No, you're not going to be a "dinosaur". 99% of extremely well compensated software engineering jobs do not involve ML. Using top large companies as a proxy of what things are going to be like in the world at large 3 years from now, maybe one in 200-300 engineers does anything in any way related to ML there. And that's a generous estimate. You do need to know what it is, roughly, but there's no need to drop everything you're doing and switch careers.
It's just that we haven't seen people protesting in the streets for some reason. Hmmm, I wonder why that is. Dropping 100k bombs, killing 2200 people with drones, and toppling several governments sure seems like a bigger deal to me than a 3 month visa ban.
The way I see it, TF is about to pull _way_ ahead thanks to XLA JIT/AOT. All of a sudden you get the ability to fuse things at a much more granular level, which could reduce memory bandwidth requirements by a lot. Frameworks like Torch can't do any fusing at all, since their computation is fully imperative. Tactical win for imperative frameworks, I suppose, but strategically functional graph is the way to go. DB people realized this in the 70s, ML people are realizing this now.
TF is way behind on UI, which is why it's making Keras its front-end. It's fairly slow on multi-GPUs compared to Torch and neon. It might pull ahead in performance on GCE, but that's just for lockin.
TF is in a fortunate position of having several UIs at this point. It's a lower level framework with a lot of power. If you don't need all that power, Keras or TFLearn or Slim are pretty great. If you do, it's there for you. I see no evidence that Google's goal with TF is to lock you into anything, and especially GCE. I'm a former Google employee, and I can tell you unequivocally — that's not how Google actually works.
It just blows my mind that Soviet Venera landers were sending digital (!) images back from the surface of Venus in early 80s. This would be a heck of an achievement even today some 35 years later.
Because if you drive a Tesla hard, battery overheats and it shuts down. Plus once you run out of battery (even if it doesn't overheat) you need several hours to recharge it. Plus Tesla is only "high performance" in a straight line. I believe they had an episode with a Tesla. It ended up on a tow truck.
Not sure why you are being downvoted. The Tesla S handles like a pig and you can't track the car because the battery can't handle it. These are valid criticisms. Though, the towing part of that episode I think was played up for the show.
That said, for 90+% of people, the Tesla S, and almost assuredly the Model 3, will be more than sufficient vehicles for people. The focus on acceleration is an amazing selling point and most people will never track their car. The ranges on these cars are pretty excellent and charging stations are becoming more and more common.
These cars aren't for me though, I'll gladly keep driving my BMW. The Tesla feels soulless while driving it and the skinny tires and the extreme weight shifts prevent you from hammering through corners which is were I believe driving is the most exhilarating.
Anyone who doesn't believe it, watch the video. Car and Driver took a Model S onto a track and in less than a minute it was limping in low-power mode as the batteries were overheating. They also ran into an issue where the brakes stopped working. This was just during one lap of a small race track.
The Model S is a luxury performance sedan, not a race car. It can't handle being pushed to its limits for any period of time. It just wasn't designed for that.
You only need basic knowledge of physics to see why a high performance car without large radiators would overheat when expending a ton of energy per unit of time. Sadly a lot of people here lack it.
They don't use Docker outside Cloud, true, but their container technology is the same as what Docker uses: cgroups. Brought to you by a couple of Google dudes a decade ago.
You're thinking about it incorrectly. Docker is not a VM. Docker is more like a chroot and a set of additional capability restrictions on top. Basically there are several things that are namespaced in Linux. Processes, network, users, IPC, mount, etc. Docker simply manages these namespaces. At a high level, when you fire up a container, a namespace gets created for it. So unless you explicitly tell Docker to expose things from the host, there's only a very limited set of things your container will see. Crucially, everything uses the same kernel, same drivers, etc, and there's zero overhead.
Think of your Linux host as simply a default namespace.
The real news here is that HuffPo agrees with Trump. I thought their editorial policy was to interpret everything Trump says in the most low-IQ and paranoid way possible. Someone needs to call them and ask if everything is OK.
