"Not Even Wrong" Podcast
Investing in fundamentally new concepts and engineering practices with large impact.
Amendment to Episode about Carlotta Pavese. 1/3 Defining intelligence via programming runs danger of circularity. 2/3 If you use intelligence to classify people make sure you know what you’re talking about, i.e only consider a skill and/or intelligence for classification if you know how to program it. The rest is gibberish. 3/3 Concept of good enough. Intelligence ought to be human centric like "be funny", "make others feel good" etc. people feel good etc.
Events discussion week 11/27-12/01 Part 2. Philosophy, Carlotta Pavone on intelligence socialism. 1/2 What is intelligence. Adaptive, flexible, learning. How about: If I can program it, it's not intelligence. Intelligence is what we cant teach machines. Human intelligence. If we reduce intelligence to intelligence socialism, what is left to distinguish us from machines. 2/2 Intelligence is a random pretext to justify hierarchy.
Events discussion week 11/27-12/01 Part 1. Cybertruk delivery event. Price, performance muted. Important technological platform for future products. Casting, voltage, battery tech. 2/2 Tierry Tambo. Flexible hardware design across stack for ML and LLMs. Next thing is multimodal. Three interesting approaches. Adaptive Float to deal with sparsity, eDRAM and customized algorithms to reduce power consumption per task, high refresh storage and Early Exit, DVFS (Dynamic Voltage Frequency Scaling) to flexibly adapt depth on neural network to requirements of workloads.
Discussing Corl 2023 Part 3. 1/2 Dieter Fox. Foundation model for robot manipulation in sim. Then sim to real. Real to sim. Bootstrap. Foundation model is supervised learning. Tokenize space. URDF. Robotics problem is putting URDF in reverse. 2/2 Sergey Levine. Offline RL. Supervised learning. Bootstrap.
❤️ Book discussion “Elizabeth Finch” by Julian Barnes. Some things science cannot tackle like beliefs and love. Literature can. Barnes tackles those two topics. 1/2 Love and happiness are learnable. “All happy couples are happy in the same way and all unhappy couples are unhappy in their own way. “ You can earn happiness by getting better at it. Like a muscle. 2/2 What if Julian the emperor had not been killed, and Hellenic pantheism prevailed in Rome and later Europe? Would there have been a need for the Renaissance? What about the Enlightenment? How come science emerged out of Christianity?
❤️ Every happy robot is happy in the same way and every unhappy robot is unhappy in their own way. Intelligence convergences. Iteration, recursive updates, learning from experience, low cost error correction. Sim to real. Real to sim. Repeat. Sim can help in reward function and policy design. Robots need a religion, something to guide them about what is right and wrong.
Discussion CORL 2023 Part 2. Highlighted papers: 1/8 LLMs for traffic planning. Prompt a traffic scenario and the simulator does it. (for example, car turns left and then sees a pedestrian 20m away right in the lane). 2/8 White paper on using MFMs for generative simulation. MFM=Multimodal Foundation Model. 3/8 RLHF, Nathan Lambert discussions RLHF and mentions canonical paper on this topic. How to estimate a reward function from behavior. What if we don’t have a reward function but we have a sense for what is good behavior? 4/8 Offline RL. Discuss Sergey’s comments and the paper. Sergey argues that every ML problem is in essence an RL problem. The paper solves the problem of off line RL, which is based on fixed data sets. What if new situation arises in inference? Solve problem of working extrapolation with regularization. 5/8 Koopman operator. Can the dynamic environment be reduced to linear functions? What about using stable diffusion and transform architecture to solve the dexterity problem? 6/8 ViNT: A Foundation Model for Visual Navigation. Tokenize vision. Transformer architecture. Zero shot learning for self driving car. 7/8 MimicLearning. Use human action to learn from and then execute with low level robot skills. Kind of what Tesla has been doing. FSD is only 2D. Easier. 8/8 Planning in a multi agent heterogenous driving scenario. Game theory, behavioral modeling. Similar to paper by Fei Miao discussed previously about tridirectional relationship among communication, learning and control.
Discussing CORL 2023 Part 1. Key takeaway for me is Sergey Levine’s idea. 1/4 Scalable learning will lead to generalizable robots. But it goes the other way, too. Generalizable robots will enable scalable learning. Researchers should prepare for that and think about data engine, benchmarks, compute stack and algorithms for that kind of environment. Simplicity, low cost. LLMs are simpler than NLP recipees before that. 2/4 Hardware. Adaptive robots must be able to make mistakes and not break all the time. 3/4 How to bootstrap such as fleet. 4/4 Tesla is on this path. Build low cost, at scale robots. Both, car and humanoid robot. Low cost drives adoption, drives data generation, drives learning, makes robots better. AI flywheel in robotics.
Events recap Part 3 week 11/13-11/17. 1/5 Joonhee Choi. Measuring entanglement entropy in a system. How much information is encoded in entanglement? Approximations and extrapolations. 2/5 Yonatan Cohen. Quantum Machines. Pulse level control of quantum computers. Better interaction with quantum hardware. Bridge classical with quantum world more efficiently. 3/5 Christoph Leuze, Augmented Reality. Assist people in tasks. Could be used to train robots. 4/5 Anqi Zhang, electrode implants for brain through blood vessels. No surgery needed. 5/5 Jens Kober, Human teacher for robot learning. How to teach robots within context of RL.
Events recap Part 2 11/13-11/17. 1/6 Daniel Worledge, Spin Transfer Tork MRAM (Magnetic Random Access Memory). 2/6 Storage X, Jiyun Kang, Cooling system for batteries. 3/6 Eleni Katifori, Fluid dynamics in systems with memristor type junctions. Complex evolution of networks. Can be used for data flow models or data flow in soft robots. 4/6 Zerina Kapetanovic, Low power communication by using ambient temperature via Johnson noise. 5/6 David Goldhaber-Gordon, create artificial atoms by squeeing electrons into small 3D space. See how they behave. Create artifacts of materials see how they behave. 6/6 Xiang Cheng. Modelling bacterial swimming behavior in fluids. Complex, nonlinear fluid, systems
Events recap Part 1 of week 11/13-11/17. 1/4 Shirin, Neural data compression. Diffusion for data compression and ML. Same thing, different angle. 2/4 Agrawal, Robotic dextrous hand manipulation, walking on ice, combing proprioceptor data with visual data. 3/4 Song, Robotics data collection, robot complete. Use LLMs for high level robot planning. Diffusion for path planning. 4/4 Raina, flexible hardware design. Configurable logic and memory tiles. Adjust instruction set architecture to compiler while flexibly adding functionality to accelerator chip.
Book discussion “One True Loves” by Taylor Jenkins Reid. 1/4 Love is something you can become good at. You need a good partner to train, like a dance. 2/4 Love is about reciprocity. 3/4 Loyalty is ephemeral. Loyalty and love don’t go hand in hand. 4/4 Identify shifts with time. Love is independent of identity but life isn’t.
Weekly seminar recap 11/6-11/10 Part 3. 1/6 Adam Kaufman on using nuclear spin for quantum information and spin squeezing for atomic clock precision. 2/6 Charles Marcus talks about quantum dots bouncing of superconductors, creating Majorana electrons. Superconducting, quantum entanglement and quantum information. 3/6 Christoph Naegerl on one dimensional bosons, ultra cold as in nano-kelvin. How can you even measure temperature at that level? How do ultra cold bosons behave. Innsbruck is powerhouse in experimental quantum physics. Naegerl works a lot with theorists. 4/6 Dave Donaldson, Economics. Measuring misallocation in economy formally through input variations in firms' reaction to demand shocks. The less shocks, the less misallocation. 5/6 Fei Fei Li book party. “The Worlds I See”. AI community has to focus on measuring human aspects of models. What is a good model? What is fair? What is true? 6/6 East Asian Philosophy. What is role of literature in forming moral compass? What is role of literature in age of AI. Who is going to teach the machines?.Confucius vs. Laozi.
Weekly event recap 11/6-11/10 Part 2. Data geometries impact neural net geometries. Use geometric algebra and inherent symmetries for robot learning. Exploit those symmetries for learning and inference, in particular when compute and power budgets are limited. Compressed data analysis with geometric priors. SueYeon Chung, Taco Cohen, He Wang, Ajil Jalal.
Weekly event recap 11/6-11/10 Part 1. 1/4 He Wang on sim-to-real robot grasping. Focus on geometry. Robotics must bootstrap itself to create data set. Tesla is doing that. Low cost, scalable. Focus on industrial application so robots scale. That way we bootstrap data set. What can robotics do for sim? 2/4 Ding Zhao on continuous learning and task specific adjustment of models. 3/4 Fei Miao on quantifying uncertainty in perception. Also multi agent behavior modeled through RL. What is collective policy, intent and reward function. 4/4 Philosophy Confucius vs Laozi. Define everything or focus on essence of things.
Robotics must bootstrap itself with low cost robots at scale. Learn from them. Build data set like internet did for NLP. Tesla is optimizing for low cost of compute per watt and low cost of actuators. Transfer learning from car for depth estimation through camera.
Discussing “The Wager” by David Grann. 1/4 Natural experiment in Anthropology. How do people behave on long confined voyages? English navy found way to organize to become global power. Who will do that for Mars? 2/4 Cast away. People adopt rules from motherland. Would that also happen with Communists? 3/4 Mutiny. Eventually rebel forces take over. 20-60-20. 4/4 Empires deal with news the way it suites them. "Truth is when you strip events from the ornament of narrative."
Event summary week of 10/30-11/3. 1/8 Active oxides for physics based in memory compute. Memsistors. Philip Wong, Carbon nanotubes for energy efficient compute. 2/8 Autonomous flight vehicles collision avoidance through Reinforcement Learning. 3/8 Automate bio labs, robotics for enzyme and protein engineering. How do you solve exploration vs. exploitation problem with science robots. 4/8 Physics, quantum spin ice. Novel properties in electric and magnetic conductivity. 5/8 Solar astrophysics. Working on space weather prediction caused by solar flairs. 6/8 Operations Management. Design flexible resource networks to adaptively satisfy stochastic demand. Why isn’t every resource allocation problem a network problem? 7/8 Jo Fox on crisis of humanities. Solution, teach machines what it means to be human. What is a novel, what is a good summary, what is fairness etc. AI needs some sort of humanities. But what? 8/8 CARS. Sustainable mobility. Papers on RL for ride share allocation. Use LLMs for edge case detection in vision.
Market rally due to macro relaxation. 1/2 housing down. Housing is a "Marie Antoinette" economy where growth allegedly comes from conspicuous consumption. No. Growth comes from more for less type innovations. 2/2 Fed is resisting monetizing government deficits. Dept. of Transport event at CARS (Center for Automotive Research Stanford) shows difference between government involved research and productive science. Mission creep. Transport cannot solve the economic divide.
Discussing Bay Area Robotics Symposium 2023 (BARS). 1/6 LLM architecture for sensorimotor data. Tokenize motion along six degrees of freedom and use masked models for training. Predict next move. 2/6 LLMs for context, for example, vision. Use language to understand WHAT, WHERE and WHY. 3/6 Repository for sensorimotor data. 4/6 Tactile sensors, soft robotics. 5/6 Multi agent robots. Aerospace and terrestrial transport. 6/6 Simulation is key. Malik.
Academic week review. 1/9 Cosmology. Find signal to noise to build model of how universe evolves. Find structure. Reverse engineer evolution of Universe back to Big Bang. 2/9 Digital Economy Lab. Research on how generative AI influences workplace. Leveling of playing field 3/9 Chemically engineer soft liquid for touch sensors. 4/9 HAI conference. Learn from machines. Questions about what is creativity, what is fair, what is good AI etc. 5/9 Physics Vladan on Atomic clocks and Rydberg states for quantum information processing. Ten logical cubits already! 6/9 Creative writing reading. Poets must process life to compete with AI. 7/9 Onur Mutlu. DRAM space is commoditized but constitutes big risk for future of compute. Compute must be data centric. 8/9 Scott Aaronson on how to make sure we know when something comes from AI. Watermarking 9/9. BARS. How to use LLMs for robotics, talked to Malik about whether we should focus on simulation (yes, but resistance in field). Nerfs + occupancy solve for depth and robotics.
Digital Economy Lab Seminar with Ethan Mollick (Wharton). Research on impact of AI at BCG. 1/3 AI levels difference of quality in employees. 2/3 80% of work can be done by 20% of employees 3/3 Creativity cannot be achieved with AI, but measured. Impact on Wharton? Use AI as baseline and teach students everything else such as leadership, interpersonal skills, risk, creativity. What is creativity? Use AI as baseline and define creativity as what humans come up with ex AI.
Baylearn 2023. 1/4 Percy Liang on benchmarking. Deep questions about AI. Example fairness can be solved for analytically in Kantian and/or Ralws way. 2/4 Christopher Re on aligning models with data. Data is the key and models need to adjust to how data flows through the stack. Adjust the stack. 3/4 Applying LLM architecture to more use cases and/or combing it with other dedicated models such as vision. 4/4 Berkeley team presents paper on using LLM architecture for video generation.
Tesla post earnings discussion. 1/4 Volume growth reset. 2/4 Margin through? 3/4 Earnings through. 4/4 Energy biz good and is silver lining of otherwise somber call. Questions for company: 1/4 AI computer architecture. Why, how will it materialize. 2/4 FSD end to end? How will it materialize. 3/4 Will Tesla sell software. 4/4 How to model energy business.
Book discussion “The Spectator Bird” by Wallace Stegner. 1/3 Fine line between love and companionship. 2/3 Living in Silicon Valley by choice. "We like others to envy us". Depth and beauty unmatched but you’re not in the thick of things. 3/3 Organized breeding and genetics is a scary technology to cope with. Attracts lunatics. Bad. But can be great if used properly.
Discussing Tesla earning call preview. Margin vs volume 1/3 amortize fixed cost 2/3 amortize software 3/3 FSD . Push volume now vs near term margins for long term cash flow. Items to mention 1/6 Cybertruk 2/6 semi 3/6 4680 progress 4/6 vertical integration (mining) 5/6 China 6/6 power market, energy prices, utility business.
Discussing HAI seminar talk at Stanford on autonomous agents. 1/3 Intrinsic motivation. Is there such a thing as an agent without intrinsic motivation? 2/3 Curiosity. Exploration vs exploitation. How do you encode curiosity with long tail payoffs? 3/3 Autonomous agents as code assistants with LLMs. What’s are they actually incentivized to do? Is self play an option?
Discussing “The Sportswriter” by Richard Ford. 1/4 Life is a film covering your body. Break out. Feel the cold air on your cheeks. Feel like a child. 2/4 Nietzsche allegories. Life happens to us. "Sports writers live in their minds and on the edge of others”. “Team talk is wrong. It’s like the dynamo of the 19th century. Leaves out the hero.” 3/4 Don’t let things happen to you. Make them happen. Sports, Academics and other hero type professions are declining because there is not enough agency. US turns into a paper pushing society. 1980s. Today better. 4/4 Death is only a problem if you don’t live life at the fullest. “Death is problem, it’s too severe, to unequivocal, a mistake in addition." Notable quotes.
Discussing our most recent essay “The Perils of Monetocracy”. 1/6 The Fed is a culmination of expedient solutions to immediate problems that have taken a life of their own. 2/6 US was built on pillars of liberty, prosperity and fairness. 3/6 Fed was established in 1913 and is orthogonal to those goals. 4/6 Key problem is toxic relationship between Congress and Fed, which is monetizing debt. 5/6 Solution is experimentation through competition among constituencies. 6/6 Uncertainty Principle of Political Economy = You can never precisely define and achieve multiple policy goals at the same time. That opens the door for experimentation. How to optimize Liberty, Prosperity and Fairness.
We discuss a potential solution to the current macro problem. What’s the problem? 1/2 Housing market bid/ask paralyzing economy. 2/2 Too much government spending. Solution 1/3 Slow adjustment of housing market by adding supply and lowering price closer to bid. Sour mortgages can be absorbed gradually by Fed. 2/3 Government constrained in spending. Must reduce entitlement spending. 3/3 Silicon Valley innovation drives productivity and growth.
Some things you can’t measure. For everything else, there’s Physics. Quantum Physics is the study of the limits of what humans can measure. Same applies to Economics. Liberty, prosperity and fairness don’t commute. In the limit they are not achievable concurrently. This is analogue to Heisenberg Uncertainty Principle. Also Goedel Incompleteness Theorem. Continuously finding new, better solutions. Newer fully right. Connect Uncertainty Principle to Popper and Deutsch. But! In order to be wrong you have to be in the right path.
The Fed is talking a stance. Rebelling against being used as ATM by Congress. No more unlimited monetization of government debt. Short term bad. Long term good. Higher for longer means “Congress, stop spending!” See interview with Thomas Hoenig.
Discussing our essay “The World Runs on Compute”. 1/6 Most productive companies dominate supply chain and eventually market. 2/6 Productivity is driven by compute. 3/6 Libertarian Paradox. Software Eats the World meets The Innovator’s Dilemma. Disruption is good. But it creates high market concentration and dominant firms. 4/6 We call for a constitution for corporates modeled after the US constitution. 5/6 Implication for investments. Applying compute to solve real business problems creates wealth and outsized returns. 6/6 Society must protect innovation. Disruptors must share wealth creation with society.
We live in a Monetocracy. Discussing the repeated intervention by Fed in bond markets 2008 and 2020. Both events were nails in coffin of liberal market democracy. Fed killing price of risk and turning into central planning agency with political agenda. Now we have Fed officials talking about influencing climate policy etc. This must stop.
Discussing book “Solito” by Javier Zamora. 1/3 Hopeless journey to the place of hope. 2/3 Poetry. Describing sensual experience and phantasy, 3/3 Glimpse of humanity in the dark.
Discussing physics seminar attended at Berkley. Cosmology is like Economics. Lots of macro data, trying to figure out patterns, build models, calibrate parameters and use them for inference. AI drives physics and physics drives AI. AI drives physics with data analysis. Physics drives AI by developing better mathematical and statistical models for data analysis. In particular, find ways to reduce data analyzed while maintaining performance.
High productivity companies will dominate markets and grow to GDP size. New discipline in economics, political economy and constitutional law is required. How to govern government size companies? When firms become dominant and large, governing them is more important than braking them apart. We need more thought about how to deal with massive power. Productivity = Fast iteration, low cost of error correction and low cost of error. The ultimate function of Anti-Trust division of government is to limit power of firms and keep government monopoly.
Discussing two papers from SITE 2023. 1/2 Firms organize around productivity within value chains. Flip this around and state that value chains with high dispersion of productivity are not going to last. Either they vertically integrate (Tesla) or they turn into lemons (Uber). Productivity dispersion happens when new software enters market. Predict more vertical integration driven by software and AI companies. 2/2 Forward pricing of options shows that FOMC and CPI announcements are priced as more risky after 2022.
SITE (Stanford) seminar on asset pricing. Economists are like algo traders - all beta, no alpha. Interesting comment: "US would have gone above 150% debt/gdp in war with Japan if not dropping the atomic bomb and stopping the war. You see what you think and think what you see.
Is Web3 water in the sand or the build out of a modern area Golden Gate Bridge? Problem with Web3 is not the tech, it’s the investors. Venture Capital is too much about “capital” and not enough about “venture”. Currently Web3 is more water in the sand than Golden Gate. Long term business needs short term bridges like Space X with launch service or Starlink.
The AI opportunity = Emergence of a new compute architecture. Every technology revolution is driven by new compute. Accelerated compute = parallel + interconnect + memory. Low cost per AI training workload, scalable and low power. Ultimately it’s the emergence of entrepreneurs with guts that drive progress, neither science nor capital. Long term it’s gutsy decisions such as DOJO, CUDA, end to end FSD etc. that drive revolutions.
End to End is the Robot’s best friend. Tesla shows off FSD version 12 which is "fully End to End". Learn to drive by learning, no explicit programming. Big step towards real world AI. Tesla at the forefront because they are pushing for it and because they can. This is as important as launch of Model S, 3 and Y. Remove explicit programming from AI training and add it to data curation.
It's size by scaling not scaling by size. Wealth creation = scaling technologies. Exploration in academia, Exploitation by entrepreneurs. Scaling is the knowledge that creates wealth. Quantify. “What gets measured gets done”. How to deal with ever larger companies and increased market power?
Discussing Ritchie Robertson “The Enlightenment”. Explanations. People are the entity that generates explanations. Explanations are substrate independent. People don’t have to be human necessarily. Democracy of explanations (not just ideas). Risk of Enlightenment = Faust. Risk to Enlightenment = Central Banking. We are still living in the Enlightenment. Rationality-Newton-differential equations. Today new compute paradigm with neural net architectures. Away from pure rationality. Risk of relativism.
Discussing Nvidia Q2. Platform shift from general purpose compute towards accelerated compute and generative AI. Nvidia has scale, reach (data center, robotics etc.) and depth (GPU, Networking, CUDA). Key advantage is Architecture. GPU, networking and software (CUDA) are combo necessary to deliver solutions in modern compute environment. Scaling is key. Nvidia is good at scaling, like Tesla.
❤️ It matters what cats can do not logic. Hardware first, then intelligence. Jitendra Malik on Robot Brains. Small Science - Big Science. My opinion - Exploitation is for entrepreneurs, not science. Exploitation = Scaling. Science leaps when time is ripe. Imagenet needed GPUs. Self driving car needed vision. Robots require soft polymers to absorb falls so they can keep falling and learning.
Tesla shares underperformance. 1/4 Disappointing Earnings Call. 2/4 Price declines in China 3/4 Departure of CFO 4/4 Higher US rates - unwind of Yen Carry Trade
❤️ Discussing David Deutsch conversation with Naval and Brett. 1/4 Knowledge is explanations tested against nature. Don’t get drawn into definitions. 2/4 Tautology. Isn’t nature just a theory humans formulate to understand the world. Where is the demarcation line between theory and nature? Isn't nature just another word for abstraction of reality, i.e. theory? 3/4 If there is no absolute knowledge, no king of knowledge then what is? How do you control for Nihilism? 4/4 Good explanations proliferate throughout the multiverse. They solve problems created by previous explanations. Kepler - Newton - Einstein.
John Schulman on Robot Brains. AI can find better ways to read nature and solve problems. Self attention in Biology could lead to new insights. Leapfrog human constraints such as thinking in liner terms. LLMs could capture more complex relationships. For example tokenize nuclear spin of Atoms in brain and find quantum operations in brain. Matthew Fisher at UCSB looks at nuclear spin of Li6 vs Li 7 isotope and finds effects on cognitive functions. Conclusion is that nuclear spin might interact with brain through quantum operations. Spintronics vs. electronics.