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In my opinion, most of the issues leading about AI "failing" in traditional organizations are due to the following: (1) Inflated expectations from higher/middl

In my opinion, most of the issues leading about AI "failing" in traditional organizations are due to the following: (1) Inflated expectations from higher/middle management which trickle down the organization. AI is seen as a high-profile case which has to lead to success (and a larger budget next year for my dept.) (2) Data quality issues. The data itself has issues, but the key issue is lack of metadata and dispersed sources. Lack of historical labels (or them being stuck in Excel or on paper) is part of this as well. Big data without any labels is mostly useless, contrary to expectations (3) Most AI or ML projects are not about ML. In fact, they're mostly about automation or rethinking an internal or customer-facing process. In many cases, such projects could be solved much better without a predictive component at all, or by simply sourcing a 1 cent per call API. AI is somehow seen as necessary, however, without which our CX can never be improved. ("We need a chatbot" vs. "No, you just need to think about your process flow") (4) Deployment issues and no clean ways to measure ROI leads to projects being in development indefinitely without someone daring to stop them early. This is also related to orgs starting 30 projects in parallel (2m lead times with one to two data scientists for each), which end up all doing kind of the same preprocessing and all lead to kind of the same propensity model. No one dares to invest in long-term deeply-impacting projects as "we want to go for the low hanging fruit first"

leto_ii 6y ago
I pretty much agree with all of your points, but I also think there may be a more fundamental issue at play here. ML doesn't actually "understand" things - it can do very sophisticated and accurate pattern matching without actually "knowing" the logic of the patterns it's matching. This in turn means that it may fail catastrophically when faced with adversarial examples or with examples that are drawn from a different distribution than that of the training set.
- falcor84 6y ago
  How is that different from a typical human given a routine boring role? Specifically in regards to adversarial input, humans are often the weakest link in terms of process security.
  - leto_ii 6y ago
    I wasn't thinking that there are no roles that can be automated, rather that there are some that can't be. > Specifically in regards to adversarial input, humans are often the weakest link in terms of process security I have yet to encounter a (healthy) person who looks at a photo of static and mistakes it for a cat.
    - duckmysick 6y ago
      How about a photo of a dress where some people say it's blue and some say it's gold?
      - leto_ii 6y ago
        Optical illusions indeed highlight limitations in human perception. However, the dress illusion seems to me far less of a problem than mistaking noise for an object. More relevant however is that we humans can understand that we're faced with an optical illusion and we can make adjustments accordingly. We have formed the concept of an "optical illusion" and we just place "The dress" in that category. A machine needs to be specifically trained on adversarial examples in order to be able to predict them. Once you come up with a different class of adversarial examples it will continue to fail to detect them. There is no understanding there, just more and more refined pattern matching. Does a machine that can match any pattern actually "understand"? I would say no. But these are already philosophical considerations :D
        duckmysick 6y ago
        > More relevant however is that we humans can understand that we're faced with an optical illusion and we can make adjustments accordingly. Broadly speaking, yes. At the same time that's not what happened in 2015. It produced so much polarizing content with people deeply entrenched in their believes. They might have recognized it as an optical illusion, but they refused to make adjustments. > A machine needs to be specifically trained on adversarial examples in order to be able to predict them. Once you come up with a different class of adversarial examples it will continue to fail to detect them. There is no understanding there, just more and more refined pattern matching. Moving away from image recognition examples, isn't that exactly what happens with humans predicting whether an email is a phishing attempt? I remember reading here on Hacker News this week about phishing tests at GitLab. It had a lot of comments about tests and training employees to spot adversarial emails. Some companies are more successful than the others. It is a complicated problem; otherwise we would have solved it already. But it's the same principle because phishers come up with different ways of tricking people. And some people will fail to detect them.
- henrik_w 6y ago
  This is one of the key points in Melanie Mitchell's book "Artificial Intelligence – A Guide for Thinking Humans". In the process of showing this, she gives really good explanations of how current AI/ML systems work. Really worth a read in my opinion. https://henrikwarne.com/2020/05/19/artificial-intelligence-a-guide-for-thinking-humans/ https://henrikwarne.com/2020/05/19/artificial-intelligence-a...
- BaronSamedi 6y ago
  I think of the current neural net based approaches as more "artificial instinct" than "artificial intelligence". The goal of the older, expert system AI paradigm was to create software that could reason about a problem, and it therefore produced systems that could answer the question "why?". ANN systems cannot answer that question. Both approaches seem useful to me, but for different problem domains.
deleted 6y ago
[deleted]
rjsw 6y ago
My background is in Standards for engineering data. I'm trying to push the idea that cleaning up a business' data before trying to apply ML to it may give better results.
- mdorazio 6y ago
  That really only solves part of the problem. Clean up your data. Clean up your processes. Then decide which processes can actually be improved in a meaningful way by ML. I've gone down the ML road twice now with companies that didn't do the second part and both times we had to kill the projects because the processes were so fuzzy and full of gotchas that no ML model could ever hope to be a net positive addition.

leto_ii 6y ago

I pretty much agree with all of your points, but I also think there may be a more fundamental issue at play here. ML doesn't actually "understand" things - it can do very sophisticated and accurate pattern matching without actually "knowing" the logic of the patterns it's matching. This in turn means that it may fail catastrophically when faced with adversarial examples or with examples that are drawn from a different distribution than that of the training set.

falcor84 6y ago
How is that different from a typical human given a routine boring role? Specifically in regards to adversarial input, humans are often the weakest link in terms of process security.
- leto_ii 6y ago
  I wasn't thinking that there are no roles that can be automated, rather that there are some that can't be. > Specifically in regards to adversarial input, humans are often the weakest link in terms of process security I have yet to encounter a (healthy) person who looks at a photo of static and mistakes it for a cat.
  - duckmysick 6y ago
    How about a photo of a dress where some people say it's blue and some say it's gold?
    - leto_ii 6y ago
      Optical illusions indeed highlight limitations in human perception. However, the dress illusion seems to me far less of a problem than mistaking noise for an object. More relevant however is that we humans can understand that we're faced with an optical illusion and we can make adjustments accordingly. We have formed the concept of an "optical illusion" and we just place "The dress" in that category. A machine needs to be specifically trained on adversarial examples in order to be able to predict them. Once you come up with a different class of adversarial examples it will continue to fail to detect them. There is no understanding there, just more and more refined pattern matching. Does a machine that can match any pattern actually "understand"? I would say no. But these are already philosophical considerations :D
      - duckmysick 6y ago
        > More relevant however is that we humans can understand that we're faced with an optical illusion and we can make adjustments accordingly. Broadly speaking, yes. At the same time that's not what happened in 2015. It produced so much polarizing content with people deeply entrenched in their believes. They might have recognized it as an optical illusion, but they refused to make adjustments. > A machine needs to be specifically trained on adversarial examples in order to be able to predict them. Once you come up with a different class of adversarial examples it will continue to fail to detect them. There is no understanding there, just more and more refined pattern matching. Moving away from image recognition examples, isn't that exactly what happens with humans predicting whether an email is a phishing attempt? I remember reading here on Hacker News this week about phishing tests at GitLab. It had a lot of comments about tests and training employees to spot adversarial emails. Some companies are more successful than the others. It is a complicated problem; otherwise we would have solved it already. But it's the same principle because phishers come up with different ways of tricking people. And some people will fail to detect them.
henrik_w 6y ago
This is one of the key points in Melanie Mitchell's book "Artificial Intelligence – A Guide for Thinking Humans". In the process of showing this, she gives really good explanations of how current AI/ML systems work. Really worth a read in my opinion. https://henrikwarne.com/2020/05/19/artificial-intelligence-a-guide-for-thinking-humans/ https://henrikwarne.com/2020/05/19/artificial-intelligence-a...
BaronSamedi 6y ago
I think of the current neural net based approaches as more "artificial instinct" than "artificial intelligence". The goal of the older, expert system AI paradigm was to create software that could reason about a problem, and it therefore produced systems that could answer the question "why?". ANN systems cannot answer that question. Both approaches seem useful to me, but for different problem domains.

deleted 6y ago

[deleted]

rjsw 6y ago

My background is in Standards for engineering data. I'm trying to push the idea that cleaning up a business' data before trying to apply ML to it may give better results.

mdorazio 6y ago
That really only solves part of the problem. Clean up your data. Clean up your processes. Then decide which processes can actually be improved in a meaningful way by ML. I've gone down the ML road twice now with companies that didn't do the second part and both times we had to kill the projects because the processes were so fuzzy and full of gotchas that no ML model could ever hope to be a net positive addition.