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Comment by platz | Hacker News Reader

Skeptical. Previously the brain was a machine consisting of drives and pulleys, just like the state of technology of the day. Now the brain is a computer that runs deep learning models. It would be one thing to just say the cortex has columns. It's another to go on and model what those columns are doing with linear algebra. The coincidence that this picture emerges at the same time as current fads in tech is too great to ignore

I am more interested in the work that is identifying different kinds of cells e.g place cells

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> The coincidence that this picture emerges at the same time as current fads in tech is too great to ignore

It's no coincidence, I see it as a kind of reframing the issue from a different perspective/approach and for that, we use whatever seems the best framework of explanation we have at hand during that time. Gotta start somewhere, can't try to paint a picture without a frame and at least some colors.

One can easily see the evolution how the frameworks we use keep getting more complex, from drives and pulleys to computers and NNs, so there clearly is some progress in how we are trying to describe the workings of the brain/consciousness.

In the end, it's also about what's the actual goal here: Trying to understand the human brain/consciousness or trying to artificially create "consciousness" or rather something resembling it. The later doesn't necessarily require the former.

Not consciousness, but intelligence. Big difference.

Is it? Personal theory is consciousness scales, like we're more conscious than a bug, but a bug still definitely conscious.

Then like A.I. overlords / borg hive mind more conscious still.

Maybe it does, but does intelligence require consciousness?

And: Is the question empirical or analytic? It is hard to discuss such matters without an agreed framework, definitions. The definitions for these terms are a long-standing analytic conundrum.

Abstractly, it is hard to understand how any performance could require consciousness unless consciousness is defined as a class of performance. If it is, then the question boils down to "Is performance class I a proper subset of performance class C?" Which is, prima facie, an analytic question.

How far down does the scale go? Are brainless animals like oysters conscious? What about the mycelium network underneath a forest?

Well maybe better way to state, less 'conscious' and more 'things they're conscious of.'

Not sure why you were downvoted. It's important to keep the two terms separate for the sake of discourse, even if "ultimately" they turn out to be the same.

Can something be more conscious but less intelligent than something else, or viceversa? We don't know for sure yet.

Are there commonly agreed definitions for those terms in the AI/ML community? I don't know, that's why I'm never sure what terms to use.

Cognitive scientists define consciousness as the ability to have subjective experiences. The definition is not mathematically concrete, but sufficient to differentiate from intelligence.

Previously the world was made from Earth, Water, Fire and Air. Then quarks and electrons. Now they are talking about how the entire universe is a computer, and how each Plank area is just a qubit.

The coincidence that this picture emerges at the same time as current fads in tech is too great to ignore.

I agree, it is suspicious, though let's distinguish between hard science and soft sciences, and between observation and theory

Alternatively if your real goal is just to develop some novel ML techniques, it would be better to not make claims about how the brain works and instead just try to draw inspiration using these thought experiments more as a 'muse'

> The coincidence that this picture emerges at the same time as current fads in tech is too great to ignore

Neural networks weren't nearly as much of a fad back in 2004 when Hawkins published On Intelligence. They did a fantastic job of illustrating how the neocortex may be a hierarchy of these pattern recognizers (columns). It's a highly engaging read!

Unfortunately you're skeptical since you're mistaking philosophy for science.

Here: [https://arxiv.org/pdf/1706.06208.pdf], for example - they use deep learning models in order to classify the behaviour of in vivo neurons.

I do believe that our understanding on how our brain works is very close to what ML is doing.

There is probably a lot to learn about the underlying core structure but the basics will not change that much anymore.

In the sense that there are neurons firing off each other sure. But it was known (and modelled in hardware) since early 1950s.

There is however a qualitative difference in the topology and function, or our very modest wetware wouldn't have been able to outperform about every deep learning model out there.

Sax Russell, is that you?

A practically identical train of thought from that character shows up in one of KSR's Mars books. Alway found it an excellent point.

Also laid out quite well in the 1990 movie "Mindwalk". [1]

A movie still worth watching even though it is dated.

1. http://www.imdb.com/title/tt0100151/?ref_=nv_sr_3

Do you have a reference for "when the brain was drives and pulleys"? I've never heard of such a theory.

I think he's referring to this https://en.wikipedia.org/wiki/Mechanism_(philosophy)

In particular, this Descartes quote "I should like you to consider that these functions (including passion, memory, and imagination) follow from the mere arrangement of the machine’s organs every bit as naturally as the movements of a clock or other automaton follow from the arrangement of its counter-weights and wheels."

> place cells

Suiting, given that your username means "place" in German :)

I'm not an expert, but isn't the causality reversed? Neural Nets were developed based on research of the physical structures in the brain, right? If that's the case, it makes sense to make analogies between the two systems.

Yes and no. Neural nets are somewhat inspired by neurons, but it's a pretty loose relation, and the more NNs improve, the less similar to the brain they look. Individual neurons are much more complex than the nodes of a neural network, and even if research into NNs provides insight into the way the brain works, it may be that the analogues of NN nodes are clusters or regions of neurons, or something more mathematically abstract.

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