The Economics of AI Today
thegradient.pub
The Economics of AI Today
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Re: The Economics of AI Today
#2That's kind of broad. Also, the robots shown have very little "AI".
Maybe machine learning has reached a peak. It's routine now to make classifiers that are about 90% accurate, and really hard to get much beyond that. What we really have are systems which extract lots of signals from an input set and construct a statistical model that maps signals to results. This works moderately well with enough data, but hits a limit at some point. It's great for the class of problems where that's good enough. Like ad targeting and search. Not so great where a wrong result is a serious problem. Like self-driving cars and medical diagnosis.
I wonder what the next idea will be. I'd like to see progress on "common sense", defined as being able to predict the consequences of real-world actions as a guide to what to do next.
Re: The Economics of AI Today
#3This worth reading - a serious and interesting write up.
I'm concerned by the language around how the economy works here "that suggests that the market expects..." The market doesn't expect a thing - the market is made of a herd that flocks from shiny thing to shiny thing. It's not a rational system and modelling it as such is surely completely debunked at this point?
Also the section on regulation is narrow; the responses cited - putting managers in, licensing a regulator to check outcomes - are weak and old fashioned. Two fundamentals are not mentioned; first an investigative and proselytizing enforcement agency as per areospace that can identify specific routes to failure and then communicate them to the professional and business actors. Secondly professions in the sense of proper engineers who are personally liable and insured and required for the use of the technology (brew what you like in your room, but if you use it on people then prison beckons). Third process and infrasturture that is required for use and that no professional would contemplate life without (like design drawings and stress analysis in engineering).
The section on the political economy of AI is interesting, it put me in mind of the latest William Gibson book "Agency" in there there is a quote - something along the lines of "at the boundary of unauthorized military research and the most reckless kind of commercial use" describing the origination of an AI. The framing and narrative of the book is much more plagent and potent than the literature of economic analysis; the data and logic of the economists so far are not as convincing as fiction and polemic. If I was an economist I would be concerned by that.
Re: The Economics of AI Today
#4That's kind of broad. Also, the robots shown have very little "AI". Maybe machine learning has reached a peak. It's routine now to make classifiers that are about 90% accurate, and really hard to get much beyond that. What we really have are systems which extract lots of signals from an input set and construct a statistical model that maps signals to results. This works moderately well with enough data, but hits a li…
Classifiers don’t need to be 100% accurate. They might need 100% precision, or 100% recall, and likely just for a controlled set of data thrown at them. The spam filter in your email client doesn’t need high accuracy, just high precision. The “recommendation system” in your favorite product doesn’t need high accuracy, it needs good content to surface to users.
And there’s a spectrum of what constitutes a classifier. It might be a fancy deep model, or more likely just be some threshold functions (perhaps crafted based upon the results from some deep model that’s not too accurate and not yet ready for production).
The hype is that these classifiers are all 90% accurate now and thus all software is going to turn into AI. That’s garbage and can disenfranchise people who don’t understand AI. That storyline only benefits investors.
What’s different today versus 2012 is we have some deep research models with impressive results. But more importantly, software has compounded itself. There are tools to store and mine data. Mobile compute is also now closer to where laptops were years ago. Competition requires taking hold of these developments in order to innovate. Products will inevitably become more intelligent, AI hype or not.
Re: The Economics of AI Today
#5That's kind of broad. Also, the robots shown have very little "AI". Maybe machine learning has reached a peak. It's routine now to make classifiers that are about 90% accurate, and really hard to get much beyond that. What we really have are systems which extract lots of signals from an input set and construct a statistical model that maps signals to results. This works moderately well with enough data, but hits a li…
> Also, the robots shown have very little "AI".
That's what happens when you change what "AI" means every time there's a breakthrough.
Re: The Economics of AI Today
#6This worth reading - a serious and interesting write up. I'm concerned by the language around how the economy works here "that suggests that the market expects..." The market doesn't expect a thing - the market is made of a herd that flocks from shiny thing to shiny thing. It's not a rational system and modelling it as such is surely completely debunked at this point? Also the section on regulation is narrow; the res…
Has any one read both Agency & the Peripheral? Which one is the lighter read?
Re: The Economics of AI Today
#7That's kind of broad. Also, the robots shown have very little "AI". Maybe machine learning has reached a peak. It's routine now to make classifiers that are about 90% accurate, and really hard to get much beyond that. What we really have are systems which extract lots of signals from an input set and construct a statistical model that maps signals to results. This works moderately well with enough data, but hits a li…
Even the best attempts at task-oriented dialog currently using purely ML techniques are pathetically bad. Having worked in the field for a while, one thing that I have observed is that chatbots simply discard the bulk of the useful information that user provide them. They then fall back to asking users direct questions. Apart from this, they can only handle limited information retrieval tasks for which training data exists.
The next challenge in my opinion is to create a task-oriented chatbot that can help users to actually solve real problems which may not be directly related to previously seen problems. Related to this, consider the problem of creating a chatbot to automate support for an entirely new product.
I have played around a little in this space and feel certain that hybrid approaches will be necessary. For example, I created a car diagnosis/remediation chatbot driven by a Bayes net model of a car's subsystems. This actually showed signs of working - sadly got distracted with other projects.
Re: The Economics of AI Today
#8That's kind of broad. Also, the robots shown have very little "AI". Maybe machine learning has reached a peak. It's routine now to make classifiers that are about 90% accurate, and really hard to get much beyond that. What we really have are systems which extract lots of signals from an input set and construct a statistical model that maps signals to results. This works moderately well with enough data, but hits a li…
Humans (doctors) aren't perfect at medical diagnosis either, so any improvement over human performance is still a net gain, even if it's still below 100% accuracy.
Re: The Economics of AI Today
#9At least in my bubble, AI discussions have been going in predictable ways.. and getting grounded in the same places.
First, we try to build the (proverbial) foundation: What is AI? What is intelligence? Is it general? Lots of places to get stuck here. Can machines understand meaning? Is general intelligence statistical. Can it be?
No real way of settling these, so we have poor foundations.
Then we ask: What can it do? When? What will it do? Why? Can it automate driving? Other stuff? How big a deal is this economically? Will all cars be taxis?
At this point, foundations crack. We're trying to predict the economic side-effects of a technology, its viability, timelines, regulation... and we're building these predictions on very abstract foundations. Obviously, it's all too squishy so we end up nowhere.
Anyway, the effects of technology are very hard to predict... especially recent ones. Computerisation of offices has not measurably increased productivity since the 80s/90s[1], for example. A PC landed on every desk. Many more people work at a desk than before. What predictions would we have made in the 90s, when this was starting to look inevitable.
[1]David Graeber, Tyler Cowen & others highlight this point. It's hard to define or measure, so most economists don't. But within wide margins of error, it does not seem to
Re: The Economics of AI Today
#10This worth reading - a serious and interesting write up. I'm concerned by the language around how the economy works here "that suggests that the market expects..." The market doesn't expect a thing - the market is made of a herd that flocks from shiny thing to shiny thing. It's not a rational system and modelling it as such is surely completely debunked at this point? Also the section on regulation is narrow; the res…
Has any one read both Agency & the Peripheral? Which one is the lighter read?
They are both pretty light and riproaring. Definitely novels of ideas not characters, fantastic language and clever plots.