The biggest problem in AI? Machines have no common sense.
Correlation doesn't equal causation — we all know this. Well, except robots.
Dr. Gary Marcus is the director of the NYU Infant Language Learning Center, and a professor of psychology at New York University. He is the author of "The Birth of the Mind," "The Algebraic Mind: Integrating Connectionism and Cognitive Science," and "Kluge: The Haphazard Construction of the Human Mind." Marcus's research on developmental cognitive neuroscience has been published in over forty articles in leading journals, and in 1996 he won the Robert L. Fantz award for new investigators in cognitive development.
Marcus contributed an idea to Big Think's "Dangerous Ideas" blog, suggesting that we should develop Google-like chips to implant in our brains and enhance our memory.
GARY MARCUS: The dominant vision in the field right now is, collect a lot of data, run a lot of statistics, and intelligence will emerge. And I think that's wrong. I think that having a lot of data is important, and collecting a lot of statistics is important. But I think what we also need is deep understanding, not just so-called "deep learning." So deep learning finds what's typically correlated, but we all know that correlation is not the same thing as causation. And even though we all know that and everybody learned that in Intro to Psych, or should have learned that in Intro to Psych they should have learned that you don't know whether cigarettes cause smoking just from the statistics we have. We have to make causal inferences and do careful studies. We all know that causation and correlation are not the same thing.
Have right now as AIs-- giant correlation machines. And it works, if you have enough control of the data relative to the problem that you're studying that you can exhaust the problem, to beat the problem into submission. So you can do that with go. You could play this game, over and over again, the rules never change. They haven't changed in 2,000 years. And the board is always the same size. And so you can just get enough statistics about what tends to work, and you're good to go. But if you want to use the same techniques for natural language understanding, for example, or to guide a domestic robot through your house, it's not going to work. So the domestic robot in your house is going to keep seeing new situations. And your natural language understanding system, every dialogue is going to be different. It's not really going to work. So yeah, you can talk to Alexa and you can say, please turn on my over and over again, get statistics on that. It's fine. But there's no machine in the world T=that we're having right now. It's just not anywhere near a reality and you're not going to be able to do it with the statistics, because there's not enough similar stuff going on.
Probably the single best thing that we could do to make our machines smarter is to give them common sense, which is much harder than it sounds like. I mean, first you might say, what is common sense? And what we settled on in the book that we wrote is that common sense is the knowledge that's commonly held. that ordinary people have. And yet machines don't. So machines are really good at things like, I don't know, converting metrics -- you know, converting from the English system to the metric system. Things that are nice, and precise, and factual, and easily stated. But things that are a little bit less sharply stated, like how you open a door, machines don't understand the first thing. So there's actually a competition right now for opening doors. So if somebody uploaded data sets from 500 different doors, and they're hoping that robots will experiment with all 500 doors and then get it. But what's more likely is that they'll get to door 501 and they'll actually have a problem or at least door 601. So every ordinary person in the west has opened a bunch of door knobs and gets the idea, right? I need to turn something, jiggle something -- might be different for the next one -- until the door itself is free.
So you can give a definition of something like that. It's hard to give a perfect definition. But we all know that. And yet nobody's ever built a robot that can do that. We made a joke about it in the book. We said, you know, in the event of a robot attack do the following six things and number one was close the door. And we added that you might need to lock it. And that was before this whole database came out. So, you know, the field advances. People are working on that. Maybe next year they'll work on teaching robots about locks. But I bet you it will take a while before robots understand all the little ways it can jiggle the key, and maybe you need to pull in the door to make it just right. We don't know how to even encode that information in a language that a computer can understand. So the big challenge of common sense is to take stuff like that-- like, how do you open the door, why do you want to open a door-- and translate into the language of the machine. It's a lot harder than a metric converter. It's a lot harder than a database. And right now the field's not even really trying to answer that question. It's so obsessed with what it can do with these big databases, which are exciting in themselves, that it's kind of lost sight of that, even though the question itself that goes back to the 1950s, when one of the founders of A.I., John McCarthy, first started writing about it in the late '50s.
But it's not-- common sense is not getting the attention that it deserves and that's one of the reasons we wrote the book. Common sense is just one step along the way to intelligence. People talk about artificial intelligence. And sometimes they talk about artificial general intelligence. There's also narrow AI. And narrow AI is the stuff that we're doing pretty well now. So, do the same problem over and over again, just solving one problem. You could think about idiot Savants that can do a calendar but can't do anything else and can tell you what day you were born on if you give them your birthday. We have a lot of narrow AI right now. We can't do narrow AI for everything that we want to do. But the dream is to have broad AI, or general AI, that can solve any problem. You think about the Star Trek computer. You can say, you know, please give me the demographics in this galaxy and cross-correlate it with this, and with that, and tell me this. And Star Trek computer says, OK. And it figures it out. So the Star Trek computer understands everything about language, and it understands pretty much everything about how the world works. And it can put those together to give you an answer.
It's not like Google, right? Google can search for pages that have the information. But then you have to put the information together. The Star Trek computer can synthesize it. And it's a great example of general intelligence. If I ask you, did George Washington have a cell phone? Well, if you have a kind of common knowledge of when cellphones were introduced, when George Washington was alive, the fact that he's dead, then you could compute the information and give me the right answer. If you Google for it, you might get a wacky answer. If it works for George Washington, maybe you search for, did Thomas Jefferson have a cell phone? And the answer should obviously be the same. But maybe that one won't be on a Google page. So if you have common sense about things like time, and space, and a lot of sort of everyday factual knowledge, that gets you a long way to intelligence. It doesn't give you all the way there. So general intelligence, first of all, has many dimensions to it. So you can think like, the SAT has verbal and math.
Those are two of the of intelligence that's Really not well-established right now in the AI community is common sense. There are other aspects of intelligence, like doing pure calculation, where the A.I. community has done a good job. But there are other things that go into intelligence as well. For example, the ability to read a graph is partly about common sense, and it's partly about understanding what people might intend, it's partly sometimes about expert knowledge. So reading a graph is another form of intelligence. And that's going to require putting together better perceptual tools than we have now, better common sense tools, probably some knowledge about politics, for example, if you're reading a graph that's relevant to the latest political campaign, and so forth. So there's a lot of stuff Right now we're trying to approximate it all with statistics. But it's never general knowledge. So you could learn to read one graph, Going help you read the next graph. So general intelligence is going to be putting together a lot of things, both some that we already understand pretty well in the A.I. community and some we just haven't been working on and really need to get back to.
- There are a lot of people in the tech world who think that if we collect as much data possible, and run a lot of statistics, that we will be able to develop robots where artificial "intelligence" organically emerges.
- However, many A.I.'s that currently exist aren't close to being "intelligent," it's difficult to even program common sense into them. The reason for this is because correlation doesn't always equal causation — robots that operate on correlation alone may have skewed algorithms in which to operate in the real world.
- When it comes to performing simple tasks, such as opening a door, we currently don't know how to encode that information — the varied process that is sometimes required in differing situations, i.e. jiggling the key, turning the key just right — into a language that a computer can understand.
Gary Marcus is the author of Rebooting AI: Building Artificial Intelligence We Can Trust.
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Most elderly individuals' brains degrade over time, but some match — or even outperform — younger individuals on cognitive tests.
- "Super-agers" seem to escape the decline in cognitive function that affects most of the elderly population.
- New research suggests this is because of higher functional connectivity in key brain networks.
- It's not clear what the specific reason for this is, but research has uncovered several activities that encourage greater brain health in old age.
At some point in our 20s or 30s, something starts to change in our brains. They begin to shrink a little bit. The myelin that insulates our nerves begins to lose some of its integrity. Fewer and fewer chemical messages get sent as our brains make fewer neurotransmitters.
As we get older, these processes increase. Brain weight decreases by about 5 percent per decade after 40. The frontal lobe and hippocampus — areas related to memory encoding — begin to shrink mainly around 60 or 70. But this is just an unfortunate reality; you can't always be young, and things will begin to break down eventually. That's part of the reason why some individuals think that we should all hope for a life that ends by 75, before the worst effects of time sink in.
But this might be a touch premature. Some lucky individuals seem to resist these destructive forces working on our brains. In cognitive tests, these 80-year-old "super-agers" perform just as well as individuals in their 20s.
Just as sharp as the whippersnappers
To find out what's behind the phenomenon of super-agers, researchers conducted a study examining the brains and cognitive performances of two groups: 41 young adults between the ages of 18 and 35 and 40 older adults between the ages of 60 and 80.
First, the researchers administered a series of cognitive tests, like the California Verbal Learning Test (CVLT) and the Trail Making Test (TMT). Seventeen members of the older group scored at or above the mean scores of the younger group. That is, these 17 could be considered super-agers, performing at the same level as the younger study participants. Aside from these individuals, members of the older group tended to perform less well on the cognitive tests. Then, the researchers scanned all participants' brains in an fMRI, paying special attention to two portions of the brain: the default mode network and the salience network.
The default mode network is, as its name might suggest, a series of brain regions that are active by default — when we're not engaged in a task, they tend to show higher levels of activity. It also appears to be very related to thinking about one's self, thinking about others, as well as aspects of memory and thinking about the future.
The salience network is another network of brain regions, so named because it appears deeply linked to detecting and integrating salient emotional and sensory stimuli. (In neuroscience, saliency refers to how much an item "sticks out"). Both of these networks are also extremely important to overall cognitive function, and in super-agers, the activity in these networks was more coordinated than in their peers.
An image of the brain highlighting the regions associated with the default mode network.
How to ensure brain health in old age
While prior research has identified some genetic influences on how "gracefully" the brain ages, there are likely activities that can encourage brain health. "We hope to identify things we can prescribe for people that would help them be more like a superager," said Bradford Dickerson, one of the researchers in this study, in a statement. "It's not as likely to be a pill as more likely to be recommendations for lifestyle, diet, and exercise. That's one of the long-term goals of this study — to try to help people become superagers if they want to."
To date, there is some preliminary evidence of ways that you can keep your brain younger longer. For instance, more education and a cognitively demanding job predicts having higher cognitive abilities in old age. Generally speaking, the adage of "use it or lose it" appears to hold true; having a cognitively active lifestyle helps to protect your brain in old age. So, it might be tempting to fill your golden years with beer and reruns of CSI, but it's unlikely to help you keep your edge.
Aside from these intuitive ways to keep your brain healthy, regular exercise appears to boost cognitive health in old age, as Dickinson mentioned. Diet is also a protective factor, especially for diets delivering omega-3 fatty acids (which can be found in fish oil), polyphenols (found in dark chocolate!), vitamin D (egg yolks and sunlight), and the B vitamins (meat, eggs, and legumes). There's also evidence that having a healthy social life in old age can protect against cognitive decline.
For many, the physical decline associated with old age is an expected side effect of a life well-lived. But the idea that our intellect will also degrade can be a much scarier reality. Fortunately, the existence of super-agers shows that at the very least, we don't have to accept cognitive decline without a fight.
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