The Polymathic AI Initiative
Towards Multidisciplinary Scientific Foundation Models
Francois Lanusse
Simons Foundation/CNRS
on behalf of Shirley Ho and the Polymathic AI team
The Rise of The Foundation Model Paradigm
-
Foundation Model approach
- Pretrain models on pretext tasks, without supervision, on very large scale datasets.
- Adapt pretrained models to downstream tasks.
- Combine pretrained modules in more complex systems.
Can we translate these innovations into a paradigm shift in machine learning for scientific applications?
Polymathic
Advancing Science through Multi‑Disciplinary AI
Our mission: to usher in a new class of machine learning for scientific data, building models that can leverage shared concepts across disciplines."
Meet the Polymathic AI Team
|
Colm-Cille
Caulfield University of Cambridge
|
Leslie
Greengard Flatiron Institute
New York University |
David
Ha Sakana AI
|
Yann
LeCun Meta AI
New York University |
|---|---|---|---|
|
Stephane
Mallat École Normale Supérieure
Collège de France Flatiron Institute |
David
Spergel Simons Foundation |
Olga Troyanskaya Flatiron Institute Princeton University |
Laure
Zanna New York University
|
Our Resources
SCIENTIFIC ADVISORY GROUP
COMPUTING RESOURCES
- Internal resources at the Flatiron Institute H100 GPUs (24 nodes equivalent to NVIDIA DGX-H100)
- External GPU grants (A100, H100)
The Foundation Model Spectrum
Language-like/less structured
Structured-data
Scientific Reasoning
Multi-Modality
Generalization to Data-Limited Domains
How can we build foundation models that jump across scientific disciplines?
- Should we treat scientific data as if we treat language?
- Should we treat scientific data the way we have been treating them in ML?
(structured as in grids, images, videos, graphs, etc...) - What is a common basis across multiple disciplines and modalities?
The Foundation Model Spectrum
Language-like/less structured
Structured-data
AstroCLIP
Cross-Modal Pretraining for Astronomical data
MPP
Multiple Physics Pretraining for Physical Surrogate Models
Scientific Reasoning
Multi-Modality
Generalization to Data-Limited Domains
xVal
A Continuous Number Encoding for LLMs
MPP
Multiple Physics Pretraining for Physical Surrogate Models
Project led by Michael McCabe, Bruno Régaldo, Liam Parker, Ruben Ohana, Miles Cranmer
Best paper award at the NeurIPS 2023 AI4Science Workshop
Physical Systems from PDEBench
Navier-Stokes
Incompressible
Compressible
Shallow Water
Diffusion-Reaction
Takamoto et al. 2022
Can we improve performance of surrogate models by pretraining on large quantities of easily simulatable systems?
Compositionality and Pretraining
MPP (Multi-Physics Pretraining): a single model for varied systems
Balancing objectives during training
Normalized MSE:
Experiment 1: Performance on Pretraining Tasks
Context size: 16 frames
Experiment 2: Transfer
Compressible Navier-Stokes
M = 0.1
M = 1.0
Going further
- Methodology improvements for long roll out predictions.
- Larger and more diverse datasets
PDEBench
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
- 55B tokens from 3M frames
=> First ImageNet scale dataset for fluids
-
18 subsets spanning problems in astro, bio, aerospace, chemistry, atmospheric science, and more.
- Simple self-documented HDF5 files, with pytorch readers provided.
=> Available early September
The Foundation Model Spectrum
Language-like/less structured
Structured-data
AstroCLIP
Cross-Modal Pretraining for Astronomical data
MPP
Multiple Physics Pretraining for Physical Surrogate Models
Scientific Reasoning
Multi-Modality
Generalization to Data-Limited Domains
xVal
A Continuous Number Encoding for LLMs
The Foundation Model Spectrum
Language-like/less structured
Structured-data
AstroCLIP
Cross-Modal Pretraining for Astronomical data
MPP
Multiple Physics Pretraining for Physical Surrogate Models
Scientific Reasoning
Multi-Modality
Generalization to Data-Limited Domains
xVal
A Continuous Number Encoding for LLMs
The Data Diversity Challenge
- Success of recent foundation models is driven by large corpora of uniform data (e.g LAION 5B).
- Scientific data comes with many additional challenges:
- Metadata matters
- Wide variety of measurements/observations
Credit: Melchior et al. 2021
Credit:DESI collaboration/DESI Legacy Imaging Surveys/LBNL/DOE & KPNO/CTIO/NOIRLab/NSF/AURA/unWISE
Towards Large Multi-Modal Data Models
Most General
Most Specific
Independent models for every type of observation
Single model capable of processing all types of observations
Towards Large Multi-Modal Data Models
Most General
Most Specific
Independent models for every type of observation
Single model capable of processing all types of observations
Bytes Are All You Need (Horton et al. 2023)
Towards Large Multi-Modal Data Models
Most General
Most Specific
Independent models for every type of observation
Single model capable of processing all types of observations
Bytes Are All You Need (Horton et al. 2023)
AstroCLIP
AstroCLIP
Cross-Modal Pre-Training for Astronomical Foundation Models
Project led by Francois Lanusse, Liam Parker, Leopoldo Sarra, Siavash Golkar, Miles Cranmer
Accepted contribution at the NeurIPS 2023 AI4Science Workshop
Published in Monthly Notices of Royal Astronomical Society
What is CLIP?
Contrastive Language Image Pretraining (CLIP)
(Radford et al. 2021)
The AstroCLIP approach
- We use spectra and multi-band images as our two different views for the same underlying object.
- DESI Legacy Surveys (g,r,z) images, and DESI EDR galaxy spectra.
Cosine similarity search
The Information Point of View
- The InfoNCE loss is a lower bound on the Mutual Information between modalities
Shared physical information about galaxies between images and spectra
=> We are building summary statistics for the physical parameters describing an object in a completely data driven way
- Redshift Estimation From Images
Supervised baseline
z_{true}
z_{true}
z_{true}
z_{true}
z_{true}
z_{true}
z_{true}
z_{true}
- Zero-shot prediction
- k-NN regression
- Few-shot prediction
- MLP head trained on top of frozen backbone
Evaluation of the model: Parameter Inference
- Galaxy Physical Property Estimation from Images and Spectra
We use estimates of galaxy properties from the PROVABGS catalog (Hahn et al. 2023) (Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and photometry method)
R^2
of regression
Negative Log Likelihood of Neural Posterior Inference
Example-based retrieval
Example of Science Application: Identifying Galaxy Tidal Features
What This New Paradigm Could Mean for Astrophysicists
-
Never have to retrain my own neural networks from scratch
- Existing pre-trained models would already be near optimal, no matter the task at hand
-
Saves a lot of time and energy
- Practical large scale Deep Learning even in very few example regime
- Searching for very rare objects in large astronomical surveys becomes possible
-
Pretraining on data itself ensures that all sorts of image artifacts are already folded in the training.
- If the information is embedded in a space where it becomes linearly accessible, very simple analysis tools are enough for downstream analysis
- In the future, survey pipelines may add vector embedding of detected objects into catalogs, these would be enough for most tasks, without the need to go back to pixels
Going Further: Collection and Curation of Scientific Data
- Development of large models requires access to "web scale" datasets
- Astrophysics generates large amounts of publicly available data,
-
BUT, data is usually not stored or structured in an ML friendly way.
-
BUT, data is usually not stored or structured in an ML friendly way.
- Accessing and using scientific data requires significant expertise, for each dataset.
=> Implies engaging with domain experts.
Credit: Melchior et al. 2021
The MultiModal Universe Project
- Goal: Assemble the first large-scale multi-modal dataset for machine learning in astrophysics.
-
Main pillars:
- Engage with a broad community of AI+Astro experts.
- Adopt standardized conventions for storing and accessing data and metadata through mainstream tools (e.g. Hugging Face Datasets).
- Target large astronomical surveys, varied types of instruments, many different astrophysics sub-fields.
Multiband images from Legacy Survey
=> Official release early September
Towards Large Multi-Modal Data Models
Most General
Most Specific
Independent models for every type of observation
Single model capable of processing all types of observations
Bytes Are All You Need (Horton et al. 2023)
AstroCLIP
Towards Large Multi-Modal Data Models
Most General
Most Specific
Independent models for every type of observation
Single model capable of processing all types of observations
Bytes Are All You Need (Horton et al. 2023)
AstroCLIP
Early Fusion Multi-modal Data Models
New Generation of Token-Based Multimodal Models
Flamingo: a Visual Language Model for Few-Shot Learning (Alayrac et al. 2022)
Chameleon: Mixed-Modal Early-Fusion Foundation Models (Chameleon team, 2024)
All-to-All Foundation Models
- Learns the joint and all conditional distributions of provided modalities:
- Can be further fine-tuned to build specialist models for news tasks.
\forall m,n \quad p(x_m | x_n)
Scientific Data Tokenization
Input
Reconstructed
Our strategy:
- Develop modality specific but universal tokenizers, i.e. a single model to embed all type of astronomical images
- This requires specific innovations to take into account the metadata of observations.
Example of strategy to embed different bands
Field Embedding Strategy Developed for Multiple Physics Pretraining (McCabe et al. 2023)
Looking
Forward at Polymathic
- Next year we are focusing on scaling up (more domains, more data, larger models) and developing the next generation of our models.
-
We are hiring!
- Postdoctoral positions
- Research engineer positions
Follow us online!
Thank you for listening!
Towards Multidisciplinary Scientific Foundation Models
By eiffl
Towards Multidisciplinary Scientific Foundation Models
Overview talk of the Polymathic AI Initiative
