8 ms·

Mark Cuban on Why You Need to Study Artificial Intelligence

191 points by applecore 10y ago

msvan 10y ago
Either he's right about machine learning, or this is exactly the kind of thing bubbles are made of.
- geodel 10y ago
  Of course he is right. I saw some dinosaur characteristics already showing up when I looked myself in mirror this morning.
  - Twisell 10y ago
    You are so lucky! I can't even look into a mirror since I learn 3 years ago that a NoSQL ninja will soon stab me in the back and steal my job because I work with that declining SQL old stuff!
    - markatkinson 10y ago
      Those made me laugh embarrassingly loudly.
evgen 10y ago
I would be far more inclined to heed this advice if Cuban have any indication of understanding ML as anything more than a magic black box; fairy dust to be sprinkled into every pitch deck to solve any objection or solve difficult problems. The bandwagon is passing through folks, jump on board with Mark or you will have fewer buzzwords with which to craft your deck...
- fixermark 10y ago
  Was he talking to engineers or owners / executives though? """ The Upfront Summit is LA's premier technology event, with more than 750 of the country's top investors, startups, and corporate executives """ If he was talking to executives, it may be sound advice. It's extremely likely the hot business opportunities in the short-term will be applying ML techniques to outstrip competitors trying to solve problems with traditional hand-coded solutions. In business-speak, "Learn ML" translates to "Familiarize yourself with the space and hire the people you need who know the topic," because that's how a company "learns" something.
- dentemple 10y ago
  As a developer, I'm perfectly okay with using a magic black box. As long as we can reasonably understand the I/O (and that use of the software can pass a security review), I don't see a problem with this.
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thomasahle 10y ago
I wonder to what degree he means. Because if he means "really" learning it, this is akind to the "learn to program in a week" books. It takes much more than three years to learn well.
- diminoten 10y ago
  You could join the top 5% of knowers about machine learning in 3 years, I bet. I doubt you'd innovate, but you could at least master the state of the art within 3 years.
- fixermark 10y ago
  I think his target audience was investors and company owners, so "Learn ML" in this context really means "Get savvy about the market and hire the people you need for your org to have some knowledge of the topic." Perhaps a good time to be an ML grad coming fresh out of college.
- vidarh 10y ago
  Here's my suggestion to people worried about this: Learn about how to use bayesian models and clustering methods, and how to recognise where they may be applicable, and you will already be able to produce things that will astound executives out there, and deliver very real value for your employer / customers, and those methods are simple - you don't even need to understand the maths to be able to make use of them, though it helps. By all means, start learning actual machine learning methods too, rather than just statistical methods, but as I pointed out elsewhere on this thread: there is low hanging fruit everywhere. Not nearly all of those will need "proper" machine learning, and even fewer will be willing to pay what it will cost to hire people with in-depth machine learning experience or pay the development costs or computational costs anytime soon. But a lot of them will buy into the buzzwords and look for a cheaper halfway-house or be open to pitches.
taytus 10y ago
Sorry, I refuse to visit such a clickbait headline.
rocky1138 10y ago
Why don't we just learn it in 3 years, then?
cstuder 10y ago
Ok, so where do I start?
- deleted 10y ago
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minimaxir 10y ago
The title is clickbait, but the problem with the religious fervor of machine learning/deep learning is that entrepreneurs/VC keep arguing that deep learning is magic and can solve any problem if you just stack enough layers. (see also: https://www.reddit.com/r/ProgrammerHumor/comments/5si1f0/machine_learning_approaches/ https://www.reddit.com/r/ProgrammerHumor/comments/5si1f0/mac...) Meanwhile, statistical methods for non-image/text data with identifiable features can often work better than neural networks, but they are not as sexy. (Good discussion on HN about this: https://news.ycombinator.com/item?id=13563892 https://news.ycombinator.com/item?id=13563892)
- thearn4 10y ago
  For whatever reason, statistics seems to be in the midst of a serious branding problem. Maybe traditional frameworks of inference are too conservative for non-researcher's tastes? I'm curious what Fisher, Neyman, and Pearson would say about the current state of the field. Especially considering how often they and other statisticians disagreed with each other throughout the 20th century.
  - estro 10y ago
    I believe your branding hypothesis is correct, and all of this hype around neural nets and machine learning is creating a startup ecosystem ripe for market correction. I've heard a million times that most machine learning problems are solvable by regression, and I expect to hear that a million times more.
    - CuriouslyC 10y ago
      Regression is great if your output is a linear function of your inputs. Neural networks don't have that limitation. For a long time people have assumed that linear functions are the rule and nonlinearity is the exception, but the fact that nonlinear predictors are trouncing linear ones in many domains seems to be proving that false. As for deep neural networks, in my opinion they are just an ensemble of neural networks, and ensemble methods have been shown to produce best-in-class results almost regardless of the base learning algorithm.
gremlinsinc 10y ago
That's as strong as a statement as Trump: Learn Machine Learning or you'll be a Dinosaur in 3 years... Maybe if it was coming from Bill Gates, Mark Zuckerberg, or another tech titan with some actual coding experience and a deeper level of learning about what ML even is. Cuban's a business man, and most CEO's I know don't have a clue about the stacks that run their own company, let alone what's popular. That said, I do think ML will be important, but I develop ecommerce apps and things of the such in Laravel, unless I move into AI and Neural nets I don't see needing to know a lot about ML (though I wouldn't mind moving in that direction as that space picks up) -- but there's still plenty of opportunities without it.
- AJ007 10y ago
  Making machines learn, applying machine learning to successfully solve problems, and positioning business assets to benefit from machine learning are three very different pieces of the "learn machine learning" puzzle. I don't know how any human would be able to go from 0 to having a good grasp of even one of these pieces in three years let alone all of them. I suppose there will be (and to some degree already are) machine learning magic wands, but they are going to be the kinds of things that suck capital out of the companies that utilize them (through loss of proprietary data and competition blocking moats.)
thedonkeycometh 10y ago
Looking forward to referring to this email in 10 years time as the sudden resurgence of Actionscript 3 takes everyone by surprise.
itg 10y ago
Good luck with that. Any place doing serious ML will require the person to have a PhD or have publications and presentations at conferences like NIPS/ICML. Even most CS grads with a bachelors do not have the math background required unless they double majored in math or stats. This is more VC/founders who are hyping up AI and need more ML folks so they can drive down costs.
- minimaxir 10y ago
  The notion that you can only do "serious ML" if you have a PhD/published papers is a No True Scotsman fallacy. The modern tools for ML/deep learning are accessible to all open source and well documented. And as I note in my top-level comment, old-fashioned statistical methods like linear regression are more than sufficient for real-world business problems, and definitely do not require a PhD to grok.
  - vidarh 10y ago
    This is what I keep telling people too - that the "old-fashioned" statistical methods aren't applied to more than a tiny little fraction of the problems they could be applied to yet.
- Avalaxy 10y ago
  > Any place doing serious ML will require the person to have a PhD or have publications and presentations at conferences like NIPS/ICML. Stop spouting this bullshit. You don't need a PhD, and you don't need to advance the field to be doing 'serious ML'. All you need to be able to do is know how and when to apply it to solve crucial business problems.
- sidlls 10y ago
  Utter nonsense. A PhD signals two things: that a person has the same degree of mastery of core material as a person with a master's degree and that he or she has the determination to do additional original research sufficient to produce a 100 page paper. It isn't required for any serious research effort, except by the accident of inertia. And it certainly isn't a necessary indicator of determination.
brilliantcode 10y ago
Normally I'd laugh off any Mark Cuban antics but he isn't wrong. AI is going to greatly reduce white collar jobs with economies of scale. Luddites of 18th century thought they would never be replaced and continued on their trajectory.
- ploika 10y ago
  In three years? No way. A lot of white-collar jobs may be automated (or otherwise changed beyond recognition due to technology) after about thirty years maybe, but not three.
  - brilliantcode 10y ago
    I agree 3 years is way too short. I'd say 15 years is even early. 30 year seems maybe, 100% in 60 years.
vidarh 10y ago
One of the things I've realised is that the more I'm looking around, the more I find opportunities where people "should" have seen the opportunities of basic bayesian models, simple clustering algorithms etc. and other simple mathematical/statistical methods 20 years, but didn't, and still don't. That has massively changed my perspective on how quickly the onslaught of machine learning will come. E.g. when I was reading up on genetic algorithms etc. 20 years ago we also expected the "revolution" to be right around the corner, and that things like genetic programming would change the world in a few years time. And while various of those methods found use some places, most places that could have used at least some of the simpler ones, still don't. In other words, I think talking about a 3 year timeline is crazy. It's getting more attention, sure, but there is so much low-hanging fruit that most developers could be busy for the next 20 years putting in place the most trivial algoriths all over the place and we still wouldn't have picked off even the low hanging fruit where the computational resources and algorithms and data to make a big impact were well within reach 20 years ago. This certainly means there is plenty of room for a lot developers to do very cool stuff and build careers on machine learning today, but it also mean most developers will not have to learn the state of the art - or anything near it - for a very long time. As a concrete example I give to people, consider all of the search boxes out there on various sites - product searches, location searches, site searches - that are straight keyword based searches that don't take into account any clickstream data to improve ranking. The proportion of search boxes I see that take advantage of the available data is vanishingly small, even though very basic analysis can improve the perceived relevance of the results massively. We certainly will see more companies invest in proper machine learning as the payoff gets higher and difficulty in taking advantage of it drops. But we will also see a huge proportion of sites that could use it continue to ignore it for years to come. There are big business opportunities in finding ways of making a dent in that portion of the market, though, and so learning this stuff can certainly be well worth it on a personal level, but I don't believe in his timeline in terms of the overall market.
- curuinor 10y ago
  Algolia, Swiftype and Constructor.io (disclaimer: I worked at the latter) all do the learning for that stuff, at a price.
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coldcode 10y ago
Machine learning would imply the machine needs to learn it not me. Knowing something about it and actually using it are quite different. While it might appear "everywhere" most of programming is still not ML and probably won't me. When I started in 1981 I didn't know C yet, that did not make me a dinosaur then either.
badthingfactory 10y ago
I'll place this in the same folder as the articles claiming Wix will eventually replace web developers.
pjungwir 10y ago
Sort of a content-free article, but the headline is an interesting bold claim that conjures a lot of thoughts: - I know enough machine learning to be dangerous, but I'm hardly ever asked to use it. I designed a Bayesian classifier for my own startup around 6 years ago, analyzing political donor networks. I've completed the Stanford ML course. Back in college I did a math minor, so I'm comfortable with linear algebra, calculus, etc. I'm pretty comfortable with statistics of both kinds. But my bread-and-butter is freelance web development . . . and I'm not really even sure how to find work doing more MLy things. - I've read over and over that the most time-consuming part of ML work is data collection & cleanup, and that matches my own experience. It is the same thing that killed so many data warehouse projects in the 90s. You don't need a Ph.D. to do it, but it is a tough and costly prerequisite. So it seems like you'll need non-ML programmers even for specifically ML projects. - In a similar vein, Google has written about the challenges of "operationalizing" machine learning projects.[1] Having a little experience collaborating with a team doing an ML project, where they did the ML engine and I did the user-facing application, I can say that many ML experts are not experts in building reliable, production-ready software. - Will there ever be a Wordpress of machine learning? If there is, the author will be rich, but you won't need a Ph.D. to operate it. But because ML requires hooks into your existing systems, I don't know if this will ever happen. What will happen I think is plugins to existing e-commerce systems for product recommendation or other off-the-shelf ML-powered features. These already exist, but I assume they will become more prevalent and powerful over time. In any case, the mainstreaming of ML for business will be inversely correlated with the expense to implement it, which suggests it will be easier and easier for non-expert developers to use (and misuse). EDIT: Added the (now-)third bullet point I forgot before. [1] https://research.google.com/pubs/pub43146.html https://research.google.com/pubs/pub43146.html
- vidarh 10y ago
  > and I'm not really even sure how to find work doing more MLy things. I think this is key - prospective clients won't ask for it because they don't understand where it could be used, and they won't understand the heavy ML methods. An approach there would be to pitch things like improving search results using a bayesian classifier applied to analytics data as a cheap upgrade when quoting other work. Until people are used to even the basic statistical approaches they won't be ready to invest in something more drastic.
mi100hael 10y ago
That page has so much JS bloat it made my top-of-the-line MBP lag just scrolling.
mad44 10y ago
(Pre-apology. I am not trolling, please don't get my comment below more than what I intended: another perspective to look at the strong reaction Cuban's comments incited.) Reading through the comments, I see that Cuban's statement upset and even angered several HN commenters. That is a strong emotional reaction. I am not saying it is the 5 stages of grief, but the first 3 fits: denial, anger, bargaining, depression and acceptance. Also from Howard Aiken: Don't worry about people stealing your ideas. If your ideas are any good, you'll have to ram them down people's throats.
- return0 10y ago
  I 'm not even sure why it provokes such reaction. Neural networks have existed for 4 decades ; without the deep- part, but we know their potential power and we still have not been replaced. Granted, this time it's different. I think what worries most of us is that neural nets need lots of data and we don't have access to it. Still, they are easy to learn and we should be learning about them (note to self).
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anupshinde 10y ago
Statements like these suggest that another AI Winter is coming (sooner than 3 years I guess) """He thinks even programming is vulnerable to being automated and reducing the number of available programming jobs.""" I believed something similar could happen within 1-2 years of learning/writing AI programs (more than 12 years back). I believed it so much that it consumed most of my weekends as I took on the Genetic Programming approach. Yes! computers can write programs - BUT trying reading those. Eventually after spending hours or days, you will be able to read those programs and you might find a simple "hello-world" program represented by a complex mathematical equation. Good luck trying to get such program fixed by humans. Imagine an experience decoding deep-learning-neural-nets. However, that is black-box from a programmer perspective. From a business/management personnel perspective - the code is a black box anyways. When they get NNs that can generate required software, they will replace the people-manager with a NN-manager (who is a programmer btw!)
- primaryobjects 10y ago
  Are you referring to something like this? Using Artificial Intelligence to Write Self-Modifying/Improving Programs http://www.primaryobjects.com/2013/01/27/using-artificial-intelligence-to-write-self-modifying-improving-programs/ http://www.primaryobjects.com/2013/01/27/using-artificial-in...
  - anupshinde 10y ago
    No, that looks like GAs with string/array representations. Similar thing worked for me when I tried randomly referencing nodes within a chromosome (say from index A to index B) - generating a graph like structure The outputs are like this for some not-so-easy targets: Op nodes ['ADD', 'ADD', 'MUL'] EXPR: ['ADD[ni_99](ADD[ni_49](I__7[ni_43](), ADD[ni_19](I__8[ni_66](), ADD[ni_79](GET_CONST_3[ni_25](), I__9[ni_71]()))), I__1[ni_61]())', 'ADD[ni_13](I__6[ni_17](), ADD[ni_49](I__7[ni_43](), ADD[ni_19](I__8[ni_66](), ADD[ni_79](GET_CONST_3[ni_25](), I__9[ni_71]()))))', 'MUL[ni_68](GET_CONST_8[ni_73](), FLOAT[ni_42](I__1[ni_91]()))'] With genetic programming (using an AST), it can solve complex equations: However, this simple equation i.e. correct answer "(a + b + c - d) / e" could be evolved and will result into either of these (depends on my luck maybe) Case1: ((int)((b+((c-d)+a))/e)&(int)((b+((c-d)+a))/e)) Case2: ((((int)a&(int)(((((((mod(e,a)c)/e)(((((int)(d-a)&(int)b)+e)/a)/e))/((((int)e&(int)c)+e)+(c+(e/a))))c)/e)e))/((((int)e&(int)c)+e)+((b/e)+(e/e))))+(((((((((mod(e,a)c)/e)(((((int)(d-e)&(int)b)+e)/a)/e))/((((int)e&(int)c)+e)+((b/e)+(e/e))))c)/e) <..........10383 characters here.........> ))))))/e))/((((int)e&(int)c)+e)+(((d-e)/e)+((d/b)/e))))+(((mod(e,a)/e)+(b/e))+(((c/b)+((d+b)/e))/e))))))))) The GP output (case 1 and 2) above was generated with a tweaked version of https://github.com/rogeralsing/go-genetic-math https://github.com/rogeralsing/go-genetic-math
feral 10y ago
Reading HN I worry that we're going to have the opposite problem - a glut of people will try and (badly?) learn ML and then realize there aren't enough ML jobs. I've a PhD and held ML-engineer positions in a few different companies - I've good industry awareness. Most applied ML, for most companies, right now, is actually relatively simple models (hand-coded rules! logistic regression! You'd be shocked how common these are.) The bulk of the work is data cleaning, gathering, integration, deployment, productisation, reliability, avoiding pathological cases, special-casing, Product, UX. You do need ML specialists who understand the stuff, to make it all work and come together - but the ratio of ML specialists to the wider team is low. Maybe 1 or 2 specialist on a team of 10 for an ML heavy product. This is going to remain the case IMO. Yes, there will be small teams, in highly resourced organizations (GOOG, FB etc), academic research labs, or occasional hard-tech startups, who do new model development. Maybe if AI becomes huge, you'll see more traditional Fortune 500s spin up similar efforts. But there'll be a much wider set of people&businesses applying and tuning well understood approaches, rather than doing new model development. And you just don't need as many ML specialists, for that approach. Even with deep learning, the tooling will advance. I mean, even look at all the research papers describing applications at the moment - so many of them are using pre-trained models. Industry will be similar. Tooling will advance, and you'll be able to do increasingly more with off-the-shelf pieces. I think ML is absolutely going to have a big impact - I buy at least some of the hype. But should all developers, or even a substantial minority of developers, start learning ML as a career imperative? I don't think so. Finally, it takes serious time to learn this stuff. Its easy to dabble (and worthwhile doing - its fun; and sometimes you can do powerful things in a using tools in a very blackbox manner!). But actually thoroughly learning it takes time. It takes serious time to build statistical intuition, as just one example. We could easily end up with a great many career developers who have a specialization in ML, frustrated they never get to use it.
- ylem 10y ago
  This is not my field, but a serious question--I once read that part of the motivation of pharmaceutical companies in hiring researchers was not so that they would all produce ground breaking independent research, but rather because they would be capable of reading the literature (again, not my field). Is that true at all for machine learning? Would companies hire people who would be up to date with the literature so that they could implement algorithms that others have developed in an academic context and put them into production?
owaislone 10y ago
I think what he meant to say was that companies should learn it in 3 years or the competition will drive them out of business. I don't think he meant individuals will be dinosaurs, but startups that fail to take advantage of ML/AI.
otikik 10y ago
Yeah. I'm going to be a clickbaitsaurus.
JustSomeNobody 10y ago
I want a nickel every time someone says programming jobs will be automated in 5 years. This goes back to the beginning of programming and it hasn't happened.
bluekite2000 10y ago
anyone knows if there is a business need for human to train/label data? I have been thinking of going to a place w/ cheap labor cost (perhaps Vietnam) and set up an operation like this.
- cr0sh 10y ago
  In the short term, perhaps. In the long term, possibly not - there are already efforts and approaches being done in ML/deep learning to get models to label unlabeled data (google "deep learning unlabeled data" for tons of research info).
anotheryou 10y ago
I think prosthetic knowledge will become deeper and more accurate in the long run. And if it scales we don't need many people building the general purpose AI. With this prosthetic knowledge we will have to learn much more what to ask and know how much the machine knows. One has to quickly grasp the abstract that is one level too high or detailed for the machine to find and than find the seperate answers of the level below to recombine them. You can't yet ask where to open a restaurant, but you can google for demographics and write a program to map ratios between income, foot-traffic density and restaurant density. Once we can ask what and where is the most profitable business in to start in town, we probably still don't get a step by step guide how to do it, interior design included. Where the rubber meets the road there is still a lot of oppertunities to decide on and complex data we can grasp more easy than the machine.
marricks 10y ago
Assuming we're all going to be deep learning programmers is quite a bit foolhardy. I think what's really relevant to consider is AI winters can and do happen[1]. I would not disagree deep learning has done some amazing things, what I would say is it does have limitations. What causes AI winters is when an advance such as deep learning can be applied to new problems and leads to increased interest. And while this new thing is really good as a subset of problems and impresses the public, of course it can't displace humans at everything and naturally has it's limitations. So funding pours in, everyone gets hyped, and then those natural limits are (re)discovered and everyone gets all anti-AI research. Of course many people knew the limitations all along, but the dream is gone and so is a lot of funding until the next thing comes along. This is probably natural to a lot of fields but AI just seems more prone to these boom and bust cycles because it's really exciting stuff. [1] https://en.wikipedia.org/wiki/AI_winter https://en.wikipedia.org/wiki/AI_winter
- sevensor 10y ago
  Exactly. 25 years later, I'm still waiting to be replaced by an Expert System. A lot of the tech that fueled the hype train (logic programming! genetic algorithms!) is still really neat, but it didn't work out the way we expected. Same thing will happen with ML. It's the Prolog of tomorrow.
dkarapetyan 10y ago
No thanks. Fundamentals and not hype is what makes one not a dinosaur.
xs 10y ago
Just like there isn't such a thing as polymaths anymore because the world has so many specialized skills, I think there won't be the possibilities of things like "full stack developer" in the near future because of the complexities for development. Some things that contribute to the complexities will be: ubiquitous controls, advanced AI, internet of things, augmented reality, machine learning, and new technologies we don't even have yet. We are in the golden age now where a single person can sometimes create a better website or app compared to a whole development team at a fortune 500 company. I think our Internet world will become so complex in the very near future that a single person simply won't be able be capable and they'll have to become specialized in only a portion of it.
deepnotderp 10y ago
I'm going to go against the grain here and (gasp) not hate on deep learning. People should realize that although many older statistical methods and "traditional" machine learning methods such as LDA, SVMs and decision trees may be good enough for business tasks, they are not the cutting edge of AI research. I think people are forgetting exactly how difficult image classification and object detection was before the advent of deep learning. People hating on "stack more layers" forget that "stack more layers" is EXACTLY what improved imagenet performance to such as massive extent. ResNets pushed the limits by figuring out how to stack more layers in a beneficial way. And let's take a look at AlphaGo, how would you do that with SVMs or decision trees? Just get over the fact that deep learning provides a level of "intuition" (Go's search space is famously greater than the estimated number of stars in the universe). I think that a part of the problem is that older ML PhDs are angry that deep learning is so easy (until the learning rate fails to provide convergence of course...) and would prefer that their preferred methods would still reign supreme. I'll end this wall of text on the note that OpenAI's Ian Goodfellow notes that all projects at OpenAI use deep learning right now, but they are not dogmatic and will consider other approaches if they work well. I think this is the path that should be taken. On the other hand, I also see a bright future for uniting traditional techniques with deep learning such as attaching a SVM to a CNN and combining decision trees with CNNs both of which have resulted in good results.
- dmreedy 10y ago
  >> I think that a part of the problem is that older ML PhDs are angry that deep learning is so easy (until the learning rate fails to provide convergence of course...) and would prefer that their preferred methods would still reign supreme. I think that's definitely a part of it, and I feel that way sometimes myself (not that I'm a PhD). But there's another side of that reluctance that lies on the axis of model accountability and explicability. A lot of modern ML/Deep Learning doesn't -feel- like we're understanding anything any more than we did ten years ago. Yes our black-box results are better according to the tests we've laid out for them, but there's something more slippery about the 'why', beyond the handwave of 'complexity'. Maybe this is just the way it will be going forward (in the spirit of Quantum's "shut up and calculate"), but it is not easy to give up something that you can wrap your head around with something that kind of just takes care of itself, especially if you're in the business of seeking knowledge instead of results.
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xamuel 10y ago
Three years ago, Machine Learning on a resume meant: "Good candidate." Currently, it means: "Jumps on bandwagons, caution." In three years, it'll mean: "Brainless buzzwords, avoid."
id122015 10y ago
It doesn't matter how far technology gets, there are some -isms and they are the real Dinosaurs that we have to get over and we are too small.
bsaul 10y ago
Honest question : once the technics will settle a bit, and libraries are created, what will be needed, apart from knowing that machine learning algorithm are based on some kind of stasticial inference, with a few settings here and there ? I mean, we don't need a phd in image compression to create a service that streams videos. We just use libraries. Same for everything in computer science, it always end up packaged in some kind of reusable code or service, and only some specialists remain working in the field to work on marginal improvements. Why would ML be any different ?
- pesfandiar 10y ago
  Unlike video streaming libraries, there's no certain way for people to verify the outcome of an ML function is correct (e.g. Would this model work on other datasets? Is the statistical inference correct?). You need at least some basic knowledge about the underlying math to avoid pitfalls.
- Eridrus 10y ago
  Once you've plugged your data into an ML system and gives you a classifier, are you done? What if the results are not good enough, do you just move on to another problem? If you don't just move on to another problem you will need people who know how to push these systems further.
  - Namrog84 10y ago
    But at some point, there is a difference between turning dials of the systems vs implementing new novel ways of these systems. Obviously we always need new novel approaches, but how much of that should stay in phd/academic/research world and how much spreads out into engineering day to day activities? I am rather newbish in the whole space, but most everything I've seen is quite often just knowing enough to turn the right dials the right way. And I do wonder how long will it be before some of these dials can start turning themselves with more iterations with a simple, "yes that's what I want, or no that's what I don't want" Sure, it might take some more cycles for it to find optimal in this example.
usgroup 10y ago
I kind of read it "learn it and you'll be a dinasaur for 3 years". It made me quite excited at the prospect. I started to practice growling and running at a curious forward angle , but on second reading I find myself disappointed.
onmobiletemp 10y ago
This is so dumb. You arent going to understand or get a job in machine learning unless you have at least a masters in the subject. Its extremely difficult and complex. I see tons of college students taking machine learning classes in anticipation of becoming a dinosaur amd none of them could get a machine learning job afterwards. Programming has always been pretty easy. The ai revolution wont be like the home computer revolution. Its going to be led by a relatively small group of academics, scientists and engineers working in prestigious research positions.
- cr0sh 10y ago
  Currently there just aren't that many ML jobs out there to apply for - but who knows what the landscape will be like in 5 years or so? Your assertion, though: > This is so dumb. You arent going to understand or get a job in machine learning unless you have at least a masters in the subject. Its extremely difficult and complex. ...couldn't be further from the truth. You can understand this stuff without a masters in the subject. It really isn't too difficult or complex. Sure, I will admit that understanding how to take a derivative might be useful, but despite not having that knowledge (but I'm working on obtaining it), I have still been able to implement successfully working ML solutions - at least in a classroom-type environment. My last success was getting a virtual car to drive around a virtual track, staying on the track and negotiating the curves, using an implementation of the NVIDIA End-to-End CNN architecture and some data I generated (plus augmentation and some other fun stuff). I used Keras and Python 3, running on my workstation at home, with a 750 ti SC as my "GPU" (I really need to upgrade this). My model converged very well after 10 epochs, but after 20 the loss was pushed pretty low to sub 1%. As far as I could tell, there wasn't evidence of overfitting (I need to do more investigation on this, though). This was all done as part of Udacity's Self-Driving Car Engineer Nanodegree, which I am taking part in. Prior to this, I also completed Udacity's CS373 course in 2012, and Andrew Ng's ML Class in 2011. My motivation for all of this has mainly been my interest in autonomous unmanned ground vehicle robotics technology. I have an ongoing side-project in developing such a platform (seemingly back-burner'd a lot, though - life getting in the way, I guess). Even so, if a job offer comes about because of it, I'm not going to complain. As it is, I believe the knowledge has helped me land positions, since it shows my dedication to improving my skills in problem domains outside of the everyday software development tasks. When potential employers have asked about it, I can show them some code I've worked on, while mentioning how some of the more simpler ML methods could help in a business problem domain. It sets me apart somewhat from other candidates, I believe. Especially those who think the topic isn't worth their time to learn, because it may be "difficult and complex".
general_ai 10y ago
No, you're not going to be a "dinosaur". 99% of extremely well compensated software engineering jobs do not involve ML. Using top large companies as a proxy of what things are going to be like in the world at large 3 years from now, maybe one in 200-300 engineers does anything in any way related to ML there. And that's a generous estimate. You do need to know what it is, roughly, but there's no need to drop everything you're doing and switch careers.
hnmot223 10y ago
"Mark also said that what happens in the next 5–10 years is just going to blow everybody away, especially in the field of automation. He thinks even programming is vulnerable to being automated and reducing the number of available programming jobs." He's talking out of his ass here. This won't be happening anytime soon (if ever)
SmokyBorbon 10y ago
Mark Cuban should study intelligence.
acd 10y ago
I am sysadmin/devops what machine learning tools and topics would you recommend to learn for that field?
slantaclaus 10y ago
Mark Cuban was a programmer dickbrain
- dang 10y ago
  We ban accounts that comment like this. Please post civilly and substantively, or not at all. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newswelcome.html https://news.ycombinator.com/newswelcome.html We detached this comment from https://news.ycombinator.com/item?id=13599487 https://news.ycombinator.com/item?id=13599487 and marked it off-topic.
bgdkbtv 10y ago
Oh yeah? Does Mark Cuban know artificial intelligence himself or is he just asking people to study it and work for him? :)

191 points by applecore 10y ago

msvan 10y ago

Either he's right about machine learning, or this is exactly the kind of thing bubbles are made of.

geodel 10y ago
Of course he is right. I saw some dinosaur characteristics already showing up when I looked myself in mirror this morning.
- Twisell 10y ago
  You are so lucky! I can't even look into a mirror since I learn 3 years ago that a NoSQL ninja will soon stab me in the back and steal my job because I work with that declining SQL old stuff!
  - markatkinson 10y ago
    Those made me laugh embarrassingly loudly.

evgen 10y ago

I would be far more inclined to heed this advice if Cuban have any indication of understanding ML as anything more than a magic black box; fairy dust to be sprinkled into every pitch deck to solve any objection or solve difficult problems. The bandwagon is passing through folks, jump on board with Mark or you will have fewer buzzwords with which to craft your deck...

fixermark 10y ago
Was he talking to engineers or owners / executives though? """ The Upfront Summit is LA's premier technology event, with more than 750 of the country's top investors, startups, and corporate executives """ If he was talking to executives, it may be sound advice. It's extremely likely the hot business opportunities in the short-term will be applying ML techniques to outstrip competitors trying to solve problems with traditional hand-coded solutions. In business-speak, "Learn ML" translates to "Familiarize yourself with the space and hire the people you need who know the topic," because that's how a company "learns" something.
dentemple 10y ago
As a developer, I'm perfectly okay with using a magic black box. As long as we can reasonably understand the I/O (and that use of the software can pass a security review), I don't see a problem with this.

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thomasahle 10y ago

I wonder to what degree he means. Because if he means "really" learning it, this is akind to the "learn to program in a week" books. It takes much more than three years to learn well.

diminoten 10y ago
You could join the top 5% of knowers about machine learning in 3 years, I bet. I doubt you'd innovate, but you could at least master the state of the art within 3 years.
fixermark 10y ago
I think his target audience was investors and company owners, so "Learn ML" in this context really means "Get savvy about the market and hire the people you need for your org to have some knowledge of the topic." Perhaps a good time to be an ML grad coming fresh out of college.
vidarh 10y ago
Here's my suggestion to people worried about this: Learn about how to use bayesian models and clustering methods, and how to recognise where they may be applicable, and you will already be able to produce things that will astound executives out there, and deliver very real value for your employer / customers, and those methods are simple - you don't even need to understand the maths to be able to make use of them, though it helps. By all means, start learning actual machine learning methods too, rather than just statistical methods, but as I pointed out elsewhere on this thread: there is low hanging fruit everywhere. Not nearly all of those will need "proper" machine learning, and even fewer will be willing to pay what it will cost to hire people with in-depth machine learning experience or pay the development costs or computational costs anytime soon. But a lot of them will buy into the buzzwords and look for a cheaper halfway-house or be open to pitches.

taytus 10y ago

Sorry, I refuse to visit such a clickbait headline.

rocky1138 10y ago

Why don't we just learn it in 3 years, then?

cstuder 10y ago

Ok, so where do I start?

deleted 10y ago
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minimaxir 10y ago

The title is clickbait, but the problem with the religious fervor of machine learning/deep learning is that entrepreneurs/VC keep arguing that deep learning is magic and can solve any problem if you just stack enough layers. (see also: https://www.reddit.com/r/ProgrammerHumor/comments/5si1f0/machine_learning_approaches/ https://www.reddit.com/r/ProgrammerHumor/comments/5si1f0/mac...) Meanwhile, statistical methods for non-image/text data with identifiable features can often work better than neural networks, but they are not as sexy. (Good discussion on HN about this: https://news.ycombinator.com/item?id=13563892 https://news.ycombinator.com/item?id=13563892)

thearn4 10y ago
For whatever reason, statistics seems to be in the midst of a serious branding problem. Maybe traditional frameworks of inference are too conservative for non-researcher's tastes? I'm curious what Fisher, Neyman, and Pearson would say about the current state of the field. Especially considering how often they and other statisticians disagreed with each other throughout the 20th century.
- estro 10y ago
  I believe your branding hypothesis is correct, and all of this hype around neural nets and machine learning is creating a startup ecosystem ripe for market correction. I've heard a million times that most machine learning problems are solvable by regression, and I expect to hear that a million times more.
  - CuriouslyC 10y ago
    Regression is great if your output is a linear function of your inputs. Neural networks don't have that limitation. For a long time people have assumed that linear functions are the rule and nonlinearity is the exception, but the fact that nonlinear predictors are trouncing linear ones in many domains seems to be proving that false. As for deep neural networks, in my opinion they are just an ensemble of neural networks, and ensemble methods have been shown to produce best-in-class results almost regardless of the base learning algorithm.

gremlinsinc 10y ago

That's as strong as a statement as Trump: Learn Machine Learning or you'll be a Dinosaur in 3 years... Maybe if it was coming from Bill Gates, Mark Zuckerberg, or another tech titan with some actual coding experience and a deeper level of learning about what ML even is. Cuban's a business man, and most CEO's I know don't have a clue about the stacks that run their own company, let alone what's popular. That said, I do think ML will be important, but I develop ecommerce apps and things of the such in Laravel, unless I move into AI and Neural nets I don't see needing to know a lot about ML (though I wouldn't mind moving in that direction as that space picks up) -- but there's still plenty of opportunities without it.

AJ007 10y ago
Making machines learn, applying machine learning to successfully solve problems, and positioning business assets to benefit from machine learning are three very different pieces of the "learn machine learning" puzzle. I don't know how any human would be able to go from 0 to having a good grasp of even one of these pieces in three years let alone all of them. I suppose there will be (and to some degree already are) machine learning magic wands, but they are going to be the kinds of things that suck capital out of the companies that utilize them (through loss of proprietary data and competition blocking moats.)

thedonkeycometh 10y ago

Looking forward to referring to this email in 10 years time as the sudden resurgence of Actionscript 3 takes everyone by surprise.

itg 10y ago

Good luck with that. Any place doing serious ML will require the person to have a PhD or have publications and presentations at conferences like NIPS/ICML. Even most CS grads with a bachelors do not have the math background required unless they double majored in math or stats. This is more VC/founders who are hyping up AI and need more ML folks so they can drive down costs.

minimaxir 10y ago
The notion that you can only do "serious ML" if you have a PhD/published papers is a No True Scotsman fallacy. The modern tools for ML/deep learning are accessible to all open source and well documented. And as I note in my top-level comment, old-fashioned statistical methods like linear regression are more than sufficient for real-world business problems, and definitely do not require a PhD to grok.
- vidarh 10y ago
  This is what I keep telling people too - that the "old-fashioned" statistical methods aren't applied to more than a tiny little fraction of the problems they could be applied to yet.
Avalaxy 10y ago
> Any place doing serious ML will require the person to have a PhD or have publications and presentations at conferences like NIPS/ICML. Stop spouting this bullshit. You don't need a PhD, and you don't need to advance the field to be doing 'serious ML'. All you need to be able to do is know how and when to apply it to solve crucial business problems.
sidlls 10y ago
Utter nonsense. A PhD signals two things: that a person has the same degree of mastery of core material as a person with a master's degree and that he or she has the determination to do additional original research sufficient to produce a 100 page paper. It isn't required for any serious research effort, except by the accident of inertia. And it certainly isn't a necessary indicator of determination.

brilliantcode 10y ago

Normally I'd laugh off any Mark Cuban antics but he isn't wrong. AI is going to greatly reduce white collar jobs with economies of scale. Luddites of 18th century thought they would never be replaced and continued on their trajectory.

ploika 10y ago
In three years? No way. A lot of white-collar jobs may be automated (or otherwise changed beyond recognition due to technology) after about thirty years maybe, but not three.
- brilliantcode 10y ago
  I agree 3 years is way too short. I'd say 15 years is even early. 30 year seems maybe, 100% in 60 years.

vidarh 10y ago

One of the things I've realised is that the more I'm looking around, the more I find opportunities where people "should" have seen the opportunities of basic bayesian models, simple clustering algorithms etc. and other simple mathematical/statistical methods 20 years, but didn't, and still don't. That has massively changed my perspective on how quickly the onslaught of machine learning will come. E.g. when I was reading up on genetic algorithms etc. 20 years ago we also expected the "revolution" to be right around the corner, and that things like genetic programming would change the world in a few years time. And while various of those methods found use some places, most places that could have used at least some of the simpler ones, still don't. In other words, I think talking about a 3 year timeline is crazy. It's getting more attention, sure, but there is so much low-hanging fruit that most developers could be busy for the next 20 years putting in place the most trivial algoriths all over the place and we still wouldn't have picked off even the low hanging fruit where the computational resources and algorithms and data to make a big impact were well within reach 20 years ago. This certainly means there is plenty of room for a lot developers to do very cool stuff and build careers on machine learning today, but it also mean most developers will not have to learn the state of the art - or anything near it - for a very long time. As a concrete example I give to people, consider all of the search boxes out there on various sites - product searches, location searches, site searches - that are straight keyword based searches that don't take into account any clickstream data to improve ranking. The proportion of search boxes I see that take advantage of the available data is vanishingly small, even though very basic analysis can improve the perceived relevance of the results massively. We certainly will see more companies invest in proper machine learning as the payoff gets higher and difficulty in taking advantage of it drops. But we will also see a huge proportion of sites that could use it continue to ignore it for years to come. There are big business opportunities in finding ways of making a dent in that portion of the market, though, and so learning this stuff can certainly be well worth it on a personal level, but I don't believe in his timeline in terms of the overall market.

curuinor 10y ago
Algolia, Swiftype and Constructor.io (disclaimer: I worked at the latter) all do the learning for that stuff, at a price.

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coldcode 10y ago

Machine learning would imply the machine needs to learn it not me. Knowing something about it and actually using it are quite different. While it might appear "everywhere" most of programming is still not ML and probably won't me. When I started in 1981 I didn't know C yet, that did not make me a dinosaur then either.

badthingfactory 10y ago

I'll place this in the same folder as the articles claiming Wix will eventually replace web developers.

pjungwir 10y ago

Sort of a content-free article, but the headline is an interesting bold claim that conjures a lot of thoughts: - I know enough machine learning to be dangerous, but I'm hardly ever asked to use it. I designed a Bayesian classifier for my own startup around 6 years ago, analyzing political donor networks. I've completed the Stanford ML course. Back in college I did a math minor, so I'm comfortable with linear algebra, calculus, etc. I'm pretty comfortable with statistics of both kinds. But my bread-and-butter is freelance web development . . . and I'm not really even sure how to find work doing more MLy things. - I've read over and over that the most time-consuming part of ML work is data collection & cleanup, and that matches my own experience. It is the same thing that killed so many data warehouse projects in the 90s. You don't need a Ph.D. to do it, but it is a tough and costly prerequisite. So it seems like you'll need non-ML programmers even for specifically ML projects. - In a similar vein, Google has written about the challenges of "operationalizing" machine learning projects.[1] Having a little experience collaborating with a team doing an ML project, where they did the ML engine and I did the user-facing application, I can say that many ML experts are not experts in building reliable, production-ready software. - Will there ever be a Wordpress of machine learning? If there is, the author will be rich, but you won't need a Ph.D. to operate it. But because ML requires hooks into your existing systems, I don't know if this will ever happen. What will happen I think is plugins to existing e-commerce systems for product recommendation or other off-the-shelf ML-powered features. These already exist, but I assume they will become more prevalent and powerful over time. In any case, the mainstreaming of ML for business will be inversely correlated with the expense to implement it, which suggests it will be easier and easier for non-expert developers to use (and misuse). EDIT: Added the (now-)third bullet point I forgot before. [1] https://research.google.com/pubs/pub43146.html https://research.google.com/pubs/pub43146.html

vidarh 10y ago
> and I'm not really even sure how to find work doing more MLy things. I think this is key - prospective clients won't ask for it because they don't understand where it could be used, and they won't understand the heavy ML methods. An approach there would be to pitch things like improving search results using a bayesian classifier applied to analytics data as a cheap upgrade when quoting other work. Until people are used to even the basic statistical approaches they won't be ready to invest in something more drastic.

mi100hael 10y ago

That page has so much JS bloat it made my top-of-the-line MBP lag just scrolling.

mad44 10y ago

(Pre-apology. I am not trolling, please don't get my comment below more than what I intended: another perspective to look at the strong reaction Cuban's comments incited.) Reading through the comments, I see that Cuban's statement upset and even angered several HN commenters. That is a strong emotional reaction. I am not saying it is the 5 stages of grief, but the first 3 fits: denial, anger, bargaining, depression and acceptance. Also from Howard Aiken: Don't worry about people stealing your ideas. If your ideas are any good, you'll have to ram them down people's throats.

return0 10y ago
I 'm not even sure why it provokes such reaction. Neural networks have existed for 4 decades ; without the deep- part, but we know their potential power and we still have not been replaced. Granted, this time it's different. I think what worries most of us is that neural nets need lots of data and we don't have access to it. Still, they are easy to learn and we should be learning about them (note to self).

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anupshinde 10y ago

Statements like these suggest that another AI Winter is coming (sooner than 3 years I guess) """He thinks even programming is vulnerable to being automated and reducing the number of available programming jobs.""" I believed something similar could happen within 1-2 years of learning/writing AI programs (more than 12 years back). I believed it so much that it consumed most of my weekends as I took on the Genetic Programming approach. Yes! computers can write programs - BUT trying reading those. Eventually after spending hours or days, you will be able to read those programs and you might find a simple "hello-world" program represented by a complex mathematical equation. Good luck trying to get such program fixed by humans. Imagine an experience decoding deep-learning-neural-nets. However, that is black-box from a programmer perspective. From a business/management personnel perspective - the code is a black box anyways. When they get NNs that can generate required software, they will replace the people-manager with a NN-manager (who is a programmer btw!)

primaryobjects 10y ago
Are you referring to something like this? Using Artificial Intelligence to Write Self-Modifying/Improving Programs http://www.primaryobjects.com/2013/01/27/using-artificial-intelligence-to-write-self-modifying-improving-programs/ http://www.primaryobjects.com/2013/01/27/using-artificial-in...
- anupshinde 10y ago
  No, that looks like GAs with string/array representations. Similar thing worked for me when I tried randomly referencing nodes within a chromosome (say from index A to index B) - generating a graph like structure The outputs are like this for some not-so-easy targets: Op nodes ['ADD', 'ADD', 'MUL'] EXPR: ['ADD[ni_99](ADD[ni_49](I__7[ni_43](), ADD[ni_19](I__8[ni_66](), ADD[ni_79](GET_CONST_3[ni_25](), I__9[ni_71]()))), I__1[ni_61]())', 'ADD[ni_13](I__6[ni_17](), ADD[ni_49](I__7[ni_43](), ADD[ni_19](I__8[ni_66](), ADD[ni_79](GET_CONST_3[ni_25](), I__9[ni_71]()))))', 'MUL[ni_68](GET_CONST_8[ni_73](), FLOAT[ni_42](I__1[ni_91]()))'] With genetic programming (using an AST), it can solve complex equations: However, this simple equation i.e. correct answer "(a + b + c - d) / e" could be evolved and will result into either of these (depends on my luck maybe) Case1: ((int)((b+((c-d)+a))/e)&(int)((b+((c-d)+a))/e)) Case2: ((((int)a&(int)(((((((mod(e,a)c)/e)(((((int)(d-a)&(int)b)+e)/a)/e))/((((int)e&(int)c)+e)+(c+(e/a))))c)/e)e))/((((int)e&(int)c)+e)+((b/e)+(e/e))))+(((((((((mod(e,a)c)/e)(((((int)(d-e)&(int)b)+e)/a)/e))/((((int)e&(int)c)+e)+((b/e)+(e/e))))c)/e) <..........10383 characters here.........> ))))))/e))/((((int)e&(int)c)+e)+(((d-e)/e)+((d/b)/e))))+(((mod(e,a)/e)+(b/e))+(((c/b)+((d+b)/e))/e))))))))) The GP output (case 1 and 2) above was generated with a tweaked version of https://github.com/rogeralsing/go-genetic-math https://github.com/rogeralsing/go-genetic-math

feral 10y ago

Reading HN I worry that we're going to have the opposite problem - a glut of people will try and (badly?) learn ML and then realize there aren't enough ML jobs. I've a PhD and held ML-engineer positions in a few different companies - I've good industry awareness. Most applied ML, for most companies, right now, is actually relatively simple models (hand-coded rules! logistic regression! You'd be shocked how common these are.) The bulk of the work is data cleaning, gathering, integration, deployment, productisation, reliability, avoiding pathological cases, special-casing, Product, UX. You do need ML specialists who understand the stuff, to make it all work and come together - but the ratio of ML specialists to the wider team is low. Maybe 1 or 2 specialist on a team of 10 for an ML heavy product. This is going to remain the case IMO. Yes, there will be small teams, in highly resourced organizations (GOOG, FB etc), academic research labs, or occasional hard-tech startups, who do new model development. Maybe if AI becomes huge, you'll see more traditional Fortune 500s spin up similar efforts. But there'll be a much wider set of people&businesses applying and tuning well understood approaches, rather than doing new model development. And you just don't need as many ML specialists, for that approach. Even with deep learning, the tooling will advance. I mean, even look at all the research papers describing applications at the moment - so many of them are using pre-trained models. Industry will be similar. Tooling will advance, and you'll be able to do increasingly more with off-the-shelf pieces. I think ML is absolutely going to have a big impact - I buy at least some of the hype. But should all developers, or even a substantial minority of developers, start learning ML as a career imperative? I don't think so. Finally, it takes serious time to learn this stuff. Its easy to dabble (and worthwhile doing - its fun; and sometimes you can do powerful things in a using tools in a very blackbox manner!). But actually thoroughly learning it takes time. It takes serious time to build statistical intuition, as just one example. We could easily end up with a great many career developers who have a specialization in ML, frustrated they never get to use it.

ylem 10y ago
This is not my field, but a serious question--I once read that part of the motivation of pharmaceutical companies in hiring researchers was not so that they would all produce ground breaking independent research, but rather because they would be capable of reading the literature (again, not my field). Is that true at all for machine learning? Would companies hire people who would be up to date with the literature so that they could implement algorithms that others have developed in an academic context and put them into production?

owaislone 10y ago

I think what he meant to say was that companies should learn it in 3 years or the competition will drive them out of business. I don't think he meant individuals will be dinosaurs, but startups that fail to take advantage of ML/AI.

otikik 10y ago

Yeah. I'm going to be a clickbaitsaurus.

JustSomeNobody 10y ago

I want a nickel every time someone says programming jobs will be automated in 5 years. This goes back to the beginning of programming and it hasn't happened.

bluekite2000 10y ago

anyone knows if there is a business need for human to train/label data? I have been thinking of going to a place w/ cheap labor cost (perhaps Vietnam) and set up an operation like this.

cr0sh 10y ago
In the short term, perhaps. In the long term, possibly not - there are already efforts and approaches being done in ML/deep learning to get models to label unlabeled data (google "deep learning unlabeled data" for tons of research info).

anotheryou 10y ago

I think prosthetic knowledge will become deeper and more accurate in the long run. And if it scales we don't need many people building the general purpose AI. With this prosthetic knowledge we will have to learn much more what to ask and know how much the machine knows. One has to quickly grasp the abstract that is one level too high or detailed for the machine to find and than find the seperate answers of the level below to recombine them. You can't yet ask where to open a restaurant, but you can google for demographics and write a program to map ratios between income, foot-traffic density and restaurant density. Once we can ask what and where is the most profitable business in to start in town, we probably still don't get a step by step guide how to do it, interior design included. Where the rubber meets the road there is still a lot of oppertunities to decide on and complex data we can grasp more easy than the machine.

marricks 10y ago

Assuming we're all going to be deep learning programmers is quite a bit foolhardy. I think what's really relevant to consider is AI winters can and do happen[1]. I would not disagree deep learning has done some amazing things, what I would say is it does have limitations. What causes AI winters is when an advance such as deep learning can be applied to new problems and leads to increased interest. And while this new thing is really good as a subset of problems and impresses the public, of course it can't displace humans at everything and naturally has it's limitations. So funding pours in, everyone gets hyped, and then those natural limits are (re)discovered and everyone gets all anti-AI research. Of course many people knew the limitations all along, but the dream is gone and so is a lot of funding until the next thing comes along. This is probably natural to a lot of fields but AI just seems more prone to these boom and bust cycles because it's really exciting stuff. [1] https://en.wikipedia.org/wiki/AI_winter https://en.wikipedia.org/wiki/AI_winter

sevensor 10y ago
Exactly. 25 years later, I'm still waiting to be replaced by an Expert System. A lot of the tech that fueled the hype train (logic programming! genetic algorithms!) is still really neat, but it didn't work out the way we expected. Same thing will happen with ML. It's the Prolog of tomorrow.

dkarapetyan 10y ago

No thanks. Fundamentals and not hype is what makes one not a dinosaur.

xs 10y ago

Just like there isn't such a thing as polymaths anymore because the world has so many specialized skills, I think there won't be the possibilities of things like "full stack developer" in the near future because of the complexities for development. Some things that contribute to the complexities will be: ubiquitous controls, advanced AI, internet of things, augmented reality, machine learning, and new technologies we don't even have yet. We are in the golden age now where a single person can sometimes create a better website or app compared to a whole development team at a fortune 500 company. I think our Internet world will become so complex in the very near future that a single person simply won't be able be capable and they'll have to become specialized in only a portion of it.

deepnotderp 10y ago

I'm going to go against the grain here and (gasp) not hate on deep learning. People should realize that although many older statistical methods and "traditional" machine learning methods such as LDA, SVMs and decision trees may be good enough for business tasks, they are not the cutting edge of AI research. I think people are forgetting exactly how difficult image classification and object detection was before the advent of deep learning. People hating on "stack more layers" forget that "stack more layers" is EXACTLY what improved imagenet performance to such as massive extent. ResNets pushed the limits by figuring out how to stack more layers in a beneficial way. And let's take a look at AlphaGo, how would you do that with SVMs or decision trees? Just get over the fact that deep learning provides a level of "intuition" (Go's search space is famously greater than the estimated number of stars in the universe). I think that a part of the problem is that older ML PhDs are angry that deep learning is so easy (until the learning rate fails to provide convergence of course...) and would prefer that their preferred methods would still reign supreme. I'll end this wall of text on the note that OpenAI's Ian Goodfellow notes that all projects at OpenAI use deep learning right now, but they are not dogmatic and will consider other approaches if they work well. I think this is the path that should be taken. On the other hand, I also see a bright future for uniting traditional techniques with deep learning such as attaching a SVM to a CNN and combining decision trees with CNNs both of which have resulted in good results.

dmreedy 10y ago
>> I think that a part of the problem is that older ML PhDs are angry that deep learning is so easy (until the learning rate fails to provide convergence of course...) and would prefer that their preferred methods would still reign supreme. I think that's definitely a part of it, and I feel that way sometimes myself (not that I'm a PhD). But there's another side of that reluctance that lies on the axis of model accountability and explicability. A lot of modern ML/Deep Learning doesn't -feel- like we're understanding anything any more than we did ten years ago. Yes our black-box results are better according to the tests we've laid out for them, but there's something more slippery about the 'why', beyond the handwave of 'complexity'. Maybe this is just the way it will be going forward (in the spirit of Quantum's "shut up and calculate"), but it is not easy to give up something that you can wrap your head around with something that kind of just takes care of itself, especially if you're in the business of seeking knowledge instead of results.

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xamuel 10y ago

Three years ago, Machine Learning on a resume meant: "Good candidate." Currently, it means: "Jumps on bandwagons, caution." In three years, it'll mean: "Brainless buzzwords, avoid."

id122015 10y ago

It doesn't matter how far technology gets, there are some -isms and they are the real Dinosaurs that we have to get over and we are too small.

bsaul 10y ago

Honest question : once the technics will settle a bit, and libraries are created, what will be needed, apart from knowing that machine learning algorithm are based on some kind of stasticial inference, with a few settings here and there ? I mean, we don't need a phd in image compression to create a service that streams videos. We just use libraries. Same for everything in computer science, it always end up packaged in some kind of reusable code or service, and only some specialists remain working in the field to work on marginal improvements. Why would ML be any different ?

pesfandiar 10y ago
Unlike video streaming libraries, there's no certain way for people to verify the outcome of an ML function is correct (e.g. Would this model work on other datasets? Is the statistical inference correct?). You need at least some basic knowledge about the underlying math to avoid pitfalls.
Eridrus 10y ago
Once you've plugged your data into an ML system and gives you a classifier, are you done? What if the results are not good enough, do you just move on to another problem? If you don't just move on to another problem you will need people who know how to push these systems further.
- Namrog84 10y ago
  But at some point, there is a difference between turning dials of the systems vs implementing new novel ways of these systems. Obviously we always need new novel approaches, but how much of that should stay in phd/academic/research world and how much spreads out into engineering day to day activities? I am rather newbish in the whole space, but most everything I've seen is quite often just knowing enough to turn the right dials the right way. And I do wonder how long will it be before some of these dials can start turning themselves with more iterations with a simple, "yes that's what I want, or no that's what I don't want" Sure, it might take some more cycles for it to find optimal in this example.

usgroup 10y ago

I kind of read it "learn it and you'll be a dinasaur for 3 years". It made me quite excited at the prospect. I started to practice growling and running at a curious forward angle , but on second reading I find myself disappointed.

onmobiletemp 10y ago

This is so dumb. You arent going to understand or get a job in machine learning unless you have at least a masters in the subject. Its extremely difficult and complex. I see tons of college students taking machine learning classes in anticipation of becoming a dinosaur amd none of them could get a machine learning job afterwards. Programming has always been pretty easy. The ai revolution wont be like the home computer revolution. Its going to be led by a relatively small group of academics, scientists and engineers working in prestigious research positions.

cr0sh 10y ago
Currently there just aren't that many ML jobs out there to apply for - but who knows what the landscape will be like in 5 years or so? Your assertion, though: > This is so dumb. You arent going to understand or get a job in machine learning unless you have at least a masters in the subject. Its extremely difficult and complex. ...couldn't be further from the truth. You can understand this stuff without a masters in the subject. It really isn't too difficult or complex. Sure, I will admit that understanding how to take a derivative might be useful, but despite not having that knowledge (but I'm working on obtaining it), I have still been able to implement successfully working ML solutions - at least in a classroom-type environment. My last success was getting a virtual car to drive around a virtual track, staying on the track and negotiating the curves, using an implementation of the NVIDIA End-to-End CNN architecture and some data I generated (plus augmentation and some other fun stuff). I used Keras and Python 3, running on my workstation at home, with a 750 ti SC as my "GPU" (I really need to upgrade this). My model converged very well after 10 epochs, but after 20 the loss was pushed pretty low to sub 1%. As far as I could tell, there wasn't evidence of overfitting (I need to do more investigation on this, though). This was all done as part of Udacity's Self-Driving Car Engineer Nanodegree, which I am taking part in. Prior to this, I also completed Udacity's CS373 course in 2012, and Andrew Ng's ML Class in 2011. My motivation for all of this has mainly been my interest in autonomous unmanned ground vehicle robotics technology. I have an ongoing side-project in developing such a platform (seemingly back-burner'd a lot, though - life getting in the way, I guess). Even so, if a job offer comes about because of it, I'm not going to complain. As it is, I believe the knowledge has helped me land positions, since it shows my dedication to improving my skills in problem domains outside of the everyday software development tasks. When potential employers have asked about it, I can show them some code I've worked on, while mentioning how some of the more simpler ML methods could help in a business problem domain. It sets me apart somewhat from other candidates, I believe. Especially those who think the topic isn't worth their time to learn, because it may be "difficult and complex".

general_ai 10y ago

No, you're not going to be a "dinosaur". 99% of extremely well compensated software engineering jobs do not involve ML. Using top large companies as a proxy of what things are going to be like in the world at large 3 years from now, maybe one in 200-300 engineers does anything in any way related to ML there. And that's a generous estimate. You do need to know what it is, roughly, but there's no need to drop everything you're doing and switch careers.

hnmot223 10y ago

"Mark also said that what happens in the next 5–10 years is just going to blow everybody away, especially in the field of automation. He thinks even programming is vulnerable to being automated and reducing the number of available programming jobs." He's talking out of his ass here. This won't be happening anytime soon (if ever)

SmokyBorbon 10y ago

Mark Cuban should study intelligence.

acd 10y ago

I am sysadmin/devops what machine learning tools and topics would you recommend to learn for that field?

slantaclaus 10y ago

Mark Cuban was a programmer dickbrain

dang 10y ago
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bgdkbtv 10y ago

Oh yeah? Does Mark Cuban know artificial intelligence himself or is he just asking people to study it and work for him? :)