A little background... I’ve been on-and-off learning about data science for around a year or so, however, I started thinking about artificial intelligence a few years ago. I have a cursory understandings of some common concepts but still not much depth. When I first learned about deep learning, my automatic response was “that’s not how our minds do it.” Deep learning is obviously an important topic, but I’m trying to think outside the black box.

I think of deep learning as being “outside-in” in that a model has to rely on examples to understand (for lack of a better term) that some dataset is significant. However, our minds seem to know when something is significant in the absence of any prior knowledge of the thing (i.e., “inside-out”).

Here’s a thing:

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I googled “IKEA hardware” to find that. The point is that you probably don’t know what this is or have any existing mental relationship between the image and anything else, but you can see that it’s something (or two somethings). I realize there is unsupervised learning, image segmentation, etc., which deal with finding order in unlabeled data, but I think this example illustrates the difference between the way we tend to think about machine learning/AI and how our minds actually work.

More examples:


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Let’s say that #1 is a stock chart. If I were viewing the chart and trying to detect a pattern, I might mentally simplify the chart down to #2. That is, the chart can be simplified into a horizontal segment and a rising segment.

For #3, let’s say this represents log(x). Even though it’s not a straight line, someone with no real math background could describe it as an upward slope that it decreasing as the line gets higher. That is, the line can still be reduced to a small number of simple ideas.

I think this simplification is the key to the gap between how our minds work and what currently exists in AI. I’m aware of Fourier transforms, polynomial regression, etc., but I think there’s a more general process of finding order in sensory data. Once we identify something orderly (i.e., something that can’t reasonably be random noise), we label it as a thing and then our mental network establishes relationships between it and other things, higher order concepts, etc.

I’ve been trying to think about how to use decision trees to find pockets of order in data (to no avail yet - I haven’t figured out to apply it to all of the scenarios above), but I’m wondering if there are any other techniques or schools of thought that align with the general theory.


1 Answer 1


It sounds like you are interested in the ideas of intrinsic motivation and attention in the context of machine learning. These are big topics, and the subject of much active research.

Intrinsic motivation says that the key to identifying interesting patterns and skills that are worth learning is to give the agent some intrinsic reason to learn to do new things. This is not dissimilar from what humans have: learning new things, and improving or exercising our capabilities to the fullest is what Aristotle identified as the good life. There are thus good reasons to think that intrinsic motivation for AI might solve the problem you identify. Current research in this domain is exploring different mathematical ways to represent intrinsic motivation.

Attention was the subject of a large burst of research in deep neural networks during the last few years. Here's a recent talk from AWS at ICML that provides a good overview. The idea here is that an agent can learn both a reasonable mapping from inputs to outputs for some problem, and a separate mapping that describes how different future inputs should "activate" certain parts of the input/output mapping that the agent has learned. Essentially attention-driven models include a second component that learns which features of the input to pay attention to when engaging in certain kinds of tasks.

  • $\begingroup$ I was not familiar with this area of research. It definitely seems like a necessary component of developing more human-like AI, but it's kind of beyond what I'm asking about. Before an AI can figure out which concepts to focus on, it needs to be able to process concepts. When I use the term "significant," I'm just referring to data that displays some kind of order, whether that be a line that can be described with a simple formula, an object that has similar visual properties within itself (e.g., the object above is roughly the same color with differing degrees of shadow), etc. $\endgroup$ Oct 6, 2019 at 2:38
  • $\begingroup$ @SuperCodeBrah perhaps you are interested in unsupervised learning approaches like the Self Organizing Map en.wikipedia.org/wiki/Self-organizing_map, or the Autoencoder en.wikipedia.org/wiki/Autoencoder? $\endgroup$ Oct 8, 2019 at 17:22
  • $\begingroup$ I think autoencoders could be relevant but the issue is that they still need training. I think for humans, we might not know what something is on a contextual level if we can't link it to other, familiar things, but we can likely still see its salient sensory features immediately (i.e., inability to contextualize or verbalize doesn't necessarily imply an absence of clarity in perception). What's the rationale for including self-organizing maps? $\endgroup$ Oct 9, 2019 at 0:49
  • $\begingroup$ @SuperCodeBrah Both autoencoders and self-organizing maps are part of a more general family of unsupervised methods in machine learning. To train them, we don't need someone to tell us what data means, we just need data. Eventually, these methods learn to recognize interesting patterns in the data (salient sensory features). This seems to me like what you may be asking about. $\endgroup$ Oct 9, 2019 at 0:51
  • $\begingroup$ Also, I added a very specific version of this question to datascience.stackexchange regarding markets or other time series. I'd be interested in hearing your thoughts on that: datascience.stackexchange.com/questions/61461/…. The way I'm framing the question doesn't necessarily capture non-linear functions like log(x) but perhaps a machine could achieve a recognition of non-linear functions by recognizing patters of near-linear segments based on their specific signatures as I tried to describe in the question. $\endgroup$ Oct 9, 2019 at 0:55

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