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Artificial Intelligence Applications, Research, and Economics
1. Ikhlaq Sidhu, content author
Ikhlaq Sidhu
Founding Faculty Director
Sutardja Center for Entrepreneurship & Technology
Department of Industrial Engineering & Operations Research
IEOR Emerging Area Professor Award
Artificial Intelligence
Applications, Research and Economics
4. Ikhlaq Sidhu, content author
Lots of Students
Our Model Brings Bay Area Executives and Entrepreneurs into the Classroom
1600 Undergraduates
100 Ph.D / Graduate Students
100 Executives
10 Global Partners
Michael Marks, KKR, former CEO, Flextronics
Shomt Ghose, Venture Partner, Onset Ventures
Udi Manber, VP Engineering, Google
Marc Andreesen, Founder, Netscape
Larry Baer, COO, San Francisco Giants
Amine Haoui, CEO, Sensys Networks
Stacey Lawson, Founder In Part, Executive Seibel
Jim Davidson, Managing Director, Silverlake Partners
Donna Dubinsky, Former CEO, Palm
Matt Caspari, co-founder, Aurora Biofuels
Richard Gorman, SVP, Siebel Systems
Mike Olson, founder and CEO, Cloudera
Brodie Keast, EVP, TiVo
David Ladd, Managing Director, Mayfield
Jeff Miller, CEO, Documentum
Eva Miranda, SVP, Sony Corporation
Ravi Mohan, Managing Director, Shasta Ventures
Ted Hoff, Inventor,of the Microprocessor
Nat Goldhaber, Managing Director, Claremont Creek Ventures
Peter Thiel, co-founder and CEO, PayPal
Victoria Hale, founder and CEO, Medicines 360
Steve Newcomb, founder , Powerset (part of Microsoft’s BING)
Pehong Cheng, CEO, Broadvision
We focus on the Mindsets & Behaviors needed for Innovation and Entrepreneurship
(in context of technology change)
14. Ikhlaq Sidhu, content author
Perfect Information vs. Real World
fully observed uncertain
discrete multi-agentsingle agent
infinite time horizon
continuous
finite
Ken Goldberg UC Berkeley
Even then, AI Cannot Solve Solve Real Life Problems Better Than Humans
And in fact, AI Can not even Work without Humans
Ken Goldberg
Leading AI
Researcher at
Berkeley
Professor and
Department Chair,
IEOR
William S. Floyd Jr.
Distinguished Chair
18. Ikhlaq Sidhu, content author
Autonomous Driving and Driver-Assist
•Communicating intent
•Driver-in-the-loop modeling
•Two-way learning: knowledge
transfer between vehicle and
driver
•Safety in autonomous and
assisted driving
Principal investigators:
Ken Goldberg
UC Berkeley
Anca Dragan
UC Berkeley
Trevor Darrell
UC Berkeley
Francesco Borrelli
UC Berkeley
Ruzena Bajcsy
UC Berkeley
Source: Ken Goldberg, CPAR, People and Robotics Initiative
22. Ikhlaq Sidhu, content author
AUTOMATION HAS BEEN
CHANGING THE JOB
LANDSCAPE FOR MANY YEARS
Over many decades:
– Routine jobs (manual or cognitive)
have declined.
– Only non-routine jobs have continued
to grow. (Source: Economist)
Now: The most famous study on Job Loss
and AI, by Carl Frey and Michael Osbourne,
predicts that 47% of the workforce is in
danger.
Automation and anxiety
Economist
24. Ikhlaq Sidhu, content author
PWC: Defining AI
- Economy to Grow $15.7T USD by
2030
- early value from productivity
(2017-2024) – $6.6T
- later value from increased
consumption (2024+) $9.1T
intelligence:
• Automated intelligence: Automation of
manual, routine tasks
• Assisted intelligence: Helping to perform
tasks faster and better
• Augmented intelligence: Helping people
to make better decisions
• Autonomous intelligence: Automating
decision-making processes without human
intervention
Figure 1: The scope of artificial intelligence
Hardwired/
specific
systems
Adaptive
systems
PwC Data &
Analytics
Human-in-the-loop No Human-in-the-loop
Assisted intelligence
AI systems that assist humans in making decisions or
taking actions. Hard-wired systems that do not learn
from their interactions.
Automation
Automation of manual and cognitive tasks that are
routine. This does not involve new ways of doing things
– automates existing tasks.
Augmented intelligence
AI systems that augment human decision making
continuously learn from their interactions with humans
and the environment.
Automation intelligence
AI systems that can adapt to different situations and
can act autonomously without human assistance.
6 The economic impact of artificial intelligence on the UK economy
Figure 3: Where will the value gains come from with AI?
During the first phase of the impact (2017-2024),
productivity growth could account for a relatively
larger share of the gains than the period that
follows, when the consumption-side impacts are
likely to dominate. This is due to the fact that it
takes time for firms to enter the marketplace and
supply new varieties of AI-enhanced products to
consumers following the stimulation in consumer
spending from higher real wages and initial product
improvements. As this takes place, competition
The potential for artificial intelligence to impact the
UK economy is slightly higher compared to the
potential in Northern Europe more generally. Our
recent report5
assesses the global potential for AI
and the likely impact for regional economies. The
analysis concludes that GDP in Northern Europe
could be up to 9.9% higher in 2030. The UK could
see larger gains as a result of having stronger
foundations in technology already – many
technology companies have their EMEA
Phase 1: Productivity-driven impact Phase 2: Consumption-side impacts dominate
£billion
0
50
100
150
200
250
2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Labour productivity Personalisation Time saved Utility
The transition timing is still a big factor
This time, it will be 10X faster and scale of 300Xthan the last
industrial revolution. We have not seen this level of displacement
before. Source McKinsey