Hi!
I am extremely interested in machine learning and AI, but am very much a novice when it comes to the academic and technical side of things. I am particularly interested in how human creativity can be augmented with ML and AI. The progress made over the last year with GANs has been super exciting to me. Also the latest Tacotron 2 (think that was what it was called) paper from google was another recent AI/ML event that blew my mind.
Soooo yeah literally everything and anything when it comes to content would be amazing! (GANs, NLP, reinforcement learning especially!)
Content with advice pertaining to how someone with education outside of computer science could work their way into the industry would be amazing and seriously helpful as well. I personally have a BS in Biology/BA in English and am currently in medical school in South Dakota, but my passion really lies in AI/ML and Architecture/Design. I am considering changing my path to a career in ML/AI, but am uncertain about how to go about making the shift. I am 25 already, but really feel my energy could be put to better use in a field like ML that I am truly passionate about, but I am a little nervous about maybe being a little too late to the party and not having the "right" background to get into the programs that would provide me this opportunity.
I have recently started teaching myself python through audacity and am hoping to work through their introductory machine learning programs this spring. Is this a good place to start in your opinion? Are non-traditional online education opportunities like audacity's nanodegrees really a feasible way to move towards a career as a ML engineer without a Bachelor's degree or MS in computer science/engineering? At this point is this just a pipe dream lol?
Sorry for the long and poorly organized reply lol. Really looking forward to your content, whatever you decide to do!
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