Summary of Jim Fan on foundation models for embodied agents and scaling data

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00:00:00 - 01:00:00

Jim Fan discusses the convergence of various AI fields towards using large foundation models like Transformers, the importance of embodiment for intelligence, and the challenges with low-level control for embodied agents. He delves into his work on training an embodied agent using Minecraft, utilizing a foundation model called myClub trained with contrastive learning, and creating an open-ended and diverse environment for agent training. Additionally, he discusses the importance of multimodality, sensory motor functionalities, and scaling data for robotics. Finally, he talks about the potential directions for research on foundation models for embodied agents and the application of Spinal, a spinal cord-inspired neural model, in other domains.

00:00:00 In this section, Jim Fan talks about how the problem engineering field may eventually become irrelevant. He explains that the reason problem engineering exists is because AI systems are often misaligned with what humans want, so engineers have to manually coerce the model into solving the task by inputting unnatural sentences. However, as AI models become more embodied and human-like, there will be less need for this type of manual intervention. Jim also shares his journey into the AI field, starting from his inspiration from sci-fi movies like Iron Man, to studying deep learning and neural networks, and finally focusing on reinforcement learning and embodied agents during his PhD.
00:05:00 In this section, Jim Fan discusses the convergence of various AI fields towards using large Foundation models like Transformers and the importance of embodiment for intelligence. He points out that domain-specific biases still exist in each subfield, and learning about each one can be exhausting. However, he sees a convergence towards two general paradigms: scaling up models and reinforcement learning for embodied agents. He cites human babies as inspiration for the importance of embodiment, with the ability to interact with a rich world and internalize experiences, something currently unachievable with large-scale foundation models.
00:10:00 In this section, Jim Fan discusses the importance of multimodality and sensory motor functionalities for embodied agents. He explains that 70% of the cortex in human brains is dedicated to visual processing, which is why having access to multiple senses is crucial in grounding knowledge. He argues that while GPT-3 can generate text and emotions, it may not be grounded in immersive experiences such as pixels, taste, and smell. Fan believes that adding decision-making and multiple modalities to AI will be essential in the post-GPT-4 era. He also talks about his prior works, where he focused on the skill universality of the environment, starting with surreal environments and scaling up reinforcement learning with PPO. He then discusses his later works, such as Waterbits, which used the web browser as a policy run environment and demonstrated the importance of observation space, action space, and a task-specific reward function.
00:15:00 In this section, Jim Fan discusses the tasks in World of Bits, a project aimed at creating a platform for embodied agents to learn how to use the Internet. He explains that the rewards for the tasks are both task-specific and varied, ranging from completing certain operations to training motor skills for using a keyboard and mouse. However, the agent's observation and action spaces are universal. Fan acknowledges that the project was ahead of its time, and while it prompted important questions regarding the justification of learning specific tasks, it did not necessarily provide answers to those questions. He expresses conflicted feelings regarding the value of web browsing as a learning environment, suggesting that it may not be necessary depending on the application.
00:20:00 In this section, Jim Fan discusses the challenges with low-level control for embodied agents and the potential high computational costs of using such controls. He notes that startups may not need to go down to the lowest level of motor skills and that it may not be necessary to always use the lowest level from a business point of view. Fan is interested to see where startup company, Adapt, will go and hopes it releases more information about its research. Fan also talks about the most important opening questions from Wallabies and highlights the weaknesses in its rewards function and lack of true embodiment. He discusses his own work in AI for Minecraft and how it addresses these issues, aiming to build something like an embodied GPT-3 where an agent can take a natural language prompt and interact in an immersive experience with true embodiment.
00:25:00 In this section, Jim Fan, the co-founder and CTO of Dojo.ai, discusses the three key ingredients essential for training an embodied agent. The first ingredient is an open-ended and diverse environment that can support an infinite number of tasks, which is why they chose Minecraft. The second ingredient is an internet skill database that teaches an agent not only how to do things but also what are the useful things worth doing, and Minecraft has approximately 140 million active players who create and share vast amounts of online knowledge. The third ingredient is a foundation model for the agent, called myClub, that is trained using contrastive learning to learn an association score between the video and the text in YouTube data, which becomes an open vocabulary reward function for the agent.
00:30:00 In this section, Jim Fan discusses his experience with Minecraft and how it inspired his work on foundation models for embodied agents. Despite being a terrible gamer and experiencing motion sickness, he watched videos and consulted online resources to learn how to play Minecraft. He realized that artificial intelligence could benefit from accessing this internet skill knowledge, especially in the context of exploration, which is intractable with random actions. Fan explains how the CLIP model by OpenAI, which is an image and text model trained through contrastive learning, could be used to train an embodied AI agent in Minecraft.
00:35:00 In this section, Jim Fan explains how the idea of using modalities to attract or repel each other depending on whether their semantic contents align can be repurposed to create a reward function for training agents in Minecraft. By using video and text as modalities, the agent can be trained to perform specific tasks based on natural language input. However, because the training is bottlenecked by hardware limitations, longer Horizon tasks such as building a house are not feasible, but this can potentially be addressed by subsampling video data and scaling up with more data. Fan highlights the promise of this approach in potentially enabling us to become an interplanetary species.
00:40:00 In this section, Jim Fan discusses the algorithm approach of video portraying (VPT) from OpenAI, which involves hiring humans to play Minecraft, recording their actions with keyboard and mouse, and using the data to train an inverse dynamics model. This allows for labeling in the wild YouTube videos that only come with pixels. The VPT approach is complementary to other approaches, such as my Dojo and Mind Clip, but has the weakness of being expensive and not language conditioned. Fan highlights the importance of language for prompting and grounding in embodied agent setups, and mentions Generally Intelligent's Avalon as a promising approach for speed and data collection.
00:45:00 In this section of the video, Jim Fan talks about the importance of computational resources and scaling data in AI research for embodied agents. Minecraft is a great game, but its complexity and lack of optimization for AI means that it is slow and becomes a bottleneck in exploration and crunching through data. However, there are platforms specifically built for AI research and optimized for speed, such as Avalon, which has a single reward function for survival and unified action and observation states across different environments. This creates a good research platform for exploring emergent properties and producing a great gaming experience where agents can collaborate with humans through chat interface.
00:50:00 In this section, Jim Fan discusses two potential directions for research on foundation models for embodied agents. The first direction is to deploy or open-source this technology for the Minecraft community, while the second direction involves learning from the training on this technology without overfitting to the specific domain of Minecraft. Jim Fan then delves further into the topic of foundation models for robotics in general, emphasizing the need for a unified architecture to push robotics forward. He explains how Vemod is taking a step in this direction by creating a single multimodal model for diverse tasks using the same format, citing examples such as visual goal conditioning and video imitation.
00:55:00 In this section, Jim Fan discusses the application of foundation models for embodied agents and scaling data for robotics. He explains how Spinal, a spinal cord-inspired neural model, allows a robot to perform various tasks by providing the robot with the ability to learn and execute new skills by demonstrating them. While the concept of a general setup that allows an expressive command to be executed is not controversial, the method itself may have some weaknesses as it currently requires a data set of expert demonstrations, which can be challenging and expensive to obtain. However, Fan sees opportunities for Spinal in other domains such as Minecraft and believes that the multimodal prompting system can be widely and safely applicable to many domains.

01:00:00 - 01:25:00

Jim Fan discusses the challenges facing robotics and the importance of planning and exploration for embodied agents. He suggests that the data problem in robotics can be addressed by using algorithms that extract reward functions instead of actions, which could help overcome the embodiment gap. Fan also discusses the potential to use large language models as reasoning engines for embodied agents, but acknowledges the risk of aligning models with particular human views or incentives. Moreover, he emphasizes the importance of feedback quality from human annotators and personalization in dialogue systems. Fan provides specific advice for researchers to start with low-hanging fruit and build a solid track record before focusing on more ambitious and promising trends that will have a lasting impact on the field.

01:00:00 In this section, Jim Fan discusses the challenges facing robotics compared to other fields such as language processing and deep learning. He points out that the problem of robotics is more difficult and involves physical properties and dynamics that are hard to capture using AI. Moreover, he notes that the hardware required for fine manipulation is expensive and challenging to develop. Additionally, the data problem is more severe in robotics as not only is there an embodiment gap, but there is also a lack of agreement on a standard hardware configuration across different labs. Nonetheless, he suggests that one way of scaling up the data is by using algorithms that extract reward functions instead of actions, which could help overcome the embodiment gap.
01:05:00 In this section, Jim Fan discusses the importance of planning and exploration for embodied agents and the challenges that arise when scaling up data. Planning is crucial for agents to complete large tasks efficiently, while exploration is necessary for agents to survive and thrive in open-ended environments. Fan believes that by carefully tuning and addressing the weaknesses of existing methods, significant progress can be made without the need for completely new or innovative approaches. One of the key open questions for Fan is how to bridge the gap between the sophisticated reasoning capabilities of current language models and the learning efficiency of human babies.
01:10:00 In this section, Jim Fan discusses the potential to use large language models as reasoning engines for embodied agents by adding vision, touch sensors, and limbs. He believes that most open-source language models are not good enough for charging continuous, but the recent paper on reinforcement from human feedback, which underlies chargpt, is exciting because it provides a general-purpose paradigm to align powerful AI systems with human intent. Fan also discusses the implications of this approach, including the potential for prop engineering to go away and the danger of aligning models with misleading or harmful information. However, there is a risk of aligning models with particular human views or incentives, which Fan acknowledges.
01:15:00 In this section, Jim Fan discusses the importance of feedback quality from human annotators in ensuring the quality of reinforcement learning for embodied agents. He also emphasizes the need for personalization in dialogue systems and the difficulty of achieving this, particularly with large language models. Fan expresses his excitement for the development of text-to-video models that can mimic the quality of human artists and pass the visual Turing test.
01:20:00 In this section of the video, Jim Fan discusses the potential game-changers in AI, such as generating videos from text that can compete with human-created content, as well as creating an AI that can score high on intelligence tests or bar exams, pushing the boundaries of domain-specific knowledge. Fan emphasizes the importance of research taste in AI researchers, stating that it is more important than implementation or coding skills. He provides specific advice for PhD students to do incremental work to build up to moonshot projects over time.
01:25:00 In this section, Jim Fan emphasizes the importance of starting with low-hanging fruit and doing incremental work to gain experience and build a solid track record in the research field. However, as one gains more experience and knowledge, they should focus on more ambitious and promising trends that will have a lasting impact on the field. Fan emphasizes that tuning the schedule and identifying the most promising directions will require hyperparameter tuning exercise.