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Artificial-Intelligence Experts Are in High Demand

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Re: Artificial-Intelligence Experts Are in High Demand

#101

Post by 0xdeadbeefbabe »

dantillberg wrote: post #92
jules wrote: post #56

Earlier quoted context omitted.

There is quite a big difference between statistics and machine learning. A lot of the most successful machine learning algorithms do not have a statistical grounding or did not when they were invented. E.g. neural networks, SVM, low rank matrix approximation, k-means, decision trees/forests. Statistics is one of the tools in the machine learning toolbox.

But, to look at one example: neural networks. Neural networks may have been inspired by attempts to recreate the structure of the biological nervous system, but the way in which they are used commonly, e.g. "learning" via back-propagation, is really just a statistical regression for a gigantic equation with many free variables. My preferred term is "predictive analytics," which I feel kind of straddles statistics and…

I feel annoyed with opaqueness of ML algorithms like neural networks. I hope ML doesn't unwittingly define itself as a field where machines learn, but humans may not learn. I'm referring to predicaments like the story about 42 from hitchhikers guide to the galaxy.

Re: Artificial-Intelligence Experts Are in High Demand

#102

Post by juliangregorian »

tansey wrote: post #19
kriro wrote: post #18

Earlier quoted context omitted.

Buy and work through "Artificial Intelligence: A Modern Approach". It's a huge book and the de facto standard for pretty much every AI 101+ course. Some of the stuff may not interest you some might but it covers a broad range (from logic based agents to Bayesian networks). It's systematic and has excellent references and further reading notes for each chapter. The focus is not on the currently sexy "data science" asp…

The AIMA book is sort of a Good Old-Fashioned AI (GOFAI) book that focuses a lot on agents and planning. The jobs this article is talking about are really machine learning ones-- taking large volumes of data and extracting knowledge, so as to build recommender systems and such. For that, Kevin Murphy's book, "Machine Learning: A Probabilistic Approach" is without a doubt the best book out there, both in terms of expl…

Murphy's book is actually subtitled "A Probabilistic Perspective" -- "Machine Learning: A Probabilistic Approach" is a different book by a different author.

Re: Artificial-Intelligence Experts Are in High Demand

#103

Post by jules »

Dn_Ab wrote: post #96
jules wrote: post #56

Earlier quoted context omitted.

There is quite a big difference between statistics and machine learning. A lot of the most successful machine learning algorithms do not have a statistical grounding or did not when they were invented. E.g. neural networks, SVM, low rank matrix approximation, k-means, decision trees/forests. Statistics is one of the tools in the machine learning toolbox.

SVMs were invented by a couple statisticians/mathematicians in the 60s. k-means also harkens back to the 60s, by mathematicians and control theorists. Decision Trees and Random forests were invented by a famous statistician, with the latter related to bootstrapping, a statstical technique. PCA and factor analysis, forms of or closely related to low rank matrix approximation, were pioneered in the early 1900s, by some…

Something that was invented by a statistician is not necessarily statistics, and that certainly applies even more to something invented by a mathematician. I guess with a broad enough notion of statistics some of these would fall in the field of statistics, but if something does not use at least one probability distribution it's probably far fetched to classify it as statistics.

It would be a lot more fair to classify machine learning as a subfield of convex optimization. Yet even that classification does not quite fit, so it makes most sense to just accept that it's a separate field which uses techniques from statistics, convex optimization, computer science, and more.

Re: Artificial-Intelligence Experts Are in High Demand

#104

Post by Florin_Andrei »

peter303 wrote: post #78

We went through a round of this in the 1980s. The first commercial graphics workstations happened to be LISP machines. So management confused non-numeric code with A.I. There was demand for workstation experts. Not to loang after this UNIX graphics workstations like Sun, Apollo and MicroVAX came out and the market switch to UNIX/Linux. Second was the expert systems boom in the mid-1980s. This was fanned by Stanford p…

No, that was the wrong approach. Back then we believed that solving AI means being strong on logic and rules, with data being a secondary aspect. Nowadays we do the exact opposite: data is king, and the rules are expected to somehow emerge from it.

Re: Artificial-Intelligence Experts Are in High Demand

#105

Post by jules »

VLM wrote: post #85
jules wrote: post #57

Earlier quoted context omitted.

Face recognition, speech recognition, machine translation, text classification and more don't exist?

That's the plot line of a hyper critical, yet insightful, joke that came out of one of the many AI downturns in past decades, that as soon as an algo or implementation works it isn't AI anymore, its (fill in the blank specialization). So AI is just the present set of algos that don't (yet?) work. So that fuzzy logic, thats not AI anymore, thats a footnote in the EE control systems theory class, isn't it? And the face…

Right, it's not yet at that point for face and speech recognition, but that certainly happened to game AI (Chess), constraint solving (Sudoku), and planning.

Re: Artificial-Intelligence Experts Are in High Demand

#106

Post by Florin_Andrei »

Daishiman wrote: post #4

This is suspiciously close "data science" and "machine learning" experts. Can't we just be honest and say that most of these are applied statistics jobs with a specialty in large volumes of data? Or is "statistics" just not fashionable enough nowadays?

> Can't we just be honest and say that most of these are applied statistics jobs with a specialty in large volumes of data?

But isn't this the approach Nature herself is taking?

The knowledge engine you carry in your head spends years just "learning" the world - which means, it absorbs huge amounts of input, sorting the good stuff from the bad. It "knows" what works simply because that stuff happens more often; it "knows" what doesn't work because that stuff doesn't happen very often.

And sure there are higher layers of integration there, but the whole process is strongly supported by a statistical approach.

Re: Artificial-Intelligence Experts Are in High Demand

#108

Post by one-more-minute »

eli_gottlieb wrote: post #99
one-more-minute wrote: post #73

Earlier quoted context omitted.

Just because you can't see unicorns doesn't mean they aren't there, but at some point you have to give up the search. It's fine to talk about how, broadly speaking, rats are more intelligent than ants, plants or microbes (which are basically I/O rules with a body), chimpanzees more so than rats, humans more so than than chimps etc. But in general there's a ton of overlap and the qualities we associate with intelligen…

Well I would say that "intelligence" is learning and inference with causal models rather than just predictive or correlative models. You can then cash it all out into a few different branches of cognition, like perception (distinguishing/classifying which available causal models best match the feature data under observation), learning (taking observed feature data and using it to refine causal models for greater accu…

It's an interesting perspective, but the thing about this kind of definition is that it's very much focused on the mechanism of intelligence rather than the behaviour that the mechanism produces – which flies in the face of our intuition about what intelligence is, I think.

If we found out that one species of chimp learns sign languages through a causal model while another learns it through an associative one (for example) we wouldn't label one more or less intelligent, because it's the end result that matters – don't you think?

Likewise, arguably the ultimate goals of AI are behavioural (machines that can think/solve problems/communicate/create etc.), even if it's been relatively focused on mechanisms lately. Any particular kind of modelling is just a means to that end. Precisely what that end is is still a bit hard to pin down, though.

Re: Artificial-Intelligence Experts Are in High Demand

#109

Post by craigching »

protonfish wrote: post #89
jfoutz wrote: post #83

Earlier quoted context omitted.

So, what's holding you up exactly? RC cars are cheap, people have done cool stuff with old android phones for sensor packages. If you need more horsepower, stream the data back to a PC, you've got wifi on the phone. New industrial arms are expensive, but you can scrounge one, or get a hobby one. sparkfun had a uArm that would probably work for you.

Because I am a software developer and intelligence hobbyist (from the biology/ethology camp.) I don't know a damn thing about RC cars or android phones nor do I have the time or desire to learn. Sadly, the hardware tinkerers don't know a damn thing about programming intelligent behavior. Until there is some sort of API to connect hardware to a software environment programmable by specialists in that domain, hobbyist…

Maybe I'm misunderstanding you, but robotics is now, and probably always will be, inherently about combining skills from electronics, mechanics, and software. I'm not sure what you're looking for, a complete robot that you can simply program? You could look into the NAO robot[1], probably too simple for you, but I think they're pretty cool. Wish I could afford one ;)

1 -- https://www.aldebaran.com/en

Re: Artificial-Intelligence Experts Are in High Demand

#110

Post by peter303 »

deanmen wrote: post #41
GigabyteCoin wrote: post #5

How can there be experts on a subject that doesn't yet exist?

AI experts don't know how to create an artificial intelligence. AI researchers study how to solve various problems in CS traditionally performed by humans that humans don't solve by carrying out an algorithm by hand like Natural Language Processing, machine learning, automated reasoning, search (e.g. chess).

There are at least two flavors of A.I. "Strong A.I." seeks to build something human-like and could pass Turings immitation game test. The new movie Ex Machina explores this test. "Weak A.I." replicates just a single cognitive skill like game playing, pattern recognition, natural language or something more trivial. Most recent A.i. worked on the latter, or its theoretical background.
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