Computerphile - No Regrets - What Happens to AI Beyond Generative? - Computerphile
The discussion highlights the limitations of current AI models, which excel in text-based tasks but struggle with decision-making and action-taking in real-world scenarios. To overcome these limitations, the focus is on moving beyond supervised learning to develop AI systems capable of trial-and-error learning, similar to humans. This involves training models in simulated environments rather than relying solely on text data, as real-world trial-and-error learning can be risky and data-intensive. The concept of 'regret' is introduced as a measure of the difference between an agent's performance and the optimal performance in a given environment. However, traditional methods of approximating regret have proven ineffective in new, more complex environments. Instead, the focus shifts to optimizing for 'learnability,' which involves creating environments where agents can learn effectively through trial and error. This approach has shown promise in improving generalization to new tasks and environments. Additionally, the discussion touches on the challenges of reinforcement learning, particularly the limitations imposed by current CPU-GPU architectures, and introduces a new approach of running environments on GPUs to enhance training efficiency. This has led to significant speed improvements, making it feasible to test algorithms faster and on more diverse environments. The introduction of a 2D physics-based simulation platform called 'kinetics' allows for the creation of diverse tasks, facilitating better training and generalization of AI models. The ultimate goal is to develop a foundation model for decision-making and action-taking, akin to the advancements seen in language models.
Key Points:
- AI models excel in text tasks but struggle with real-world decision-making.
- Training in simulated environments is crucial for developing action-taking AI.
- Traditional regret approximation methods are ineffective in complex environments.
- Optimizing for 'learnability' improves AI generalization to new tasks.
- Running environments on GPUs significantly speeds up reinforcement learning.
Details:
1. 🔍 Rethinking AI: Beyond Supervised Learning
- Current AI models, particularly generative models, are excellent for tasks like Q&A and chatbots due to their simplicity and ability to predict future data points using large data corpora.
- However, these models face significant limitations in real-world decision-making, trial and error learning, and long-term planning, highlighting the need for AI systems that can perform beyond supervised learning.
- The necessity for AI to evolve includes developing capabilities for complex reasoning and interaction with the real world, addressing tasks that require more than just prediction based on existing data.
- For example, AI's struggle with long-term planning can be seen in autonomous driving, where real-time decision-making and adaptation to unpredictable environments are crucial.
2. 🚀 Learning Through Experience: AI's New Frontier
- AI systems must learn through experience similar to humans, but this introduces risks when applied in real-world scenarios.
- Trial and error learning in AI systems can be risky in real-world applications, necessitating controlled environments for training.
- To develop effective AI models, there is a need for an 'internet of environments' akin to the 'internet of text' used for text-based models.
- AI models should be trained in simulated environments to facilitate complex decision-making and action-taking capabilities.
- Relying solely on text corpus data is insufficient for nurturing AI models capable of advanced multi-term decision making.
- An 'internet of environments' would provide diverse and controlled settings for AI to safely learn through trial and error, similar to how text models use the internet of text.
- Examples of controlled environments include simulated urban settings for autonomous vehicles or virtual marketplaces for economic models.
3. 🌐 Creating an 'Internet of Environments' for AI
- AI systems require virtual environments for training due to limited availability of human data and the rise in computing power.
- Faster computing technology facilitates 'compute only scaling,' which is essential for AI innovation.
- Virtual environments enable agents to engage in trial and error learning, which is crucial given the scarcity of real-world human data.
- Ensuring that virtual environments accurately mimic real-world conditions is a significant challenge for effective AI training.
- Creation and maintenance of virtual environments involve sophisticated simulations that require ongoing updates to reflect realistic scenarios.
- Examples of successful virtual environments include OpenAI's Gym and Google's DeepMind Lab, which have set benchmarks for AI training.
- The importance of these environments lies in their ability to accelerate AI development by providing scalable and diverse learning contexts.
4. 🔧 AI Training in Complex Virtual Worlds
- Designing task distributions for AI to ensure robustness across varied real-world instances, emphasizing the importance of diverse and unpredictable environments for comprehensive AI training.
- Developing methods for AI to generalize across multiple grid world environments, highlighting techniques that allow AI to adapt to different scenarios and tasks efficiently.
- Optimizing AI navigation from start to goal in unpredictable environments by utilizing advanced algorithms that enhance learning and adaptability.
- Creating simulation environments that allow for comprehensive training beyond random distributions, ensuring AI systems can handle complex and unexpected challenges effectively.
5. 📈 From Regret to Learnability in AI Development
- Develop methods robust to real-world variability by simulating all possible layouts.
- Key question: What distribution should training use to minimize regret at test time?
- Regret is the performance gap between a trained agent and an optimal agent in an environment.
- Regret for environment 'E' is calculated as Optimal Performance (J*Pi) on E minus Agent Performance (J Pi) on E.
- The policy (Pi) in reinforcement learning maps observations to action distributions.
- To enhance learnability, focus on creating adaptable models that can learn efficiently from diverse scenarios.
- Use real-world feedback loops to reduce the performance gap (regret) and improve model robustness.
- Implementing continuous learning mechanisms can turn minimized regret into a foundation for adaptive learnability.
6. 🛠️ Advancing AI with Multi-Agent Simulations
- 'Optimal policy' aims for the highest expected reward, like a cleaning robot maximizing dirt collection, but requires careful reward specification to avoid unintended behaviors.
- Simulations assume a known, computable environment distribution, crucial for realistic training scenarios.
- Regret is emphasized as a key metric for identifying learning opportunities by comparing actual and optimal performance.
- Transitioning from simple to complex environments, such as continuous 2D navigation tasks for multiple robots, revealed limitations in standard methods when slightly moving out of previous research distributions.
- Despite 6 months of effort, adapting existing methods to new environments proved challenging, highlighting the need for optimizing regret to ensure learning opportunities and bounded suboptimal performance.
- Real-world examples could include robots in dynamic environments, where unexpected changes test the robustness of AI learning models.
7. 🔄 Optimizing AI: From Regret to Learnability
- After six months of effort, the team revisited their approach to regret approximations and discovered they did not align well with an intuitive notion of learnability.
- A graph was plotted with estimated regret on the x-axis and learnability on the y-axis to test their correlation.
- Learnability was defined where the agent sometimes succeeds, indicating tasks are neither too easy nor too difficult.
- The proxy for learnability was set as p * (1 - p), where p is the probability of success, aiming to correlate it with estimated regret.
- However, results showed no correlation or even negative correlation between regret and learnability.
- A strategic shift to optimize directly for learnability rather than regret led to immediate improvements in generalization to new environments.
- Within a day, methods optimizing for learnability showed better generalization to hold-out environments than previous approaches.
- The experiment highlights the importance of reevaluating research paradigms and ensuring methods can generalize beyond specific task distributions.
8. 🖥️ Revolutionizing AI with GPU Acceleration
8.1. GPU Acceleration in Deep Learning
8.2. Challenges in Reinforcement Learning with GPU Acceleration
9. 🔄 Designing Diverse AI Environments
- Implementing the environment on the GPU allows running the environment, the policy, and the training loop on the same GPU, eliminating CPU-GPU communication and enabling scaling by adding batch dimensions.
- This integration resulted in a speed increase by a factor of 10,000, making it feasible to test algorithms faster and evaluate across multiple environments simultaneously with just a couple of GPUs.
- Despite the computational efficiency, the diversity of tasks has been limited to those implemented in the 'Jack' framework, prompting the development of more environments in Jax, including the multi-root environment in Tod.
- A new system called 'Kinetics' has been developed, which includes an N2N GPU accelerated system with a task generation editor, a GPU accelerated physics engine, and a UI for human interaction, supporting a wide range of 2D tasks.
- Kinetics allows the implementation of diverse reinforcement learning environments, such as 2D robotics tasks (e.g., walking, Hopper), games (e.g., marble shooting), and the lunar lander, all within a 2D physics engine, simplifying the development process.
- The GPU implementation faced challenges in maintaining task diversity, which led to the exploration and integration of the 'Kinetics' system to expand task variety and utility.
- The advancements in GPU-based environments and the 'Kinetics' system significantly enhance the ability to conduct complex AI experiments efficiently and provide a scalable solution for diverse AI task generation.
10. 🤔 Future Directions: Scaling AI Training in Simulation
- Parameterization enables running different environments efficiently on a single GPU by parallelizing tasks, enhancing training capabilities significantly.
- Training agents on random distributions through optimized curriculums leads to improved performance on unrelated tasks, demonstrating versatility.
- Zero-shot improvement is achieved by pre-training on arbitrary distributions with curriculum application, resulting in substantial performance gains on new, unseen tasks.
- Fine-tuning pre-trained models on target tasks accelerates training and enhances outcomes, outperforming models trained from scratch.
- The shift towards developing agentic foundation models for decision-making, rather than just prediction, marks a significant evolution in AI capabilities.
- This methodology's scalability suggests potential for expansion into complex 3D environments, using the same principles to maintain efficiency.
- Computational power remains a limitation, but current techniques provide a solid foundation for future AI training advancements in simulation.