When someone describes their worries about AI taking over the world, I usually think to myself “I recently bookmarked a good article about why this is silly and I should point this person to it”, but in that instant I can’t remember what the article was. I recently re-read a few and thought I’d summarize them here in case anyone wants to point their friends to some sensible discussions of why such worries are unfounded.

The impossibility of intelligence explosion by François Chollet

Chollet is an AI researcher at Google and the author of the Keras deep learning framework and the Manning books “Deep Learning with Python” and “Deep Learning with R”. Like some of the other articles covered here, his piece takes on the idea that we will someday build an AI system that can build a better one on its own, and then that one will build a better one, and so on until the singularity.

His outline gives you a general idea of his line of reasoning; the bulleted lists in his last two sections are also good:

• A flawed reasoning that stems from a misunderstanding of intelligence

• Intelligence is situational

• Our environment puts a hard limit on our individual intelligence

• Most of our intelligence is not in our brain, it is externalized as our civilization

• An individual brain cannot implement recursive intelligence augmentation

• What we know about recursively self-improving systems

• Conclusions

One especially nice paragraph:

In particular, there is no such thing as “general” intelligence. On an abstract level, we know this for a fact via the “no free lunch” theorem – stating that no problem-solving algorithm can outperform random chance across all possible problems. If intelligence is a problem-solving algorithm, then it can only be understood with respect to a specific problem. In a more concrete way, we can observe this empirically in that all intelligent systems we know are highly specialized. The intelligence of the AIs we build today is hyper specialized in extremely narrow tasks – like playing Go, or classifying images into 10,000 known categories. The intelligence of an octopus is specialized in the problem of being an octopus. The intelligence of a human is specialized in the problem of being human.

‘The discourse is unhinged’: how the media gets AI alarmingly wrong by Oscar Schwartz

This Guardian piece focuses on how the media encourages silly thinking about the future of AI. As the article’s subtitle tells us,

Social media has allowed self-proclaimed ‘AI influencers’ who do nothing more than paraphrase Elon Musk to cash in on this hype with low-quality pieces. The result is dangerous.

Much of the article focuses on the efforts of Zachary Lipton, a machine learning assistant professor at Carnegie Mellon, to call out bad journalism on the topic. One example is an article that I was also guilty of taking too seriously: Fast Company’s AI Is Inventing Languages Humans Can’t Understand. Should We Stop It? The actual “language” was just overly repetitive sentences made possible by recursive grammar rules, which I had experienced myself many years ago doing a LISP-based project for a Natural Language Processing course. Schwartz quotes the Sun article Facebook shuts off AI experiment after two robots begin speaking in their OWN language only they can understand as saying that the incident “closely resembled the plot of The Terminator in which a robot becomes self-aware and starts waging a war on humans”. (The Sun article also says “Experts have called the incident exciting but also incredibly scary”; according to the Guardian article, “These findings were considered to be fairly interesting by other experts in the field, but not totally surprising or groundbreaking”.)

Schwartz’s piece describes how the term “electronic brain” is as old as electronic computers, and how overhyped media coverage of machines that “think” as far back as the 1940s led to inflated expectations about AI that greatly contributed to the several AI winters we’ve had since then.

Ways to Think About Machine Learning by Benedict Evans

If you’re going to read only one of the articles I describe here all the way through, I recommend this one. I don’t listen to every episode of the a16z podcast, but I do listen to every one that includes Benedict Evans (this week’s episode, on Tesla and the Nature of Disruption, was typically excellent), and I have subscribed to his newsletter for years. He’s a sharp guy with sensible attitudes about how technologies and societies fit together and where it may lead.

One theme of many of the articles I describe here is the false notion that intelligence is a single thing that can be measured on a one-dimensional scale. As Evans puts it,

This gets to the heart of the most common misconception that comes up in talking about machine learning - that it is in some way a single, general purpose thing, on a path to HAL 9000, and that Google or Microsoft have each built *one*, or that Google ‘has all the data’, or that IBM has an actual thing called ‘Watson’. Really, this is always the mistake in looking at automation: with each wave of automation, we imagine we’re creating something anthropomorphic or something with general intelligence. In the 1920s and 30s we imagined steel men walking around factories holding hammers, and in the 1950s we imagined humanoid robots walking around the kitchen doing the housework. We didn’t get robot servants - we got washing machines.

Washing machines are robots, but they’re not ‘intelligent’. They don’t know what water or clothes are. Moreover, they’re not general purpose even in the narrow domain of washing - you can’t put dishes in a washing machine, nor clothes in a dishwasher (or rather, you can, but you won’t get the result you want). They’re just another kind of automation, no different conceptually to a conveyor belt or a pick-and-place machine. Equally, machine learning lets us solve classes of problem that computers could not usefully address before, but each of those problems will require a different implementation, and different data, a different route to market, and often a different company. Each of them is a piece of automation. Each of them is a washing machine.

After bringing up relational databases as a point of comparison for what new technology can do (“Relational databases gave us Oracle, but they also gave us SAP, and SAP and its peers gave us global just-in-time supply chains - they gave us Apple and Starbucks”), he asks “What, then, are the washing machines of machine learning, for real companies?” He offers some good suggestions, some of which can be summarized as “AI will allow the automation of more things”.

He also discusses low-hanging fruit for what new things AI may automate. As an excellent followup to that, I recommend Kathryn Hume’s Harvard Business Review article How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist.

The Myth of a Superhuman AI by Kevin Kelly

In this Wired Magazine article by one of their founders, after a discussion of some of the panicky scenarios out there we read that “buried in this scenario of a takeover of superhuman artificial intelligence are five assumptions which, when examined closely, are not based on any evidence”. He lists them, then lists five “heresies [that] have more evidence to support them”; these five provide the structure for the rest of his piece:

• Intelligence is not a single dimension, so “smarter than humans” is a meaningless concept.

• Humans do not have general purpose minds, and neither will AIs.

• Emulation of human thinking in other media will be constrained by cost.

• Dimensions of intelligence are not infinite.

• Intelligences are only one factor in progress.

A good point about how artificial general intelligence is not something to worry about makes a nice analogy with artificial flight:

When we invented artificial flying we were inspired by biological modes of flying, primarily flapping wings. But the flying we invented – propellers bolted to a wide fixed wing – was a new mode of flying unknown in our biological world. It is alien flying. Similarly, we will invent whole new modes of thinking that do not exist in nature. In many cases they will be new, narrow, “small,” specific modes for specific jobs – perhaps a type of reasoning only useful in statistics and probability.

(This reminds me of Evans writing “We didn’t get robot servants - we got washing machines”.) Another good metaphor is Kelly’s comparison of attitudes about superhuman AI with cargo cults:

It is possible that superhuman AI could turn out to be another cargo cult. A century from now, people may look back to this time as the moment when believers began to expect a superhuman AI to appear at any moment and deliver them goods of unimaginable value. Decade after decade they wait for the superhuman AI to appear, certain that it must arrive soon with its cargo.

19 A.I. experts reveal the biggest myths about robots by Guia Marie Del Prado

This Business Insider piece is almost three years old but still relevant. Most of the experts it quotes are actual computer scientist professors, so you get much more sober assessments than you’ll see in the panicky articles out there. Here’s a good one from Berkeley computer scientist Stuart Russell:

The most common misconception is that what AI people are working towards is a conscious machine, that until you have a conscious machine there’s nothing to worry about. It’s really a red herring.

To my knowledge, nobody, no one who is publishing papers in the main field of AI, is even working on consciousness. I think there are some neuroscientists who are trying to understand it, but I’m not aware that they’ve made any progress.

As far as AI people, nobody is trying to build a conscious machine, because no one has a clue how to do it, at all. We have less clue about how to do that than we have about build a faster-than-light spaceship.

From Pieter Abbeel, another Berkeley computer scientist:

In robotics there is something called Moravec’s Paradox: “It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”.

This is well appreciated by researchers in robotics and AI, but can be rather counter-intuitive to people not actively engaged in the field.

Replicating the learning capabilities of a toddler could very well be the most challenging problem for AI, even though we might not typically think of a one-year-old as the epitome of intelligence.

I was happy to see the article quote NYU’s Ernie Davis, whose AI class I took over 20 years ago while working on my master’s degree there. (Reviewing my class notebook I see a lot of LISP and Prolog code, so things have changed a lot.)

This article implicitly has a nice guideline for when to take predictions about the future of AI seriously: are they computer scientists familiar with the actual work going on lately? If they’re experts in other fields engaging in science fiction riffing (or as the Guardian article put it more cleverly, paraphrasing Elon Musk), take it all with a big grain of salt.

I don’t mean to imply that the progress of technologies labeled as “Artificial Intelligence” has no potential problems to worry about. Just as automobiles and chain saws and a lot of other technology invented over the years can do harm as well as good, the new power brought by advanced processors, storage, and memory can be misused intentionally or accidentally, so it’s important to think through all kinds of scenarios when planning for the future. In fact, this is all the more reason not to worry about sentient machines: as the Guardian piece quotes Lipton, “There are policymakers earnestly having meetings to discuss the rights of robots when they should be talking about discrimination in algorithmic decision making. But this issue is terrestrial and sober, so not many people take an interest.” Sensible stuff to keep in mind.