I am a research scientist at Anthropic. My research focuses on sociotechnical alignment of AI systems: how to evaluate and build AI systems that interact positively with people and the societies we live in. Concretely, this looks like:
Developing scalable evaluations for both present-day and emerging harms
Creating interpretability methods that let us better understand and steer models
Running human studies to understand new ways that humans and AI systems can interact
Understanding the limits of multimodality and emerging applications on the horizon
Designing ways of eliciting values from groups of people and training models using those values
If this sounds interesting, we're hiring!
Previously, I completed my PhD in Computer Science at Stanford, where I was advised by Noah Goodman and part of the Stanford AI Lab and Stanford NLP Group.
Publications
Evaluating and Mitigating Discrimination in Language Model Decisions [🐦thread]
Alex Tamkin, Amanda Askell, Liane Lovitt, Esin Durmus, Nicholas Joseph, Shauna Kravec, Karina Nguyen, Jared Kaplan, Deep GanguliArXiv PreprintPress: [VentureBeat] [TechCrunch]Eliciting Human Preferences with Language Models [🐦thread]
Belinda Z. Li*, Alex Tamkin*, Noah D. Goodman, Jacob AndreasArXiv PreprintPress: [VentureBeat]Codebook Features: Sparse and Discrete Interpretability for Neural Networks [🐦thread][📝blogpost]
Alex Tamkin, Mohammad Taufeeque, Noah D. GoodmanArXiv PreprintSocial Contract AI: Aligning AI Assistants with Implicit Group Norms
Jan-Philipp Fränken, Sam Kwok, Peixuan Ye, Kanishk Gandhi, Dilip Arumugam, Jared Moore, Alex Tamkin, Tobias Gerstenberg, Noah D. GoodmanSoLaR NeurIPS 2023 WorkshopOperationalising the Definition of General Purpose AI Systems: Assessing Four Approaches
Risto Uuk, Carlos Ignacio Gutierrez, Alex TamkinArXiv Preprint Wai Tong Chung, Bassem Akoush, Pushan Sharma, Alex Tamkin, Ki Sung Jung, Jacqueline Chen, Jack Guo, Davy Brouzet, Mohsen Talei, Bruno Savard, Alexei Y Poludnenko, Matthias IhmeNeurIPS 2023BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors
Kathryn Wantlin, Chenwei Wu, Shih-Cheng Huang, Oishi Banerjee, Farah Dadabhoy, Veeral Vipin Mehta, Ryan Wonhee Han, Fang Cao, Raja R. Narayan, Errol Colak, Adewole S. Adamson, Laura Heacock, Geoffrey H. Tison, Alex Tamkin*, Pranav Rajpurkar*ArXiv PreprintFeature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
Alex Tamkin, Margalit Glasgow, Xiluo He, Noah GoodmanNeurIPS 2023Multispectral Contrastive Learning with Viewmaker Networks
Jasmine Bayrooti, Noah Goodman, Alex TamkinCVPR 2023 Workshop on Perception Beyond the Visible SpectrumTask Ambiguity in Humans and Language Models
Alex Tamkin*, Kunal Handa*, Avash Shrestha, Noah GoodmanICLR 2023Oolong: Investigating What Makes Crosslingual Transfer Hard with Controlled Studies [🐦thread]
Zhengxuan Wu*, Isabel Papadimitriou*, Alex Tamkin*EMNLP 2023DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision [🐦thread]
Alex Tamkin, Gaurab Banerjee, Mohamed Owda, Vincent Liu, Shashank Rammoorthy, Noah GoodmanNeurIPS 2022Active Learning Helps Pretrained Models Learn the Intended Task [🐦thread]
Alex Tamkin*, Dat Nguyen*, Salil Deshpande*, Jesse Mu, Noah GoodmanNeurIPS 2022DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning [🌐site] [🐦thread]
Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, Colin Schultz, Noah GoodmanNeurIPS 2021Press: [Redshift Magazine] [AIM Magazine] [Stanford HAI]Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study
Ananya Karthik, Mike Wu, Noah Goodman, Alex TamkinNeurIPS 2021 Workshop on Self-Supervised Learning - Theory and PracticeC5T5: Controllable Generation of Organic Molecules with Transformers
Daniel Rothchild, Alex Tamkin, Julie Yu, Ujval Misra, Joseph GonzalezArXiv PreprintOn the Opportunities and Risks of Foundation Models
Center for Research on Foundation Models (full list of authors)– Section 4.2: Training and Self-Supervision, Alex Tamkin– Section 4.9: AI Safety and Alignment, Alex Tamkin, Geoff Keeling, Jack Ryan, Sydney von Arx– Coauthor: Sections §2.2: Vision, §3.3: Education, §4.1 Modeling, §5.6: Ethics of ScalePress: [Forbes] [The Economist] [VentureBeat]Viewmaker Networks: Learning Views for Unsupervised Representation Learning [📝blogpost] [🐦thread]
Alex Tamkin, Mike Wu, Noah GoodmanICLR 2021Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models [📝blogpost]
Alex Tamkin*, Miles Brundage*, Jack Clark, Deep GanguliArXiv Preprint Press: [WIRED] [VentureBeat] [Datanami] [Slator]Language Through a Prism: A Spectral Approach for Multiscale Language Representations [🐦thread] [📝blogpost]
Alex Tamkin, Dan Jurafsky, Noah GoodmanNeurIPS 2020Investigating Transferability in Pretrained Language Models [🐦thread]
Alex Tamkin, Trisha Singh, Davide Giovanardi, Noah GoodmanFindings of EMNLP 2020; Presented at CoNLL 2020Distributionally-Aware Exploration for CVaR Bandits.
Alex Tamkin, Ramtin Keramati, Christoph Dann, Emma Brunskill. NeurIPS 2019 Workshop on Safety and Robustness in Decision Making; RLDM 2019Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy
Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill. AAAI 2020Recursive Routing Networks: Learning to Compose Modules for Language Understanding.
Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua D Greene, Dan Jurafsky, Christopher Potts, Lauri KarttunenNAACL 2019Drone.io: A Gestural and Visual Interface for Human-Drone Interaction.
Jessica R Cauchard, Alex Tamkin, Cheng Yao Wang, Luke Vink, Michelle Park, Tommy Fang, James A Landay. HRI 2019 Andrew Vanderburg, Christopher Shallue, Liang Yu, Anne Dattilo, Alex Tamkin. American Astronomical Society Meeting Abstracts, 2019Media
Quanta Magazine - How Quickly Do Large Language Models Learn Unexpected Skills?
VentureBeat - Anthropic leads charge against AI bias and discrimination with new research
TechCrunch - Anthropic’s latest tactic to stop racist AI: Asking it ‘really really really really’ nicely
VentureBeat - How can AI better understand humans? Simple: by asking us questions
WIRED Magazine - Chatbots Got Big—and Their Ethical Red Flags Got Bigger
Abrupt Future Podcast - Alex Tamkin on ChatGPT and Beyond: Navigating the New Era of Generative AI
AI Artwork in PC Magazine (twitter thread: DALL-E Meets WALL-E: an Art History)
The Gradient Podcast - Alex Tamkin on Self-Supervised Learning and Large Language Models
Press: [Communications of the ACM]The Engineered Mind Podcast - Alex Tamkin on NLP, AI Ethics & PhD Life
Personal
Other topics I think a lot about:
Societal impacts of technology, especially machine learning and large language models
Mentoring, teaching and fostering a healthy and inclusive research culture
Scientific communication and breaking down walls between fields
I also like making art, especially ceramics and photography!