3 ms·

Computer vision totally qualifies as AI as it can grant an agent artificially intelligent behavior.

sleepybrett 2y ago
Based on what is said in the article, it seems like a VERY simple algorithm. It clusters the pixels in the image by color and reports any small blobs of unusual color. That's not AI by any of the stupid definitions we've come up with recently.
- morkalork 2y ago
  Clustering and outlier detection is not AI?
  - short_sells_poo 2y ago
    I mean, if something as traditional as simple clustering is AI, then so is linear regression and Excel Sheets have been doing AI/ML for the past 2 decades. At some point we just have to stop with the breathless hype. I'm sure labelling it as AI gets more clicks and exposure so I know exactly why they do it. Still, it's annoying.
    - wizzwizz4 2y ago
      You're only saying this because we're in a hype cycle. Circa 2018, there was no problem at all with calling this AI: in fact, it was normal.
      - kjkjadksj 2y ago
        Back then we still called things image classifiers or machine learning, and when you said AI most people probably had an image of Arnold Schwarzenegger or Cortana flash in their mind.
      - sleepybrett 2y ago
        it was not.
    - morkalork 2y ago
      Yes! AI is any sort of machine intelligence and its been around for more than 2 decades, the 80s even had its own "AI winter" after all.
      - Gud 2y ago
        There is no intelligence here, only pattern matching.
        kvakerok 2y ago
        The same could be said about many people.
    - YeGoblynQueenne 2y ago
      At least until recently any introductory machine learning course would teach linear regression and clustering, the latter as an example of unsupervised learning.
      - sleepybrett 2y ago
        Sure, but as a stepping stone. There is no model here, there is no neural net.
  - LorenPechtel 2y ago
    To me the fundamental difference is that AI is trained, algorithms are not. There's not training here, it's a simple frequency count looking for outliers. While it's an approach a human would take the human is doing it in a very different fashion. And the human is much more sensitive to form, this is much more sensitive to color. They are definitely right that our (I am a hiker) gear tends to stand out against nature. Not only is it generally in colors that do not appear in any volume in nature, but almost nothing in the plant and mineral kingdoms is of uniform color. A blob of uniform color is in all probability either a monochromatic animal (the sheep their system detects) or man made. What surprises me about this is that it hasn't been tried before.
    - KolmogorovComp 2y ago
      You are confusing AI and Machine Learning, the latter being a subset of the former.
      - kxrm 2y ago
        This really gets at one of my issues with the term "AI". There is a very scientific, textbook definition of what Artificial Intelligence is however, this term carries baggage from sci-fi. Using a term like "AI" to describe this is like using a term "Food" to describe pickles. Poor analogy but "AI" is just so vast that most lay readers or those not familiar with this phrase in regular computer science discussions aren't grounded in the consequence. I feel that we as an industry need to do better and use terms more responsibly and know our audience. There is a big difference between a clustering algorithm that detects pixels and flags them and a conscious, self-aware system. However both of those things are "AI" and both have very different consequences.
    - YeGoblynQueenne 2y ago
      This is the list of discussion topics from the Dartmouth Workshop on Artificial Intelligence (1955) where the term was first introduced: The following are some aspects of the artificial intelligence problem: 1 Automatic Computers If a machine can do a job, then an automatic calculator can be programmed to simulate the machine. The speeds and memory capacities of present computers may be insufficient to simulate many of the higher functions of the human brain, but the major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have. 2. How Can a Computer be Programmed to Use a Language It may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture. From this point of view, forming a generalization consists of admitting a new word and some rules whereby sentences containing it imply and are implied by others. This idea has never been very precisely formulated nor have examples been worked out. 3. Neuron Nets How can a set of (hypothetical) neurons be arranged so as to form concepts. Considerable theoretical and experimental work has been done on this problem by Uttley, Rashevsky and his group, Farley and Clark, Pitts and McCulloch, Minsky, Rochester and Holland, and others. Partial results have been obtained but the problem needs more theoretical work. 4. Theory of the Size of a Calculation If we are given a well-defined problem (one for which it is possible to test mechanically whether or not a proposed answer is a valid answer) one way of solving it is to try all possible answers in order. This method is inefficient, and to exclude it one must have some criterion for efficiency of calculation. Some consideration will show that to get a measure of the efficiency of a calculation it is necessary to have on hand a method of measuring the complexity of calculating devices which in turn can be done if one has a theory of the complexity of functions. Some partial results on this problem have been obtained by Shannon, and also by McCarthy. 5. Self-lmprovement Probably a truly intelligent machine will carry out activities which may best be described as self-improvement. Some schemes for doing this have been proposed and are worth further study. It seems likely that this question can be studied abstractly as well. 6. Abstractions A number of types of ``abstraction'' can be distinctly defined and several others less distinctly. A direct attempt to classify these and to describe machine methods of forming abstractions from sensory and other data would seem worthwhile. 7. Randomness and Creativity A fairly attractive and yet clearly incomplete conjecture is that the difference between creative thinking and unimaginative competent thinking lies in the injection of a some randomness. The randomness must be guided by intuition to be efficient. In other words, the educated guess or the hunch include controlled randomness in otherwise orderly thinking. From: https://web.archive.org/web/20070826230310/http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html https://web.archive.org/web/20070826230310/http://www-formal... So, no, the fundamental difference is not that "AI is trained, algorithms are not". Some hand-crafted algorithms fall under the purview of AI research. A modern example is graph-search algorithms like MCTS or A*.
  - tpxl 2y ago
    Novel stuff is AI, old stuff is statistics. Decision trees used to be called AI :)
KaiserPro 2y ago
The fuck it does. for it to be AI, it needs some sort of ML basis. otherwise its just fancy "classical" computer vision. (this is from someone who's been working in the field for far too long, and remembers a time before "deep", "ML" and "ai" were part of every paper. )

Based on what is said in the article, it seems like a VERY simple algorithm. It clusters the pixels in the image by color and reports any small blobs of unusual color. That's not AI by any of the stupid definitions we've come up with recently.

morkalork 2y ago
Clustering and outlier detection is not AI?
- short_sells_poo 2y ago
  I mean, if something as traditional as simple clustering is AI, then so is linear regression and Excel Sheets have been doing AI/ML for the past 2 decades. At some point we just have to stop with the breathless hype. I'm sure labelling it as AI gets more clicks and exposure so I know exactly why they do it. Still, it's annoying.
  - wizzwizz4 2y ago
    You're only saying this because we're in a hype cycle. Circa 2018, there was no problem at all with calling this AI: in fact, it was normal.
    - kjkjadksj 2y ago
      Back then we still called things image classifiers or machine learning, and when you said AI most people probably had an image of Arnold Schwarzenegger or Cortana flash in their mind.
    - sleepybrett 2y ago
      it was not.
  - morkalork 2y ago
    Yes! AI is any sort of machine intelligence and its been around for more than 2 decades, the 80s even had its own "AI winter" after all.
    - Gud 2y ago
      There is no intelligence here, only pattern matching.
      - kvakerok 2y ago
        The same could be said about many people.
  - YeGoblynQueenne 2y ago
    At least until recently any introductory machine learning course would teach linear regression and clustering, the latter as an example of unsupervised learning.
    - sleepybrett 2y ago
      Sure, but as a stepping stone. There is no model here, there is no neural net.
- LorenPechtel 2y ago
  To me the fundamental difference is that AI is trained, algorithms are not. There's not training here, it's a simple frequency count looking for outliers. While it's an approach a human would take the human is doing it in a very different fashion. And the human is much more sensitive to form, this is much more sensitive to color. They are definitely right that our (I am a hiker) gear tends to stand out against nature. Not only is it generally in colors that do not appear in any volume in nature, but almost nothing in the plant and mineral kingdoms is of uniform color. A blob of uniform color is in all probability either a monochromatic animal (the sheep their system detects) or man made. What surprises me about this is that it hasn't been tried before.
  - KolmogorovComp 2y ago
    You are confusing AI and Machine Learning, the latter being a subset of the former.
    - kxrm 2y ago
      This really gets at one of my issues with the term "AI". There is a very scientific, textbook definition of what Artificial Intelligence is however, this term carries baggage from sci-fi. Using a term like "AI" to describe this is like using a term "Food" to describe pickles. Poor analogy but "AI" is just so vast that most lay readers or those not familiar with this phrase in regular computer science discussions aren't grounded in the consequence. I feel that we as an industry need to do better and use terms more responsibly and know our audience. There is a big difference between a clustering algorithm that detects pixels and flags them and a conscious, self-aware system. However both of those things are "AI" and both have very different consequences.
  - YeGoblynQueenne 2y ago
    This is the list of discussion topics from the Dartmouth Workshop on Artificial Intelligence (1955) where the term was first introduced: The following are some aspects of the artificial intelligence problem: 1 Automatic Computers If a machine can do a job, then an automatic calculator can be programmed to simulate the machine. The speeds and memory capacities of present computers may be insufficient to simulate many of the higher functions of the human brain, but the major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have. 2. How Can a Computer be Programmed to Use a Language It may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture. From this point of view, forming a generalization consists of admitting a new word and some rules whereby sentences containing it imply and are implied by others. This idea has never been very precisely formulated nor have examples been worked out. 3. Neuron Nets How can a set of (hypothetical) neurons be arranged so as to form concepts. Considerable theoretical and experimental work has been done on this problem by Uttley, Rashevsky and his group, Farley and Clark, Pitts and McCulloch, Minsky, Rochester and Holland, and others. Partial results have been obtained but the problem needs more theoretical work. 4. Theory of the Size of a Calculation If we are given a well-defined problem (one for which it is possible to test mechanically whether or not a proposed answer is a valid answer) one way of solving it is to try all possible answers in order. This method is inefficient, and to exclude it one must have some criterion for efficiency of calculation. Some consideration will show that to get a measure of the efficiency of a calculation it is necessary to have on hand a method of measuring the complexity of calculating devices which in turn can be done if one has a theory of the complexity of functions. Some partial results on this problem have been obtained by Shannon, and also by McCarthy. 5. Self-lmprovement Probably a truly intelligent machine will carry out activities which may best be described as self-improvement. Some schemes for doing this have been proposed and are worth further study. It seems likely that this question can be studied abstractly as well. 6. Abstractions A number of types of ``abstraction'' can be distinctly defined and several others less distinctly. A direct attempt to classify these and to describe machine methods of forming abstractions from sensory and other data would seem worthwhile. 7. Randomness and Creativity A fairly attractive and yet clearly incomplete conjecture is that the difference between creative thinking and unimaginative competent thinking lies in the injection of a some randomness. The randomness must be guided by intuition to be efficient. In other words, the educated guess or the hunch include controlled randomness in otherwise orderly thinking. From: https://web.archive.org/web/20070826230310/http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html https://web.archive.org/web/20070826230310/http://www-formal... So, no, the fundamental difference is not that "AI is trained, algorithms are not". Some hand-crafted algorithms fall under the purview of AI research. A modern example is graph-search algorithms like MCTS or A*.
- tpxl 2y ago
  Novel stuff is AI, old stuff is statistics. Decision trees used to be called AI :)

KaiserPro 2y ago

The fuck it does. for it to be AI, it needs some sort of ML basis. otherwise its just fancy "classical" computer vision. (this is from someone who's been working in the field for far too long, and remembers a time before "deep", "ML" and "ai" were part of every paper. )