Preface to a Proposal for a New Mode of Inquiry

Here's an issue of style and presentation: Would you mind editing your text (or your future texts), striving to remove self-reference and cheerleading ("fluff")?

A small number of uses of "I/my" and colorful language ("amazing, terrifying, bright, beautiful, silent, immense, multifaceted") is reasonable, but the discipline of focusing almost entirely on the ideas being discussed helps both you and your readers understand what the ideas actually are.

As far as I can tell, the content of your post is "I will be posting over the next couple of weeks.", and the rest is fluff. Since you did invest some time in writing this post, you must have believed there was more to it. The fluff has either confused you (into believing this post was substantial) or confused me (preventing me from seeing the substantial arguments).

You maybe should have mentioned the earlier discussion of your idea on the open thread, in which I believed I spotted some critical problems with where you're going: you seem to be endorsing a sort of "blank slate" model in that humans have a really good reasoning engine, and the stimuli humans get after birth are sufficient to make all the right inferences.

However, all experimental evidence tells us (cf. Pinker's The Blank Slate) that humans make a significantly smaller set of inferences on our sense data than are logically possible under constraint of Occam's razor; there are grammatical errors that children never make in any language; there are expectations babies all have, at the same time, though none has gathered enough postnatal sense data to justify such inferences, etc.

I conclude that it is fruitless to attempt to find "general intelligence" by looking at what general algorithm would make the inferences human do, given postnatal stimuli. My alternative suggestion is to identify human intelligence as a combination of general reasoning and pre-encoding of environment-specific knowledge that humans do not have to entirely relearn after birth because the brain wiring-up in the womb already filters out inference patterns that don't win.

That knowledge can come from the "accumulated wisdom" of the evolution history, meaning you need to account for how that data was transformed in a human's present internal model.

ETA: Wow, I was sloppy when I wrote this; hope the point was able to shine through. Typos and missing words corrected. Should make more sense now.

I'm intrigued and looking forward to reading your articles. I suggest you change your title-writing algorithm, though. To my ears, "Preface to a Proposal for a New Mode of Inquiry" sounds like a softcover edition of a book co-authored by a committee of the five bastard stepchildren of Kant and Kafka.

To suggest that computer science should be an important influence on AI is a bit like suggesting that woodworking should be an important influence on music, since most musical instruments are made out of wood.

"Computer science is no more about computers than astronomy is about telescopes." -- E. Dijkstra

Our decision making algorithm is not much more than: invent a plan, try to predict what will happen based on that plan, and if the prediction seems good, implement the plan. All the "magic" really comes from the ability to make accurate predictions.

You need to locate a reasonable hypothesis before there is any chance for it to be right. A lot of magic is hidden in the "invent a plan".

To suggest that computer science should be an important influence on AI is a bit like suggesting that woodworking should be an important influence on music, since most musical instruments are made out of wood.

It's been brought up in multiple comments already, but I also wanted to register my disapproval of this statement. The first four minutes of the first SICP video lecture has the best description of computer science that I've ever heard, so I quote:

"The reason that we think computer science is about computers is pretty much the same reason that the Egyptians thought geometry was about surveying instruments, and that is when some field is just getting started and you don't really understand it very well, it's very easy to confuse the essence of what you're doing with the tools that you use...I think in the future, people will look back and say, "well yes, those primitives in the 20th century were fiddling around with these gadgets called 'computers,' but really what they were doing was starting to learn how to formalize intuitions about process: how to do things; starting to develop a way to talk precisely about 'how-to' knowledge, as opposed to geometry that talks about 'what is true.'" - Hal Abelson

That said, I'm looking forward to your upcoming posts.

Er, have you given much thought to friendliness?

Anna Salamon once described the Singularity Institute's task as to "discover differential equations before anyone else has discovered algebra". The idea being that writing an AI that will behave predictably according to a set of rules you give it is much more difficult than building an AI that's smart enough to do dangerous stuff. It seems to me that if your ideas about AI are correct, you will be contributing to public knowledge of algebra.

To suggest that computer science should be an important influence on AI is a bit like suggesting that woodworking should be an important influence on music, since most musical instruments are made out of wood.

Something of a jarring note in an otherwise interesting post (I'm at least curious to see the follow-up), in that you are a) reasoning by analogy and b) picking the wrong one: the usual story about music is that it begins with plucked strings and that the study of string resonance modes gave rise to the theories of tuning and harmony.

I think I understand better now.

Your proposal seems to involve throwing out "sophisticated mathematics" in favor of something else more practical, and probably more complex. You can't do that. Math always wins.

The problem with math is that it's too powerful: it describes everything, including everything you're not interested in. In theory, all you need to make an AI is a few Turing machines to simulate reality and Bayes theorem to pick the right ones. In practice this AI would take an eternity to run. Turing machines live in a world of 0s and 1s, but we live a world made of clouds and birds, and a machine that talks in binary about clouds and birds would be complicated and hard to find. For a practical AI, you need a model of computation that regards nouns, verbs and people as the building blocks of reality, and regards Turing machines as very weird examples of nouns. This model would perform worse than a Turing machine if presented with a freakish alternate universe with no concept of time or space, but otherwise it's fine. The hard part is compromising between simplicity and open-mindedness.

The same applies to neural networks. In theory, the shape can be anything you like as long as it's big enough.(I'm leaving out a lot of details here, sorry.)Math is just the general framework that you build reality inside.

Empirical methods are upside down. You're starting with the gritty details, hoping that as everything piles up something more powerful than bayesian inference will emerge. That won't happen. Instead you'll get a lousy, brittle copy of bayesian inference that can't handle anything too different from what it was designed for... like a human.

(Edited for grammar)

I am unsure whether this is LW material. There are plenty of people with ideas about AI and it tends to generate more heat than light, from my experience. I'll reserve judgement though, since there is a need for a place to discuss things.

First I agree with the need to take AI in different directions.

However I'm sceptical of the Input Output view of intelligence. Humans aren't pure functions that always map the same input to the same output, it relies on their history as well. So even if you have a system that corresponds with what a human does for the time t to t+n it may not correspond at times greater than t+n.

The way forward, for me, is to look at altering the software ecosystem. Currently the programs we write are static rigid structures with limited awareness of their surrounding software. They are like this because it is easier for the human system administrator to deal with. We need to write software that looks at its computing environment and reasons about it to manage itself and the (virtual) machines that enable this to be done in an controlled fashion.

"(and why did it take so long for people to figure out the part about empirical verification)?"

Most of the immediate progress after the advent of empiricism was about engineering more than science. I think the biggest hurdle wasn't lack of understanding of the importance of empirical verification, but lack of understanding of human biases.

Early scientists just assumed that they were either unbiased or that their biases wouldn't affect the data. They had no idea of the power of expectation and selection biases, placebo effects, etc. It wasn't until people realized this and started controlling for it that science took off.

'An important aspect of my proposal will be to expand the definitions of the words "scientific theory" and "scientific method"'

I have to admit that this idea makes me extremely wary, but that's probably because I'm used to statements like this coming from people with a harmful agenda (i.e. creationists). I'll try to keep an open mind when I read your future posts in this series.

Have you heard of the methodology proposed by cyberneticists and systems engineers and how is it similar or different from what you are proposing?

Edited for diplomacy/clarity.

So... what's your proposal?

I am going to post an outline of my proposal over the next couple of weeks. I expect most of you will disagree with most of it, but I hope we can at least identify concretely the points at which our views diverge. I am very interested in feedback and criticism, both regarding material issues (since we reason to argue), and on issues of style and presentation. The ideas are not fundamentally difficult; if you can't understand what I'm saying, I will accept at least three quarters of the blame.

Aw come on, just one little hint? Most posts have a tl;dr paragraph or a "related to" to help people understand.

To suggest that computer science should be an important influence on AI is a bit like suggesting that woodworking should be an important influence on music, since most musical instruments are made out of wood.

Computer science is probably not what you think it is. AI is included in it; but so is applied stuff like hacking. I think time (not watchmaking, just time) would make a better example.

Edited for trying/failing not to sound mean/weird.

Thoughts I found interesting:

The failures indicate that, instead of being threads in a majestic general theory, the successes were just narrow, isolated solutions to problems that turned out to be easier than they originally appeared.

Interesting because I don't think it's true. I think the problem is more about the need of AI builders to show results. Providing a solution (or a partial solution or a path to a solution) in a narrow context is a way to do that when your tools aren't yet powerful enough for more general or mixed approaches. Given the variety of identifiable structures in the human brain that gives us intelligence I strongly expect that an AI will be built by combining many specialized parts that will probably be based on multiple research areas we'd recognize today.

One obvious influence comes from computer science, since presumably AI will eventually be built using software. But this fact appears irrelevant to me, and so the influence of computer science on AI seems like a disastrous historical accident.

Interesting because it forced me to consider what I think AI is outside the context of computer science - something I don't normally do.

In my view, AI can and must become a hard, empirical science, in which researchers propose, test, refine, and often discard theories of empirical reality.

Interesting because I'm very curious to see what this means in the context of your coming proposal.

I work in machine translation research. Google might have a little more data, but there are several groups doing equally good work.

This sounds really good and interesting, and is well written, but it also sounds incredibly ambitious. Maybe a little more conservative presentation would be more convincing for me.

Human intelligence, in contrast, is based on a complex inductive foundation, supplemented by minor deductive operations.

You'd be hard-pressed to formalize this statement, since any notion of "induction" can find a deductive conceptualization.

One thing the world has is an abundance of human minds. We actually do not need machines that think like humans - we have humans. What we need is two other things: machines that do thinking that humans find difficult (like the big number crunchers) and one-off machines that are experimental proofs-of-concept for understanding how a human brain works (like Blue Brain). As far as getting the glory for doing what many said was impossible and unveiling a mechanical human-like intelligence, forget the glory because they will just move the goal posts.

I believe that what is needed is to leave sequential operations and learn how to effectively use parallel operations. This would get close to a human intelligence and would also advance the power of computing of a non-human but useful kind.

I think you are so very right about the importance of prediction. !!! And looking forward to later posts.