Good morning. It’s Wednesday, October ninth.
Did you recognize: On today in 2006, Google announced their purchase of YouTube for $1.65 billion in stock.
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Today’s trending AI news stories
Hugging Face’s recent tool lets devs construct AI-powered web apps with OpenAI in only minutes
Hugging Face’s latest tool, openai-builtoffers developers a simplified technique of constructing AI-powered web apps. With just a couple of lines of Python, developers can spin up interactive apps using OpenAI’s language models, slashing development time from months to minutes. By abstracting the backend complexity, openai-gradio lets small teams concentrate on innovation slightly than infrastructure headaches.
The tool provides flexibility for personalization, from input fields to output formats, making it easy to tailor AI interfaces for specific use cases. The simplicity of the setup means startups and enterprises alike can experiment with AI without breaking a sweat—or the bank. Hugging Face has essentially handed developers a quick pass to scalable AI, removing the same old technical roadblocks. Read more.
LLMs are ‘consensus machines’ much like crowdsourcing, Harvard study finds
A recent Harvard study uncovers that giant language models (LLMs) function very similar to crowdsourcing platforms, generating responses based on the statistical likelihood of word pairings slightly than expert knowledge. Researchers Jim Waldo and Soline Boussard found that while LLMs excel at providing accurate answers on well-trodden topics, they hit upon specific or contentious questions.
The study notes, “A GPT will tell us that grass is green since the words ‘grass is’ are mostly followed by ‘green.’ It has nothing to do with the colour of the lawn.” This highlights the models’ tendency to supply consensus-driven responses while glossing over the subtleties of less popular subjects.
The authors advocate for a critical lens when interpreting LLM outputs, especially in complex scenarios where consensus could also be lacking, reinforcing the notion that “crowdsourced” knowledge is not infallible. Read more.
China’s upgraded light-powered ‘AGI chip’ is now 1,000,000 times more efficient than before, researchers say
China’s Taichi-II chiplet has elevated light-powered AI processing, achieving a million-fold increase in energy efficiency and a 40% enhancement in classification accuracy over its predecessor. Powered entirely by photons, this chip employs a “fully forward mode” training method, enabling real-time learning without the cumbersome baggage of iterative processing. Its modular design allows for impressive scalability, with multiple chiplets able to simulating nearly 14 million artificial neurons—outpacing rivals.
Operating at over 160 trillion operations per watt, Taichi-II positions itself as a frontrunner in the search for low-energy, high-performance computing. Because it strides toward practical applications, it’s not only a chip; it’s a step closer to the elusive realm of artificial general intelligence, albeit with the same old caveats about its future implications. Read more.
6 recent AI-powered tools from around the net
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