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Forecasting the Impact of Artificial Intelligence: Another Voice Lawrence Vanston THE INTERNATIONAL JOURNAL OF APPLIED FORECASTING THE INTERNATIONAL JOURNAL OF APPLIED FORECASTING FORECASTING THE IMPACT OF ARTIFICIAL INTELLIGENCE: Another Voice Lawrence Vanston RESPONSE Spyros Makridakis FORECASTER IN THE FIELD FEATURE Lawrence Vanston Become a Foresight subscriber and member: Join the International Institute of Forecasters foresight.forecasters.org ARTIFICIAL INTELLIGENCE Forecasting the Impact of Artificial Intelligence: Another Voice LAWRENCE VANSTON PREVIEW FROM AUTHOR Over the past year I have had the pleasure to read Foresight’s five- part series “Forecasting the Impact of Artificial Intelligence” by Spyros Makridakis. Recently I was asked to offer my thoughts on these articles as a forecaster with a deep interest in AI. Since I have a lot to say, I agreed. First, Spyros has done a masterly job conveying a complex and broad-ranging topic. He has raised many of the crucial issues and made the importance of this topic very clear. There are few people with his insight and experience in forecasting who could have done this, and he has done it well. Here I will add my voice, focusing on • areas in which I thought the article was weak (e.g, AI performance forecasts); • areas that Spyros covers well, but are crucial enough to deserve further illumination (AI’s impact on employment and the dangers of AI); and • areas of disagreement (brain-computer interfaces, blockchain, and AI for forecasting). FORECASTING AI’s limit that cannot be overcome, further COMPUTATIONAL POWER improvement has no utility, or there is a fundamental change in technologi- y first reaction to the AI perfor- cal approach. The last often results in a Mmance forecasts cited by Spyros discontinuity or a change in the rate of was “Is that all there is?” For a field as im- improvement. The transition from dis- portant to humanity as AI, I was expect- crete circuits to integrated circuits is an ing more. We can’t really blame Spyros, example. That transition increased the because when his series appeared there rate of improvement and began Moore’s weren’t many compelling forecasts of law, but it is not evidence of a super- AI performance that I could find either. exponential trend, as Kurzweil argues. Since then I have made some progress Another example of a discontinuity is the on forecasting AI performance, which I transition from analog modems to broad- shared last summer at ISF2018 (Vanston, band, where we saw a dramatic bump in 2018) and will highlight in this section. data rates and then a return to the previ- ous rate of improvement. Even when two A key component of forecasting AI per- exponential trends combine, as happened formance is forecasting computational for a while when we increased clock rates power. Although Ray Kurzweil has argued and wavelengths simultaneously in opti- for a future of superexponential improve- cal transmission systems, the combined ment in computer performance (Kurzweil, trend is still exponential. Setting aside a 2005), the actual evidence supports con- major discontinuity from quantum com- tinued exponential progress along the puting, for example, or the oft-predicted lines of Moore’s law (see Sidebar A for a but yet-to-happen death of Moore’s law, primer on growth curves). the logical working forecast is exponen- Exponential progress is typical of tech- tial at historical rates. nologies unless there is a fundamental https://foresight.forecasters.org FORESIGHT 39 A trove of data has been compiled by the Electronic Frontier Foundation for AI applications such as games, image rec- ognition, speech recognition, machine translation, natural language processing, and computer programming (Electronic Frontier Foundation, 2018). These data, along with insightful analyses in blogs by Miles Brundage (2018) and Sarah Constantin (2017), reveal that, in some areas, AI has passed human performance, while in others it has a long way to go. In some cases, deep learning has caused a discontinuity in performance improve- ment; in others, it simply enabled con- tinuation of the trend. Surprisingly, when Sidebar A: Growth Rate Primer measured in the customary units applied With linear growth, performance increases by a to a specific application (for example, the constant b units each year. Linear growth is not Elo rating for chess or BLEU score for typical of performance improvement in high tech, machine translation), AI progress has but as we discuss in the text, there is evidence typically not been exponential, but linear that progress in many AI applications is linear. (Vanston 2018). With exponential growth, performance increases This suggests that for those AI applica- by a constant percentage r each year, where tions still far below human performance, r = eb – 1. Exponential growth implies a constant AI parity may be years away, barring doubling time, d = ln 2 / b. Moore’s law is the breakthroughs. This probably explains classic example of exponential growth with a why 92.5% of the AI fellows in Oren doubling time of two years and an annual growth Etzioni’s survey (Etzioni, 2016) cited by rate of 41%. Exponential growth yields a straight Spyros said superintelligence wouldn’t line when performance is plotted on a log scale as be achieved for 25 years, if ever. And why shown in the graph. almost every speech I hear from an AI expert starts with “Forget about AI and With double exponential growth, the annual human intelligence, current AI has the IQ growth increases exponentially, so it plots on a of a slug. Nothing to worry about here!”— log scale like an exponential would on a linear before they move on to whatever they’re scale. This is the curve to use if you want to fore- selling. cast insane future performance improvement, but such improvement is unsustainable in the Is there really nothing to worry about? long run. It’s easy to mistake one-time increases See Sidebar B for my thoughts on that in the exponential growth constant b for double topic, but we are flying blind until we have exponential growth, especially if you’re looking better technology forecasts than those for it. provided by Kurzweil (who is a great vi- sionary relying on a highly questionable One-time changes in b can occur when the tech- forecast) and the offhand opinions of nology approach changes (as when we went from experts (who know everything about AI, discrete to integrated circuits), or when two ex- but may not know much about technol- ponentially improving processes are combined. ogy forecasting). The latter results in a new exponential curve since the product of two exponential curves is AI’S IMPACT ON EMPLOYMENT 1t b2t bt another exponential: f(t) = a1eb * a2e = ae Spyros does a balanced job of laying out where b = b1 + b2. the arguments regarding the impact of AI on employment. This issue, and how we handle it, may be as important and more immediate than our existential concerns 40 FORESIGHT Spring 2019 about AI. Before you continue, note that I Economists would say that if you must do lean optimist/realist even though I sound income redistribution, it’s most efficient like a pessimist! to just give people cash. I’m not so sure. Optimists argue that we have had techno- Perhaps pushing the money through pro- logical revolutions before, and each time grams (grants, microloans, fellowships, we have ended up with more, but differ- etc.) and institutions (schools, charities, ent, jobs. That’s true, but each of those churches, nonprofits, sports leagues, etc.) revolutions was unique and there have makes more sense for some individuals. not been so many to make broad gener- Lastly, while AI will impact employment, alizations. We can indeed say based on it likely won’t be dramatic in the next 10 this experience that we could end up with years. So we may have some time for fore- more jobs, but saying we will without spe- casters to forecast, analysts to analyze, cifics is speculative. It is argued that we planners to plan, entrepreneurs to entre- just can’t imagine the jobs yet. Perhaps… preneur, and think tanks to develop policy but if you can’t imagine it, you can’t count positions consistent with their politics. If on it. Same for happy historical examples there are indeed problems, I believe there such as still having bank tellers in spite of are solutions. The question is whether we ATMs. pay enough attention to them. Even if we do end in a happy place of full employment, there’s no guarantee it will be a smooth transition. The impacts of Sidebar B: Is There Nothing to Worry About? employment dislocation can be substan- Are Elon Musk and the late Stephen Hawking, tial and consequential. The last American not to mention the other authors cited by presidential election turned on a few Spyros, wrong to be deeply worried about AI? states where the displacement of indus- In my opinion, absolutely not, but perhaps for trial jobs by technology or offshoring is different reasons. My reasons are independent either a reality or a realistic fear. What’s it of when and whether computers achieve human going to be like when the factories return intelligence or human consciousness. home, but the parking lots are empty? 1. Rather than the threats of single computers, AI will doubtless generate many jobs in you should worry about the interactions of AI and managing AI, but realistically, how millions of interconnected computers, the many compared to the jobs lost? And not actions of each dictated by an AI algorithm everyone is suited by talent, disposition, optimized for its own purposes. Really smart or inclination for those jobs. Optimists humans managed to design complex deriva- say that creative and intellectual jobs are tives that were impossible for really smart not threatened and that AI will supple- humans to unwind, almost bringing down our ment, not replace, them.
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