Chris Lattner Joins Google Brain

Well, if he was trying 1M combinations every 40 seconds, for $7 per hour, and he didn't need to use hundreds of dollars per hour of commute time, let's say 10 hours = $70. That's 900M combinations per hour, so 9B combinations in 10 hours.

If he was trying combinations using upper-case characters, lower-case characters, numbers, and let's say 20 symbols, that's 82 possible combinations for each one. We'd expect him to find the password after exhausting half of the search set, so we want log base 82 of 18B. That suggests 5 characters.

If he let's say just used lower-case characters and numbers, that's log base 36 of 18B, which suggests 7 characters.

It may have to do with the fact that he just wanted some new challenges to sink his teeth into. As an old school hacker myself, I can relate. Even really cool technologies start to feel mundane and tedious after you've done them for long enough.

The real challenge with senior positions is adjusting to new environment/dynamics, not to mention the expectations that come from "legend" status. I'm not a legend, but I certainly had a much easier time in the earlier stages in career just focusing on the tech direct. When you're the hot new hire, a certain group at the new company expects a lot form you, while others who have been there longer think it's just hype. (I'm assuming something similar happened at Tesla).

Wish him all the luck on the new adventure! And being an AI geek myself: NO AI cannot solve everything. Not even close. (at least for now)

Sentient machine conversations are straight up funny to me. Still, I can't help get over the cool/geek factor of working on Machine Learning/Deep learning or exploring new algorithms that could significantly improve the field.

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

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

[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.

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?

>'If someone was to create AI that is human-equivalent (aka strong AI)'

Creating AI with human level intelligence isn't going to be so great, most people are complete idiots. Creating AI with above human intelligence is a different story.