Towards Foundation Models for Science

Francois Lanusse, Mariel Pettee, Bruno Regaldo-Saint Blancard

on behalf of Shirley Ho and the Polymathic AI team

What are foundation models?

The Key Ideas

  • Large models pre-trained on task-agnostic objectives on massive & diverse datasets
     
  • Through fine-tuning, they can be adapted downstream for a variety of tasks/datasets
     
  • They may contain advantageous inductive biases about particular domains or data structures
  • They are actively changing workflows:
    • "around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted." (arXiv:2303.10130 [econ.GN])

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 H100 GPUs resources at the Flatiron Institute 
  • External 500k GPU hours (V100 and A100)
  • In the process of securing additional O(10^2) dedicated H100 GPUs

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

xVal
 A Continuous Number Encoding for LLMs

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

Project led by Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti
Accepted contribution at the NeurIPS 2023 AI4Science Workshop

See Mariel's talk from Monday

The problem: existing LLMs are not suitable for reliable zero-shot numerical operations.

They make erratic, discontinuous predictions.

Even fine-tuning exhaustively does not grant out-of-distribution generalization abilities.

xVal in a Nutshell: a continuous numerical encoding for language models

xVal in a Nutshell: a continuous numerical encoding for language models

This encoding strategy has 3 main benefits:

  • Continuity

    • It embeds key information about how numbers continuously relate to one another, making its predictions more appropriate for many scientific applications.

  • Interpolation

    • It makes better out-of-distribution predictions than other numerical encodings.

  • Efficiency

    • By using just a single token to represent any number, it requires less memory, compute resources, and training time to achieve strong results.

 

xVal in a Nutshell: a continuous numerical encoding for language models

xVal shows improved predictions for out-of-distribution values.

xVal in a Nutshell: a continuous numerical encoding for language models

When evaluated on multi-digit multiplication tasks, xVal performs comparably well, and is less prone to large outliers:

And when evaluated on compound operations of basic arithmetic, xVal shows the strongest performance:

xVal in a Nutshell: a continuous numerical encoding for language models

Future directions: improving the dynamic range of the embedded values.

MPP

Multiple Physics Pretraining for Physical Surrogate Models

Project led by Michael McCabe,  Bruno Régaldo, Liam Parker, Ruben Ohana, Miles Cranmer
Oral presentation at the NeurIPS 2023 AI4Science Workshop

Context

  • Previous works on large domain-specific pretrained models: chemistry, medicine, astrophysics, climate, ...
    → extension on surrogate modeling of spatiotemporal physical systems
  • Spatiotemporal predictions motivated by faster surrogates for PDE solvers, systems that are hard to simulate with current models/hardware
  • Situations where data is expensive…

Time

Ex: N-body simulation

Springel et al. 2005

Can we  build a foundation model that
would be finetunable on a few training examples?

Background

  • Main pretraining strategies
     - autoregressive prediction
     - masked reconstruction
     - contrastive learning
  • No conditioning on physical parameters
  • Spatiotemporal physics: PDEs from a physical system with typically conservations and symmetries…
         → Suggests that there are some learnable shared features

Natural choice for physical surrogate modeling

Physical Systems from PDEBench

Navier-Stokes

Incompressible

Compressible

Shallow Water

Diffusion-Reaction

Takamoto et al. 2022

Compositionality and Pretraining

Architecture for MPP

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

Fun Fact: Finetuning with VideoMAE works quite well…

Tube masking

ViT

ViT

ViT

Trained on reconstructing masked pixels on natural videos (SSV2 and K400)

Tong et al. 2022

Experiment 3: Broader Downstream Tasks

Regression Problems on Incompressible Navier-Stokes

Mixed results

Long time predictions on Navier-Stokes?

Conclusion

  • MPP-pretraining approach with shared embedding space and autoregressive predictions
  • A single transformer model matches/outperforms baselines for each task without finetuning
  • Transfer capabilities were demonstrated
  • Evaluation on broader downstream tasks
  • Open code and pretrained models:                      https://github.com/PolymathicAI/multiple_physics_pretraining

AstroCLIP

Cross-Modal Pre-Training for Astronomical Foundation Models

Project led by Francois Lanusse, Liam Parker, Siavash Golkar, Miles Cranmer
Accepted contribution at the NeurIPS 2023 AI4Science Workshop

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:DESI collaboration/DESI Legacy Imaging Surveys/LBNL/DOE & KPNO/CTIO/NOIRLab/NSF/AURA/unWISE

Towards Large Multi-Modal Observational 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 Observational 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 Observational 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

What is CLIP?

Contrastive Language Image Pretraining (CLIP)
(Radford et al. 2021)

One model, many downstream applications!

Flamingo: a Visual Language Model for Few-Shot Learning (Alayrac et al. 2022)

Hierarchical Text-Conditional Image Generation with CLIP Latents (Ramesh et al. 2022)

Contrastive Learning in Astrophysics

Self-Supervised similarity search for large scientific datasets (Stein et al. 2021)

Example of Science Application: Identifying Galaxy Tidal Features

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.
     
  • Once trained, we can do example retrieval by nearest neighbor search.

How we do it

We take a two steps approach:

  • Build self-supervised model separately for images and spectra
    • Images: Start from pre-trained ResNet 50 from Stein et al. (2021)
    • Spectra: Pretrain by mask modeling a GPT-2 like transformer on spectra
  • Train an embedding module on top of each backbone under InfoNCE loss
    • Images: Simple MLP
    • Spectra: Cross-attention module 

More examples of retrieval

Image Similarity

Spectral Similarity

Image-Spectral Similarity

Visualizing properties of the embedding space

UMAP representation of spectra embeddings

Testing Structure of Embedding Space by k-NN Regression

Comparison to image-only SSL (from Stein et al. 2021)

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

Thinking about data from a hierarchical Bayesian model point of view

y \sim \underbrace{p_{obs}( y | x )}_{\text{instrument specific}} \qquad \underbrace{p(x | \theta)}_{\text{generative process}} \qquad \underbrace{p(\theta)}_{\text{physical parameters}}

=> We are building summary statistics for the physical parameters describing an object in a completely data driven way

What comes next!

  • Idea of finding a common shared representation diverse observational modalities
    • Next steps would be, embedded data from different instruments, different filters, etc... to build a universal embedding for types of galaxy observations
       
  • These pretrained models will act as strong off-the-shelf model for many downstream tasks:
    • Never need to train your own CNN anymore

Teaser for what comes next

We are just getting started!

Thank you for listening!

Towards Foundation Models for Science

By eiffl

Towards Foundation Models for Science

Overview talk of the Polymathic AI Initiative

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