11 ms·

Artificial-Intelligence Experts Are in High Demand

224 points by mwytock 11y ago

nerdy 11y ago
AI is very interesting but not very accessible because it's so specialized. I have a fairly strong programming background but feel like I'd need to study theory for a significant amount of time to even get my feet wet with AI. If you have (condensed, especially) AI resources that you think would help bridge that gap, please share! Toy-scale project ideas would also be appreciated.
- ignoramous 11y ago
  There was a tutorial link trending on HN a few months back, I can't seem to find it. But these links are helpful (though not condensed) as well: https://github.com/ChristosChristofidis/awesome-deep-learning https://github.com/ChristosChristofidis/awesome-deep-learnin... https://github.com/owainlewis/awesome-artificial-intelligence https://github.com/owainlewis/awesome-artificial-intelligenc...
  - iyn 11y ago
    These links can be helpful too: https://news.ycombinator.com/item?id=7783550 https://news.ycombinator.com/item?id=7783550 https://news.ycombinator.com/item?id=9432952 https://news.ycombinator.com/item?id=9432952 https://news.ycombinator.com/item?id=2645671 https://news.ycombinator.com/item?id=2645671 https://news.ycombinator.com/item?id=5508261 https://news.ycombinator.com/item?id=5508261 https://news.ycombinator.com/item?id=9004689 https://news.ycombinator.com/item?id=9004689
- kriro 11y ago
  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" aspects though (however you will find plenty of material that is relevant). The edX class from Berkeley is pretty fun and hands on. It uses Pacman as a running example and essentially teaches the agents stuff from AIAMA: https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x-0 https://www.edx.org/course/artificial-intelligence-uc-berkel... The Stanford class by Thrun and Norvig himself (one of the authors of AIAMA) is also good but I prefer the edX one: https://www.udacity.com/course/intro-to-artificial-intelligence--cs271 https://www.udacity.com/course/intro-to-artificial-intellige... Edit: changed to direct links for the courses
  - tansey 11y ago
    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 explaining things from the ground up and being the most comprehensive/up-to-date source.
    - kriro 11y ago
      There's still quite a bit of material on Bayesian networks (with the dreadfull dentist example :D), neural networks and support vector machines but overall you're right the focus is on agents. The relevant chapters are great staring points though and as always filled with great reference material for further reading. + I'm pretty sure if you apply for an AI job somewhere and it's labaled AI and not "data science" they'll expect that you know the material in AIAMA.
    - juliangregorian 11y ago
      Murphy's book is actually subtitled "A Probabilistic Perspective" -- "Machine Learning: A Probabilistic Approach" is a different book by a different author.
  - wimagguc 11y ago
    +1 for the Stanford course. Great intro to AI and super easy to follow - I've done it after my uni class elsewhere, and it helped to internalise what I've learned there.
  - musername 11y ago
    reading the book still requires a significant amount of time
- roel_v 11y ago
  'AI' is not programming, it's mathematics (well the current 'flavor' of statistics-based AI, that is - the 1980's style AI people I used to work with were philosophers, legal scholars and the like). Anyway, there is no 'bridging the gap' - you need to start from a good foundation of statistics (and the 'prerequisites' - algebra, calculus, linear algebra) and in the end, the technicalities of the software and the theory come together naturally. (source: have tried to 'bridge the gap' for 2 year, including taking MSc courses, before admitting to myself that it's a lost battle. Am now starting to build a solid math foundation before revisiting ML applications.)
  - afshin 11y ago
    This is exactly my experience as well. I'm an alright programmer, but it is insufficient, because machine learning and AI are a table resting on four legs: linear algebra, calculus, statistics, and programming. I've also found myself going back to build up those foundations.
- syllogism 11y ago
  Here are two tutorials I've written on natural language understanding: https://honnibal.wordpress.com/2013/09/11/a-good-part-of-speechpos-tagger-in-about-200-lines-of-python/ https://honnibal.wordpress.com/2013/09/11/a-good-part-of-spe... https://honnibal.wordpress.com/2013/12/18/a-simple-fast-algorithm-for-natural-language-dependency-parsing/ https://honnibal.wordpress.com/2013/12/18/a-simple-fast-algo...
raverbashing 11y ago
However, with the abysmal standards of hiring, I'm sure a lot of companies would pass on very good candidates because they won't write FizzBuzz on the board for you, or companies would pass on Peter Norvig because his code is not Pep8 compliant
- rifung 11y ago
  Not sure if you're being serious but are you sure they would even care whether these kinds of candidates could program? I am skeptical they hired them to code; I imagine they mostly spend their time doing research and then have the SDEs implement things.
  - jsweojtj 11y ago
    It happens all the time, and it comes from a mix of things. One way it happens is that you get a PhD in astrophysics with years of data analysis experience in for a data science job. Have a software engineer interview her and he might find that she doesn't know a number of basic computer sciences concepts [traversing a linked list, tail recursion, implement breadth-first-search]. His knowledge background says these basic ideas are fundamental, there are therefore serious questions about the technical ability of the interviewee.
    - raverbashing 11y ago
      This is a good example. At the same time this CS interviewer may not know what's the second central moment of a probability density function.
      - deleted 11y ago
        [deleted]
      - eli_gottlieb 11y ago
        Uhhh... the variance?
  - kriro 11y ago
Daishiman 11y ago
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?
- higherpurpose 11y ago
  I imagine the "AI experts" will be paid significantly more than data scientists and machine learning experts, just like "software engineers" are paid more than "software developers" and "programmers".
- fit2rule 11y ago
  These subjects (plural) are all plagued by the same problem: definitions of terms. One mans intelligence is another mans dire stupidity, and so on and on it goes, chasing its tail. The most value I got from this article was in the realization that, every few years or so, the academic globes align well enough (some paper de joure becomes well-read I suppose) that .. for a brief instant .. terms are defined well enough, and gain enough agreement, that progress is made .. which progress attracts more eyeballs, who tend to want to break off a chunk for themselves, and the terms begin to differ again and we have a whole new 'sub-sub-sub-' variety of the subject. So its all about globes aligning, basically. I will now go off and implement an AI technique based entirely on the description of globes, alignment, and little chunks breaking off every now and then .. see you at the top of the AI heap in a year or ten.
- Nvn 11y ago
  As mentioned in the article, AI is the broader field that encompasses Machine Learning, and to a large extent also Data science (and Computer vision, NLP, Pattern recognition, etc.). And while data science might utilize a lot of statistical techniques, it is a huge stretch to consider the whole AI field to be 'statistics'. In general, AI borrows many more techniques from mathematics than it does from statistics. However, the field of AI has been quite established since the 1960's, and many techniques have been developed within that field as AI techniques, it's more about being accurate than about being fashionable as AI simply isn't 'just' statistics.
GigabyteCoin 11y ago
How can there be experts on a subject that doesn't yet exist?
- ankurdhama 11y ago
  Actually there can be anything possible in the articles that are published by so called Tech Journalists who have no idea of the fundamentals of the tech.
- myrryr 11y ago
  What do you mean it doesn't exist? I work in that field, it exists.
  - blumkvist 11y ago
    What do you mean it exists? How do you definite AI? Can your project reason with you? Or is it simply an Input-Output type of program? Just because you use natural language with it instead of punch cards doesn't mean it is intelligent. People tried to do AI in the 60s to 90s era. It is dubbed symbolic AI. It didn't work out. A good chance that it never will. Today machine learning algos and a bunch of automated statistics is called "Artificial Intelligence". It's not intelligence at all. Intelligence implies something more than I/O computation.
    - one-more-minute 11y ago
      I tend to agree that AI today is a long way from what the founders of the subject imagined – it's become something more like "Applied Computer Science". But what's now called "Artificial General Intelligence" isn't dead, and people are still working on it. Also, it's more tricky than you'd think to narrow down what counts as intelligence. There aren't really any hard lines between an I/O program and an intelligent agent, even though they seem pretty far apart.
      - protonfish 11y ago
        Just because you don't see the hard lines doesn't mean they aren't there. We are deluding ourselves by avoiding a hard definition of intelligence so we can keep believing that we are creating AI when its really nothing of the sort.
lowglow 11y ago
I'm starting an SF-based robotics/AI/ML workshop/meetup/club next week. Hit me up at dan@techendo.com if you want an invite -- or join this group: https://www.facebook.com/groups/762335743881364/?ref=br_rs https://www.facebook.com/groups/762335743881364/?ref=br_rs
wimagguc 11y ago
I wonder what all the AI is going to be used for. Is everyone working on their own Siri and recommendation engine now? (Is anyone building an AI that can come up with its own agenda?)
- aangjie 11y ago
  I think (not sure, as I don't closely follow the work) AGI people and MIRI work along the lines. (i.e: how to make sure a super AI doesn't go wrong, but comes with Objective functions beneficial to humans. https://intelligence.org/ https://intelligence.org/)
julianpye 11y ago
This trend is in most companies business-driven, in others it is technical-driven. Few companies have technical leadership that can manage true AI resources. If you remember the ML courses from Uni and experts in that field, you can imagine why. In many universities AI departments are assigned to schools of psychology and philosophy. Only companies with a deep engineering culture as those mentioned here can build up true AI departments. The other driver is business-driven. And this is where management demands 'AI experts', when what they really want is data-miners. And in many cases management prides themselves on 'AI algorithms', but we know that this is a term for anything that gets the results that management wants and may be far from intelligent and in most corporate cases a bunch of SQL scripts.
- netcan 11y ago
  What's the potential path forward (say projecting 10 years ahead) from the current growth in demand for data mining centric people? I mean people go and study in response to demand. They learn data mining and AI at Universities. I think it's often people with backgrounds or aptitude in maths. What will the 22 year old with an aptitude for maths that is learning R, SQL, AI-for-business and such be doing in 10 years? I don't know if the starting point matters much. "Results Driven," even if its optimising inventory or making ad purchasing decisions or data mining old DBs is not a bad place to "search" for advancements. Not everything needs to be fundamental research.
  - deleted 11y ago
    [deleted]
  - nkassis 11y ago
    I doubt the 22 year old you are referring too will run out of problems to solve. I also feel at some point it will be like a lot of software engineering is today. Working for companies implementing solutions similar to what already exists but tailored to the context of that companies specific needs. As of now I feel that this field is so young that a lot of the solutions are almost completely custom built to the problem at hand and that a lot of work is needed to abstract away those solutions into higher level reusable pieces.
100timesthis 11y ago
when the wsj writes about it means that the trend is over
aajtechmail 11y ago
OHH
rayalez 11y ago
The big thing that prevents me from getting into AI is the lack of practical projects that I can build. It is a very interestimg field, but as a self-taught programmer I'm used to learning by building things, and it's hard for me to come up with some project that would be practically useful and yet doable. Does anyone have any ideas?
- ASlave2Gravity 11y ago
  Game AIs, Stock market bots, character recognition, adaptations to game of life, computer art wherein you get an AI to paint or to draw and can seed it values. There's lots of cool stuff you can hack away at. Even training a neural net to recognise a '3' is quite interesting.
  - protonfish 11y ago
    These things suck. I want a robot with sensors, the ability to move and an arm that can be programmed with a language that is appropriate for AI. That doesn't sound like something technically difficult.
    - jfoutz 11y ago
      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.
      - protonfish 11y ago
        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 robotics will remain in the realm of Battlebots.
graycat 11y ago
Considering being an employee such as in the OP, I have two reactions: (1) Take statistics, machine learning, neural nets, artificial intelligence (AI), big data, Python, R, SPSS, SAS, SQL Server, Hadoop, etc., set them aside, and ask the organization looking to hire: "What is the real world problem or collection of problems you want solved or progress on?" Or, look at the desired ends, not just the means. (2) Does the hiring organization really know what they want done that is at all doable with current technical tools or only modest extensions of them? Or, since artificial intelligence is such a broad field, really, so far of mostly unanswered research questions, and the list of topics I mentioned is still more broad, I question if many organizations know in useful terms just what those topics would do for their organization. So, for anyone with a lot of technical knowledge in, say, the AI, etc., topics, it is important for them to be able to evaluate the career opportunity. I.e., is there a real career opportunity there, say, one good to put on a resume and worth moving across country, buying a house, supporting a family, getting kids through college, meeting unusual expenses, e.g., special schooling for an ADHD child, providing for retirement, making technical and financial progress in the career, etc.? So, some concerns: (A) If an organization is to pay the big bucks for very long, e.g., for longer than some fashion fad, then they will likely need some valuable results on their real problems for their real bottom line. So, to evaluate the opportunity, should hear about the real problems and not just a list of technical topics. (B) For the opportunity for the big bucks to be realistic, really should know where the money is coming from and why. That is, to evaluate the opportunity, need to know more about the money aspects than a $10/hour fast food guy. (C) As just an employee, can get replaced, laid off, fired, etc. So, to evaluate the opportunity, need to evaluate how stable the job will be, and for that need to know about the real business and not just a list of technical topics. (D) For success in projects, problem selection and description and tool selection are part of what is crucial. Is the hiring organization really able to do such work for AI, etc. topics? Or, mostly organizations are still stuck in the model of a factory 100+ years ago where the supervisor knew more and the subordinate was there to add muscle to the work of the supervisor. But in the case of AI, etc., what supervisors really know more or much of anything; what hiring managers know enough to do good problem and tool selection? Or, if the supervisors don't know much about the technical topics, then usually the subordinate is in a very bad career position. This is an old problem: One of the more effective solutions is some high, well respected professionalism. E.g., generally a working lawyer is supposed to report only to a lawyer, not a generalist manager. Or there might be professional licensing, peer review, legal liability, etc. Or, being just an AI technical expert working for a generalist business manager promises in a year or so to smell like week old dead fish. (E) If some of the AI, etc., topics do have a lot of business value, then maybe someone with such expertise really should be a founder of a company, harvest most of the value, and not be an employee. So, what are the real problems to be solved. That is, is there a startup opportunity there? Really, my take is that the OP is, net, talking about a short term fad in some topics long surrounded with a lot of hype. Not good, not a good direction for a career. AI and hype? Just why might someone see a connection there?
peter303 11y ago
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 professor Fegeinbaum who wrote the infamous book The 5th Generation about expert system computers being the future and Japan was building the best ones. These would either be LISP machines or an interesting French niche language called prologic. Prologic basically traversed a databse "if-then" rules (modus pons). These machines went nowhere and Japan economy tanked in the early 90s. Lot of Silicon Valley VCs lost big on this. Prof Feigenbaum may still be correct, but 40 years early. However the new A.I. is driven by massive database matching possible in modern peta-level computers and not so much logical computing.
- peter303 11y ago
  The first four generations were defined by hardware: (1) vacuum tubes, (2) transisitors, (3) integrated circuit boards, (4) microprocessor full CPUs on a chip. I would define (5) clusters and (6) mobile. Candidates for next generates include huge data engine clouds, wearables and internet of things.
- mangeletti 11y ago
  What you're referring to is the advent of AI bubbles, and thus AI Winters - http://en.wikipedia.org/wiki/AI_winter http://en.wikipedia.org/wiki/AI_winter Stating that a bubble cycle has emerged in the means only accentuates the importance of the end, to note that the desire for AI is so strong that futility hasn't kept people from trying. Virtual reality is another such example.
- VLM 11y ago
  That would be prolog. You'll have much better luck googling for prolog. I played with "turbo prolog" in the 80s and accomplished nothing (from the same place as turbo C or turbo pascal or turbo basic (am I forgetting any?)). A modern variant (of a logic oriented language) can be seen here: https://github.com/clojure/core.logic/wiki/A-Core.logic-Primer https://github.com/clojure/core.logic/wiki/A-Core.logic-Prim... It tends to suffer from management by scalable procedure disease. Its possible to successfully replace a human assembly line worker with a robot arm and a very small shell script, which inevitably leads overactive imaginations to think of replacing engineers or doctors with an immense set of unfortunately undefinable unscalable procedures and rulesets, so it always collapses with complexity at implementation time. Its like moths to a flame, you should be able to replace an engineer with a very long list of if/then statements, but it turns out to be impossible in practice. Meanwhile the more advanced techniques butts up against the rapidly scaling "DBA" "IT" type of traditional solutions or non-traditional big-data techniques. Its hard to find something to logic program that isn't less verbose in a non-logic language or unwritable in any language including logic programming. Its like the Perl regex thing where you got a problem, so you write a regex, and now you got two problems. Its a very narrow although interesting niche. Finding something that fits would be pretty cool, although probably very difficult to maintain.
astrocyte 11y ago
When true strong A.I hits, I feel the confusion will quickly lift. You'll know because all of the people with weak A.I : > Used mainly to strip information value from people without compensation > Who are dumping money into foundations to prevent the coming of it's more true form will be screaming 'It's the end of the world'. Until then, enjoy the algorithms. It's the nature of business to over-sell. Don't be too upset by it.
tvsaugt 11y ago
I wish there were any position like this available in Germany ...
- iamcurious 11y ago
  Interesting. Do you know if there are any positions in the UK or any other part of Europe?
  - tvsaugt 11y ago
    I think the Amazon ML Group have something going on in the UK.
    - iamcurious 11y ago
      I will check them out. Thanks!

224 points by mwytock 11y ago

nerdy 11y ago

AI is very interesting but not very accessible because it's so specialized. I have a fairly strong programming background but feel like I'd need to study theory for a significant amount of time to even get my feet wet with AI. If you have (condensed, especially) AI resources that you think would help bridge that gap, please share! Toy-scale project ideas would also be appreciated.

ignoramous 11y ago
There was a tutorial link trending on HN a few months back, I can't seem to find it. But these links are helpful (though not condensed) as well: https://github.com/ChristosChristofidis/awesome-deep-learning https://github.com/ChristosChristofidis/awesome-deep-learnin... https://github.com/owainlewis/awesome-artificial-intelligence https://github.com/owainlewis/awesome-artificial-intelligenc...
- iyn 11y ago
  These links can be helpful too: https://news.ycombinator.com/item?id=7783550 https://news.ycombinator.com/item?id=7783550 https://news.ycombinator.com/item?id=9432952 https://news.ycombinator.com/item?id=9432952 https://news.ycombinator.com/item?id=2645671 https://news.ycombinator.com/item?id=2645671 https://news.ycombinator.com/item?id=5508261 https://news.ycombinator.com/item?id=5508261 https://news.ycombinator.com/item?id=9004689 https://news.ycombinator.com/item?id=9004689
kriro 11y ago
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" aspects though (however you will find plenty of material that is relevant). The edX class from Berkeley is pretty fun and hands on. It uses Pacman as a running example and essentially teaches the agents stuff from AIAMA: https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x-0 https://www.edx.org/course/artificial-intelligence-uc-berkel... The Stanford class by Thrun and Norvig himself (one of the authors of AIAMA) is also good but I prefer the edX one: https://www.udacity.com/course/intro-to-artificial-intelligence--cs271 https://www.udacity.com/course/intro-to-artificial-intellige... Edit: changed to direct links for the courses
- tansey 11y ago
  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 explaining things from the ground up and being the most comprehensive/up-to-date source.
  - kriro 11y ago
    There's still quite a bit of material on Bayesian networks (with the dreadfull dentist example :D), neural networks and support vector machines but overall you're right the focus is on agents. The relevant chapters are great staring points though and as always filled with great reference material for further reading. + I'm pretty sure if you apply for an AI job somewhere and it's labaled AI and not "data science" they'll expect that you know the material in AIAMA.
  - juliangregorian 11y ago
    Murphy's book is actually subtitled "A Probabilistic Perspective" -- "Machine Learning: A Probabilistic Approach" is a different book by a different author.
- wimagguc 11y ago
  +1 for the Stanford course. Great intro to AI and super easy to follow - I've done it after my uni class elsewhere, and it helped to internalise what I've learned there.
- musername 11y ago
  reading the book still requires a significant amount of time
roel_v 11y ago
'AI' is not programming, it's mathematics (well the current 'flavor' of statistics-based AI, that is - the 1980's style AI people I used to work with were philosophers, legal scholars and the like). Anyway, there is no 'bridging the gap' - you need to start from a good foundation of statistics (and the 'prerequisites' - algebra, calculus, linear algebra) and in the end, the technicalities of the software and the theory come together naturally. (source: have tried to 'bridge the gap' for 2 year, including taking MSc courses, before admitting to myself that it's a lost battle. Am now starting to build a solid math foundation before revisiting ML applications.)
- afshin 11y ago
  This is exactly my experience as well. I'm an alright programmer, but it is insufficient, because machine learning and AI are a table resting on four legs: linear algebra, calculus, statistics, and programming. I've also found myself going back to build up those foundations.
syllogism 11y ago
Here are two tutorials I've written on natural language understanding: https://honnibal.wordpress.com/2013/09/11/a-good-part-of-speechpos-tagger-in-about-200-lines-of-python/ https://honnibal.wordpress.com/2013/09/11/a-good-part-of-spe... https://honnibal.wordpress.com/2013/12/18/a-simple-fast-algorithm-for-natural-language-dependency-parsing/ https://honnibal.wordpress.com/2013/12/18/a-simple-fast-algo...

raverbashing 11y ago

However, with the abysmal standards of hiring, I'm sure a lot of companies would pass on very good candidates because they won't write FizzBuzz on the board for you, or companies would pass on Peter Norvig because his code is not Pep8 compliant

rifung 11y ago
Not sure if you're being serious but are you sure they would even care whether these kinds of candidates could program? I am skeptical they hired them to code; I imagine they mostly spend their time doing research and then have the SDEs implement things.
- jsweojtj 11y ago
  It happens all the time, and it comes from a mix of things. One way it happens is that you get a PhD in astrophysics with years of data analysis experience in for a data science job. Have a software engineer interview her and he might find that she doesn't know a number of basic computer sciences concepts [traversing a linked list, tail recursion, implement breadth-first-search]. His knowledge background says these basic ideas are fundamental, there are therefore serious questions about the technical ability of the interviewee.
  - raverbashing 11y ago
    This is a good example. At the same time this CS interviewer may not know what's the second central moment of a probability density function.
    - deleted 11y ago
      [deleted]
    - eli_gottlieb 11y ago
      Uhhh... the variance?
- kriro 11y ago

Daishiman 11y ago

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?

higherpurpose 11y ago
I imagine the "AI experts" will be paid significantly more than data scientists and machine learning experts, just like "software engineers" are paid more than "software developers" and "programmers".
fit2rule 11y ago
These subjects (plural) are all plagued by the same problem: definitions of terms. One mans intelligence is another mans dire stupidity, and so on and on it goes, chasing its tail. The most value I got from this article was in the realization that, every few years or so, the academic globes align well enough (some paper de joure becomes well-read I suppose) that .. for a brief instant .. terms are defined well enough, and gain enough agreement, that progress is made .. which progress attracts more eyeballs, who tend to want to break off a chunk for themselves, and the terms begin to differ again and we have a whole new 'sub-sub-sub-' variety of the subject. So its all about globes aligning, basically. I will now go off and implement an AI technique based entirely on the description of globes, alignment, and little chunks breaking off every now and then .. see you at the top of the AI heap in a year or ten.
Nvn 11y ago
As mentioned in the article, AI is the broader field that encompasses Machine Learning, and to a large extent also Data science (and Computer vision, NLP, Pattern recognition, etc.). And while data science might utilize a lot of statistical techniques, it is a huge stretch to consider the whole AI field to be 'statistics'. In general, AI borrows many more techniques from mathematics than it does from statistics. However, the field of AI has been quite established since the 1960's, and many techniques have been developed within that field as AI techniques, it's more about being accurate than about being fashionable as AI simply isn't 'just' statistics.

GigabyteCoin 11y ago

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

ankurdhama 11y ago
Actually there can be anything possible in the articles that are published by so called Tech Journalists who have no idea of the fundamentals of the tech.
myrryr 11y ago
What do you mean it doesn't exist? I work in that field, it exists.
- blumkvist 11y ago
  What do you mean it exists? How do you definite AI? Can your project reason with you? Or is it simply an Input-Output type of program? Just because you use natural language with it instead of punch cards doesn't mean it is intelligent. People tried to do AI in the 60s to 90s era. It is dubbed symbolic AI. It didn't work out. A good chance that it never will. Today machine learning algos and a bunch of automated statistics is called "Artificial Intelligence". It's not intelligence at all. Intelligence implies something more than I/O computation.
  - one-more-minute 11y ago
    I tend to agree that AI today is a long way from what the founders of the subject imagined – it's become something more like "Applied Computer Science". But what's now called "Artificial General Intelligence" isn't dead, and people are still working on it. Also, it's more tricky than you'd think to narrow down what counts as intelligence. There aren't really any hard lines between an I/O program and an intelligent agent, even though they seem pretty far apart.
    - protonfish 11y ago
      Just because you don't see the hard lines doesn't mean they aren't there. We are deluding ourselves by avoiding a hard definition of intelligence so we can keep believing that we are creating AI when its really nothing of the sort.

lowglow 11y ago

I'm starting an SF-based robotics/AI/ML workshop/meetup/club next week. Hit me up at dan@techendo.com if you want an invite -- or join this group: https://www.facebook.com/groups/762335743881364/?ref=br_rs https://www.facebook.com/groups/762335743881364/?ref=br_rs

wimagguc 11y ago

I wonder what all the AI is going to be used for. Is everyone working on their own Siri and recommendation engine now? (Is anyone building an AI that can come up with its own agenda?)

aangjie 11y ago
I think (not sure, as I don't closely follow the work) AGI people and MIRI work along the lines. (i.e: how to make sure a super AI doesn't go wrong, but comes with Objective functions beneficial to humans. https://intelligence.org/ https://intelligence.org/)

julianpye 11y ago

This trend is in most companies business-driven, in others it is technical-driven. Few companies have technical leadership that can manage true AI resources. If you remember the ML courses from Uni and experts in that field, you can imagine why. In many universities AI departments are assigned to schools of psychology and philosophy. Only companies with a deep engineering culture as those mentioned here can build up true AI departments. The other driver is business-driven. And this is where management demands 'AI experts', when what they really want is data-miners. And in many cases management prides themselves on 'AI algorithms', but we know that this is a term for anything that gets the results that management wants and may be far from intelligent and in most corporate cases a bunch of SQL scripts.

netcan 11y ago
What's the potential path forward (say projecting 10 years ahead) from the current growth in demand for data mining centric people? I mean people go and study in response to demand. They learn data mining and AI at Universities. I think it's often people with backgrounds or aptitude in maths. What will the 22 year old with an aptitude for maths that is learning R, SQL, AI-for-business and such be doing in 10 years? I don't know if the starting point matters much. "Results Driven," even if its optimising inventory or making ad purchasing decisions or data mining old DBs is not a bad place to "search" for advancements. Not everything needs to be fundamental research.
- deleted 11y ago
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- nkassis 11y ago
  I doubt the 22 year old you are referring too will run out of problems to solve. I also feel at some point it will be like a lot of software engineering is today. Working for companies implementing solutions similar to what already exists but tailored to the context of that companies specific needs. As of now I feel that this field is so young that a lot of the solutions are almost completely custom built to the problem at hand and that a lot of work is needed to abstract away those solutions into higher level reusable pieces.

100timesthis 11y ago

when the wsj writes about it means that the trend is over

aajtechmail 11y ago

OHH

rayalez 11y ago

The big thing that prevents me from getting into AI is the lack of practical projects that I can build. It is a very interestimg field, but as a self-taught programmer I'm used to learning by building things, and it's hard for me to come up with some project that would be practically useful and yet doable. Does anyone have any ideas?

ASlave2Gravity 11y ago
Game AIs, Stock market bots, character recognition, adaptations to game of life, computer art wherein you get an AI to paint or to draw and can seed it values. There's lots of cool stuff you can hack away at. Even training a neural net to recognise a '3' is quite interesting.
- protonfish 11y ago
  These things suck. I want a robot with sensors, the ability to move and an arm that can be programmed with a language that is appropriate for AI. That doesn't sound like something technically difficult.
  - jfoutz 11y ago
    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.
    - protonfish 11y ago
      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 robotics will remain in the realm of Battlebots.

graycat 11y ago

Considering being an employee such as in the OP, I have two reactions: (1) Take statistics, machine learning, neural nets, artificial intelligence (AI), big data, Python, R, SPSS, SAS, SQL Server, Hadoop, etc., set them aside, and ask the organization looking to hire: "What is the real world problem or collection of problems you want solved or progress on?" Or, look at the desired ends, not just the means. (2) Does the hiring organization really know what they want done that is at all doable with current technical tools or only modest extensions of them? Or, since artificial intelligence is such a broad field, really, so far of mostly unanswered research questions, and the list of topics I mentioned is still more broad, I question if many organizations know in useful terms just what those topics would do for their organization. So, for anyone with a lot of technical knowledge in, say, the AI, etc., topics, it is important for them to be able to evaluate the career opportunity. I.e., is there a real career opportunity there, say, one good to put on a resume and worth moving across country, buying a house, supporting a family, getting kids through college, meeting unusual expenses, e.g., special schooling for an ADHD child, providing for retirement, making technical and financial progress in the career, etc.? So, some concerns: (A) If an organization is to pay the big bucks for very long, e.g., for longer than some fashion fad, then they will likely need some valuable results on their real problems for their real bottom line. So, to evaluate the opportunity, should hear about the real problems and not just a list of technical topics. (B) For the opportunity for the big bucks to be realistic, really should know where the money is coming from and why. That is, to evaluate the opportunity, need to know more about the money aspects than a $10/hour fast food guy. (C) As just an employee, can get replaced, laid off, fired, etc. So, to evaluate the opportunity, need to evaluate how stable the job will be, and for that need to know about the real business and not just a list of technical topics. (D) For success in projects, problem selection and description and tool selection are part of what is crucial. Is the hiring organization really able to do such work for AI, etc. topics? Or, mostly organizations are still stuck in the model of a factory 100+ years ago where the supervisor knew more and the subordinate was there to add muscle to the work of the supervisor. But in the case of AI, etc., what supervisors really know more or much of anything; what hiring managers know enough to do good problem and tool selection? Or, if the supervisors don't know much about the technical topics, then usually the subordinate is in a very bad career position. This is an old problem: One of the more effective solutions is some high, well respected professionalism. E.g., generally a working lawyer is supposed to report only to a lawyer, not a generalist manager. Or there might be professional licensing, peer review, legal liability, etc. Or, being just an AI technical expert working for a generalist business manager promises in a year or so to smell like week old dead fish. (E) If some of the AI, etc., topics do have a lot of business value, then maybe someone with such expertise really should be a founder of a company, harvest most of the value, and not be an employee. So, what are the real problems to be solved. That is, is there a startup opportunity there? Really, my take is that the OP is, net, talking about a short term fad in some topics long surrounded with a lot of hype. Not good, not a good direction for a career. AI and hype? Just why might someone see a connection there?

peter303 11y ago

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 professor Fegeinbaum who wrote the infamous book The 5th Generation about expert system computers being the future and Japan was building the best ones. These would either be LISP machines or an interesting French niche language called prologic. Prologic basically traversed a databse "if-then" rules (modus pons). These machines went nowhere and Japan economy tanked in the early 90s. Lot of Silicon Valley VCs lost big on this. Prof Feigenbaum may still be correct, but 40 years early. However the new A.I. is driven by massive database matching possible in modern peta-level computers and not so much logical computing.

peter303 11y ago
The first four generations were defined by hardware: (1) vacuum tubes, (2) transisitors, (3) integrated circuit boards, (4) microprocessor full CPUs on a chip. I would define (5) clusters and (6) mobile. Candidates for next generates include huge data engine clouds, wearables and internet of things.
mangeletti 11y ago
What you're referring to is the advent of AI bubbles, and thus AI Winters - http://en.wikipedia.org/wiki/AI_winter http://en.wikipedia.org/wiki/AI_winter Stating that a bubble cycle has emerged in the means only accentuates the importance of the end, to note that the desire for AI is so strong that futility hasn't kept people from trying. Virtual reality is another such example.
VLM 11y ago
That would be prolog. You'll have much better luck googling for prolog. I played with "turbo prolog" in the 80s and accomplished nothing (from the same place as turbo C or turbo pascal or turbo basic (am I forgetting any?)). A modern variant (of a logic oriented language) can be seen here: https://github.com/clojure/core.logic/wiki/A-Core.logic-Primer https://github.com/clojure/core.logic/wiki/A-Core.logic-Prim... It tends to suffer from management by scalable procedure disease. Its possible to successfully replace a human assembly line worker with a robot arm and a very small shell script, which inevitably leads overactive imaginations to think of replacing engineers or doctors with an immense set of unfortunately undefinable unscalable procedures and rulesets, so it always collapses with complexity at implementation time. Its like moths to a flame, you should be able to replace an engineer with a very long list of if/then statements, but it turns out to be impossible in practice. Meanwhile the more advanced techniques butts up against the rapidly scaling "DBA" "IT" type of traditional solutions or non-traditional big-data techniques. Its hard to find something to logic program that isn't less verbose in a non-logic language or unwritable in any language including logic programming. Its like the Perl regex thing where you got a problem, so you write a regex, and now you got two problems. Its a very narrow although interesting niche. Finding something that fits would be pretty cool, although probably very difficult to maintain.

astrocyte 11y ago

When true strong A.I hits, I feel the confusion will quickly lift. You'll know because all of the people with weak A.I : > Used mainly to strip information value from people without compensation > Who are dumping money into foundations to prevent the coming of it's more true form will be screaming 'It's the end of the world'. Until then, enjoy the algorithms. It's the nature of business to over-sell. Don't be too upset by it.

tvsaugt 11y ago

I wish there were any position like this available in Germany ...

iamcurious 11y ago
Interesting. Do you know if there are any positions in the UK or any other part of Europe?
- tvsaugt 11y ago
  I think the Amazon ML Group have something going on in the UK.
  - iamcurious 11y ago
    I will check them out. Thanks!