Issue #10: ML in Finance, Disinformation, AI Superpowers

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This edition has 879 words which will take you about 5 minutes to read.

“We’re going to need technological solutions, but I don’t think they’re going to solve the problem.”  – Hany Farid

Data Exchange podcast

  • Identifying and mitigating liabilities and risks associated with AI   As AI and machine learning become more widely deployed, lawyers and technologists need to collaborate more closely so they can identify and mitigate liabilities and risks associated with AI.  Andrew Burt, is the Chief Legal Officer at Immuta and co-founder and Managing Partner of BNH.ai, the first law firm run by lawyers and technologists focused on helping companies identify and mitigate those risks.
  • How machine learning is being used in quantitative finance  Quants use ML alongside other modeling techniques. While ML clearly makes sense for extracting signals from unstructured and nontraditional sources of data, I’ve long wondered as to how much quants use modern tools like deep learning or reinforcement learning. In this episode, Arum Verma, Head of Quantitative Research Solutions at Bloomberg describes the growing use of ML in finance.  

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Machine Learning tools and infrastructure

Virtual Conferences

  • Hany Farid at the Spark+AI Summit   The battle between teams that generate and detect deepfakes has profound implications for the general public and for policy makers. Hany Farid is considered by many to be the “father of digital forensics”, a field that now finds itself at the center of the battle against deepfakes. At this year’s conference, he will provide an overview of the creation of deep fakes, and he will also describe emerging techniques for detecting them. This is a FREE event, register here.
  • Pulsar Summit   A free virtual conference centered on Apache Pulsar, a fast-growing messaging system originally developed by Yahoo. This must attend event for data engineers and data architects takes place June 17-18.
  • The Future of Transfer Learning in NLP  A survey talk by Thomas Wolf of Hugging Face, one of the most popular natural language processing libraries, and one of my favorite open source tools. The beauty of Hugging Face is it makes all the complex research that Thomas describes in his talk, available to developers. Hugging Face engineers learn and implement all the cutting edge research, in turn, developers benefit through their easy-to-use libraries. Learn how to fine-tune language models by attending the free event – The Road to AutoML – register here.

Work and Hiring

[Image: Entering the Vortex 1 by Dean Wampler.]

Recommendations

    • Verification Handbook  A free ebook with contributions from journalists who have had to wade through user-generated content. The list of verification tools and strategies in this book is worth scanning. We are awash in disinformation and we are about to start a contentious phase in the Presidential election campaign in the US. 
    • Computer algorithms that scan everything spit out flawed tenant screening reports   You’ve heard me talk about the importance of model governance and for tools for managing risks in ML, it’s precisely because models are being used in many important settings. Granted this example is less about models but more about data unification, companies in this space probably depend on entity resolution tools that combine rules and models.
    • Symbolic Mathematics Finally Yields to Neural Networks  An accessible overview of recent research into the use of machine learning to solve complex integrals and differential equations. While there is much to be excited about, these are preliminary results. The deep learning based solver only tackled equations with one variable and only involved elementary functions. The results are promising enough that I expect more researchers to focus on applications of ML to symbolic math. Look for AI to augment research mathematicians – not just Wall Street quants and traders – in the near future!
    • Protesters sing Lean on Me The late Bill Withers is watching over all of us.