
No Phd, No Problem: New Schemes Teach the Masses to Build AI - jkuria
https://www.economist.com/business/2018/10/27/new-schemes-teach-the-masses-to-build-ai
======
mindcrime
The 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.

~~~
halflings
Andrew Ng's course definitely requires _some_ linear algebra knowledge, and
explains how things work under the hood. Same goes for fast.ai

I think even that level of knowledge is not always necessary. Often, just
having the intuitive understanding can be enough to get great results e.g:
understand that this is a black box that takes features in, predicts things,
and you have to help it by giving good features; or understand that word2vec
builds vector representations based on words that co-occur in the same
context.

~~~
ivan_ah
Speaking of learning/reviewing linear algebra, I wrote the NO BULLSHIT guide
to LINEAR ALGEBRA which covers all the material from first year in a very
concise manner.

preview:
[https://minireference.com/static/excerpts/noBSguide2LA_previ...](https://minireference.com/static/excerpts/noBSguide2LA_preview.pdf)
condensed 4 page tutorial:
[https://minireference.com/static/tutorials/linear_algebra_in...](https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf)
reviews on amazon:
[https://www.amazon.com/dp/0992001021/noBSLA](https://www.amazon.com/dp/0992001021/noBSLA)

~~~
vazamb
Hey, I just finished your book. Loved it! I am out of uni already and uses it
to brush up.

~~~
ivan_ah
Nice! Did you work though some of the exercises and problems? Don't be a
tourist in the land of math!

If you don't want to bother with pen-and-paper (which is the best, but takes
very long), you should at least try some problems using the computer-assisted
approach. See samples solutions to first few chapters:
[https://github.com/minireference/noBSLAnotebooks](https://github.com/minireference/noBSLAnotebooks)

------
sixhobbits
For 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
_For 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.

------
dcx
This 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)

~~~
thatcat
What is the course you are referring to? This is a pay walled article.

~~~
ekr
[http://course.fast.ai/](http://course.fast.ai/)

------
DrNuke
An 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
I see both arguments in this thread and elsewhere.

------
master_yoda_1
I 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
As 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
"it turns out many phds are just smart and curious" So does non-PhD my friend
:)

~~~
seanmcdirmid
Yep. Having a PhD is just one indicator.

~~~
master_yoda_1
i Disagree and there is no data to support this

~~~
seanmcdirmid
What are you disagreeing with? Having a PhD is an indicator that you at least
got a PhD. Better if it’s a good school or they know your advisor. You can’t
take it for much more than that, but it isn’t an empty achievement either.

Also, there is no data for a lot of things.

------
ThePhysicist
From 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
Well, 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.

------
minimaxir
A 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
I 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
> 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.)

~~~
janoc
The problem is that when you put people without formal training in charge of
building and training models, you get also results that match - models with
horrible biases, models that don't reflect the reality at all, models that are
worse than taking random guesses.

Running through a bunch of tutorials and then taking a deep learning toolkit
(e.g. Tensorflow or Keras) and starting to feed data into it won't teach you
squat about the importance of having a representative sample, about removing
biases from the data, about correctly handling outliers, about doing some
basic statistical analysis on the data to see whether they are even relevant
to the problem at hand and so on and so forth.

Or even how to build a questionnaire/experiment so that you don't get only a
load of expensive garbage instead of data out of it.

This is what a doctorate and the associated research training generally give
you. Of course, it is not anything that couldn't be learned without doing a
formal PhD, publishing research papers and defending a thesis but it is
typically the background you won't get from these MOOCs or various online (and
offline) "data science" trainings.

~~~
sizzle
Great answer. What if you learned basic stats, calculus etc during a masters
or BSci degree?

------
mto
Personally 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.

------
mlthoughts2018
I 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.

------
tootie
I 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
Chatbots 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
Yeah. 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.

------
LeanderK
Just 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?

------
fermienrico
Why 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?

~~~
whatshisface
Because having a PhD is proof that you can do research, and there are so many
PhDs available that Intel has absolutely no motivation to increase their
hiring risk one iota by extending their net past that market. Even if a BA was
only 0.000001% less likely to pan out than a PhD it would not be worth it. The
only way this could change would be if the number of PhDs available to Intel
was drastically reduced, which might happen if the president gets his way with
immigration, or if the now commonly repeated message "getting a PhD will not
be worth ten years when measured by career progress" sinks in to US students.

~~~
gaius
_Because having a PhD is proof that you can do research, and there are so many
PhDs available that Intel has absolutely no motivation to increase their
hiring risk one iota by extending their net past that market. Even if a BA was
only 0.000001% less likely to pan out than a PhD it would not be worth it._

There's such a shortage of STEM graduates that PhDs get hired to do stuff you
can actually do with a MOOC (or to carry a pager at Google).

~~~
whatshisface
I know way too many unemployed physicists to believe for one second that there
is a shortage of STEM graduates. The shortage must be of some other trait or
skill.

~~~
collyw
Are they trying to stay in Physics? I know ex-physicists who have switched to
biology of programming.

------
kmax12
The 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
just 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?

------
akkartik
What could possibly go wrong?

[https://news.ycombinator.com/item?id=17785162](https://news.ycombinator.com/item?id=17785162)

------
QML
The 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.

------
tschellenbach
Weird 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.

------
sytelus
Unpaywalled 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)

------
code4tee
There’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.

------
usgroup
Why 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.

------
DeonPenny
Finally, 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.

------
musgrove
The 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
I 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
Then 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.

------
ateesdalejr
I 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?

