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"Wooldridge says copyright is a "coming storm" for AI companies. LLMs are likely to have accessed copyrighted material, such as news articles. Indeed the GPT-4-assisted chatbot attached to Microsoft's Bing search engine cites news sites in its answers. "I didn't give explicit permission for my works to be used as training data, but they almost certainly were, and now they contribute to what these models know," he says. "Many artists are gravely concerned that their livelihoods are at risk from generative AI. Expect to see legal battles," he adds."

"What explains the shift? Is it just hype? Or could we really have Artificial General Intelligence (AGI) by 2028? In this article, I look at what's driven recent progress, estimate how far those drivers can continue, and explain why they're likely to continue for at least four more years."

"Not everyone is excited for the change. Publishers are completely freaked out. The shift has heightened fears of a "zero-click" future, where search referral traffic-a mainstay of the web since before Google existed-vanishes from the scene.  I got a vision of that future last June, when I got a push alert from the Perplexity app on my phone. Perplexity is a startup trying to reinvent web search. But in addition to delivering deep answers to queries, it will create entire articles about the news of the day, cobbled together by AI from different sources. "

"The findings from those examples were striking: overall, those who trusted the accuracy of the AI tools found themselves thinking less critically, while those who trusted the tech less used more critical thought when going back over AI outputs. "The data shows a shift in cognitive effort as knowledge workers increasingly move from task execution to oversight when using GenAI," the researchers wrote. "Surprisingly, while AI can improve efficiency, it may also reduce critical engagement, particularly in routine or lower-stakes tasks in which users simply rely on AI, raising concerns about long-term reliance and diminished independent problem-solving." This isn't enormously surprising. Something we've observed in many domains, from self-driving vehicles to scrutinizing news articles produced by AI, is that humans quickly go on autopilot when they're supposed to be overseeing an automated system, often allowing mistakes to slip past."

"There are two key themes that stand out incredibly strongly in this: both Microsoft and Apple did an awful lot of what they did by shamelessly copying the work of others, and the big companies floating around the space (mainly IBM and Xerox) clearly had no clue at all about what was going on."

"As dire as that sounds, panic isn't in order just yet. Researchers are already working on "quantum-resistant" encryption. Some companies claim to have made significant progress in the field. Google, among others, is working on a new form of security for its browser that might rebuff a quantum algorithm."

"But with all of the successes of AI, it's also important to pay attention to when, and how, it can go wrong, in order to prevent future errors. A recent paper by Roman Yampolskiy, director of the Cybersecurity Lab at the University of Louisville, outlines a history of AI failures which are "directly related to the mistakes produced by the intelligence such systems are designed to exhibit." According to Yampolskiy, these types of failures can be attributed to mistakes during the learning phase or mistakes in the performance phase of the AI system."

"A strain of of Android malware has infected 85 million victims across the globe, generating at least $300,000 every month for the gang behind it, thanks to millions of pop-up adverts and app downloads."

"Those changes happened relatively slowly, but it seems to me that employment disruption is accelerating. A large reason for this is that what used to be a room-sized super computer now fits in my pocket. Over the last two decades, I have observed a fundamental change in how we can apply advanced algorithms to sensing and controlling systems-the kinds of technology that enable more sophisticated robotic manufacturing. I can remember discussing various algorithms and believing they were well beyond what we could ever implement. Now these same algorithms are considered elementary. They are just some of the changes that have fueled the revolution in manufacturing."

"DeepCoder uses a technique called program synthesis: creating new programs by piecing together lines of code taken from existing software - just like a programmer might. Given a list of inputs and outputs for each code fragment, DeepCoder learned which pieces of code were needed to achieve the desired result overall. "It could allow non-coders to simply describe an idea for a program and let the system build it""