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Andrew Ng: Unbiggen AI

spectrum.ieee.org

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Re: Andrew Ng: Unbiggen AI

#61

Post by mdp2021 »

ThalesX wrote: post #58
mdp2021 wrote: post #54

Earlier quoted context omitted.

I'd just note that if pushed by circumstances (if one was willing to be interviewed in spite of their ways), the interview environment could be (would be) on a throwaway virtual machine... Possibility which, by the way, makes the interviewer's cautionary move generally useless.

Or, in 2022, one could reach into their pocket just use a phone, making the interviewer's cautionary move generally useless.

> just use a phone

I assumed that keeping looking in the direction of the camera was relevant (in their idea).

Re: Andrew Ng: Unbiggen AI

#62

Post by mdp2021 »

cloogshicer wrote: post #55
notsag-hn wrote: post #32

I was going to interview at LandingAI. I was asked before the interview to install a spyware browser extension to monitor my traffic to detect if I was cheating during the interview. I respectfully declined and didn't have that interview.

Well, Ng is also one of those people who believe that we should all work 70+ hours per week: https://news.ycombinator.com/item?id=15251769

~80hrs on topic A squeezes the available time for being acquainted with the rest. [Edited because there was little way not to make the former formulation read, unwillingly, nasty]

Some of us believe instead on the advantage of being a polymath, (also) to be able to export wisdom from other contexts into the current work.

Also in terms of the proper ground to facilitate innovation.

Re: Andrew Ng: Unbiggen AI

#63

Post by kavalg »

DeathArrow wrote: post #20

Pretty interesting. Mr. Ng claims that for some applications having a small set of quality data can be as good as using huge set of noisy data. I wonder if, assuming the data is of highest quality, with minimal noise, having more data will matter for training or not. And if it matters, on what degree?

It is relatively easy to turn small and accurate data to bigger and less accurate data with various forms of augmentation. The opposite is harder.

Re: Andrew Ng: Unbiggen AI

#64

Post by xtiansimon »

mdp2021 wrote: post #61
ThalesX wrote: post #58

Earlier quoted context omitted.

Or, in 2022, one could reach into their pocket just use a phone, making the interviewer's cautionary move generally useless.

> just use a phone I assumed that keeping looking in the direction of the camera was relevant (in their idea).

I have multiple monitors

Re: Andrew Ng: Unbiggen AI

#66

Post by nuclearnice1 »

onethought wrote: post #23
vasco wrote: post #7

Earlier quoted context omitted.

Interestingly, when you learn how to drive you need to see approximately one example and you're able to identify them all.

when you learn to drive you need [approximately 16 years training your visual system hardware that took 6 million years to evolve and] to see approximately one example and you're able to identify them all. FTFY. Yet Tesla have been working on both the hardware and software for 10 years? Amazing progress right?

If you’re going back 6 million years on evolution, would it make sense to push back the start date for Tesla’s work as well?

It seems the right parallel to evolution might include predecessor inventions in vision, computation, and beyond.

Re: Andrew Ng: Unbiggen AI

#68

Post by YeGoblynQueenne »

riku_iki wrote: post #13
Datenstrom wrote: post #4

Earlier quoted context omitted.

> simply saying "we should make this better thing" isn't enough. Besides the references to his company which has customers and a product that already works on these principles the literature currently shows that this is very much possible if you dig into the correct niches. Besides the SOTA in few-shot and meta-learning it is possible to smartly choose the correct few samples for the network that yield the same resul…

> it is possible to smartly choose the correct few samples for the network that yield the same results. And then, someone is using pretrained 500B model, and fine-tuning your few examples, and getting new SOTA.

They might get new SOTA because the metric is accuracy, but if the metric was accuracy weighted by sample efficiency, then SOTA would look a lot less impressive.

Simplest way to weigh by sample efficiency: multiply accuracy by ratio of test set to training set sizes. Everyone's training/testing on 80/20 splits, so everybody's SOTA would go down by 3/4s.

Re: Andrew Ng: Unbiggen AI

#69

Post by machiaweliczny »

a_square_peg wrote: post #42

I’ve been wondering about the limits of data-centric approach – there seems to be this implicit notion that more data equals better performing ML or AI. I think it would be interesting to imagine a point of diminishing return on additional data if we consider that our ability to perceive is probably largely based on two parts - sensory input and knowledge. Note that I’m making an explicit distinction here on the diff…

I have a feeling that too much knowledge might slow learning process as it's harder to spot/test observe steepest gradient. At least that's how it feels intuitively from human PoC. From computation that would be just little more computation but I guess would mean slower convergence also. Taking math as more extreme example it's hard to understand something complex unless you understand basic algebra.

Anyone knows if this might be true mathematically speaking? Does order of data matters?

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