Dario Amodei

Combining scaling and long-term AI alignment methods to build steerable, interpretable, and robust AI systems.

* 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

Team Lead for AI Safety

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/).

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.

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)

- 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.

• 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.

- 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.

• 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.

• 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 - (Bio)physics

BS - Physics

Progress towards BS - Physics