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Adventures in Improving AI Economics

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Re: Adventures in Improving AI Economics

#31

Post by jmatthews »

motohagiography wrote: post #18

Good analysis and great of them to share their thinking. Does feel like this could have been a tweet that said the necessary condition for successful ML solution is applying it to a problem that has asymmetric upside. Great for telling people they should get tested for diseases, terrible for diagnosis. In the alerting first case, consequences of being wrong are no better than base rate as they wouldn't have been test…

I appreciate the thoughtful commentary. I couldn't disagree more with you more of course.

There are 2 instances where AI breaks the mold you've cast.

Executing rote tasks that no humans need do, and relatedly, while there does seem to be a tough hurdle when it comes to "better than human" execution there is also an inverted survivors bias. Once a technology is production ready it is no longer AI. Cars aren't robots, antilock brakes aren't AI, Once a system outperforms a human it's technology, not intelligence.

Re: Adventures in Improving AI Economics

#32

Post by logicslave12 »

Swizec wrote: post #23
eanzenberg wrote: post #19

In my experience, there are just a lot of "bad" AI/ML engineers who don't fundamentally understand what data can do, what ML algorithms can handle, and how to piece it together to produce something of value to the end user. A couple of these people on a team can torpedo a project. Worse are those who sabotage projects or are general pain points of hindering progress. These may be jaded people who don't believe that M…

Correct me if I’m wrong but isn’t this fundamentally true for any team working on any project?

No, there’s more ambiguity in machine learning projects. When you develop a website, aside from the design, it works or it doesn’t. Whether some kind of ml product can work at all is often team dependent

Re: Adventures in Improving AI Economics

#33

Post by rland »

LifeIsBio wrote: post #25
gilgoomesh wrote: post #14

Earlier quoted context omitted.

Yeah, I find this kind of abbreviation annoying. But there's a few words that are commonly abbreviated like this: i18n -> internationalization l10n -> localization g11n -> globalization l12y -> localizability a11y -> accessibility It bothers me because my brain does not jump from the abbreviation to the underlying word. I really need to stop and think about each one. And I get the numbers wrong when writing them.

There was a period of a few months when I was first learning about web apps where I saw "i18n" multiple times. The first time I came across it, I tried to sound it out: i18n -> I-one-eight-n -> iwonation I was already a couple of rabbit holes deep at the time and didn't have the mental capacity to look it up and wrap my head around yet another new concept. "Oh boy." I thought to myself, "One more word I've never hear…

Wait, can you clarify what is actually going on? Is there any rhyme or reason or is are these shortenings just random?

I'm having trouble parsing the grandparent comment...

Re: Adventures in Improving AI Economics

#34

Post by websight »

rland wrote: post #33
LifeIsBio wrote: post #25

Earlier quoted context omitted.

There was a period of a few months when I was first learning about web apps where I saw "i18n" multiple times. The first time I came across it, I tried to sound it out: i18n -> I-one-eight-n -> iwonation I was already a couple of rabbit holes deep at the time and didn't have the mental capacity to look it up and wrap my head around yet another new concept. "Oh boy." I thought to myself, "One more word I've never hear…

Wait, can you clarify what is actually going on? Is there any rhyme or reason or is are these shortenings just random? I'm having trouble parsing the grandparent comment...

It's the number of letters between the start and end letters. Yeah, it's annoying.

Re: Adventures in Improving AI Economics

#35

Post by jointpdf »

mdorazio wrote: post #11
mensetmanusman wrote: post #3

Good analogy about discovery of Pharma molecules. It’s really fun to think about the fact that Tesla has more than enough data to unlock autonomous vehicles, but all that is missing is the correct AI architecture to get it working... Who will figure out how to code that? Will it be a breakthrough, or can sub-optimal architectures eventually reach equilibrium with 10x or 100x the amount of time/data processing.

> Tesla has more than enough data to unlock autonomous vehicles Many people in the automotive industry, myself included, disagree with this statement pretty strongly. Driving data quantity is not equivalent to quality and they are severely lacking in advanced sensor data.

So is the claim by Elon Musk that current iterations of Tesla vehicles have all of the sensors and compute power needed to be fully autonomous (Level 4+ I guess?) in the future, via software updates only, a specious one?

Re: Adventures in Improving AI Economics

#36

Post by nl »

jointpdf wrote: post #35
mdorazio wrote: post #11

Earlier quoted context omitted.

> Tesla has more than enough data to unlock autonomous vehicles Many people in the automotive industry, myself included, disagree with this statement pretty strongly. Driving data quantity is not equivalent to quality and they are severely lacking in advanced sensor data.

So is the claim by Elon Musk that current iterations of Tesla vehicles have all of the sensors and compute power needed to be fully autonomous (Level 4+ I guess?) in the future, via software updates only, a specious one?

It's hard to be absolutist on the response to that: anything is possible, and humans can drive without LIDAR.

But at the moment it seems a strange position to take: we know LIDAR data is useful in many circumstances, and we know it can solve a number of the hard parts of computer vision.

Re: Adventures in Improving AI Economics

#37

Post by nl »

antipaul wrote: post #4
mensetmanusman wrote: post #3

Good analogy about discovery of Pharma molecules. It’s really fun to think about the fact that Tesla has more than enough data to unlock autonomous vehicles, but all that is missing is the correct AI architecture to get it working... Who will figure out how to code that? Will it be a breakthrough, or can sub-optimal architectures eventually reach equilibrium with 10x or 100x the amount of time/data processing.

Perhaps. It seems it’s still an open question whether AI is just about memorizing your data, or can it actually make reliable decisions during previously unseen scenarios. Have we already observed, or collected, all that is possible in the “driving” world?

> It seems it’s still an open question whether AI is just about memorizing your data,

No - it's not at all, and this is a well understood problem in building machine learning systems. There are some cases where this occurs but usually this is just overfitting.

Good AI systems generalize well on unseen data.

Re: Adventures in Improving AI Economics

#38

Post by nl »

KorfmannArno wrote: post #27
antipaul wrote: post #4

Earlier quoted context omitted.

Perhaps. It seems it’s still an open question whether AI is just about memorizing your data, or can it actually make reliable decisions during previously unseen scenarios. Have we already observed, or collected, all that is possible in the “driving” world?

I've yet to see an "AI" that is not just memorizing data.

Then you haven't really looked.

Most credible machine learning systems work well on unseen data, which by definition isn't memorizing.

Re: Adventures in Improving AI Economics

#39

Post by antipaul »

KorfmannArno wrote: post #30
KorfmannArno wrote: post #27

Earlier quoted context omitted.

I've yet to see an "AI" that is not just memorizing data.

But then again, how does something like the following work? https://twitter.com/GoogleAI/status/1293970520753369088 Any idea how it could be fooled?

Via a fancy “adversarial” patch

https://www.theverge.com/2019/4/23/18512472/fool-ai-surveill...

Re: Adventures in Improving AI Economics

#40

Post by nl »

KorfmannArno wrote: post #30
KorfmannArno wrote: post #27

Earlier quoted context omitted.

I've yet to see an "AI" that is not just memorizing data.

But then again, how does something like the following work? https://twitter.com/GoogleAI/status/1293970520753369088 Any idea how it could be fooled?

What do you mean by "fooled"?

I'm very familiar with the BlazeFace and FaceMesh models (which are related to this in Google's MediaPipe framework).

They have weaknesses - they aren't designed for running upside down for example, so if they get data that is that way oriented they will tend to fail.

They aren't designed for "life" detection, so you can show printed pictures of a face and it will detected it.

But they give you confidence scores etc, so if you give it something like a caricature of a face it will return reasonable confidence numbers indicating it isn't as sure of its predictions.

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