DL is already bearing fruit everywhere - the key is that the places where it works are narrow domains. In general the larger the scope of intelligent behavior the less successful it has been.
Someone has already mentioned face unlock, but also dictation is miles ahead of where it used to be. Similarly text-to-speech is absurdly better than it used to be and is approaching indistinguishability from human speech in some cases (again, narrow domains are more successful!)
Smartwatches are capable of detecting falling motion and alerting emergency responders, and are increasingly able to detect (some types of) cardiac incidents. Again here the theme is intelligent behavior in very narrow domains, rather than some kind of general omni-capable intelligence.
The list goes on, but I think there's a problem where so many companies have overpromised re: AI in more general circumstances. Voice assistants are still pretty primitive and unable to understand the vast majority of what users want to speak about. Self-driving still isn't here. To some degree I think the overpromising and underdelivering re: larger-scoped AI has poisoned the well against what is working, which is intelligent systems in narrow domains, where they are absolutely rocking it.
> the key is that the places where it works are narrow domains
I've observed that not only are the domains narrow, but the domains of domains are narrow. In other words the real-world applications are mostly limited to pattern recognition, reconstruction, and generation.
What I wonder is this. Is DL a dead end?
Are we going to reach a ceiling and only have Face ID, Snapchat filters, spam detection, and fall detection to show for it? Certainly there'll be creative people that'll come up with very clever applications of the technology. Maybe we'll even get almost-but-not-really-but-still-useful-actually vehicle autonomoy.
I can't imagine a world without the transistor, the internet, ink, smart phones, satellites, etc. What I'm seeing coming out of DL is super cool but it feels like a marginal improvement on what we have now and no more. And that's fine... but a lot of very smart people that I know are heavily investing in AI because they're banking on it being the new big technological leap.
> What I'm seeing coming out of DL is super cool but it feels like a marginal improvement on what we have now and no more
"Marginal" here seems to be doing a lot of heavy lifting and IMO isn't fair. The ultimate point of technology isn't to inspire a Jetsons-like sense of wonder (though it is nice when it happens), it's to make life better for people generally. The best technology winds up disappearing into the background and is unremarked-upon.
Like better voice recognition or text-to-speech. We've become accustomed to computers being able to read things without sounding like complete robots - and the technology has become so successful that it's simply become the baseline expectation - nobody says "wow Google Assistant sounds so natural" - but if you trotted out a pre-DL voice synthesis model it would be immediately rejected.
I also wouldn't characterize "ability to automatically detect cardiac episodes and summon help" as some kind of marginal improvement!
I think there's a bit of confusion here re: a desire for DL to be the revolutionary discovery that enables a sci-fi expectation of AI (self driving cars! a virtual butler!), vs. the reality of DL being a powerful tool that enables vast improvements in various narrow domains - domains that can be highly consequential to everyday life, but ultimately isn't very sci-fi.
Does that make DL a dead-end? For those who practice it we aren't close to the limits of what we can do - and there are vast, vast use cases that remain to be tackled, so no? But for those whose expectations are predicated on a sci-fi-inspired expectation, then maybe? It's likely DL in and of itself won't lead us to a fully-conversant virtual butler, for example.
[edit] And to be fair - the sci-fi-level expectations were planted by lots of people in the industry! Lots of it was mindless hype by self-described thought leaders and various other folks wanting to suck up investment money, so it's not fair to blame folks generally for having overinflated expectations about ML. There's been a vast amount of confusion about the technology in large part because companies themselves have vastly overstated what it is.
> The best technology winds up disappearing into the background and is unremarked-upon.
Very much agree, but what I've seen is that DL based solutions do not disappear into the background.
It's so rare for them to disappear into the background that, sitting here at my computer right now, thinking real hard, I can't come up with a single consumer DL product that works reliably. I'm pretty sure there are a few things but it's soooo rare.
Face ID works most of the time but the success rate for me is like 1 in 50. It's very very cool technology but it's also very unreliable. Also if face ID never existed I don't think my life would be worse off in any way.
The same basic issue I can apply to ever DL solution I can think of. The best way I can describe it is they feel... janky. Always janky.
I've had similar conversations before and, after some back and forth, the bullish-on-AI person ends up saying much of what you said. Here's where we end up in a weird stalemate...
> I also wouldn't characterize "ability to automatically detect cardiac episodes and summon help" as some kind of marginal improvement!
Maybe not a marginal improvement, but there's a lot of amazing technology in the medical, industrial, and military sectors. For example people are surprised that FLIR was actively used in the military in the early 90s!
I have no doubt that DL is going to drive a lot of the innovation in highly specialized areas.
What I'm talking about (and terrible at communicating, honestly) is general purpose consumer applications. Can DL significantly improve the lives of every day people? Right now I'm seeing a lot of toy applications, innovation in highly specialized areas, and only hopeful ambition for general use.
What I'm waiting for is that magic moment when I use a technology that a) works flawlessly and b) changes how I live my life. As soon as I see a DL based solution that does that then I'm sold. I just haven't seen it yet.
Deep learning is narrowly applicable to every domain, that's the beauty of it. It's delivering 1.1-10x efficiency improvements for a lot of common workflows, which might not seem that impressive but really adds up.
My cousin is an MMA fighter in another country, just today he got a contract from an american agent and asked me to translate it. I was able to throw it into google translate and in under 2 seconds it produced a flawless translation of 20 pages of legalese.
I have a fairly affordable Hyundai that's able to drive 80 miles on a highway without me touching the steering wheel.
I built an app that uses image recognition to automate food logging, from the surveys that we did it cut down the time to log from 15minutes a day to under 2.
I've worked on systems to monitor patients at risk of falling in a hospital setting.
> My cousin is an MMA fighter in another country, just today he got a contract from an american agent and asked me to translate it. I was able to throw it into google translate and in under 2 seconds it produced a flawless translation of 20 pages of legalese.
Flawless sounds like an overstatement. I would hope that you use a professional before signing contract? That's serious stuff.
> I have a fairly affordable Hyundai that's able to drive 80 miles on a highway without me touching the steering wheel.
Are you referring to lane assist or OpenPilot? In both cases you need to be focused enough on the road that (IMO at least) it doesn't make that big of a difference either way. Certainly not life changing.
> I built an app that uses image recognition to automate food logging, from the surveys that we did it cut down the time to log from 15minutes a day to under 2.
Can it detect hot dogs?
> I've worked on systems to monitor patients at risk of falling in a hospital setting.
See my response wrt specialized (medical, industrial, military) settings. There's a lot of other incredible technology at work in hospitals.
> My friend built Tonal, which can track your exercise form
People exercised just fine before this. I'd classify Tonal as a marginal improvement, at best. I've actually found that removing technology and falling back to simple calisthenics (done properly of course) is having a much greater impact than adding more technology, for various reasons.
> Alphafold will be a huge deal for drug discovery.
I agree, but it falls under the category of specialized use cases. It's very exciting though.
> My cousin is an MMA fighter in another country, just today he got a contract from an american agent and asked me to translate it. I was able to throw it into google translate and in under 2 seconds it produced a flawless translation of 20 pages of legalese.
You do realize that todays translation services often reverses the meaning of sentences? They are useful for reading random posts where you don't care about the results, but they should never be used when you absolutely need to know the meaning of statements.
What you did is akin to putting your sleeping friend into a tesla, turn on the autopilot and see the teslan leave on the road, then posting "See, the tesla drove away perfectly, AI really automated driving!". You don't even know if it arrived safely, and even if it did the tech isn't reliable enough to safely do what you did.
I've worked on similar systems and am aware of these issues. I said that the translation was flawless because I'm bilingual and read it to make sure that there were no mistakes, which I would have expected to see. It was a fairly standard contract and it probably also helps that a lot of the machine translation datasets contain a ton of EU legal documents since they need to be translated for all member states (see https://www.statmt.org/europarl/)
Someone has already mentioned face unlock, but also dictation is miles ahead of where it used to be. Similarly text-to-speech is absurdly better than it used to be and is approaching indistinguishability from human speech in some cases (again, narrow domains are more successful!)
Smartwatches are capable of detecting falling motion and alerting emergency responders, and are increasingly able to detect (some types of) cardiac incidents. Again here the theme is intelligent behavior in very narrow domains, rather than some kind of general omni-capable intelligence.
The list goes on, but I think there's a problem where so many companies have overpromised re: AI in more general circumstances. Voice assistants are still pretty primitive and unable to understand the vast majority of what users want to speak about. Self-driving still isn't here. To some degree I think the overpromising and underdelivering re: larger-scoped AI has poisoned the well against what is working, which is intelligent systems in narrow domains, where they are absolutely rocking it.