For me he puts words onto something I've felt recently, that what we're doing is cool and all, but just doesn't feel like the right way to approach it. We're just putting loads of data and computing power into something that produces results that looks intelligent, but digging deeper bares no resemblence to what a neuro scientist would call intelligent..
> Even Google Translate, which pulls off the neat trick of approximating translations by statistically associating sentences across languages, doesn’t understand a word of what it is translating.
This is just another incarnation of "AI is the thing we haven't done." He's parroting Chomsky's disdain for statistical models and John Searle's fundamental misunderstanding of AI. For the former, Norvig has a fair rundown of Chomsky's complaints (http://norvig.com/chomsky.html).
> bears no resemblance to what a neuroscientist would call intelligent
TensorFlow gets results. The neuroscientist can claim it's a P-zombie, but they need to point to some criteria for accepting something as intelligence. Otherwise we're just moving goalposts.
>This is just another incarnation of "AI is the thing we haven't done."
I don't think so - it appears to be an objectively correct assessment of the current state of the art.
> Otherwise we're just moving goalposts.
The first movement of the goalposts was to call '80s technology AI. Now they are drifting back to where they started.
On the other hand, I am surprised by the claim that AI is stuck; my outsider's impression is that progress has accelerated. Perhaps the impression of being stuck comes from more people realizing how difficult a problem it is.
Deep learning made practical a large number of applications that were previously intractable by neural network approaches. Advances over the last 10 years have pushed the boundaries of what machine learning systems are capable of doing. However machine learning has algorithmic limits to what it can accomplish, and we are starting to hit those limits. A change in paradigm is required to begin making real progress again. Either a change to something new or a regression to older ideas that were temporarily put on the back burner.
That's not a universally held view, but I think it is the sentiment behind this editorialized title.
How deep an understanding is required to meet the threshold? The skepticism feels like "no true Scotsman" applied to the definition of understanding.
I observe the following in young children when exposed to a new word:
0. First exposure to totally new word used in a sentence with more familiar words.
1. Brief pause
2. Mimic pronunciation 1-2 times
3. Process for minutes, hours, or days.
4. Use the word in a less than 100% correct way
5a. Maybe hear the phrase repeated back with the error "corrected" (hello internet)
5b. Maybe hear more usage of the word in passing from others (with varying degrees of "correctness")
6. Recurse for life.
At what point did the person understand the word? How is AI translation substantially different?
I'm not sure I understand any word in a way that would satisfy AI skeptics.
Ironically, your last sentence has 'no true Scotsman'-like reasoning, along the lines of 'no true AI sceptic would fairly evaluate a claim of machine understanding.'
BTW, I am not a skeptic of the potential of AI, though I am skeptical of some claims being made.
We can understand how individual chemical reactions in Einstein's brain work, but that doesn't make us smarter than him.
As for sentience, we don't understand it, so the only way we'd recognize it is if it was extremely similar to human sentience.
If your objectives are in medicine, cognitive science or philosophy of the mind, you might want simulations which are isomorphic to biological minds. You probably hope that AI work will provide illumination into how the mind works, or why it sometimes fails, or how to improve it.
If your goals are in computing and product engineering, you want predictable, reproducible, and adaptive methods for making smarter tools on time and on budget. You may want the product to have behaviors compatible with humans (as a product feature) but you shouldn't care whether the implementation technique in any way resembles an actual human mind. Behaviorism is all that matters for a product evaluation. The design and marketing teams can take care of imbuing the product with intangible properties imagined by consumers.
And honestly, if you want a biological mind, we already have techniques to build them: go find a mate, procreate, and raise your offspring. Nobody tasked to deliver a commercial AI product is actually going to want a solution that behaves like real human minds, where individual units off the same assembly line may require psychotherapy, develop self-destructive habits, or worse slip through QA with an undetected sociopathy or psychopathy which creates a manufacturer liability.
Some of us old school engineering types may harbor a disdain for the current neural net renaissance because it feels a little too black box to us. Deep down, we'd prefer a tool-building tool that had more directly visible logic and rules in it, because we tend to believe (rightfully or not) that such a method is more amenable to engineering practices and iterative designs. But, the risk in this mindset is in forgetting that even complex, logical systems can exhibit emergent properties and chaotic behavior. We probably need to engage in more statistical methods whether we like it or not...
But 10 years is a long time in internet years. I would not be at all surprised if there's another word2vec (or two) arose before then to displace the tectonic plates yet again.
If you honestly think the author should be discredited because you don't find is PhD appealing, you need to take a step back and seriously readjust your arrogance level.
Achievements are perhaps the best yardstick for expertise. Whether a person has worked at a startup or has contrarian viewpoints are both irrelevant.
Gary Marcus was co-founder and CEO of Geometric Intelligence, successfully raised some money and grew a team, [1] then successfully sold the company to Uber, after which he directed Uber's AI lab. He has a PhD from MIT in cognitive science. And he's been a professor of Neural Science at NYU for nearly 20 years. [2]
I'm no expert, but he seems like one to me.
[1] https://www.crunchbase.com/organization/geometric-intelligen... [2] https://www.linkedin.com/in/gary-marcus-b6384b4/