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Artificial Intelligence Applications, Research, and Economics

GMIC Conference in Hong Kong, 2017.

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Artificial Intelligence Applications, Research, and Economics

  1. 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
  2. 2. Ikhlaq Sidhu, content author My Topics for Today: 1. Technical evolution of AI/ML 2. Berkeley research perspective 3. Job Loss, Economics, and Singularity vs Multiplicity
  3. 3. Ikhlaq Sidhu, content author My Perspective: Sutardja Center for Entrepreneurship & Technology College of Engineering, UC Berkeley Approach Berkeley Method: ØEntrepreneurship ØInnovation Leadership
  4. 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)
  5. 5. Ikhlaq Sidhu, content author • Detection of fake news • Prediction of long-term energy prices to solve Wall Street problem • Prediction applications stock market, sports betting, and more • AI for crime detection, traffic guidance, medical diagnostics, etc. • A version of Zillow that is recalculated with the effects of AirBnB income and many more… My newest course: IEOR 135 Applied Data Science with Venture Applications Sample Data-X Projects
  6. 6. Ikhlaq Sidhu, content author Data and AI Technology: Where is it going?
  7. 7. Ikhlaq Sidhu, content author Traditionally 2 Tasks: Classification & Predictive Scoring The most famous application has been recommendation: “which other user is most like you” Extracted Data often in Table Format Classification: Cats and Dogs, Speech Recognition Movie Recommendation Scoring: Credit Score, Movie Rating Heath Score, Any Isoquant…
  8. 8. Ikhlaq Sidhu, content author X Y X Y X YML Algorithms Guess this function F(x) We have now switched to Neural Networks as Function Approximators
  9. 9. Ikhlaq Sidhu, content author Neural net results are close t human results
  10. 10. Ikhlaq Sidhu, content author Peter Abbeel – Deep Reinforcement Learning Peter Abbeel Professor at UC Berkeley
  11. 11. Ikhlaq Sidhu, content author
  12. 12. Ikhlaq Sidhu, content author Most Recent AI News
  13. 13. Ikhlaq Sidhu, content author Does this mean AI Can Do Everything Better than Humans
  14. 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
  15. 15. Ikhlaq Sidhu, content author Acknowledgement to Ken Goldberg UC Berkeley AI Systems Only Work because of Human are Part of the System Google Operations People Write Web Pages People at Google Tune the Results People Click on What They Want Result Feedback By clicks Massive Data There is no “intelligence”, “Desire”, or “Existence” in AI without People There are only people who “invest in, design and operate the machines”
  16. 16. Ikhlaq Sidhu, content author 37 faculty At Berkeley, we have a lot of research on “How Machines Will Work as Part of Larger Systems that Work with People”
  17. 17. Ikhlaq Sidhu, content author My Drive Home From Berkeley
  18. 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
  19. 19. Ikhlaq Sidhu, content authorSource: Ken Goldberg, CPAR, People and Robotics Initiative Safety in Human-Robot Interaction: Guarantees and Verification Safety-constrained motion planning for efficiency in factory human-robot interaction Learning and prediction for safety in HRI Provably safe human-centric autonomy Masayoshi Tomizuka UC Berkeley Principal investigators: Claire Tomlin UC Berkeley Francesco Borrelli UC Berkeley
  20. 20. Ikhlaq Sidhu, content author Longer Term Future Narratives: Multiplicy vs Singularity Apocalypse Multiplicity: diverse combinations of people and machines work together to solve problems and innovate. Multiplicity is not science fiction. Extending on the combination of machine learning, the wisdom of crowds, and cloud computing already underlies many complex tasks performed everyday Ken Goldberg, UC Berkeley MultiplicitySingularity Singularity: Google’s director of Engineering, Ray Kurzweil, has predicted singularity to take place around 2045 Singularity is a tipping point when the robots powered by artificial intelligence will become more intelligent than human beings. Singularity is the time when artificial intelligence won’t require human intervention to become more intelligent. Computers will be self- sufficient, they will make their own decisions http://techbakbak.com/singularity-will-happen/ https://www.wsj.com/articles/the-robot-human-alliance-1497213576
  21. 21. Ikhlaq Sidhu, content author AI and Job Loss
  22. 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
  23. 23. Ikhlaq Sidhu, content authorIkhlaq Sidhu, University of California, Berkeley Textile vs Hand weaving: During the 19th century, amount of cloth a single weaver in America could produce = 50X gain. Labor required fell by 98%. Result: cloth became cheaper, demand greater, 4X more jobs were created in the same sector. Economists and historians claim that job disruption actually helped the economies that participated. Auto vs Horse-based transportation: This led to a decline in horse- related jobs. However, the automobile industry itself grew fast. Jobs were also created in different sectors, e.g. motel and fast- food industries that arose to serve motorists and truck drivers. ATM Machines at Banks: Automated teller machines (ATMs) reduce the number of bank clerks (20/bank in 1988 to 13/bank in 2004) by taking over some of their routine tasks. However, bank branches grew in numbers by 43% and total employees grew. Reference: Do we understand the impact of artificial intelligence on employment? | Bruegel One Caveat: The McKinsey Global Institute estimates that, compared with the Industrial Revolution of the late 18th and early 19th centuries, AI’s disruption of society is happening ten times faster and at 300 times the scale.
  24. 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
  25. 25. Ikhlaq Sidhu, content author Argument Narrative • Could you have stopped the first industrial revolution? No, if you chose to not participate, you would become irrelevant: – Who benefited: i) people/governments who created the tools. ii) people who learned to “operate the machine”, iii) those that invent or design the use-case or process. – Who suffered: displaced workers and displaced economies. • Mass displacement also means there will be a lack of spending, but so far no economists have validated this concern. • Innovative people with entrepreneurial behaviors are best suited to survive transition. Retraining will be essential. Psychology is the biggest factor. We should consider how to best re-train for this change.
  26. 26. Ikhlaq Sidhu, content author Contact: Ikhlaq Sidhu Founding Faculty Director, Center for Entrepreneurship & Technology IEOR Emerging Area Professor, UC Berkeley sidhu @berkeley.edu, scet.berkeley.edu