6 ms·

I enjoy that we are seemingly aware about our lack of understanding of the workings of the brain but are happy and eager to proclaim the advent of intelligent m

I enjoy that we are seemingly aware about our lack of understanding of the workings of the brain but are happy and eager to proclaim the advent of intelligent machines who will replace humans in all aspects imaginable. What we are really building are pretty good pattern recognition machines, often with surprising capabilities. But we overreach when we equate that with intelligence. At least it's useful to discuss the ethics due to buzzwords, media hype and industry demands before we are actually able to produce something truly artificially intelligent. Until then, we continue to add DL algorithms to our Madam Tousseau-esque collection of artificial intelligence.

michaelmcmillan 8y ago
Intelligent machines are not predicated on understanding the brain, necessarily.
- JackFr 8y ago
  This. We didn't reach the moon by studying birds.
  - neuronic 8y ago
    But we did reach it by understanding physics...
- lurquer 8y ago
  Your statement presupposes that there is an abstract notion of 'intelligence' that can manifest itself in qualitatively different types of systems. That might be true. But, so far, the only kind of 'intelligence' that seems recognizeable to us as such is the product of squishy bunches of biological neurons... maybe squisky bunches of biological neurons are essential to 'intelligence.'
  - michaelmcmillan 8y ago
    It is unlikely that our future scientific endeavours respect "What we know so far".
dorkwood 8y ago
I feel like there’s a lot of people out there under the impression that we’re on the brink of human-like AI (in the self-driving car sphere, for example). Surely there’s going to be some sort of crash when they collectively realise that’s not the case.
- ben_w 8y ago
  Hmm. Domain-specific AI is often already superhuman in performance, once trained. What we lack seems to be: (1) generalisation, as a trained AI is often useless in separate tasks; (2) efficient training, as AI take far more data to reach the same performance as a human (I have seen suggestions this is related to (1)); and (3) any idea at all what counts as self-awareness/conscious/qualia/etc., which may not be important from a performance point of view, but very much influences how people regard the AI and their future potential.
  - YeGoblynQueenne 8y ago
    How can an AI system be "superhuman in performance" when it can't generalise outside its training set and requires vast amounts of data to have good predictive accuracy on that single training set? What is really the case is that some AI systems (in particular, deep neural net classifiers) have superhuman accuracy and that again only in classification tasks. Edit: I'm not being contrary for the sake of it. I think there is a very useful insight to draw from the success of deep neural network classifiers: that it is possible to perform classification without any kind of understanding of the objects, or their classes, at all. In the past, AI researchers operated under the assumption that reasoning and inference would be required to perform this task, but we know now that such abilities are not necessary if the task is only to classify objects. It's also clear that in some cases dumb classification can replace reasoning- as long as a reasoning problem can be reduced to dumb classification. However- humans clearly have the ability to reason and draw inferences (regardless of how often we do that successfully). There must be some explanation for this. Why do we have an intelligence that goes beyond simple object recognition? What is the point of having a broad intelligence? It must mean that there is more to intelligence than tasks that can be reduced to classification. So reducing "performance" to "accuracy" (of classification) risks fudging an important difference between the "superhuman" abilities of machines and human abilities. In the end, the question is what are humans good at and why can't machines do the same things, even though they can beat us roundly in other tasks? What is the difference between tasks that are easy for machines and tasks that are easy for humans?

michaelmcmillan 8y ago

Intelligent machines are not predicated on understanding the brain, necessarily.

JackFr 8y ago
This. We didn't reach the moon by studying birds.
- neuronic 8y ago
  But we did reach it by understanding physics...
lurquer 8y ago
Your statement presupposes that there is an abstract notion of 'intelligence' that can manifest itself in qualitatively different types of systems. That might be true. But, so far, the only kind of 'intelligence' that seems recognizeable to us as such is the product of squishy bunches of biological neurons... maybe squisky bunches of biological neurons are essential to 'intelligence.'
- michaelmcmillan 8y ago
  It is unlikely that our future scientific endeavours respect "What we know so far".

dorkwood 8y ago

I feel like there’s a lot of people out there under the impression that we’re on the brink of human-like AI (in the self-driving car sphere, for example). Surely there’s going to be some sort of crash when they collectively realise that’s not the case.

ben_w 8y ago
Hmm. Domain-specific AI is often already superhuman in performance, once trained. What we lack seems to be: (1) generalisation, as a trained AI is often useless in separate tasks; (2) efficient training, as AI take far more data to reach the same performance as a human (I have seen suggestions this is related to (1)); and (3) any idea at all what counts as self-awareness/conscious/qualia/etc., which may not be important from a performance point of view, but very much influences how people regard the AI and their future potential.
- YeGoblynQueenne 8y ago
  How can an AI system be "superhuman in performance" when it can't generalise outside its training set and requires vast amounts of data to have good predictive accuracy on that single training set? What is really the case is that some AI systems (in particular, deep neural net classifiers) have superhuman accuracy and that again only in classification tasks. Edit: I'm not being contrary for the sake of it. I think there is a very useful insight to draw from the success of deep neural network classifiers: that it is possible to perform classification without any kind of understanding of the objects, or their classes, at all. In the past, AI researchers operated under the assumption that reasoning and inference would be required to perform this task, but we know now that such abilities are not necessary if the task is only to classify objects. It's also clear that in some cases dumb classification can replace reasoning- as long as a reasoning problem can be reduced to dumb classification. However- humans clearly have the ability to reason and draw inferences (regardless of how often we do that successfully). There must be some explanation for this. Why do we have an intelligence that goes beyond simple object recognition? What is the point of having a broad intelligence? It must mean that there is more to intelligence than tasks that can be reduced to classification. So reducing "performance" to "accuracy" (of classification) risks fudging an important difference between the "superhuman" abilities of machines and human abilities. In the end, the question is what are humans good at and why can't machines do the same things, even though they can beat us roundly in other tasks? What is the difference between tasks that are easy for machines and tasks that are easy for humans?