Everything depends on taste but I love Neuromancer, it's really good at building a complicated world that isn't overburdened and I find the notion that it is "boring" kind of crazy! It almost reads like a thriller. Immediately one of my favorite books when I picked it up. It's certainly of our time, one of those books that you won't believe was written when it was. And it doesn't really feel dated, we might still be heading towards the world it portrays, hard to say... haha
Thanks for the reply. I read Do Androids Dream of Electric Sheep, by Philip K. Dick and had almost the exact experience you describe with world building. I'll definitely give Neuromancer a read when I finish the three or so books I've started but not finished (it's not an issue of boredom - I'm just very busy at the moment).
Some of Facebook's primary competitors are traditional news media outlets. Chris Hughes still owns Facebook stock (I think). If Hughes can take down a newspaper, that's one less competitor for Facebook, and his stock goes up.
Yea but that's okay right... people can write that. It's actually probably more effective than an "objective" piece. It's persuasive because the author's clearly loves the vibe, and describes it in a weirdly beautiful way. Which got me to buy into it, as a software developer myself. And the contrast with how the valley is portrayed normally is refreshing. (FD I don't live there, but I did really like this piece.)
This doesn't seem like it has anything to do with the technology discussed... and what is your comparison country? Which US airports have you been too?
I work with a group using machine learning for drug discovery (I'm not a biologist/chemist) but the bio people around me loved to talk poorly about Watson's drug discovery tool.
Lots of focus on the algorithms in the comments here, but from what I could glean generally they lacked domain experts when developing the datasets... we spend 90% just finding the best data... and even then it's tricky. I think they may have had lower standards for the input into the system... garbage in garbage out.
I guess it depends on... what "work" means. So I worked on Deep Networks for quantum chemistry (I'm not a physicist or chemist but) I can tell you people were ecstatic about the possibility that the approximations that the neural nets come up with might get closer to real physics than current approximations right now. This w/o any needed advancements. Some challenges are so difficult in these areas that approximations are the best that are possible. It's kind of similar to drug discovery now.. like if there are models which can help narrow down potential molecules / targets, that has tremendous potential even if the system needs to be double checked by a person. So it's hard to see "don't work" as anything but buzzy. BUT I will agree with you neuroscience will help develop our understanding of cognition.
I just wanted to blast these other applications, because I think people get this idea that AI has to be AI for anything interesting to happen... but there are really niche applications where people don't think these tools are experimental. And what you describe may already be happening, Geoff Hinton's critique of modern deep nets seems to be a call to get more biological. (Thinking of capsules nets).
What is considered to be "top of game" in deep learning in your experience... does that apply to just researchers like Ian Goodfellow who come up with completely novel methods for ML algorithms, or does it extend as far as people who are just using the methods that others developed effectively or in new ways? I know thats a weird question, but I am planning on looking into deep learning jobs after finishing my (MS) curious what the market value is for people who have experience implementing the systems , vs the people inventing new architectures. Because I won't have a PHD... It seems like somewhere along the way its a pretty extreme jump to ask for 1,2M or even 500K instead of just 100K~200K... wondering if you have any advice for how someone new might prove themselves... I guess beyond the standard stuff (have a nice github, try to replicate papers etc...)
I think a smart, hard working person who re-uses modern results from others well and in potentially new ways can create a vast amount of value. Short term, more than the top guys, as a lot of their work may be speculative, and yours would be getting-it-done. Long term, they'll invent some method that leaves you in the dust, but that's fine, just learn that too!
