
Confession of a so-called AI expert - parinvachhani
https://huyenchip.com/2017/07/28/confession.html
======
bllguo
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
>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
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).

------
deft
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

~~~
jefft255
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
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.

~~~
jefft255
I totally agree with your last point, and I wish more university acknowledged
this when deciding who teaches classes. My favourite CS course was taught by a
first year MS student. For most universities though, letting an undergraduate
teach a class is a no-no and it surprises me that Stanford, one of the most
prestigious university in the world, would make an exception to that.

Edit: Commenter above clarified to me that the student is not teaching a full
blown 3 unit course.

------
Dzugaru
> 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.

~~~
jcoffland
Pattern matching is the one thing DL is good for. Which is why it's a good
match for CV. Calling DL AI in the first place was a mistake or at least over
zealous marketing.

Playing go or chess or matching patterns are all things intelligent begins can
do but that does not imply that doing those thing means you are intelligent.

~~~
PeterisP
One can argue that the parts of our brain that make us intelligent, the
prefrontal cortex that is so much bigger than in "lesser" animals, is
essentially an overgrown, glorified pattern matching engine. Pattern matching
is the one thing our brains are good for - there's good reason to suppose that
quite many intelligence-related tasks can be reduced to a form of pattern
matching.

~~~
jcoffland
One could argue a lot of things. Humans once argued quite seriously that the
human brain was composed of microscopic gears because that was the technology
of the time.

------
rubatuga
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?

~~~
hvmonk
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
Given that creating and improving AI is a programming job, once you've
replaced programming jobs you've replaced everything else too.

------
burritofanatic
> 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
Safety concerns?

~~~
semi-extrinsic
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
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.

~~~
posterboy
That's distorting the numbers and a ridiculous question because the choice
isn't based on the statistic, unless you are planning an economy.

~~~
wfunction
It sounds like we're agreeing? I was also saying, just like you, that the
choice is not based on the statistics. The parent one is the one that used the
statistic to justify the choice of biking.

------
apetresc
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
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.

~~~
jtmcmc
posted above but [https://registrar.stanford.edu/staff/student-initiated-
cours...](https://registrar.stanford.edu/staff/student-initiated-courses-sics)

~~~
jboggan
Thanks, this makes a lot more sense, my impression from the post was that this
was a normal CS course.

------
onuralp
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
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.

------
master_yoda_1
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
:)

------
frgtpsswrdlame
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?

~~~
wakkaflokka
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."

~~~
gaius
_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.

------
msla
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
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
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.

~~~
b4ux1t3
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
_... 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?

------
kafkaesq
_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.

------
throw2016
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.

------
perlgeek
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.

------
ScottBurson
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!

------
bitanarch
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.

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

------
gmarx
Big Head!

------
fageyogurtspoon
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.

------
nthcolumn
Is she having doubts brought on by the recent Google Manifesto perhaps:

[http://huyenchip.com/2017/08/09/sexism-in-silicon-
valley.htm...](http://huyenchip.com/2017/08/09/sexism-in-silicon-valley.html)

posted earlier.

