When the bubble bursts
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Amount of industries and businesses that can benefit not from the state of the art but from stuff that been known for years now and just works because it was developed for harder problems is huge. The big return/promise companies are vacuuming up all the talent while niches all over the place can benefit from someone spending few month cleaning up data and using some transfer learning to save them a ton of $$.
For these application you don't need PhD you need an engineer who knows how to ship stuff but on other hand also knows how to work with current DL frameworks.
On app side.. Cookpad added this neat feature last year, where it scans your pictures and adds to you cfood/cook log if it is food. The had a PhD do it, but this is because they want to do a lot more with it later I guess so building expertise and team. Food in general I think has a lot of neat computer vision apps that will happen eventually.
It’s a big hype in the public sector in these years, but it’s really all talk. We did a project where we used ML and 1000 server instances in Azure to go through millions of employee cases, to flag cases that didn’t have a certain document. Because the the is new, and this wasn’t something that could be delayed, we also had 10 employs do the same task to make sure it got completed.
The ML project took 2 really expensive employs and 3 months to train the algorithm, then 5 hours to go through all the documents + we had to spend 1 week of 5 employs to clean up the stuff our algorithm had marked as “unreadable” due to terrible scans. The human employs did it in the same amount of time, but made a few more errors.
Over all the ML was more expensive and our politicians won’t favor it again.
On other things where ML might work, our datasets are turning out to be too small, unless we work together with other municipalities and then we’re facing GDPR violations that may be hard to pass through our legal team. Legal is a big issue on a lot of things, it’s not currently legal to automate any sort of process that require any form of validation (as long as it has to do with case working).
That being said, I’m sure stuff like facial recognition will change the world.
Why would you use server instances in Azure to do ML? Something like Google CloudML (I'm sure that the other major cloud providers do managed Tensorflow as well I've just never tried it on their platforms) would be a better fit to a project with only two technical staff. Your two staff probably spent a combined total of one-person-month working on infrastructure.
Your issue with small data is very real. People need to stop trying to do ML on small datasets. The results will be sub-optimal.
ML in general can absolutely be used with small datasets. ML is all about finding the right model complexity to fit to the data to maximize out-of-sample performance. If your dataset is small, all that means is that your model will have to be more crude. A simple cross-validated regularized linear regression or a shallow decision tree are ML models too, and you can usefully apply them to a dataset of just 100 samples.
So if you've got an analyst sitting on that issue - then your problem is solved anyway.
So again, why hire somebody with a trendy specialty who is <probably> full of BS, when you can hire someone with some respect for classics?
I do think that people that think that ML = big data are mistaken. ML is about making the most of your data, however much of it you have.
In my view, the main difference between ML and plain statistics is that with the latter, you come up with the appropriate model apriori, and then make sure the data satisfies the assumptions so that you can draw conclusions from the in-sample fit of the model. You control for overfitting by choosing the simplest model that is reasonable - often univariate linear regression.
Whereas with ML, you let the data dictate how complex a model you should use. You choose the appropriate model complexity using techniques like cross-validation, and verify the effectiveness of your model empirically.
ML is often used interchangeably with ANNs which I think is a mistake. Take structured data problems on Kaggle and you would very rarely see ANNs as a major predictor in the winning models.
There is bitcoin, then there are shitcoins.
Every year someone releases a new altcoin with a youtube video that promises the end of war, starvation, etc... Then suckers buy it. 3 years ago, it was developing 'the ultimate privacy coin', because the libertarians ate that shit up.
Using blockchain outside of currency seems expensive and unnecessary.
I dont see bitcoin bubble popping unless they have a security issue. The rest of the coins might be as good as useless when people realize how expensive it is to run 7 computers to do the job of 1.
Even if this bubble bursts, bigger and greater things will arise because of it. With only 2 samples of these bubbles the p-value is miserable, but history has taught us to see these bubbles through.
Wisdom is not the sum of success, it is the sum of failure. We need to learn just how far we can push deep learning - within reasonable moral degrees of freedom.
That doesn't make any sense. Failing is not implicitly useful and (while we are on the topic of morality) must not be held up as a goal.
They were simultaneously different points and part of the same point in my argument. Separately, failure is a good thing - as any CEO would tell you. Together, they are horrific.
It's very complicated, which is a far cry from "idiotic" as the article portrays.
How can you possibly say failure can't be useful? How do you iterate your plans and designs? I don't think you appreciate how knowledge works. Your weird demonization of failure (including it somehow being a moral issue?) won't force success, it's just going to stop you from realistically evaluating your goals.
Aside from that explanation, I'm not dying to debate you or list exhaustive examples or whatever because you're belligerent and arrogant. People like you can't be convinced, you have to go through it yourselves several times to figure it out, if ever.
The upside of failure is that it teaches more reliable lessons than success, there's no survivorship bias at play when learning from failures like there is with success. Jack Ma's lengthy interview at Davos this year touches on the subject if you'd like to see that perspective defended by someone with authority.
It depends on who is doing it. Maybe you understand what you said, but others have an interest in not understanding it because they have an obstacle in themselves.
> The upside of failure is that it teaches more reliable lessons than success, there's no survivorship bias at play when learning from failures like there is with success.
I would like to improve your abilities or otherwise open up your wisdom. Any "upside of failure" is by definition a success. Therefore people's statements about success and failure, as if the nature of the result can be captured by a singular datapoint, are self-contradictory, and meaningful only as a reflection of the individual's limitation of understanding.
> if you'd like to see that perspective defended by someone with authority.
We're talking about wisdom here, so the only authority is a perfectly enlightened being, not an ordinary human being who has knowledge but no way to confirm the degree of exactness of their own ability to make confirmations. No one without enlightenment perceives the causes of why things happen to them. If you want to convince me someone is an authority on how a good and a bad thing happens then I will need to see any proof at all they realize the principle by which the world operates. Otherwise you should realize that's merely their own ideas, of which some percentage may be correct, despite their inability to confirm it.
I am currently a proponent of self-driving cars, as I believe that human reflexes are designed to operate at, on average, 6mph. Almost anything could be better than us, at least in theory. Testing the limits of self-driving cars is how we determine what we can't do with them (and asking too much is how we zero-in on the failure point). This has complex moral consequences. Will more people be killed due to failure, or fewer? Thats great if it works out, but horrific if it doesn't. It's incredibly difficult to draw a line given that we don't know what the whole landscape is.
This bubble is worsening unemployment. One ripple arising from that is UBI. Another is a positive-sum world. ML is currently successful in medicine. It's not all doom and gloom - we are already reaping long-term benefits.
By what measure?
The problem is not reflexes, the problem is some drivers being dumb and a bunch of others distracted. You could mandate a very limited systento prevent both without solving the self driving car problem at all. (I recommend looking at trains for this solution.)
It is being done because it is attractive, not because it is necessary.
The outcome of the dotcom bubble was the build-out of the infrastructure (dark fibre). A similar bubble built out the railway infrastructure the century before that.
Agile is a bubble in and of itself.
http://webcache.googleusercontent.com/search?q=cache:hunch.n...
The real experts are those who are working for universities and getting paid peanuts. Those who value knowledge above money.
I might be alone but it's feeling a little bubbly to me in a few different areas. ML is certainly one of them.