It’s an unusually beautiful written article, well worth the read just for the prose. As for the main sentiment that we have a new AI winter, I’m not so sure. My lay person view is that we see quite a lot of commercial success with these systems so the current wave will be well funded for at least a decade.
What are the big successes? Speech recognition? Which still seems rather bad to me.
Language translation (which for the languages I’m interested in (Japanese) is still almost totally unusable?
Self driving cars? Which are not yet in production (and where the social issues are probably far harder than the technical ones, and likely have been since the 90s).
Is there some big application if ML that I’m missing that is a clear win?
A lot of ML behind the scenes. Search, knowledge systems, map routing, etc. You know, the boring stuff that isn’t AI yet. I don’t really disagree with your broader point. I expect a lot of things many think are just around the corner are going to be many years away.
Amazon's system for finding related products is eerily accurate. The more you buy, the better it gets at anticipating what you want to buy.
... but the larger wins they've had are behind the scenes, in the predictive modeling that helps them get product into warehouses in front of demand spikes (and into geographically-relevant warehouses to satisfy the spikes).
> Amazon's system for finding related products is eerily accurate.
Amazon's system for finding related products fucking sucks. After I buy an X, I see ads for more X for months, which is completely useless. Sometimes I'll see ads for the exact thing I bought... what?!
Yep, I see this all the time too. Buy a printer? Amazon thinks you want to buy a new printer every day for months.
Google too. I bought a Pixel 2 online, from Google, signed in to Google. Now I see ads on Google ad networks all day long to buy a Pixel 2. It has been 5 months of daily ads from Google to buy the phone I just bought. Ridiculous.
Ad networks are a clear financial win for AI - but they also show the ridiculousness and are clear windows into the failures so far.
This. For all the talk about how Facebook is mining data to hyper-target customers, it still seems like they're still trying to throw stuff at the wall to see if it sticks.
That is amazing too me. When I did inside sales for a VAR in college in the 90s, we wrote a little app that would note the customer's account notes a few months after a printer installation to ask about toner.
It had a high conversion rate and better still got us referrals to the head executive assistant (who was usually the boss's assistant).
I find that particular, oft-repeated bug fascinating. It's a known issue, but one that seems to spread across many vendors and ad networks. I have to assume there's something very stubborn about the system design that makes it harder to fix than it feels like it should be.
(One hypothesis: Perhaps for privacy reasons or Amazon-not-wanting-to-give-away-the-whole-farm-on-sale-conversion reasons, ad networks only have visibility onto what you've seen, not whether you actually closed the purchase).
What I normally do is searching on Google on monday morning a "cool" thing, like "drone" or "camera" and then I enjoy all the week these "targeted" ads, thus avoiding quite a few about "random" things I am not interested in like toasters, diapers, and similar.
Not that I am actually interested in drones or cameras.
I'm not sure that things like Amazon's "Customers who bought this also bought that" is particularly amazing or clever; that ain't rocket surgery, you know! Sure, every once in a while I'll go "Yep, I probably need me some of that, too", but mostly it's "Nope - I've already got plenty of that, thank you. Moving on ..." And like everybody else, I too get sick of being pitched even more of this, or different variations of this, after I've already made my purchase. I'm sure that all of it does lead to at least an incremental increase in sales, though, so it serves its purpose.
A while back I saw an article on the success of an "A.I." that was being used for marketing purposes. And as I read through all of its supposed "breakthrough suggestions" I thought to myself "You know, you could have saved yourself an awful lot of time and trouble and money if you'd just read pretty much any sales and marketing book from, say, the last 100 years or so." Because there was really nothing there that wasn't already pretty much common knowledge within that realm.
I work on Amelia at IPSoft, things seem to be going well.
Customer Service is a huge, overlooked industry mostly based around classifying requests and then performing a rote action, which is a great use case for AI.
I used to work at IPsoft as well. I think the big difference is between "ML as a tool to solve a 80%+ of relatively standard customer and employee service requests" vs "we've created self-reasoning AI that will solve all your problems".
In my opinion, sales at IPsoft faced some of the same challenges initially (selling the idea of AI vs selling a well working solution for a specific problem), but has become more focused now that they have proven use cases in the field.
As you say, service desks are a great opportunity for ML. They're seen as cost centers, typically plagued by high attrition of employees, provide inconsistent service levels, and spend the majority of their time on requests that are relatively standard. Still, it is not an automatic effort: integrating with back-end systems, creating a dataset to start classifying incoming requests, and defining the processes to handle them are (mostly) still manual work.
Not sure what you are using, but speech recognition is amazing on my android device and has been for years. I rarely ever type anything into my phone anymore. It's a usability gamechanger.
anyway, that tangent aside, considering how many of the individual elements in a phone tend to be sourced from Asian countries. I guess not not using my phone, im actively subverting and appropriating Asian culture(s because yes i know there's more than one and I was getting too into character—
anyway i know that totally isn't your implication, I was just taking the expression that I perceived and the meaning I inferred from to the logical extreme to demonstrate my distaste with this whole white male native speaker archetype. sure I would certainly count myself as one, but those are just a bunch of adjectives serving to do nothing more than to elucidate what it means about you to think these things. i can certainly tell you that I and the majority of white male native speaking individuals are nothing like the stereotype you're trying to evoke. we aren't all scientists, aren't all engineers, we aren't all beauticians, and no single one of us lives in a world that is solely their own. Every part of society and the corpus of human knowledge has really just been a super long drawn out game of telephone where the telephone line never terminates and just loops back around. That and there are several telephones attached to it in such a way that physically proximate ones will simultaneously reproduce the vibration traveling across the telephone line. perhaps a bit abstruse
but I think it brings home an important point. namely, i don't think a white person had their hands on this device between it being sealed and being opened at the store.
ML is a "big" application at the tools level. Categorization in particular.
You're already seeing it in product. Consumer level security video equipment with human detection is pretty accessible now. I can ask my iPhone for pictures of my kids in snow in 2014 and get a pretty good output. Enterprise level categorization of photo and video is a thing.
"Smarter" machines are just like smart people, by themselves not very exciting. But give them a purpose or application, and things get exciting.
For safety reasons, self-driving cars on the road don't rely on ML as much as you may think. It's much better to run algorithms with provable guarantees rather than black-box neural nets on a multi-ton vehicle. When the judge asks your company if you did everything in your power to prevent the death at hand, it helps to point to decades-old well proven techniques, rather than band new unproven, untested, trends.
> Self driving cars? Which are not yet in production
Yes they are. What do you mean, not in production? Maybe there aren't millions of fully autonomous cars on the road today, but there will be soon. They sure are in production. Companies like Tesla are making production hardware self-driving cars right now - full autonomy coming soon, of course.
> Speech recognition? Which still seems rather bad to me.
Speech recognition is 100% amazing. The phones never ever hear me wrong any more, not ever, not once. The main problem I have with it is interpretation, which can be shocking, shocking bad. (Ask Google for a joke, it'll tell you one and then prompt to you ask "one more". As soon as you do, it'll say "one more what? I don't understand", which is pretty funny.)
> Is there some big application if ML that I’m missing that is a clear win?
Well yeah. I would say millions. ML has been embedded into the world now and makes nearly every daily interaction with technology better than it was before.
> Yes they are. What do you mean, not in production? Maybe there aren't millions of fully autonomous cars on the road today, but there will be soon. They sure are in production.
Huh? They are in production but not yet in production, but they sure are in production? Can I go to the store and buy one? No. So they are still building the things!
> Speech recognition is 100% amazing. The phones never ever hear me wrong any more, not ever, not once. The main problem I have with it is interpretation, which can be shocking, shocking bad.
I simply do not believe your first point, based on my own experience. 100%? Really? Moreover, your second point, that the interpretation is off, is most of the problem. That's where 99% of the work for the foreseeable future will be spent on this problem.
Perhaps it is beautifully written but unusual? It seems pretty baseline to me. Certainly doesn't seem to meet the bar of worth reading, regardless of its argument. I mean if you only read twitter all day, then ok.
