
Chris Lattner Joins Google Brain - janober
https://techcrunch.com/2017/08/14/swift-creator-chris-lattner-joins-google-brain-after-tesla-autopilot-stint
======
guelo
I find it strange that probably the most successful and influential compiler
and PL developer in a generation doesn't seem to be interested in compilers or
PL anymore.

~~~
chubot
There is an obvious overlap between programming languages and machine learning
at Google: TensorFlow.

For example, see "A Computational Model for TensorFlow"
[https://research.google.com/pubs/pub46196.html](https://research.google.com/pubs/pub46196.html)

There are tons of interesting things going on in TensorFlow from a programming
language perspective. It has optimization at the low level, like CUDA and SIMD
back ends via the Eigen library [1] (which is pretty crazy C++ metaprogramming
in its own right).

But it also has optimization at the high level / cluster level, e.g. deciding
which nodes to put computations on, to minimize data movement across various
networks, etc.

It also has multiple front ends. Python is the main one, but IIRC people were
developing others (maybe not at Google).

I worked adjacent to the TensorFlow team, and A LOT of people had BOTH ML and
PL skills [2]. It's not an either-or thing. It's best when you have the same
person with both sets of expertise.

I think a big problem with large parts of the academic PL community is they're
not exposed enough to real applications. Difficulty isn't proportional to
real-world benefit. It's certainly difficult to invent type systems to
statically detect minor problems, but that doesn't mean it's important for
creating and maintaining software. (Sorry, had to rant about that.)

Machine learning is a domain rife with programming language problems, but of
course it takes a long time to develop that expertise. I'm sure Lattner would
be a good person to synthesize knowledge in the different domains.

[1]
[http://eigen.tuxfamily.org/index.php?title=Main_Page](http://eigen.tuxfamily.org/index.php?title=Main_Page)

[2] edit: I should really say ML and distributed computing skills. But most
people with distributed computing skills know a decent amount about
programming languages; they overlap in MapReduce-type big data frameworks too.

~~~
leereeves
Makes me wonder if Google is working on a programming language for machine
learning...

~~~
chubot
That's what TensorFlow is. It happens to use the Python interpreter as the
front end, and for metaprogramming, but the language has its own semantics
(see the paper above).

You could probably an invent your own syntax for it (and I'm sure someone
has), but that's a small part of the picture.

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eanzenberg
Super interesting developments..

Also, re:

>AI can't democratize itself (yet?) so I'll help make it more accessible to
everyone!

This needs to happen more on the software side. You can buy a world-class
quad-gpu machine for about half the cost of a vehicle. You can build a world-
class single-gpu machine (what I have) for a fifth of that. It's literally
amazing how accessible the hardware is, compared to almost any other science
which requires 7 figure funding to get world-class results.

The software, the learning, the math, all need to become more and more
accessible so more people can pick up and train an algorithm.

~~~
mathperson
I mean...you can't make math simpler. like...math is math. you can create
resources that make it easier to teach math (and for the record high quality
textbooks on AI/ML ABOUND). I think you want software and libraries so you can
build things without understanding the math. To some degree this already
possible (see my english major linkedin friends playing withs sci-kit etc
etc). Fundamentally though, I think you need at least some basic understanding
of the mathematics to interpret results/create new ideas. Arguing otherwise is
like saying you can code without knowing what a queue is...I mean sure you can
probably create something that compiles and runs but your horizons will be
more narrow than someone with a full computer science education and grounding.
just like trying to do ml without at least some understanding of the math is
like a blind man trying to open a door.

case-in-point: I am on hn right now because my research is going badly because
I am using another researcher's code and I don't fully understand the
correspondence between the paper and the code. I am getting terrible results
on some multi-class classification (i.e. approximately random). I am not sure
if I am using some wrong parameters or because the signal I am looking for
doesn't exist in the data. Not knowing at least some mathematics would make
this a LOT harder

~~~
gaius
_mean...you can 't make math simpler. like...math is math._

Notation makes a huge difference. LinAlg is a good example of this. And a PL
_is_ notation.

~~~
jakobegger
Simpler notation might make it easier to implement algorithms without fully
understanding them. Not sure how much good is going to come from that.

But if you fully understand the problem, notation is not an issue.

~~~
gaius
I disagree. Try using Roman numerals for example. Or doing linear programming
or any sort of optimisation without using matrix notation.

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bitL
Just wondering - does a normal person stand a chance to join Google Brain, or
do you need to make node.js, swift, own self-driving car or another crazy
project to get in?

~~~
litzer
Not sure about in general, but it seems like their residency program [0] gives
a good shot at landing on the team afterwards and is accessible to "normal"
people passionate about ML

[0]
[https://research.google.com/teams/brain/residency/](https://research.google.com/teams/brain/residency/)

~~~
dontreact
Another way is to do 20% and then transfer in which is what I did. That seems
doable (I don't have PhD for example).

~~~
jadekler1
What do you mean by 20%?

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dontreact
Start on another team at Google (preferably using ML) and do 20% time with
Brain.

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tambourine_man
>…he created the Land compiler and LLVM.

I think you mean _Clang_ and LLVM

~~~
melling
[http://nondot.org/sabre/](http://nondot.org/sabre/)

"I lead and am the original author of the LLVM Compiler Infrastructure, an
open source umbrella project that includes all sorts of toolchain related
technology: compilers, debuggers, JIT systems, optimizers, static analysis
systems, etc. I started both LLVM and Clang and am still the individual with
the most commits."

~~~
dbaupp
The parent is likely talking about the "Land" typo.

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ChuckMcM
It was probably a clang->land auto correct on a phone. It used to be I only
had to worry about my fingers hitting the wrong keys, now I have to worry
about my computer not believing that what I wanted to type was what I
_actually_ typed.

~~~
melling
My mistake. I skimmed over the word "Land" without really noticing it.

Add voice dictation and things get even weirder. A lot of times it's magical
with some crazy word thrown in.

~~~
ChuckMcM
Agreed on the voice thing. I've found some really odd sound alikes in my
comments when I've used it (trust me it sucks to type a long response on a
phone keyboard :-) A common one is substituting 'by' and 'buy' at different
times.

~~~
mikeash
It's always fun to try to dictate "wreck a nice beach." It's such a classic
example of sound-alikes gone wrong that it seems engines go out of their way
to interpret it as "recognize speech" unless you take extreme measures.

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tyingq
Is Google's work culture more like Apple or Tesla? Curious if this is likely
to address whatever about Telsa wasn't keeping him happy. Or maybe it was more
about the specific work he was tasked to do?

~~~
nostrademons
It's like neither, but probably pretty similar to the academic UIUC culture
where LLVM got started. Having both worked at Google and followed Chris
Lattner's work on LLVM since before he was at Apple, I suspect he'll be
happier there than at Tesla.

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kensai
I was kind of worried he would not find anything... :D

Seriously, I wonder if stars like Lattner even need to submit a CV or their
mail simply floods with offers.

~~~
samfisher83
I am guessing he doesn't have to write code on a whiteboard.

~~~
kensai
Damn, I always thought interviews of guys like him went along these lines:
[https://www.youtube.com/watch?v=XUhsNIeIgpY](https://www.youtube.com/watch?v=XUhsNIeIgpY)

~~~
gordon_freeman
I think they don't even have to explicitly show attitude like in the video you
shared. Their past accomplishments in managing complex systems and projects
can more than speak for themselves. :)

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tambourine_man
I have zero experience with this, but I imagine working for three major
competitors in such a short time can lead to some complicated situations
regarding industrial secrets.

Compilers (specially open source) may be a stretch, but Tesla AI and Google
AI? Can a contract clause protect one company when you're working for the
other on the exact same field?

Again, I have no idea what I'm talking about, just curious.

~~~
yawaramin
Non-compete clauses are illegal in California. That's one of the big reasons
for the tech boom there.

~~~
zuck9
So, how does that work with, non-California companies, like Microsoft? Former
MS employee joins FB/Google. Now can MS sue them?

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tambourine_man
Worrisome for Apple that its AI efforts weren't interesting enough to keep
Lattner, or win him back.

~~~
yborg
I would think that the whole Siri fiasco would keep anybody in AI/ML the hell
away from Apple.

~~~
netheril96
Care to elaborate on the Siri fiasco? I'm out of the loop.

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s73ver_
Swift on Android confirmed.

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tatoalo
Interesting as a company change (IMHO), from privacy-focused Apple to
Google... :D

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bitmapbrother
Privacy focused? Oh, you mean how they pivoted when their Ad business failed.

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benzoate
Didn't their ad business fail because they refused to share/collect as much
user data, and hence advertisers were less willing to spend on their platform?

~~~
bitmapbrother
Not to my recollection. They exited the Ad business because it was a financial
failure. Do you really think Apple would have exited a business if they were
on track to make Google like revenue from it? No way - the shareholders would
have raided the building.

