hharrison

I am aware of Minsky's work, see my other reply. I was too flippant in my comment. Perhaps I should have said intelligent behavior, rather than intelligent structures, is self-organized.

But, I don't think any approach to consciousness can be considered a large arm of cognitive science. Most cognitive scientists don't want to touch consciousness with a ten-foot pole. But of course you're right that connectionism lives on.

Let me try to explain the kind of self-organization I have in mind. Consider the fundamental question "how is behavior organized"? The behaviorists pointed to organization in the environment. Cognitivists point to organization of internal representations. Connectionists and similar approaches point to organization of neural structures. Yes, something intelligent emerges from simple, perhaps self-organized, components in this scheme. But they are unwilling to take self-organization to the level of behavior.

In my opinion, a true self-organizational approach to behavior is to say that behavior emerges from the interaction between organism and environment. This is the level at which we need to accept self-organization. It is far from the mainstream. The mainstream approach to vision, for example, starts with the retinal image and asks what can be inferred from it. Yes, maybe they say that this inference engine is itself a self-organized structure. But it still reifies an input-process-output view of cognition. The sensory system receives input, constructs a model of the world. The "higher cognitive" centers formulate plans from this model. The action system instantiates these plans.

To bring it back to the ants: The ants demonstrate what can be done without explicit planning. Modern cognitive science studies explicit planning, even if they agree that this capability emerges from simple components.

As I said in the other post, I could provide literature if you are interested in any of these specific debates.

Yes and no. I am aware of the status of the field (well, AI less so). I myself am a psychologist working on the self-organization of behavior. Yes, we have been using examples like termites and ants for years. But we are far from the mainstream. For example, in Pinker's How the Mind Works, he dismisses self-organization as "fairy dust".

Some aspects of our work have been trickling into the mainstream. But if you don't assume that the mechanisms of mind are computations on representations, then it is very difficult to get a job without compromising your principles.

And perhaps you could argue that the mainstream thinks this system of computations is the result of self-organization on some lower level. And I wouldn't disagree. But for the majority of the field that is just lip service. Their research goals are to uncover the algorithms that the mind is running. There's no self-organization at that level.

Of course there are always exceptions. But if anyone is still reading this and is interested I can provide literature staking out the relevant positions.

Yes. AI/cog sci could learn a lot from this. Intelligent structures are self-organized, the order isn't imposed from above or from some "central executive."

The analogy that a mind is like a computer is just that, an analogy. It's used to present the high-level structure of the brain in a accessible, understandable way, not as a logical argument.

Actually, this analogy is the foundation of our modern understanding of the mind and is accepted by nearly all experimental Psychologists/Cognitive Scientists. It is certainly pervasive enough to be arguing against. Few of them seriously recognize its limitations.

This analogy is not the justification for why people believe that "the mind can be replicated on a computer". And yet the article tries to disprove the latter, deep and meaningful point by attacking the relatively superficial analogy between [current] computers and minds.

I think you're missing the point. No one cares whether the mind can be replicated on a computer. Researchers/theorists care about whether the mind is a computer. The solar system can be modeled on a computer, and yet there isn't an entire theoretical approach to astronomy predicated on uncovering the algorithms running on the solar system computer. This line of discussion about the capabilities of math and computation are beside the point.

Yes, thank you. So many people here are missing the point. The debate isn't whether or not minds are simply too special to be modeled by a computer. The debate is whether or not the mind itself is a computational system

I mean, consider the solar system. Can the solar system be modeled by a computer? Yes of course. Is the solar system a computational system? Is it organized on computational principles? Of course not. These questions are not the same thing, and yet conversation about the latter always seems to get drowned out by people saying "How could you possibly deny the former!"

I feel like I should share a perspective from Cognitive Science/Psychology.

In theoretical/experimental Psychology, the dominant paradigm is a computational/representational paradigm. Take vision for example. The accepted facts are that we receive as input an impoverished view of the world, insufficient to know what's really out there. So we have to take that input and build upon it, based on assumptions and past experiences and what-have-you, until we have an internal representation of the external world. And then we can reason with this internal representation, we can refer to it when planning actions, etc. So in this view, we are not really in contact with the external world, only our reconstruction of it in our heads.

This view is probably familiar to you in some form. I am part of a group of scientists pushing an alternative view, however within Psychology we are considered fringe for questioning this dogma. We take a non-representational view of the mind. Going back to the vision debate, if you assume a stationary vantage point and a single snapshot of an "image", and if you assume that the final "output" of visual cognition is a representation in 3D-coordinates, then yes, the visual input is underspecified. However, if you assume a moving point of observation, if you realize that the really rich information is not in the snapshot but in the way the light changes over time, if you realize that in order to successfully control actions you don't need a full 3D map of the world, then there is enough input. Some of what we do is to work out mathematically that the information is there to support certain actions, then to demonstrate experimentally that indeed, people do seem to use these "shortcut" strategies that don't require intermediate representations.

Of course, there's a lot more to it than that. I could take about thermodynamics, self-organization, and lots of other interesting stuff. But what I wanted to show is that the debate about computation is alive and well within Psychology, and indeed the computation side is extremely dominant. It may take the tack of representationalism vs non-representationalism, however the representational theories are firmly computational. Research has the explicit goal of figuring out what is the storage/transmission format of these representations? What operations are performed on the input to create them? What operations are performed on them to use them? Etc.

Also, despite what some of the comments here suggest, at issue is not whether or not computers can model a mind. All the behaviors that we think are done without representations? We model them and study them with the aid of computer models. Of course. But that's not really interesting at all. And yes if you modeled a brain physically, you might get a mind (I'd argue you would need to model the body as well, not to mention quite a bit of environment). But that's not really the point. Today, Psychologists do research with the idea that they are setting out to discover the software that the brain is running. This is very different from the claim that a computer could model a brain, and it pervades how we think about minds, even (and especially) among experts in the field.

Any good editor should be able to figure out the indents when pasting. I'm not an emacs user, but I'd be surprised if there wasn't a plugin with smart python pasting.

Fair enough. And interesting, I hadn't thought of some of this.

SciPy users are certainly doing scientific computing in Python, but it is surprising to get that attitude from the developers.

Yep, ggplot is the one thing that I keep coming back to R for, plus the odd statistical model I can't find in statsmodels - which is rarer and rarer.

One of the many awesome features of IPython - the interactive python shell and notebook, is that you can call code blocks in R just by prefacing with %%R. So my plotting habits are usually first to try the python port of ggplot, and if that can't handle my situation I just jump into R without having to switch windows or do any complicated data transfer.

It's worth mentioning that matplotlib is designed to mimic Matlab's plotting API, so for people coming from Matlab there's very little change, plus there's all the benefits of the other plotting libraries others have mentioned.

Yeah, I'm a Python convert like the author, though coming mostly from Matlab rather than R, and everyone in my field reacts with surprise when I tell them I prefer Python. They're open-minded, and I'm hoping to convert a few myself, but I don't think the mass migration has happened yet.

Regarding your second comment- you're correct of course, but what makes this a "blind spot"? After all, if the user is writing code in Python, they're doing scientific computing in Python, regardless of what the Python library calls behind the scenes. In my experience, a lot of people doing scientific computing--particularly those more interested in the science than the computing--could care less about what's going on behind the curtain. Any moment they have to think about implementation is a moment not thinking about science and therefore a waste of time. So it's actually a benefit for an ecosystem to hide the underlying mechanics--calling it a "bizarre blind spot" seems to imply they're doing something wrong.

Either the blind person is responding to some low-energy distribution (scattered sound waves, perhaps, or past samplings of the energy distribution, i.e. memory) or the blind person isn't perceiving any more than the spark is (in this example).

In any case my post above implied a definition for perception: responding to low-energy distributions with an asymmetric high-energy response.

Wind is a local potential in this example. An intelligent wildfire would be one whose sparks can go against the wind, because it perceives more fuel in that direction.

Also: my definition is meant to include fish and birds, even plants, as intelligent.

I'll try: a system is intelligent if it is able to respond to low-energy deposits (information) with high-energy reactions (e.g., movement) in order to seek non-local sources of negentropy to dissipate.

But then again I'm more interested in the intelligence that differentiates a slime mold from a hurricane than the intelligence that differentiates a human from a chimpanzee.

For example: hurricanes are self-organized, constituted by a structured flow of energy and matter rather than specific pieces of matter. But a hurricane is a slave to the local potential. It will dissipate all the negentropy in its wake, and in doing so maintain its structure. But once there is no more energy differential to dissipate, the hurricane will itself dissipate as it is not able to break free of the local potential and use information to seek out non-local negentropy sources. The question for research is what is necessary to make that jump from self-organization to intelligence, given that operationalization.

Well, to the extent that it's a scientific problem, it sure would help to have a theory. I sure don't expect that theory to enter the public consciousness anytime soon, if at all.

But I do agree with your sentiment as far as the way intelligence is usually discussed, even among the science-literate.

I think the first true strong AI will come from research on non-equilibrium thermodynamics. We need to get down to the basics: where do entities come from that self-organize, more specifically that are able to use information in structured energy arrays to find and dissipate negentropy deposits, and dissipate that energy in order to maintain their own state away from equilibrium and hence avoid dissipating themselves? In short, strong AI will not come from top-down research on problem solving or learning, but bottom-up research on what makes autonomy and agency possible.

Goedel machines might actually be the closest thing to this in the computing literature, my reaction is less about the work itself than the rhetoric surrounding it, to be honest. JS should collaborate with a physicist on the thermodynamic side of the problem.

If you're intrigued, you could start with the article I linked above, or if you have journal access, anything from the same special issue. I chose that article just because it's the only one not behind a paywall.

I'm already familiar with some of his work. What did you have in mind specifically? In any case, he may be one of the best in the traditional computational approach to AI, but I think framing intelligence in terms of computation is inherently misguided. Of course I'm not going to get far with that unorthodox perspective on HN :)

Read the paper I linked and you'll understand why I'm not impressed.

Eventually, but strong AI just means intelligence matching or exceeding human intelligence. We already have billions of entities with human intelligence, and it is taking us a long time to produce something smarter than ourselves. If the first strong AI is just a little smarter than us, and if it chooses to put its energy toward the creation of more AIs, then maybe it will eventually produce something smarter than itself. But it's not so simple that we just get to the singularity straight away.

We are far, far from hard AI. If anything, this article shows that we're only just now starting to ask the right questions. And that's even debatable. Plus they're very hard questions.

The problem is we have no theory of intelligence, no theory of psychology. Research in the cognitive fields is fractured, all about tiny insignificant phenomena with little relation to anything else. Our best theory is "the brain is like a computer" which is, frankly, a terrible theory.

Here's something I find more promising: On Intelligence From First Principles: Guidelines for Inquiry Into the Hypothesis of Physical Intelligence [1]

In short, what we really need to understand is self-organization and non-equilibrium thermodynamics. Not image labeling.

[1] http://www.tandfonline.com/doi/pdf/10.1080/10407413.2012.645...

I'm taking a course with this book right now... and yes, I stole it at the authors' recommendation. Textbooks are too expensive and I don't want to carry it around anyway.

True that! Okay, you've convinced me to make it a long-term goal.

Nope, Psychology with a focus on complex systems, statistical physics, dynamical systems, that sort of thing. Everything from time series analyses that require hundreds of thousands of data points to plain old factorial ANOVAs.

Psychology is probably one of the worst sciences for the attitude described in the article. Being in the most "mathy" corner of the field doesn't really help.

In MATLAB, R, or numpy, it's the difference between `mean(n)` and manually looping. It's not an issue of algorithmic efficiency, it's an issue of lost productivity because they don't even write a function to reuse (all they understand is scripting) they recode the loop every single time they have to sum or take the mean of something.

Well I'm self-taught so I have to start somewhere. I'm not sure I could put together a stand-alone tool and still complete my Ph.D. program. Anyways I've found most stand-alone tools just aren't flexible enough and I don't feel like making something I wouldn't use myself.

All true, but the issue isn't really that they could improve algorithmic complexity with more technical skills, it's that they could improve their overall productivity.

There's a huge resistance to using source control, so lots of time gets spent searching through deep folder structures and finding just which 'file1_v3 (4).doc' is the right one. Data gets lost due to simple mistakes.

They spent 20 minutes coding the Runge-Kutta algorithm every time they need to run a numerical simulation without realizing or caring that (a) they could spend 25 minutes to create a function that they could reuse or (b) that the function already exists.

In short, the issue isn't computing time, it's researcher time. But the idea of spending some time now to save time later is so foreign because of the focus on getting the publication out as quick as possible.

Hey, I have no problems with my taxes going toward science. The way it's done is far from perfect but the answer isn't to take away funding.

This is so true. I'm in a Ph.D. program and everyone around me is wasting so much time by reinventing the wheel every time they need to code something. So I spend my time making libraries to help them out, but then I get scolded because that's time that's not going directly toward getting publications. And few people use my code because they don't trust software as up to the scientific standard unless (a) they spent thousands of dollars on it, a la MATLAB, or (b) they wrote it themselves and, e.g., take a mean by manually iterating over an array, "just to make sure" the mean is calculated correctly. Ugh. It doesn't matter how many tests I an point them to. I can't wait to get out of here and work somewhere where coding is appreciated, where I can actually get paid, and where I have some choice as to which state I live in.

I'm surprised that this point hasn't come up yet in this conversation: the whole point of insurance is to pool risk. The basic principle is that of everyone who pays in, the lucky ones subsidize the unlucky ones.

Obviously, this works best the more people who pay in. When people don't get insurance until they need it, it raises the cost for everyone. When people don't get insurance and still end up burdening those who did (by going bankrupt when faced with monstrous E.R. bills, for a common example), costs go up for everyone. In these situations, there's only way to make the system work: enforce that everyone buys in. That's why I support single-payer.

Here's another car analogy: car insurance is mandatory for drivers in the U.S., and most countries. Because the system only works when everyone buys in.

Of course it offends the libertarian sensibility to force people to buy something. How barbaric! Personally, I'm a libertarian-to-leftist convert. I think society needs a monopoly on force, and that monopoly goes to the government. The use of that force should be minimized, but one of reasons we need it is to force people to chip in for the common good. When it comes to pooled risk for catastrophic events, it's worth it. I have no moral qualms. It's not a matter of who's doing the planning, it's a matter of societal choices that just plain don't work if not everyone's on board. You're right that central planning is not voluntary. That's the whole point.

But outside of "character" or other intangibles, there's no incentive to avoid the safety nets—in fact there's every incentive to use them, lest you be a sucker. Only suckers pay for things.

"Yeah, look at all those suckers, paying for things, how stupid. Why bother trying to make more money? It's not like I value character-building." - single mom making $12,000/year, and (gasp) receiving government assistance

Do you really think government assistance makes poverty appealing?

All the more odd that Data Science is meant to be less scientific than Statistics (if the taxonomy I described in another post is correct).

Sounds like you agree with nfoz... you don't always need mathematical expertise ('scientific validity') to play with data, but when you do, you need a statistician.

Really, it comes down whether you need to confirm your assumptions and quantify your uncertainty, etc. Often, in applied situations, you don't. In science, you do.