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AI and the Professions: Past, Present and Future


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A presentation to the National Conference of Lawyers and CPA’s - December 11, 2017. Describes the history of AI, explains why the legal and accounting professions are at a turning point, and predicts changes in the professions from AI adoption.

Analytic Law, LLC helps law firms and departments discover how to solve legal problems using analytic techniques, including data analytics, prediction systems, machine learning, game theory and behavioral economics.

Published in: Law
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AI and the Professions: Past, Present and Future

  1. 1. Presented to the National Conference of Lawyers and CPA’s December 11, 2017 Warren E. Agin, Esq. Analytic Law, LLC
  2. 2. Change is Coming The Past The Present The Future Predictions
  3. 3. Richard Susskind The Future of the Professions: How Technology Will Transform the Work of Human Experts
  4. 4. Myth number one: AI is Artificial “Intelligence”
  5. 5.  Myth number two: The Singularity is Coming
  6. 6.  Myth number three An AI Winter is Coming.
  7. 7. ML Techniques Regression Expert systems Neural networks k-Nearest neighbor Support vector machines Decision trees (random forests) Math Techniques and Computer Algorithms that Do Useful Things
  8. 8. ML Can Group objects into categories Discover categories of objects Discover and remember relationships between objects Find the best path through layers of decisions For example:
  9. 9. We examine the past, view the present, and look at the future to understand how machine learning is about to transform professional services.
  10. 10.  Alan Turing invents the Bombe, the first universal computing machine  McCulloch and Fitts publish “A logical calculus of ideas immanent in nervous activity  Neumann and Morgenstern invent game theory
  11. 11.  The first program with machine learning – checkers  Frank Rosenblatt introduces the perceptron  Solomonoff introduces bayesian methods  Weizenbaum creates the first NLP system – ELIZA  Edward Feigenbaum creates the first expert based system
  12. 12.  Backward propagation advances neural networks  Expert systems commercialized  Data exploring techniques advance  Random forest decision trees introduced  Support vector machines introduced
  13. 13.  IBM Watson wins at Jeopardy  AlphaGo defeats Ke Jie at Go  IBM Watson out performs humans at diagnosing cancer  Facebook’s facial recognition system reaches human levels of performance  ML as a service from IBM Watson, Amazon and Microsoft
  14. 14.  The foundations for machine learning are from the 1940’s – universal computing machines and structures to allow computer software to perform “human” tasks  The basic techniques – perceptrons, expert systems, bayesian methods, clustering algorithms – were invented in the 1960’s  By the turn of the century we already had sophisticated machine learning based algorithms  But, the impact on our professional life was very small
  15. 15. Machine learning techniques are computationally expensive. They require massive computing power, and systems with enough memory to store and manipulate large data sets. This was the greatest barrier to building and commercializing useful machine learning systems.
  16. 16. This is an Intel Paragon XP-S- 140. Hottest supercomputer in 1994. If you were a data scientist working on machine learning in 1994 you only wished you had access to this much power
  17. 17. This is a Samsung Galaxy 7. It is more powerful than an Intel Paragon XP- S-140.
  18. 18. Some desktop computer processing speeds: 1990 – 32 Mhz 2000 – 1 Ghz (Pentium III) 2017 – 3.4Ghz (AMD Ryzen)
  19. 19. Machine learning techniques require large data sets to learn because they mostly learn from “experience.”  Today, because everything is “online,” we have that data.
  20. 20.  In 2014 IDC predicted that available data will grow by 40% a year into the following decade – doubling the amount of available information every two years.  By 2020 37% of that data will be in formats usable by machine learning systems Eric Schmidt of Google – August 4, 2010
  21. 21. Car is packed top to bottom – back to front – with computing gear Note the external air conditioner on top to keep the equipment from over heating
  22. 22. Fully autonomous cars in active commercial use in Phoenix. The computer fits in the back trunk.
  23. 23. Or – so you still think you can outperform a machine?
  24. 24. Ken Jennings had won 74 consecutive Jeopardy games, while Brad Rutter amassed the game’s largest ever jackpot.
  25. 25. Humans had failed to identify the disease after months of work. Once the patient’s genetic information was provided Watson, it was able to provide a diagnosis in ten minutes.
  26. 26. Considered the world’s most strategically complex game.
  27. 27. DeepStack, from the University of Alberta, defeated eleven professional players at heads-up no-limit Texas Hold ‘Em. DeepStack can run on a standard gaming laptop at real time speeds. Designed at Carnegie Mellon the program beat four top-ranked professional poker players over 20 days of play.
  28. 28. So, where are we today in using machine learning in the legal and accounting fields? No one has invented the artificial lawyer or accountant yet.
  29. 29. Tasks Voice recognition Sort documents into categories Find clauses in documents Predict linear relationships Identifying common behaviors Simple Applications
  30. 30. Watson Natural language interface Natural language classifiers Tone analyzer Language translators Many other tools Amazon Chat bot building Image recognition Regression models Other ML tools Machine Leaning as a Service
  31. 31. Vendors
  32. 32. Vendors
  33. 33.  “Tax is basically just a big AI problem, it’s all about rules and data, it’s about matching one thing with another.”  ‘We’re taking a 15 hour project for one of our staff down to about three seconds…” Harry Gaskell, UK CIO, Ernst & Young
  34. 34. Firms funding or incubating legal technology
  35. 35. Firms that provided speakers at the AI for Professional Services conference in the UK in November
  36. 36. This is the big question. But maybe it’s the wrong question.
  37. 37. They Can Drive a car Play poker Recognize someone you meet on the street Read a book (or millions of books) and find a relevant passage Review documents for specific things (faster and with higher accuracy) Remember things Do math
  38. 38. Humans Already designed Years to train skills Years to build knowledge Very flexible Work slowly Get only one human Machines Years to design Train skills quickly Builds knowledge quickly Inflexible Work quickly Once trained, easy to duplicate
  39. 39.  Machine learning systems need to be built for each use case. For complex use cases, this can take years and the process requires human expertise.  Machine learning systems can’t adopt to new situations – humans need to do work to adopt a system to a new use case.  Machine learning systems can require more work and investment up front. Machine learning systems are single use systems
  40. 40.  Google designed AlphaGo Zero to learn to play Go by playing itself over and over again.  After three days AlphaGo Zero beat the original AlphaGo 100 games to 0.  Waymo cars have logged more than three million miles to reach their current capabilities – enormous human labor is also needed in the training process. Machine learning systems can train new skills as fast or faster than humans But not always
  41. 41.  Machine learning systems once built can outperform humans consistently  Machine learning systems can process documents, and data, at electronic speeds. Machines can process information faster and more accurately than humans
  42. 42.  Each human needs to go through a training process  For machines, once the development and training process is done, you do not have to do it again.  Instead of training 1,000 accountants to do something, you just have to build one machine. Once developed, a machine learner is developed.
  43. 43. 40 years according to a 2016 study of AI researchers’ opinions about when machine learning systems will reach human level competence in various fields
  44. 44. Predictions from 2016 Survey  Poker – by 2019 (already achieved)  Write a high school essay – by 2026  Play Go – by 2028 (already achieved)  Write a NY Times bestseller – by 2051  Any human intelligence task – by 2060
  45. 45. Computers won’t replace human professionals because each brings something to the table
  46. 46. In a 2011 study at MIT, hybrid predictions of outcomes out- performed either humans or algorithms.
  47. 47. “If you merge millions of games played by computers with high-caliber human games, you get something that is quantitatively and qualitatively superior to any commercial product…” Nelson Hernandez, Freestyle Chess Player Teams of humans working with chess playing computing programs can outperform the programs alone.
  48. 48. “More and more asset managers are realizing that, when combined with human input, systematic, IE rules-based investing, can improve risk-adjusted performance by removing harmful emotions from the decision process while keeping useful human judgment in the equation.” Pierre E Mendelsohn, Founder and CEO, Alpima
  49. 49. Pitted People v a Random Tree Algorithm at Predicting Supreme Court Decisions Legal Expert Alone 60% Crowd 70% SuperCrowd 84.29% Algorithm 70.9% Crowd + Algorithm Predicted to outperform either alone Run by Daniel M. Katz and Michael Bommarito
  50. 50.  Investment in machine learning systems for legal applications is being accelerated by funding for legal tech generally  Hundreds of new companies being started – most in narrow verticals or horizontals  Baring a market crash, this trend will accelerate, creating large numbers of entrepreneurs trying to “solve” discrete problems in law and accounting  Most will fail commercially, but the effort will generate new sets of machine learning based tools  Competitive pressures between firms, with alternative service providers, with clients, and between industries will drive innovation • Lexis buys Lex Machina • ROSS Intelligence raises $8.7m in venture funding • $10.5m venture round for Atrium LTS • $1 million from Greylock Ventures for DoNotPay
  51. 51.  Development and training is needed to build out a machine learner for a specific task.  The question is: which tasks will automate first?  Simple tasks will automate before complex tasks.  Highly repetitive tasks will automate before infrequent tasks.  Data rich tasks will automate before tasks without data capture  High value/low risk tasks will automate before low value or high risk tasks  Tasks alternative providers can do will automate before those only law firms and accounting firms can do. Machine learning technology is currently capable of replacing many tasks performed by professionals
  52. 52.  Professionals will no longer do the work.  Professionals will define the work – addressing changes in the law, accounting methods, and business practices  Professionals will build and manage the systems that do the work.  Professionals will address the “edge” cases, where building machine learning systems is not cost effective. Now professionals define, manage, and do the work
  53. 53.  Will more services devolve to alternative legal providers and outsourcing agencies (like Turbotax for consumer tax work)?  Will larger clients find it more effective to centralize legal processes?  What might centralization and predictive tools mean for negotiation and dispute resolution processes?  As we adjust how we work to accommodate machine learning, how will that change the legal and accounting professions? Machine learning and prediction will change what services clients need
  54. 54. John von Neumann 1948