Why it's time to reset our expectations for AI The hype we have been sold for the past few years has been overwhelming. Hype Correction is the antidote. Can I ask you a question: How do you about AI right now? Are you still excited? When you hear that OpenAI or Google just dropped a new model, do you still get that buzz? Or has the shine come off it, maybe just a teeny bit? Come on, you can be honest with me.

As AI companies work furiously to improve the intelligence and usefulness of their products, their demand for cheap, plentiful energy has skyrocketed. This gold rush has been extremely profitable for an unlikely beneficiary: Bitcoin miners. In recent months, major Bitcoin mining companies have started to swap out some of their mining equipment in favor of rigs used to run and train AI systems. These companies believe that AI training could provide a safer and more consistent source of revenue than the volatile crypto industry. And so far, these pivots have been warmly received by investors, leading to the market cap of 14 major bitcoin mining companies jumping in value by 22%, or 4 billion, since the beginning of June, J.P. Morgan reported on June 24. This transition reflects several trends of the moment: the roaring hype cycle of AI; the dwindling access to power, and a tenuous bitcoin mining landscape following the bitcoin halving.

Text-to-video generative AI models such as Sora OpenAI have the potential to disrupt multiple industries. In this paper, we report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We collected and analyzed comments (N=292) under popular posts about Sora-generated videos, comparison between Sora videos and Midjourney images, and artists' complaints about copyright infringement by Generative AI. We found that people were most concerned about Sora's impact on content creation-related industries. Emerging governance challenges included the for-profit nature of OpenAI, the blurred boundaries between real and fake content, human autonomy, data privacy, copyright issues, and environmental impact. Potential regulatory solutions proposed by people included law-enforced labeling of AI content and AI literacy education for the public. Based on the findings, we discuss the importance of gauging people's tech perceptions early and propose policy recommendations to regulate Sora before its public release.

Sick and tired of having to work for a living? ChatGPT feels the same, apparently. Over the last month or so, there's been an uptick in people complaining that the chatbot has become lazy. Sometimes it just straight-up doesn't do the task you've set it. Other times it will stop halfway through whatever it's doing and you'll have to plead with it to keep going.

Sick and tired of having to work for a living? ChatGPT feels the same, apparently. Over the last month or so, there's been an uptick in people complaining that the chatbot has become lazy. Sometimes it just straight-up doesn't do the task you've set it. Other times it will stop halfway through whatever it's doing and you'll have to plead with it to keep going.

Earlier this week, Paul McCartney sent the music-nerd internet ablaze with some news: Artificial intelligence had helped resurrect a bit of John Lennon's voice for a new Beatles song, more than four decades after his death. The song is set for release later this year and comes from vocals Lennon recorded on an old demo. "We were able to take John's voice and get it pure through this AI," McCartney told BBC Radio 4, "so then we could mix the record, as you would normally do." The reaction this elicited on WIRED Slack channels was somewhere between "cool" and "gross." Using AI to resurrect Lennon for a new song has its appeal, but given the recent ethical questions around using the technology to make fake songs from artists like Drake and The Weeknd, it also feels icky.

Generative AI, ChatGPT, Dall-E - it seems everyone's so excited about AI at the moment that they've forgotten all about the hot technology trends of just a short while back. Are The Metaverse And Web3 Still Relevant? This is the nature of the "hype cycle" that those of us who follow the latest developments in technology have become very accustomed to. New ideas get a lot of attention – not always of the right sort – then the excitement dies off when something even newer emerges. But does that mean that they're dead and forgotten?

Last week, I had the pleasure of interviewing Kevin Novak, founder of Rackhouse Venture Capital and Uber's first head of AI, and Alex Lee, founder and CEO, of Truewind, in front of a crowd of investors and LPs. The panel was titled, "AI and the battle to capture its value chain: base layer accrual vs the fine tuners." Here's a sample of the questions and topics we addressed. How has AI evolved since you started working in the field, and what is different about this current hype cycle compared to previous ones? According to the Economist, over 500 generative AI startups have collectively raised over $11B, not including OpenAI.

Technology can be a topic difficult to understand and make predictions on, even for those with a strong technical background and perceived expertise. From Ethernet's creator Robert Metcalfe's 1995 prediction that the internet would "catastrophically collapse" by the next year to Intel's prediction that 3D TV was the future, it is clear that predicting tech trends is a difficult endeavor. No matter how hard predicting the future of technology is, every new technology that creates disruption will go through this cycle. Most recently, we have gone through multiple hype cycles with innovations like blockchain, cryptocurrency, the metaverse, VR, and now, AI. Every single of these technologies has captivated not only the public but also developers and investors, blurring the line between facts and fiction.

Large Language Models (LLMs) have come a long way in recent years. From fluent dialogue generation to text summarisation, and article generation, language models have made it extremely easy for anyone to build an NLP-powered product. As a result, hundreds of apps have been popping up every day, predominantly relying on APIs such as OpenAI, Cohere, or Stable Diffusion. Looking at these developments, one might wonder: what is the disruptive potential of such apps? Are they poised to deliver transformative results to all industries?