AI and Future of Professions

Jeffrey Funk
Technology Consultant
Retired Associate
Professor
For information on other presentations, see
http://www.slideshare.net/Funk98/presentations

Artificial Intelligence Continues to Improve,
Supported by Moore’s Law and Growing Internet

Error Rate for Image Recognition Performance
has Fallen since 2010*
*Measured by ImageNet classification
https://srconstantin.wordpress.com/2017/01/28/performance-trends-in-ai/
Chess Performance
(ELO Ratings) Rose
Between 1990 and 2014
(50% better)

Error Rate
for
Automated
Speech
Recog-
nition
(ASR)
Has Also
Fallen
https://srcons
tantin.wordpr
ess.com/201
7/01/28/perfo
rmance-
trends-in-ai/
Automated
Chat Boxes
Have
Become
Common on
Websites

ELO ratings for best computer chess engines
rose from 2000 in 1990 to 3300 by 2014
https://srconstantin.wordpress.com/2017/01/28/
performance-trends-in-ai/
Didn’t even double

 Will AI, in combination with Moore’s Law,
Internet speed, cloud computing, and Big Data,
lead to high levels of unemployment?
 Will it Require Basic Guaranteed Income?
 Or will it merely change the required skills, as
computers and Big Data have done?
• Leading to better productivity enhancing tools that must
be mastered by professionals

 Not as rapid as Moore’s Law
• Doubling every 18 months versus no doubling in 14 years
 These improvements are also less relevant than
Moore’s Law
• Not all applications require better image or speech
recognition, or even skills at playing chess
• They require different types of skills– but what are they?
 We shouldn’t expect AI to have the impact that
Moore’s Law had on computers

 Are numbers of them growing? Will they continue to
grow?
 How have computers (and Cloud Computing, Big
Data) impacted on their work over last 30 years?
• What types of new skills have emerged?
 How might impact of AI be different?
• Are algorithms or machine learning different from previous
improvements?
• What types of new skills might be required?

Routine Non-Routine
Manual Assembly line:
mostly automated
Nail Salon; probably
won’t be automated
Cognitive Many Jobs are
being Auto-
mated!!!!
Hardest to Automate.
Where you want to be
Easier to make computers exhibit adult (calculations)
than child (perception and mobility) behavior
Low-level sensor motors still require much
computational resources

Most Job Growth is for Non-Routine Cognitive -
Routine Cognitive Work is Flat
http://moderndiplomacy.eu/index.php?option=com_k2&view=item
&id=2094:trump-vs-the-robots-us-jobs-and-promises&Itemid=206

http://moderndiplomacy.eu/index.php?option=com_k2&view=item
&id=2094:trump-vs-the-robots-us-jobs-and-promises&Itemid=206
Unemployment Rate Much Higher for
Routine than Nonroutine Cognitive

 Legal
 Journalism
 Accounting
 Architecture
 Engineering

http://leiterlawschool.typepad.com/leiter/2015/03/the-absence-of-
evidence-for-structural-change-growth-in-lawyer-employment-
and-earnings-michael-simko.html
The Total Number of Employed Lawyers
has Grown Since 1976

ABA: American
Bar Association
BLS: Bureau of
Labor Statistics
http://www.mybudget360.com/la
w-school-bubble
-law-tuition-law-degrees-in-
bubble-applications-down/
Employed
Passed
bar exam
Law School
Graduates
Millions of Graduates, Passed Bar Exam,
and Employed Lawyers

Leading to Low Salaries for Some Graduates
How can
they repay
$120K for
Law School
Tuition?

https://openlegalservices.org/2016/09/good-news-unemployment-among-new-lawyers-is-finally-decreasing/
The Problems Continue
Supply
Demand

https://openlegalservices.org/2016/09/good-news-
unemployment-among-new-lawyers-is-finally-
decreasing/
Higher Supply than Demand
Leads to “Surplus” (Red
Line) and Unemployment
(Blue Line)
Leading to a “Surplus” of New Law Graduates

http://www.thefacultylounge.org/2014/04/2-supply-how-many-law-students-will-
graduate-in-2017-2018.html
Recent Low Number of Law School
Entrants Suggests that the Supply is
Finally Falling (30% drop)

 Old work involved paper
• Asking clients to fill out forms
• Now done with online
questionnaires
 Large legal cases involve
researching legal precedents,
which can now be done with
computer searches
 Computers and AI will
continue to change legal jobs
Richard and Daniel Susskind, The Future of the Professions, Oxford University Press

 Computers can “learn” some tasks
• Finding and scanning relevant documents
• Judging simple cases in which rules are clear and facts
are not disputed
 More difficult for computers to “learn” less
routine and more ambiguous tasks
• writing legal briefs
• negotiating and appearing in court
 Likely scenario: low-level jobs will be
eliminated in UK and US, but other jobs will
be created
https://www.ft.com/content/f809870c-26a1-11e7-8691-d5f7e0cd0a16?mhq5j=e2
https://badplatitude.wordpress.com/2017/02/19/reverse-engineering-claims-of-ais-impact-on-lawyer-jobs/

 Ravn extracts data from official title deeds
produced by UK Land Registry
• Serves legal notices on correct property owners in
real estate cases
 LinkRFI analyzes 16 UK and European
regulatory registers to check client names
for banks
 Luminance’s machine-learning technology
enables lawyers to see all governing law in
clauses within global sales contracts
https://www.theatlantic.com/magazine/archive/2017/04/rise-of-the-robolawyers/517794/. https://www.ft.com/content/f809870c-26a1-
11e7-8691-d5f7e0cd0a16?mhq5j=e2; https://www.nytimes.com/2017/03/19/technology/lawyers-artificial-intelligence.html

 Lex Machina analyzes millions of court
decisions to help law firms
• Determine which judges tend to favor plaintiffs,
summarize legal strategies of opposing lawyers
based on their case histories, and determine
arguments most likely to convince specific judges
 Lexoo facilitates outsourcing of legal work
• Uses data and algorithms to match prices from
experienced and self-employed lawyers with work
for mid-size companies

 DoNotPay asks questions about parking ticket and
generates message to appropriate agency
• Were signs clearly marked?
• Were you parked illegally because of a medical emergency?
 Apply to deportation, bankruptcy, divorce
disputes?
• They involve lengthy and confusing statutes that have been
interpreted thousands of times
• All exceptions, loopholes, and historical cases can be
analyzed to determine best path forward
 Robolawyers could help address needs of poor

Law schools should be helping students
• understand changes in work
• how to use existing and future tools
• how to develop next generation ones
Law schools should also be helping
students understand the larger picture
• What types of overall value can lawyers provide
society?
• How can lawyers help reduce litigation time and
cost?

http://www.thepassivevoi
ce.com/2016/06/internet-
publishers-vs-
newspapers/
Employment in Newspapers
vs. Internet Publishing

Newspapers
Motion Picture and Video Production
Periodicals/Magazines
Books
Internet Publishing and Broadcasting
Employment is Changing from Newspapers,
Books, and Magazines to Internet Publishing
and Motion Pictures

 Number of articles written by
robots is growing rapidly
• Just input facts and let algorithm
write paper
 Associated Press (AP) creates
more than 3,000 financial
reports per quarter
 Some estimate that 90 percent
of news could be
algorithmically generated by
the mid-2020s
http://www.nytimes.com/2015/03/08/opinion/sunday/if-an-algorithm-wrote-this-how-would-you-even-know.html?rref=opinion
&module=Ribbon&version=origin&region=Header&action=click&contentCollection=Opinion&pgtype=article

https://onlinejournalismblog.com/2015/01/07/the-
metajournalist-and-the-return-of-personalised-news-
research-on-automated-reporting/

1. Marketing Managers
2. Customer Service Representatives
3. Public Relations Specialists
4. Executive Secretaries and Administrative
Assistants
5. Editors
6. Writers and Authors
7. Sales Representatives and Miscellaneous
Services
8. Market Research Analysts
9. Public Relations Managers
Journalism
Majors are Also
Looking
Elsewhere for
Work
Top Ten Occupations by
Interest from Journalism
Majors
/http://blog.indeed.com/2016/06/16/jobs-data-insights-future-of-
journalism

http://blog.indeed.com/2016/06/16/jobs-data-insights-future-of-journalism/
Should Journalism Majors Try
to be Content Marketing Roles?

 New systems include templates
 Journalists choose topic and system
• accesses data sources
• identifies key trends in data
• Learns to do this better over time
 Journalist chooses direction of article
and adds key phrases
 System writes article about trends and
updates data analysis for future articles
https://www.wired.com/2017/02/robots-wrote-this-story/

 Washington Post (acquired by Jeff Bezos in
2013) Editors use Heliograf
 For an area of interest
• Hook Heliograf up to source of structured data
(e.g., election data from VoteSmart.org)
• Create narrative templates for stories, including
key phrases that account for different potential
outcomes
• Heliograf identifies relevant data, matches it to
corresponding phrases in template, and then
publishes different versions across different
platforms

 In November 2012,
• it took four employees 25 hours to compile and post
just a fraction of election results manually
 In November 2016,
• Heliograf created more than 500 articles, with little
human intervention, that had more than 500,000 clicks
 Future
• use Heliograf to keep data in stories up-to-date
• E.g., someone shares a Tuesday story on Thursday,
and the facts have changed, Heliograf automatically
updates story with most recent facts

Systems automatically analyze data
• Government, open source, physical sensors
• Financial, demographic, climate, crime, sports,
traffic, marriage, birth, social media
• Identify trends and changes from historical
patterns
Journalists write articles using templates
• Input key phrases
• Systems build article around phrases
Systems learn to do this better over time
https://www.cjr.org/innovations/artificial-intelligence-journalism.php

 Should be helping students
• understand these changes
 Should also be preparing students for
broader types of work
• Marketing
• Public Relations
• Research Analysts
Or should they reduce student intakes
like Law Schools have done?

2015 Trends in the Supply of Accounting Graduates and the Demand for Public Accounting Recruits
Accounting Degrees Awarded
Have Seen Ups and Downs
Enron
WorldCom
Scandals

Hiring by CPA (Certified Public Accountant)
Firms has also Seen Ups and Downs

Number of people taking CPA Exam Also Has Ups and
Downs, but overall up from 1971

Since about ½ of Candidates Pass, Number Passing
Exam in 2014 about equalled number hired
45,000
Passed
and
43,000
hired in
2014,
But
over-
supply
in
previous
years

What Else is Happening? CPA Firms are Hiring More
Experts in Taxation, MIS, and Other
2015 Trends in the Supply of Accounting Graduates and the Demand for Public Accounting Recruits,

 Accounting continues to
become more automated
• Began with spreadsheets
• Moved to tax preparation
 Spreadsheets continue to
become more sophisticated
• Cash flow done with QuickBook,
Xero, Kashflows
• Compliance checked automatically
• Enables accountants to focus on
problem solving, like collecting
payments

https://www.wsj.com/articles/wesurvived-spreadsheets-and-well-
survive-ai-1501688765

 Automates most accounting functions
• Bank and credit card account feeds
• Invoicing
• Accounts payable
• Expense claims
• Fixed asset depreciation
• Purchase orders
• Other aspects of compliance
 Dramatically reduced cost of accounting
for small and medium size businesses
http://www.zdnet.com/article/how-artificial-intelligence-has-morphed-accountants-into-business-advisors/

 Compliances done with TurboTax, H&R
Block, At Home TaxACT
But even planning is threatened; planning
and compliance are different sides of same
coin
Compliance works forward from rules and
regulations while planning works
backwards from these rules and regulations

 Samples (chosen by heuristics)
used in past to minimize calculations
 Big Data enables software to analyze
100% of the data, and continuously
 Governments use software and big
data to assess tax returns, estimate
chances of fraud
• Many require original electronic
records, as opposed to paper
• Electronic invoices are harder to fake
than are paper ones
http://www.journalofaccountancy.com/issu
es/2014/apr/automated-audits-
20127039.html

 Machine Learning will automate some tasks,
pushing accountants to higher-level problem
solving
 Fraud Prevention
• Automatically monitor phone calls by traders for signs of
wrongdoing, such as insider trading.
• Machine-learning excels in spotting unusual patterns of
transactions, which can indicate fraud
 Risk Exposure
• Use real-time tracking of risk exposures to enable
companies to monitor capital requirements at all times
https://www.economist.com/news/finance-and-economics/21722685-fields-trading-credit
-assessment-fraud-prevention-machine-learning

 Connect different types of data to
produce financial reports
 Take data directly out of a client's bank
account, to produce financial reports
right through to tax returns
 Better visualize data with dashboards
 Accountants can spend less time on
compliance and more time on higher-
level problem solving
http://www.zdnet.com/article/how-artificial-intelligence-has-morphed-accountants-into-business-advisors/

 SMACC’s software uses more than 60 data
points to review receipts and invoices
 Checks whether math is accurate
 Verifies issuer with details like Value
Added Tax ID numbers
 Places receipts and invoices in proper
category
 When software has “learned” how to
handle each supplier, tasks are
subsequently handled automatically
https://www.thebalance.com/is-artificial-intelligence-the-future-of-accounting-4083182

 Huge degree of complexity in interpreting
and administering tax laws
• thus time consuming for accountants
 Machine “learning” can interpret
legislative and case law changes
• Identify where they are relevant to individual clients
• Make recommendations to clients
• Help companies more easily operate in multiple
countries
Machines will become better over time
https://www.acuitymag.com/technology/leveraging-artificial-intelligence-in-accounting

• Understand AI and their impact on accounting
work
• How to do higher-level problem solving
being the drivers of these changes
• New software tools require developers who
understand accounting
• Accountants can help develop next generation
tools

http://www.acsa-
arch.org/resources/data-
resources/acsa-atlas-project
Architecture Degrees Are Growing, but not
as Fast as Other Degrees
Master Degrees
Bachelor Degrees
18,000 in 2012
1970 1980 1990 2000 2010
All Degrees
9,000 bachelor
degrees in 2012

Number of Licensed Architects is Also Increasing,
but not at Fast as Number of Graduates
9,000 bachelor degrees a year means 90,000 in
last 10 years, 180 in last 20 years.
How many unemployed or under-employed
architects? More than 100,000?
http://www.archdaily.com/tag/ncarb

http://www.cons4arch.com/tag/recession/
$41,000 in 1990$
Salaries Have Grown Some, But not a Lot (1% per Year)

 Software eliminates
wooden models
• Use CAD and CAE ( VR and
AR below) to create more
design possibilities
• Input objectives and designs
are proposed
• Computations done
automatically to test more
radical designs

Reduce the need for plywood mock ups and allow architects
to change plans and allow clients an immersive view
3D CAD Enables Better Design

Examples of 3D CAD for Buildings
Left are real buildings, Right is 3D CAD

 View designs at multiple levels and evaluate
them in multiple ways
• Including new needs such as smart homes
 Do calculations
• Best layout of spaces
 Ensure compliance with safety and
environmental regulations
• Distances to exits
• Number of elevators
 Propose possible designs, different ways of
construction, new forms of urban planning
http://futurearchitectureplatform.org/news/28/ai-architecture-intelligence/

Examples of Different Levels, Different Views

 Architects work closely with clients, local
governments, local citizens and other entities
 Architects have access to open source designs
• Sketchup3d has one million designs
• Grab Cad has 660,000 designs
• Designs shared on many sites (e.g., Pinterest)
 Regulatory compliance done automatically
 Generative design is next step

 AI-based systems propose many possible
designs
• Goal is to preserve design intent, instead of
compromising on sub-optimal solutions
 AI-based system must include
• Logic of design
• Aesthetics
• Typography
 System learns to do this over time as
architects provide feedback on designs
generated by AI-system
http://archinect.com/features/article/149995618/the-architecture-of-artificial-intelligence

 AI-based systems can create thousands of
design variations based on inputs like
• sun exposure, views, or pedestrian movement
 Some of this technology comes from web
design
• Applies slight changes to color, title treatment, and
cropping of images
 Just as machine learning helped with web
design, it will help with architectural design
http://archinect.com/features/article/149995618/the-architecture-of-artificial-intelligence

Iris VR Architecture Demo Walkthrough
https://www.youtube.com/watch?v=BjEcA2p_mMY
Iris VR CAD can do even better:
Allowing Users to See the Inside of the Building

• understand these changes
• How AI can propose new designs and learn from
feedback
being the drivers of these changes
• New software tools for architecture require
developers who understand architecture
• Architects can help develop next generation
tools

 Legal
 Journalism
 Accounting
 Architecture
 Engineering
• Some data will be for STEM (Science,
Engineering,Technology, Mathematics)

Thousands of STEM
Workers in U.S. -
Growing Quickly
https://www.nap.edu/read/13467/chapter/5#40

https://www.nap.edu/read/13467/chapter/5#40
Still Growing
Now

IT is Much Bigger
than Engineering

IT Also Growing
Faster than
Engineering

https://en.wikipedia.org/wiki/Engineering_education
Mech Eng
Elec Eng
CS w/i Eng
Comp Eng
Civil Eng
Chem Eng
100,000
80,000
60,000
40,000
20,000
Annual Number of Engineering Graduates (B.S.)
Also Growing Quickly, per
2006 2008 2010 2012 2014

https://www.geekwire.com/2014/analysis-examining-computer-
science-education-explosion/
Also Growing Quickly with PC bubble in
1985 and IT bubble in 2003

https://www.timeshighereducation.com/features/what-should-we-be-teaching-the-next-generation-of-
computer-scientists#survey-answer
More Recent Data Shows Same Pattern

High Annual Salaries (Mean) for Engineers (and Computer)
Only Lower than Doctors, Dentists, and Lawyers

http://dpeaflcio.org/programs-publications/issue-fact-
sheets/the-stem-workforce-an-occupational-overview/
Unemployment Rates for STEM and All Occupations:
Much Lower than Other Occupations
All
occupations

 Calculators from late 1960s
 CAD and CAE from 1970s
• These changes have continued
and still continue
• Automation of calculations and
design
• Improving productivity of
engineers and expanding their
work
http://personal.stevens.edu/~ffisher/me345/how_software_changes_way_eng_work.pdf

 Better software keeps coming
• automates low-level design work
 Most engineering analysis has
been automated
• Mechanical Eng: fluid and heat flow
• Electrical Eng: chip and board
design
• Civil Eng: stress analysis
• Yet little de-emphasis by
engineering programs on
mathematical analysis
http://personal.stevens.edu/~ffisher/me345/how_software_changes_way_eng_work.pdf

 Lower cost software also
changes work
• Done online with software-as-a-
service
• Enables more design options to be
considered by small firms,
individuals, emerging economies
• For example, water flow analysis for
fish farms
 Enables more collaboration
• More data sharing and team work

 Generative Design is one big change that is
now occurring
 As with architectural design, new
engineering designs are proposed by a
CAD system
 Teams of designers input constraints and
system proposes designs
 System learns from feedback
 Airbus uses such a system from AutoCAD to
design structural parts
https://www.autodesk.com/solutions/generative-design

Proposed New Structural Partition on Airbus
Key constraint is weight
Minimize weight while providing sufficient strength

 Primarily utilizes 3D models and finite
element analysis for determining load paths
• specified geometries, supports, boundary conditions
and volumes
 3D model loaded into FEA software
• FEA software identifies load paths
• Software removes superfluous material from non-load
bearing areas
 Result is lighter part https://www.autodesk.com/solutions/generative-design

 Similar to topology
optimization
• Goal is weight minimization using
FEA solver and iterative 3D design
process
 Difference
• weight minimization by filling load-
bearing spaces and voids with
variable density meshes, not by
removing material
• Can system “learn” to propose
better designs

 A car was fitted with dozens of sensors
 Sensors recorded everything that was
happening to car during a drive,
including the forces to which it was
subjected
 Resulting data - literally billions of data
points - were plugged into AutoCAD’s
generative design tool
 Can system “learn” to propose better
designs over time
https://redshift.autodesk.com/machine-learning/

 Accelerate generative design by noticing
designers’ reactions to what systems
propose
 Incorporate unspoken preferences into
design process
 Prepare process instructions including
those for robots
 Use input from new digital nervous system,
also known as IoT, to perceive and react
intelligently to real world.
https://redshift.autodesk.com/machine-learning/

 Engineering schools are far ahead of other
professions
• Providing students with tools
• Electrical Engineering helps some students
understand the fundamentals of tools and develop
new ones
 But they rarely help students understand
drivers of change, nor how it impacts work
• Still too much focus on math and engineering
calculations
• Not enough high-level design and design projects

 Engineering schools need to place more
emphasis on high- than low-level work
 Design projects
 Conceptual design
 How to generate new ideas
 How to deal with systems
• Not just physical systems
• Economics of systems, including changing
economics
• Competition among firms in systems

 AI is impacting on many types of work
 Not just routine manual work
 It is now impacting on routine cognitive
work
• Lawyers
• Journalists
• Accountants
• Architects
• Engineers

 Employment data doesn’t suggest that
demand for work is falling
• Some mismatch of demand (number working) and
supply (number graduating)
• Journalism and lawyers may be most affected
 But work is changing
• From low-level to high-level work
• Low-level work is being automated
High-level work requires skills in
productivity enhancing tools

 For what types of tasks will machine
learning work well?
 First applications of AI were games such
as Chess, Go, and Jeopardy, which have
• very constrained situations
• involve many routine cognitive tasks that can be
better done by computers

But work of professions is more complex
• Fewer constraints means more complex
problems and more non-routine cognitive tasks
• Machine learning requires constraints in order
for feedback to occur
• Humans can provide feedback but this type of
learning will be slower
Thus, elimination of low-level tasks will
occur slowly, as professions move to
higher-level work

 They need to help students understand
work and how it is changing
 Emphasize
• high-level tasks more than low-level tasks
• job opportunities and challenges
 Help students
• Adapt and change
• Make better career decisions

 Professional work involves computers and
Internet
• When not in front of desktop or laptop computer,
they are connected to Internet via smart devices
 The future is high-level work
• Engineers, architects, scientists must do high-level
conceptual design because computers do drawings
and calculations
• Accountants and financial analysists must think more
strategically about a business because computers
do most calculations and even audits

 Students are taught very little about tools or
the likely demand for professions
• because tools and drivers of them are not published in
disciplinary journals that professors emphasize
 Professors follow disciplinary journals
because
• they are measured by publications in them
• journals purportedly represent core knowledge of
field
 Engineering programs generally do the best
• But they don’t cover future of tools and their drivers
• Even electrical engineering and computer science
programs discuss these tools only in special courses

 Help students better understand technology
change and its impact on work
• including free market, how it works, and technology
change
• Students should be learning about what is happening
now and what will likely happen in the near future
 These issues are not covered in history of
technology courses
• Recent, current and future changes are most relevant for
students
• Knowledge of industry and what is happening in
industry is essential for helping students understand
existing and future work

 Research and teaching addresses technology
change
• Impact of better integrated circuits, Internet speed and cost,
and smart phones on emergence of new types of products,
services, and content including new forms of productivity
enhancing tools
 Received NTT DoCoMo Mobile Science Award in
2004 for lifetime contributions to social science
aspects of mobile communication
 My course slides and group presentations are
available on my slideshare accounts.
• http://www.slideshare.net/Funk98/presentations;
http://www.slideshare.net/Funk97/presentations

 Internet of Things
http://www.slideshare.net/Funk98/sensors-mems-
internet-of-things
 Bio-sensors for health care data
http://www.slideshare.net/Funk98/bioelectronics-
biosensors-smart-phones-and-health-care
 Better displays
• http://www.slideshare.net/Funk98/the-future-of-
displays
 Virtual and augmented reality, wearable
computing
• http://www.slideshare.net/Funk98/wearable-
computing-and-human-computer-interfaces

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