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?
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.
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.
... 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 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?!
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.
It had a high conversion rate and better still got us referrals to the head executive assistant (who was usually the boss's assistant).
(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).
Not that I am actually interested in drones or cameras.
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.
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.
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.
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.
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.