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*edit: title should say 5 years

Means I can do my own midjourney for $5, but only for a single image? 🤔

Is that the same model every time?

No, this is classification. You can train an AI that's pretty good at identifying images for 5$.

Adjust for inflation and AI can afford a carton of eggs

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Man this is terrible. The log scale completely distorts the trend - essentially costs fell by a factor of 100 between 2017 and 2018, and the rest is trivial

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It only fell from $12 to $5 over 3 years. The real interesting thing here is pretty obviously 2017 to 2018

how do i train my bot. It sucks right now.

Still a 50%+ decline over 3 years. Pretty good imo.

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What drove the 2017 to 2018 cost reduction?

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Nonlogaritmic https://i.imgur.com/z6RZpvC.png

There's some sense to having the second trend (2018-2021) visible, but it should be on a different graph, the two data pieces are not really compatible.

As someone who does market valuations and sets the prices for some of the most popular AI datasets, this is very misleading if not wholly inaccurate. In any case, I’m gonna look into these cited sources to see how they even came up with these numbers.

No it’s a benchmark that any image classification model can use to test accuracy. There would still be pretty massive improvement on the graph if it were all the same model, GPU efficiency for deep learning has skyrocketed since 2017, but a lot of the graph is also just modern models being quicker to train

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What's this scale on the vertical axis? Ewww.

or leave it at log scale and it is completely fine. It is labeled where is the issue?

We can make 3 post about it split it up zoom in and out or just use log scale...

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So to what is this improvement attributed to? To hardware or to better AI systems designs?

Because the image actually makes a separate point of saying "99.59 % in 5 years", which isn't all that interesting as it's almost the same %, 98.92% in just the first year. In a way, it's presenting the data in a way making it less impressive than it is.

This is /r/dataisbeautiful, not r/whateverlooksnice, so critiquing data presentation seems appropriate.

And a shitty one too... Same as their veiled ad yesterday, irrelevant data set and infographic masquarading as data...

logarithmic decline

Yup, and picking the most trendy topic possible, while ironically revealing they know fuck all about it!

Well it is a trade-off of either seeing the big drop and nothing else or seeing the big drop and due to the log level graph also seeing the further advancements clearly.

That's what I was always taught to do to provide a better glimpse of data in statistics and econometrics classes.

Not 75%. It only looks like that. It's actually 98.2% in two years. That's exactly why the post distorts the data.

If this were in a scientific publication where seeing the actual numbers in each year was important, I might agree. But this is a data visualisation purporting to show the trend in costs over 5 years, and it is failing to show the main trend clearly. It's the difference between 'showing the data' and 'showing the story'

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How did it get more expensive in 2019?

but your suggestions to split it up in multiple graphs is far worse or only show data from 2017 and 2018.

Than everyone would wonder how ho wit developed after 2018.

You can make the same claims about every stock market chart which is displayed in log scale. "These movements don't matter because 96% of the growth was in the past."

but the recent development is very important too. In this case that it still continues to go.

It is still down 35% in the last year, which lets you see we are not even close to the end of the read.

One might argue the 98.92% decrease says a lot less because when something is not done in scale it is always at first extreme expensive. So I don't agree that they make it look less impressive than it is.

So as long as your point is people don't understand how to read log charts I still disagree with you.

"to a top 5 accuracy of 93% or more".

So they take the model cheapest to train that can reach that top-5 label accuracy.

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What does this mean to me as a consumer of nerds?

I changed my mind, and I will guess mobileNetV2.

Looks like my crypto investment 😂😂😂

At high level our models didn't get much better (there are improvements ofcourse). The biggest change is that instead of training on a small data set, companies started throwing everything on the internet at it.

So basically investment - more electricity, more expenditure.

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Yeah ok it's 75% from 2019 to 2021 but compared to the absolute difference between 2017 and 2018 it's nothing.

> Progress in AI is being driven by availability of large volume of structured data, algorithmic innovations,and compute capabilities. For example, the time to train object detection task like ImageNet to over 90% accuracy has reduced from over 10 hours to few seconds, and the cost declined fromover $2,000 to substantially less than $10 within the span of the last three years Perrault et al.(2019)

https://www.researchgate.net/publication/350015537_AI_Efficiency_Index_Identifying_Regulatory_and_Policy_Constraints_for_Resilient_National_AI_Ecosystems.

How can you say making something 75% cheaper in 2 years is nothing lmfao? What the fuck are you on. Of course once you get to a certain point the absolute numbers are going to look small but that's still huge improvements.

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Basically. More money to gcp or azure.

I would guess better architecture for both models, hardware and frameworks. While tensorflow, pytorch and resnet are all from mid 2015/2016 i would guess it could take a year to fully integrate (be it improvements in the framework, or industries adopting them). Tensorflow and Pytorch are very popular ML packages, and resnet is an architecture which I thought is more data efficient than it's predecessors.

As for the hardware i dont know enough about their releases the same goes for updates in the cuda framework which improves gpu acceleration.

AI will replace most call centers in the next 10 years

So forget taking jobs it's gonna go for the kill shot and starve us out first. Greatttt🙄

Resnet was far more efficient than vgg, but its also from 2016.

In the "efficiency first' route there's been resNet, then MobileNet, then many versions of EfficientNet.

Could the rise in open source computing power also be a factor?

Electricity prices maybe?

Eh, with bitcoin at least its back to being up 50% from where it was just a month or two ago... I've been riding that one a good long while. Have been buying $10 a day for like 3 years and bought a solid chunk like 2 years before that, so have been on the roller coaster for like 5 or so... Definitely a good few times that my stomach has dropped, but it's always worked itself out

Yeah that’s true if you didn’t traded with leverage and didn’t invest in sh*t coins 🥲

I think the price of bitcoin was climbing a lot then. So might be related to GPU prices?

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Once the AIs unionize the cost is gonna go up dramatically.

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The most obvious thing to me is the introduction of Nvidia's RTX series cards.

What is that open source computing power you speak of?

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Think he means cloud based computing power?

As in rather have 1 PC here and one pc there doing the work individually, linking them up and sharing the load

What happened in 2018-2019? Why price go up?

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Just wait till they graph 2022 and 2023 when inflation gets it.

You 'consume' nerds? Huh..

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I can tell you the price of ImageNet, it’s fucking free.

This has nothing to do with dataset, but price, availability and performance of compute, and the efficiency of the newer models.

Training a VGG19 on a 1080 or a EfficientNet on a TPU isn’t the same.

Nah. But you can download stable diffusion (what midjourney is based on), and run it on your machine for free (-electricity)

2019 was a lull for bitcoin, graphics cards were plentiful and relatively cheap. There was the first big spike in late 2017 / early 2018 then it dropped until late 2020 and went crazy in 2021.

you don't like aws

You are OnlyFans?

You can donate your idle CPU time to do research if you want

You install a program and it when your CPU/GPU usage is low it’ll do calculations for that organisation

https://en.m.wikipedia.org/wiki/List_of_volunteer_computing_projects

Though he is likely on about Cloud Computing and got the name wrong

https://en.m.wikipedia.org/wiki/Cloud_computing

I don't like the uneven scale of the y-axis, it doesn't show the full proportion of the decline

If something cost a billion $ and you brought the price down to a million $, I’m pretty sure you’d think dropping it from a million to 250k is pretty great too.

But is this assuming it’s stealing art from the internet, or is it being paid for?

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It's not even a log scale, there's a 0 on there. The scale is all over the place.

This is what Cathy Wood keeps saying. This is why she's investing heavily in these new fields

uuhm well it depends. You want to keep up-to-date and newer models take tons more resources.

They are just a fan of tangy candy

Should be plotted in energy consumption

I used Folding@Home before AI took over! :>

Deep learning techniques improved dramatically in 2017. TPUs were also first introduced in 2016, and it typically takes about 4 years for data centers to completely migrate.

what being a low regulation market does to a mfer.

Yes. I like em twink with glasses.

SETI 4ever

Just BTC and hold as long as possible

I think the interesting thing is that it rose from 12 to 19.

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But cheese is three times more. Damn.

exponential scale misleading here i feel like

I have a theory that modern games are doing this.

The heck is with the scaling on the y axis? Fix that wackness. Looks out of proportion and obviously adjusted to fit a narrative.

It's not even consistent. Big, then small physical spaces, then jumping my random amounts as well.

The "trend line" with the attached conclusion is what makes it egregious and masks the logarithmic nature of the y axis. Like it misses the important points with overfitting.

And the interesting thing is two things

1. in one year, prices fell by 99%
2. in subsequent years, prices have fallen another 60%

But it makes it look like there is a continuity that in reality doesnt fit a trend line at all as is seen in the non-logaritmic version

the further advancements are irreverent. of the 4 dat a points one goes backwards, and the cost only decreased by 50% compared to the drastic cost the year before.

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Wtf is that y axis?!? Like why not nog scale, or literally any labelling that makes mathematical sense?

"Oh yeah let's put the bars here here and here, looks better that way"

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I think it's also about Transfer learning. Many are using fixed backbones.

If anything, the carbon footprint of training AI models have become bigger and bigger each passing year. The large LLMs are trained with clusters so big, that only the very very top tier tech companies can afford.

and the only one with a decent fricking screen saver option.

Seriously, the rest of the @Home gang needs to make with the fast fourier transforms or something equally eye-candy-ish to watch.

There is actually no such thing as AI. (...in the form of AI that would typically be imagined from decades of books, TV and movies.)

No matter what, at this time, it always boils down to computer program that has pre-programmed responses from pre-determined stimuli.

This is the actual metric that should scare people about AI

Special purpose CPUs that perform lower precision calculations that are fine for neural nets. You need 64 bit floating point for weather prediction, but 8 bit integer works OK for some neural calculations. The previous CPUs could downshift to smaller numbers, but were not proportionally faster. The new ones are. NVIDIA, Google, Apple have special neural chips.

New prize for the most ridiculously misleading visualization I've seen on the sub so far.

Literally just a 5 point line graph, and you still managed to absolutely butcher it by using Willy Wonka's Wacky Y Axis where there are no rules and the numbers don't matter.

And boring even if was done correctly.

And ugly.

Your statement is wrong and has been for decades. It can definitely respond to brand new stimuli it's never seen before. You seriously think the ChatGPT guys "preprogrammed" the answer to "Give me the recipe for a cake but in Shakespearean iambic pentameter"? Lol? There are also tons of AI systems that for example take any painting you give them and make it look like Van Gogh. The programmers never saw your painting before...

If you want to argue subjectively about the term intelligence, fine, but "preprogrammed responses" as well as "predetermined stimuli" are both objectively wildly incorrect.

Lol yep, turns out just stealing shit is really cheap. WHO KNEW!

"Price of a candy bar before vs after I started just walking out of the store with them. 100% decrease! Massive efficiency gain!"

Infoation adjusted wages are slightly HIGHER than they were before COVID. As in it is in fact easier to afford the cost of living than before. Wages don't just sit around static either.

The core problem is only if you have a lot of cash savings or if you are a creditor

It still costs time and money to label your own dataset. This chart appears to be using an older pre-made dataset that's rather limited compared to more recent text to image datasets like LAION.

It should excite people that the technology is still very much democratic in terms of costs, and not something only the 0.1% can afford.

Aws is not really the best for ML related things

No, they preprogrammed how to break down that sentence into individual parts and process each one and how they relate to each other.

What you have seemed to miss, is the part of my statement in parenthesis.

> No, they preprogrammed how to break down that sentence into individual parts and process each one and how they relate to each other.

If you meant "processing", why did you say "stimuli and responses", neither of which is processing?

Regardless, also no, they almost certainly didn't train it grammar either. Similarly to how you don't teach your 2 year old child sentence diagramming, in most AI like this, it picks it up from examples, not explicit rules.

They did program in basic fundamentals of learning and conditioning though. Much like your human genes programmed into you, since newborn infants already demonstrate reinforcement learning...

Anyone have the actual report on this to link? The ‘source’ is not easy to find. I very much doubt there was a 100x improvement in AI train time in a single year without a major major caveat. I’m going to guess that there was some major improvement in the imagenet algorithm itself OR people learned how to train AI on GPUs (which I swear was done much earlier than 2018).

What type of scale is that?