Hacker News Reader

Comment by axegon_ | Hacker News Reader

At one point I lost interest in both and ML/AI in general. I think eventually I got frustrated with the insane amounts of abuses for marketing purposes and never truly delivering what was promised(I know, I know, fake it till you make it). For better or worse far too few managed to make it, so most stuck with faking. I think I lost interest completely around 2019. But even back then they were starting to seem like twins - practically identical with some minor but sometimes subtle differences. Looking at random sections of the documentation, all you gotta do is ignore the semantics...

> tf.nn.conv2d(input, filters, strides, padding, data_format='NHWC', dilations=None, name=None)

> torch.nn.functional.conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1)

View on HN

Yeah, since the release of TF2 in 2019 they're a lot more similar. PT is still more pythonic and easier to debug, but TF has a way better industry infrastructure. The documentation is comically similar though! lol

Have you checked out Google's Coral devices? DL has definitely been abused for marketing purposes, but I think the lack of delivery had more to do with the fact that DL was progressing far faster than the tools around them which make their intelligence actionable.

Part of this is because so many DL applications had to be delivered in a SaaS way, when local AI makes much more sense for a lot of applications. I think the TF -> TFLite -> Coral Device pipeline has the potential to revolutionize a LOT of industries.

Now that you mention it Coral products are the only thing still of some interest to me. Though with so many alternatives around I'm not completely sure they are justified. Say the SBC - even though it is relatively smaller, it's still more expensive than the Jetson which is in a different category in terms of specs. In all fairness I'm interested to get some opinions on the jetson since I have a project where it might come in handy(automotive related). I'm still on the fence as to what I want to use and if I should consider a USFF desktop computer and have an x86 at my disposal and avoid risking having to dig deep into some library when something goes wrong. The one thing that I'm keeping my eyes on are the M.2 Coral devices, though I personally have no use for them.

Unless I missed something, I recently tried to buy a Coral. It was not available anywhere except a few places at 10X RRP. You can buy a Jetson easily. I just got one a few weeks ago.

I have done the tutorials and they all work. They seem to be very well maintained.

People I know said they never got the google Coral SDK working. Unfortunately they wouldn't give me their Corals. :(

Depends which of their products you are after and where you are located. At this very moment Mouser[1] has a very limited quantity in stock.

[1] https://eu.mouser.com/c/?b=Coral

> At one point I lost interest in both and ML/AI in general.

Kind of offtopic but yeah, same. I'm a data scientist and right now I'm learning Django.

In my experience web developers don't get paid as much as data scientists, unfortunately. But I feel you.

Both frameworks have matured a lot and I think we're coming up on some really awesome applications in the coming years. The tools around them which make it easier to apply DL everywhere are really the bottleneck at this point!

Same here-spent so much time having to spin up all of the infrastructure and data I needed everywhere I’ve worked that I basically do more architecture and engineering that going back to TF or PyTorch or figuring out the new framework/model arch de-jour just lost all appeal.

Personally I wish we'd get beyond these low level abstractions by now. Machine learning is so wonderfully mathematical that making abstractions from the details should be incredibly easy and powerful. I can't believe like 8 years after the big ML wave e.g. frontend javascript developers aren't enjoying the fruits of these labors (despite there being no good reason for them not to be able to).

Have you checked out TensorFlow.js? May be what you're looking for...

There are high level abstractions like keras and tensorflow.js (or even higher level GUI tools). All of them are fairly accessible to people with some basic programming knowledge.

I don't get your point about JS developers not enjoying the fruits of these labors - they don't need to enjoy them because they work in a different domain. And if they're interested in playing around with deep learning, the higher level APIs are easy to pick up. I'm not sure what you're expecting to see.

Yeah they made ml5.js for this reason: https://ml5js.org/

I do feel like Google could do better communicating all of their different tools though. Their ecosystem is large and pretty confusing - they've got so many projects going on at once that it always seems like everyone gets fed up with them before they take a second pass and make them more friendly to newcomers.

Facebook seems to have taken a much more focused approach as you can see with PyTorch Live

What do you envision that would help JavaScript devs take advantage of ML? There is tensorflow.js. Are you thinking completely different ‘building blocks’ that provide higher level apis like fast.ai geared towards frontend devs or something else?

They actually made ml5.js for this reason, not sure if you've seen it https://ml5js.org/

Pretty cool imo

You can work within a niche domain or an applied industry, for which ML/AI is just another tool in the bag (admittedly: sometimes revolutionary, many other times irrelevant); or you may want to do bleeding-edge research, only to find that you just cannot compete against the top dogs (even the wonderful fast.ai couldn’t follow suit without refactoring heavily every six or twelve months). What’s the point, then? Set yourself a clearly interesting and achievable target (learn and find a job, get a paper published, release an applied library, etc.) and a challenging deadline with milestones (say, 3-6-9-12 months). After that, wrap up and move forward or move on.

Reply on news.ycombinator.com