John Lewis’ Post

Unsupervised Machine Learning. 😅 This led to the Foundation models of today. 😂 Btw, check out our videos on Foundation models. https://lnkd.in/guDcbRHM

As the 1yo voyage-code-2 is already unparalleled in code retrieval, voyage-code-3 pushes the boundaries even further. With a new quantization aware training objective and improved matryoshka, voyage-code-3 has much better storage cost-performance tradeoff compared to OpenAI models.

We've included the new OpenAI model GPT-4o-mini into xplainable. - Priced at $0.15 per 1M input tokens and $0.6 per 1M output tokens, more than 60% cheaper than GPT-3.5 Turbo. Based on our initial testing, the auto-train recommendations and business intelligence insights generated by this model are superior even to those of GPT-4. The GPT-4o-mini not only provides more accurate and actionable recommendations but also does so at a fraction of the cost, making machine learning accessible to businesses of all sizes.

Vertical embedding model with Matryoshka Learning and Quantization-Aware Training for the win.

You can try out this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models so you can quickly get started with ML. In this post, we walk through how to discover and deploy the DBRX model.

Very exciting week in AI! 😀 We've all had our feeds filled with everyone sharing that video of OpenAI O1 "thinking" (by generating chain-of-thought tokens lol) and making a game with just a prompt. But I think we need to focus on a few things: - O1 is for specific purposes: Not everything requires immense thinking. If you don't believe me, check this out https://lnkd.in/gkU4gCK4 - The pricing is outrageous: $15/1M input tokens and $60/1M output tokens 💀 For these reasons, we need some sort of a router that decides the nature of the query and whether it needs so much compute and "thinking". Otherwise, we'll just end up burning thousands of tokens to output 'ok' after 20 seconds 😄

Just spent 20 minutes listening to a session at the Open Source Summit on CI/CD practices for Machine Learning with MLOps. I liked it! What really stood out? The speaker ( Robert Hafner ) not an ML professional, made MLOps accessible by stripping away the buzzwords. A key takeaway that resonated with me: "AI is software; everything that applies to software applies to AI. MLOps is 80% software." This session was a refreshing reminder that MLOps doesn't have to be intimidating. Simplifying the approach makes adopting best practices so much easier! Check it out here: https://lnkd.in/d_GNePNW #OpenSourceSummit #MLOps #CICD #MachineLearning #SoftwareEngineering #AI #TechTalk

OpenAI dropping a $200/m pro model is very interesting, especially when my advent of ai tip tomorrow covers this exact subject matter 👀

🚀 OpenAI Introduces O3 and O3-Mini: Next-Gen Reasoning Models OpenAI has launched its latest reasoning models, O3 and O3-mini, showcasing groundbreaking performance on the ARC-AGI challenge. Low-compute mode: 75.7% accuracy for just $20/task. High-compute mode: 87.5% accuracy, leveraging advanced compute resources. While the high-compute mode is costly, these models represent more than just raw power—they push the boundaries of AI reasoning and open up new scientific frontiers.