Earlier quoted context omitted.
> Deep Learning makes feature engineering obsolete I think even if you avoid constructing features, you are basically doing a similar process where a single change in a hyper-parameter can have significant effects: - internal structure of a model (what types of blocks are you using and how do you connect them, what are they capable of together, how do gradients propagate?) - loss function (great results come only if…
Yep, and in doing so, you just traded "feature engineering" for graph design and data prep, no? And that's my response to these sorts. And their usual response to me is to grumble that I don't know what I'm doing. I've started tuning them out of my existence because they seem to have nothing to contribute to it.
AI winter is well on its way
491–500 of 518 posts
Re: AI winter is well on its way
#492Earlier quoted context omitted.
> AI winters are a result of a massive disparity between the expectations of the general public and the reality of where the technology currently sits. I think they also happen when the best ideas in the field run into the brick wall of insufficiently developed computer technology. I remember writing code for a perceptron in the '90s on an 8 bit system, 64 k RAM - it's laughable. But right now compute power and data…
I wish GPUs were 1000x faster... Then I could do some crazy magic with Deep Learning instead of waiting weeks for training to be finished...
Re: AI winter is well on its way
#493Re: AI winter is well on its way
#494Earlier quoted context omitted.
"Just by using DenseNet-BC-100-12 I ended up with 83% ROC AUC after a few hours of training." OK, but 83% ROC/AUC is nothing to be bragging about. ROC/AUC routinely overstates the performance of a classifier anyway, and even so, ~80% values aren't that great in any domain. I wouldn't trust my life to that level of performance, unless I had no other choice. You're basically making the author's case: deep learning clea…
Yeah, but the point here was that radiologists on average fared even worse. 83% is not impressive, but better than what we have right now in real-world with real people, as sad as it is. Obviously, best radiologists would outperform it right now, but average ones, likely stressed under heavy workload might not be able to beat it. And of course, this classifier probably works on certain visual structures better than h…
Re: AI winter is well on its way
#495Earlier quoted context omitted.
>Bayesian can be seen as a subset of deep learning or hell a superset. eh-hem DIE, HERETIC! eh-hem Ok, with that out of my system, no, Bayesian methods are definitely not a subset of deep learning, in any way. Hierarchical Bayes could be labeled "deep Bayesian methods" if we're marketing jerks, but Bayesian methods mostly do not involve neural networks with >3 hidden layers. It's just a different paradigm of statisti…
My mentor was very very adamant about Bayesian network and hierarchical as being deep learning. He sees the latent layer in the hierarchical model as the hidden layer and the Bayesian just have a strict restrictions/assumptions to the network where as the deep learning is more dumb and less assuming. A few of my professor thinks that PGM, probability graphical model is a super set of deep learning/neural network. Thi…
Re: AI winter is well on its way
#496When computers first came on the scene a lot of people had a very poor conception of what it was the human mind did, computationally. So when computers turned out to be good at things that were challenging "intellectual" tasks for humans like chess and calculus many were duped into thinking that computers were somehow on a similar level to human brains and "AI" was just around the corner. The reality was that one of the most important tasks that the human brain performs: contextualization, categorization, and abstraction was taken for granted. We've since discovered that task to be enormously computationally difficult, and one of the key roadblocks towards "true AI" development.
Now, of course, we're at it again. We have the computational muscle to make inference engines that work nothing like the human brain good at tasks that are difficult to program explicitly (such as image and speech recognition) and we've built other tools that leverage huge data sets to produce answers that seem very human or intelligent (using bayesian methods, for example). We look at this tool and too many say "Is this AI?" No, it might be related to AI, but it's just a tool. Meanwhile, because of all the AI hype people overpromise on neural networks / "deep learning" projects and people get lazy about programming. Why bother sitting down for 15 minutes to figure out the right SQL queries and post processing when you can just throw your raw data at a neural network and call it the future?
One of the consistently terrible aspects of software development as a field is that it continues to look for shortcuts and continues to shirk the basic responsibilities of building anything (e.g. being mindful of industry best practices, understanding the dangers and risks of various technologies and systems and being diligent in mitigating them, etc.) Instead the field consistently and perversely ignores all of the hard-won lessons of its history. Consistently ignores and shirks its responsibilities (in terms of ethics, public safety, etc.) And consistently looks for the short cut and the silver bullet that will allow them to shirk even the small vestiges of responsibility they labor under currently. There's a great phrase on AI that goes: "machine learning is money laundering for bias", which points to just one facet among so many of what's wrong with "AI" as it's practiced today. We see "AI" used to sell snake oil. We see "AI" used to avoid responsibility for the ethical implications inherent in many software projects. We see "AI" integrated into life critical systems (like self-driving cars) without putting in the effort to ensure it's robust or protect against its failures, with the result being loss of life.
AI is just the latest excuse by software developers to avoid responsibility and rigor while cashing checks in the meantime. At some point this is going to become obvious and there is going to be a backlash. Responsible developers should be out in front driving for accountability and responsibility now instead of waiting until a hostile public forces it to happen.
Re: AI winter is well on its way
#497Earlier quoted context omitted.
Yeah, but the point here was that radiologists on average fared even worse. 83% is not impressive, but better than what we have right now in real-world with real people, as sad as it is. Obviously, best radiologists would outperform it right now, but average ones, likely stressed under heavy workload might not be able to beat it. And of course, this classifier probably works on certain visual structures better than h…
I think you're missing a very important part here that maybe you've considered: Domain knowledge. I'm assuming your radiologic images were hand labeled by other radiologists. How did they come to that diagnosis? By only looking at the image? This was a severe limitation of the Andrew Ng paper on CheXnet for detection of pneumonia from chest x Rays. CheXnet was able to outperform radiologists on detection of pneumonia…
The main critique for CheXNet I've read was focused on the NIH dataset itself, not the model. The model generalizes quite well across multiple visual domains, given proper augmentation.
Re: AI winter is well on its way
#498I was recently "playing" with some radiology data. I had no chance to identify diagnoses myself with untrained eyes, something that probably takes years for a decent radiologist to master. Just by using DenseNet-BC-100-12 I ended up with 83% ROC AUC after a few hours of training. In 4 out of 12 categories this classifier beat best human performing radiologists. Now the very same model with no other change than adjust…
AI winters are a result of a massive disparity between the expectations of the general public and the reality of where the technology currently sits. Just like an asset bubble, the value of the industry as a whole pops as people collectively realize that AI, while not being worthless, is worth significantly less than they thought. Understand that in pop-sci circles over the past several years the general public is be…
A symptom of capitalism and marketing trying to push shit they don't understand
Re: AI winter is well on its way
#499Earlier quoted context omitted.
I wish GPUs were 1000x faster... Then I could do some crazy magic with Deep Learning instead of waiting weeks for training to be finished...
That's more a matter of budget than anything else. If you problem is valuable enough spending the money in a short time-frame rather than waiting for weeks can be well worth the investment.
Re: AI winter is well on its way
#500Earlier quoted context omitted.
That's more a matter of budget than anything else. If you problem is valuable enough spending the money in a short time-frame rather than waiting for weeks can be well worth the investment.
I cannot fit a cluster of GPUs into a phone where I could make magic happen real-time though :(