Spectrum: Another point you’ve made regarding the failure of neural realism is that there is nothing very neural about neural networks.
Michael Jordan: There are no spikes in deep-learning systems. There are no dendrites. And they have bidirectional signals that the brain doesn’t have.
We don’t know how neurons learn. Is it actually just a small change in the synaptic weight that’s responsible for learning? That’s what these artificial neural networks are doing. In the brain, we have precious little idea how learning is actually taking place.
[---]
Spectrum: What are some of the things that people are promising for big data that you don’t think they will be able to deliver?
Michael Jordan: I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.
Spectrum: What will happen if people working with data don’t heed your advice?
Michael Jordan: I like to use the analogy of building bridges. If I have no principles, and I build thousands of bridges without any actual science, lots of them will fall down, and great disasters will occur.
Similarly here, if people use data and inferences they can make with the data without any concern about error bars, about heterogeneity, about noisy data, about the sampling pattern, about all the kinds of things that you have to be serious about if you’re an engineer and a statistician—then you will make lots of predictions, and there’s a good chance that you will occasionally solve some real interesting problems. But you will occasionally have some disastrously bad decisions. And you won’t know the difference a priori. You will just produce these outputs and hope for the best.
And so that’s where we are currently. A lot of people are building things hoping that they work, and sometimes they will. And in some sense, there’s nothing wrong with that; it’s exploratory. But society as a whole can’t tolerate that; we can’t just hope that these things work. Eventually, we have to give real guarantees. Civil engineers eventually learned to build bridges that were guaranteed to stand up. So with big data, it will take decades, I suspect, to get a real engineering approach, so that you can say with some assurance that you are giving out reasonable answers and are quantifying the likelihood of errors.
[---]
Spectrum: When you say “intelligent,” are you just using it as a synonym for “useful”?
Michael Jordan: Yes. What our generation finds surprising—that a computer recognizes our needs and wants and desires, in some ways—our children find less surprising, and our children’s children will find even less surprising. It will just be assumed that the environment around us is adaptive; it’s predictive; it’s robust. That will include the ability to interact with your environment in natural language. At some point, you’ll be surprised by being able to have a natural conversation with your environment. Right now we can sort of do that, within very limited domains. We can access our bank accounts, for example. They are very, very primitive. But as time goes on, we will see those things get more subtle, more robust, more broad. As some point, we’ll say, “Wow, that’s very different when I was a kid.” The Turing test has helped get the field started, but in the end, it will be sort of like Groundhog Day—a media event, but something that’s not really important.
- More Here
Michael Jordan: There are no spikes in deep-learning systems. There are no dendrites. And they have bidirectional signals that the brain doesn’t have.
We don’t know how neurons learn. Is it actually just a small change in the synaptic weight that’s responsible for learning? That’s what these artificial neural networks are doing. In the brain, we have precious little idea how learning is actually taking place.
[---]
Spectrum: What are some of the things that people are promising for big data that you don’t think they will be able to deliver?
Michael Jordan: I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.
Spectrum: What will happen if people working with data don’t heed your advice?
Michael Jordan: I like to use the analogy of building bridges. If I have no principles, and I build thousands of bridges without any actual science, lots of them will fall down, and great disasters will occur.
Similarly here, if people use data and inferences they can make with the data without any concern about error bars, about heterogeneity, about noisy data, about the sampling pattern, about all the kinds of things that you have to be serious about if you’re an engineer and a statistician—then you will make lots of predictions, and there’s a good chance that you will occasionally solve some real interesting problems. But you will occasionally have some disastrously bad decisions. And you won’t know the difference a priori. You will just produce these outputs and hope for the best.
And so that’s where we are currently. A lot of people are building things hoping that they work, and sometimes they will. And in some sense, there’s nothing wrong with that; it’s exploratory. But society as a whole can’t tolerate that; we can’t just hope that these things work. Eventually, we have to give real guarantees. Civil engineers eventually learned to build bridges that were guaranteed to stand up. So with big data, it will take decades, I suspect, to get a real engineering approach, so that you can say with some assurance that you are giving out reasonable answers and are quantifying the likelihood of errors.
[---]
Spectrum: When you say “intelligent,” are you just using it as a synonym for “useful”?
Michael Jordan: Yes. What our generation finds surprising—that a computer recognizes our needs and wants and desires, in some ways—our children find less surprising, and our children’s children will find even less surprising. It will just be assumed that the environment around us is adaptive; it’s predictive; it’s robust. That will include the ability to interact with your environment in natural language. At some point, you’ll be surprised by being able to have a natural conversation with your environment. Right now we can sort of do that, within very limited domains. We can access our bank accounts, for example. They are very, very primitive. But as time goes on, we will see those things get more subtle, more robust, more broad. As some point, we’ll say, “Wow, that’s very different when I was a kid.” The Turing test has helped get the field started, but in the end, it will be sort of like Groundhog Day—a media event, but something that’s not really important.
- More Here
No comments:
Post a Comment