Llion Jones cowrote "Attention Is All You Need," the seminal paper that introduced the transformer — the architecture that launched the generative AI revolution. Now he warns that the industry that grew out of this breakthrough is stifling the next one. Learn why the current corporate arms race is killing true innovation and how we can get back to bold exploration. Hosted on Acast. See acast.com/privacy for more information.
The Transformer architecture (which powers ChatGPT and nearly all modern AI) might be trapping the industry in a localized rut, preventing us from finding true intelligent reasoning, according to the person who co-invented it. Llion Jones and Luke Darlow, key figures at the research lab Sakana AI, join the show to make this provocative argument, and also introduce new research which might lead the way forwards. **SPONSOR MESSAGES START** — Build your ideas with AI Studio from Google - http://ai.studio/build — Tufa AI Labs is hiring ML Research Engineers https://tufalabs.ai/ — cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — **END** The "Spiral" Problem – Llion uses a striking visual analogy to explain what current AI is missing. If you ask a standard neural network to understand a spiral shape, it solves it by drawing tiny straight lines that just happen to look like a spiral. It "fakes" the shape without understanding the concept of spiraling. Introducing the Continuous Thought Machine (CTM) Luke Darlow deep dives into their solution: a biology-inspired model that fundamentally changes how AI processes information. The Maze Analogy: Luke explains that standard AI tries to solve a maze by staring at the whole image and guessing the entire path instantly. Their new machine "walks" through the maze step-by-step. Thinking Time: This allows the AI to "ponder." If a problem is hard, the model can naturally spend more time thinking about it before answering, effectively allowing it to correct its own mistakes and backtrack—something current Language Models struggle to do genuinely. https://sakana.ai/ https://x.com/YesThisIsLion https://x.com/LearningLukeD TRANSCRIPT: https://app.rescript.info/public/share/crjzQ-Jo2FQsJc97xsBdfzfOIeMONpg0TFBuCgV2Fu8 TOC: 00:00:00 - Stepping Back from Transformers 00:00:43 - Introduction to Continuous Thought Machines (CTM) 00:01:09 - The Changing Atmosphere of AI Research 00:04:13 - Sakana’s Philosophy: Research Freedom 00:07:45 - The Local Minimum of Large Language Models 00:18:30 - Representation Problems: The Spiral Example 00:29:12 - Technical Deep Dive: CTM Architecture 00:36:00 - Adaptive Computation & Maze Solving 00:47:15 - Model Calibration & Uncertainty 01:00:43 - Sudoku Bench: Measuring True Reasoning REFS: Why Greatness Cannot be planned [Kenneth Stanley] https://www.amazon.co.uk/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 https://www.youtube.com/watch?v=lhYGXYeMq_E The Hardware Lottery [Sara Hooker] https://arxiv.org/abs/2009.06489 https://www.youtube.com/watch?v=sQFxbQ7ade0 Continuous Thought Machines [Luke Darlow et al / Sakana] https://arxiv.org/abs/2505.05522 https://sakana.ai/ctm/ LSTM: The Comeback Story? [Prof. Sepp Hochreiter] https://www.youtube.com/watch?v=8u2pW2zZLCs Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis [Kumar/Stanley] https://arxiv.org/pdf/2505.11581 A Spline Theory of Deep Networks [Randall Balestriero] https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf https://www.youtube.com/watch?v=86ib0sfdFtw https://www.youtube.com/watch?v=l3O2J3LMxqI On the Biology of a Large Language Model [Anthropic, Jack Lindsey et al] https://transformer-circuits.pub/2025/attribution-graphs/biology.html The ARC Prize 2024 Winning Algorithm [Daniel Franzen and Jan Disselhoff] “The ARChitects” https://www.youtube.com/watch?v=mTX_sAq--zY Neural Turing Machine [Graves] https://arxiv.org/pdf/1410.5401 Adaptive Computation Time for Recurrent Neural Networks [Graves] https://arxiv.org/abs/1603.08983 Sudoko Bench [Sakana] https://pub.sakana.ai/sudoku/