This is so far from being my area of expertise. Just one observer's thoughts/questions. Insane how far we have come so quickly, at any rate.
That is because much of the machine learning research is coming from industry, particularly the mega-corps. And for extremely important disruptive events such as AI, they are willing to drop $$ into collecting a stable of research scientists to not fall behind in this space. So while the AI winter was driven by a drop in academic and government spending on research, I think our present situation is far less likely to result in a similar drying up.
Start ups could get hit, however.
Because the company is a king, and the employees are the property of the crown?
I found the migration of experts from CMU to Uber fascinating — for two reasons: 1. The geographical proximity in which the acquisition occurred (i.e. a rust belt town with an awesome CS school and a well known Silicon Valley startup on location). 2. The moving from the academy to the private sector of said professors/researchers happened in a way I hadn't seen before. I mean lots of academics work within industry at some point, but this move seemed to carry more weight in the media — maybe because of the institution involved.
Anyway, with that said, lots of the articles and speculation I read about it, for better or worse, painted the acquisition of academics by Uber as a bit unfair. I.e. Uber could and did pay way more than CMU and provided super interesting problems maybe outside the realm of what a professor normally faces in their research, I don't know. It was the "unfair" element portrayed in articles/opinion pieces I based my wording off of. I am in no way suggesting these profs and researchers did anything wrong or did anything but make the right choice for themselves, something we all have to do.
I digress. Word choice noted, problematic history and other associations noted and hopefully I cleared up my choice a bit.
- TTS and speech synthesis have lots of uses in Android (phones, Wear, Auto, etc.)
- Object recognition is very useful for photo search
- Face recognition is also useful for photo search (if you tag people in Google Photos)
- Neural machine translation provides better translation accuracy (for languages which we have enough training data for)
It's possible Google will cut back on some more speculative ML research efforts, but certainly not those four, I would think.
As you've mentioned, object detection (which is a considerable portion of CV) is marketable. Meta learning (Vizier, "Learning to learn with gradient descent with gradient descent") are incredibly valuable from a business perspective. Model compression is important for neural networks on mobile/embedded devices, etc.
One of the very interesting things about ML being such a corporation driven field is that it's incredible how quickly relatively recent research makes its way into products.
I think this is just true for really anything that falls purely within the domain of the computational sciences. For a ground breaking drug to hit the market, it takes 10 years of FDA trials, for high speed trains it requires large teams of construction workers only after years of safety work.
In the world of software as it stands today, deploying a new model for something can be virtually instant in some cases, and even in the worst cases, it isn't taking people years to improve a small attribute of their product.
How much money does Google Photos make? Last time I used it, there were no ads in it.
Isn't speech recognition nearly at human levels of comprehension, at least for US English and outside of highly technical jargon? Speech patterns change slowly, so would Android continue to make money if investment in speech R&D were cut back? My guess is yes, it'd be fine.
Does Google Translate make money in some specific, measurable way? I don't mean "well it's neat to have translation links in web search", I mean, would people cut back on the commercial queries that are Google's main source of income if Translate quality stopped improving? I doubt it.
Much of their products are like this. They're products but they aren't businesses. It's very easy to lose sight of the basics of business when inside the Google bubble. The things you mention are not providing business value in the conventional sense of the term because they aren't yielding independent profits. So they're all vulnerable.
That said, vulnerable to what? I think Google is at risk of losing a lot of trust around web search and political and informational search more generally, but commercial queries are probably pretty much impregnable.
Google Photos is, in a sense, a big ad with prominent calls to action for selling storage space.
> Isn't speech recognition nearly at human levels of comprehension, at least for US English and outside of highly technical jargon?
Recognition, maybe (though I think no). Applying recognized text to get the desired outcome, not even close.
> Does Google Translate make money in some specific, measurable way?
It powers a paid API, so, yes, it makes money in a specific, direct, measurable way.
FYI: there is no such thing as a "neutral accent" - unless you mean neutral to your locale. I could describe an object as having "neutral temperature", but you'd need to know how hot/cold its environment is before it makes sense.
It's true that this accent generally lacks the strong regionalisms that some other US accents do. But it's mostly still an outgrowth of a general region even if its use is cultivated more widely.
Recognition of technical jargon is an easier problem than recognition common speech. Jargons are far more regular and have limited lexical and idiomatic variation, while common speech has a huge number of variations and is constantly innovating new usages. This is evidenced by the fact that the earliest speech recognition systems were only useful in highly restricted domains (medical transcription, phone support tree navigation).
It will be either one of those two, if it doesn't happen in the next 20 years I would bet against it happening at all until AGI is a reality, assuming it will ever happen in the first place.
But if it is possible with an incremental change to present day technology I fully expect it to happen and likely a lot faster than those 20 years. The economic incentives are too large to ignore and there are many players wealthy enough to finance such an incremental step.
It's not sexy and doesn't fulfill autonomous taxis everywhere fantasies but for the vast majority of people who don't live in or near urban cores it would likely be a significant quality of life improvement.