Forecasting the Impact of Artificial Intelligence: Another Voice Lawrence Vanston

THE INTERNATIONAL JOURNAL OF APPLIED FORECASTING THE INTERNATIONAL JOURNAL OF APPLIED FORECASTING

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 recognition, 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 improvement; 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. I don’t doubt financial system. At least we know how those that AI will help doctors be better doctors algorithms reasoned. Is the same true for AI? and architects be better architects. But 2. Emerging technologies provide computers suppose AI makes architects twice as ef- with the types of inputs and outputs that ficient at designing buildings. Does that heretofore only humans and fellow animals mean we will have twice as many build- had. This not only provides data fodder for ings or half as many architects? machine learning, but assumes you can just Optimists who concede that jobs will be pull the plug or take out the battery. (Try that lost look on the bright side and offer the on your iPhone!) prospect of more leisure time to pursue 3. Further, AI is advancing to the point of being their personal interests, perhaps with able to use those skills in spite of the second the benefit of a Universal Basic Income. half of Moravec’s Paradox (Moravec, 1988)— I will never run out of creative, produc- that it’s “difficult or impossible to give them tive, fulfilling things to do, but that’s [computers] the skills of a one-year-old when not true for everyone; witness people on it comes to perception and mobility.” Our unemployment who fall prey to opioids.

https://foresight.forecasters.org FORESIGHT 41 BLOCKCHAIN, IA, AND FORECASTING machines are already approaching a one-year- old’s level in many ways. Consider this list of Most of Part 4 of the Makridakis series “easy for one-year-olds, hard for computers” is devoted to blockchain. That Spyros put skills: “recognizing a face, moving around in blockchain on the same plane as AI sur- space, judging people’s motivations, catching prised me, and I disagree with so many of a ball, recognizing a voice, setting appropri- his observations and conclusions that the ate goals, paying attention to things that are margins of my copy are full. Here are my interesting; anything to do with perception, main objections: attention, visualization, motor skills, social AI doesn’t need blockchain. Neither does skills” (Wikipedia, 2018). On some of these IoT or any other cutting-edge technol- measures our machines seem equal to a one- ogy, so why link them? Blockchain is an year-old and working on the terrible twos. interesting character, but it is not crucial 4. This brings us to the other half of Moravec’s to the AI story. Paradox: “It is comparatively easy to make Blockchain claims to have the potential to computers exhibit adult-level performance on replace banks and other financial inter- intelligence tests or playing checkers.” As ma- mediaries, even government institutions. chines begin to reach fundamental levels of This outcome assumes that the recording sensory perception, motor skill, rudimentary of transactions is the most important language skills, learning ability, and pseudo- aspect of the financial entity’s business. reasoning, how far is it really to human in- However, anyone who has bought a house telligence? If what sets us apart from other knows that recording the deed at the vertebrates is massive amounts of pliable, county courthouse (or the equivalent in general-purpose wiring, is that really so hard your country) is a tiny part of the transac- to duplicate? Add in the human knowledge tion. The institutions involved—agents base, already conveniently compiled, and we for the buyer and seller, the title com- could be a lot closer than is now apparent. pany, the lending bank, federal lending 5. Finally, AI doesn’t need human intelligence agencies, etc.—provide expertise, broad or human consciousness to be dangerous. trust, fiduciary responsibility, insurance, Microbes with sub-slug intelligence kill mil- money in the form of loans and loan guar- lions of us and theoretically could kill us all, so antees, and force of law. AI isn’t the first intelligence to challenge hu- Similarly, the main function of Amazon mans! Once we leave programming decisions and the source of its “monopolistic/ to AI we don’t know what intelligence—pos- oligopolistic power” is not the recording sibly far inferior to ours, but just as danger- of the financial transactions. If it were, ous—will emerge. Nor do we know whether Amazon could easily be replaced, with or its evolution will be on pace with vertebrate without blockchain. The reason it has not evolution or microbal evolution. is that for a reasonable price Amazon pro- These concerns exist in the absence of human vides a host of useful services and a form greed, malice, and bias, intentional or otherwise. of trust that is far broader than block- In their presence, they are deeply troubling. There chain’s narrow definition of trust. are indeed ways to address these problems. My Blockchain is basically a narrow technol- fear regards the will, not the way. It’s hard to say ogy attempting to replace existing techno to useful, cool technology. In the U.S. we have nologies that are already providing trans- seen our own technology used against us to influ- action documentation quickly, reliably, ence the fundamental course of events, including and cheaply. Maybe it will be widely ad- our global alliances and trade relationships, even opted, maybe it won’t, but it won’t change without AI. The problems arising from AI may be the world. And if it does change the world, harder to fix. Will we be quick enough? And, in it may be for the worse; the Internet is a the current winner-take-all world, what are the dangerous place. consequences if we aren't?

42 FORESIGHT Spring 2019 INTELLIGENCE AUGMENTATION Sidebar C: Drivers and Constraints Part 4 ends with a discussion of intel- This simple method is a great tool for navigating ligence augmentation, in which Spyros the shoals of overoptimistic and overpessimistic views AI and humans helping each technology forecasts. It’s been likened to force- other, leveraging each other’s strengths. field analysis, but it emphasizes the dynamic Nothing new or controversial there—the instead of the static, which is the whole point of future is an extension of the past. He then forecasting. Basically, it is a series of questions links the future of IA with direct human- begetting more questions until you have an an- machine interfaces. The latter technology swer, or at least an informed opinion! may be closer than we think and may have immediate medical, military, and other • What are the drivers for adoption? benefits. However, the risks and benefits - How strong are they? of direct human-machine interfaces need • What are the constraints on adoption? to be assessed on their own, regardless of - How strong are they? Can they be AI. In fact, this technology may increase overcome? our susceptibility to AI domination as • What is the balance of drivers and much as ameliorate it. constraints? - Will this change? THE LONG-TERM FUTURE • What are the important areas of uncertainty Part 5 of the series starts with a discus- that need to be resolved? sion of the pitfalls of long-term forecast- - How can these be addressed to ing and the top 10 emerging trends. I find everyone’s satisfaction? a lot here to agree with. I absolutely be- Incidentally, besides preventing avoidable ship- lieve in the necessity of steering between wrecks, the exercise of going through these the shoals of being too conservative or questions provides a foundation for quantita- too optimistic. That’s why I never start tive forecasts as well as a research agenda for a forecast without doing a drivers and both forecasting and the technology itself. For constraints analysis (see Sidebar C) that more information on drivers and constraints, see usually includes the four factors Spyros Vanston, 2008. lists, among others. Along with AI itself, half of his top 10 trends are on my own list of top information/communications While we are on the topic of long-term technology trends, and the others are ar- forecasting, I will take the opportunity to eas I know well enough to agree with their plug my own field, technological forecast- presence on the list. ing (or technology forecasting)—which includes the kinds of “not so statistical forecasting” that we need when we require rigor and proven methods, but don’t have much data. These methods include alternative scenarios, expert opinion, Delphi, substitution analysis, performance analysis, analogies, cross- impact analysis, and ideation tools like nominal groups and impact wheels, to name a few. Technological Forecasting and Social Change is the field’s classic journal. Martino (1983) and Porter (2011) are a couple of the classic texts. Technological forecasting was essential in the 1960s as the digital revolution began, it was essential for the forecasting we did during the 1990s and early 2000s for the last wave of new technology (Vanston and Hodges,

https://foresight.forecasters.org FORESIGHT 43 2004)), and it will be essential for fore- cites the alleged slow progress in using casting AI and other new-wave technolo- AI for forecasting as an example of the gies in the future. weaknesses of AI. First, it’s not a repre- A few selected cautions regarding some of sentative example. More importantly, Spyros’s specific conclusions: it masks some fundamental changes in forecasting. The M competitions mea- • Don’t assume that all computing will sure the ability to forecast the next few end up in the cloud. Computing has a numbers in a long series of numbers with long pendulum when it comes to cen- no contextual information except the tralization and decentralization. “Easy frequency of the data. Years of research as using electricity” sounds a lot like and prior contests have honed statistical the “computing utility” mantra from methods so that they would be hard to the late 1970s, right before the PC be beat by any emerging technology. By revolution decentralized computing. not having labeled or contextual informa- Currently, edge computing is as much a tion, the strengths of AI forecasting are trend as cloud computing. With edge missed. The advantages statistical meth- computing, information is stored and ods have in these contests are such that processed close to the user (thus, at some AI forecasting experts have refused the network’s edge) to serve applica- to participate. tions such as autonomous vehicles that require high reliability and low latency. Even then, the top finisher in the 2018 Most likely there will continue to be lo- M4 competition, Slawek Smyl of Uber cal, edge, and cloud computing in some Technologies, used a hybrid of AI and con- combination. ventional forecasting (Smyl, Ranganthan & Pasqua, 2018). This is more an example • I think Spyros sticks his neck out un- of teaching AI to use statistical forecast- necessarily when he predicts that a ing so it doesn’t have to learn it on its wristwatch will be the interface of own than an example of happy machine/ choice for communicating with com- human collaboration. ISF2018 this sum- puters. It might be, but AR/VR using mer was instructive. Four major presenta- glasses or headsets may reemerge as a tions on AI for forecasting were authored contender as well. Or maybe a drone by employees of Amazon, Microsoft, and will fly beside us! Or maybe our current Uber, leaders in the application of tech- array of laptops, pads, smartphones, nology. All of these presentations were and big screens will survive. Choosing positive regarding AI for forecasting and among alternative technologies that optimistic that the constraints on AI (e.g., do the same thing is high on the list of that it’s data and computation intensive the risky forecasting tasks (Vanston, and that it’s prone to overfitting and in- 2008). Better to avoid it if possible! stability) will be overcome. Reading the • As I’ve noted above, brain-computer article by the Amazon team, ironically in interfaces may have a place, but they the same issue of Foresight as Part 5, re- are not necessary to survive AI. We inforces this position (Januschowski and should ask for more analyses of driv- colleagues, 2018). ers/constraints and risks/benefits be- Will AI eventually replace most statisti- fore agreeing with Spyros’s optimistic cal forecasting? It’s too soon to tell, but conclusion regarding this technology. it’s possible: parity has been reached in Otherwise, we risk having another un- some applications, improvements in AI fortunate example of forecasts gone are likely, and constraints on AI can likely wrong. And very odd that he ends his be overcome. Some of the objections to opus on AI on this point! AI I’ve heard remind me of those raised AI FOR FORECASTING by people engaged with prior old technologies such as traditional telephony, Spyros makes one other point in Part 5 dial-up modems, and circuit switching. that I’d like to challenge vigorously. He I’d mention slide rules, but that would

44 FORESIGHT Spring 2019 Will AI eventually replace most statistical forecasting? It’s too soon to tell, but it’s possible: parity has been reached in some applications, improvements in AI are likely, and constraints on AI can likely be overcome. date me. And then there are the classic Martino, J. (1983). Technological Forecasting for Decision examples: steam locomotives and prop Making, 2nd Ed., North-Holland. planes. Maybe forecasters need to spon- Moravec, H. (1988). Mind Children, Harvard University sor their own long-range technology Press. forecast, starting with a good drivers and Moravec, H. (1998). When Will Computer Hardware constraints analysis! Even if the news is Match the Human Brain? Journal of Evolution and Technology, 1. bad, I think the skill sets are highly trans- ferable with a little training, and job secu- Porter, A. L., Cunningham, S.W., Banks, J., Roper, A.T., rity will be more like that of doctors than Mason, T.W. & Rossini, F.A. (2011). Forecasting and Management of Technology, 2nd Ed., Wiley. architects. Smyl, S., Ranganthan, J. & Pasqua, A. (2018, June SUMMARY 25). M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model, Retrieved from Spyros has done a masterful job cover- Uber Engineering: https://eng.uber.com/ ing a complex, wide-ranging subject. I’m m4-forecasting-competition/ genuinely impressed. I have highlighted Vanston, L. (2018). Forecasting AI Performance, 38th the areas of disagreement rather than the International Symposium on Forecasting, Boulder, CO, much broader areas of agreement because USA: International Institute of Forecasters. those are the ones that need to be talked Vanston, L. (2017). Forecasting Issues for the New about more. For the same reason, I have World, 23rd IIF Workshop on Predictive Analytics and also addressed the more controversial Forecasting (20-25), Munich: International Institute concerns and dangers of AI more than of Forecasting, Retrieved from http://predictive- its immense benefits. Forecasters of all analyticsandforecasting.com/wp-content/ stripes have a crucial role to play in help- uploads/2017/10/MUNICH-WORKSHOP- VANSTON-Sepember-2017.pdf ing sort these issues for the benefit of our clients and humanity. As my mentor the Vanston, L. (2008). Practical Tips for Forecasting late Ralph Lenz once told me: “The high- New Technology Adoption, Telektronikk 3/4.2008, Retrieved from http://tfi.com/pubs/forecasting- est value of a forecast is to raise the level tips.pdf of discussion.” Let’s talk. Vanston, L. & Hodges, R. (2004). Technology Forecasting REFERENCES for Telecommunications, Telektronikk, 100(4), 32-42, Brundage, M. (2018). Blog Posts, Retrieved from Retrieved from http://tfi.com/pubs/w/pdf/telek- https://www.milesbrundage.com/blog-posts tronikk_peer.pdf Constantin, S. (2017). Performance Trends Wikipedia (2018). Moravec’s Paradox, Retrieved in AI, Retrieved from Otium: https://srcon- from Wikipedia: https://en.wikipedia.org/wiki/ stantin.wordpress.com/2017/01/28/ Moravec%27s_paradox performance-trends-in-ai/ Electronic Frontier Foundation (2018). AI Progress Measurement, Retrieved from https://www.eff. org/ai/metrics Etzioni, O. (2016, September 20). No, the Experts Don’t Think Superintelligent AI Is a Threat to Humanity,MIT Lawrence Vanston is President of Technology Review, Retrieved from https://www. Technology Futures, Inc. http://www.tfi. technologyreview.com/s/602410/no-the- com. See his Forecaster in the Field inter- experts-dont-think-superintelligent-ai-is-a- view on page 48 of this issue. threat-to-humanity/ [email protected] Januschowski, T., Gasthaus, J., Wang, Y., Rangapuram, S. S. & Callot, L. (Fall 2018). Deep Learning for Forecasting: Current Trends and Challenges, Foresight, 42-47. Kurzweil, R. (2005). The Singularity Is Near, New York: Viking Books.

https://foresight.forecasters.org FORESIGHT 45 Response to Lawrence Vanston SPYROS MAKRIDAKIS

’m extremely flattered that two futur- My main comments center on Larry’s Iists as distinguished as Owen Davies four major disagreements with what I and Lawrence Vanston have thought well have written, and present my reasoning to comment on my five-partForesight pa- as to why I believe I’m on the right track, per “Forecasting the Impact of Artificial although it will be many years before we Intelligence.” My sincere thanks to both know who’s right and who’s mistaken. for their insightful remarks and the op- Additionally, I will outline my view of the portunity they provided me to update my two biggest AI/IA issues facing human- thinking about AI and check the rational- kind and explain why dealing with them ity of my predictions. Their commentaries may prove extremely difficult. have considerably improved my original article, and I’m pleased that there is a fair THE FOUR DISAGREEMENTS amount of agreement among us. This re- Blockchain sponse is devoted to Larry’s commentary, Larry states that blockchain is basically as Owen’s was a coda to my original paper a narrow technology trying to become with additional expert opinion gathered mainstream. I disagree. I believe block- by TechCast’s Delphi forecasts. chain possesses three major advantages The Nobel laureate Paul Krugman in 1998 of great value that are not available in the made some profoundly off-the-mark traditional Internet: predictions that Owen Davies used as an ➞ it provides trust among unknown epigraph to his coda in the Winter 2019 participants who can interact with issue—including Krugman’s statement each other with confidence; that the Internet’s effect on the world ➞ it ensures enlarged safety in economy would be no greater than that of transactions; the fax machine—illuminating the short- ➞ sightedness of forecasters. Of course it permits the construction of fast, Krugman did not notice that a year ear- trusted, and safe local networks. lier IBM’s Deep Blue computer had beaten These may not seem like much, but let’s world chess champion Garry Kasparov, or consider the implications of hacking that algorithms in 1998 could read hand- houses that are connected to the Internet written characters in what were preludes of Things (IoT), smart contracts, autono- to AI. mous vehicles, and even the possibility of At the other extreme, forecasters should getting your thoughts stolen through a not fall into the trap of the science-fiction BCI (brain-to-computer interface). Unless hype advocating that General Purpose AI perfect safety could be assured, the opera- (GAI) and singularity are just around the tion of IoT, smart contracts, and AVs, not corner, competing with us and threaten- to mention BCI, might be impossible. The ing our human dominance. I would like to same would go for local networks that reiterate my position that while AI is su- could re-create the equivalent of the vil- perb in games and image, speech, and text lage square where neighbors can share recognition, it is incapable of understand- communications and services. They must ing meaning, making causal links, and operate in a trusted and safe environ- exhibiting common sense. Many experts, ment; otherwise, they would be shunned. to paraphrase Lawrence Vanston, say that The Cloud “current AI has the IQ of a slug.” My own The second and third disagreements prediction is that AI will take a long time are about the cloud and its accessibility to achieve the abilities of a one-year-old without a computer, say through a digi- child to interact with its external world. tal watch or other device. I understand

46 FORESIGHT Spring 2019 Larry’s objections, but I still believe that OTHER MAJOR ISSUES we are at the very beginning of the utili- zation of cloud computing. AI will give rise to two major, interrelated issues. The first will have to do with the Two factors would contribute to moving “winner takes all” syndrome. AI applica- the future toward my prediction. First, tions, once developed, could be exploited G5 and higher-level communication at a global level at little additional expen- networks will bring Wi-Fi to all places, diture, providing a huge advantage to facilitating connections from anywhere their inventor(s). to the cloud. Second, once voice com- mands become widespread and reliable, The second will have to do with the income they would make keyboards obsolete: this inequality created by such a syndrome as would substantially increase the utiliza- a single firm, or a very few firms, in some tion of the cloud, whether this would be advanced countries would dominate the done by a cheap computer, a smartphone, AI market. Furthermore, income inequal- or a digital watch. In my view, there will ity will become much worse between be no reason to own and maintain an advanced AI nations and the rest of the expensive computer if the same service world that would not have the special- can be obtained more cheaply and easily ized scientists or the vast research funds from the cloud, allowing one to access and required to keep up. process information from any place at any Worse, as AI will be automating more and time without having to carry a big device more repetitive labor tasks it will reduce or worry about where such information is the advantage of less-developed countries stored. to attract firms through low labor costs, Forecast Accuracy as robots and machines will be capable Will AI improve forecasting accuracy? of operating the factories of advanced My strong view is that improvements, if countries at similar or even lower costs. any, will be limited and will be due not to The big challenge will be to reduce income AI learning to forecast more accurately inequality within advanced AI countries but rather to the ability of computers to through some form of guaranteed mini- optimize the parameters of the forecast- mum income. However, it may be much ing model more precisely. The top two harder to do so between countries. People winners of the M4 Competition (Smyl’s seem unwilling to help poorer countries/ model from Uber, and Montero-Manso regions economically while, at the same from University of A Coruña, Spain) did time, remaining unwilling to allow immi- not learn to forecast more accurately by grants to enter their wealthier countries. studying past patterns or analyzing historical data. Rather, they simply found more effective ways to choose the most appropriate model and/or compute the best weights to combine the various forecasting methods used. AI forecasting Spyros Makridakis is Professor, models cannot work well when there are University of Nicosia and Director of the Future. structural changes in the data or when See our Forecaster in the Field interview with Spyros in the Fall 2017 issue of Foresight. patterns change. Our own experience has indicated that there is still a long way to [email protected] go before AI forecasting models will be able to improve their accuracy through such learning.

https://foresight.forecasters.org FORESIGHT 47 Interview with Lawrence Vanston, President, Technology Futures, Inc.

So, what do you want to do when you were more interested in right decisions grow up? than theory. Myself, I was interested in I want to help people understand technol- both. ogy change so they can make good choices Tell us more about your career to date. for themselves and the world. Luckily, I’ve lived in interesting times— Isn’t that what you do already? especially for the communications in- Yes, but I want to do it when I grow up, dustry, home to most of my clients. The too, and better—especially the part about glory days were the late 80s and early 90s people understanding. I definitely have when we were simultaneously forecast- my Cassandra days. ing digital wireless, digital TV, consumer broadband, fiber optics, Internet applica- What do people get wrong about the tions, and local competition, which revo- future? lutionized the industry. Happily, most of They forget that things change; what’s my clients survived, I think in part due true today mightn’t be true tomorrow. to good forecasting. The 2000s have been When you hear the words “never/not more evolutionary—2G-to-5G wireless; in my lifetime,” alarms should blare. 1Mbs-to-1Gbs broadband; HD, 4K, 8K Surprisingly, experts are the worst. Then digital television—but still very interest- there’s the opposite problem: people ing to a forecaster. think change will occur overnight, are dis- How do you see the future? mayed when it doesn’t, then say “no one It’s a new glory era for technology fore- saw it coming” when it finally does. We casting. The aforementioned technology keep making these mistakes, and simple wave changed everything over 30 years; tools for avoiding them have been around this new wave will change things as much for decades. in the next three. AI, VR/AR, robotics, You’re a forecaster? autonomous vehicles, the Internet of I call myself a “technology forecaster”…an Things, all raise issues of timing, likeli- actual thing! I deal with what’ll change in hood, path to the future, and potential the next five years plus, rather than short- impacts, the issues that technology fore- term variations. Although there’s plenty casting addresses for good people to make of overlap with short-term forecasting, I good decisions. There will be lots to do rely more on understanding the principles when I grow up! of technology change, less on statistical What do you do when you’re not analysis. And I rarely have nice data to forecasting? work with. Besides spending time with family and How did you get started in forecasting? friends, my passions are social dancing I was in grad school in the late 70s study- and art. You can spot me out dancing ing operations research. My father, John in Austin, the live-music capital of the Vanston, taught a course in technology world. Every November, my house be- forecasting that I took and enjoyed. About comes Art 84 where I welcome 1,200 or so that time, he started Technology Futures, visitors as part of the East Austin Studio Inc. (TFI). I graduated and took a dream Tour featuring art, music, and dance. I’m job in network planning at Bell Labs. After also on the board of Fisterra Projects, the 1984 Bell System breakup, I moved an Austin-based arts-and-sciences non- back to Austin, joined TFI, and been there profit. And I love to travel…a good thing, ever since. Best of all, I learned technolog- since my kids, clients, and conferences are ical forecasting from the founders, who far flung!

48 FORESIGHT Spring 2019