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I like this article very much. As someone who makes a living from R&D funding into hyped technologies, this made me do some amount of introspection. AI, howeve

by syats 6y ago

I like this article very much. As someone who makes a living from R&D funding into hyped technologies, this made me do some amount of introspection. AI, however much we love it, and however good computers are at chess and go, has done relatively little to increase productivity in already existing processes. Yes, it's nice to tell apart cats from dogs with F1=0.98.. but, what bushiness actually added image classification into their processes and made money out of it? Furthermore, as any one searching for specific stuff on Google and being frustrated by synonyms being substituted or search terms being ranked in importance, can tell you, F1=0.98 isn't enough to really outsource human "intelligent" work onto computers. The same goes for big data.. yes, we love our fancy Sparks and Airflows and Kafkas, yes, I've pushed to build "big data" pipelines several times.. yes, fancy dashboards can come up. But any increase in productivity or quality of life could have also been achieved with shell scripts and a bit of patience. Uber your say? they use big data to foo and to bar...yes, but not in real time, for real money making purposes: even if you ignore the fact that they are still in the negatives, the computations that have to be done real fast for their service to be usable, are all "small" as in, they-can-be-done-in-a-mobile-phone small.

calebkaiser 6y ago
> AI, however much we love it, and however good computers are at chess and go, has done relatively little to increase productivity in already existing processes. Is that true? Uber's ETA prediction is, to me, a very impactful and technologically impressive application of ML. Recommendation engines--from Netflix/Spotify to paid advertising platforms--seem to be pretty powerful. Image classifiers, from automated tagging to monitoring solutions, seem to have made a big impact. This isn't just Facebook suggested tags or whatever, Wildlife Protection Services recently doubled their detection rates of poachers throughout nature reserves by introducing ML. And what about smartphones? Speech interfaces and conversational assistants are a standard part of every flagship phone now. Mobile cameras use ML to improve images without upgrading hardware. I agree that there is a lot of snake oil AI nonsense out there, but I think it's a bit of a false dilemma to deciding whether or not machine learning is "valuable" by evaluating the over-promises of sales teams. The reality is that almost all of the most popular apps on your phone (Uber, Lyft, Maps, Gmail, Spotify, Netflix, Facebook, Instagram, Snapchat, etc.) all incorporate ML.
- syats 6y ago
  Indeed, ML is vastly deployed, I do not doubt that. And maybe those "AI-powered" companies are making money, or making lives better. But I was referring explicitly to existing processes. Your poacher detection example is a good one, there was a process (based on some other tech/skill/k9 units?)and then it was improved with AI. My assertion is that there are very few such examples. Contrast that to new enterprises that use AI/ML. Of course, if you create a whole new market and corner it, whatever technology you use might seem like the recipe of your success. But in the case of ride hailing apps, they created a brand new market of taxis-called-directly-by-the-user (and still not making money), Facebook created a whole new market of personalized matchmaking between people and ads, etc. Yes, they are kings in their markets, yes, they use "AI", but what portion of their market dominance is due to the use of that specific technology? I would say close to 0.

by syats 6y ago

calebkaiser 6y ago

> AI, however much we love it, and however good computers are at chess and go, has done relatively little to increase productivity in already existing processes. Is that true? Uber's ETA prediction is, to me, a very impactful and technologically impressive application of ML. Recommendation engines--from Netflix/Spotify to paid advertising platforms--seem to be pretty powerful. Image classifiers, from automated tagging to monitoring solutions, seem to have made a big impact. This isn't just Facebook suggested tags or whatever, Wildlife Protection Services recently doubled their detection rates of poachers throughout nature reserves by introducing ML. And what about smartphones? Speech interfaces and conversational assistants are a standard part of every flagship phone now. Mobile cameras use ML to improve images without upgrading hardware. I agree that there is a lot of snake oil AI nonsense out there, but I think it's a bit of a false dilemma to deciding whether or not machine learning is "valuable" by evaluating the over-promises of sales teams. The reality is that almost all of the most popular apps on your phone (Uber, Lyft, Maps, Gmail, Spotify, Netflix, Facebook, Instagram, Snapchat, etc.) all incorporate ML.

syats 6y ago
Indeed, ML is vastly deployed, I do not doubt that. And maybe those "AI-powered" companies are making money, or making lives better. But I was referring explicitly to existing processes. Your poacher detection example is a good one, there was a process (based on some other tech/skill/k9 units?)and then it was improved with AI. My assertion is that there are very few such examples. Contrast that to new enterprises that use AI/ML. Of course, if you create a whole new market and corner it, whatever technology you use might seem like the recipe of your success. But in the case of ride hailing apps, they created a brand new market of taxis-called-directly-by-the-user (and still not making money), Facebook created a whole new market of personalized matchmaking between people and ads, etc. Yes, they are kings in their markets, yes, they use "AI", but what portion of their market dominance is due to the use of that specific technology? I would say close to 0.