ai
Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).
Cosine AI has received government backing to build the UK's first sovereign AI, leveraging compute resources from the Izzimbad supercomputer. This initiative aims to position the UK at the forefront of AI development.
Alistair Pullen of Cosine AI explains how their business model as an inference company allows them to operate with significantly less capital than US-based AI giants. By licensing technology rather than hosting models, they avoid the massive data center costs associated with inference.
Alistair Pullen discusses the importance of both total and active parameters in LLM architecture, citing examples like Mistral's 675B model and Anthropic's Sonnet and Opus. He suggests that architectural decisions are heavily influenced by inference capabilities and practical deployment constraints.
Alistair Pullen argues that the difficulty in running large models, like Deep Seek V4 Pro with 1.6 trillion parameters, is a major reason why open-source models struggle to match the performance of commercial offerings. He highlights that the need for extensive hardware resources for inference limits adoption.
Alistair Pullen notes that the recent ban of 'Fable' due to export controls has accelerated discussions around sovereign AI. He links this to Cosine AI's expertise in model training and building, which enabled them to pursue the sovereign AI mandate.
Thomas Ahle discusses the complexity of chip design, comparing it to software development and highlighting the high costs associated with commercial verification tools. He notes that while AI can generate extensive code for chip design, ensuring its correctness remains a significant challenge.
Thomas Ahle explains the concept of thermodynamic computing, where noise is intrinsic to the computation process, drawing parallels to probabilistic machine learning. This approach aims to model chips as stochastic differential equations, allowing noise to settle and achieve computational results that would otherwise be prohibitively expensive.
Thomas Ahle mentions that Normad Computing has released its first chip, the CN 101, which is now in silicon. While it currently targets a narrow band of probabilistic workloads, the key test will be its performance as the technology scales up, and how benchmarks reflect this progress.
Thomas Ahle raises concerns about the long-term implications of AI in chip design, questioning what level of human understanding will be retained. He posits that as AI becomes more capable in generating complex code, humans might become "dumber" in the process, leading to a potential loss of deep comprehension.
Thomas Ahle highlights the significant cost of commercial tools for hardware development, citing examples of $10,000 per seat for CPU kernels and potentially $10 billion for large-scale AI deployments in data centers. This high cost, along with a lack of readily available open-source code for training AI models, hinders AI adoption in the hardware space.
Thomas Ahle compares Anthropic's success in reproducing a C compiler with 40,000 agents to his own projects. He expresses concern that agentic coding can lead to complex, unmanageable codebases ('spaghetti monsters') that even their creators may not fully understand, posing a challenge for long-term maintenance and verification.
Thomas Ahle suggests that advancements in AI models, such as Fable and GPT-4.5, can be deceptive. He notes that while newer models may appear superior to older versions, this rapid progress might obscure underlying issues or a lack of true understanding, comparing it to "smoking mirrors" and emphasizing the importance of rigorous, objective testing.