Google's Gemini 3 model keeps the AI hype train going - for now Google's latest model reportedly beats its rivals in several benchmark tests, but issues with reliability mean concerns remain over a possible AI bubble Gemini 3 is Google's latest AI model Google's latest chatbot, Gemini 3, has made significant leaps on a raft of benchmarks designed to measure AI progress, according to the company. These achievements may be enough to allay fears of an AI bubble bursting for the moment, but it is unclear how well these scores translate to real-world capabilities. What's more, persistent factual inaccuracies and hallucinations that have become a hallmark of all large language models show no signs of being ironed out, which could prove problematic for any uses where reliability is vital. AI may blunt our thinking skills - here's what you can do about it In a blog post announcing the new model, Google bosses Sundar Pichai, Demis Hassabis and Koray Kavukcuoglu write that Gemini 3 has "PhD-level reasoning", a phrase that competitor OpenAI also used when it announced its GPT-5 model . As evidence for this, they list scores on several tests designed to test "graduate-level" knowledge, such as Humanity's Last Exam, a set of 2500 research-level questions from maths, science and the humanities.

This week, we're looking back at our hellish 2024 and trying to figure out where to go from here. We began the year with enormous hype around artificial intelligence, but that's cooled off after seeing how useless many AI features have been. It's also clear that many companies, including Microsoft and Apple, are trying to push half-baked AI concepts onto users. Looking forward, we're expecting a rough few years for the tech industry (not to mention the world as a whole). Listen below or subscribe on your podcast app of choice. If you've got suggestions or topics you'd like covered on the show, be sure to email us or drop a note in the comments!

Building his own large language model (LLM) is out of the realm of possibility for startup founders like Zhang Haiwei. He'd need hundreds of millions of dollars, and he'd be competing with China's internet giants, who have a long head start. The likes of Baidu and IFlyTek have been working on LLMs--the foundation of artificial intelligence systems that can mimic human intelligence--for years, long before the current AI boom took off. Instead, Zhang's motion-capture startup, Chingmu, is using OpenAI's models trained with its own data to analyze how people and objects move, to use in animation and sports training. "My view of this year is involution," Zhang says, applying a popular term in China which describes a cycle of manic competition that leads to everyone working harder and harder for fewer rewards.

When I started my data science career at Accenture in 2013, everyone in the industry -- from automotive to telecommunication -- was into "big data." In fact, according to Gartner's Hype Cycle that year, big data was at the "peak of inflated expectations." Being a cool kid on the data science block meant you were required to have Hadoop or something similar. And if you had tools like Spark, MapReduce, or Hive in your repertoire you were a rock star. Fast forward to 2017 and the hype around big data has mostly quieted, only to be replaced by artificial intelligence (AI), a sub-branch of machine learning.