> “deep learning,” teaching machines to recognize images and understand natural language using software that operates a bit like the networks of neurons in the human brain.
'understand natural language'?
Very far from it...
> I saw the movie, where the main actor's wife was so angry - but I was having a great day
A solid group of talent, welcome to the club. I am interested to see where the "deep learning" start-ups end up in ten years time with such a wide-array of problem sets and industries.
Well, you can somewhat discriminate between them. Just look at people who work at that startup. Have they been doing GPU / Deep Learning staff at around 2010, when it had started showing promise? (this group of people is probably limited to a bunch of grad students and some small communities around a few open source projects). If yes, then you don't need to worry. After all, they've been smart enough to pick up deep learning half a decade earlier, before it had became a buzzword. And now they probably have experience in the area. A solid bet.
On the other hand, if these folks are just some bozos, who picked up a buzz-word and now are trying to hack some stuff together. Well. It's your call.
(I'm not in any way affiliated with that startup. and I haven't checked on the background of that PhD guy. Although I do have some small vested interest in the field of "deep learning" in general. And I don't want it to become yet another dead buzz word. And WTF - why Google is being used in the headline?)
Machine Learning itself is kind of rebranding to avoid itself from being called yet another AI trick. It is AI as much as Deep Learning is. Main difference between traditional machine learning and deep learning is that, deep learning (neural networks) is inspired by biology.
They are closely related but entirely different approaches. Most machine learning is relatively simple statistical models. Deep learning means ridiculously large models. Sometimes they have millions of parameters and require rooms full of GPUs running for weeks to train. But the capacity means they can learn far more complicated functions (like machine vision or language.)
Machine learning isn't an approach, its an entire discipline. Deep learning is just a specific category of implementation of a subset (neural nets) of machine learning.
They aren't entirely different approaches, considering deep learning is a form of machine learning...
Real deep learning[0] is a very particular type of machine learning that has recently been shown to be quite useful for certain specific tasks. But as often happens with technical terms, it's been regularly abused once it started getting press.
Well, they are just trying to disrupt the big data space with enterprise level deep learning allowing better branding position and lessening inertia to allow quicker pivots. It makes sense to do this because we are at a inflection point where an autonomous thought leader can appropriate the mind share of the by market by leaning in to this space and bringing to bear value added innovation. Also, hacking.
My firm offers an entire framework built around this service, except it is an enterprise version for 12000% more where the server is placed back into your company's data center.
It is "an approach to AI based on enabling computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of of its relation to simpler concepts." [1]
Relatedness of Sentences (beta)
1 - not related at all
5 - an almost perfect paraphrase
Two men are taking a break from a trip on a snowy road
Two men are taking a break from a trip on a road covered by snow 4.05
Two men are taking a break from a trip on a road covered by rocks 4.13
Two men are taking a break from a trip on a road covered by mushrooms 4.23
Two men are taking a break from a trip on a road covered by hobbits 4.27
Plenty of work ahead :)