Comment by rm999 | Hacker News Reader

Comment by rm999 | Hacker News Reader

This article mirrors some huge frustrations I've had in recent years as a long-time lover and pusher of machine learning. I spoke to a Watson booth employee for a few minutes at a machine learning conference a couple years ago, and almost right away had similar feelings. I don't like the term 'fraud' here though, 'insanely oversold' seems more appropriate. I looked more into Watson, and realized it's really just a large number of traditional (and not very innovative) machine learning algorithms wrapped into a platform with a huge marketing budget.

>AI winter is coming soon.

Perhaps, if so Watson is certainly evidence of this. It frustrates me to no end that machine learning has so much potential but is often lost in a sea of noise and buzz words (as much as I love deep learning, I'm almost tempted to lump that in here too given its outsized media coverage). Machine learning is in its infancy of impact, but the overselling by mediocre enterprise companies and ignorant press may shoot its credibility for years to come.

Machine learning is doing fine, please don't worry about that!

My wife was trying to find an old photo tonight and all I had to do was suggest that she go to google photos and search based on description. Same thing with Facebook recognizing images.

I am getting into using deep learning for NLP, and that looks promising. Google's parser is interesting but I want to see how quickly they can get many layer networks to do things like anaphora resolution (match up pronouns with previous noun phrases).

Better parsers are interesting, but I feel like the parsers we have already are 'good enough.'

The biggest hole in NLP is going from a parsed sentence to 'meaning.' Once we get that right, and are able to feed meaning back into the parser, then all our parsing problems will be fixed imo.

Let me suggest that the meaning will require working effectively with concepts. That is, humans, who actually have real intelligence, can read words and identify and work with the underlying concepts, but apparently so far no one knows how to program a computer to do this.

The parsers available are good enough for English. Sadly, that's absolutely not true for other languages.

As a French with a strong accent, Siri probably understands only half of what I say and I have to make lots of efforts.

So you are saying, all we need is true AI to fix our parsing problems? Well then, all is clear and easy now ...

(How else would the computer understand "meaning" without beeing strong AI)

Sort of, more like, "our parsing is good enough for our use cases, we'd make more progress if we focused instead on getting closer to true AI"

We don't even need true understanding of meaning, even a lousy approximation would be an improvement at this point (or I should say, a slightly less lousy approximation compared to what we have now).

I agree machine learning isn't going anywhere - and yes the big companies are going to solve increasingly complex problems using increasingly complex methods using their data and resources.

The trend I'm seeing is medium sized companies (think 10's of millions to low billions market cap) not solving simple, highly impactful problems using potentially simple machine learning techniques. A basic regression model that was state of the art 75 years ago can often add millions of dollars in immediate value if applied to the right problems. Instead, these companies often prefer to do what they know best, which is hire people to perform repeatable-but-not-easily-scriptable tasks.

The moment machine learning-illiterate executives start getting burned (one CEO to the other: "oh yeah, we spent X dollars on <insert hyped machine learning platform here> and it was useless"), is the moment that the hype can start destroying value.

> The moment machine learning-illiterate executives start getting burned (one CEO to the other: "oh yeah, we spent X dollars on <insert hyped machine learning platform here> and it was useless"), is the moment that the hype can start destroying value.

This is a perfect description of how AI winters come to be. However, I think the upcoming winter will be more like an AI Fall. Some technologies over hyped and over sold will fall out of favor and people will be skeptical. However, companies like Google, Facebook, Microsoft, Apple who are using AI internally and can quantify improvements will continue to do so. The hype cycle is definitely going toward the slowly rising plateau rather than another crash. There is critical mass. Maybe this is what people though before in AI hype cycles (we are definitely in one), but in previous cycles we didn't have near human image recognition essentially solved.

> a large number of traditional (and not very innovative) machine learning algorithms wrapped into a platform with a huge marketing budget.

Maybe that's the path to intelligence -- a bunch of ML and reasoning algorithms we already know about just working together and fed with a lot more data...

Once you take apart any AI thing (or any complicated thing n computing) you discover it is just a neural network, just SVD, just NLP + expert system etc. At least that is how I felt about it when I studied in college. The veil of mystery was lifted and it was just algorithms + math.

You know, all the AI research DARPA paid for, pre-Winter, paid for itself many times over in planning the logistics for Gulf War 1. But the funding dried up anyway. Perception is important.

What do you mean by this? Do you have any references? I'd love to read more about how DARPA used AI for the gulf war.

https://en.m.wikipedia.org/wiki/Dynamic_Analysis_and_Replann...

https://en.m.wikipedia.org/wiki/Dynamic_Analysis_and_Replann...

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