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I'm one of these persons with no formal training in any scientific fields. I don't think any of what you assume people like me think in your stereotype. I know

I'm one of these persons with no formal training in any scientific fields. I don't think any of what you assume people like me think in your stereotype. I know it's not magic, it's brute force problem solving. Once you are capable of training systems like this, you get to solutions which are really powerful. All you see is numbers and functions, and all I see is problems being solved by these AI/ML methods and often in quantifiably better ways than intelligent humans can. NN-s are the building blocks. Neurons in your brain are dumb as well, it's the quality of the network that matters. So instead of moving goalposts, what do you think is required to implement intelligence?

mjburgess 5y ago
Well, I'm not making a sterotype -- I'm offering an explanation of the people i've met. It's very hard to converse with people whose academic background is discrete mathematics, in an essentially empirical domain (that of modelling intelligence). There's a lot which is odd (and dubious) in AI/ML that traces is origins to this peculilar situation of a discipline (csci) in the "early phases" of modelling an empirical phenomenon, without yet, an intra-disciplinary theoretical support for it -- csci people take geometry and classical physics to make video games. They dont yet take the equivalent to make intelligent systems (which would include stuff on learning in animals, humans; and in my view, more applied math). In any case, to answer your question about implementation, see https://news.ycombinator.com/threads?id=mjburgess#30579711 https://news.ycombinator.com/threads?id=mjburgess#30579711
AlotOfReading 5y ago
Just because you can brute force things doesn't mean it's a practical way to solve certain problems. The set of all C++ programs humans will ever write is finite and therefore parseable with a regular language, but no one's out there writing C++ compilers that way for obvious reasons. That's the essence of what I think GP is getting at. I don't know if NNs are sufficiently powerful escape that argument because my formal understanding of "the world" simply isn't good enough, but it's not obvious to me that they are.
- tiborsaas 5y ago
  I've seen a case when someone implemented an algorithm after an existing AI was created. I can't remember unfortunately, but the hand made version was better so it's certainly a possibility. NN-s automate human problem solving as it was demonstrated recently by DeepMind's AlphaCode. It can be quite practical though as we failed to come up with solutions that were previously out of reach before deep neural networks (and massive increases in hardware capabilities).
  - YeGoblynQueenne 5y ago
    AlphaCode works by first generating many millions of programs from a large language model trained on all of github and fine-tuned on a dataset of problems like the ones it's supposed to be solving. Then, those millions of problems are reduced to several thousand, by "filtering" on input/output examples of the target problem. Finally, these many thousands of examples are further reduced by hand-crafted heuristics, clustering and ranking, that finally select k prorgams, 10 in the paper introducing AlphaCode. This is not a practical approach. It's a maddenlingly resource-intensive approach that spams programs at an insane rate and hopes to get lucky and catch a few that solve a programming task. In the evaluations listed in the AlphaCode paper, it performs awfully against the test set of its training dataset and a pre-existing benchmark. DeepMind have drummed up their systems ability to do about as well as the average participant in a code contest, but that's no measure of coding ability. Deep neural nets are certainly very far from automating human problem solving, like you say in your comment. Your comment is exactly the kind of overhyped misunderstanding of the state-of-the-art that the OP is warning against.
    - tiborsaas 5y ago
      Thanks for the deeper explanation. Even though it's not practical, it's an approach that previously wasn't possible so at least some cheering for it is valid. Problem solving is getting automated, but to what extent is debatable, we are indeed in the early stages. I haven't suggested that it's already AGI.
      - 5y ago
netizen-936824 5y ago
There is far more computational power in a single neuron than any NN that I've heard of. One of the single approximated "neurons" in a AI/ML NN doesn't even operate anywhere close to a real neuron. Real neurons are oscillators which exhibit nonlinear dynamic behaviors
beaconstudios 5y ago
NNs are clever, but what they do is essentially reverse programming. Rather than you write a function that translates a -> b, you give it a -> b mappings and the training writes the function. What GP was saying is something that most programmers should already know: that writing the function is usually the easy bit, and the hard bit is defining and then modelling the problem; NNs can't do that.
YeGoblynQueenne 5y ago
[NOT THE OP] >> All you see is numbers and functions, and all I see is problems being solved by these AI/ML methods and often in quantifiably better ways than intelligent humans can. Maybe in playing chess and go, but in any other task that neural networks have been compared to humans directly, the ability to outperform humans is more informative of the limitations of the measures of performance, than of the capabilities of the tested systems (typically, deep neural networks). Take large language models (LLMs) for example. Last time I looked -it was a while ago but- LLMs like BERT, ERNIE and their famous friends scored very highly and even above humans in natural language understanding tasks, such as those in the GLUE and SUPERGLUE benchmarks. You'd think that a system that scores 85% in a language understanding task has developed at least _some_ ability to understand language. And yet this is not the case. LLMs, despite their ability to score highly in formal benchmarks, remain dumb as bricks and as incapable of understanding human language as a rock. We know this because every once in a while someone in the field of NLP looks away from their favourite leaderboard for long enough to ask serious questions about the performance of systems, as for example [1] who started by asking why a language model like BERT would be any good at argument comprehension (BERT scored only three points below humans in a relevant benchmark task). Everytime anyone undertakes this kind of analysis of the abiltiy of LLMs to understand language, the conclusion is that they don't, and instead they learn "cheap tricks" and overfit to statistical regularities of the benchmark datasets. Similar analyses have found similar results in machine vision (e.g. [2], [3]) etc. The question then is, in what sense, exactly, is an automated system solving problems "in quantifiably better ways than intelligent humans can", when that system is not even solving the problem it's supposed to be solving, like the problem of understanding language by LLMs? The idea that those systems "solve problems", when they 're really only overfitting to their training sets, is an artifact of the shoddy scholarship that is typical in deep learning research, and the OP is right to be concerned that there is little understanding between machine learning researchers of what, exactly, it is that their systems are doing- or even trying to do. And, as the OP points out, I'm afraid that really is the result of a poor background, not only in sciences and mathematics, but in AI and even machine learning itself, among the current generation of machine learning researchers. Contrary to what the OP says, though, this paucity of relevant background is the result of an influx to the field of people from the sciences, who have a background in continuous mathematics, but not in AI or computer science. It is, indeed, a background in computer science that can best guard against misconceptions about computers being wonder machines with magickal intelligence abilities. At the very least, you don't have to explain to a computer scientist why solving one instance of a class of problems does not mean a general solution for the entire class. That comes built-in to most who have graduated in the discipline. But is it built-in to phycisists, or biologists, who study very different systems? ________________ [1] Probing Neural Network Comprehension of Natural Language Arguments https://aclanthology.org/P19-1459/ https://aclanthology.org/P19-1459/ [2] Intriguing properties of neural networks https://research.google/pubs/pub42503/ https://research.google/pubs/pub42503/ [3] The Elephant in the Room https://arxiv.org/abs/1808.03305 https://arxiv.org/abs/1808.03305
sifar 5y ago
I think what the OP is implying that the intelligence we find in nature is not isolated computation but is physically situated - it can sense and manipulate it's environment. The current DNN based approach do not do that. Sure you can create an autonomous vehicle that uses NN to help navigate - but it does not update it's inference if it is incorrect. That immediate feedback doesn't exist or rather it is not accessible to the system itself. One has to collect the data, retrain the network and deploy it again to close the loop.

mjburgess 5y ago

Well, I'm not making a sterotype -- I'm offering an explanation of the people i've met. It's very hard to converse with people whose academic background is discrete mathematics, in an essentially empirical domain (that of modelling intelligence). There's a lot which is odd (and dubious) in AI/ML that traces is origins to this peculilar situation of a discipline (csci) in the "early phases" of modelling an empirical phenomenon, without yet, an intra-disciplinary theoretical support for it -- csci people take geometry and classical physics to make video games. They dont yet take the equivalent to make intelligent systems (which would include stuff on learning in animals, humans; and in my view, more applied math). In any case, to answer your question about implementation, see https://news.ycombinator.com/threads?id=mjburgess#30579711 https://news.ycombinator.com/threads?id=mjburgess#30579711

AlotOfReading 5y ago

Just because you can brute force things doesn't mean it's a practical way to solve certain problems. The set of all C++ programs humans will ever write is finite and therefore parseable with a regular language, but no one's out there writing C++ compilers that way for obvious reasons. That's the essence of what I think GP is getting at. I don't know if NNs are sufficiently powerful escape that argument because my formal understanding of "the world" simply isn't good enough, but it's not obvious to me that they are.

tiborsaas 5y ago
I've seen a case when someone implemented an algorithm after an existing AI was created. I can't remember unfortunately, but the hand made version was better so it's certainly a possibility. NN-s automate human problem solving as it was demonstrated recently by DeepMind's AlphaCode. It can be quite practical though as we failed to come up with solutions that were previously out of reach before deep neural networks (and massive increases in hardware capabilities).
- YeGoblynQueenne 5y ago
  AlphaCode works by first generating many millions of programs from a large language model trained on all of github and fine-tuned on a dataset of problems like the ones it's supposed to be solving. Then, those millions of problems are reduced to several thousand, by "filtering" on input/output examples of the target problem. Finally, these many thousands of examples are further reduced by hand-crafted heuristics, clustering and ranking, that finally select k prorgams, 10 in the paper introducing AlphaCode. This is not a practical approach. It's a maddenlingly resource-intensive approach that spams programs at an insane rate and hopes to get lucky and catch a few that solve a programming task. In the evaluations listed in the AlphaCode paper, it performs awfully against the test set of its training dataset and a pre-existing benchmark. DeepMind have drummed up their systems ability to do about as well as the average participant in a code contest, but that's no measure of coding ability. Deep neural nets are certainly very far from automating human problem solving, like you say in your comment. Your comment is exactly the kind of overhyped misunderstanding of the state-of-the-art that the OP is warning against.
  - tiborsaas 5y ago
    Thanks for the deeper explanation. Even though it's not practical, it's an approach that previously wasn't possible so at least some cheering for it is valid. Problem solving is getting automated, but to what extent is debatable, we are indeed in the early stages. I haven't suggested that it's already AGI.
    - 5y ago

netizen-936824 5y ago

There is far more computational power in a single neuron than any NN that I've heard of. One of the single approximated "neurons" in a AI/ML NN doesn't even operate anywhere close to a real neuron. Real neurons are oscillators which exhibit nonlinear dynamic behaviors

beaconstudios 5y ago

NNs are clever, but what they do is essentially reverse programming. Rather than you write a function that translates a -> b, you give it a -> b mappings and the training writes the function. What GP was saying is something that most programmers should already know: that writing the function is usually the easy bit, and the hard bit is defining and then modelling the problem; NNs can't do that.

YeGoblynQueenne 5y ago

[NOT THE OP] >> All you see is numbers and functions, and all I see is problems being solved by these AI/ML methods and often in quantifiably better ways than intelligent humans can. Maybe in playing chess and go, but in any other task that neural networks have been compared to humans directly, the ability to outperform humans is more informative of the limitations of the measures of performance, than of the capabilities of the tested systems (typically, deep neural networks). Take large language models (LLMs) for example. Last time I looked -it was a while ago but- LLMs like BERT, ERNIE and their famous friends scored very highly and even above humans in natural language understanding tasks, such as those in the GLUE and SUPERGLUE benchmarks. You'd think that a system that scores 85% in a language understanding task has developed at least _some_ ability to understand language. And yet this is not the case. LLMs, despite their ability to score highly in formal benchmarks, remain dumb as bricks and as incapable of understanding human language as a rock. We know this because every once in a while someone in the field of NLP looks away from their favourite leaderboard for long enough to ask serious questions about the performance of systems, as for example [1] who started by asking why a language model like BERT would be any good at argument comprehension (BERT scored only three points below humans in a relevant benchmark task). Everytime anyone undertakes this kind of analysis of the abiltiy of LLMs to understand language, the conclusion is that they don't, and instead they learn "cheap tricks" and overfit to statistical regularities of the benchmark datasets. Similar analyses have found similar results in machine vision (e.g. [2], [3]) etc. The question then is, in what sense, exactly, is an automated system solving problems "in quantifiably better ways than intelligent humans can", when that system is not even solving the problem it's supposed to be solving, like the problem of understanding language by LLMs? The idea that those systems "solve problems", when they 're really only overfitting to their training sets, is an artifact of the shoddy scholarship that is typical in deep learning research, and the OP is right to be concerned that there is little understanding between machine learning researchers of what, exactly, it is that their systems are doing- or even trying to do. And, as the OP points out, I'm afraid that really is the result of a poor background, not only in sciences and mathematics, but in AI and even machine learning itself, among the current generation of machine learning researchers. Contrary to what the OP says, though, this paucity of relevant background is the result of an influx to the field of people from the sciences, who have a background in continuous mathematics, but not in AI or computer science. It is, indeed, a background in computer science that can best guard against misconceptions about computers being wonder machines with magickal intelligence abilities. At the very least, you don't have to explain to a computer scientist why solving one instance of a class of problems does not mean a general solution for the entire class. That comes built-in to most who have graduated in the discipline. But is it built-in to phycisists, or biologists, who study very different systems? ________________ [1] Probing Neural Network Comprehension of Natural Language Arguments https://aclanthology.org/P19-1459/ https://aclanthology.org/P19-1459/ [2] Intriguing properties of neural networks https://research.google/pubs/pub42503/ https://research.google/pubs/pub42503/ [3] The Elephant in the Room https://arxiv.org/abs/1808.03305 https://arxiv.org/abs/1808.03305

sifar 5y ago

I think what the OP is implying that the intelligence we find in nature is not isolated computation but is physically situated - it can sense and manipulate it's environment. The current DNN based approach do not do that. Sure you can create an autonomous vehicle that uses NN to help navigate - but it does not update it's inference if it is incorrect. That immediate feedback doesn't exist or rather it is not accessible to the system itself. One has to collect the data, retrain the network and deploy it again to close the loop.