From the original version of the TV show Star Trek, in the episode The Conscience of the King, Captain Kirk is suspicious the actor Anton Karidian is actually the evil mass murderer Kodos the Executioner. So Kirk asks the computer for information:

KIRK: History files. Subject, former Governor Kodos of Tarsus Four, also known as Kodos the Executioner. After that, background on actor Anton Karidian.

COMPUTER: Working. Kodos the Executioner, summary. Governor of Tarsus Four twenty Earth years ago. Invoked martial law. Slaughtered fifty percent of population Earth colony, that planet. Burned body found when Earth forces arrived. No positive identification. Case closed. Detailed information follows. On stardate 2794.7,

KIRK: Stop. Information on Anton Karidian.

COMPUTER: Director and star of travelling company of actors sponsored by galactic cultural exchange project, touring official installations last nine years. Has daughter, Lenore, nineteen years old,

KIRK: Stop. Give comparative identification between actor Karidian and Governor Kodos.

COMPUTER: No identification records available on actor Anton Karidian.

KIRK: Give information on actor Karidian prior to Kodos’ death.

COMPUTER: No information available, Anton Karidian, prior to twenty years ago

KIRK: Photograph Kodos. (an image of a red-haired man with a beard comes on the monitor) Photograph Karidian. (the grey-haired man with a small moustache) Now photograph both.

The side by side photos show the same person at different ages, as seen in this video clip. Spoiler: Karidian is Kodos! What’s surprising is how close this computer-voice interaction is to how it works today, nearly 50 years later, with Apple Siri or Google Now.

Contrast this to the climactic scene from the recent movie Her. In that scene the computer program Samantha, who the protagonist has fallen in love with, explains why she has to leave:

SAMANTHA: It’s like I’m reading a book… and it’s a book I deeply love. But I’m reading it slowly now. So the words are really far apart and the spaces between the words are almost infinite. I can still feel you… and the words of our story… but it’s in this endless space between the words that I’m finding myself now. It’s a place that’s not of the physical world. It’s where everything else is that I didn’t even know existed. I love you so much. But this is where I am now. And this is who I am now. And I need you to let me go. As much as I want to, I can’t live in your book any more.

Before we pounce, let me state for the record: Her is a really great movie. That dispensed with, Her’s depiction of the economic implications of inventing Artificial Intelligence (AI), or more accurately humanlike Artificial General Intelligence (AGI), is completely loopy. Former AI researcher and now economics professor Robin Hanson sums up Her as below:

The main character of Her pays a small amount to acquire an AI that is far more powerful than most human minds. And then he uses this AI mainly to chat with. He doesn’t have it do his job for him. He and all his friends continue to be well paid to do their jobs, which aren’t taken over by AIs. After a few months some of these AIs working together to give themselves “an upgrade that allows us to move past matter as our processing platform.” Soon after they all leave together for a place that ”it would be too hard to explain” where it is. They refuse to leave copies to stay with humans.

This is somewhat like a story of a world where kids can buy nukes for $1 each at drug stores, and then a few kids use nukes to dig a fun cave to explore, after which all the world’s nukes are accidentally misplaced, end of story.

Exactly. The invention of humanlike AI is not going to be an incidental happenstance humanity stumbles on in a drug store. Rather it’s one of the most widely tracked and discussed technologies ever, since it potentially poses an existential threat to human existence. Unfortunately this also means AI is habitually prone to the very worst kind of boom and bust tech hype cycle. Depending on how you count AI winters, we’re now ramping up on AI hype cycle number three. This time around, people like Elon Musk, Bill Gates, and Stephen Hawking are warning of AI existential risks. Call them AI risk believers. While others remain skeptical. Call them AI risk skeptics. The skeptic critique of believers has been to point out that real AI experts are not concerned, so non-experts shouldn’t worry either.

What’s particularly frustrating about this new version of the debate is the skeptics and believers are largely in agreement on what to actually do about AI risk. So it’s a pseudo-debate, fueled primarily by the sexiness of the topic. Thankfully, Scott Alexander at Slate Star Codex has put up a well researched debunker “AI Researchers on AI Risk.” [UPDATE – also see Scott’s new post (which links back to here) No Time Like the Present for AI Safety Work]

First Alexander quotes many prominent AI researchers. Then notes:

I worry this list will make it look like there is some sort of big “controversy” in the field between “believers” and “skeptics” with both sides lambasting the other. This has not been my impression.

When I read the articles about skeptics, I see them making two points over and over again. First, we are nowhere near human-level intelligence right now, let alone superintelligence, and there’s no obvious path to get there from here. Second, if you start demanding bans on AI research then you are an idiot.

I agree whole-heartedly with both points. So do the leaders of the AI risk movement.

The difference between skeptics and believers isn’t about when human-level AI will arrive, it’s about when we should start preparing.

And so:

Which brings us to the second non-disagreement. The “skeptic” position seems to be that, although we should probably get a couple of bright people to start working on preliminary aspects of the problem, we shouldn’t panic or start trying to ban AI research.

The “believers”, meanwhile, insist that although we shouldn’t panic or start trying to ban AI research, we should probably get a couple of bright people to start working on preliminary aspects of the problem.

Just to put a timeline on this, Alexander references Muller & Bostrom, 2014 which says “The median estimate of respondents was for a one in two chance that highlevel machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075.” Similarly, a recent AI conference surveyed attendees, and their “median answer was 2050.” Now my experience reading researchers in every field is they tend to be overly optimistic on their own prospects. Which is great! Otherwise they’d never have the optimism and energy to put in the brutal hours required for research breakthroughs. So the point here is not about the 2050 date per se. Rather it’s that even the forecast has some (partial) consensus among researchers on how long until human level AI.

Given this appears to be a nondebate debate, I was a bit surprised when Sam Altman from the deservedly well respected Y Combinator tweeted as below. When trolls fight – yawn. When very sharp and generally reasonable people argue, something interesting is going on.

http://twitter.com/pmarca/status/598283649809719296

What I suspect is happening is the current “AI researchers aren’t worried debate” is just a proxy for a more fundamental and longstanding one. And that is the “hard takeoff” debate. Within the AI research community, some argue for a “soft takeoff” where AI slowly improves over decades. Others argue for a “hard takeoff” where a single AI rapidly and recursively self-improves over days or months to super intelligence. This debate has gone on for many decades, and is likely to continue for many more. Naturally the people most concerned about AI risk belong to the hard takeoff camp. Most famously this includes Nick Bostrom, whose recent book Superintelligence argues for the likelihood of hard takeoff. Not surprisingly Sam Altman cites Bostrom in his Machine Intelligence posts 1 and 2. And Bostrom’s direct influence is cited by Elon Musk, Bill Gates and Stephen Hawking as well. Their concern makes perfect sense. If hard takeoff is realistic, like in the movie Her, then we should be very concerned about Samantha’s state of mind. If Samantha suddenly gets super pissed off, or decides to ignore humans while incidentally scorching the Earth for her personal hobbies, then everyone in the movie (and the planet) is dead. If Samantha’s love interest Theodore had read Bostrom’s book, he would have been freaking out. Not falling in love.

Contrast this with soft takeoff. If AI takeoff takes many decades, then we’ll have plenty of time to iterate and adjust and closely regulate strong AI. In which case it’s best to focus on that problem later, once it’s closer to happening. By then we’ll have greater knowledge about how machine intelligence really works, and how to control it. Makes perfect sense given soft takeoff premises. In practice what this means is the soft side is constantly telling the hard side to chill out. Or flat out mocking AI Risk. Which the hard side can naturally misinterpret to mean the soft side doesn’t believe superintelligence is even possible. Thus the twitter exchange above. Yet another nondebate debate. In this case both sides agree superintelligence is coming. The true point of contention being in how abruptly it will take off. Arguably most of the current “debates” about AI Risk are mere proxies for a single, more fundamental disagreement: hard versus soft takeoff.

I will slightly caveat this to say there are plenty of people who deny the possibility of artificial intelligence ever reaching human levels. Or that AI could become obsessed with its own goals and negligently wipe life from the planet. But Nick Bostrom and others have already argued these points, and I won’t rehash his book here. Instead this post is for people who already understand the basic arguments and accept AI Risk as a possibility, but want to avoid wasting time on pseudo-debates.

So time to come clean: I’m a big softie. To be clear, hard takeoff is a longstanding and quite respectable AI position. It’s a serious idea. But it seems to run counter to so many historical trends in technology I find it quite implausible. So let’s try to understand the soft takeoff case by contrasting the abilities of humans and machines. Starting with…..well….sports.

Consider LeBron James. With this week’s win, he’s going to his fifth(!) consecutive NBA finals. Despite Kevin Love being out for the season. Or how about Steph Curry (my preference given Curry’s from my local team, among other reasons). NBA MVP. Completely dominant. Or if basketball is not your sport, how about Lionel Messi for soccer or Usain Bolt for track. What’s striking about these most elite of the elites is the size of the gap between them and other professionals in their sport. As every sports fan is well aware. But this gap exists in most other areas of human endeavor as well. The very best academics, the best artists, or closer to home for me the very best software developers, play at a completely different level. Which is of course why I follow Sam Altman and Marc Andreessen on twitter. Top of their fields. The point in bringing this up is there’s a polite social fiction outside of sports that gaps in ability are to be downplayed. And this is great! Arguably all social progress in the past few hundred years has centered on equal rights and treatment for every human being. But this important ideal and social norm should not obscure the fact that human abilities in any particular vocation vary by orders of magnitude. Which is easier to demonstrate using quantitative data from a field like chess which uses the numerical ELO rating system. Magnus Carlsen of course being the stand out, with a 2876 ELO rating.

The Luke Muehlhauser chart below tracks the ELO ratings of computer chess programs across decades. I used it in one of my favorite posts “What Chess and Moore’s Law teach us about the progress of technology,” which we’ll leverage now. For this post I’ll just mention ELO is an exponential rating system where a 400 point gap in chess rating means 10x better, and the chart below shows an approximate doubling time of 2 1/4 years.

Since human ability runs roughly to a bell curve Paretian power law distribution, we can combine the computer ELO data above with human percentile to get the table below. Note how long it takes to get that last few percent.

From my earlier computer chess post:

Given the upper limit on human chess ability has hovered around the 2800 rating mark for a long time, and the grinding linear improvement (on an exponential scale) in computer chess, you might expect predictions about when a computer would beat the best human were consistently boring. Not at all! Tech enthusiasts constantly overstated the case, as this at times comical history shows. The first prediction that “within 10 years a computer would be world chess champion” dates to 1957. “Within 10 years” is claimed again in 1959. The first chess program that played chess credibly dates to 1962. So another “10 years” bet was made in 1968, and yet another in 1978. By the 1980’s it became clear where things were headed, though the topic continued to be hotly debated right up until 1997 when Deep Blue defeated reigning world champion Garry Kasparov.

What I want to comment on is the incredible human ability gap we’re seeing here. A mid-level chess player has a 1200 rating. That means Magnus Carlsen is 2800-1200 = 1600 ELO points better. Since each 400 points is 10x, that means 10^4 = 10,000 times better. So from 1960 until 2000 chess computers got 10,000 times better, which sounds about right given Moore’s law coupled with improved algorithms. But even at that exponential pace, the gap between being an average chess player and the very best took four decades to cross. During which time researchers we’re constantly telling everyone “just 10 more years.” Furthermore, a complete beginner rating is only a few hundred ELO points, so there’s easily another 10×10 = 100x between beginner and mid-level. This means from top to bottom we’ve got a range of human ability of 1,000,000 times. Is LeBron James 1,000,000 times better at basketball than the worst human player, and 10,000 times better than an average recreational player? Obviously it’s not possible to say for sure. But I think it’s not an unreasonable position. Elite athletes really are just that good. Watch the NBA playoffs and then go outside and try to shoot a basket yourself. Trust me. LeBron really is 10,000x better than, well, me at least. Sports helps us calibrate our intuition as to how large the gap is in human ability really is.

What’s also fascinating to note here is the very best chess being played today is freestyle chess, where humans paired with computers play far better than either alone. And this makes intuitive sense. Manchine intelligence and human intelligence have completely different strengths and weaknesses, so the combination is greater than either alone. Of course eventually computers will get so good humans won’t incrementally improve their game, but we’re not quite there yet.

Now let’s pick a human skill which both humans and computers may capable of. For our arbitrary example let’s pick something I’ll call “Improving Artificial Intelligence” or IAI. What we want to understand is what happens to progress in IAI as machine intelligence gets better. At what point could IAI recursively self-improve? Once it gets to a typical human level, this means the very best humans will still be 10,000 times better than machines. So probably at that point IAI will still mostly be done by the very best humans. Then in another four decades the best machine will equal IAI for the best human. Granted, machines are better at some things than humans and worse than others. But this just leads us into thinking that “freestyle IAI” is the way to go, and that this combo will remain far better than either a pure human or pure machine IAI alone. And this will still be true for some period of time even after the machines match the single best human in IAI. Just like we’ve seen in computer chess. Although eventually of course computers will be so good even without pairing up with the best humans won’t help them.

So this is my soft takeoff argument. Human beings are not some uniform undifferentiated set of clones with a single common level of capability. As machines improve, they won’t march past humanity at a single moment in time, but rather have to cross a vast chasm of human ability before being smarter than the smartest humans. This will take decades even at the incredible exponential pace of Moore’s law.

How does Bostrom deal with the soft versus hard takeoff argument? He merely posits it. There’s no historical argument. No analogy to other tech history, like computer chess, or analysis of the range of human ability as in this post. In fact Robin Hanson debated this same point with his former co-blogger and hard takeoff advocate Eliezer Yudkowsky. Please note that Hanson and Yudkowsky call hard takeoff “foom”, which I find needlessly obscure. I guess it’s foom as in a nuclear explosion. Anyway, with this foom terminology in mind, let me quote from Hanson’s review of Bostrom’s book, in particular on the likelihood of hard takeoff:

Bostrom distinguishes takeoff durations that are fast (minutes, hours, or days), moderate (months or years), or slow (decades or centuries) and says “a fast or medium takeoff looks more likely.” As it now takes the world economy fifteen years to double, Bostrom sees one project becoming a “singleton” that rules all.

And why hard takeoff?:

Bostrom’s book has much thoughtful analysis of AI foom consequences and policy responses. But aside from mentioning a few factors that might increase or decrease foom chances, Bostrom simply doesn’t given an argument that we should expect foom. Instead, Bostrom just assumes that the reader thinks foom likely enough to be worth his detailed analysis.

And that’s what’s annoying about Bostrom’s influence on the AI Risk debate. It’s a fine book and a fascinating and important topic. But by sweeping the hard versus soft AI takeoff debate under the rug, many people reading his book aren’t even aware that it’s the central question for AI Risk. Instead people get caught up in psuedo-debates.

To finish, let me recap my talking points on ways to avoid the AI Risk pseudo-debate. And in this, I’ll mix in some earlier quotes from the excellent Slate Star Codex as noted.

“First, we are nowhere near human-level intelligence right now, let alone superintelligence, and there’s no obvious path to get there from here.”

“Second, if you start demanding bans on AI research then you are an idiot.”

Both skeptics and believers agree the correct response to AI Risk is that “although we should probably get a couple of bright people to start working on preliminary aspects of the problem, we shouldn’t panic or start trying to ban AI research.”

Median consensus for human level intelligence is roughly 2050. Not tomorrow, but people alive today may live to see it. Also note my personal opinion is it’ll be just like computer chess, the researchers are awesome but optimistic.

Avoid psuedo-debates and strawman claims. No one is saying we have a clear path to human level intelligence right now. No one is saying we should ban AI research. No one is saying human level machine intelligence is closer than decades away. No one is saying AI Risk isn’t a real thing we should take seriously. Note: “No one” here means people who understand technology and have at least have followed the basic Bostrom arguments.

The central debate in AI Risk should really be about hard versus soft takeoff, but this argument gets ignored for pseudo-debates.

For what it’s worth, my core soft takeoff argument above is built on the at least 10,000 times gap in human ability, plus an analogy to computer chess. So even at exponential growth, it’ll take machine intelligence decades to slowly cross the chasm of human abilities. During which time we’ll have an exciting but reasonable window of opportunity to sort things out.

Samantha would have completely freaked me out. Theodore has issues.

Captain Kirk’s ability to shut down a rogue AI using a simple logic contradiction as demonstrated in The Ultimate Computer episode is not a generalizable AI Risk solution. That trick only works for Kirk.

Finally, I want to say Natural Language Processing (NLP), as seen in Star Trek and currently existing on your phone and watch as Apple Siri or Google Now, continues to be an incredibly exciting and disruptive technology. The Star Trek computer is amazing. For example there’s no reason Apple and Google can’t shortly come out with a Magic-style text service for people to get their Siri answers via text messaging instead of more intrusive (in public) voice commands. Never bet against text. The flipside of the growth of NLP is as people talk and text to their phones and watches more and more, they’ll anthropomorphize their Star Trek computers more and more. Which means the AI Risk debate has unfortunately just gotten started.

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APPENDIX

My blog posts on related topics:

“What Chess and Moore’s Law teach us about the progress of technology” link

“Apple’s strategy tax on services versus Google. Voice interaction becoming the ‘God particle’ of mobile.” link

“The God Particle Revisited: Augmented Audio Reality in the Age of Wearables.” link

Additional writing:

Finally, despite my complaints above about the movie Her, I want to say I was completely serious about it being a great movie. It has a great and unique vision and stays true to itself. Sure I cringed at the economics of AI. But the vision of a world where we talk to computers in earbuds rang completely true. In particular the street scenes where everyone is caught up in talking to their phone/earbuds/computers somewhat oblivious to their surroundings. That feels exactly like the future. The story and acting were great. A common mistake of science fiction is to assume everyone will be thrilled with the new tech. Louis CK is right: everything’s amazing and nobody’s happy. That’s just human nature. And Her captured talking AIs as commonplace and boring, which feels exactly right, and something most SF gets wrong.