5 ms·

> the knowledge graph that Google uses, Bayesian modelling, economic and social simulation and so on... Those are not "AI TECHNOLOGY" -they're just math (the

> the knowledge graph that Google uses, Bayesian modelling, economic and social simulation and so on... Those are not "AI TECHNOLOGY" -they're just math (the examples you list are basically all linear regression). Treating "AI" anything as if it were an actual subject outside of very, very obscure research projects is about 90% of the hype problem here. The use of the term is very much history repeating itself: people actually thought "AI" was a real thing back in the 1980s -the hype back then was even more ridiculous, and it was based on stuff which amounted to "we will use interpreters and parse trees."

michelpp 8y ago
Yup. “All the impressive achievements of deep learning amount to just curve fitting,” - Judea Pearl
- RosanaAnaDana 8y ago
  Ok. So can you make an argument that any kind of intelligence or learning is anything more than curve fitting. I think the goalpost moving that's gone on with AI (deep blue, AlphaGo, etc) is as ridiculous as the 'its overhyped and just curve-fitting', when no ones settled into the difficult task of rigorously defining what 'intelligence' is. So consider the possibility that all forms of intelligence are some flavor of curve fitting. Is that such a problem? If that were the case, what would be so wrong with calling non-biological systems which can optimize curve fitting 'intelligent, artificial'?
  - cik2e 8y ago
    I had the identical sentiment after reading that dismissive oversimplification. Here’s the source interview: https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/ https://www.quantamagazine.org/to-build-truly-intelligent-ma.... For a man of his experience, I am surprised that he is forgetting the incremental nature of science. Most high level researchers are not visionaries but are really damn good at what they do. Their work will provide the building blocks for game-changing advances. His goal is to draw attention to the loftier problems in AI but doing so by belittling the work of others is immature and ineffective as a motivational tool. Who’s going to switch gears to exploring radically different approaches because what we’re doing now is so boring and lame? I would have loved to read more about his proposed avenues of research instead of how unimpressed he is with curve fitting.
    - sgt101 8y ago
      Plenty of chances : http://bayes.cs.ucla.edu/jp_home.html http://bayes.cs.ucla.edu/jp_home.html
- sgt101 8y ago
  I would cite Professor Pearl's work as some of the most interesting artefacts derived from the program of Artificial Intelligence. Causal modelling is a big deal! Causal models are one of the things I call AI technology.
esoterica 8y ago
Who gave you the authority to decide on behalf of the world what the phrase AI means? You don’t get to arbitrarily redefine common words and phrases. Machine learning techniques are AI, as the term has been used in industry and academia for decades.
- F-0X 8y ago
  > Machine learning techniques are AI, as the term has been used in industry and academia for decades. As the poster said, most of them are just statistics; the term has been used in industry and academia for _centuries_. Predictive models based on accumulated data is just what statistics is applied to do. Calling it AI academically is a big stretch, calling it AI in industry is a marketing technique.
  - esoterica 8y ago
    I repeat: > Who gave you the authority to decide on behalf of the world what the phrase AI means?
- scottlocklin 8y ago
  > Machine learning techniques are AI ... No, they absolutely are not: this is why we have a separate term and subject known as "machine learning." It is only pointy headed managers and regurgitators of public relations press releases (aka modern journalists) who refer to machine learning as "AI." Oh yeah, I guess I should include people who think the latter are some kind of authority on the topic. From the damn Miriam Webster dictionary of the English language: "artificial intelligence: : a branch of computer science dealing with the simulation of intelligent behavior in computers 2 : the capability of a machine to imitate intelligent human behavior machine learning noun Definition of machine learning : the process by which a computer is able to improve its own performance (as in analyzing image files) by continuously incorporating new data into an existing statistical model"
  - Bartweiss 8y ago
    There's a distinction to be drawn, certainly. If you walk into a university CS department, entry-level AI and ML are likely to be separate entries on the class roster, and you can make useful predictions about what you'll find in each course. But the complaint is that "computer intelligence" is the same sort of god-of-the-gaps concept as "human intelligence". Is tool use strictly human? Tool invention? Meta-tool use? Observational learning? Language? Coining new words? In the same way that the list of human-only behaviors has spent a century in retreat, the tasks which constitute "intelligent behavior" in computers are fundamentally undefined, and outside of the Turing Test they mostly consist of whatever problems appear at least 5-10 years out of reach. "ML techniques" is literally synonymous with AI, obviously. One is a group of algorithms, the other's an abstract concept. But a great many tasks currently handled under the label ML would have been widely accepted as "AI problems" very recently. A computer winning Jeopardy would have sounded like a shocking AI advance in 1997 when Deep Blue won, let alone in 1964 when the show debuted. In 2011, people scrambled to be fastest and most scathing in labelling it "not real intelligence" and mocking it for mistakes smaller than many human errors. The difference is usually semantic, and some responsibility for the sliding definition certainly lies with hype-happy journalists and over-optimistic AI researchers. (Had Watson won Jeopardy back in 1980, it would have roughly matched Minsky and Simon's predicted timelines.) But that distinction is increasingly used for the very specific equivocation which enables this article. "AI isn't doing that", meaning "it's ML instead", is conflated with "AI isn't doing that" meaning "that isn't happening". If AI doesn't take your job, but a deep-learning network developed by AI researchers does, it seems pretty fair to complain that the two terms aren't actually distinct.
basetop 8y ago
I think you are confusing "General AI" ( or conscious AI ) with AI. AI is most certainly real and is used everywhere from trading software to text expansion to mortgage qualifications to chess to self driving cars and beyond. AI is everywhere. General or "conscious" AI, the stuff we imagine in sci-fi movies, isn't anywhere close to being realized. And it may never be created for all we know. Conscious AI is on the level of free fusion energy. We don't even know if it is possible.
sgt101 8y ago
I describe them as AI technology because they are spinoffs from the program of research (ongoing, nascent) into Artificial Intelligence. Reasoning over knowledge graphs is not linear regression. Bayesian Modelling is not linear regression (although you can do Bayesian Linear Regression) Simulation systems are not linear regression. Google translate is not linear regression. AlphaZero is not linear regression. None of the above are Artificial Intelligence, they are not trivial either.

michelpp 8y ago

Yup. “All the impressive achievements of deep learning amount to just curve fitting,” - Judea Pearl

RosanaAnaDana 8y ago
Ok. So can you make an argument that any kind of intelligence or learning is anything more than curve fitting. I think the goalpost moving that's gone on with AI (deep blue, AlphaGo, etc) is as ridiculous as the 'its overhyped and just curve-fitting', when no ones settled into the difficult task of rigorously defining what 'intelligence' is. So consider the possibility that all forms of intelligence are some flavor of curve fitting. Is that such a problem? If that were the case, what would be so wrong with calling non-biological systems which can optimize curve fitting 'intelligent, artificial'?
- cik2e 8y ago
  I had the identical sentiment after reading that dismissive oversimplification. Here’s the source interview: https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/ https://www.quantamagazine.org/to-build-truly-intelligent-ma.... For a man of his experience, I am surprised that he is forgetting the incremental nature of science. Most high level researchers are not visionaries but are really damn good at what they do. Their work will provide the building blocks for game-changing advances. His goal is to draw attention to the loftier problems in AI but doing so by belittling the work of others is immature and ineffective as a motivational tool. Who’s going to switch gears to exploring radically different approaches because what we’re doing now is so boring and lame? I would have loved to read more about his proposed avenues of research instead of how unimpressed he is with curve fitting.
  - sgt101 8y ago
    Plenty of chances : http://bayes.cs.ucla.edu/jp_home.html http://bayes.cs.ucla.edu/jp_home.html
sgt101 8y ago
I would cite Professor Pearl's work as some of the most interesting artefacts derived from the program of Artificial Intelligence. Causal modelling is a big deal! Causal models are one of the things I call AI technology.

esoterica 8y ago

Who gave you the authority to decide on behalf of the world what the phrase AI means? You don’t get to arbitrarily redefine common words and phrases. Machine learning techniques are AI, as the term has been used in industry and academia for decades.

F-0X 8y ago
> Machine learning techniques are AI, as the term has been used in industry and academia for decades. As the poster said, most of them are just statistics; the term has been used in industry and academia for _centuries_. Predictive models based on accumulated data is just what statistics is applied to do. Calling it AI academically is a big stretch, calling it AI in industry is a marketing technique.
- esoterica 8y ago
  I repeat: > Who gave you the authority to decide on behalf of the world what the phrase AI means?
scottlocklin 8y ago
> Machine learning techniques are AI ... No, they absolutely are not: this is why we have a separate term and subject known as "machine learning." It is only pointy headed managers and regurgitators of public relations press releases (aka modern journalists) who refer to machine learning as "AI." Oh yeah, I guess I should include people who think the latter are some kind of authority on the topic. From the damn Miriam Webster dictionary of the English language: "artificial intelligence: : a branch of computer science dealing with the simulation of intelligent behavior in computers 2 : the capability of a machine to imitate intelligent human behavior machine learning noun Definition of machine learning : the process by which a computer is able to improve its own performance (as in analyzing image files) by continuously incorporating new data into an existing statistical model"
- Bartweiss 8y ago
  There's a distinction to be drawn, certainly. If you walk into a university CS department, entry-level AI and ML are likely to be separate entries on the class roster, and you can make useful predictions about what you'll find in each course. But the complaint is that "computer intelligence" is the same sort of god-of-the-gaps concept as "human intelligence". Is tool use strictly human? Tool invention? Meta-tool use? Observational learning? Language? Coining new words? In the same way that the list of human-only behaviors has spent a century in retreat, the tasks which constitute "intelligent behavior" in computers are fundamentally undefined, and outside of the Turing Test they mostly consist of whatever problems appear at least 5-10 years out of reach. "ML techniques" is literally synonymous with AI, obviously. One is a group of algorithms, the other's an abstract concept. But a great many tasks currently handled under the label ML would have been widely accepted as "AI problems" very recently. A computer winning Jeopardy would have sounded like a shocking AI advance in 1997 when Deep Blue won, let alone in 1964 when the show debuted. In 2011, people scrambled to be fastest and most scathing in labelling it "not real intelligence" and mocking it for mistakes smaller than many human errors. The difference is usually semantic, and some responsibility for the sliding definition certainly lies with hype-happy journalists and over-optimistic AI researchers. (Had Watson won Jeopardy back in 1980, it would have roughly matched Minsky and Simon's predicted timelines.) But that distinction is increasingly used for the very specific equivocation which enables this article. "AI isn't doing that", meaning "it's ML instead", is conflated with "AI isn't doing that" meaning "that isn't happening". If AI doesn't take your job, but a deep-learning network developed by AI researchers does, it seems pretty fair to complain that the two terms aren't actually distinct.

basetop 8y ago

I think you are confusing "General AI" ( or conscious AI ) with AI. AI is most certainly real and is used everywhere from trading software to text expansion to mortgage qualifications to chess to self driving cars and beyond. AI is everywhere. General or "conscious" AI, the stuff we imagine in sci-fi movies, isn't anywhere close to being realized. And it may never be created for all we know. Conscious AI is on the level of free fusion energy. We don't even know if it is possible.

sgt101 8y ago

I describe them as AI technology because they are spinoffs from the program of research (ongoing, nascent) into Artificial Intelligence. Reasoning over knowledge graphs is not linear regression. Bayesian Modelling is not linear regression (although you can do Bayesian Linear Regression) Simulation systems are not linear regression. Google translate is not linear regression. AlphaZero is not linear regression. None of the above are Artificial Intelligence, they are not trivial either.