Google Pathways (report)

An Exploration of the Pathways Architecture from PaLM to Parti

Alan D. Thompson
LifeArchitect.ai
August 2022
24 pages incl title page, references, appendices.

Download PDF (2.3MB).

Updates to the Pathways family since publication

1. 16/Aug/2022: PaLM + Robots: Google PaLM-SayCan: announce, research site, video.

2. 15/Sep/2022: PaLI: Google Pathways Language and Image model: paper.

Reviews
Received by at least one major Western government; used in policy analysis.

Abstract

With over a million subscribed users, GPT-3 and related models have received a lot of press coverage and public attention. Much like a flashy Porsche driving down the Autobahn, these models look impressive, and are performing well. However, it is only a matter of time until they are overtaken by a much larger supercar. And that vehicle is already rapidly approaching. Google Pathways was announced at the end of 2021, and we are seeing several of its components in 2022: beginning with PaLM, PaLM-Coder, Parti, and Minerva. While all of these models are closed—only available for Google’s internal research—it is anticipated that a future Pathways model will be publicly released. This report explores the accomplishments of the Pathways models so far, with PaLM and its related language models already at more than triple the size of GPT-3.

Contents

1. Overview
1. Google Pathways (Oct/2021)
2. Google Pathways: The Pathways System (Mar/2022)
3. Google Pathways: PaLM 540B (Apr/2022)
3.1. PaLM Dataset Summary
3.2. PaLM Capabilities & Performance
4. Google Pathways: PaLM-Coder 540B (Apr/2022)
5. Google Pathways: Parti 20B (Jun/2022)
6. Google Pathways: Minerva 540B (Jun/2022)
6.1. The Polish National Math Exam
7. Following the Trail to Transformative AI
8. Further reading
Appendix A: PaLM 540B vs GPT-3 175B vs Jurassic-1 178B vs human

More videos and images

Videos

1. PaLM 540B (Apr/2022)

2. Parti 20B (Jun/2022)

3. Report card referencing PaLM

Images (selected)


References, Further Reading, and How to Cite

To cite this report: 

Thompson, A. D. (2022). Google Pathways: An Exploration of the Pathways Architecture from PaLM to Parti. https://LifeArchitect.ai/pathways

Further reading

For brevity and readability, footnotes were used in this paper, rather than in-text citations. Additional reference papers are listed below, or please see http://lifearchitect.ai/papers for the major foundational papers in the large language model space.

Pathways System announcement (Pathways blog)

Dean, J. (2021). Introducing Pathways: A next-generation AI architecture.

https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/

Pathways System paper

Barham, P., Chowdhery, A., Dean, J., Ghemawat, S., Hand, S., Hurt, D., Isard, M., Lim, H., Pang, R., Roy, S., Saeta, B., Schuh, P., Sepassi, R., Shafey, L. E., Thekkath, C. A., and Wu, Y. (2022). Pathways: Asynchronous Distributed Dataflow for ML. https://arxiv.org/abs/2203.12533 

PaLM announcement (PaLM blog)

Narang, S. & Chowdhery, A. (2022). Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance.

https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html

PaLM paper (includes PaLM-Coder)

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N. (2022). PaLM: Scaling Language Modeling with Pathways. https://arxiv.org/abs/2204.02311 

Parti paper

Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., Hutchinson, B., Han, W., Parekh, Z., Li, X., Zhang, H., Baldridge, J., & Wu, Y. Scaling Autoregressive Models for Content-Rich Text-to-Image Generation. https://arxiv.org/abs/2206.10789 

Parti demo

Google. (2022). https://parti.research.google/ 

Minerva announcement (Minerva blog)

Dyer, E., & Gur-Ari, G. (2022). Minerva: Solving Quantitative Reasoning Problems with Language Models.

https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html 

Minerva paper

Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., Wu, Y., Neyshabur, B., Gur-Ari, G., & Misra, V. (2022). Solving Quantitative Reasoning Problems with Language Models. https://arxiv.org/abs/2206.14858

Minerva sample demo

Google. (2022). https://minerva-demo.github.io/


Image credit: Thanks to jesssaysno for the header image on this page.


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Dr Alan D. Thompson is an AI expert and consultant. With Leta (an AI powered by GPT-3), Alan co-presented a seminar called ‘The new irrelevance of intelligence’ at the World Gifted Conference in August 2021. His applied AI research and visualisations are featured across major international media, including citations in the University of Oxford’s debate on AI Ethics in December 2021. He has held positions as chairman for Mensa International, consultant to GE and Warner Bros, and memberships with the IEEE and IET. He is open to consulting and advisory on major AI projects with intergovernmental organisations and enterprise.

This page last updated: 15/Sep/2022. https://lifearchitect.ai/pathways/