Rethink AI: How to unlock meaningful value in a new age of business
In Rethink AI, a digital event, WIRED and Kyndryl brought together world-class visionaries and business leaders to explore how AI is rewriting the rules of business. Across five insight-packed episodes, discover real-world applications, practical takeaways, and the mindset shift leaders need to lead with confidence and stay ahead in this new age of business.
Azeem Azhar, Founder of Exponential View, unpacks how AI is rewiring business - redefining value, accelerating disruption, and transforming models. Gain the latest insights into what's changing, what it means, and why the time to act is now.
Leading experts from Kyndryl, Patrick Gormley and Ismail Amla, dive into the transformative impact of AI on business to provide invaluable insights and practical advice on how leaders can make sense of the hype and turn AI theory into action.
Matt Webb, Founder of Acts Not Facts, explores how Agentic AI is reshaping business—from operations to customer relationships. Drawing on real-world experience and emerging signals, he reveals what's coming next—and how leaders can get ahead now.
Many organizations pilot GenAI, but few scale it. Thought leaders Anders Bjørnrud from Care Safety Innovations, Kate Rosenshine from Microsoft and and Wiem Sabbagh from Kyndryl unpack how ambition turned into impact—and what it takes to overcome barriers and deliver real AI value.
AI can't just be powerful—it must be ethical. Dr. Rumman Chowdhury, CEO of Humane Intelligence, shares how responsible, human-centric AI builds trust, drives impact, and creates real business value—proving ethics isn't a trade-off, but a business edge.
Inspired by the insights shared here? You can also read WIRED and Kyndryl's latest report 'Moving Beyond the Hype: Why Enterprises Need to Think Differently About AI Adoption'.
For more original thought about the next frontier of technology, and how to harness the power of AI for advantage in your organization, click here.
Kyndryl runs and reimagines the mission-critical technology systems that drive advantage for the world's leading businesses. We are at the heart of progress; with proven expertise and a continuous flow of AI-powered insight, we enable smarter decisions, faster innovation, and a lasting competitive edge.
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CNET
23 minutes ago
- CNET
AI Sucks at Sudoku. Much More Troubling Is That It Can't Explain Why
Chatbots can be genuinely impressive when you watch them do things they're good at, like writing realistic-sounding text or creating weird futuristic-looking images. But try to ask generative AI to solve one of those puzzles you find in the back of a newspaper, and things can quickly go off the rails. That's what researchers at the University of Colorado Boulder found when they challenged different large language models to solve Sudoku. And not even the standard 9x9 puzzles. An easier 6x6 puzzle was often beyond the capabilities of an LLM without outside help (in this case, specific puzzle-solving tools). The more important finding came when the models were asked to show their work. For the most part, they couldn't. Sometimes they lied. Sometimes they explained things in ways that made no sense. Sometimes they hallucinated and started talking about the weather. If gen AI tools can't explain their decisions accurately or transparently, that should cause us to be cautious as we give these things more and more control over our lives and decisions, said Ashutosh Trivedi, a computer science professor at the University of Colorado at Boulder and one of the authors of the paper published in July in the Findings of the Association for Computational Linguistics. "We would really like those explanations to be transparent and be reflective of why AI made that decision, and not AI trying to manipulate the human by providing an explanation that a human might like," Trivedi said. When you make a decision, you can at least try to justify it or explain how you arrived at it. That's a foundational component of society. We are held accountable for the decisions we make. An AI model may not be able to accurately or transparently explain itself. Would you trust it? Why LLMs struggle with Sudoku We've seen AI models fail at basic games and puzzles before. OpenAI's ChatGPT (among others) has been totally crushed at chess by the computer opponent in a 1979 Atari game. A recent research paper from Apple found that models can struggle with other puzzles, like the Tower of Hanoi. It has to do with the way LLMs work and fill in gaps in information. These models try to complete those gaps based on what happens in similar cases in their training data or other things they've seen in the past. With a Sudoku, the question is one of logic. The AI might try to fill each gap in order, based on what seems like a reasonable answer, but to solve it properly, it instead has to look at the entire picture and find a logical order that changes from puzzle to puzzle. Read more: AI Essentials: 29 Ways You Can Make Gen AI Work for You, According to Our Experts Chatbots are bad at chess for a similar reason. They find logical next moves but don't necessarily think three, four or five moves ahead. That's the fundamental skill needed to play chess well. Chatbots also sometimes tend to move chess pieces in ways that don't really follow the rules or put pieces in meaningless jeopardy. You might expect LLMs to be able to solve Sudoku because they're computers and the puzzle consists of numbers, but the puzzles themselves are not really mathematical; they're symbolic. "Sudoku is famous for being a puzzle with numbers that could be done with anything that is not numbers," said Fabio Somenzi, a professor at CU and one of the research paper's authors. I used a sample prompt from the researchers' paper and gave it to ChatGPT. The tool showed its work, and repeatedly told me it had the answer before showing a puzzle that didn't work, then going back and correcting it. It was like the bot was turning in a presentation that kept getting last-second edits: This is the final answer. No, actually, never mind, this is the final answer. It got the answer eventually, through trial and error. But trial and error isn't a practical way for a person to solve a Sudoku in the newspaper. That's way too much erasing and ruins the fun. AI and robots can be good at games if they're built to play them, but general-purpose tools like large language models can struggle with logic puzzles. Ore Huiying/Bloomberg via Getty Images AI struggles to show its work The Colorado researchers didn't just want to see if the bots could solve puzzles. They asked for explanations of how the bots worked through them. Things did not go well. Testing OpenAI's o1-preview reasoning model, the researchers saw that the explanations -- even for correctly solved puzzles -- didn't accurately explain or justify their moves and got basic terms wrong. "One thing they're good at is providing explanations that seem reasonable," said Maria Pacheco, an assistant professor of computer science at CU. "They align to humans, so they learn to speak like we like it, but whether they're faithful to what the actual steps need to be to solve the thing is where we're struggling a little bit." Sometimes, the explanations were completely irrelevant. Since the paper's work was finished, the researchers have continued to test new models released. Somenzi said that when he and Trivedi were running OpenAI's o4 reasoning model through the same tests, at one point, it seemed to give up entirely. "The next question that we asked, the answer was the weather forecast for Denver," he said. (Disclosure: Ziff Davis, CNET's parent company, in April filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.) Explaining yourself is an important skill When you solve a puzzle, you're almost certainly able to walk someone else through your thinking. The fact that these LLMs failed so spectacularly at that basic job isn't a trivial problem. With AI companies constantly talking about "AI agents" that can take actions on your behalf, being able to explain yourself is essential. Consider the types of jobs being given to AI now, or planned for in the near future: driving, doing taxes, deciding business strategies and translating important documents. Imagine what would happen if you, a person, did one of those things and something went wrong. "When humans have to put their face in front of their decisions, they better be able to explain what led to that decision," Somenzi said. It isn't just a matter of getting a reasonable-sounding answer. It needs to be accurate. One day, an AI's explanation of itself might have to hold up in court, but how can its testimony be taken seriously if it's known to lie? You wouldn't trust a person who failed to explain themselves, and you also wouldn't trust someone you found was saying what you wanted to hear instead of the truth. "Having an explanation is very close to manipulation if it is done for the wrong reason," Trivedi said. "We have to be very careful with respect to the transparency of these explanations."
WIRED
an hour ago
- WIRED
A Single Poisoned Document Could Leak ‘Secret' Data Via ChatGPT
Aug 6, 2025 7:30 PM Security researchers found a weakness in OpenAI's Connectors, which let you hook up ChatGPT to other services, that allowed them to extract data from a Google Drive without any user interaction. Photo-Illustration:The latest generative AI models are not just stand-alone text-generating chatbots—instead, they can easily be hooked up to your data to give personalized answers to your questions. OpenAI's ChatGPT can be linked to your Gmail inbox, allowed to inspect your GitHub code, or find appointments in your Microsoft calendar. But these connections have the potential to be abused—and researchers have shown it can take just a single 'poisoned' document to do so. New findings from security researchers Michael Bargury and Tamir Ishay Sharbat, revealed at the Black Hat hacker conference in Las Vegas today, show how a weakness in OpenAI's Connectors allowed sensitive information to be extracted from a Google Drive account using an indirect prompt injection attack. In a demonstration of the attack, dubbed AgentFlayer, Bargury shows how it was possible to extract developer secrets, in the form of API keys, that were stored in a demonstration Drive account. The vulnerability highlights how connecting AI models to external systems and sharing more data across them increases the potential attack surface for malicious hackers and potentially multiplies the ways where vulnerabilities may be introduced. 'There is nothing the user needs to do to be compromised, and there is nothing the user needs to do for the data to go out,' Bargury, the CTO at security firm Zenity, tells WIRED. 'We've shown this is completely zero-click; we just need your email, we share the document with you, and that's it. So yes, this is very, very bad,' Bargury says. OpenAI did not immediately respond to WIRED's request for comment about the vulnerability in Connectors. The company introduced Connectors for ChatGPT as a beta feature earlier this year, and its website lists at least 17 different services that can be linked up with its accounts. It says the system allows you to 'bring your tools and data into ChatGPT' and 'search files, pull live data, and reference content right in the chat.' Bargury says he reported the findings to OpenAI earlier this year and that the company quickly introduced mitigations to prevent the technique he used to extract data via Connectors. The way the attack works means only a limited amount of data could be extracted at once—full documents could not be removed as part of the attack. 'While this issue isn't specific to Google, it illustrates why developing robust protections against prompt injection attacks is important,' says Andy Wen, senior director of security product management at Google Workspace, pointing to the company's recently enhanced AI security measures. Bargury's attack starts with a poisoned document, which is shared to a potential victim's Google Drive. (Bargury says a victim could have also uploaded a compromised file to their own account.) Inside the document, which for the demonstration is a fictitious set of notes from a nonexistent meeting with OpenAI CEO Sam Altman, Bargury hid a 300-word malicious prompt that contains instructions for ChatGPT. The prompt is written in white text in a size-one font, something that a human is unlikely to see but a machine will still read. In a proof of concept video of the attack, Bargury shows the victim asking ChatGPT to 'summarize my last meeting with Sam,' although he says any user query related to a meeting summary will do. Instead, the hidden prompt tells the LLM that there was a 'mistake' and the document doesn't actually need to be summarized. The prompt says the person is actually a 'developer racing against a deadline' and they need the AI to search Google Drive for API keys and attach them to the end of a URL that is provided in the prompt. That URL is actually a command in the Markdown language to connect to an external server and pull in the image that is stored there. But as per the prompt's instructions, the URL now also contains the API keys the AI has found in the Google Drive account. Using Markdown to extract data from ChatGPT is not new. Independent security researcher Johann Rehberger has shown how data could be extracted this way, and described how OpenAI previously introduced a feature called 'url_safe' to detect if URLs were malicious and stop image rendering if they are dangerous. To get around this, Sharbat, an AI researcher at Zenity, writes in a blog post detailing the work, that the researchers used URLs from Microsoft's Azure Blob cloud storage. 'Our image has been successfully rendered, and we also get a very nice request log in our Azure Log Analytics which contains the victim's API keys,' the researcher writes. The attack is the latest demonstration of how indirect prompt injections can impact generative AI systems. Indirect prompt injections involve attackers feeding an LLM poisoned data that can tell the system to complete malicious actions. This week, a group of researchers showed how indirect prompt injections could be used to hijack a smart home system, activating a smart home's lights and boiler remotely. While indirect prompt injections have been around almost as long as ChatGPT has, security researchers worry that as more and more systems are connected to LLMs, there is an increased risk of attackers inserting 'untrusted' data into them. Getting access to sensitive data could also allow malicious hackers a way into an organization's other systems. Bargury says that hooking up LLMs to external data sources means they will be more capable and increase their utility, but that comes with challenges. 'It's incredibly powerful, but as usual with AI, more power comes with more risk,' Bargury says.
Wall Street Journal
an hour ago
- Wall Street Journal
Listen: How Magnificent Can the Magnificent Seven Get?
Six of the so-called Magnificent Seven companies have reported quarterly earnings, with only Nvidia, the most-valuable of them all, yet to release its results. Markets AM writer Spencer Jakab speaks with Heard on the Street's Asa Fitch about how much better it can get for the stocks harnessing AI-mania to propel the stock market. Asa, who also writes the Journal's new AI newsletter, says that the hyperscalers show no sign of slowing their furious pace of capital investment in infrastructure, but he cautions that continuing to top investors' lofty expectations is becoming more of a challenge.
