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.

1. Presented to the
National Conference of Lawyers and CPA’s
December 11, 2017
Warren E. Agin, Esq.
Analytic Law, LLC
AnalyticLaw.com

2. Change is Coming
The Past
The Present
The Future
Predictions

4. Richard Susskind
The Future of the
Professions: How
Technology Will
Transform the Work of
Human Experts

7. Myth number one:
AI is Artificial
“Intelligence”

8.  Myth number two:
The Singularity is
Coming

9.  Myth number three
An AI Winter is
Coming.

10. 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

11. 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:

14. We examine the past,
view the present, and
look at the future to
understand how
machine learning is
about to transform
professional services.

17.  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

18.  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

19.  Backward propagation advances
neural networks
 Expert systems commercialized
 Data exploring techniques
advance
 Random forest decision trees
introduced
 Support vector machines
introduced

20.  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

21.  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

26. 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.

27. 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

28. This is a Samsung Galaxy
7. It is more powerful
than an Intel Paragon XP-
S-140.

29. Some desktop computer
processing speeds:
1990 – 32 Mhz
2000 – 1 Ghz (Pentium III)
2017 – 3.4Ghz (AMD
Ryzen)

30. Machine learning
techniques require large
data sets to learn because
they mostly learn from
“experience.”
 Today, because
everything is “online,” we
have that data.

31.  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

33. 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

34. Fully autonomous cars
in active commercial
use in Phoenix.
The computer fits in the
back trunk.

39. Or – so you still
think you can
outperform a
machine?

40. Ken Jennings had
won 74 consecutive
Jeopardy games,
while Brad Rutter
amassed the game’s
largest ever jackpot.

41. 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.

42. Considered the
world’s most
strategically
complex game.

43. 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.

44. 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.

45. Tasks Voice recognition
Sort documents into categories
Find clauses in documents
Predict linear relationships
Identifying common behaviors
Simple Applications

46. 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

47. Vendors

48. Vendors

49.  “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

50. Firms funding or
incubating legal
technology

51. Firms that provided
speakers at the AI
for Professional
Services conference
in the UK in
November

53. This is the big
question. But
maybe it’s the
wrong question.

54. 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

57. 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

58.  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

59.  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

60.  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

61.  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.

62. 40 years according to a
2016 study of AI
researchers’ opinions
about when machine
learning systems will
reach human level
competence in various
fields

63. 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

64. Computers won’t
replace human
professionals
because each brings
something to the
table

65. In a 2011 study at
MIT, hybrid predictions
of outcomes out-
performed either
humans or algorithms.

67. “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.

68. “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

69. 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

72.  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

73.  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

74.  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

75.  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

76. John von Neumann
1948