On Wednesday, Meta, Google, and Microsoft reported record-breaking profits alongside unprecedented investments in AI infrastructure. This surge in spending has sparked discussions about the potential formation of an AI market bubble.
An intense ideological conflict about the methodology tech giants should use to report AI data center emissions has escalated to the global stage, implicating the Greenhouse Gas Protocol in the dispute.
The new AI-driven Wikipedia alternative, Grokipedia, has been criticized for promoting false information, including claims that pornography worsened the AIDS epidemic and that social media may be contributing to an increase in transgender individuals.
This $226 million initiative harnesses ocean wind energy and natural seawater cooling to power a cutting-edge undersea data center, showcasing innovative sustainable technology.
As cryptocurrency-based prediction platforms gain popularity and attract significant investment, the former US president's social media network, Truth Social, is gearing up to introduce a competitor in this space.
I spoke with the scholars who literally wrote the definitive book on tech bubbles—and put their analytical framework to work on AI.
Occasionally, large language models (LLMs) exhibit unexpected malicious or harmful behavior, and the reasons behind this phenomenon remain unclear.
Chinese creator Tianran Mu gained viral attention by replicating the eerie and unsettling style commonly found in AI-generated videos. However, all of his content is completely handcrafted, showcasing human creativity rather than artificial intelligence.
Open source language models have played a key role in driving AI innovation by providing accessible tools and frameworks. Similarly, the emergence of open source robotics models promises to revolutionize the development and capabilities of physical robots. These models, which think in three dimensions, could enable more advanced and adaptable robotic systems, fostering innovation in robotics as open source language models have done for AI.
Recent research indicates that exposing large language models to low-quality but high-engagement content from social media platforms can deteriorate their cognitive performance, reducing their overall effectiveness and accuracy.