deep learning
deep learning on Beyond Market Intelligence: a running collection of 100 stories we have gathered and hand-picked because they are worth your time. Every post here touches on deep learning in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around deep learning, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
whats the best and complete way to keep up with ai/ml news? [D]
Staying current in the rapidly evolving AI/ML landscape can feel overwhelming, especially when a single newsletter isn't enough. To ensure you're not left behind, prioritize a multi-faceted approach. Begin with curated aggregators and industry publications, then supplement with focused Twitter/X lists of leading researchers and practitioners. Finally, actively participate in relevant online communities. For deeper insights into related trends, explore our recent article, "Neil Rimer thinks the AI money is coming back out," which offers a valuable perspective on market dynamics.
How a former DeepMind researcher raised at a $300M pre-seed valuation before launching a product
Andrew Dai, a former DeepMind researcher with over a decade of experience shaping influential AI systems—including work that informed ChatGPT—is pioneering a new frontier: visual AI. He recently secured a remarkable $300 million pre-seed valuation before even launching his product, signaling immense confidence in this emerging field. Dai articulates a clear vision for how visual AI will transform data management. For further insights into the evolving landscape of AI, explore our recent article, "Google continues its renaming streak by turning NotebookLM to Gemini Notebook."
How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured)
Running Large Language Models (LLMs) locally presents a compelling alternative to cloud-based solutions, but what's the real cost? We measured the actual GPU electricity consumption for eight different local LLMs on a single RTX 3090, revealing surprising results – the most efficient wasn't necessarily the smallest or largest. Discover how costs vary per million tokens, and gain practical insights into optimizing your local LLM deployment. For a deeper dive into the computational challenges of generative AI, explore "A Gentle Introduction to Autoencoders & Latent Space."
12 Ways to Reduce LLM Latency and Inference Costs in Production
Scaling large language models (LLMs) effectively moves beyond simply adding more GPUs. It demands a rigorous focus on optimizing request efficiency. This article details 12 proven strategies to reduce LLM latency and inference costs in production environments. Ranked by impact, these methods address wasted work within each request—from caching and quantization to optimized prompting and batching. Discover practical techniques to empower your LLM deployments and maximize performance.