AI can predict heart attacks more accurately than doctors • Hacker News Board

Re: AI can predict heart attacks more accurately than doctors

#11

Post by snackai » Mon, Apr 17, 2017, 9:49 PM UTC

Kenji wrote: post #8

brandonb wrote: post #5

As others pointed out, it's an overreach to call this artificial intelligence. What the authors showed is that by training standard machine learning algorithms (random forests, logistic regression, gradient boosting, and a shallow neural net) on readily-available signals from the medical record (e.g., prior diagnoses), you can increase the c-statistic from 0.728 to 0.764. These machine learning techniques are well-su…

What's the difference between AI and 'standard machine learning algorithms'? Seems like they are used interchangeably today. Especially if you count neural nets as a standard algorithm too - which, let's be frank - it is in today's world.

Machine Learning is just a tool belt. AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. Therefore of course AI can utilize the tool belt that is Machine Learning.

Re: AI can predict heart attacks more accurately than doctors

#12

Post by jasode » Mon, Apr 17, 2017, 10:19 PM UTC

snackai wrote: post #11

Kenji wrote: post #8

Earlier quoted context omitted.

What's the difference between AI and 'standard machine learning algorithms'? Seems like they are used interchangeably today. Especially if you count neural nets as a standard algorithm too - which, let's be frank - it is in today's world.

Machine Learning is just a tool belt. AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. Therefore of course AI can utilize the tool belt that is Machine Learning.

>AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own.

From the perspective of a language descriptivist instead of prescriptivist, that strict definition of AI is already long gone. That ship has sailed.

Even wikipedia says that colloquial uses of "artificial intelligence" stands for machine learning that mimics intelligence. It's understandable that The NYT and all mainstream non-compsci publications will use "artificial intelligence" that way. One of the researchers (Stephen Weng) cited in the article also uses the term "artificial intelligence" in his LinkedIn page and in the PDF research article itself.

If and when "solving/recognizing on its own" truly becomes reality, I suspect the world will adapt and call it "True-AI" or "Lifelike-AI" or "Strong-AI". We'll find another term to distinguish it from the watered down "artificial intelligence".

Re: AI can predict heart attacks more accurately than doctors

#13

Post by mortehu » Mon, Apr 17, 2017, 10:38 PM UTC

snackai wrote: post #11

Kenji wrote: post #8

Earlier quoted context omitted.

What's the difference between AI and 'standard machine learning algorithms'? Seems like they are used interchangeably today. Especially if you count neural nets as a standard algorithm too - which, let's be frank - it is in today's world.

Machine Learning is just a tool belt. AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. Therefore of course AI can utilize the tool belt that is Machine Learning.

I think you might be talking about what other people call Artificial General Intelligence (AGI)?

https://en.wikipedia.org/wiki/Artificial_general_intelligenc...

Re: AI can predict heart attacks more accurately than doctors

#14

Post by qbrass » Mon, Apr 17, 2017, 11:47 PM UTC

brandonb wrote: post #7

epalmer wrote: post #6

How do I interpret Missing BMI as a risk factor. In the ML: Neural Networks run BMI Missing was considered a top 10 risk factor. The body text said this about BMI Missing: "This study suggests that missing values, in particular, for routine biometric variables such as BMI, are independent predictors of CVD." I'm having a hard time wrapping my brain around this concept. Thanks

This could be a signal that people who don't visit the doctor often are more likely to develop CVD. It makes sense—if caught early, risk factors like high BMI, high blood pressure, and high LDL cholesterol can be treated and thereby prevent heart attacks and other cardiovascular events.

Or it's selection bias from people who don't normally go to the doctor mostly going when there's a problem.

The people who don't go to the doctor because they feel fine aren't included in the study.

Re: AI can predict heart attacks more accurately than doctors

#15

Post by gnaritas » Mon, Apr 17, 2017, 11:49 PM UTC

snackai wrote: post #11

Kenji wrote: post #8

Earlier quoted context omitted.

What's the difference between AI and 'standard machine learning algorithms'? Seems like they are used interchangeably today. Especially if you count neural nets as a standard algorithm too - which, let's be frank - it is in today's world.

Machine Learning is just a tool belt. AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. Therefore of course AI can utilize the tool belt that is Machine Learning.

The timeless problem of AI, anything that starts out AI ends up being rebranded as not AI once it's understood. It's the no true Scotsman fallacy. Machine learning is AI, it's just not human level AI.

Re: AI can predict heart attacks more accurately than doctors

#16

Post by davak » Mon, Apr 17, 2017, 11:52 PM UTC

epalmer wrote: post #6

How do I interpret Missing BMI as a risk factor. In the ML: Neural Networks run BMI Missing was considered a top 10 risk factor. The body text said this about BMI Missing: "This study suggests that missing values, in particular, for routine biometric variables such as BMI, are independent predictors of CVD." I'm having a hard time wrapping my brain around this concept. Thanks

In our ICU data we found that missing fields also predicted worse outcomes. My theory is that in sicker patients nurses are less worried about collecting all the data. But who knows.

Re: AI can predict heart attacks more accurately than doctors

#17

Post by jgalt212 » Tue, Apr 18, 2017, 10:58 AM UTC

gnaritas wrote: post #15

snackai wrote: post #11

Earlier quoted context omitted.

Machine Learning is just a tool belt. AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. Therefore of course AI can utilize the tool belt that is Machine Learning.

The timeless problem of AI, anything that starts out AI ends up being rebranded as not AI once it's understood. It's the no true Scotsman fallacy. Machine learning is AI, it's just not human level AI.

> Machine learning is AI

I disagree. Machine Learning is just super-charged linear regression.

If you build a ML system that autonomously chooses it factors, and automatically adjusts to model drift (by either adjusting existing coefficients (easy) or adding/removing factors (hard), then ML drifts into AI.

Re: AI can predict heart attacks more accurately than doctors

#18

Post by gnaritas » Tue, Apr 18, 2017, 4:27 PM UTC

jgalt212 wrote: post #17

gnaritas wrote: post #15

Earlier quoted context omitted.

The timeless problem of AI, anything that starts out AI ends up being rebranded as not AI once it's understood. It's the no true Scotsman fallacy. Machine learning is AI, it's just not human level AI.

> Machine learning is AI I disagree. Machine Learning is just super-charged linear regression. If you build a ML system that autonomously chooses it factors, and automatically adjusts to model drift (by either adjusting existing coefficients (easy) or adding/removing factors (hard), then ML drifts into AI.

> I disagree. Machine Learning is just super-charged linear regression.

Machine learning is neural nets, which started out as an approach to AI and which yes boil down to linear regression, but that's about as useful as saying brains are just super super charged linear regression. And that's the point, AI is a label that keeps getting cast off of things that started out as AI but once understood people decided they no longer were, you are committing the no true Scotsman fallacy. If it started off as AI, it's AI, that doesn't change because you understand the math underneath that it boils down to.

Re: AI can predict heart attacks more accurately than doctors

#19

Post by felippee » Tue, Apr 18, 2017, 8:57 PM UTC

gnaritas wrote: post #15

snackai wrote: post #11

Earlier quoted context omitted.

Machine Learning is just a tool belt. AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. Therefore of course AI can utilize the tool belt that is Machine Learning.

The timeless problem of AI, anything that starts out AI ends up being rebranded as not AI once it's understood. It's the no true Scotsman fallacy. Machine learning is AI, it's just not human level AI.

I'd disagree, there is more here than the no true Scotsman fallacy. It is equivalent to saying that any magic trick ceases to be magic once exposed as a trick. We don't have a good definition of I in AI therefore such arguments go on forever, yet somehow we know that the "I as in human being "I" is different than the "I" as in "AI" statistical smoke and mirrors. My opinion is that a good definition could be established that would clear such BS once and for all, some attempt here: http://blog.piekniewski.info/2017/04/13/ai-confuses-intellig...

Re: AI can predict heart attacks more accurately than doctors

#20

Post by gnaritas » Tue, Apr 18, 2017, 10:39 PM UTC

felippee wrote: post #19

gnaritas wrote: post #15

Earlier quoted context omitted.

The timeless problem of AI, anything that starts out AI ends up being rebranded as not AI once it's understood. It's the no true Scotsman fallacy. Machine learning is AI, it's just not human level AI.

I'd disagree, there is more here than the no true Scotsman fallacy. It is equivalent to saying that any magic trick ceases to be magic once exposed as a trick. We don't have a good definition of I in AI therefore such arguments go on forever, yet somehow we know that the "I as in human being "I" is different than the "I" as in "AI" statistical smoke and mirrors. My opinion is that a good definition could be establish…

You're conflating human level intelligence with artificial intelligence for one, AI doesn't have to be human level to be AI. Secondly "statistical smoke and mirrors" is the Chinese room fallacy; it doesn't matter what it is under the covers, if it behaves intelligently then it's intelligent regardless of whether or not it can be boiled down to some math. Your link covers that near the beginning when he discusses duck typing, but he comes to the wrong conclusion. The article should have ended there, if it behaves intelligently, then it's intelligent, that simply is the truth and looking at the implementation is cheating because we don't know brains aren't doing the same thing.

What's really going on is you are branding "human intelligence" special because we don't understand its implementation and labeling everything else not intelligent because we do, for all we know the human mind itself could be nothing more than statistical smoke and mirrors. The only problem here is human ego.

A car that can drive me somewhere on its own simply by being given a destination, is AI, not matter how it's implemented as long as it's the computer doing the driving and is operating locally by actually having sensors that see the road. It doesn't have to be able to ponder its own existence to be AI.

Neural nets were an attempt to model how the brain works; they are by definition AI regardless of whether they boil down to some maths. Everything a computer does boils down eventually to some maths, that is not an escape hatch to claim something isn't AI.

Machine learning is AI. It is not AGI, but it most certainly is AI.