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Not exactly on topic but after skimming the article and then the comments, there seems to be — albeit a very understandable — misunderstanding of what is being

Not exactly on topic but after skimming the article and then the comments, there seems to be — albeit a very understandable — misunderstanding of what is being done in the field and how the public views and uses terminology. People hear or read the words Machine Learning and assign a HAL like technology to it. It’s definitely hard to explain an idea like SVM, it’s applications, and how it works/what it does without a background in some linear algebra. And then to broach more complicated topics like temporal real time neural networks in computer vision for something like anomaly detection. From my conversations with people because it’s something that isn’t understood there is almost a cognitive dissonance. One of which expects so much now, but at the same time denies the future application space when the conversation becomes more personal. “Why aren’t self driving cars a thing yet...” and then “A machine couldn’t do what I do.” It’s worrying that there will definitely be regulation coming to the field, where the people writing the laws and the people the writers represent have a hard time understanding what they are trying to regulate. Add in the fact that the tech itself is somewhat cheap and will only get less expensive. You only have to look as far as the Deep Fakes fiasco on Reddit. The guy creating fakes of celebrities being superimposed on porn is a self proclaimed amateur and was able to accomplish a quite a bit at home with Keras. Edit: I do want to mention that a lot of the papers I’ve read recently on real time anomaly detection rely on automated labeling and not having human labelers. It’s only important to have human readable labels if a human has to interpret directly.

andreyk 7y ago
Since it's related, a little self-plug here : I've started and have been running the project Skynet Today (https://www.skynettoday.com/ https://www.skynettoday.com/) to try and make present day AI more understandable for non AI researchers. Always could use more help! https://www.skynettoday.com/contribute https://www.skynettoday.com/contribute
- _fullpint 7y ago
  What type of help are you looking for?
sgt101 7y ago
Which papers are those?
- _fullpint 7y ago
  I’ll look it up later but if you look at any of the recent computer vision conferences and search for things like anomaly detection with either or attention or lstm networks you should be able to find stuff.
  - sgt101 7y ago
    But those papers (the ones I'm familiar with) are human labelled - the challenge with anomaly detection is winnowing out the events that humans believe are really anomalies from the coincidences and permutations in the data which normal statistics sees as very unusual.
    - _fullpint 7y ago
      Maybe I'm mistaken but from what I remember I had read a couple that were not human labeled. Let me take a gander later today.
      - sgt101 7y ago
        Thank you - I'm really interested as anomaly detection is such a pain!
pjmorris 7y ago
> It’s only important to have human readable labels if a human has to interpret directly I admit it's a failure of imagination on my part, but in what context would a human never need to interpret the result? It seems to me that for anything with human consequences, there's going to still be a human in the loop somewhere.
- _fullpint 7y ago
  In the example I gave about anomaly detection, what’s being detected as normal or an anomaly doesn’t need to be labeled by a human. This just comes down to probability of temporal data. This temporal data can come from segmentation which again doesn’t need human readable labeling and can be produced from an automated process. You can automate such a system to alert someone if something is an anomaly. Edit: Also want to add there are more traditional avenues of which non-labeled data can be used. Unsupervised Learning where you read in arbitrary data and cluster for some sort of probability based system. Another good thing to look at is a system with “some” labeling like Semi-supervised Learning. Any situation where you can do binary classification doesn’t need any true labeling as long as you can evaluate attributes of some object, complex or not.
jointpdf 7y ago
>People hear or read the words Machine Learning and assign a HAL like technology to it. It’s definitely hard to explain and idea like SVM, it’s applications, and how it works/what it does without a background in some linear algebra. I agree with your first point—there is a general lack of understanding about ML/AI, even among knowledgeable laypeople. But on your second point, I think this illustrates the tendency for technical folks (e.g. ML engineers) to overemphasize the importance of specific algorithms (e.g. SVMs, CNNs) and implementations or frameworks (e.g. TensorFlow). These are the things that are important to us, so we try to convey that when connecting with others. Even when I intentionally intending to simplify things to share enthusiasm and understanding with a non-technical audience, it is easy to slip into unintentionally alienating statements like, “machine learning is just matrix multiplication and gradient descent done on a GPU”. It might be a subconscious way of justifying our hard-won knowledge and its value. But, I think it’s possible to have demystifying conversations that help people build a genuine understanding of ML/AI. And it’s also possible to do so in a way that instills a sense of fascination and respect for the field and the work, skill, and resources it involves to do well. The themes of probability, statistics, and linear algebra can be honored and elucidated by discussing their core relevance: Probability—What does a statement like “there is a 70% probability that this image is of a Fuji apple” mean? How does that differ from a statement about an event in the future like “there is a 70% probability that it will rain in London tomorrow?”. How do those probabilities change depending on factors that we can measure (conditional probability—the heart of statistics and ML)? What is an expected value and how does it relate to the ideas of risk and optimal decisions? Statistics—What is a statistical model and what does it mean to “build” one? How much data do we need to collect for this building process, and in what format and subject to what assumptions and methodology? What are the inputs and outputs of a model that are relevant to my problem? What is “ground truth”, and how do we get enough examples of it with enough confidence? How do I know my model will actually work in the real world (generalization), and how bad is it if the model is wrong (will my users suffer an injustice or die, or just eat the wrong flavor of apple)? What are sampling bias and statistical bias, and how do they relate (or not) to bias in AI systems? What is a distribution? An anomaly? What are clusters, and how do we define whether two things are similar or not? Linear algebra—How do we store “unstructured” data like an image or document so that a machine can work with it? How can we use math and computers to (efficiently) transform the data from input to output? What does it mean for a machine to “learn”? What is a tensor and why is it flowing? Wait, you want how much money to spend on graphics cards? I get variants of the above (non-trivial) questions from interested but largely uninitiated stakeholders in government and business quite often. Approaching these conversations in a “big picture” way that respects peoples’ intelligence and curiosity is tremendously more rewarding and productive than getting down into eyeglaze-inducing technical/architectural rabbit holes.
jinfiesto 7y ago
> It’s definitely hard to explain an idea like SVM, it’s applications, and how it works/what it does without a background in some linear algebra. I don't actually think this is the case. The basic idea is that you can represent data as points in n-dimensional space and draw decision boundaries in that space. I think most people should at least be able to understand this geometrically for n=2/3 and then accept that it possibly extrapolates to n > 3.
- bigchewy 7y ago
  huh?
- bashinator 7y ago
  What is a "decision boundary?" What does it mean to represent data as a point in space? Why would the number of dimensions of space matter? How does any of that relate to AI or machine learning?
  - jammygit 7y ago
    Those details matter a lot, you're right. However, pictures help a lot imho, and I bet my parents could get the jist of this: https://qph.fs.quoracdn.net/main-qimg-968b9df8608f76d41586a0c257381821 https://qph.fs.quoracdn.net/main-qimg-968b9df8608f76d41586a0...
    - bashinator 7y ago
      Is that teaching a computer to distinguish between males and females based on height and weight?
      - jinfiesto 7y ago
        More or less, yes.
- jdietrich 7y ago
  >most people should at least be able to understand this geometrically Most people can't reliably read a bus timetable, calculate a 10% tip, or multiply 537 by 12. A concept like SVM is absolute voodoo to the overwhelming majority of the population and always will be, no matter how you try and explain it. https://nces.ed.gov/naal/sample.asp https://nces.ed.gov/naal/sample.asp

andreyk 7y ago

Since it's related, a little self-plug here : I've started and have been running the project Skynet Today (https://www.skynettoday.com/ https://www.skynettoday.com/) to try and make present day AI more understandable for non AI researchers. Always could use more help! https://www.skynettoday.com/contribute https://www.skynettoday.com/contribute

_fullpint 7y ago
What type of help are you looking for?

sgt101 7y ago

Which papers are those?

_fullpint 7y ago
I’ll look it up later but if you look at any of the recent computer vision conferences and search for things like anomaly detection with either or attention or lstm networks you should be able to find stuff.
- sgt101 7y ago
  But those papers (the ones I'm familiar with) are human labelled - the challenge with anomaly detection is winnowing out the events that humans believe are really anomalies from the coincidences and permutations in the data which normal statistics sees as very unusual.
  - _fullpint 7y ago
    Maybe I'm mistaken but from what I remember I had read a couple that were not human labeled. Let me take a gander later today.
    - sgt101 7y ago
      Thank you - I'm really interested as anomaly detection is such a pain!

pjmorris 7y ago

> It’s only important to have human readable labels if a human has to interpret directly I admit it's a failure of imagination on my part, but in what context would a human never need to interpret the result? It seems to me that for anything with human consequences, there's going to still be a human in the loop somewhere.

_fullpint 7y ago
In the example I gave about anomaly detection, what’s being detected as normal or an anomaly doesn’t need to be labeled by a human. This just comes down to probability of temporal data. This temporal data can come from segmentation which again doesn’t need human readable labeling and can be produced from an automated process. You can automate such a system to alert someone if something is an anomaly. Edit: Also want to add there are more traditional avenues of which non-labeled data can be used. Unsupervised Learning where you read in arbitrary data and cluster for some sort of probability based system. Another good thing to look at is a system with “some” labeling like Semi-supervised Learning. Any situation where you can do binary classification doesn’t need any true labeling as long as you can evaluate attributes of some object, complex or not.

jointpdf 7y ago

>People hear or read the words Machine Learning and assign a HAL like technology to it. It’s definitely hard to explain and idea like SVM, it’s applications, and how it works/what it does without a background in some linear algebra. I agree with your first point—there is a general lack of understanding about ML/AI, even among knowledgeable laypeople. But on your second point, I think this illustrates the tendency for technical folks (e.g. ML engineers) to overemphasize the importance of specific algorithms (e.g. SVMs, CNNs) and implementations or frameworks (e.g. TensorFlow). These are the things that are important to us, so we try to convey that when connecting with others. Even when I intentionally intending to simplify things to share enthusiasm and understanding with a non-technical audience, it is easy to slip into unintentionally alienating statements like, “machine learning is just matrix multiplication and gradient descent done on a GPU”. It might be a subconscious way of justifying our hard-won knowledge and its value. But, I think it’s possible to have demystifying conversations that help people build a genuine understanding of ML/AI. And it’s also possible to do so in a way that instills a sense of fascination and respect for the field and the work, skill, and resources it involves to do well. The themes of probability, statistics, and linear algebra can be honored and elucidated by discussing their core relevance: Probability—What does a statement like “there is a 70% probability that this image is of a Fuji apple” mean? How does that differ from a statement about an event in the future like “there is a 70% probability that it will rain in London tomorrow?”. How do those probabilities change depending on factors that we can measure (conditional probability—the heart of statistics and ML)? What is an expected value and how does it relate to the ideas of risk and optimal decisions? Statistics—What is a statistical model and what does it mean to “build” one? How much data do we need to collect for this building process, and in what format and subject to what assumptions and methodology? What are the inputs and outputs of a model that are relevant to my problem? What is “ground truth”, and how do we get enough examples of it with enough confidence? How do I know my model will actually work in the real world (generalization), and how bad is it if the model is wrong (will my users suffer an injustice or die, or just eat the wrong flavor of apple)? What are sampling bias and statistical bias, and how do they relate (or not) to bias in AI systems? What is a distribution? An anomaly? What are clusters, and how do we define whether two things are similar or not? Linear algebra—How do we store “unstructured” data like an image or document so that a machine can work with it? How can we use math and computers to (efficiently) transform the data from input to output? What does it mean for a machine to “learn”? What is a tensor and why is it flowing? Wait, you want how much money to spend on graphics cards? I get variants of the above (non-trivial) questions from interested but largely uninitiated stakeholders in government and business quite often. Approaching these conversations in a “big picture” way that respects peoples’ intelligence and curiosity is tremendously more rewarding and productive than getting down into eyeglaze-inducing technical/architectural rabbit holes.

jinfiesto 7y ago

> It’s definitely hard to explain an idea like SVM, it’s applications, and how it works/what it does without a background in some linear algebra. I don't actually think this is the case. The basic idea is that you can represent data as points in n-dimensional space and draw decision boundaries in that space. I think most people should at least be able to understand this geometrically for n=2/3 and then accept that it possibly extrapolates to n > 3.

bigchewy 7y ago
huh?
bashinator 7y ago
What is a "decision boundary?" What does it mean to represent data as a point in space? Why would the number of dimensions of space matter? How does any of that relate to AI or machine learning?
- jammygit 7y ago
  Those details matter a lot, you're right. However, pictures help a lot imho, and I bet my parents could get the jist of this: https://qph.fs.quoracdn.net/main-qimg-968b9df8608f76d41586a0c257381821 https://qph.fs.quoracdn.net/main-qimg-968b9df8608f76d41586a0...
  - bashinator 7y ago
    Is that teaching a computer to distinguish between males and females based on height and weight?
    - jinfiesto 7y ago
      More or less, yes.
jdietrich 7y ago
>most people should at least be able to understand this geometrically Most people can't reliably read a bus timetable, calculate a 10% tip, or multiply 537 by 12. A concept like SVM is absolute voodoo to the overwhelming majority of the population and always will be, no matter how you try and explain it. https://nces.ed.gov/naal/sample.asp https://nces.ed.gov/naal/sample.asp