Similar Stories to Here's How Indycar Aerodynamics Have Changed In The Last 100 Years on Bing News

Enlarge (credit: DuckDuckGo) On Thursday, DuckDuckGo unveiled a new "AI Chat" service that allows users to converse with four mid-range large language models (LLMs) from OpenAI, Anthropic, Meta, and Mistral in an interface similar to ChatGPT while attempting to preserve privacy and anonymity. While the AI models involved can output inaccurate information readily, the site allows users to test different mid-range LLMs without having to install anything or sign up for an account. DuckDuckGo's AI Chat currently features access to OpenAI's GPT-3.5 Turbo, Anthropic's Claude 3 Haiku, and two open-source models, Meta's Llama 3 and Mistral's Mixtral 8x7B.

Enlarge / The R1S and R1T don't look much different from the electric trucks we drove in 2022, but under the skin, there have been a lot of changes. (credit: Rivian) Rivian provided flights from Los Angeles to Seattle and accommodation so Ars could drive the new R1S and R1T.

Artificial intelligence systems like ChatGPT could soon run out of what keeps making them smarter — the tens of trillions of words people have written and shared online. A new study released Thursday by research group Epoch AI projects that tech companies will exhaust the supply of publicly available training data for AI language models by roughly the turn of the decade — sometime between 2026 and 2032. Comparing it to a “literal gold rush” that depletes finite natural resources, Tamay Besiroglu, an author of the study, said the AI field might face challenges in maintaining its current pace of progress once it drains the reserves of human-generated writing. In the short term, tech companies like ChatGPT-maker OpenAI and Google are racing to secure and sometimes pay for high-quality data sources to train their AI large language models – for instance, by signing deals to tap into the steady flow of sentences coming out of Reddit forums and news media outlets. In the longer term, there won’t be enough new blogs, news articles and social media commentary to sustain the current trajectory of AI development, putting pressure on companies to tap into sensitive data now considered private — such as emails or text messages — or relying on less-reliable “synthetic data” spit out by the chatbots themselves. “There is a serious bottleneck here,” Besiroglu said.

If you are one of the millions of people who suddenly went puzzle-crazy during the pandemic lockdowns of 2020, then you may have heard of Liberty Puzzles, a 19-year-old Boulder company that specializes in manufacturing high-end, hand-cut wooden jigsaw puzzles. Liberty’s works became highly sought-after commodities for people who were looking to spend their time doing something creative during their downtime.

The celebrities who blow into Rockmount Ranch Wear’s Denver store tend to be touring musicians, whether they’re headlining nearby Ball Arena or playing sold-out shows at Red Rocks Amphitheatre. They’re some of the biggest names in the business. “When Bruce Springsteen came in last spring, he spent over an hour and a half here, then invited us all to his concert at (Ball Arena),” said Steve Weil, the third-generation owner of the Western wear manufacturer.

Enlarge (credit: Aurich Lawson | Getty Images) We’ve been living through the generative AI boom for nearly a year and a half now, following the late 2022 release of OpenAI’s ChatGPT. But despite transformative effects on companies’ share prices, generative AI tools powered by large language models (LLMs) still have major drawbacks that have kept them from being as useful as many would like them to be.