9 ms·

Confession of a so-called AI expert

203 points by parinvachhani 9y ago

frgtpsswrdlame 9y ago
The article is an interesting mix of imposter syndrome and bubble speculation. I guess a good question is: if you know you're in a bubble and you feel like an imposter, are you right?
- 23o3o9d 9y ago
  I sincerely feel for the author of this post, and am not really sure how to explain my reaction while still being supportive, but... Based on what the author is saying, part of me thinks imposter syndrome and bubble are both justifiable ways of thinking about what he's describing, but to me as an outsider the bigger problem it reveals is the way hiring and career development happens. Without exaggerating anything about me, or without this coming from a place of jealously (although I can't deny I'm a bit jealous), it seems that I could easily teach the course they're teaching, with a deeper understanding of the material, and more justification for teaching it in many ways. I know that if I taught that course it would be fairly easy and not really stressful--fun in fact. I've taught courses on equally complex stats and math, and published in related areas. And yet, there are no recruiters pounding on my door. If I applied for jobs most places would throw out my application for all sorts of reasons. This person seems competent enough, so I do think there's impostor syndrome going on. Part of what they're describing is a normal process of teaching higher ed for the first time. And there probably is a bubble--the stuff they're describing is part and parcel of hype that goes along with bubbles, and although extremely useful, I think there's also a lot of problems with AI being swept under the rug. This post really touches a nerve for me, because it gets at a problem with careers, at least in the US, which is that the bases of hiring decisions (and by hiring I mean broadly, not just as an employee) are so incredibly superficial. My guess is this person would function fine in AI, but I think anyone who knew me would have to bet that, between the two of us, I would be better qualified and better able to work in that area. But because this person taught an AI course at Stanford, they're more sought after than me, who doesn't even have a CS degree (although I do have a PhD) and certainly not a degree from an elite school. I'm really at a difficult place in my life because I'm at a point in my career where I should be happy, and lots of people would say I'm successful, but to me I feel professionally typecast and trapped, by stereotypes and superficial appraisals. All the time you hear admonishments that degrees don't matter, etc. but then the reality is, they not only matter but matter in the most superficial ways possible, where it's not just having a degree and publishing and doing research in closely related areas, but having a degree covering exactly what is the focus of a hot plasma-magnitude bubble, from an elite university no less. Most of the time at this point I just want a job that pays enough, and where I can live in a nice, comfortable safe place that I love. I've started to feel like the whole concept of meritocracy is a huge lie, and not because the people benefiting from it are incompetent--not because of false positives--but because of the huge problem of false negatives that lies in the shadows.
  - ElijahLynn 9y ago
    s/thinking about what he's describing/thinking about what she's describing https://huyenchip.com/ https://huyenchip.com/
  - ericjang 9y ago
    she's
- _qbxp 9y ago
  Sort of tangential, but I felt this exact same way when I moved from being a post-doc in neuroscience to being a data scientist. The impostor syndrome was so strong it was painful. It has subsided now a bit because I know I'm able to bring value to my company - despite the fact that I know there are much more capable and qualified data scientists (by a large margin) out there, and despite the fact that by-and-large 'ML' and 'AI' is definitely a buzzword around here. But it really, really motivates me to strengthen where I'm lacking. The funny thing is, it took me about a year to find this position after a good amount of rejections. About a year after I got my data scientist title, I've been contacted by recruiters from places I would have never expected to be contacted from (Amazon, Microsoft, FB, etc.). Did a few interviews, and realized during those interviews that I still have a lot to learn. For one of the interviews, they gave me a take home assignment where they literally duplicated a column in the feature matrix... I didn't catch it, and during the phone part of the interview I get asked 'do you know notice something interesting about those two feature distribution plots you have there?' "Hrmm, no I don't. Oh, wait, they look pretty similar." "They're exactly the same." "... shit."
  - deleted 9y ago
    [deleted]
  - gaius 9y ago
    they literally duplicated a column in the feature matrix This is called colinearity - you can check for it by comparing the rank of the matrix to its number of columns. In R qr(X)$rank. Good to add this to your EDA workflow.
Dzugaru 9y ago
> Even though I’m one of the beneficiary of this AI craze, I can’t help but thinking this will burst. I don't think it will. Level off - maybe. I've started my work in Computer Vision with classical algorithms (SIFT features, geometry, correlation filters and things alike people were researching for decades). These really worked like garbage, it was a nightmare. Then we jumped on DL bandwagon - and CV just clicked for me. Now I see it working, not perfectly, not at human level yet, but it works, it's better than everything else and it certainly brings value - not just in CV! Maybe there will be some expectations delayed or even ruined (AGI, fully self-driving cars, dunno), but the tech isn't going anywhere. At it requires at least some experience and a specific mindset, slightly unusual for a generic programmer. So I don't see a problem with experts, courses, degrees and the like.
- visarga 9y ago
  Computer vision is the part of DL that is most suited to produce economic value. CV will be worth trillions of dollars in a couple of decades. All those cars, drones, agricultural equipment, medical scanners, robots and security cameras will be able to understand what they see and act intelligently. It's like the most universally useful thing since the invention of the wheel.
  - rubidium 9y ago
    "It's like the most universally useful thing since the invention of the wheel." I'll choose refrigeration, combustion engine, concrete, and probably hundreds of other things before computer vision.
    - visarga 9y ago
      I said universally useful - a refrigerator is just that. A CV system can be used in hundreds of totally different applications. Like the wheel and yes, the engine. The engine is universal as well, you could see it as the upgrade of the wheel.
- pron 9y ago
  > not at human level yet Not even at insect level yet. There's no doubt things will improve, and there's already great value, but I hate calling ML "AI". It's been over 70 years of ML research (specifically neural networks) and I don't know how long it's going to take to reach insect-level behavior (which is still far from basic intelligence) let alone so-called AGI (which, BTW, people in the '50s were certain is just around the corner), even though I think we'll get there eventually. We'd better stop using the term "AI" to mean anything other than a field of research or an aspiration, and definitely stop using it to describe existing software.
msla 9y ago
Is it really a bubble if it's producing real value? The answer is yes. Bubbles are investment and financial entities, decoupled from the value the sector is producing, and a bubble burst can indeed destroy real value. So AI being in a bubble says nothing about whether AI is valuable.
- AndrewKemendo 9y ago
  It's not really a bubble from the financial risk perspective if there isn't a way to "correct" or collapse it. Machine Learning is a feature set inside an application inside a market. It's not an industry of it's own where massive swaths of an industry place their money or livelihoods, like e-commerce or derivatives. So in that sense there isn't any bubble to burst. The majority of ML applications are happening INSIDE massive technology companies, not as stand alone companies. Even then, the stand alone companies have a product that they are selling that ML functions with. So SaaS with ML, or Image Captioning or Translation service etc...
  - TrickyRick 9y ago
    Investing billions of dollars in companies and paying employees crazy salaries simply because their title contains the word "data", how is there no way to collapse that? One day the time comes to reap the reward and when the rewards are a lot smaller than expected funding will be withdrawn or at the very least scaled back.
    - AndrewKemendo 9y ago
      Where are all of these companies paying crazy salaries simply because their title contains the word data?
    - b4ux1t3 9y ago
      Honestly? Data works. It makes companies billions of dollars. Machine learning and AI are getting increasingly good at parsing the data that already exists. It makes sense that companies, who have always made money on data, would invest in something that promises to make them even more money, and has demonstrated the ability to do just that.
HillaryBriss 9y ago
... a rigged system can’t be sustainable ... is this even true? aren't legacy admissions to Ivy League schools, the government protected status of Wall Street banks, generations of nepotism in Hollywood, Ticketmaster, the red-blue lock on politics, prosecution-protected city police officers, and Time Warner cable all dandy examples of sustainable rigged systems?
bllguo 9y ago
Well, I can understand why the author would come to this conclusion. A 3rd-year undergrad being called by companies and VCs for insight? I'm impressed by the author but disdainful of these solicitors. It reflects poorly on them. To me they seem like people who don't understand what's happening but are anxious not to miss out on a new wave of get-rich-quick schemes. Like other commenters have mentioned, largely due to misbranding and sensational media hype. Fearmongering from people like Elon Musk hasn't helped. But the key impact of machine learning for me is to make better and more efficient decisions that are informed by data - and that is not going to go away.
- nnfy 9y ago
  >but disdainful of these solicitors. It reflects poorly on them. To me they seem like people who don't understand what's happening but are anxious not to miss out on a new wave of get-rich-quick schemes. This is an odd point of view to have. While op may see this as pressure because of imposter syndrome, I would have DREAMED of an opportunity like this in college. These entrepreneur may not quite understand what they're getting into, but make no mistake, they are very good at recognizing experts, both established and rising [although they do cast a wide net] and this kind of behavior drives life changing opportunities and make it substantially easier, in a difficult world, for the cream of the crop to be unlocked to exceptional success at an early age. And the rising tide helps all of us. The smartest people in college are usually the ones who ate able to learn what they need to know outside of college on their own, once they have an income, and entrepreneurs unlock that potential.
  - bllguo 9y ago
    I can see your point of view as well... I would've been pretty excited to have this opportunity also. However call me idealistic, naive, whatever - I don't like the motivations of these solicitors, even though in a pragmatic sense their actions will have positive side-effects for us (the "rising tide" you mention).
onuralp 9y ago
This reminds me of Ferenc's reminder that 'deep learning is easy' - 'http://www.inference.vc/deep-learning-is-easy/ http://www.inference.vc/deep-learning-is-easy/
- bitL 9y ago
  I am not sure he even understands complexity of DL itself. DL can be formulated as a non-linear optimization problem; that means it's one of the most difficult computational problems and the few types of topology we know are working with current "simple" non-linear optimizers are quite miraculous; I don't think anybody understands why these simple methods work so well when we restrict/structure the number of connections between layers and why fully-connected networks have such a terrible performance even if theoretically they should be able to handle everything better. So there is IMO a plenty of space for everyone to figure out their own niche with best performing algorithm in production and enable magical things in their apps.
rubatuga 9y ago
One of my friends is in finance, and the other in biology, and judging by the way that they talk about it, they believe that AI is about to take over the world, and they believe there is a huge monolithic black box that can solve all the world's problems. So yes, there is a huge bubble. The question is how exactly will the bubble pop? Or will it pop?
- specialist 9y ago
  I've started paying attention to AI (again). This ML hype cycle feels like when I did optimization stuff 15 years ago. Huge interest, lots of monkey motion, then it just fell off everyone's radar. Not equating ML to OR, directly. With today's horsepower and data sets, it is a brave new world. But we're in the elevated expectation phase of the hype cycle.
- frgtpsswrdlame 9y ago
  Or how can I make money from it popping??
  - rubatuga 9y ago
    Short every AI startup you see.
- hvmonk 9y ago
  We have seen many a bubbles in the past. The AI bubble will also burst eventually. I have a first hand experience of it in the ML/NLP world, and I can safely say it is about 30% works plus 70% hype. Large amount of data has helped, so AI systems are better than before (with lots of training data), but that's pretty much it. It is not going to replace programming jobs, leave aside solving world problems.
  - Will_Parker 9y ago
    Given that creating and improving AI is a programming job, once you've replaced programming jobs you've replaced everything else too.
master_yoda_1 9y ago
I get similar impression from other side. I have met stanford phd having very poor math background. Yes it is true but unbelivable. I think the problem is with higher education system not the hype. Standford/cmu should make their degree more rigorous. I am sure if cs231n include fisher vector in their course and some maths derivation in their assigment,the number of student would drop logarithmically :)
apetresc 9y ago
What I don't understand is how you get a gig teaching a course at Stanford while being an undergraduate student at Stanford. Is this some sort of special seminar course or something?
- jboggan 9y ago
  I didn't come off of this article feeling very impressed with Stanford's CS department. Someone with contextual knowledge please explain why I am wrong. I've had my own calculus / discrete math / math for bio courses before but that was after several years as a doctoral student and TA at Georgia Tech. I can't imagine that there isn't a PhD candidate with more experience under their belt both teaching and using TensorFlow. The author even admits they volunteered to teach the course to stimulate learning the material themselves.
  - bitL 9y ago
    It's probably not a standard course or Stanford is experimenting with practical student-led courses. Why not? I'd do the same...
  - jtmcmc 9y ago
    posted above but https://registrar.stanford.edu/staff/student-initiated-courses-sics https://registrar.stanford.edu/staff/student-initiated-cours...
    - jboggan 9y ago
      Thanks, this makes a lot more sense, my impression from the post was that this was a normal CS course.
- ataki12 9y ago
  I come from a similar background as the author - BS/MS in Stanford CS, but I left just as the deep learning craze was booming. Stanford allows undergrads to propose and teach some of these more "practical" courses for upcoming technologies - a few examples are classes for NodeJS, cryptocurrency, Spark, and this one for Tensorflow. It's student-lead, very hands-on, and intended to give a specific industry experience. The classes the author mentioned are first year pre-requisites for the AI specialization and doing research at labs, and gives enough background for a student to be instructive when explaining concepts such as perceptrons and svm's, without necessarily the mathematical rigor. This is the level needed to interact with Tensorflow.
ElijahLynn 9y ago
If anyone feels like this, I highly recommend reading the book Learned Optimism.
deft 9y ago
No offense to the author. But serious question: why whenever articles like this come does everyone say "oh you're not a fraud at all!!". You know what, maybe she is a fraud. If she's being honest in this post she sounds a bit like one. She's crafted the perfect resume to attract attention about AI and it works. But she doesn't really know all that much. Possible that she has severe imposter syndrome and she sounds above average. But maybe she really isn't as great as everyone thinks she is and she wants people to know that. And maybe people shouldn't pat her on the back and say "no dude you're great don't say that" *disclaimer: basing this on blog comments and some comments here
- dsfjksdf 9y ago
  Unless she lied on her resume, it is hardly her fault if recruiting agencies rank her resume so highly. Maybe they should fix their algorithms.
  - deft 9y ago
    Agreed, but she exploited their algorithms which is definitely something 'frauds' do. I'm not even saying it's a bad thing, it's working for her. She shouldn't feel bad but she shouldn't feel like a genius either.
- jefft255 9y ago
  Also, there's something I don't get: how does an undergrad ends up teaching a full blown course at Stanford? Especially considering she doesn't seem to be some kind of super genius, only a probably excellent student. I'm not trying to demotivate her, I actually think it's amazing that he/she is doing this, but how did this happen?
  - ataki12 9y ago
    I have the same background as the author, but a few years older. Stanford undergrads and grads alike can teach a course as long as they have (1) the necessary background, (2) passion and proficiency in the tech, and (3) motivation to teach and manage course overhead. Being a "super genius" doesn't correlate with being informative and having intrinsic instructional value.
fageyogurtspoon 9y ago
Boo hoo, she's going to be making $300k straight out of school, with a 10 minute commute and free food, while managing people twice her age.
burritofanatic 9y ago
> It’s a phenomenon that Richard Socher, the dishevelled 30-something (or 20-something?) lecturer who just sold his company for several hundred millions yet still biked to campus, mentioned in his class: “Companies keep asking my students to drop out to work for them.” Why would anyone not continually bike for commuting purposes once they are wealthy? Biking is such a joy whether you're 7 or 70, rich, or poor.
- wfunction 9y ago
  Safety concerns?
  - semi-extrinsic 9y ago
    Several studies have shown that the net health benefit of bicycling to/from work is large even when you account for accidents. Not hard to believe when you hear it halves the risk of cardiovascular disease, which is the most common cause of death in the US. E.g. these authors find the health improvement statistically increases your life expectancy by up to 14 months, while traffic accidents statistically reduce it by up to 9 days. That's a ratio of 47 to 1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2920084/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2920084/
    - wfunction 9y ago
      I don't think people think in terms of life expectancy. Do you? Is a 1% chance of dying on your next ride and otherwise living 100 years equivalent to not riding your bike and living 99 years to you? Edit regarding your comment: Oh, I see why you're confused. I didn't need to do the math because I was talking about a 1-time bike ride as an example to just get the point across -- it's already scary for 1 ride. But if you want the actual math for a lifetime, there's a ~1/5000 lifetime odds of dying in 1 year of biking. That's still pretty damn high. I don't know about you but I'd rather just give up on the 78th expected year of my life and lose the 0.1% chance of dying in the next 5 years.
      - 9y ago
deleted 9y ago
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kafkaesq 9y ago
A French company, during our interview, told me that they ran hundreds of resumes through their algorithm and mine miraculously landed on top. That's the problem - these AI companies... believe their own hype a bit too much.
deleted 9y ago
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ScottBurson 9y ago
OT, but I can't resist: > Like my friend Delenn said, [...] I've wondered if Delenn would ever show up as a girl's name. I guess now is about when you'd expect to hear about the children of people who watched Babylon 5 as teenagers. Cool!
throw2016 9y ago
There is a difference between exaggeration, hype and wilful misleading and AI proponents have long crossed the line. Now policy makers, and all sorts of busy bodies are contemplating solutions to the 'ai problem' which does not exist and will not exist for some time to come, if it comes. Pattern matching and image recognition are valuable on their own but passing it off as AI makes a complete mockery of the word and scientific communication. Engineers and scientists are supposed to be precise and even giving leeway for hype and excitement within the realm of what is possible.
gmarx 9y ago
Big Head!
bitanarch 9y ago
No billionaire startup CEO, or anyone at the top of their field, was born knowing how to do it. Just believe in yourself. It's the people who have the courage that'll end up as leaders. You've just got a taste of it.
perlgeek 9y ago
In any fast-growing field, the distribution of expertise is pyramid shaped, with a broad base of inexperienced folks, and comparably fewer senior people. Outsiders calling on the expertise of relatively junior people is a seems to be a pretty natural consequence of this distribution, though maybe not to the extent described in this blog post.
nthcolumn 9y ago
Is she having doubts brought on by the recent Google Manifesto perhaps: http://huyenchip.com/2017/08/09/sexism-in-silicon-valley.html http://huyenchip.com/2017/08/09/sexism-in-silicon-valley.htm... posted earlier.

203 points by parinvachhani 9y ago

The article is an interesting mix of imposter syndrome and bubble speculation. I guess a good question is: if you know you're in a bubble and you feel like an imposter, are you right?

23o3o9d 9y ago
I sincerely feel for the author of this post, and am not really sure how to explain my reaction while still being supportive, but... Based on what the author is saying, part of me thinks imposter syndrome and bubble are both justifiable ways of thinking about what he's describing, but to me as an outsider the bigger problem it reveals is the way hiring and career development happens. Without exaggerating anything about me, or without this coming from a place of jealously (although I can't deny I'm a bit jealous), it seems that I could easily teach the course they're teaching, with a deeper understanding of the material, and more justification for teaching it in many ways. I know that if I taught that course it would be fairly easy and not really stressful--fun in fact. I've taught courses on equally complex stats and math, and published in related areas. And yet, there are no recruiters pounding on my door. If I applied for jobs most places would throw out my application for all sorts of reasons. This person seems competent enough, so I do think there's impostor syndrome going on. Part of what they're describing is a normal process of teaching higher ed for the first time. And there probably is a bubble--the stuff they're describing is part and parcel of hype that goes along with bubbles, and although extremely useful, I think there's also a lot of problems with AI being swept under the rug. This post really touches a nerve for me, because it gets at a problem with careers, at least in the US, which is that the bases of hiring decisions (and by hiring I mean broadly, not just as an employee) are so incredibly superficial. My guess is this person would function fine in AI, but I think anyone who knew me would have to bet that, between the two of us, I would be better qualified and better able to work in that area. But because this person taught an AI course at Stanford, they're more sought after than me, who doesn't even have a CS degree (although I do have a PhD) and certainly not a degree from an elite school. I'm really at a difficult place in my life because I'm at a point in my career where I should be happy, and lots of people would say I'm successful, but to me I feel professionally typecast and trapped, by stereotypes and superficial appraisals. All the time you hear admonishments that degrees don't matter, etc. but then the reality is, they not only matter but matter in the most superficial ways possible, where it's not just having a degree and publishing and doing research in closely related areas, but having a degree covering exactly what is the focus of a hot plasma-magnitude bubble, from an elite university no less. Most of the time at this point I just want a job that pays enough, and where I can live in a nice, comfortable safe place that I love. I've started to feel like the whole concept of meritocracy is a huge lie, and not because the people benefiting from it are incompetent--not because of false positives--but because of the huge problem of false negatives that lies in the shadows.
- ElijahLynn 9y ago
  s/thinking about what he's describing/thinking about what she's describing https://huyenchip.com/ https://huyenchip.com/
- ericjang 9y ago
  she's
_qbxp 9y ago
Sort of tangential, but I felt this exact same way when I moved from being a post-doc in neuroscience to being a data scientist. The impostor syndrome was so strong it was painful. It has subsided now a bit because I know I'm able to bring value to my company - despite the fact that I know there are much more capable and qualified data scientists (by a large margin) out there, and despite the fact that by-and-large 'ML' and 'AI' is definitely a buzzword around here. But it really, really motivates me to strengthen where I'm lacking. The funny thing is, it took me about a year to find this position after a good amount of rejections. About a year after I got my data scientist title, I've been contacted by recruiters from places I would have never expected to be contacted from (Amazon, Microsoft, FB, etc.). Did a few interviews, and realized during those interviews that I still have a lot to learn. For one of the interviews, they gave me a take home assignment where they literally duplicated a column in the feature matrix... I didn't catch it, and during the phone part of the interview I get asked 'do you know notice something interesting about those two feature distribution plots you have there?' "Hrmm, no I don't. Oh, wait, they look pretty similar." "They're exactly the same." "... shit."
- deleted 9y ago
  [deleted]
- gaius 9y ago
  they literally duplicated a column in the feature matrix This is called colinearity - you can check for it by comparing the rank of the matrix to its number of columns. In R qr(X)$rank. Good to add this to your EDA workflow.

Dzugaru 9y ago

> Even though I’m one of the beneficiary of this AI craze, I can’t help but thinking this will burst. I don't think it will. Level off - maybe. I've started my work in Computer Vision with classical algorithms (SIFT features, geometry, correlation filters and things alike people were researching for decades). These really worked like garbage, it was a nightmare. Then we jumped on DL bandwagon - and CV just clicked for me. Now I see it working, not perfectly, not at human level yet, but it works, it's better than everything else and it certainly brings value - not just in CV! Maybe there will be some expectations delayed or even ruined (AGI, fully self-driving cars, dunno), but the tech isn't going anywhere. At it requires at least some experience and a specific mindset, slightly unusual for a generic programmer. So I don't see a problem with experts, courses, degrees and the like.

visarga 9y ago
Computer vision is the part of DL that is most suited to produce economic value. CV will be worth trillions of dollars in a couple of decades. All those cars, drones, agricultural equipment, medical scanners, robots and security cameras will be able to understand what they see and act intelligently. It's like the most universally useful thing since the invention of the wheel.
- rubidium 9y ago
  "It's like the most universally useful thing since the invention of the wheel." I'll choose refrigeration, combustion engine, concrete, and probably hundreds of other things before computer vision.
  - visarga 9y ago
    I said universally useful - a refrigerator is just that. A CV system can be used in hundreds of totally different applications. Like the wheel and yes, the engine. The engine is universal as well, you could see it as the upgrade of the wheel.
pron 9y ago
> not at human level yet Not even at insect level yet. There's no doubt things will improve, and there's already great value, but I hate calling ML "AI". It's been over 70 years of ML research (specifically neural networks) and I don't know how long it's going to take to reach insect-level behavior (which is still far from basic intelligence) let alone so-called AGI (which, BTW, people in the '50s were certain is just around the corner), even though I think we'll get there eventually. We'd better stop using the term "AI" to mean anything other than a field of research or an aspiration, and definitely stop using it to describe existing software.

msla 9y ago

Is it really a bubble if it's producing real value? The answer is yes. Bubbles are investment and financial entities, decoupled from the value the sector is producing, and a bubble burst can indeed destroy real value. So AI being in a bubble says nothing about whether AI is valuable.

AndrewKemendo 9y ago
It's not really a bubble from the financial risk perspective if there isn't a way to "correct" or collapse it. Machine Learning is a feature set inside an application inside a market. It's not an industry of it's own where massive swaths of an industry place their money or livelihoods, like e-commerce or derivatives. So in that sense there isn't any bubble to burst. The majority of ML applications are happening INSIDE massive technology companies, not as stand alone companies. Even then, the stand alone companies have a product that they are selling that ML functions with. So SaaS with ML, or Image Captioning or Translation service etc...
- TrickyRick 9y ago
  Investing billions of dollars in companies and paying employees crazy salaries simply because their title contains the word "data", how is there no way to collapse that? One day the time comes to reap the reward and when the rewards are a lot smaller than expected funding will be withdrawn or at the very least scaled back.
  - AndrewKemendo 9y ago
    Where are all of these companies paying crazy salaries simply because their title contains the word data?
  - b4ux1t3 9y ago
    Honestly? Data works. It makes companies billions of dollars. Machine learning and AI are getting increasingly good at parsing the data that already exists. It makes sense that companies, who have always made money on data, would invest in something that promises to make them even more money, and has demonstrated the ability to do just that.

HillaryBriss 9y ago

... a rigged system can’t be sustainable ... is this even true? aren't legacy admissions to Ivy League schools, the government protected status of Wall Street banks, generations of nepotism in Hollywood, Ticketmaster, the red-blue lock on politics, prosecution-protected city police officers, and Time Warner cable all dandy examples of sustainable rigged systems?

bllguo 9y ago

Well, I can understand why the author would come to this conclusion. A 3rd-year undergrad being called by companies and VCs for insight? I'm impressed by the author but disdainful of these solicitors. It reflects poorly on them. To me they seem like people who don't understand what's happening but are anxious not to miss out on a new wave of get-rich-quick schemes. Like other commenters have mentioned, largely due to misbranding and sensational media hype. Fearmongering from people like Elon Musk hasn't helped. But the key impact of machine learning for me is to make better and more efficient decisions that are informed by data - and that is not going to go away.

nnfy 9y ago
>but disdainful of these solicitors. It reflects poorly on them. To me they seem like people who don't understand what's happening but are anxious not to miss out on a new wave of get-rich-quick schemes. This is an odd point of view to have. While op may see this as pressure because of imposter syndrome, I would have DREAMED of an opportunity like this in college. These entrepreneur may not quite understand what they're getting into, but make no mistake, they are very good at recognizing experts, both established and rising [although they do cast a wide net] and this kind of behavior drives life changing opportunities and make it substantially easier, in a difficult world, for the cream of the crop to be unlocked to exceptional success at an early age. And the rising tide helps all of us. The smartest people in college are usually the ones who ate able to learn what they need to know outside of college on their own, once they have an income, and entrepreneurs unlock that potential.
- bllguo 9y ago
  I can see your point of view as well... I would've been pretty excited to have this opportunity also. However call me idealistic, naive, whatever - I don't like the motivations of these solicitors, even though in a pragmatic sense their actions will have positive side-effects for us (the "rising tide" you mention).

onuralp 9y ago

This reminds me of Ferenc's reminder that 'deep learning is easy' - 'http://www.inference.vc/deep-learning-is-easy/ http://www.inference.vc/deep-learning-is-easy/

bitL 9y ago
I am not sure he even understands complexity of DL itself. DL can be formulated as a non-linear optimization problem; that means it's one of the most difficult computational problems and the few types of topology we know are working with current "simple" non-linear optimizers are quite miraculous; I don't think anybody understands why these simple methods work so well when we restrict/structure the number of connections between layers and why fully-connected networks have such a terrible performance even if theoretically they should be able to handle everything better. So there is IMO a plenty of space for everyone to figure out their own niche with best performing algorithm in production and enable magical things in their apps.

rubatuga 9y ago

One of my friends is in finance, and the other in biology, and judging by the way that they talk about it, they believe that AI is about to take over the world, and they believe there is a huge monolithic black box that can solve all the world's problems. So yes, there is a huge bubble. The question is how exactly will the bubble pop? Or will it pop?

specialist 9y ago
I've started paying attention to AI (again). This ML hype cycle feels like when I did optimization stuff 15 years ago. Huge interest, lots of monkey motion, then it just fell off everyone's radar. Not equating ML to OR, directly. With today's horsepower and data sets, it is a brave new world. But we're in the elevated expectation phase of the hype cycle.
frgtpsswrdlame 9y ago
Or how can I make money from it popping??
- rubatuga 9y ago
  Short every AI startup you see.
hvmonk 9y ago
We have seen many a bubbles in the past. The AI bubble will also burst eventually. I have a first hand experience of it in the ML/NLP world, and I can safely say it is about 30% works plus 70% hype. Large amount of data has helped, so AI systems are better than before (with lots of training data), but that's pretty much it. It is not going to replace programming jobs, leave aside solving world problems.
- Will_Parker 9y ago
  Given that creating and improving AI is a programming job, once you've replaced programming jobs you've replaced everything else too.

master_yoda_1 9y ago

I get similar impression from other side. I have met stanford phd having very poor math background. Yes it is true but unbelivable. I think the problem is with higher education system not the hype. Standford/cmu should make their degree more rigorous. I am sure if cs231n include fisher vector in their course and some maths derivation in their assigment,the number of student would drop logarithmically :)

apetresc 9y ago

What I don't understand is how you get a gig teaching a course at Stanford while being an undergraduate student at Stanford. Is this some sort of special seminar course or something?

jboggan 9y ago
I didn't come off of this article feeling very impressed with Stanford's CS department. Someone with contextual knowledge please explain why I am wrong. I've had my own calculus / discrete math / math for bio courses before but that was after several years as a doctoral student and TA at Georgia Tech. I can't imagine that there isn't a PhD candidate with more experience under their belt both teaching and using TensorFlow. The author even admits they volunteered to teach the course to stimulate learning the material themselves.
- bitL 9y ago
  It's probably not a standard course or Stanford is experimenting with practical student-led courses. Why not? I'd do the same...
- jtmcmc 9y ago
  posted above but https://registrar.stanford.edu/staff/student-initiated-courses-sics https://registrar.stanford.edu/staff/student-initiated-cours...
  - jboggan 9y ago
    Thanks, this makes a lot more sense, my impression from the post was that this was a normal CS course.
ataki12 9y ago
I come from a similar background as the author - BS/MS in Stanford CS, but I left just as the deep learning craze was booming. Stanford allows undergrads to propose and teach some of these more "practical" courses for upcoming technologies - a few examples are classes for NodeJS, cryptocurrency, Spark, and this one for Tensorflow. It's student-lead, very hands-on, and intended to give a specific industry experience. The classes the author mentioned are first year pre-requisites for the AI specialization and doing research at labs, and gives enough background for a student to be instructive when explaining concepts such as perceptrons and svm's, without necessarily the mathematical rigor. This is the level needed to interact with Tensorflow.

ElijahLynn 9y ago

If anyone feels like this, I highly recommend reading the book Learned Optimism.

deft 9y ago

No offense to the author. But serious question: why whenever articles like this come does everyone say "oh you're not a fraud at all!!". You know what, maybe she is a fraud. If she's being honest in this post she sounds a bit like one. She's crafted the perfect resume to attract attention about AI and it works. But she doesn't really know all that much. Possible that she has severe imposter syndrome and she sounds above average. But maybe she really isn't as great as everyone thinks she is and she wants people to know that. And maybe people shouldn't pat her on the back and say "no dude you're great don't say that" *disclaimer: basing this on blog comments and some comments here

dsfjksdf 9y ago
Unless she lied on her resume, it is hardly her fault if recruiting agencies rank her resume so highly. Maybe they should fix their algorithms.
- deft 9y ago
  Agreed, but she exploited their algorithms which is definitely something 'frauds' do. I'm not even saying it's a bad thing, it's working for her. She shouldn't feel bad but she shouldn't feel like a genius either.
jefft255 9y ago
Also, there's something I don't get: how does an undergrad ends up teaching a full blown course at Stanford? Especially considering she doesn't seem to be some kind of super genius, only a probably excellent student. I'm not trying to demotivate her, I actually think it's amazing that he/she is doing this, but how did this happen?
- ataki12 9y ago
  I have the same background as the author, but a few years older. Stanford undergrads and grads alike can teach a course as long as they have (1) the necessary background, (2) passion and proficiency in the tech, and (3) motivation to teach and manage course overhead. Being a "super genius" doesn't correlate with being informative and having intrinsic instructional value.

fageyogurtspoon 9y ago

Boo hoo, she's going to be making $300k straight out of school, with a 10 minute commute and free food, while managing people twice her age.

burritofanatic 9y ago

> It’s a phenomenon that Richard Socher, the dishevelled 30-something (or 20-something?) lecturer who just sold his company for several hundred millions yet still biked to campus, mentioned in his class: “Companies keep asking my students to drop out to work for them.” Why would anyone not continually bike for commuting purposes once they are wealthy? Biking is such a joy whether you're 7 or 70, rich, or poor.

wfunction 9y ago
Safety concerns?
- semi-extrinsic 9y ago
  Several studies have shown that the net health benefit of bicycling to/from work is large even when you account for accidents. Not hard to believe when you hear it halves the risk of cardiovascular disease, which is the most common cause of death in the US. E.g. these authors find the health improvement statistically increases your life expectancy by up to 14 months, while traffic accidents statistically reduce it by up to 9 days. That's a ratio of 47 to 1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2920084/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2920084/
  - wfunction 9y ago
    I don't think people think in terms of life expectancy. Do you? Is a 1% chance of dying on your next ride and otherwise living 100 years equivalent to not riding your bike and living 99 years to you? Edit regarding your comment: Oh, I see why you're confused. I didn't need to do the math because I was talking about a 1-time bike ride as an example to just get the point across -- it's already scary for 1 ride. But if you want the actual math for a lifetime, there's a ~1/5000 lifetime odds of dying in 1 year of biking. That's still pretty damn high. I don't know about you but I'd rather just give up on the 78th expected year of my life and lose the 0.1% chance of dying in the next 5 years.
    - 9y ago

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kafkaesq 9y ago

A French company, during our interview, told me that they ran hundreds of resumes through their algorithm and mine miraculously landed on top. That's the problem - these AI companies... believe their own hype a bit too much.

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ScottBurson 9y ago

OT, but I can't resist: > Like my friend Delenn said, [...] I've wondered if Delenn would ever show up as a girl's name. I guess now is about when you'd expect to hear about the children of people who watched Babylon 5 as teenagers. Cool!

throw2016 9y ago

There is a difference between exaggeration, hype and wilful misleading and AI proponents have long crossed the line. Now policy makers, and all sorts of busy bodies are contemplating solutions to the 'ai problem' which does not exist and will not exist for some time to come, if it comes. Pattern matching and image recognition are valuable on their own but passing it off as AI makes a complete mockery of the word and scientific communication. Engineers and scientists are supposed to be precise and even giving leeway for hype and excitement within the realm of what is possible.

gmarx 9y ago

Big Head!

bitanarch 9y ago

No billionaire startup CEO, or anyone at the top of their field, was born knowing how to do it. Just believe in yourself. It's the people who have the courage that'll end up as leaders. You've just got a taste of it.

perlgeek 9y ago

In any fast-growing field, the distribution of expertise is pyramid shaped, with a broad base of inexperienced folks, and comparably fewer senior people. Outsiders calling on the expertise of relatively junior people is a seems to be a pretty natural consequence of this distribution, though maybe not to the extent described in this blog post.

nthcolumn 9y ago

Is she having doubts brought on by the recent Google Manifesto perhaps: http://huyenchip.com/2017/08/09/sexism-in-silicon-valley.html http://huyenchip.com/2017/08/09/sexism-in-silicon-valley.htm... posted earlier.