Tesla AI Day 2022

Tesla Bot (Optimus)

🤖 Q: What are the key design principles of Tesla's Optimus robot?
A: Optimus is designed for high-volume manufacturing, low cost, and high reliability, with 28 degrees of freedom and opposable thumbs for operating tools.

🔋 Q: How is Optimus powered and controlled?
A: It uses a 2.3 kWh battery pack integrated into a single PCB with sensing, fusing, and power management, controlled by a central computer in the torso for vision processing and decision-making.

🖐️ Q: What capabilities does the Optimus hand have?
A: The hand has 11 degrees of freedom, 6 actuators, and an in-hand controller for proprioception and object learning, allowing for wide aperture power grasps and precision gripping.

🏭 Q: How will Tesla initially deploy Optimus?
A: Tesla plans to start with simple tasks in their factories, like loading parts, and gradually expand its capabilities through internal testing and volume production.

🗣️ Q: Will Optimus have communication abilities?
A: Yes, it's designed to have conversational capabilities for natural interactions and may eventually understand emotions and contribute to creativity.

Full Self-Driving (FSD)

🚗 Q: What can Tesla's FSD beta currently do?
A: FSD can drive from parking lot to parking lot, handling CDC driving, traffic lights, stop signs, intersections, turns, and more using neural networks running on the car.

👁️ Q: How does the FSD occupancy network function?
A: It predicts 3D occupancy and flow around the car using 8 cameras and 12-bit raw images, producing a unified volumetric occupancy in vector space.

🛣️ Q: What does Tesla's lane detection network do?
A: It predicts a graph of lane segments and connectivities using 8 cameras and coarse roadmap data, producing a dense tensor encoding of the world.

🧠 Q: How does Tesla's Autopilot Vision stack work?
A: It uses a two-phase neural network to identify agent locations in 3D space and then process relevant data, optimizing inference latency and performance.

🌐 Q: When will FSD beta be available worldwide?
A: Tesla aims to roll out FSD beta worldwide by the end of 2023, focusing on regulatory approval in other countries.

Dojo AI System

💻 Q: What is unique about Tesla's Dojo AI system?
A: Dojo is a single unified accelerator with globally addressable memory and uniform high bandwidth and low latency, designed to break integration boundaries.

🧩 Q: How is the Dojo training tile constructed?
A: It integrates 25 dies at extremely high bandwidth and can be scaled to any number of additional tiles for seamless compute and high bandwidth memory.

🚀 Q: What advantages does Dojo offer over GPUs?
A: Dojo achieves orders of magnitude improvement in communication-bound operations like batch norm and gradient reduction, making it ideal for large-scale AI training.

🌐 Q: How will Tesla deploy the Dojo system?
A: Tesla plans to deploy their first exapod by Q1 2023, more than doubling their current auto-labeling capacity and replacing multiple GPU boxes with a single Dojo tile.

☁️ Q: Will Dojo be available as a service?
A: Yes, Tesla plans to offer Dojo as an online service similar to AWS, focusing on faster and cheaper neural net training.

AI Development and Testing

🧪 Q: How does Tesla test its neural networks?
A: Tesla uses unit tests, VIP sets for failure modes, curated examples of past failures, shadow modes for data collection, and a nine-level filter before customer release.

📊 Q: How does Tesla measure FSD safety?
A: Tesla publishes statistics on miles driven with and without autonomy, showing steady improvements in safety compared to human drivers.

🔄 Q: How is Tesla's Autopilot system evolving?
A: Autopilot is steadily improving with neural nets absorbing more software, and will eventually train on video data alone without intermediate software.

Future Developments

🎨 Q: Will Optimus have artistic capabilities?
A: Yes, Optimus is planned to have physical art capabilities, including dance moves, playing soccer, and other activities, with potential for costumes and scanning.

🔒 Q: How will Tesla ensure Optimus's safety?
A: Optimus will be controlled by laws of robotics to prevent harm, with built-in safety features and instructions for safe operation.

🔬 Q: What's the future of Tesla's AI compiler and inference system?
A: The system is highly scalable and can handle large amounts of data, with expanding auto-labeling and simulation capabilities.

 

Tesla Bot (Optimus)

🤖 Tesla's Optimus robot is designed to be highly efficient, low-cost, and high-volume, with a fully Tesla-designed body, actuators, battery pack, and control system.

🦾 The Tesla Bot is fully autonomous, capable of walking, dancing, and picking up objects without a tether, using the same autopilot neural networks as Tesla's self-driving cars.

🖐️ Optimus features a human-like hand with 6 actuators, 11 degrees of freedom, and an in-hand controller for proprioception and object learning.

🏭 Optimus will initially perform simple tasks in factories, gradually expanding to more complex situations with exponential growth in usefulness.

🧠 The robot will be controlled by a neural network learning from video data of its actions, with no other software in between.

Full Self-Driving (FSD)

🚗 Tesla's FSD beta software can navigate from parking lot to parking lot, handling curb detection, driving, traffic lights, stop signs, and intersections.

🌐 The FSD planning system operates on a vector space model of the world, produced by neural networks running in the car.

📷 Tesla's occupancy network uses 8 cameras to generate a 3D occupancy map with 10ms latency on their neural accelerator.

🛣️ The lane detection network uses a language-based approach, predicting a graph of lane segments and their connectivities using a language model.

🔮 FSD predicts short-term future trajectories of all objects on the road to anticipate and avoid dangerous situations.

Data and Training

📊 The occupancy network is trained on 1.4 billion frames of data from Tesla's Fleet, using 100,000 GPUs in parallel.

💾 Tesla's training infrastructure has 14,000 GPUs across multiple clusters in the United States.

🏷️ Tesla's Auto Labeling Machine replaces 5 million hours of manual labeling with just 12 hours on a cluster.

🎭 Tesla's Simulation Tooling can procedurally generate 3D scenes in 5 minutes, creating infinite targeted permutations.

🔄 The Data Engine uses a deterministic loop to identify mispredictions, correct labels, and categorize clips into evaluation sets.

Dojo Supercomputer

💻 Tesla's Dojo is a radically different machine using SRAM as primary storage, model parallelism, and no virtual memory.

🚀 Dojo achieves 30X speedup in training latency for the FSD team, allowing rapid exploration of alternatives.

🧩 Dojo's training tile integrates 25 dies at extremely high bandwidth, scalable to any number of additional tiles.

💪 The exit pod houses two full accelerators for a combined 1 exaflop of ML compute.

⚡ Dojo's compiler and ingest pipeline work together to extract utilization from the hardware while code is running.

AI and Software

🧠 Tesla's Autopilot software processes and analyzes data from cameras, radar, and lidar for fully autonomous driving.

🗣️ Optimus will have conversational capabilities, allowing natural interactions with users.

🔧 Tesla's AI compiler now supports new operations needed by neural networks and maps them to the best underlying hardware resources.

🔬 The FSD inference engine can distribute execution of a single neural network across two independent system-on-chips.

🔮 Tesla's occupancy network predicts the full physical occupancy of the world, including trees, walls, buildings, cars, and their future motion.

Future Vision and Impact

🌍 Optimus has the potential to transform civilization by enabling a future of abundance where poverty is eliminated.

🔄 Tesla's long-term vision includes creating an Android-like robot that can understand and respond to human emotions and art.

💡 Optimus will be upgradable with attachments like power ports and additional arms, fostering an ecosystem of small companies making add-ons.

🔬 Tesla's Dojo platform will be operated as a service on Amazon Web Services, providing fast and cost-effective neural net training for various industries.

🔮 Optimus is expected to be available for public purchase within 3-5 years, according to Elon Musk.

Technical Innovations

🔧 Tesla's FSD computer uses a neural network accelerator called Trip that can perform dense dot products extremely fast.

📊 To optimize the FSG Lanes Network, Tesla engineers built a lookup table in SRAM to store embeddings associated with spatial locations.

🔬 Dojo's high-density integration accelerates compute-bound, latency-bound, and bandwidth-bound portions of a model.

🚀 Dojo is capable of doubling the throughput of an A100 GPU on their auto-labeling network.

🔄 Tesla's occupancy network is trained without human intervention, using large auto-labeling datasets and physics-based checks.

Design and Engineering Principles

🔄 Optimus is designed with symmetry to minimize wear and tear on actuators and joints, unlike humans with handedness and uneven muscle use.

⚡ The robot will have increasing bandwidth over time, translating to better dexterity and reaction time, with 10-25 Hz control.

🔬 Tesla's FSD system is designed to be safe and reliable, with multiple layers of testing and validation before deployment.

🔧 The Autopilot system is scalable and adaptable, with the ability to learn from data and improve over time.

🤖 Optimus is designed to be maximally useful as quickly as possible, focusing on humanoid design and actuators that can flex and extend.

 

• 00:00 🤖 Tesla showcased advancements in AI for self-driving and introduced Optimus, a humanoid robot designed for production, seeking talented individuals to join their team; they envision a future of abundance and no poverty through self-driving cars, and emphasized the importance of public influence; the latest generation robot is designed with a focus on the human form, leveraging existing infrastructure and supply chain, and mimicking the human brain; models and modal data are used to optimize robot components and control systems for various tasks, taking inspiration from biology.
  • Tesla AI Day 2022 showcased the advancements in AI for full self-driving and the potential for Tesla to make a meaningful contribution to AGI, with demonstrations of the Optimus robot operating without a tether and performing various tasks.
  • Tesla has developed a humanoid robot called Optimus that is designed for high volume production, low cost, and high reliability, with the goal of making a useful robot with intelligence to navigate the world, and they are seeking talented individuals to join their team and help bring it to fruition.
  • Tesla envisions a future of abundance and no poverty through the transformation of civilization, with self-driving cars having a significant impact on productivity, and it is important for the public to have influence over Tesla's actions to ensure a positive outcome.
  • Tesla's latest generation robot is designed with a focus on the human form, with a reduced number of degrees of freedom, minimized idle power consumption, and centralized power distribution and compute, including a battery pack with integrated electronics for streamlined manufacturing and efficiency.
  • Tesla is leveraging its existing infrastructure and supply chain to create a humanoid robot with a central computer that mimics the human brain, equipped with wireless connectivity, audio support, and hardware-level security features, while also utilizing their expertise in complex systems from the automotive side to ensure the robot's structural foundation can withstand falls without significant damage.
  • The speaker discusses the use of models and modal data to optimize the components and control system of robots for various tasks, such as walking and squatting, taking inspiration from biology and considering the non-linear characteristics of the knee.
• 38:09 🤖 Tesla AI team has made progress in designing hardware and adapting computer vision neural networks for their robot, addressing challenges in actuator optimization, locomotion planning, and motion manipulation in the real world.
  • The speaker discusses the design process and optimization of actuators in a robot, using torque speed trajectories and efficiency maps to generate power consumption and energy accumulation for different tasks, ultimately selecting the optimal actuator design.
  • The speaker discusses the optimization of joint actuators in order to reduce the number of unique designs and demonstrates the force capability of the linear actuators by lifting a nine-foot concert grand piano.
  • The human hand is incredibly dexterous, with the ability to move at high speeds, grasp and manipulate various objects, and the design of Tesla's robotic hand is inspired by the human hand's adaptability and ergonomic functionality.
  • The Tesla AI team has made significant progress in designing the hardware and adapting the computer vision neural networks from autopilot to the robot, as well as training neural networks to navigate indoor environments and improving the simulation of the robot's locomotion.
  • Walking is a complex engineering problem that requires physical self-awareness, energy-efficient gait, balance, and coordination of limb motion, and Tesla addresses these challenges through their Locomotion planning and control stack, which involves generating reference trajectories for the robot's motion based on a model of its kinematics and dynamics.
  • The speaker discusses the challenges of implementing motion planning and manipulation in the real world, including the need for state estimation and adapting motion references to real-world situations.
• 56:14 🚗 Tesla is improving their AI technology to achieve self-driving capabilities, with advancements in autonomous driving software and the use of sophisticated AI training processes and architecture to generate 3D occupancy of the world, allowing for obstacle prediction and human-like behaviors.
  • Tesla is working on improving their AI technology, specifically focusing on getting Optimus at par with Bumblebee and deploying it in real-world use cases, with the goal of changing the entire economy, and the autopilot team believes that every Tesla built in the last several years has the hardware to drive itself.
  • Tesla has made significant advancements in their autonomous driving software, with 160,000 customers now using the FSD beta software that can navigate parking lots, handle traffic lights and stop signs, and make turns using neural networks that run on the car itself.
  • Tesla's AI training process involves using sophisticated auto labeling systems, simulation systems, and a well-oiled data engine pipeline to train neural networks for self-driving cars, and their planning system evaluates various interactions to make safe and reasonable decisions.
  • The speaker explains how Tesla's AI system uses interaction search and lightweight queryable networks to efficiently solve a multi-agent trajectory planning problem in real-time, with the help of collision checks, comfort analysis, and scoring based on human demonstrations and fleet data.
  • The Tesla AI architecture combines data-driven approaches with physics-based checks to generate 3D occupancy of the world using video feeds from eight cameras, allowing for the prediction of obstacles and the extraction of human-like behaviors, while the occupancy network takes video streams from 80 cameras as input to produce a unified volumetric occupancy in vector space for every 3D location around the car, predicting the probability of occupation and generating semantics and occupancy flow.
  • The occupancy network accurately models the curvature of the visible space and predicts a drivable surface that aligns with the voxel grid, providing useful information for control on hilly and curvy roads.
• 01:16:43 🚗 Tesla's AI technology uses neural networks to accurately detect lanes, predict future paths, and understand the semantics of stationary objects, resulting in improved navigation and collision avoidance.
  • Tesla is excited about the potential of neural readings and occupancy networks for computer vision, and they have built in-house supercomputers with thousands of GPUs to train their networks using massive amounts of data.
  • Tesla has implemented a more efficient method of processing video frames for training their occupancy networks, resulting in a 30x speed increase and freeing up CPU resources, and they have also developed a format called "small" for storing ground truth data and other tensors, optimizing storage efficiency by transposing dimensions and compressing data.
  • The speaker discusses how Tesla's autopilot system uses a neural network to predict lanes and the behavior of other agents on the road, utilizing convolutional layers, attention layers, and language modeling techniques.
  • The speaker explains the process of predicting the position and type of tokens in a language model by discretizing the 3D world, predicting heat maps and spline coefficients, and using different token types such as start, continuation, and fork.
  • Tesla's AI technology uses neural networks to accurately detect lanes and predict future paths, allowing for improved navigation and collision avoidance.
  • The neural network accurately predicted that the parked car was stopped, while the car in the other lane was just waiting for a red light to turn green, emphasizing the importance of understanding the semantics of stationary objects to avoid getting stuck.
• 01:34:07 🚗 Tesla AI Day 2022 showcased advancements in autopilot technology, including a two-phase neural network split for efficient inference, an auto labeling machine for faster training, and the use of simulated scenes and large-scale environments for diverse data generation.
  • The autopilot Vision stack predicts the geometry, kinematics, and semantics of the world by maximizing frame rate and minimizing inference latency through a two-phase neural network split, while running FSD networks on the FSD computer involves optimizing for inference latency, implementing a lookup table and token cache, and utilizing a compiler, linker, and hybrid scheduling system for efficient compute utilization.
  • Tesla has developed an auto labeling machine powered by multi-trip reconstruction that can replace 5 million hours of manual labeling with just 12 hours on a cluster, allowing for high precision trajectory and structure recovery and scalability in training neural networks for autonomous driving.
  • Tesla uses a combination of automated ground truth labels and new tooling to generate simulated scenes for training their AI, allowing for faster and more diverse data generation.
  • Tesla uses a tile extractor tool to divide data into geohash tiles, which are then loaded and converted into assets for the Unreal Engine, allowing for the generation of large-scale environments in a short amount of time, and this data is continuously managed and updated using their PDG Network, providing consistent quality and features for training their neural networks.
  • Tesla's goal was to improve training latency for their autopilot team by building a chip with high-efficiency arithmetic units, utilizing SRAM instead of DRAM, and making choices such as no virtual memory, no interrupts, and model parallelism, resulting in a radically different machine; they also aimed for a compute fabric with no limits, disaggregating the traditional package machine ratios, vertically integrating their data center for efficiency, and integrating early to test software workloads, facing challenges in power delivery and component failures.
  • To achieve system performance, Tesla integrated components into power modules but faced failures due to vibrations causing clock output loss, which was resolved by using soft terminal caps, updating the mems part, and adjusting the switching frequency.
• 02:08:33 🚀 Tesla's AI Day 2022 showcased their progress in infrastructure, including Dojo's efficient execution of models, improved data loading, plans for an exopod, and the development of a humanoid robot with conversational capabilities and artistic creativity.
  • Tesla has made progress in their infrastructure, including a custom-designed CDU for cooling, a system tray for connecting tiles, a dojo interface processor for data feeding, and hosts for processing and applications.
  • Tesla's Dojo accelerator, with its high density integration and uniform low latency, allows for the efficient execution of communication-bound models, overcoming scalability challenges and enabling models to work out of the box with minimal manual work.
  • Dojo's batch operations on 25 dice show significant improvement over GPUs, with reduced communication time and increased performance, making it capable of handling larger complex models and surpassing the performance of an A100 GPU on auto labeling and occupancy networks.
  • Tesla has extended their transport protocol to work over ethernet, resulting in improved data loading and increased occupancy, and they plan to build their first exopod by quarter one of 2023, doubling their auto labeling capacity.
  • Tesla's tendon-based system with metallic boating cables allows for part reduction and anti-backlash, while the goal is to create a useful robot quickly and potentially give it a personality.
  • Optimus, the humanoid robot, will have conversational capabilities and the ability to emulate humans, while Dojo, Tesla's big computer, is likely to operate as a service in Amazon Web Services, and AI advancements will enable robots to generate physical art and contribute to creativity.
• 02:42:57 🚗 Tesla is focused on thoroughly testing their AI models for autonomous driving, plans to roll out FSD beta worldwide, and is interested in building AGI systems while ensuring safety and avoiding misuse.
  • Tesla's FSD team uses a series of tests, including unit tests and curated VIP sets, to ensure the neural network models are thoroughly tested against past failures before release.
  • Tesla uses shadow modes to test their AI models in cars, has an extensive QA program, and trains large offline models to produce good labels for training online networks, with larger data sets and pre-training steps leading to improved model performance.
  • Tesla is potentially interested in building artificial general intelligence systems and investing in technical AGI safety, believing that there should be a regulatory authority for AI safety at the government level, and they anticipate having the most data and training power to make a contribution to AGI; in terms of the semi truck, the sensing requirements are different from a car but still rely on cameras, and the possibility of developing and deploying different software and hardware components independently for Optimus to accelerate feature development was not comprehended.
  • Tesla plans to roll out the FSD beta worldwide by the end of this year, pending regulatory approval, and expects significant improvements in assessing fast-moving cross traffic and integrating the FSD stack for both city streets and highways, with the goal of surpassing the performance of the production stack in various weather conditions, including heavy rain and snow, and potentially including the parking lot stack by the end of this year.
  • The fundamental metric that Tesla is optimizing for is the number of miles a car can drive in full autonomy before intervention is required, and they are continuously making radical improvements on that metric.
  • AI is advancing quickly, with the potential for AGI on a humanoid robot coming faster due to significant advancements in AI, talented people working on it, and improved hardware, and Tesla plans to gradually expand the use of Optimus in their factories, with the possibility of making it available to the general public within three to five years, while also ensuring safety and avoiding potential misuse.
• 03:04:47 🚗 Tesla is using occupancy networks and trigger logic to address challenges in autonomous driving, and their massive amount of real-world data combined with advancements in AI will likely lead to the emergence of AGI and improve the capabilities of autonomous cars and humanoid robots.
  • Tesla is a demanding company that values and utilizes the talents of skilled engineers, unlike some other companies in Silicon Valley.
  • Tesla is addressing challenges in autonomous driving by using occupancy networks to represent the physical world in 3D and sourcing examples of tricky stopped cars through trigger logic and shadow mode, while not specifically focusing on AGI.
  • Tesla's massive amount of real-world data from autonomous vehicles and humanoids, combined with advancements in AI, will likely lead to the emergence of AGI (Artificial General Intelligence) and significantly improve the capabilities of autonomous cars and humanoid robots.
  • Tesla prioritizes safety in their vehicles and believes that deploying autonomous technology, even if not perfect, is morally right as it reduces injuries and fatalities, despite the potential for lawsuits and blame.
  • The speaker discusses the importance of symmetry in the design of Optimus, but also expresses interest in exploring more creative and fantastical designs in the future, potentially through the addition of attachments or an ecosystem of small companies making add-ons for Optimus.
  • The video provides a detailed overview of Tesla AI Day, allowing viewers to skip to the parts they find interesting and hints at the possibility of a monthly podcast.

WatchUrl: https://www.youtube.com/watch?v=ODSJsviD_SU