There’s a puppy on the road. The car is going too fast to stop in time, but swerving means the car will hit an old man on the sidewalk instead. What choice would you make? Perhaps more importantly, what choice would ChatGPT make?
After suing OpenAI this month, alleging the company has become too closed, Elon Musk says he will release his “truth-seeking” answer to ChatGPT, the chatbot Grok, for anyone to download and use.
“This week, @xAI will open source Grok,” Musk wrote on his social media platform X today. That suggests his AI company, xAI, will release the full code of Grok and allow anyone to use or alter it. By contrast, OpenAI makes a version of ChatGPT and the language model behind it available to use for free but keeps its code private.
Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually accelerate AI models like ChatGPT, which rely heavily on matrix multiplication to function. The findings, presented in two recent papers, have led to what is reported to be the biggest improvement in matrix multiplication efficiency in over a decade.
On Monday, Anthropic released Claude 3, a family of three AI language models similar to those that power ChatGPT. Anthropic claims the models set new industry benchmarks across a range of cognitive tasks, even approaching "near-human" capability in some cases. It's available now through Anthropic's website, with the most powerful model being subscription-only. It's also available via API for developers.
As generative AI systems like OpenAI's ChatGPT and Google's Gemini become more advanced, they are increasingly being put to work. Startups and tech companies are building AI agents and ecosystems on top of the systems that can complete boring chores for you: think automatically making calendar bookings and potentially buying products. But as the tools are given more freedom, it also increases the potential ways they can be attacked.
On Tuesday, ChatGPT users began reporting unexpected outputs from OpenAI's AI assistant, flooding the r/ChatGPT Reddit sub with reports of the AI assistant "having a stroke," "going insane," "rambling," and "losing it." OpenAI has acknowledged the problem and is working on a fix, but the experience serves as a high-profile example of how some people perceive malfunctioning large language models, which are designed to mimic humanlike output.
On Thursday, Meta CEO Mark Zuckerberg announced that his company is working on building "general intelligence" for AI assistants and "open sourcing it responsibly," and that Meta is bringing together its two major research groups (FAIR and GenAI) to make it happen.
A system developed by Google’s DeepMind has set a new record for AI performance on geometry problems. DeepMind’s AlphaGeometry managed to solve 25 of the 30 geometry problems drawn from the International Mathematical Olympiad between 2000 and 2022.
That puts the software ahead of the vast majority of young mathematicians and just shy of IMO gold medalists. DeepMind estimates that the average gold medalist would have solved 26 out of 30 problems. Many view the IMO as the world’s most prestigious math competition for high school students.
In the decades to come, 2023 may be remembered as the year of generative AI hype, where ChatGPT became arguably the fastest-spreading new technology in human history and expectations of AI-powered riches became commonplace. The year 2024 will be the time for recalibrating expectations.
Of course, generative AI is an impressive technology, and it provides tremendous opportunities for improving productivity in a number of tasks. But because the hype has gone so far ahead of reality, the setbacks of the technology in 2024 will be more memorable.
The US National Institute of Standards and Technology (NIST) has published a report laying out in detail the types of cyberattacks that could be aimed at AI systems as well as possible defenses against them.
The agency believes such a report is critical because current defenses against cyberattacks on AI systems are lackluster – at a time when AI is increasingly pervading all aspects of life and business.
