4 ms·

Systems that outperform doctors in some specific area of diagnostics aren't new. One of the earliest examples of such systems is Mycin [1], which also was devel

by romaniv 10y ago

Systems that outperform doctors in some specific area of diagnostics aren't new. One of the earliest examples of such systems is Mycin [1], which also was developed at Stanford, but around forty-something years ago. Never went to production because of practical issues that have nothing to do with its accuracy. It's interesting that all of those "practical issues" are no longer relevant, and yet we don't see a widespread use of similar software. [1] - https://en.wikipedia.org/wiki/Mycin https://en.wikipedia.org/wiki/Mycin

lucidrains 10y ago
Comparing apples and oranges here. Mycin is an expert system dealing with changing rulesets with A LOT of manual teaching. The current papers deals with computers discerning visual patterns by itself.
- throwaway729 10y ago
  I disagree. Data isn't magic fairy dust, and romaniv is correct -- plenty of expert systems-based approaches did perform well in practice. Many of the barriers standing in the way of wide-spread use diagnostic software are not technological in nature. Ignore those barriers at your own risk.
  - lucidrains 10y ago
    I am assuming you are referring to the expert system used for infectious diseases. They performed well, but could not adapt to the constantly changing guidelines, medications. Data input was inefficient back in the days of terminals. It took about 5 thousands rules to be on par with a junior infectious disease doctor if I remember my studies on this subject. The deep neural net visual diagnostics are different. They are learned on pure pixels as we would from photons striking our retina, signals traveling all the way back to our visual cortex for learning. There is no assembling of thousands of rules here and therefore the system is less brittle. These new systems get more powerful with more data given to them. Expert systems required humans to craft rules from the data, and therefore required constant maintenance and is fallible to human error. So yes, some of the barriers are definitely technological in nature.
    - romaniv 10y ago
      >It took about 5 thousands rules to be on par with a junior infectious disease doctor if I remember my studies on this subject. IIRC, MYCIN had several hundred rules. The researchers in this article had to pre-process 130,000 labeled examples. If you see a misclassification in an expert system you can at least backtrack and identify the individual rules that contributed to the failure. AFAIK, systemic errors in training data are much more difficult to detect and fix. I think people tend to overstate the practical issues with expert systems and understate the issues with deep learning, partly because we have decades of experience with real-life deployments of the former and relatively little experience with the latter.
      - lucidrains 10y ago
        Oh yea, I'm confusing it with INTERNIST. https://en.wikipedia.org/wiki/Internist-I https://en.wikipedia.org/wiki/Internist-I Yes, the explainability of expert systems is the only thing going for it. Yes, there are issues with both, but we are really debating different solutions for different problems. For visual recognition, there is no doubt in my mind that deep learning is king.
brandonb 10y ago
I wrote a bit about the systematic issues preventing promising AI research from moving to production in medicine, including a very brief history of MYCIN: https://blog.cardiogr.am/three-challenges-for-artificial-intelligence-in-medicine-dfb9993ae750 https://blog.cardiogr.am/three-challenges-for-artificial-int... I think now really is different. Part of that is algorithmic advances like deep learning, as shown in this Nature paper. An even larger part of it is that the financial incentives are flipping due to value-based care. In 1979, a hospital that implemented an expert system for accurate diagnosis may, paradoxically, see its revenue fall. Nowadays, with ACOs, risk-based contracting, and bundled payments, the financial incentives create tailwinds rather than headwinds for large-scale adoption of AI in medicine. Contrary to popular belief, the medical system can absorb new techniques very quickly--when incentives are aligned. And they are now becoming aligned.
- deleted 10y ago
  [deleted]
leereeves 10y ago
I hope someday soon we'll develop systems that allow us to "ask" a ML algorithm what factors led to a decision (diagnosis in this case). It would be interesting to compare that with the current state of the art in the field, and see if ML can contribute new scientific/medical theory as well.
- derefr 10y ago
  I'm not so sure we could ask the algorithm itself, in any literal sense. An algorithm trained to introspect might actually be wrong about its own "memories" or "motives"—just like a human might! (Though, likely, without the penchant toward political rationalizing.) This is most simply because, whatever the algorithm is trained to do, it's certainly trained better to do that thing than to introspect. Introspection is a separate skill! But there's also a more insidious element: introspection (in humans, at least) tends to result in the creation of a lot of "personal concepts" that don't map to well-known common concepts. An introspection on one mind must necessarily result in a taxonomy that contains terms for the tiny, unique features that only that mind has—which makes it very, very hard to communicate one's personal introspections to others. (You might call this a kind of overfitting: the introspection capability becomes optimized for that one mind, but ceases to translate well to features in other minds—like human minds.) I'd place a much stronger bet on our ability to train one AI to "stare at the brain" of other AIs as they make decisions [tons of them, as its training data], with the expected output being a general theory on common AI features responsible for the given calculation step. A computer psychologist, of sorts. :) Of course, you could include such a pre-trained model as a "module" alongside the AI itself, and call the combined system "one AI" if you like.
  - leereeves 10y ago
    > Introspection is a separate skill! Indeed it is. It's something that a second algorithm (perhaps ML, perhaps not) would do. And this is beginning to remind me of Society of Mind. https://en.wikipedia.org/wiki/Society_of_Mind https://en.wikipedia.org/wiki/Society_of_Mind
- romaniv 10y ago
  Well, there are ML algorithms that can be interrogated about their reasoning. One type is symbolic systems that use data to generate or "tune" their rules. Another one is stuff like this: https://www.youtube.com/watch?v=UqPcq0n59rQ https://www.youtube.com/watch?v=UqPcq0n59rQ (it's a kind of ensemble system that uses a linear combination of human-interpretable classifiers). Both kinds are pretty interesting, because you can manually inspect and tune them after training.

by romaniv 10y ago

lucidrains 10y ago

Comparing apples and oranges here. Mycin is an expert system dealing with changing rulesets with A LOT of manual teaching. The current papers deals with computers discerning visual patterns by itself.

throwaway729 10y ago
I disagree. Data isn't magic fairy dust, and romaniv is correct -- plenty of expert systems-based approaches did perform well in practice. Many of the barriers standing in the way of wide-spread use diagnostic software are not technological in nature. Ignore those barriers at your own risk.
- lucidrains 10y ago
  I am assuming you are referring to the expert system used for infectious diseases. They performed well, but could not adapt to the constantly changing guidelines, medications. Data input was inefficient back in the days of terminals. It took about 5 thousands rules to be on par with a junior infectious disease doctor if I remember my studies on this subject. The deep neural net visual diagnostics are different. They are learned on pure pixels as we would from photons striking our retina, signals traveling all the way back to our visual cortex for learning. There is no assembling of thousands of rules here and therefore the system is less brittle. These new systems get more powerful with more data given to them. Expert systems required humans to craft rules from the data, and therefore required constant maintenance and is fallible to human error. So yes, some of the barriers are definitely technological in nature.
  - romaniv 10y ago
    >It took about 5 thousands rules to be on par with a junior infectious disease doctor if I remember my studies on this subject. IIRC, MYCIN had several hundred rules. The researchers in this article had to pre-process 130,000 labeled examples. If you see a misclassification in an expert system you can at least backtrack and identify the individual rules that contributed to the failure. AFAIK, systemic errors in training data are much more difficult to detect and fix. I think people tend to overstate the practical issues with expert systems and understate the issues with deep learning, partly because we have decades of experience with real-life deployments of the former and relatively little experience with the latter.
    - lucidrains 10y ago
      Oh yea, I'm confusing it with INTERNIST. https://en.wikipedia.org/wiki/Internist-I https://en.wikipedia.org/wiki/Internist-I Yes, the explainability of expert systems is the only thing going for it. Yes, there are issues with both, but we are really debating different solutions for different problems. For visual recognition, there is no doubt in my mind that deep learning is king.

brandonb 10y ago

I wrote a bit about the systematic issues preventing promising AI research from moving to production in medicine, including a very brief history of MYCIN: https://blog.cardiogr.am/three-challenges-for-artificial-intelligence-in-medicine-dfb9993ae750 https://blog.cardiogr.am/three-challenges-for-artificial-int... I think now really is different. Part of that is algorithmic advances like deep learning, as shown in this Nature paper. An even larger part of it is that the financial incentives are flipping due to value-based care. In 1979, a hospital that implemented an expert system for accurate diagnosis may, paradoxically, see its revenue fall. Nowadays, with ACOs, risk-based contracting, and bundled payments, the financial incentives create tailwinds rather than headwinds for large-scale adoption of AI in medicine. Contrary to popular belief, the medical system can absorb new techniques very quickly--when incentives are aligned. And they are now becoming aligned.

deleted 10y ago
[deleted]

leereeves 10y ago

I hope someday soon we'll develop systems that allow us to "ask" a ML algorithm what factors led to a decision (diagnosis in this case). It would be interesting to compare that with the current state of the art in the field, and see if ML can contribute new scientific/medical theory as well.

derefr 10y ago
I'm not so sure we could ask the algorithm itself, in any literal sense. An algorithm trained to introspect might actually be wrong about its own "memories" or "motives"—just like a human might! (Though, likely, without the penchant toward political rationalizing.) This is most simply because, whatever the algorithm is trained to do, it's certainly trained better to do that thing than to introspect. Introspection is a separate skill! But there's also a more insidious element: introspection (in humans, at least) tends to result in the creation of a lot of "personal concepts" that don't map to well-known common concepts. An introspection on one mind must necessarily result in a taxonomy that contains terms for the tiny, unique features that only that mind has—which makes it very, very hard to communicate one's personal introspections to others. (You might call this a kind of overfitting: the introspection capability becomes optimized for that one mind, but ceases to translate well to features in other minds—like human minds.) I'd place a much stronger bet on our ability to train one AI to "stare at the brain" of other AIs as they make decisions [tons of them, as its training data], with the expected output being a general theory on common AI features responsible for the given calculation step. A computer psychologist, of sorts. :) Of course, you could include such a pre-trained model as a "module" alongside the AI itself, and call the combined system "one AI" if you like.
- leereeves 10y ago
  > Introspection is a separate skill! Indeed it is. It's something that a second algorithm (perhaps ML, perhaps not) would do. And this is beginning to remind me of Society of Mind. https://en.wikipedia.org/wiki/Society_of_Mind https://en.wikipedia.org/wiki/Society_of_Mind
romaniv 10y ago
Well, there are ML algorithms that can be interrogated about their reasoning. One type is symbolic systems that use data to generate or "tune" their rules. Another one is stuff like this: https://www.youtube.com/watch?v=UqPcq0n59rQ https://www.youtube.com/watch?v=UqPcq0n59rQ (it's a kind of ensemble system that uses a linear combination of human-interpretable classifiers). Both kinds are pretty interesting, because you can manually inspect and tune them after training.