7 ms·

Seems like a weirdly timed article given the new S curve is about to start with ML finding numerous real world applications.

brandall10 4y ago
If anything, I take articles like this in mainstream media as a canary in the coalmine that something big is about to happen.
- meghan_rain 4y ago
  Can you elaborate what you mean?
  - kqr 4y ago
    Not the GP, but this is how one can view it: When the leaps in progress are so large that the general public does not understand that they have happened, or see them somehow as lateral moves rather than forward moves, a lot has happened in a short span of time.
  - dvngnt_ 4y ago
    something something lagging indicator
  - dpkirchner 4y ago
    If progress slows or plateaus, the inevitable breakthrough will really shine.
- grugagag 4y ago
  Some kind of technological shift that will make nowadays programming seem like chiseling furniture by axe. And we’re all just going to watch dumbfounded when things are going to get to complicated to understand by man.
  - jmoak3 4y ago
    Love this post for some similar thinking: https://www.cold-takes.com/this-cant-go-on/ https://www.cold-takes.com/this-cant-go-on/ The world is a crazy place, could get crazier. Or not. I don't know.
pinewurst 4y ago
It’s a promotional piece because Smil has (yet another) new book coming out. I’ve seen it (thanks Z-Library) and am distinctly underwhelmed. It’s about tech hype and failure but says nothing new and even the rehashes are poorly done, judging from the areas I personally know best. Vaclav Smil started off as a knowledgeable commentator on sci-tech (his fertilizer book is especially nice) but has devolved to basically a brand, with the work (such as it is) done by an army of grad assistants. Very surface stuff beloved by the likes of Bill Gates who confuse surface with depth.
endisneigh 4y ago
What are the real world applications you’re referring to?
- cosmodisk 4y ago
  It can be anything really. Better medication design, discovery and modelling of new compounds, disease discovery, better understanding of our environment, etc.
  - OnlineGladiator 4y ago
    So why aren't we already aware of what these applications are since ML has already been well-funded and hyped for about a decade? Basically what is the inflection point? Obviously this is subjective, but underwhelmed would be a compliment for how I feel about ChatGPT. I've been hearing about these near-term breakthroughs for nearly a decade, even working in the industry. And yet, the real-world progress is nothing compared to the hype (or funding). So why now? What's changed?
    - lostlogin 4y ago
      I’m not the OP, and I’m a radiographer. Big things are happening with image processing in radiology. ML/Deep Learning or whatever the marketing people call it is what is being done behind the scenes in Siemens ‘Deep Resolve’ for MR systems. It’s utterly transformed what we do. It adds signal in an initial processing stage in the k-space domain (I’d estimate 25%-30%) then reconstructs the image. Then it doubles the resolution (or does it quadruple?) with double the pixels in the x direction, double in the y. It does this based on a training dataset of paired images, one high resolution, one low resolution. Images are now obtained quicker and are higher resolution than ever. Imaging protocols have extra sequences as time constraints are reduced. Patients can stay still for the short scans we are doing. Not ever scan benefited the same way, and some sequences don’t have the tech yet. The images are fantastic. It’s a larger change than the move to high field magnets (1.5T to 3T) and the hype about it isn’t anywhere near enough. I’m lucky to be able to compare imaging with and without the technology applied, to be able to mess about with it and find the rough edges (they are unexpected and a little counter intuitive) but I’d be trying to avoid systems without it (or an equivalent) as a system user or a patient. The future is very very bright.
deleted 4y ago
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cnqso 4y ago
The presence of currently unfolding areas of innovation doesn't discredit the fact that the exponential progress we've enjoyed since the Industrial Revolution has slowed suddenly over the past few decades.
jszymborski 4y ago
Between this and the "Save money by skipping breakfast" piece, the WSJ is quickly affirming all my suspicions that it's editorial section can be safely ignored.
- speedgoose 4y ago
  Well, it’s also owned by Rupert Murdoch.
peacefulhat 4y ago
True. The article is about hardware, though. Luckily, we're getting a lot more use out of GPUs and SSDs at a time when improvement in CPUs and RAM has drastically slowed.
shrimp_emoji 4y ago
ML is capitalizing on the remaining facets of tech that aren't slowing down (parallel computing as in GPUs and memory capacity). Core speeds? RIP. :'(
plaidfuji 4y ago
It’s funny you say “about to start” - I feel like I’m in a minority here but I’m kind of suspicious that we’re at the end of the S-curve of ML, and the real-world applications are well-known: - A/B testing for UI design - Ml modeling for ad targeting and optimization - fraud detection - image classification, object recognition and segmentation (lots of neat Snap filters, many niche CV apps with drones, industrial sensors, medical diagnostic tools, self-driving cars???) - text classification (sentiment analysis) - text generation (translation, document summaries, writing assistants) - niche academic/scientific applications (simulation acceleration) Empirical modeling has been around for more than a century, there’s just been an explosion of image and text data that have demanded a new generation of empirical models (ML) - it’s resulted in a vast array of decision-acceleration applications, but aside from ChatGPT it feels like the boundaries are starting to be felt more concretely these days.
- hnfong 4y ago
  It remains to be seen how far GPT-like models can go. If we can teach it to be consistently better at humans in doing tasks that require intelligence, and if the costs of running the AI models are smaller than hiring people to do those things, things could get out of hand pretty quickly. One could doubt whether GPTs really can outperform humans consistently in areas that matter, but we haven't even tried yet.
deleted 4y ago
[deleted]

brandall10 4y ago

If anything, I take articles like this in mainstream media as a canary in the coalmine that something big is about to happen.

meghan_rain 4y ago
Can you elaborate what you mean?
- kqr 4y ago
  Not the GP, but this is how one can view it: When the leaps in progress are so large that the general public does not understand that they have happened, or see them somehow as lateral moves rather than forward moves, a lot has happened in a short span of time.
- dvngnt_ 4y ago
  something something lagging indicator
- dpkirchner 4y ago
  If progress slows or plateaus, the inevitable breakthrough will really shine.
grugagag 4y ago
Some kind of technological shift that will make nowadays programming seem like chiseling furniture by axe. And we’re all just going to watch dumbfounded when things are going to get to complicated to understand by man.
- jmoak3 4y ago
  Love this post for some similar thinking: https://www.cold-takes.com/this-cant-go-on/ https://www.cold-takes.com/this-cant-go-on/ The world is a crazy place, could get crazier. Or not. I don't know.

pinewurst 4y ago

It’s a promotional piece because Smil has (yet another) new book coming out. I’ve seen it (thanks Z-Library) and am distinctly underwhelmed. It’s about tech hype and failure but says nothing new and even the rehashes are poorly done, judging from the areas I personally know best. Vaclav Smil started off as a knowledgeable commentator on sci-tech (his fertilizer book is especially nice) but has devolved to basically a brand, with the work (such as it is) done by an army of grad assistants. Very surface stuff beloved by the likes of Bill Gates who confuse surface with depth.

endisneigh 4y ago

What are the real world applications you’re referring to?

cosmodisk 4y ago
It can be anything really. Better medication design, discovery and modelling of new compounds, disease discovery, better understanding of our environment, etc.
- OnlineGladiator 4y ago
  So why aren't we already aware of what these applications are since ML has already been well-funded and hyped for about a decade? Basically what is the inflection point? Obviously this is subjective, but underwhelmed would be a compliment for how I feel about ChatGPT. I've been hearing about these near-term breakthroughs for nearly a decade, even working in the industry. And yet, the real-world progress is nothing compared to the hype (or funding). So why now? What's changed?
  - lostlogin 4y ago
    I’m not the OP, and I’m a radiographer. Big things are happening with image processing in radiology. ML/Deep Learning or whatever the marketing people call it is what is being done behind the scenes in Siemens ‘Deep Resolve’ for MR systems. It’s utterly transformed what we do. It adds signal in an initial processing stage in the k-space domain (I’d estimate 25%-30%) then reconstructs the image. Then it doubles the resolution (or does it quadruple?) with double the pixels in the x direction, double in the y. It does this based on a training dataset of paired images, one high resolution, one low resolution. Images are now obtained quicker and are higher resolution than ever. Imaging protocols have extra sequences as time constraints are reduced. Patients can stay still for the short scans we are doing. Not ever scan benefited the same way, and some sequences don’t have the tech yet. The images are fantastic. It’s a larger change than the move to high field magnets (1.5T to 3T) and the hype about it isn’t anywhere near enough. I’m lucky to be able to compare imaging with and without the technology applied, to be able to mess about with it and find the rough edges (they are unexpected and a little counter intuitive) but I’d be trying to avoid systems without it (or an equivalent) as a system user or a patient. The future is very very bright.

deleted 4y ago

[deleted]

cnqso 4y ago

The presence of currently unfolding areas of innovation doesn't discredit the fact that the exponential progress we've enjoyed since the Industrial Revolution has slowed suddenly over the past few decades.

jszymborski 4y ago

Between this and the "Save money by skipping breakfast" piece, the WSJ is quickly affirming all my suspicions that it's editorial section can be safely ignored.

speedgoose 4y ago
Well, it’s also owned by Rupert Murdoch.

peacefulhat 4y ago

True. The article is about hardware, though. Luckily, we're getting a lot more use out of GPUs and SSDs at a time when improvement in CPUs and RAM has drastically slowed.

shrimp_emoji 4y ago

ML is capitalizing on the remaining facets of tech that aren't slowing down (parallel computing as in GPUs and memory capacity). Core speeds? RIP. :'(

plaidfuji 4y ago

It’s funny you say “about to start” - I feel like I’m in a minority here but I’m kind of suspicious that we’re at the end of the S-curve of ML, and the real-world applications are well-known: - A/B testing for UI design - Ml modeling for ad targeting and optimization - fraud detection - image classification, object recognition and segmentation (lots of neat Snap filters, many niche CV apps with drones, industrial sensors, medical diagnostic tools, self-driving cars???) - text classification (sentiment analysis) - text generation (translation, document summaries, writing assistants) - niche academic/scientific applications (simulation acceleration) Empirical modeling has been around for more than a century, there’s just been an explosion of image and text data that have demanded a new generation of empirical models (ML) - it’s resulted in a vast array of decision-acceleration applications, but aside from ChatGPT it feels like the boundaries are starting to be felt more concretely these days.

hnfong 4y ago
It remains to be seen how far GPT-like models can go. If we can teach it to be consistently better at humans in doing tasks that require intelligence, and if the costs of running the AI models are smaller than hiring people to do those things, things could get out of hand pretty quickly. One could doubt whether GPTs really can outperform humans consistently in areas that matter, but we haven't even tried yet.

deleted 4y ago

[deleted]