How a Toronto professor’s research revolutionized artificial intelligence
thestar.com
thestar.com
Such science "journalism" is seriously hurting the field, including the researchers that are lionized in these articles. It also makes large groups of researchers who've been working hard on advancing the field think they don't matter in the eyes of the public, and that can be quite demoralizing.
That "unsupervised cats" paper from 2012 had nothing new in it at the time, although it was a feat of parallelization on a cluster of CPUs. Three years later its (uninteresting) results are not used anywhere. But Google used it as a PR piece at the time, and hundreds of journalists have been presenting it as some kind of game-changing breakthrough. It continues to this day.
For a country and city that has a heavy inferiority complex and suffers greatly from the "Brain Drain" of technical talent to the US, this is one of those articles that's meant to inspire. Not an article for Canadians/Torontonians to puff their chests.
So I wouldn't be so critical about it's so called "propaganda" aspect.
If you're interested in this area, my company is designing hardware accelerators for these algorithms. Our versions of the algorithms have a few extra twists to them to account for the analog nature of the hardware. I'm probably a bit biased, but I think this is a really interesting place to be working right now
Below is what posted on this month's Who is Hiring:
Isocline - Austin, TX - Software Engineer for High Performance Computing and Modeling
We are looking for two people - one interested in neural networks and one interested in GPS.
We are developing microchips that yield a 10-1000x improvement in performance & energy-efficiency compared to digital ASICs, GPUs, and FPGAs. We are a bootstrapped company and are fully funded through mid 2016. Patents pending.
$70K – $120K Salary
0.5% – 1.2% Equity
Full Job Description: https://angel.co/isocline/jobs/38767-software-engineer
Company website: http://isoclineengineering.com/
Email me directly if you do not have an AngelList profile
Toronto Professor, contributes to computer science subfield AI.
Vs
How a Toronto Professor, changed the world with Deep Learning.
Which one would you click ? This article is framed towards an audience that doesn't exactly care about technical details.
It's amazing that neural networks actually work now. They've been around since the 1960s, and for four decades, they sucked. They're only a little smarter; it's mostly sheer compute power applied to algorithms that fit on one screen of Matlab.
- proper initialization of weights (should have been obvious, but no one really saw it)
- rectified linear units and dropout