4 ms·

It's absolutely not just you: the author is completely clueless and is basically making shit up. For example: "The reason is not only because race is used as a

It's absolutely not just you: the author is completely clueless and is basically making shit up. For example: "The reason is not only because race is used as a factor in assigning risk (not unlike Amazon’s system picking “female” as a proxy for “inadequate”), but also because lenders are obviously incentivized to find new ways to make money on loans and insurance rates. " This is complete horse shit. I've done this sort of risk assessment for banks. Race is absolutely not used as a feature in assigning risk, and you have to work REALLY HARD to remove things such as zip codes which are simple proxies for race, AND you have to have an explainable model (GLM, decision tree, 2-3 layer neurons, etc) which you can defend as not being racist. This is a legal requirement, and I assume it is possible that banks have done overtly racist things in the past, but they certainly don't do this now: it's something that could cost them money. The over all point is legit: AI isn't real and anyone who thinks it is should have their head examined, machine learning is gratuitously overhyped, and its use by powerful groups is what you should fear. How he got there is not legit.

sgt101 8y ago
>AI isn't real and anyone who thinks it is should have their head examined, machine learning is gratuitously overhyped Whooohaaa! ARTIFICIAL INTELLIGENCE is not real, but there is a lot of AI TECHNOLOGY that is real and useful - for example the knowledge graph that Google uses, Bayesian modelling, economic and social simulation and so on. Both AI and ML are overhyped at the moment, data quality and knowledge engineering are killer problems, but there will be incremental improvements in both of these continually from now on, and this will enable both AI and ML tech to be much more easily applied than now.
- scottlocklin 8y ago
  > the knowledge graph that Google uses, Bayesian modelling, economic and social simulation and so on... Those are not "AI TECHNOLOGY" -they're just math (the examples you list are basically all linear regression). Treating "AI" anything as if it were an actual subject outside of very, very obscure research projects is about 90% of the hype problem here. The use of the term is very much history repeating itself: people actually thought "AI" was a real thing back in the 1980s -the hype back then was even more ridiculous, and it was based on stuff which amounted to "we will use interpreters and parse trees."
  - michelpp 8y ago
    Yup. “All the impressive achievements of deep learning amount to just curve fitting,” - Judea Pearl
    - RosanaAnaDana 8y ago
      Ok. So can you make an argument that any kind of intelligence or learning is anything more than curve fitting. I think the goalpost moving that's gone on with AI (deep blue, AlphaGo, etc) is as ridiculous as the 'its overhyped and just curve-fitting', when no ones settled into the difficult task of rigorously defining what 'intelligence' is. So consider the possibility that all forms of intelligence are some flavor of curve fitting. Is that such a problem? If that were the case, what would be so wrong with calling non-biological systems which can optimize curve fitting 'intelligent, artificial'?
      - cik2e 8y ago
        I had the identical sentiment after reading that dismissive oversimplification. Here’s the source interview: https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/ https://www.quantamagazine.org/to-build-truly-intelligent-ma.... For a man of his experience, I am surprised that he is forgetting the incremental nature of science. Most high level researchers are not visionaries but are really damn good at what they do. Their work will provide the building blocks for game-changing advances. His goal is to draw attention to the loftier problems in AI but doing so by belittling the work of others is immature and ineffective as a motivational tool. Who’s going to switch gears to exploring radically different approaches because what we’re doing now is so boring and lame? I would have loved to read more about his proposed avenues of research instead of how unimpressed he is with curve fitting.
        sgt101 8y ago
        Plenty of chances : http://bayes.cs.ucla.edu/jp_home.html http://bayes.cs.ucla.edu/jp_home.html
    - 8y ago
- drb91 8y ago
  > for example the knowledge graph that Google uses, Bayesian modelling Note how these have concrete descriptions and you don't have to call it AI—which, again, is meaningless. Correspondingly, referring to "AI" as a real thing (including calling search and modeling algorithms AI) is a red flag. > economic and social simulation What relation does this have to AI?

sgt101 8y ago

>AI isn't real and anyone who thinks it is should have their head examined, machine learning is gratuitously overhyped Whooohaaa! ARTIFICIAL INTELLIGENCE is not real, but there is a lot of AI TECHNOLOGY that is real and useful - for example the knowledge graph that Google uses, Bayesian modelling, economic and social simulation and so on. Both AI and ML are overhyped at the moment, data quality and knowledge engineering are killer problems, but there will be incremental improvements in both of these continually from now on, and this will enable both AI and ML tech to be much more easily applied than now.

scottlocklin 8y ago
> the knowledge graph that Google uses, Bayesian modelling, economic and social simulation and so on... Those are not "AI TECHNOLOGY" -they're just math (the examples you list are basically all linear regression). Treating "AI" anything as if it were an actual subject outside of very, very obscure research projects is about 90% of the hype problem here. The use of the term is very much history repeating itself: people actually thought "AI" was a real thing back in the 1980s -the hype back then was even more ridiculous, and it was based on stuff which amounted to "we will use interpreters and parse trees."
- michelpp 8y ago
  Yup. “All the impressive achievements of deep learning amount to just curve fitting,” - Judea Pearl
  - RosanaAnaDana 8y ago
    Ok. So can you make an argument that any kind of intelligence or learning is anything more than curve fitting. I think the goalpost moving that's gone on with AI (deep blue, AlphaGo, etc) is as ridiculous as the 'its overhyped and just curve-fitting', when no ones settled into the difficult task of rigorously defining what 'intelligence' is. So consider the possibility that all forms of intelligence are some flavor of curve fitting. Is that such a problem? If that were the case, what would be so wrong with calling non-biological systems which can optimize curve fitting 'intelligent, artificial'?
    - cik2e 8y ago
      I had the identical sentiment after reading that dismissive oversimplification. Here’s the source interview: https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/ https://www.quantamagazine.org/to-build-truly-intelligent-ma.... For a man of his experience, I am surprised that he is forgetting the incremental nature of science. Most high level researchers are not visionaries but are really damn good at what they do. Their work will provide the building blocks for game-changing advances. His goal is to draw attention to the loftier problems in AI but doing so by belittling the work of others is immature and ineffective as a motivational tool. Who’s going to switch gears to exploring radically different approaches because what we’re doing now is so boring and lame? I would have loved to read more about his proposed avenues of research instead of how unimpressed he is with curve fitting.
      - sgt101 8y ago
        Plenty of chances : http://bayes.cs.ucla.edu/jp_home.html http://bayes.cs.ucla.edu/jp_home.html
  - 8y ago
drb91 8y ago
> for example the knowledge graph that Google uses, Bayesian modelling Note how these have concrete descriptions and you don't have to call it AI—which, again, is meaningless. Correspondingly, referring to "AI" as a real thing (including calling search and modeling algorithms AI) is a red flag. > economic and social simulation What relation does this have to AI?