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1 Copyright 2018, Technology Futures, Inc. 1
2 Forecasting Artificial Intelligence Lawrence Vanston, Ph.D. President, Technology Futures, Inc TFI 2018 January 25-26, 2018 Marriott Courtyard Downtown Austin, Texas 84 Waller St Austin, Texas (512) Copyright 2018, Technology Futures, Inc. 2
3 Excerpted from: Copyright 2018, Technology Futures, Inc. 3
4 Outline Fundamental Driving Forces Old and New Adoption Forecast for the New Transformative Technologies AI is in the Middle of Everything Performance Trend Basics Performance Trends for AI Implications Copyright 2018, Technology Futures, Inc. 4
5 Fundamental Driving Forces s View Physical Movement Analog Low Bandwidth Wireline Electronic Circuit Switching Telecommunications Digital Communications High Bandwidth Wireless Optical Packet Switching Mass Marketing Niche Marketing Copyright 2018, Technology Futures, Inc. 5
6 New Fundamental Driving Forces Humans Machines People Communicating Things Communicating Computing AI /Cognitive Computing Screen-based Augmented/Virtual Reality Client/Server Cloud Computing Small Data Big Data Slow Data Fast Data These overlap & support each other! Copyright 2018, Technology Futures, Inc. 6
7 Humans Machines Robots Self Driving Vehicles Drones Copyright 2018, Technology Futures, Inc. 7
8 Kognit Cognitive Assistants for Dementia Patients Copyright 2018, Technology Futures, Inc. 8
9 Adoption Forecast for the New Transformative Technologies Copyright 2018, Technology Futures, Inc. 9
10 TFI Wireless Generations Forecast Copyright 2018, Technology Futures, Inc. 10
11 TFI Wireless Connection Forecast Millions of Connections Total 400 Standard M2M Year Historical Data Source:CTIA Source: Technology Futures, Inc. Wireless Forecast 2017 Copyright 2018, Technology Futures, Inc. 11
12 U.S. Broadband Lifecycles Percentage of Households 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 6 Mb/s 0% Data Source: FCC. Data excludes mobile wireless broadband All Broadband Households 1.5 Mb/s 24 Mb/s Year 50 Mb/s 300 Mb/s & Above 100 Mb/s Source: Technology Futures, Inc. Broadband Access 2016 Lifecycle (t) = Substitution (t) Next Substitution (t) Copyright 2018, Technology Futures, Inc. 12
13 Ultra-HD Households (aka 4K) 100% 90% Percentage of Households 80% 70% 60% 50% 40% 30% 20% 10% HDTV Households Published Forecasts HDTV Analogous Rate Ultra-HDTV Households 0% HDTV 216 Historical data sources: Misc, Leichtman Research Year Source: Technology Futures, Inc. UHDTV Data Source (Red Squares): Strategy Analytics Copyright 2018, Technology Futures, Inc. 13
14 Major Transitions of our Time Percent Substitution 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Broadband, Digital, Fiber, Wireless, IP 0% Year Machines, AI, AR/VR, IoT Source: Technology Futures, Inc. Forecasting Future 2017 Copyright 2018, Technology Futures, Inc. 14
15 AI is in the Middle of Everything AI AI CLOUD AI AI INPUTS AI Clever Programming OUTPUTS AI Copyright 2018, Technology Futures, Inc. 15
16 Performance Trends Basics Copyright 2018, Technology Futures, Inc. 16
17 Moore s Law 1,000, ,000 Pentium 4 Processor Pentium III Processor Transistors in 1000s 10,000 1,000 Pentium Processor 486 DX Processor 386 Processor 286 Pentium II Processor Year Copyright 2018, Technology Futures, Inc. 17
18 Constant Percentage Rate of Advance (Exponential) Most new technologies progress this way Measure must reflect utility Rates will continue if: The improvement is technically possible Utility and demand continues Basic approach remains the same If approach changes, look for Discontinuities Changes in improvement rate Copyright 2018, Technology Futures, Inc. 18
19 Analog Modem & Broadband Speeds Nominal Data Rate (Mb/s) Broadband Analog Modems Performance Increases 4 times every 4 years (42% annually) Broadband Access 2003 Year Source: Technology Futures, Inc. Copyright 2018, Technology Futures, Inc. 19
20 Maximum Data Rates for Commercial Fiber Optic Systems 1,000, , Assumes 150 Tb/s Limit; estimates vary Pre-2000 Trend Gigabits/Second (Gb/s) 10, , DWDM Single Wavelength Post-bubble Blues Year Fiber Data Rates 2011 Source: Technology Futures, Inc. Copyright 2018, Technology Futures, Inc. 20
21 Average transactions-per-minute per processor Source: Raghunath Nambiar, Cisco, Copyright 2018, Technology Futures, Inc. 21
22 Performance Trends for AI Copyright 2018, Technology Futures, Inc. 22
23 Note this is double exponential growth! -LKV Kurzweil s Computer Performance Forecast This is exponential growth! By Courtesy of Ray Kurzweil and Kurzweil Technologies, Inc. en:pptexponentialgrowthof_computing.jpg, CC BY 1.0, Copyright 2018, Technology Futures, Inc. 23
24 By Steve Jurvetson - CC BY 2.0, Copyright 2018, Technology Futures, Inc. 24
25 Exponential Performance Trends Exponential f(t) = e bt Typical Multiple Exponentials f(t) = e b 1t e b 2t = e (b 1+b 2 )t = e bt Note that Multiple Exponential is Exponential True Double Exponential f(t) = e b = e (b ) x x Happens NO! Copyright 2018, Technology Futures, Inc. 25
26 Multiple vs Double Exponentials 1,000,000, ,000,000 10,000,000 1,000, ,000 Double Exponential Multiple Exponential Exp 1 * Exp 2 Performance 10,000 1, Year Exp 2 Exp 1 Source: Technology Futures, Inc. Sample Trend 2017 Copyright 2018, Technology Futures, Inc. 26
27 Elon Musk on AI Progress and Threat at International Space Station Research and Development Conference, July 19, 2017 Clip from CNN Money/Tech Full interview at Copyright 2018, Technology Futures, Inc. 27
28 A Premise for Moving Forward Kurzweil means true double exponential, based on logic that better argues for multiple exponential. Musk means multiple exponential when he says double exponential (I think) Let s assume that exponential is the most solid basis to explore the promise and impact of AI However, there s a catch Copyright 2018, Technology Futures, Inc. 28
29 Hard vs Easy Problems Easy Problems Computations grow linearly or polynomially with problem size n. f(n) = a + bn f(n) = a + b 1 n + b 2 n 2 + b 3 n b k n k Hard Problems Computations grow exponentially with problem size n f(n) = e cn Copyright 2018, Technology Futures, Inc. 29
30 Decision Tree Copyright 2018, Technology Futures, Inc. 30
31 Performance Trends for Problem Solving - Advanced Exponential increase in processing speed, x(t)=e b 1t Exponential increase in efficiency, y(t)=e b 2t Combined performance, e (b 1+b 2 )t = e bt Solution time = s(t) = f(n)/ e bt = e -bt f(n) The maximum problem size solvable in S seconds grows with n: Exponentially if f(n) is polynomial (Easy) Linearly if f(n) is exponential (Hard) Copyright 2018, Technology Futures, Inc. 31
32 Maximum Problem Size in S seconds Copyright 2018, Technology Futures, Inc. 32
33 Maximum Problem Size in S seconds (Example) Max Problem Size in S Seconds Polynomial Difficulty (Easy Problems) Year S = 1000 Exponential Difficulty (Hard Problems) Source: Technology Futures, Inc. Sample Trend 2018 Copyright 2018, Technology Futures, Inc. 33
34 Chess Discontinuity Due to Hardware Improvement, Not Deep Learning Copyright 2018, Technology Futures, Inc. 34
35 Go AlphaGo performed notably better than its predecessors (all Monte Carlo Tree Search programs, but without deep learning or GPUs.) It uses much more hardware and more data. AlphaGo doesn t represent that much of a surprise given the improvements in hardware and data (and effort). Miles Brundage Miles Brundage ( erformance-trends-in-ai/ Copyright 2018, Technology Futures, Inc. 35
36 Prior AI Go Improvement Found at: Referenced source for data: "Sensei's Library: KGSBotRatings". Retrieved Copyright 2018, Technology Futures, Inc. 36
37 AlphaGo Continued Progress blog/alphago-zerolearning-scratch/ Copyright 2018, Technology Futures, Inc. 37
38 Image Recognition Copyright 2018, Technology Futures, Inc. 38
39 Image Recognition Copyright 2018, Technology Futures, Inc. 39
40 SPEECH RECOG- NITION 2011: contextdependent deep neural network hidden Markov models ( Copyright 2018, Technology Futures, Inc. 40
41 Speech Recognition Copyright 2018, Technology Futures, Inc. 41
42 Machine Translation Copyright 2018, Technology Futures, Inc. 42
43 Natural Language Processing (SAT) (a) SAT Analogies (b) SAT Multiple Choice (c) SAT Paraphrase ID a. Analogies: No Deep Learning b. Mult Choice: Neural Nets in 2014 c. Paraphrase ID: Top Performer was Matrix Factorization 28/performance-trends-in-ai/ Copyright 2018, Technology Futures, Inc. 43
44 Generating computer programs from specifications Copyright 2018, Technology Futures, Inc. 44
45 Summary for AI Progress Wide Range of: Problem difficulty Solution status Metrics Progress rates and patterns Distance to go Discontinuities and rate changes Deep learning has varied impact on rate Linear progress typical Copyright 2018, Technology Futures, Inc. 45
46 Other Thoughts on AI Progress What are the characteristics of problems that decide the above? Do the problems become easier or harder as we move toward General Intelligence? How will AI systems play with each other? How will AI interact with the outside world? Does the argument that AI will bootstrap itself hold water? Copyright 2018, Technology Futures, Inc. 46
47 What does it mean for... Commerce and Industry? Communications? Mankind? Copyright 2018, Technology Futures, Inc. 47
48 For Commerce and Industry Wide range of applications across industries Ubiquitous AI embedded in other transformative technologies Important across processes, products, and services Many opportunities, but highly disruptive Huge employment implications Copyright 2018, Technology Futures, Inc. 48
49 Implications for Communications Rapidly increasing performance requirements Rapidly improving technologies Widespread and massive investments Frequent upgrades and rapid obsolescence Changes in workforce Copyright 2018, Technology Futures, Inc. 49
50 Implications for Mankind Tremendous opportunity for good and evil Existential Threats Real and Imagined Challenges political, economic, and social Copyright 2018, Technology Futures, Inc. 50
51 Copyright 2018, Technology Futures, Inc. 51
52 It s up to us. Copyright 2018, Technology Futures, Inc. 52
53 (512) Your Bridge to the Future Copyright 2018, Technology Futures, Inc. 53
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