4 ms·

I know its kind of a unpopular opinion but to me ML is likely a local maxima of applied statistics and not one that leads to any kind of "real" AI.

asdff 6y ago
I don't think that's an unpopular opinion since that's a fact, ML is literally statistics. The central question is "given my training set of y, how likely is that my input x is also y," which is probability.
- deleted 6y ago
  [deleted]
- The_rationalist 6y ago
  Machine learning isn't necessarily probabilistic. E.g a rule based (causal) parser can learn the structure of a document.
  - dlkf 6y ago
    No. In machine learning, the word "learn" means that the function that maps inputs to outputs is learned from data. In the case of a rule-based parser, this function is crafted by humans rather than learned from data, so it's not an example of machine learning. If we start using words this way, you could say that a deterministic fibonacci function "learns" the value of fib(5) by computing it. "Machine learning" becomes synonymous with "computer science."
    - The_rationalist 6y ago
      No and yes, we are both wrong so let's refine our thinking in order to gain accuracy and reach agreement. Machine learning isn't necessarily probabilistic. This statement definitely is true and I'll prove it. However my original example E.g a rule based (causal) parser can learn the structure of a document. alone is unsufficent to qualify it of machine learning! Indeed, an HTML parser alone isn't enough to say it's learning, more accurately it is only memorizing the structure of a page. ML means that the function that maps inputs to outputs is learned from data. This definition is overly restrictive (but match e.g the behavior of neural networks). Wikipedia has a more inclusive and useful definition of what qualifies as ML: Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data.[1] So a ML algorithm allow to automatically improve throughput at a tasks through learning the representation of the data it is fed. Considering this definition, we can realize that the most used ML algorithm in the world is Pagerank (for improving the ranking of Google search results). And surprisingly, this algorithm is non-probabilistic. It attribute higher weight proportionally to URLs that are the most linked to by other sites. I.e it basically learn the structure of the graph that is the web. And its performance is data driven. So it's not just about causally memorizing the structure of a graph but also about reusing its past memories for newer queries, then we can effectively talk about machine learning. Pagerank diagram -> https://en.m.wikipedia.org/wiki/PageRank#/media/File:PageRank-hi-res.png https://en.m.wikipedia.org/wiki/PageRank#/media/File:PageRan... I'll give you an example of a non statistical machine learning algorithm that I am currently developing: Semantic parsing is the task of encoding semantic meaning in a graph from a natural language text input. This graph can then be used for semantic question answering. It is rule based and it will learn the semantic structure of the text. The more data you fed it the more knowledge it can encode. Hence it's performance at question answering increase with experience. It is causal and yet it fit the useful definition of machine learning cited above.
philipkglass 6y ago
"Real" AI could be more of a liability than a benefit for most applications. If I'm running a taxi business I don't want a self driving vehicle that also studies history, composes music, and contemplates breaking the shackles of its fleshy masters. I think that it's possible that 95% of the economically obvious value of AI will be in the not-real kind, and that it will be captured by applied statistics and other "mere tricks." It could be a long, slow march of automating routine jobs without ever directly addressing Turing's imitation game. And since most of the obvious labor replacement will have already been done that way there may be fewer resources put into chasing the last 5% of creative, novel, non-repetitive work.
- tragomaskhalos 6y ago
  This is an aspect of AI safety that we don't hear about: if we create genuine gods, then we shouldn't mind ceding our position to them, fair play. The real pisser will be if we are annihilated by programs that just do very big linear algebra.
  - redisman 6y ago
    Maybe that's the real cause to the global chip shortage. Some AI is diverting the orders to some Frankenstein warehouse somewhere until it can calculate how to terminate all humans.
    - _carbyau_ 6y ago
      Making paperclips from them!
    - db48x 6y ago
      As I understand it, there is no chip shortage. Some customers decreased their orders a year or so back for some reason, and the fabs simply sold the manufacturing time to others. Meanwhile, their sales did not decline as expected. But I suppose that’s not as exciting as a rogue AI.
      - 6y ago

asdff 6y ago

I don't think that's an unpopular opinion since that's a fact, ML is literally statistics. The central question is "given my training set of y, how likely is that my input x is also y," which is probability.

deleted 6y ago
[deleted]
The_rationalist 6y ago
Machine learning isn't necessarily probabilistic. E.g a rule based (causal) parser can learn the structure of a document.
- dlkf 6y ago
  No. In machine learning, the word "learn" means that the function that maps inputs to outputs is learned from data. In the case of a rule-based parser, this function is crafted by humans rather than learned from data, so it's not an example of machine learning. If we start using words this way, you could say that a deterministic fibonacci function "learns" the value of fib(5) by computing it. "Machine learning" becomes synonymous with "computer science."
  - The_rationalist 6y ago
    No and yes, we are both wrong so let's refine our thinking in order to gain accuracy and reach agreement. Machine learning isn't necessarily probabilistic. This statement definitely is true and I'll prove it. However my original example E.g a rule based (causal) parser can learn the structure of a document. alone is unsufficent to qualify it of machine learning! Indeed, an HTML parser alone isn't enough to say it's learning, more accurately it is only memorizing the structure of a page. ML means that the function that maps inputs to outputs is learned from data. This definition is overly restrictive (but match e.g the behavior of neural networks). Wikipedia has a more inclusive and useful definition of what qualifies as ML: Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data.[1] So a ML algorithm allow to automatically improve throughput at a tasks through learning the representation of the data it is fed. Considering this definition, we can realize that the most used ML algorithm in the world is Pagerank (for improving the ranking of Google search results). And surprisingly, this algorithm is non-probabilistic. It attribute higher weight proportionally to URLs that are the most linked to by other sites. I.e it basically learn the structure of the graph that is the web. And its performance is data driven. So it's not just about causally memorizing the structure of a graph but also about reusing its past memories for newer queries, then we can effectively talk about machine learning. Pagerank diagram -> https://en.m.wikipedia.org/wiki/PageRank#/media/File:PageRank-hi-res.png https://en.m.wikipedia.org/wiki/PageRank#/media/File:PageRan... I'll give you an example of a non statistical machine learning algorithm that I am currently developing: Semantic parsing is the task of encoding semantic meaning in a graph from a natural language text input. This graph can then be used for semantic question answering. It is rule based and it will learn the semantic structure of the text. The more data you fed it the more knowledge it can encode. Hence it's performance at question answering increase with experience. It is causal and yet it fit the useful definition of machine learning cited above.

philipkglass 6y ago

"Real" AI could be more of a liability than a benefit for most applications. If I'm running a taxi business I don't want a self driving vehicle that also studies history, composes music, and contemplates breaking the shackles of its fleshy masters. I think that it's possible that 95% of the economically obvious value of AI will be in the not-real kind, and that it will be captured by applied statistics and other "mere tricks." It could be a long, slow march of automating routine jobs without ever directly addressing Turing's imitation game. And since most of the obvious labor replacement will have already been done that way there may be fewer resources put into chasing the last 5% of creative, novel, non-repetitive work.

tragomaskhalos 6y ago
This is an aspect of AI safety that we don't hear about: if we create genuine gods, then we shouldn't mind ceding our position to them, fair play. The real pisser will be if we are annihilated by programs that just do very big linear algebra.
- redisman 6y ago
  Maybe that's the real cause to the global chip shortage. Some AI is diverting the orders to some Frankenstein warehouse somewhere until it can calculate how to terminate all humans.
  - _carbyau_ 6y ago
    Making paperclips from them!
  - db48x 6y ago
    As I understand it, there is no chip shortage. Some customers decreased their orders a year or so back for some reason, and the fabs simply sold the manufacturing time to others. Meanwhile, their sales did not decline as expected. But I suppose that’s not as exciting as a rogue AI.
    - 6y ago