4 ms·

People spend an awful lot of time talking about current successes in AI without often reflecting on how much (or little actually) AI impacts their lives. Despit

People spend an awful lot of time talking about current successes in AI without often reflecting on how much (or little actually) AI impacts their lives. Despite all of the energy put into current gen AI, as far as every day impacts the biggest things I can think of are: - Spam filtering/email sorting - Web search - GPS/Wayfinding - Voice assistants These are the only practical applications of "AI" that I use more or less everyday (I'd be happy to reminded of others). Of these 4 I personally have found spam filtering to be getting worse recently, as well as web search. The first 3 were all more or less solved over a decade ago, and, while I find Siri convenient, I wouldn't mind much at all if voice assistants completely disappeared tomorrow. I'm not denying we've had an amazing decade of pushing the research needle further. There have been tons of impressive AI projects out there. However the practical, day-to-day improvements we've seen with the existence of AI seem to be few and far between, and this is even more true when you start asking about any AI work done in the last decade. I was happier with the state of "AI" in my life in 2006 than I am today. I just find it a bit fascinating how much energy has gone into both generic data science as well as more serious AI research and yet how little the reach of AI has grown in the last 10 years. All of the cool AI that I use existed before data science was declared the "sexiest job".

flooo 4y ago
Most novel AI serves the user perfectly well. And since this type of AI requires lots of labelled data, these users are typically large data harvesting organisations. Some other applications that you may use daily are translate and face unlock/recognition. It’s interesting that you mention search and spam filtering which both include an adversarial component. It seems to me that the adversarial AI has become better, in line of expectation from the democratisation of AI tools and knowledge.
vagrantJin 4y ago
I agree with your take mostly. But also, some things are getting better work-wise like video/image editing. Where something can take an animator a week or two now takes all of 1 minute or less. Some startups in the motion capture space are doing some wild things and in a few years even small indie game studios will have mocap parity with AAAs.
tomrod 4y ago
You might be surprised where AI shows up. Use a credit card? Fraud monitoring, KYC, and other financial models run through (e.g. Early Warning service). Log into a website? Application monitoring with anomaly detection. Own a 401k with shares in a financial vehicle like an ETF? AI used to predict the market for in-the-money trades. Gone to the ER? Risk levels of mortality, sepsis, etc. are constantly pushed to your medical record (in many top-tech hospitals, like Parkland Hospital in Dallas and similar).
- woopwoop 4y ago
  Do any of those applications use neural nets in any non-trivial way? I'm pretty sure that kind of stuff is all classical statistical modeling.
  - tomrod 4y ago
    Yep, they do. But remember, neural nets are a part of ML, not the universe of ML. The amount of sunshine between statistical learning and ML and mostly negligible.
- ripe 4y ago
  I believe most of these applications are based on statistical methods, but not what is commonly known as AI/ML.
  - tomrod 4y ago
    This is like when the Scooby Doo gang pulls the mask off the mayor. ML and statistical learning are broadly the same thing. There is arguably some, but not much, sunshine in the area of RL. AI itself is a superset of whatever difference one may see between Stats and ML, as algos don't specifically require uncertainty.

flooo 4y ago

Most novel AI serves the user perfectly well. And since this type of AI requires lots of labelled data, these users are typically large data harvesting organisations. Some other applications that you may use daily are translate and face unlock/recognition. It’s interesting that you mention search and spam filtering which both include an adversarial component. It seems to me that the adversarial AI has become better, in line of expectation from the democratisation of AI tools and knowledge.

vagrantJin 4y ago

I agree with your take mostly. But also, some things are getting better work-wise like video/image editing. Where something can take an animator a week or two now takes all of 1 minute or less. Some startups in the motion capture space are doing some wild things and in a few years even small indie game studios will have mocap parity with AAAs.

tomrod 4y ago

You might be surprised where AI shows up. Use a credit card? Fraud monitoring, KYC, and other financial models run through (e.g. Early Warning service). Log into a website? Application monitoring with anomaly detection. Own a 401k with shares in a financial vehicle like an ETF? AI used to predict the market for in-the-money trades. Gone to the ER? Risk levels of mortality, sepsis, etc. are constantly pushed to your medical record (in many top-tech hospitals, like Parkland Hospital in Dallas and similar).

woopwoop 4y ago
Do any of those applications use neural nets in any non-trivial way? I'm pretty sure that kind of stuff is all classical statistical modeling.
- tomrod 4y ago
  Yep, they do. But remember, neural nets are a part of ML, not the universe of ML. The amount of sunshine between statistical learning and ML and mostly negligible.
ripe 4y ago
I believe most of these applications are based on statistical methods, but not what is commonly known as AI/ML.
- tomrod 4y ago
  This is like when the Scooby Doo gang pulls the mask off the mayor. ML and statistical learning are broadly the same thing. There is arguably some, but not much, sunshine in the area of RL. AI itself is a superset of whatever difference one may see between Stats and ML, as algos don't specifically require uncertainty.