[Medical] AI Engineering
[ Level: Easy ][ Time to Read: 14 minutes]In this Google DeepMind podcast episode featuring Professor Anna Fry interviewing Dr Shane Legg, a co-founder and Chief AGI Scientist at DeepMind the conversation focuses extensively on Artificial General Intelligence (AGI), defining it across different levels, from “minimal AGI” (human-level cognitive capability) to “full AGI” and eventually Artificial Super Intelligence … Read more
[ Level: Easy ][ Time to Read: 13 minutes]Understanding the risk posed by AGI is like standing at the foot of an accelerating train track; you can debate the exact speed or the final destination, but the wisdom lies in recognizing the immediate need to step off the tracks before the exponential approach leaves you … Read more
[ Level: Easy ][ Time to Read: 4 minutes]For many years, I have had the privilege of training aspiring Machine Learning and Deep Learning students, helping them transition from coders to effective AI practitioners. To date, I have trained more than 600 AI practitioners, with student backgrounds ranging impressively from medical doctors to high school … Read more
[Level: Intermediate ][ Time to Read: 7 minutes] A prominent use case for AI in medicine is in diagnostic imaging, particularly in radiology and pathology.1 AI-powered tools can analyze medical images, such as X-rays, CT scans, MRIs, and pathology slides, to assist healthcare professionals in detecting abnormalities and making more accurate, faster diagnoses.2 How it … Read more
[Level: Complex ][ Time to Read: 7 minutes] Retrieval-Augmented Generation (RAG) is a powerful technique that enhances a model’s ability to generate responses by dynamically retrieving relevant information from external memory sources and incorporating it into the model’s context. This approach addresses key limitations of large language models (LLMs) and large multimodal models (LMMs), often … Read more
As an AI Engineer I’ve found that Chip Huyen’s book, “AI Engineering: Building Applications with Foundation Models,” serves as a comprehensive and practical guide for professionals seeking to design and deploy generative AI applications in production. It feels to me like it provides timeless foundational knowledge for building scalable AI products, rather than focusing on … Read more
Mechanistic interpretability (Mech Interp) is a fascinating and rapidly evolving field within AI research that aims to reverse-engineer neural networks to understand their internal algorithms and mechanisms. Chris Olah, one of the pioneers of this field, likens it to neurobiology, as AI models are not programmed in the traditional sense but rather “grown” through training, … Read more
Scaling, often referred to as the Scaling Hypothesis or scaling laws, is a fundamental concept in AI development that posits that by increasing certain key “ingredients,” AI models will continuously improve in their performance and intelligence. Dario Amodei notes that this concept suggests a fundamentally positive future for AI. Here’s a breakdown of what scaling … Read more
In the rapidly evolving world of artificial intelligence, new models and companies emerge at a dizzying pace. Among them, Anthropic has distinguished itself not only by creating a powerful AI named Claude but also by its foundational commitment to safety. As podcast host Lex Fridman noted, CEO Dario Amodei and the Anthropic team have been … Read more
My take on the absolutely wonderful article/blog post by Dario Amodei entitled “Machines of Loving Grace.” The public conversation around artificial intelligence often swings between breathless hype and existential fear. We hear about job displacement, runaway risks, and “doomer” scenarios that paint a grim picture of our technological future. It’s a narrative heavy with caution, … Read more