Latent Space Podcast 6/20/23 [Summary] - Commoditizing the Petaflop — with George Hotz of the tiny corp

Original Link: Commoditizing the Petaflop — with George Hotz of the tiny corp

Summary of Latent Space Podcast with Swyx, Alessio, and Guest Geohot (George Hotz)

Introduction:

• Swyx is the writer and editor of Latent Space.

• Alessio is Partner and CTO in residence at Decibel Partners.

• Geohot (George Hotz) is the guest, known for his pioneering hacks such as unlocking the first iPhone, breaching the PS3 system, and founding Comma.ai. He has a controversial history with tech giants and regulatory authorities.

Discussion Points:
Geohot has concerns about the closed nature of some major tech players in the AI space, emphasizing the need for open-source and accessible tools.

The interview is peppered with technical insights, discussions on AI's future, and Geohot's experiences and beliefs.

Geohot's Achievements: Traded the unlocked iPhone for a car and iPhones, hacked PS3, faced a lawsuit from Sony, started Comma.ai but faced governmental restrictions. He clarifies that his products are for developers, emphasizing the difference between a dev kit and a standard product.

Hero's Journey: Discussion on Geohot's blog post relating to the concept of "The Hero's Journey" and its relation to TinyGrad, a project he's now heavily involved in.

Concerns on AI Regulation: Geohot expresses concern about potential government restrictions on AI, using Sam Altman's congressional hearing as a pivotal moment that made him realize the importance of his work.

TinyGrad & TinyCorp: Geohot emphasizes the need for simplicity in AI and machine learning models, comparing complex and reduced instruction sets. He advocates for a "RISC" approach, simplifying the ML process.

AI Chips Debate: The discussion revolves around the efficiency of AI chips and the infrastructure required for optimal performance. George suggests that if one can't develop an efficient ML framework for standard GPUs, they can't for a unique chip.

Turing Completeness: Both George and Swyx discuss the downsides of Turing Completeness in ML. Turing Completeness makes it easier to write codes but isn't always the most efficient. The conversation touches on TPUs, how they are a better option than CUDA, and the problem with closed-source systems like Google's TPU.

Explanation of Systolic Arrays: An attempt to demystify the concept of Systolic Arrays, which are efficient for power but may not be the best fit for all computations.

Exploring TinyGrad: George's Innovation for Streamlined AI Frameworks

In a conversation between Alessio, Swyx, and George, the trio delves into the intricacies and developments surrounding the 'TinyGrad' framework.

• George introduces TinyGrad, highlighting how it was initially restricted to 1,000 lines of code to ensure efficiency. This constraint was eventually lifted once the core framework was established.

• He contrasts TinyGrad with platforms like PyTorch, emphasizing the boilerplate code in PyTorch and its complexity.

• TinyGrad's frontend has a design similar to PyTorch, but with additional features and better support for ONNX, exceeding even Core ML's capabilities.

• George elaborates on the problems with PyTorch, pointing out its inability to handle complex operations without unnecessary memory transactions. He also touches upon the limitations of PyTorch Lightning and describes his modifications to optimize TinyGrad's API.

• A significant feature of TinyGrad is its operation fusing using a concept called 'laziness'. This allows TinyGrad to achieve better efficiency by delaying the execution of operations until necessary, thereby optimizing resource usage.

• The framework also offers enhanced debugging tools. By simply activating the "debug=2" feature, users can view the precise details of the GPU operations, offering a more intuitive approach to performance analysis.

• Despite its advancements, TinyGrad currently lags in performance on NVIDIA and x86 platforms. However, it has proved to be twice as fast as Qualcomm's library when run on Qualcomm GPUs.

• A notable highlight is that TinyGrad has been successfully deployed in the OpenPilot model for half a year, further validating its practical application.

Overall, while still a work in progress, TinyGrad represents George's vision for a streamlined, efficient, and intuitive AI framework.

TinyGrad's Pursuit of Developer Efficiency in the AI World

George, in conversation with Swyx and Alessio, highlights the considerable lead NVIDIA has in the market due to the millions of man hours put into it compared to Qualcomm. George's venture, TinyGrad, focuses on increased developer efficiency to bridge the performance gap with competitors. He acknowledges some challenges faced with other platforms, such as the complexity of AMD's framework and PyTorch's occasional performance inconsistencies.

AMD's kernel issues were a pain point, causing frequent system crashes for George. However, after reaching out to AMD's CEO, Lisa Su, he received considerable support and a fixed driver. This experience highlighted the importance of responsive open-source culture in software development.

George praises the importance of good CI (Continuous Integration) for TinyGrad and mentions a possible API adjustment if PyTorch compatibility becomes a hindrance. The conversation briefly touches upon Mojo, an AI project by Chris Lattner, emphasizing the differences in their project paths.

While TinyGrad's main goal is to commoditize the petaflop, George's immediate business aspiration is to sell his computers at a profit. Some humor was shared around the visual representation of the TinyGrad project and George's quip on "giving up."

Decoding Hardware Choices for Advanced AI Implementation

Alessio, George, and Swyx engage in a discussion about the complexities of hardware design, especially with regards to GPUs for AI. Key highlights include:

• Alessio asks George about the metrics he considers when looking at hardware design. George mentions teraflops per second and memory bandwidth.

• George discusses the concept of "luxury AI computers" for users, specifically mentioning the potential of running large AI models such as the "Large Llama" or "Large Falcon". Swyx mentions the FB-16 Llama.

• George and Swyx then dive deep into the technicalities of quantization, specifically int8 and FB16 standards. George is skeptical about some of the quantization standards that are not based on research papers.

• Swyx suggests that when dealing with hundreds of billions of parameters, individual quantization may not matter much.

• George emphasizes the challenges in hardware design, especially when trying to integrate multiple GPUs into one system. He explains the complexities of trying to ensure the system is quiet, efficient, and has enough power while remaining practical for a user.

• Alessio and George talk about the potential of "tiny boxes" as personal compute clusters or mini data centers for individual developers. George envisions these devices as AI hubs for homes, especially for tasks like home robotics. He argues that it's better than sending data to the cloud due to latency and cost concerns.

• Swyx points out the benefits of having a personal compute cluster, and George emphasizes its legality under NVIDIA's licensing terms.

• They wrap up with a discussion on the limitations of PCIe connections and the need for bandwidth, especially for training massive models.

Efficiency in AI: A Glimpse into the Future of Compute and Models

Swyx and George discuss the advancements in AI and the push towards more parameter-efficient models. George highlights Comma's approach, emphasizing the importance of cooling and powering AI models in cars. They touch on the privacy and security aspects, emphasizing how significant breaches could lead to a permanent ban of hardware from their network.

George underlines his skepticism about federated training over the internet due to bandwidth limitations. He delves into the idea of "tiny boxes" that aim to be the pinnacle of "flops per dollar" and "flops per watt." He sees these tiny boxes as potentially revolutionary for businesses seeking efficient training methods.

George's mention of the comparison between compute power and human cognition leads to a discussion on GPT-4, where he outlines the model's parameters and the strategies behind its training. He hints at the consistent improvements in training techniques, highlighting the importance of newer embedding technologies.

Closing on a philosophical note, the conversation veers towards the "Bitter Lesson" from Rich Sutton and the age-old debate between compute power and intricate model design.

Blending AI and Human Perspectives in Tomorrow's Tech Landscape.

George discusses advancements in AI, mentioning how it has surpassed expectations. He contemplates having multiple AI models like LLMs engage in discussions before producing an answer. He believes that traditional coding and AI model expectations need a shift in perspective. At his company, TinyCorp, he aims to be at the forefront of these technological advancements, integrating machine learning seamlessly.

George moves on to discuss remote work for TinyCorp, emphasizing the company's evolving culture. He introduces a fresh hiring perspective, highlighting the importance of a practical approach over traditional technical screens. George believes in the symbiotic relationship between tools and humans. He introduces the concept of an "API line" to explain the distinction between those controlled by technology and those who control it. He sees the future as a hybrid where humans are supercharged by AI tools, rather than being completely replaced by them.

Swyx brings up the "API line," hinting at its origin. The conversation pivots to the evolving dynamics of task allocation and management, touching upon the concept of a "Kanban board."

Alessio shifts the conversation towards physical robots, specifically humanoid ones. George contrasts Tesla's complex approach to robotics with Comma's simplified version, which uses minimal hardware to turn the robotics problem into a software challenge.

Swyx mentions "segment anything" from Facebook, suggesting it as a significant development in computer vision. George expresses his interest in it but emphasizes the value of integrating speech into AI, wishing for a more natural conversational experience with models like LLMs.

Exploring the Future of AI: From Machine Integration to Eternal Existence

In an intriguing dialogue, George shares his future plans for AI, illustrating a timeline that moves from building hardware infrastructure with Comma, to software with TinyCorp, culminating in the creation of an 'AI Girlfriend.' George challenges the prevalent notion of neural merging through methods like Neuralink, suggesting that a deeper, more organic connection can exist between humans and AI. Referring to the vast amount of his own data already available online, he speculates on the concept of immortality through digitization. The conversation also delves into the nature and challenges of machine learning, with George emphasizing the importance of accessibility and the quest for improved efficiency. The chat winds down with George discussing 'six tricks' that might propel AI to new heights and sharing insights about transformers in AI.

Effective Accelerationism, Avatar 2 Critiques, and the AI-Human Content Dilemma

Swyx opens the discussion with Mark Andreessen's support for effective accelerationism (IAC) and George's criticism of it. George feels that only the left takes ideologies seriously, suggesting that they can effectively mobilize energy around ideologies, unlike the right.

The conversation transitions to two figures named Sam. One is incarcerated, while both are seeking regulation in their respective domains. Swyx and George debate the intentions behind certain key figures' advocacy for IAC and EA. They also discuss Mark Andreessen's late realization about potential deceit in the political sphere, which George thinks was already apparent to many others.

Alessio shifts the topic to the movie, "Avatar 2." George expresses disappointment with the film, saying he rewrote the script to better emphasize character emotions. Swyx attributes the movie's shortcomings to its writing, even though it had impressive CGI.

Alessio and George discuss the possibility of AI, like ChatGPT, being responsible for the movie's script. George raises concerns about distinguishing between AI and human-written content, especially in contexts like spam. He suggests a model where sending emails could have a small cost, deterring spammers.

Closing the discussion, George promotes TinyGrad, aspiring for it to be a significant competitor in the tech space and to eventually innovate in numerous areas, starting with GPUs and scaling up to self-reproducing robots.

Alessio and Swyx thank George for his insights.