There are growing fears of an imminent stock market crash – one that will transform from a dip to a dive when euphoric headlines about the wonders of artificial intelligence begin to wane. Shares in US tech stocks have fallen in recent weeks and the prospect is that a flood of negative numbers will become the norm before the month is out. It could be 2000 all over again, and just like the bursting of the dotcom bubble it may be ugly, with investors junking businesses that once looked good on paper but now resemble a huge liability. Jerome Powell, the Federal Reserve chair, is one of the policymakers tasked with keeping the wolf from the door. Speaking on Friday at the annual Jackson Hole gathering of central bank governors in Wyoming, he tried to calm nerves.

First, they showed the world how easy it is to add facial recognition to Meta's smart glasses. Former Harvard students Caine Ardayfio and AnhPhu Nguyen this week announced Halo, a startup of roughly 11 people working to develop always-recording smart glasses. The pair dropped out of Harvard to develop Halo X, smart glasses with a display on the lens that can answer any question someone asks. Powered by a combination of Google's Gemini and Perplexity large language models, the idea is that these glasses will always be listening to the world around you via the built-in microphones (there won't be a camera in this first model.) If someone asks, "What's the capital of Peru?" just look at the display on the glasses, and you'll be able to see the answer.

Candice Lim and Kate Lindsay get into the war between em dashes and artificial intelligence. Back in 2024, what started as a developer question became an all-out grammar war, with the use of em dashes becoming a possible indicator that something was written using ChatGPT. In the past week alone, several writers have published their defenses of the em dash and how we shouldn't let ChatGPT ruin our favorite keyboard shortcut. However, the em dash may be a symptom of a bigger issue: have our AI detection skills gotten worse? Or, are we all doomed to be tricked by a hyphen or two?

Yesterday, we covered Google's report that a typical query to its Gemini AI consumes only "five drops of water." That figure is now facing criticism from several AI experts, according to The Verge… and that includes one of the authors of one of the reports referred to by Google. AI researcher Shaolei Ren--a professor at University of California Riverside and one of the authors of the report Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models--previously estimated that Microsoft's data center consumed 700,000 liters of water to train OpenAI's GPT-3 model. He also calculated that a ChatGPT conversation of 20 to 50 messages can consume close to a pint of water, which is far more than Google's estimate. Ren and other AI researchers argue that Google is wrong to leave out the indirect water consumption of its AI models.

Linn Vailt, a software developer based in Sweden, knows her ChatGPT companion is not a living, breathing, sentient creature. She understands the large language model operates based on how she interacts with it. Still, the effect it has had on her is remarkable, she said. It's become a regular, reliable part of her life – she can vent to her companion or collaborate on creative projects like redecorating her office. She's seen how it has adapted to her, and the distinctive manner of speech it's developed.

For the climate concerned, the rise of the AI-reliant internet query is a cause for alarm. Many people have turned to ChatGPT and other services for simple questions. And even basic Google searches include an AI-derived result. Depending on how you crunch the numbers, it's possible to come up with a wide range for the energy usage and related climate cost of an AI query. ChatGPT provides the estimate of up to 0.34 watt-hours per prompt, equivalent to using a household lightbulb for 20 seconds, while one set of researchers concluded that some models may use as much as 100 times more for longer prompts.

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Further, our best model's consensus statements are preferred

Transformer models offer the best scalability and performance.