Also, holy sh*t. I literally just went back and read your 1st post. You work at google brain? That is so badass. I would love to hear about what your personal experience working at that level in the industry has been like and how you worked to get yourself there.
Is it as exciting and inspiring as it seems?
The reason I want to move into ML is that I just have endless questions and ideas about potential applications, but little understanding about its current limitations. I want to be able to understand it from the ground up so I can actually start helping apply it to real world problems and my own personal areas of interest. Like, I want to know how far you can push generative design and Architecture. Can we use AI/ML techniques to generate architecturally-significant, site-specific, environmentally sustainable, psychologically-positive, life-enriching housing that is uniquely optimized to an individuals needs, budget, and location? Like, there has to be a way to cut the median cost of a US family home by 10x while still doing all of these things. I refuse to believe that the god awful new-built homes being sold for ~$200k in South Dakota where I live are the best we can do at this time... Case in point lol. All dreams, no concrete knowledge of limitations to build a foundation on.
Hey, thanks for the comments!
GANs have definitely been achieving some unbelievable results and will for sure continue to be one of the most promising areas in ML in 2018. In addition, I personally think that we'll also see a lot of great work this year in: unsupervised/self-supervised learning, exploration, attention, and evolutionary strategies.
It's definitely not too late at all to move into ML! One of the best things about ML is the (relatively) low barrier to entry. In most other fields, in order to push the bleeding edge, you need to study decades of theory in order to reach the boundary of what's been explored. In ML, it seems that you can reach that point much faster, thanks to the wealth of accessible online materials and immense levels of open research communication.
So... where to start? I personally don't have experience with Udacity's nanodegree programs, but I've looked at the syllabus before and it seems reasonable. What I've seen a lot of people do is Andrew Ng's famous Coursera class, which is an online version of Stanford's introductory ML course.
Personally, I would do something like:
Regardless of what path you choose though... it's definitely not too late! ML is definitely here to stay. Calling it Software 2.0 might be slight hyperbole, but I have no doubt that ML systems (and people who work with them!) will be very crucial to how our society works in the next decades.
And yes, Google Brain is amazing. I love going to work every day with such smart colleagues and working on problems I care about.
Generative models for architectural design is definitely a very interesting problem! I'm not aware of any work that approaches anywhere near what you are proposing. I think a lot of the challenges stem from the very very complex dynamics that go into ensuring a building is structurally safe; while we may be far away from an end-to-end system that can output detailed, architecturally meaningful blueprints that are safe and useful, I think it is very plausible that deep learning systems can at least automate some mundane parts of the architectural design process. Which could very much drive prices down 10x in the future :)
Hope that was helpful! Happy to answer any other questions.
Seriously. Thank you so much. All of this info is unbelievably helpful and exactly the kind of direction I've been looking for. I started khan academy courses for multivariable calc and linear algebra for a refresher last week and this outline will help me immensely with my next steps. I just heard about Fast.ai in this panel conversation between Vivienne Ming, Jeremy Howard, and Peter Diamandis yesterday, and am excited to take the course. Dr. Ming's work has been super inspiring for me.
Thanks again for taking the time to respond so thoroughly! I will definitely take you up on that as questions come up through my studies :)
-Richard