Work Experience
CEO and Co-Founder
Combining scaling and long-term AI alignment methods to build steerable, interpretable, and robust AI systems.
Co-founder & CEO / Anthropic
Dario Amodei is an American artificial intelligence researcher and entrepreneur, best known as the co-founder and CEO of Anthropic, a public benefit corporation focused on building safe, steerable, and interpretable AI systems like Claude. Prior to founding Anthropic in 2021, he was the Vice President of Research at OpenAI, where he led the development of the groundbreaking GPT-2 and GPT-3 models. Amodei holds a PhD in Computational Neuroscience from Princeton and has a strong background in physics and deep learning research from his time at Google Brain and Baidu.
CEO and Co-Founder
Combining scaling and long-term AI alignment methods to build steerable, interpretable, and robust AI systems.
Vice President of Research
* I led the efforts to build GPT-2 and GPT-3 (https://arxiv.org/abs/2005.14165).
* I am one of two people who sets overall research direction at OpenAI and writes its annual research roadmap.
* In addition I have built and lead several teams focused on long-term safety research, including how to make AI systems more interpretable and how to embed human preferences and values in future powerful AI systems. Vice President of Research Dec 2019 - Dec 2020 · 1 yr 1 mo
Research Director
Research Director
Team Lead for AI Safety
Team Lead for AI Safety
Senior Research Scientist
I worked as a deep learning researcher on the Google Brain team, working to extend the capabilities of neural networks. I have also worked on the safety and reliability of AI systems, and recently published a paper (with Chris Olah and several other researchers) laying out some key problems for preventing accidents in AI systems. Our work (https://arxiv.org/abs/1606.06565) is described here (https://www.technologyreview.com/s/601750/google-gets-practical-about-the-dangers-of-ai/).
Research Scientist
I work with Andrew Ng and a small team of AI scientists and systems engineers to solve hard problems in deep learning and AI, including speech recognition and natural language processing.
* Performed the majority of machine learning research for Deep Speech 2 (http://arxiv.org/abs/1512.02595), listed by MIT Technology Review as one of the top 10 technological breakthroughs of 2016 (https://www.technologyreview.com/s/600766/10-breakthrough-technologies-2016-conversational-interfaces/)
* Conceived, prototyped and implemented a neural network architecture that achieved a 35% relative reduction in the word error rate (WER) of the lab's English speech system.
* Conceived, prototyped and implemented a neural network architecture that achieved a 15% relative reduction in the WER of the lab's Chinese speech system.
* Increased speed of decoder by 10x in Chinese and English, removing a key obstacle to deployment of the lab's speech system to Baidu users.
* Increased speed of training for Chinese system by 27% and reduced memory usage by 50%, resulting in a substantial increase in lab-wide productivity and computational efficiency.
* Co-developer (with Jim Fan and Jesse Engel) of Baidu's internal neural network library
* Awarded "Star Employee of the Quarter" for Q2 2015.
Software Developer (part-time)
Skyline is a comprehensive software suite essential to the research of a large fraction of the proteomics community: 30,000 unique installs, 7000 instances/week, 300K lines of C# code, 10 professional programmers
• Personally contributed 14K lines of code (net, human-generated) + 50 pages tutorials and docs.
• Invented and implemented peak picking, binary classification, Storey-Tipshirani, and signal processing algorithms for mass spectrometry data, now widely employed by Skyline’s user base.
• Instructor at weeklong course (Zurich, February 2014) attended by 40 on how to use Skyline, invited to teach second course in Barcelona (October 2014).
• Featured speaker at Skyline user group meeting (June 2014, attendees: 250), and IMSC Work-
shop (August 2014, attendees: 100+)
• Regularly handle support requests from Skyline users (3-4 requests/week)
Postdoctoral Scholar
- Researching improved computational methods for acquisition and analysis of high-throughput protein expression data (C#, Python, Matlab).
- Invented, developed, and implemented protein detection and statistical confidence algorithms and tools that have driven the field-wide adoption of next-generation high throughput mass spectrometry.
- Developed multiplexing technique that increases peptides detected per run by 25%.
- Contributed to open source proteomics software projects, including Skyline and Proteowizard.
- Developed method for distinguishing cancer cells from normal cells using biophysical modeling and linear classification.
Consulant
• Help invent and develop breakthrough products for industry and government.
• Design and conduct simulations to address unusually difficult physics and math problems.
• Experience with electromagnetic devices, complex mechanical systems.
PhD Student in Computational Neuroscience
- Dissertation awarded Hertz Doctoral Thesis Prize, 2012.
- Co-authored grant proposal leading to McKnight Technological Innovations in Neuroscience Award (4 awards given out of 97 proposals).
- Developed Hopfield-style statistical models for real (biological) neural networks in retina and cortex (implemented in C/Matlab).
- Helped develop novel algorithms to solve neural mixed-signal (spike sorting) problems at 300 neuron scale (C/C++). Performed first ever recording of a complete 0.5x0.5mm patch of retina.
- Co-invented and developed novel device for in vivo intracellular recording, personally built initial prototypes in fabrication facility.
Geophysicist
• Invented and developed highly mathematical techniques in computational seismology.
• Published non-confidential portions of my research in Physical Review E.
• Sole author on Schlumberger confidential report (35 pages) on remaining research.
• Wrote 5000 lines of Matlab, Fortran to implement theoretical work.
Research Intern
• Designed robust, fault-tolerant, highly mathematical algorithms for parallel distributed systems.
• Participated actively and independently in whole development pipeline, from theoretical algo-
rithm development to software simulation to building a physical network of microcontrollers.
• Results published in confidential AMI report.
PhD
PhD - (Bio)physics
BS
BS - Physics
Progress towards BS
Progress towards BS - Physics
A foundation in theoretical physics and computational neuroscience, leading to extensive expertise in modern AI/ML development.
Machine Learning
Algorithms
Mathematical Modeling
Deep Learning
AI Safety
Python
C#
Matlab
Simulations
LaTeX
Physics
Bioinformatics
Computational Neuroscience
Proteomics