15 ms·
No Phd, No Problem: New Schemes Teach the Masses to Build AI
- sytelus 8y agoUnpaywalled on Chinese website :) http://www.tianfateng.cn/20740.html http://www.tianfateng.cn/20740.html Sara Hooker who is prominently features in this article as success has rebuked the story by saying that she has spent over 4 years in machine learning: https://medium.com/@sarahooker/slow-learning-d9463f6a800b https://medium.com/@sarahooker/slow-learning-d9463f6a800b
- usgroup 8y agoWhy not! A bit of benefit multiplied by a large number of applicants should pay some dividend for some time. But there is a big difference between the craft and the science ... and phd or not , ratively few practitioners have the chops for the science.
- dcx 8y agoThis kind of looks like submarine marketing ala pg [1]. But I'm very interested in this topic nevertheless. Has anyone taken this course? Or if not, would anyone be able to recommend the current best resources for acquiring useful AI / machine learning skills, for the average software engineer? [1] http://www.paulgraham.com/submarine.html http://www.paulgraham.com/submarine.html
- deleted 8y ago[deleted]
- deleted 8y ago[deleted]
- gaius 8y agowould anyone be able to recommend the current best resources for acquiring useful AI / machine learning skills, for the average software engineer? EdX has a good one, I wrote about my experience of it here https://gaius.tech/2018/08/08/microsoft-professional-program-artificial-intelligence/ https://gaius.tech/2018/08/08/microsoft-professional-program... The tooling and libraries are very good now; assuming you already had the data in a good shape, you could do something commercially useful with Keras in a day or two from first picking it up. There is still quite a lot of low hanging fruit around but these skills are well on their way to becoming commoditized so I don’t know how much longer that will last.
- nlowell 8y agoI've taken the course. It's the best out there. Jeremy and Rachel do a phenomenal job and they're taking a contrarian stance to a lot of others in how they encourage anyone with even a little bit of programming experience to go for it. It's also a free course so it's not like they'd make tons of cash on covert advertising operations...
- roymurdock 8y agoHave you been able to apply the learnings to any problems at work, or to switch jobs?
- nlowell 8y agoFrom taking the course I realized that I wanted to do deep learning rather than what I was going into otherwise (likely some other kind of data science or fintech). I quit my job to go harder on deep learning studying and apply to jobs, but I ended up getting excited about doing a startup. Now I'm working on Stowbots (stowbots.com) and trying to bring the power of deep learning to everyone's internet browsing.
- roymurdock 8y agoAwesome. Good luck!
- thatcat 8y agoWhat is the course you are referring to? This is a pay walled article.
- ekr 8y agohttp://course.fast.ai/ http://course.fast.ai/
- deleted 8y ago[deleted]
- halhod 8y agoHi, I wrote the piece. No PR was involved. I’ve been following fast.ai for a while and I thought it was part of an interesting trend My basic rule with stories is that they must come from me casting my attention out into the world, not via a bidding war for it in my inbox. I run a whitelist and never read inbound PR. I only email comms people when I a) can’t find a person’s details b) the person asks me to
- roymurdock 8y agoDoes the economist have a standard definition of AI they run with, and/or do you? I find a lot of technologies get bundled together/labeled AI and it makes it hard to discuss or analyze such a broad swathe of things that could be AI depending on how the org has labeled them.
- thwy12321 8y agofast.ai is known for extensive marketing here on HN.
- nl 8y agoOr maybe many here (such as me) have done and loved the fast.ai course
- jph00 8y agoCan you be specific? fast.ai has no revenue at all, no investment dollars at all, no marketing team, and no PR budget. It's entirely self-funded out of my pocket personally in order to help people. The courses are all free, there are no ads, and the software is all open source. Neither I nor anyone else at fast.ai has ever asked anyone to comment on an HN post, or to post anything to HN. edit: Just noticed last time fast.ai was mentioned on HN you that made a similar claim. @dang and I both responded to you then. So this is not just some misunderstanding, but repeated and intentional falsehoods. https://news.ycombinator.com/item?id=18096807 https://news.ycombinator.com/item?id=18096807
- meowface 8y agoThey may just be genuinely unaware of how organically successful you guys are. But maybe I'm being overly charitable.
- wmurmann 8y agoI started with Andrew Ng's Coursera class. There are 5 different courses and I went through the first course and 3/4 through the 4th on conv nets. I would say he does a really good job of explaining the basics but the code is a little dated as AI is moving so fast. I'm a few weeks in to Fast.ai and the professor keeps it high level and has more focus on application than theory. You jump straight into working on image classification which is broken down into clear concise steps that gives you some confidence moving forward. If you do take the course make sure to plug in your own data and get a feel for how everything works before jumping to the next video. I would say its good to have some knowledge of the basics, so watch a few weeks worth of Andrews course while it's still free(7 day trial). Skip over the programming assignments because they are too low level and verbose. Then when you think you have a good sense how things work jump over to Fast.ai and start coding.
- minimaxir 8y agoA similar HN submission came up a few days ago about how MOOCs can be a replacement for a PhD: https://news.ycombinator.com/item?id=18293418 https://news.ycombinator.com/item?id=18293418 Data-oriented MOOCs like Andrew Ng’s Coursera course on Machine Learning and fast.ai’s course on Deep Learning are good academic introductions to the theory and terminology behind data science and other related fields. Although MOOCs have many practice problems to solve, they don’t make you an expert in the field capable of handling messier real-world problems, nor claim to do so. (my longer blog post on the subject: https://minimaxir.com/2018/10/data-science-protips/ https://minimaxir.com/2018/10/data-science-protips/) More importantly, actually getting the job nowadays is near impossible without a Masters/PhD due to the competition. (the statistical trick with MOOC/boot camp job placement is that the candidate often has a Masters/PhD in a different field, not necessarily in AI)
- jefft255 8y agoI don't think MOOCs can come even close to be a replacement for a PhD (and from what I understand, neither do you). If a candidate who learned with MOOCs can apply for a job which previously "required" a PhD, then that requirement was simply misguided in the first place. A PhD in (AI, CS, stats or whatever else) does not teach you how to be a good all-around data-scientist, it teaches you how to conduct scientific research. In AI this means either developing or improving algorithms, theory work or applying AI to one particular problem for four years. That kind of expertise is not needed for most DS jobs and never was. In my opinion however it is a good rule of thumb to assume that someone with a PhD in a relevant field will become a good data scientist after an adjustment period.
- minimaxir 8y ago> If a candidate who learned with MOOCs can apply for a job which previously "required" a PhD, then that requirement was simply misguided in the first place. Therein lies the problem. A lot of people I've talked with in leadership positions but without a statistical background believe that data science/AI requires a PhD, and since there's a healthy supply of candidates with the PhDs, there isn't much reason to reevaluate that position. (that's more for traditional job positions; obviously research positions will benefit more from a PhD.)
- fermienrico 8y agoWhy is PhD valued so much for engaging in R&D? Pretty much any labs (Intel labs, Amazon Lab126, etc) require PhD. What is objectively beneficial for having a PhD in doing R&D? Is it the ability to conduct research - methods, process, analysis, discipline? Or is it the fact that they have knowledge in that particular field? Every time I speak to someone with a PhD at work (I work in top 10 tech companies in US), in 100% of the cases in my experience do not engage in anything related to their thesis. So why is there this arbitrary blockade for having a PhD in R&D/Labs?
- Retric 8y agoGetting a PhD is mostly about doing research. So, it's directly relevant.
- fermienrico 8y agoI’d like to do research but it’s impossible to get into these labs because having a PhD is an absolute non-negotiable requirement.
- jefft255 8y agoIt is not "absolute" as you say, you can get in most research lab with a MS if you are an exceptional candidate. My advice, if you really, really want to be a scientist in a major research lab, is to just bite the bullet and do a PhD.
- marmaduke 8y agoYou don’t just “do research” (nowadays) without having had training, which is precisely the point of a PhDd
- thrmsforbfast 8y agoa) it's not absolute b) you could apply to phd programs and get a phd. But yes, it's impossible to get into R&D labs if you've never done research. You can self-teach and publish in top conferences on your own (this is probably much harder than you think it is but is not impossible!), or you can defer income to get an education from one of the many structured educational programs designed to teach you how to do research. I'm not sure why you expect industry R&D labs (as a researcher, that is) to hire people who do not already know how to do the job. They are companies, not guilds or universities. Software Engineering positions certainly require people to already know how to program, after all. How is this any different?
- LeanderK 8y agoJust some thoughts, never took the course: These AI-Bootcamps can, i would expect, only complement existing skills. I think they are a bit misleading, since I expect the typical day to day work for the graduates to be more Data-Sciency than ML-engineer (I would expect the graduates to be quickly out of breath in a real ML-(research?) engineer position). The challenges in many Data-Science roles lie mostly in understanding the data and not in the algorithms. Domain-Knowledge is also very important in many Data-Science (especially non-big data related) roles. I think the day to day tasks of a lot of data-scientists are not what what you usually associate with "AI". Any real experience to validate/invalidate my thoughts?
- master_yoda_1 8y agoI disagree that for becoming an expert you don't need PhD., or some kind of 3-4 years focused work in a particular topic in AI. But the question is are all Ph.D equal, in my view many students fraudulently awarded Ph.D without enough rigorous work, earlier those won't get hired, but because of AI hype they get hired. Also many old PhDs brand them-self as AI expert even though they don't know much. Hiring is still runs on hype and there are many bias (including gender bias) exists in industry.(e.g. Facebook's Mark does not like to hire 30+ people)
- seanmcdirmid 8y agoAs a PL PhD, many of my peers have gone into ML. They actually have competence in it, it turns out many phds are just smart and curious (eg people like Jeff Dean who has a PL background also). I haven’t gotten into it myself, but that is more of an interest issue.
- master_yoda_1 8y ago"it turns out many phds are just smart and curious" So does non-PhD my friend :)
- seanmcdirmid 8y agoYep. Having a PhD is just one indicator.
- master_yoda_1 8y agoi Disagree and there is no data to support this
- ychen306 8y agoIt takes a special kind of something to think that there's no correlation between intelligence and having a PhD.
- 8y ago
- tschellenbach 8y agoWeird that they talk about this topic without mentioning market leaders such as https://www.datacamp.com/ https://www.datacamp.com/ and https://www.udacity.com/ https://www.udacity.com/. Coursera also has some excellent courses. And of course Galvanize also teaches a course about data science.
- DrNuke 8y agoAn often untold story these days is you still need specialistic domain knowledge and a lot of your own data to make good use of fast.ai’s very clever lessons. Being able to achieve state of the art results with copy-pasting & modding any of the Dogs vs Cats image classifiers out there, eg. from Keras/Tensorflow, fast.ai/PyTorch, PyImageSearch/OpenCV etc, is worth almost nothing without your own business or research case, your own data and your own targets / metrics.
- collyw 8y agoI see both arguments in this thread and elsewhere.
- ThePhysicist 8y agoFrom my experience, to be a good applied AI researcher / implementer it’s not very important that you have a PhD in a specific field but it is important that you have experience working with and debugging complex problems. A PhD is usually a good way to gain such experience as you will work on a challenging problem and have access to people that can teach you how to debug / get unstuck when you’re stuck. Often I see people without such experience getting stuck on something when building a ML model and not being able to get unstuck as they lack the ability to properly debug the issue without external help. I think it’s absolutely possible to teach people how to write models in Keras/Tensorflow etc. but IMHO it won’t do them much good unless they also learn to effectively debug their models.
- lordnacho 8y agoWell, why would you need a PhD? A lot of applied engineering doesn't require you to be doing cutting edge things. You still need to spend time learning existing things in the field, but that is a lot less uncertain than finding something new to write about. Also a lot of places where recent ML type stuff is useful requires some knowledge other than ML. For instance in quantitative finance this stuff might be useful, so a lot of people who've done finance will do a short course to complement existing skills rather than taking several years out to do research.
- QML 8y agoThe comparison of AI to the Internet doesn’t really make sense: “nerds” are still the ones building out applications; only difference is that those applications can be used by a wider audience. The comparison could be that “nerds” build out AI systems, and that they’re used by wider audiences—this is what is already happening; look at assistants like Siri or Alexa, recommendation systems like YouTube’s, etc. I think what this article is getting at, is the notion that AI will become “professionalized”: the role of “AI specialist” will be as common place as software engineers; the salary premium will close. All of this is banking on, however, the idea that AI systems will needed and be implemented by most companies—which I disagree with.
- akkartik 8y agoWhat could possibly go wrong? https://news.ycombinator.com/item?id=17785162 https://news.ycombinator.com/item?id=17785162
- mindcrime 8y agoThe one thing I see over and over and over again, in these articles and associated discussion, is failure to recognize the distinction between "ML research" and "applied ML". You always get people in the threads yelling "you can't do ML without graduate level knowledge of measure theory, real analysis, information theory, ..." and so on, ad infinitum. And that is very likely true for most ML research. But OTOH, you can absolutely take off the shelf libraries, the level of knowledge you would get from, say, taking Andrew Ng's Coursera class, and then create value for your company using ML. No measure theory or linear algebra needed. And of course all this happens along a spectrum from "requires the least theory/math" to "requires the most theory/math". It's not a strictly binary dichotomy. So is the person completing the fast.ai course going to be the inventor of the next great new NN training algorithm? Maybe not. Probably not, even. But are they going to be able to apply ML to solve real problems? Yeah, most likely.
- DelightOne 8y agoIs such a course enough to be able to discern what data to best collect for this and that ML model?
- mindcrime 8y agoIs such a course enough to be able to discern what data to best collect for this and that ML model? For any arbitrary range of values for $DATA_TO_COLLECT and $ML_MODEL? Maybe not. For many real world scenarios? I'd say yes. Heck, sometimes the data you have available simply "is what it is" and there is no question of "what data to best collect". Let me also add re-iterate that learning to use ML is a continuum... and I would not posit that one could take Andrew's class, or the fast.ai class, and claim to be "done" with their ML education. To me either (or both) of those is just step one on a journey that could continue more or less forever (or until ASI comes along and enslaves the human race, ala The Matrix). Personally I took Andrew's (original) Coursera course, but since then I've continued to study Linear Algebra and Calculus through other forums, have been working through several other ML books, and have a whole litany of stuff queued up to study, up to and including the aforementioned Measure Theory, Complex Analysis, etc. I definitely value that stuff, I just think you can accomplish some useful tasks before you get to that level.
- mto 8y agoPersonally I liked Andrew Ng’s course much more. While fast.ai seems to have some practical gems hidden, it's just so much noise surrounding it. So much time wasted just by stating over and over again why their top down approach is so great. And even more with how to install this and that, dealing with the command line, setting up stuff, using AWS etc. Things the "coders" the course is targeted at should be able to do anyway. I always found myself jumping around the videos to find the useful parts (with the help of the time markers in the wiki) but then dropped it every time and did Andrew Ng’s course in combination with "the" deep learning book instead. They just have much less noise.
- musgrove 8y agoThe only thing a Ph.D. does is teach you how to formulate and defend a thesis, according to traditional philosophical tenents, and possibly using the statistics and relevant courses you took while getting a masters. It doesn't take you further or deeper into your chosen path of study. It enables you to publish research articles, for when/if you become a professor and learn how to navigate ridculous politics within University departments. That's it. If you want ti build AI, then go learn how to do it. Don't waste your time getting a PhD. Your time willbe much better spent and rewarded.
- sabana 8y agoI don't have a Ph.D and I'm not pursuing one, but when you say "It doesn't take you further or deeper into your chosen path of study"that says to me that you don't understand what a Ph.D is...
- musgrove 8y agoThen how can you derive that opinion? I understand all too well what a PhD is because of several credible reasons. It's a doctorate of Philosophy. The reason for that degree being in Philosphy, rather than Marketing, or whatever the the chosen path of study is, is because what you're learning to do is create a theory, evaluate it in a philosophical manner using theorems, and defend it, whether it pans out or not. It's to teach the candidate how to do research and present it in research journals, which is what makes a Ph.D. more valuable than, say, an instructor or an adjunct professor, who doesn't need a Ph.D but can also teach graduate-level courses, as I have. Instructors in a University know as much as or more about the subject they teach as a Ph.D. And they often do know more. In some instances, much more. But they don't have the other two responsibilities tenure-track professors have which is research and service, which set tenure-track professors apart in salary and prestige. It's not a fair system, but schools don't represent the real world in any shape or form, so that's just how it is.
- tootie 8y agoI see a lot of companies asking how they can make use of AI/ML because they think they're supposed to. Aside from the sort of recommenders we've been doing forever, it's mostly just niche applications. ChatBots are already waning. I don't think we need all that many AI experts right now.
- thrower123 8y agoChatbots need to go away faster - I've almost managed to punt on having to deal with them this cycle, if they get back into the trough of disillusionment soon, I'll be in the clear. Most people just want a super dumb dialog tree, but that's not sexy and whitepaper friendly.
- tootie 8y agoYeah. I did a voice doodad for a client not too long ago and it was just a whizzy version of a classic IVR system. Worked really well and no one was the wiser.
- sixhobbits 8y agoFor the people saying that incomplete knowledge of theoretical foundations will lead to dangerous or "bad" AI - go read some papers from respected academics and 'industry leaders'. Incomplete knowledge is something we have to deal with one way or the other. For the people saying that people won't be able to do anything useful after a 7 week course, go look at what beginner programmers are creating these days. There are so many resources out there now. I am constantly surprised at the impressive and practically valuable scripts that programmers are writing after a several week course. I was excited to get a hangman game working weeks after first learning to code - but now it's not uncommon to build multi-player games or complicated, good looking web apps with less experience. We've definitely moved a level of abstraction higher in the last decade - it just wasn't as clear cut as MIPS to Java. I think the highest value created here might be in the really non-sexy "oh so that's what 'AI' means" revelations that more and more people are having every day. Before I spent two years studying ML I had the same crazy ideas around AGI that many people with no understanding have. And I got super lost in the same non-productive discussions about what AI might do, what it could currently do, and what it all actually meant. If courses like fast.ai can get a critical mass of people to understand core ML concepts like classification and overfitting and accuracy and precision and recall I think we'd collectively get closer to stopping the hype train and focusing attention on where advancement is actually possible and currently happening. I know dozens of people taking this course, from all walks of life, and I am convinced that they're all gaining useful knowledge, and are likely to benefit society in some way as well. Maybe they're not going to invent GANs from scratch, but neither is every one who learned what an if statement is going to turn into Linus. Gate keeping isn't cool. This course is (though it actively claims that it isn't).
- joe_the_user 8y agoFor the people saying that incomplete knowledge of theoretical foundations will lead to dangerous or "bad" AI - go read some papers from respected academics and 'industry leaders'. It's not models that would necessarily be bad when made by something like "script-machine-learners". What has a lot of potential for a lot of badness is taking these models as given by "magic". Machine learning is essentially a giant exercise in piecing-together correlations by sort-of clever, sort-of brute-force methods. What's hard is knowing the proper application areas and the improper application areas.
- mlthoughts2018 8y agoI did 3 years of PhD work in computer vision, then dropped out of the PhD program to work in finance, eventually found my way back to a career in deep learning for image processing and NLP and some other smaller stats problems in causal inference. My undergrad degree was a very advanced pure math curriculum as well, so I had already done multiple years worth of linear algebra, measure theory and measure theoretic probability theory even before the PhD work. I think in terms of being effective at utilizing machine learning or statistics in a company that creates products, there is absolutely no value whatsoever, emphatically none, not even in terms of mathematical thinking, formalism or ability to grok research publications, associated with measure theory, measure theoretic probability, formal derivations of common ML algorithms or optimization problems, theoretical topics in convergence, etc. None. The absolute most critical thing you need is skepticism that algorithms are not working. After that you need a great understanding of all the complex failure cases that are possible, which includes tons of things that business people will not think of, from multi-collinearity to mode collapse to unsound reasoning based on p-values to overfitting to missing data treatments and so on. If you can grok basic linear algebra and algorithms, can assemble modern machine learning library components efficiently and have good judgment about statisical fallacies and unsound statistical reasoning, then it does not matter what other credential you have at all, period. In fact, I have worked with very decorated PhD level ML researchers who had such horrible programming skills that it was nearly impossible to incorporate their work into actual products. I’ve also worked with decorated PhD level ML researchers who did not understand basic things about general statistics outside the scope of loss function optimization, for example like topics in MCMC sampling, or cases where reasoning about a model’s goodness of fit needs to holistically consider residual analysis, outlier analysis, posterior predictive checking and plausible effect sizing from literature reviews. They argued and argued that purely optimizing log-loss (with appropriate controls for overfitting) should always be the best model, which is just very naive. The people saying these things had PhDs in top programs, many publications and conference presentations, and usually considerable software engineering skills. Truly, credentials in ML really don’t mean anything. It’s about work experience and what you know about pragmatically analyzing statistical problems in the service of product development, and academic training is just not a very important part of this.
- kmax12 8y agoThe challenge with machine learning today isn't that it doesn't work -- many organizations have successfully applied it to their problems -- but rather many people struggle to use it. There is massive potential if we can just make it easier to use the machine learning techniques that have already been proven to work. Phds are useful if you're trying to use the state of the art, but that's not what the masses need to benefit from machine learning. The scikit-learn library is a great example of this. It provides a clean fit()/predict() API that developers can leverage in their applications, which little understanding of how the implementations of each algorithms works. Another area ripe to be made easier for new practitioners of machine learning is feature engineering. Without proper feature engineering it is difficult to create accurate models. That is why I work on an open source python library for automated feature engineering called, Featuretools (https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/). It can help when your raw data is still too granular for modeling or comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: https://www.featuretools.com/demos https://www.featuretools.com/demos. In the future, I expect even more tools to emerge to help with things like defining a specific prediction problem and extracting labeled training examples, frameworks for robust testing, etc.
- budadre75 8y agojust curious, can it select sets of features from a predefined sets of features? like knowing which of the tuples from (A,B,C),(D,E,F),(G,H,I) is the best tuple?
- code4tee 8y agoThere’s a difference between “building things with ML/AI” and “building ML” just like you don’t need to know how to design microprocessors to buy and use a computer. These articles always fail to report on the difference between the two.
- ateesdalejr 8y agoI know this isn't particularly constructive but, before reading did anyone else think they were making a new mass-market dialect of scheme/lisp to teach people AI programming?
- DeonPenny 8y agoFinally, I never got why they had this limit. ML even if you know the theory and mathematical knowledge isn't out the realms of undergrads. If you black box it can easily be done.