Forbes blogger Alex Knapp, who often covers advanced technology and futurist topics, recently wrote a post titled Ray Kurzweil’s Predictions for 2009 Were Mostly Inaccurate... Some of Knapp’s posts are annoyingly opinionated and closed-minded, but this one was well-put together, and I made a lengthy comment there, which I repeat here.
You should read his post first to get the context… And also, once you read his post, you might want to read Ray’s rebuttal to Michael Anissimov’s earlier critique of his predictions.
Ray rates himself as 90% right out of 100+ predictions; Michael looks at only a handful of Ray’s predictions and finds most of them unfulfilled.
Looking at the “90% right” that Ray claims, it seems to me about half of these are strong wins, and the other half are places where the technologies Ray has forecast DO now exist, but aren’t as good or as prevalent as he had envisioned.
On the other hand, Alex Knapp in Forbes took Ray’s top 10 predictions rather than the full 100+, and found a lower accuracy for these.
An excerpt from my comment to Alex’s post on the Forbes site (with light edits) is:
One thing that should be clarified for the general readership is that the vast majority of those of us in the “Singularitarian” community do not, and never did, buy into all of Ray Kurzweil’s temporally-specific predictions. We love Ray dearly and respect him immensely—and I think the world owes Ray a great debt for all he’s done, not only as an inventor, but to bring the world’s attention to the Singularity and related themes. However, nearly all of us who believe a technological Singularity is a likely event this century, prefer to shy away from the extreme specificity of Ray’s predictions.
Predicting a Singularity in 2045 makes headlines, and is evocative. Predicting exactly which technologies will succeed by 2009 or 2019 makes headlines, and is evocative. But most Singularitarians understand that predictions with this level of predictions aren’t plausible to make.
The main problem with specific technology forecasts, is highlighted by thinking about multiple kinds of predictions one could make in reference to any technology X:
1) How long would it take to develop X if a number of moderately large, well-organized, well-funded teams of really smart people were working on it continuously?
2) How long would it take to develop X if a large, well-funded, bloated, inefficient government or corporate bureaucracy were working on it continuously?
3) How long would it take to develop X if there were almost no $$ put into the development of X, so X had to be developed by ragtag groups of mavericks working largely in their spare time?
4) How long would it take to develop X if a handful of well-run but closed-minded large companies dominated the X industry with moderately-functional tools, making it nearly impossible to get funding for alternate, radical approaches to X with more medium-term potential
When thinking about the future of a technology one loves or wants, it’s easy to fall into making predictions based on Case 1. But in reality what we often have is Case 2 or 3 or 4.
Predicting the future of a technology is not just about what is “on the horizon” in terms of science and technology, but also about how society will “choose” to handle that technology. That’s what’s hard to predict.
For example a lot of Ray’s failed top predictions had to do with speech technology. As that is pretty close to my own research area, I can say pretty confidently that we COULD have had great text to speech technology by now. But instead we’ve had Case 4 above—a few large companies have dominated the market with mediocre HMM-based text to speech systems. These work well enough that it’s hard to make something better, using a deeper and more ultimately promising approach, without a couple years effort by a dedicated team of professionals. But nobody wants to fund that couple years effort commercially, because the competition from HMM based systems seems too steep. And it’s not the kind of work that is effectively done in universities, as it requires a combination of engineering and research.
Medical research, unfortunately, is Case 2. Pharma firms are commonly bloated and inefficient and shut off to new ideas, partly because of their co-dependent relationship with the FDA. Radical new approaches to medicine have terrible trouble getting funded lately. You can’t get VC $$ for a new therapeutic approach until you’ve shown it to work in mouse trials or preferably human trials—so how do you get the $$ to fund the research leading up to those trials?
Artificial General Intelligence, my main research area, is of course Case 3. There’s essentially no direct funding for AGI on the planet, so we need to get AGI research done via getting funding for other sorts of projects and cleverly working AGI into these projects…. A massive efficiency drain!!
If speech-to-text, longevity therapy or AGI had been worked on in the last 10 years with the efficiency that Apple put into building the iPad, or Google put into building its search and ad engines, then we’d be a heck of a lot further advanced on all three.
Ray’s predictive methodology tries to incorporate all these social and funding related factors into its extrapolations, but ultimately that’s too hard to do, because the time series being extrapolated aren’t that long and depend on so many factors.
However, the failure of many of his specific predictions, does not remotely imply he got the big picture wrong. Lots of things have developed faster than he or anyone thought they would in 2009, just as some developed more slowly.
To my mind, the broad scope of exponential technological acceleration is very clear and obvious, and predicting the specifics is futile and unnecessary—except, say, for marketing purposes, or for trying to assess the viability of a particular business in a particular area.
The nearness of the Singularity does not depend on whether text-to-speech matures in 2009 or 2019—nor on whether AGI or longevity pills emerge in 2020 or 2040.
To me, as a 45 year old guy, it matters a lot personally whether the Singularity happens in 2025, 2045 or 2095. But in the grand scope of human history, it may not matter at all….
The overall scope and trend of technology development is harder to capsulize in sound bites and blog posts than specific predictions—hence we have phenomena like Ray’s book with its overly specific predictions, and your acute blog post refuting them.
Anyway, anyone who is reading this and not familiar with the issues involved, I encourage you to read Ray’s book the Singularity is Near—and also Damien Broderick’s book “The Spike.”
Broderick’s book made very similar points around a decade earlier,—but it didn’t get famous. Why? Because “Spike” sounds less funky than “Singularity”, because the time wasn’t quite ripe then, and because Broderick restricted himself to pointing out the very clear general trends rather than trying and failing to make overly precise predictions!
After I wrote this, Ray Kurzweil wrote his own rebuttal of Alex’s argument.
Ray then emailed me, thanking me for my defense of his predictions, but questioning my criticism of his penchant for focusing on precise predictions about future technology. I’m copying my reply to Ray here, as it may be of general interest…
I wrote that blog post in a hurry and in hindsight wish I had framed things more carefully there…. But of course, it was just a personal blog post not a journalistic article, and in that context a bit of sloppiness is OK I guess…
Whether YOU should emphasize precise predictions less is a complex question, and I don’t have a clear idea about that. As a maverick myself, I don’t like telling others what to do! You’re passionate about predictions and pretty good at making them, so maybe making predictions is what you should do .... And you’ve been wonderfully successful at publicizing the Singularity idea, so obviously there’s something major that’s right about your approach, in terms of appealing to the mass human psyche.
I do have a clear feeling that the making of temporally precise predictions should play a smaller role in discussion of the Singularity than it now does. But this outcome might be better achieved via the emergence of additional, vocal Singularity pundits alongside you, with approaches complementing your prediction-based approach—rather than via you toning down your emphasis on precise prediction, which after all is what comes naturally to you…
One thing that worries me about your precise predictions is that in some cases they may serve to slow progress down. For example, you predict human-level AGI around 2029—and to the extent that your views are influential, this may dissuade investors from funding AGI projects now ... because it seems too far away! Whereas if potential AGI investors more fully embraced the uncertainty in the timeline to human-level AGI, they might be more eager for current investment.
Thinking more about the nature of your predictions ... one thing that these discussions of your predictive accuracy highlights is that the assessment of partial fulfillment of a prediction is extremely qualitative. For instance, consider a prediction like “The majority of text is created using continuous speech recognition.” You rate this as partially correct, because of voice recognition on smartphones. Alex Knapp rates this as “not even close.” But really—what percentage of text do you think is created using continuous speech recognition, right now? If we count on a per character basis, I’m sure it’s well below 1%. So on a mathematical basis, it’s hard to justify “1%” as a partially correct estimate of “>50%. Yet in some sense, your prediction *is* qualitatively partially correct. If the prediction had been “Significant subsets of text production will be conducted using continuous speech recognition”, then the prediction would have to be judged valid or almost valid.
One problem with counting partial fulfillment of predictions, and not specifying the criteria for partial fulfillment, is that assessment of predictive accuracy then becomes very theory-dependent. Your assessment of your accuracy is driven by your theoretical view, and Alex Knapp’s is driven by his own theoretical view.
Another problem with partial fulfillment is that the criteria for it, are usually determined *after the fact*. To the extent that one is attempting scientific prediction rather than qualitative, evocative prediction, it would be better to rigorously specify the criteria for partial fulfillment, at least to some degree, in advance, along with the predictions.
So all in all, if one allows partial fulfillment, then precise predictions become not much different from highly imprecise, explicitly hand-wavy predictions. Once one allows partial matching via criteria defined subjectively on the fly, “The majority of text will be created using continuous speech recognition in 2009” becomes not that different from just saying something qualitative like “In the next decade or so, continuous speech recognition will become a lot more prevalent.” So precise predictions with undefined partial matching, are basically just a precise-looking way of making rough qualitative predictions
If one wishes to avoid this problem, my suggestion is to explicitly supply more precise criteria for partial fulfillment along with each prediction. Of course this shouldn’t be done in the body of a book, because it would make the book boring. But it could be offered in endnotes or online supplementary material. Obviously this would not eliminate the theory-dependence of partial fulfillment assessment—but it might diminish it considerably.
For example the prediction “The majority of text is created using continuous speech recognition.” could have been accompanied with information such as “I will consider this prediction strongly partially validated if, for example, more than 25% of the text produced in some population comprising more than 25% of people is produced by continuous speech recognition; or if more than 25% of text in some socially important text production domain is produced by continuous speech recognition.” This would make assessment of the prediction’s partial match to current reality a lot easier.
I’m very clear on the value of qualitative predictions like “In the next decade or so, continuous speech recognition will become a lot more prevalent.” I’m much less clear on the value of trying to make predictions more precisely than this. But maybe most of your readers actually, implicitly interpret your precise predictions as qualitative predictions… in which case the precise/qualitative distinction is largely stylistic rather than substantive.