Continual learning

Jun 21, 2026

⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai

Ronuk Malde, CEO of Trajectory.ai, discusses his journey from building AI coding agents at Windsurf to his current focus on continual learning for enterprise AI. He shares insights on leveraging real-world user data, the unique challenges of model acquisition, and how Trajectory.ai's platform, powered by innovations like scaled SDPO and a novel training stack, enables dynamic, always-learning AI models for diverse industries from legal to finance.

Jun 21, 2026

6 Things to Know about AIE World's Fair 2026

Discover the AI Engineering World's Fair 2026, the largest iteration yet, offering an unparalleled deep dive into AI engineering with expanded tracks on auto research, GPU specialization, and new verticals like finance and healthcare. Highlights include an innovative expo experience, exclusive leadership initiatives like the "Token Billionaires Program," and unique side events fostering community, including "Posters on AI" where attendees can defend their tweets. This event is designed to be a curated hub for practical, cutting-edge insights and networking in the AI/ML professional landscape.

Apr 29, 2026

How to Build the Future: Demis Hassabis

Demis Hassabis, CEO of Google DeepMind, outlines the remaining challenges on the path to AGI, including memory, continual learning, and true reasoning. He discusses how learnings from AlphaGo are shaping agent development, the strategic importance of powerful small models like Gemma, and his vision for AI as the ultimate tool for scientific discovery, offering a framework for identifying breakthrough opportunities and advice for founders building in the age of AI.

Dec 31, 2025

The Algorithm That IS The Scientific Method [Dr. Jeff Beck]

Dr. Jeff Beck argues that the future of AI lies not in scaling up large language models, but in building systems that mirror the brain's approach to understanding the world. He posits that true intelligence is grounded in physics and object-centered models, not language. Beck's vision involves creating AI composed of numerous small, modular models—much like a video game engine—that can be dynamically combined, updated through continual learning, and understand the world through causal relationships and forces. This approach, he claims, will solve key challenges in generalization, robotics, and alignment by enabling machines to 'know what they don't know' and reason about the physical world in a way that is fundamentally similar to humans.

Dec 23, 2025

What are we scaling?

A critical analysis of AI progress, arguing that short AGI timelines are unlikely given the current reliance on pre-baking skills via reinforcement learning. The author contends that true AGI requires on-the-job, continual learning—a capability current models lack. The modest economic impact of AI is presented not as a diffusion lag but as direct evidence of this capability gap. The future of AI will be a gradual, competitive race to solve continual learning, not a sudden takeoff.

Oct 04, 2025

Some thoughts on the Sutton interview

A reflection on Richard Sutton's "Bitter Lesson," arguing that while his critique of LLMs' inefficiency and lack of continual learning is valid, imitation learning is a complementary and necessary precursor to true reinforcement learning, much like fossil fuels were to renewable energy.