Google just launched Simplify in its iOS app, powered by Gemini models designed for minimally-lossy text simplification. Unlike basic summarization that cuts content, the system preserves nuance and original details while making dense language readable. In trials, it improved reading comprehension accuracy on complex medical texts by 15%.
UC San Diego’s Hao AI Lab just pitted top AI models against Super Mario Bros. using a custom framework. Anthropic’s Claude 3.7 took the top spot, while reasoning heavyweights like OpenAI’s o1 flunked out because taking several seconds to "think" in a platformer means instant death.
Meta just introduced Llama 3.3 70B, a lightweight text model that delivers the punch of its massive 405B predecessor while running significantly cheaper. Armed with advanced post-training, it tops GPT-4o and Gemini 1.5 Pro on key benchmarks like MMLU, and is available for download right away.
Google and Sourcegraph just shared test results from plugging Gemini 1.5 Flash's 1M-token context into the Cody coding assistant. The massive context dropped hallucination rates from 18.97% down to 10.48% on complex codebase tasks, while custom caching trimmed initial response latency from 40 seconds to roughly 5.
Google just rolled out updated versions of its Gemini 1.5 Pro and Flash models, boosting math reasoning by around 20% and cutting unnecessary refusals. The company also slashed 1.5 Pro input token costs by 64% and output tokens by 52%, while ramping up API rate limits. Why it matters: Developers can now run faster and sharper frontier models at substantially lower operating costs.
Google just rolled out three experimental models: an ultra-compact Gemini 1.5 Flash-8B, a significantly upgraded Flash, and a stronger Pro model that levels up coding and complex prompt reasoning. Both maintain 1M token context windows and are available to test for free in Google AI Studio and the Gemini API.
OpenAI just took back the #1 spot on the LMSYS Chatbot Arena leaderboard with its updated ChatGPT-4o model, edging past Google's Gemini-1.5-Pro-Exp with a 1314 score. Tested anonymously across over 11,000 community votes, the new checkpoint shows noticeable gains in coding (up 30+ points) and hard prompts, and developers can already call it in the API.
Google DeepMind just hooked up Gemini 1.5 Pro to its discontinued Everyday Robots hardware, turning them into autonomous office navigators. The bot can process spoken commands and even follow hand-drawn whiteboard maps across a 9,000-square-foot office, notching a 90% success rate across 50 employee interactions.
Google just opened up Gemini 1.5 Pro's 2-million-token context window to all developers, removing the waitlist. Both 1.5 Pro and Flash now also feature built-in Python code execution in an offline sandbox to tackle math and data reasoning, alongside Gemma 2 landing in Google AI Studio. Why it matters: developers can now feed entire codebases into Gemini while letting the model verify its own math by running code.
Google just announced its next-gen AI model Gemini 1.5, led by 1.5 Pro built on a Mixture-of-Experts architecture. It matches flagship 1.0 Ultra quality with far less compute while shattering context limits with support for up to 1 million tokens—enough for an hour of video or 30,000 lines of code. It's rolling out now in limited preview for developers and enterprise users.
