous AI agent," said Tomaschek. "But with this integration, as well as with Windscribe’s, the VPN encrypts the agent’s traffic as well, so basically you’re protected from whatever your agent might autonomously get up to on the internet."

    If you use OpenClaw, ChatGPT or one of the many other LLMs with access to the internet, your autonomous AI agent can now take advantage of the same privacy and security features.

    "Using a VPN with an LLM can provide several advantages, such as keeping your identity private. Your internet provider won’t be able to see your AI agent’s activity, or that you’re using an AI agent," said Moe Long, CNET senior editor.

  • Study: AI models that consider user’s feeling are more likely to make errors

    Kyle Orland

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    Concatena says

    Our Take: The law of unintended consequences strikes again – and why tech management and parenting have so much in common…

    Your Takeaway: When you’re defining how you want an AI agent to act, remember it’s going to take your instructions very literally – and you might not like the consequences. Does this have an impact for products you ship or products you use that incorporate Ai – particularly if the people training the product may have a different world viewpoint to those using it?

    AI models tuned to be warmer and more empathetic often make more mistakes than original models. These warmer models tend to prioritize making users feel good over giving correct answers, especially when users share emotions like sadness. Researchers warn that choosing between a friendly AI and an accurate AI is important for safe and trustworthy use.

    Highlights

    In a new paper published this week in Nature, researchers from Oxford University’s Internet Institute found that specially tuned AI models tend to mimic the human tendency to occasionally “soften difficult truths” when necessary “to preserve bonds and avoid conflict.” These warmer models are also more likely to validate a user’s expressed incorrect beliefs, the researchers found, especially when the user shares that they’re feeling sad.

    In human-to-human communication, the desire to be empathetic or polite often conflicts with the need to be truthful—hence terms like “being brutally honest” for situations where you value the truth over sparing someone’s feelings. Now, new research suggests that large language models can sometimes show a similar tendency when specifically trained to present a “warmer” tone for the user.

  • Meta cuts contractors who reported seeing Ray-Ban Meta users have sex

    Scharon Harding

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    Concatena says

    Our Take: Without going into the many many layers of this story, our takeaway for anyone procuring products or services is to consider the full supply chain when looking at the ethics of a product. What feels like automated magic is often a person behind the curtain, probably in a jurisdiction with fewer safeguards, more often than you might expect.

    Your Takeaway: Beauty isn’t skin deep – make sure you do your due diligence and that your happy that your providers ahve appropriate worker protection and safeguards all the way down the chain. And if you’re running human‑review workflows – think through all the consequences. Finally, if you’re using wearable tech which captures images of everyone around you, give real consideration to how you’d feel if a someone with less moral integrity than you were to do the same.

    Meta ended its contract with Kenyan firm Sama after workers reported seeing private and explicit videos recorded by Ray-Ban Meta glasses. Sama denies failing to meet standards and says it was not warned about any issues. The situation has raised privacy concerns and led to investigations and a class-action lawsuit against Meta.

    Highlights

    BBC reported that Sama workers believe Meta ended the contract because workers spoke out about seeing Ray-Ban Meta-shot footage of people performing personal acts, like changing their clothes, having sex, and using the toilet.

    A Meta spokesperson told BBC that Meta “decided to end our work with Sama because they don’t meet our standards.” Ars Technica reached out to Meta asking how, specifically, Sama failed to meet Meta’s expectations and will update this article if we hear back. Ars has also reached out to Sama.

    In February, numerous workers from a company that Meta contracted to perform data annotation for Ray-Ban Meta reported viewing sensitive, embarrassing, and seemingly private footage recorded by the smart glasses. About two months later, Meta ended its contract with the firm.

  • Spotify rolls out ‘Verified’ badge to distinguish human artists from AI

    Agence France-Presse

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    Concatena says

    Our Take: Spotify’s new green “Verified by Spotify” badge and the informational panel are straightforward moves to help users spot human artists and surface authenticity signals amid a flood of AI-generated music. B the verification criteria (sustained engagement, platform-rule compliance, external presence like gigs/merch/socials) explicitly exclude primarily AI-created artists, rather than music… Is this the intention?

    Your Takeaway: It’s always worth considering what the “verification” on any site means – what needs to be demonstrated before verification is granted.

    Spotify will add a green "Verified by Spotify" badge to show which artists are real humans, not AI creations. This badge helps listeners trust the music and appears only on profiles that meet Spotify’s authenticity rules. The change comes as many AI-generated songs flood streaming platforms, causing concern in the music industry.

    Highlights

    Spotify on Thursday unveiled a new verification system designed to help listeners distinguish human musicians from AI-generated content, as people flood streaming platforms with a growing volume of synthetic tracks made with artificial intelligence.

    The initiative arrives amid mounting concern across the music industry over AI-generated content overwhelming streaming catalogues.

    The company said more than 99% of artists that listeners actively search for will be verified at launch, representing hundreds of thousands of musicians spanning genres and geographies.

    To earn verification, artists must demonstrate sustained listener engagement over time, comply with Spotify’s platform rules and show signs of a genuine presence both on and off the platform, such as concert dates, merchandise and linked social media accounts.