Stanford ECON295/CS323 I 2024 I AI and Creativity, Anima Anandkumar
11 Sep 2024 (7 days ago)
Generative AI and its Applications
- Generative AI differs from discriminative AI, which focuses on understanding and classifying existing data, by generating new samples from complex distributions.
- Language models are trained by predicting the next word in a sequence, learning the underlying meaning of words and how they appear in different contexts.
Embodiment and Language Models
- While language models excel at understanding and generating text, they lack embodiment, the ability to translate words into actions within the physical world.
- Language models can be used by AI agents to continuously query for information and improve their skills to solve increasingly complex problems in environments like Minecraft.
Verifying Language Models
- Combining language models with formal verifiers like Lean can eliminate hallucinations in domain-specific applications, such as mathematics, by verifying the correctness of each step in a proof.
Genomic Language Models
- Genomic language models, trained on massive datasets of DNA sequences, can learn the functionality of genes and predict mutations in viruses and bacteria, enabling a broader understanding of their evolution compared to traditional biological studies.
- A language model trained on genomes, having been trained on the Alpha and Beta variants of COVID-19, accurately predicted the emergence of the Delta and Omicron variants.
Limitations of Genomic Language Models
- Studying the genomic sequence alone does not provide sufficient information to determine the severity of a variant; understanding the biological processes, such as protein interactions and transmission mechanisms, is crucial.
AI in Scientific Modeling
- While mathematical equations, like Newton's laws of motion, can describe physical phenomena, simulating these processes at scale, especially at the quantum level, presents a significant computational challenge.
- Scientific phenomena can be modeled using AI by incorporating mathematical equations, either through simulators or by directly integrating physical laws into the models.
Challenges of Traditional Numerical Solvers
- Traditional numerical solvers, while attempting to directly solve equations, face limitations in capturing fine-scale details, leading to high computational costs and memory requirements, particularly for simulations involving large spatial areas or fine resolutions.
Neural Operators: A Solution
- Neural operators, akin to vector graphics in their ability to represent data continuously, offer a solution by learning the underlying functions from data, eliminating the need to predefine resolution and enabling accurate predictions at arbitrary resolutions.
Limitations of Fourier Transforms and Fixed Resolution Neural Networks
- Fourier Transforms use sine waves, which can be too limiting when a model is not sparse in terms of sine waves.
- Neural networks with fixed resolution are limited in that the input and output must be at the same resolution, unlike models that allow for flexible resolution.
Successes of Neural Operators
- Plasma evolution in nuclear fusion simulations saw a million times improvement in compute, while weather prediction saw tens of thousands of times improvement.
Neural Operators: An Extension of Neural Networks
- Neural operators are an extension of neural networks that can handle any resolution of input and output.
- Fourier neural operators are particularly well-suited for applications in fluid dynamics because they can describe interactions across multiple scales.
Weather Modeling with Neural Operators
- Weather modeling is a challenging application for neural operators because it involves high-resolution data with many channels and a relatively small number of samples.
- Neural operators were applied to weather forecasting in the late 2020s as a challenging use case that could impact the real world.
Deep Learning for Weather Forecasting
- This deep learning based method using neural operators is significantly faster, up to 455,000 times faster, than traditional numerical models run on supercomputers.
- While traditional weather forecasting relies on assumptions and limited data calibration, neural-based models can adapt and learn from data, potentially leading to more accurate predictions, especially in regions with limited data like the global South.
Skepticism and Success of Deep Learning in Weather Forecasting
- There was initial skepticism, even an opinion piece titled "Can Deep Learning Beat Numerical Weather Models?" concluding it couldn't, about deep learning's ability to model weather, particularly rare events like hurricanes.
- Despite skepticism, deep learning models like the one discussed, trained on weather data, proved to be competitive with traditional numerical weather models, even predicting hurricanes accurately.
Adoption of Deep Learning in Weather Forecasting
- Traditional weather agencies like the European Center for Medium Range Weather Forecasting (ECMWF) and the North American Atmospheric Administration (NOAA) are now using and improving upon these deep learning models.
Advantages of Deep Learning Models
- Deep learning models, when compared to numerical solvers, are generally faster, especially when dealing with non-linear and unstable phenomena that require a fine grid in numerical models.
GraphCast: A New Approach to Weather Prediction
- GraphCast, a model from DeepMind, builds upon the principles discussed, using a hierarchical graph approach to represent relationships between neighboring grid points.
Future of Weather Prediction Models
- A new architecture for weather prediction models will be released soon. This architecture is a hybrid that uses both local and global information while treating the geometry as a sphere.
- Using a spherical geometry in weather prediction models, as opposed to a rectangular one, leads to more stable long-term predictions, especially for climate modeling.
Limitations of Current Weather Models
- Current weather models used by organizations like the ECMWF only use around 50 ensemble members (perturbations) to determine sensitivity in weather predictions, which is a relatively small number and can lead to inaccurate statistical bounds.
Importance of Reducing Computational Cost in Climate Modeling
- Climate models require numerous runs to effectively predict long-term trends and extreme events. Reducing the cost of these runs is crucial for improving the accuracy of uncertainty quantification.
AI in Biology
- Biology, as a field, exhibits a greater degree of openness to incorporating AI and machine learning compared to fields like physics. This receptiveness stems from the inherent complexities and uncertainties present in biological systems, making data-driven approaches particularly valuable.
Data Quality in Scientific Models
- Data used in scientific models, such as those for genomics, protein folding, and weather prediction, is often carefully curated and validated, leading to higher quality datasets compared to the vast but less structured data used in language models.
Understanding vs. Memorization in Language Models
- Yan Lun believes that large language models are primarily memorizing data rather than demonstrating true understanding.
Improving Language Models
- Current research focuses on improving models through repeated interactions and reinforcement learning, aiming to enhance performance on complex tasks.
Importance of Theoretical Constraints in AI Models
- While increasing data generally improves model performance, incorporating theoretical constraints and structure can enhance learning, especially with limited data.
AI in Design Optimization
- AI models can be used to optimize designs for real-world applications, such as medical catheters that reduce bacterial contamination.
- AI can be used to create designs that are not only aesthetically pleasing but also functional and manufacturable, such as aircraft and drones.
- AI can be used to optimize mask design in lithography, which is an important part of chip manufacturing, especially for the latest GPUs.
Towards a Comprehensive Understanding of Science
- There is a need for models that can understand all of science in a comprehensive way, going beyond simply reading textbooks and providing information.
Grounding AI Models in Physics
- A model called Sora learned physics indirectly from videos, but directly grounding models in physics is crucial.
- A new model, similar in size to GPT-2, has been trained on various physics concepts, including fluids, wave equations, and materials, with the goal of simulating and designing across multiple physics phenomena.