8 ms·

The AI Index 2019 Report

103 points by smt1 7y ago

account73466 7y ago
>> Diversifying AI faculty along gender lines has not shown great progress, with women comprising less than 20% of the new faculty hires in 2018. I am wondering what is % of women doing PhD in the same field and whether it is growing. Without the latter number growing it would be hard to have a greater % of women among new faculty hires.
- Proven 7y ago
  But that has nothing to do with AI and in fact should be off topic
- grammarxcore 7y ago
  We'd also need to see numbers on new positions and freshly available positions. Even if the number of candidates is relatively stagnant, they can't get jobs if their number is larger than the number of positions available. Many of the doctoral candidates I knew in school couldn't find positions in academia because of market saturation.
  - account73466 7y ago
    >> Many of the doctoral candidates I knew in school couldn't find positions in academia because of market saturation. The situation is now drastically different compared to say 40 years ago. That is why so many PhDs leave for industry to be better paid. The latter is possible for CS guys but in other domains the situation is pretty bad since they don't have easily accessible highly paid industry jobs.
scottlocklin 7y ago
I'm pretty sure there is a ~2002 "Nanotech index report" out there which looks a lot like this. Oh looksie; I am right: http://www.nanotech-now.com/CMP-reports/NOR_White_Paper-July2002.pdf http://www.nanotech-now.com/CMP-reports/NOR_White_Paper-July...
- account73466 7y ago
  True but I am not sure "nanotech" was used by top S&P 500 companies and their engineers on a daily basis.
  - scottlocklin 7y ago
    Newsflash: neither is "AI." Unless you expand the word "nanotech" to include things like toothpaste and paint, or the word "AI" to mean things like logistic regression and random forests.
    - MLpractitioner 7y ago
      AI for standard machine learning is definitely widely used in most industries
      - OnlineGladiator 7y ago
        I'm curious - how do you know it's used for most industries? I'm sure there are lots of industries that use ML, and lots that don't. Is there a report on this somewhere? I just get a lot of worthless hype articles when I google it.
        account73466 7y ago
        You see that from papers published at top ML conferences. Top contributors are often from FAANG. Some papers are clearly applications.
        OnlineGladiator 7y ago
        FAANG isn't "most industries" by any stretch. This ignores healthcare, automotive, pharmaceutical, agricultural, and so many more industries. All you've done is point out that there are a lot of ML papers coming from tech companies. Nobody is disputing that. Why do you assume this generalizes to most industries?
keithyjohnson 7y ago
I get that these bullets points are answering What instead of Why but for those that are more readily discernible, like "In a year-and-a-half, the time required to train a large image classification system on cloud infrastructure has fallen from about three hours in October 2017 to about 88 seconds", what's causing this? Are models getting smaller without a loss in accuracy? Is training distributed over a greater amount of cheaper machines? Personally, I'd be more excited about the former rather than the latter. We can't all afford MegatronLM-type experiments - https://nv-adlr.github.io/MegatronLM https://nv-adlr.github.io/MegatronLM.
- ummonk 7y ago
  Both. Companies are certainly building bigger and bigger clusters for training. At the same time though, consumer GPUs have gotten significantly faster (compare e.g. an Nvidia 2080TI to a 980TI), and learning algorithms keep improving / better learning algorithms become more widely used (e.g. Adam instead of stochastic gradient descent).
  - antpls 7y ago
    And also, architectural search allowed for neural networks to use more efficient builtin blocks, using many less parameters, and achieving the same accuracy with smaller models (and lowering training cost)
- cyorir 7y ago
  The improvements in the report are mainly from improvements in cloud infrastructure, but that's not to say there haven't been improvements in developing small, efficient models as well. One notable model that was introduced in 2017 was MobileNet, which aimed to create a model that could function on a mobile device without much loss in accuracy. There have been many more attempts to shrink models for use on devices with limited resources since 2017. These smaller models tend to have lower training times as well.
- hooande 7y ago
  read the actual report instead of just the bullet points. the speed improvement is a function of cost on cloud hardware
deleted 7y ago
[deleted]
utopian3 7y ago
> In a year-and-a-half, the time required to train a large image classification system on cloud infrastructure has fallen from about three hours in October 2017 to about 88 seconds in July 2019. During the same period, the cost to train such a system has fallen similarly. 3 hours to 88 seconds? Wow... I wonder if that's been further reduced today (December 2019)

103 points by smt1 7y ago

>> Diversifying AI faculty along gender lines has not shown great progress, with women comprising less than 20% of the new faculty hires in 2018. I am wondering what is % of women doing PhD in the same field and whether it is growing. Without the latter number growing it would be hard to have a greater % of women among new faculty hires.

Proven 7y ago
But that has nothing to do with AI and in fact should be off topic
grammarxcore 7y ago
We'd also need to see numbers on new positions and freshly available positions. Even if the number of candidates is relatively stagnant, they can't get jobs if their number is larger than the number of positions available. Many of the doctoral candidates I knew in school couldn't find positions in academia because of market saturation.
- account73466 7y ago
  >> Many of the doctoral candidates I knew in school couldn't find positions in academia because of market saturation. The situation is now drastically different compared to say 40 years ago. That is why so many PhDs leave for industry to be better paid. The latter is possible for CS guys but in other domains the situation is pretty bad since they don't have easily accessible highly paid industry jobs.

scottlocklin 7y ago

I'm pretty sure there is a ~2002 "Nanotech index report" out there which looks a lot like this. Oh looksie; I am right: http://www.nanotech-now.com/CMP-reports/NOR_White_Paper-July2002.pdf http://www.nanotech-now.com/CMP-reports/NOR_White_Paper-July...

account73466 7y ago
True but I am not sure "nanotech" was used by top S&P 500 companies and their engineers on a daily basis.
- scottlocklin 7y ago
  Newsflash: neither is "AI." Unless you expand the word "nanotech" to include things like toothpaste and paint, or the word "AI" to mean things like logistic regression and random forests.
  - MLpractitioner 7y ago
    AI for standard machine learning is definitely widely used in most industries
    - OnlineGladiator 7y ago
      I'm curious - how do you know it's used for most industries? I'm sure there are lots of industries that use ML, and lots that don't. Is there a report on this somewhere? I just get a lot of worthless hype articles when I google it.
      - account73466 7y ago
        You see that from papers published at top ML conferences. Top contributors are often from FAANG. Some papers are clearly applications.
        OnlineGladiator 7y ago
        FAANG isn't "most industries" by any stretch. This ignores healthcare, automotive, pharmaceutical, agricultural, and so many more industries. All you've done is point out that there are a lot of ML papers coming from tech companies. Nobody is disputing that. Why do you assume this generalizes to most industries?

keithyjohnson 7y ago

I get that these bullets points are answering What instead of Why but for those that are more readily discernible, like "In a year-and-a-half, the time required to train a large image classification system on cloud infrastructure has fallen from about three hours in October 2017 to about 88 seconds", what's causing this? Are models getting smaller without a loss in accuracy? Is training distributed over a greater amount of cheaper machines? Personally, I'd be more excited about the former rather than the latter. We can't all afford MegatronLM-type experiments - https://nv-adlr.github.io/MegatronLM https://nv-adlr.github.io/MegatronLM.

ummonk 7y ago
Both. Companies are certainly building bigger and bigger clusters for training. At the same time though, consumer GPUs have gotten significantly faster (compare e.g. an Nvidia 2080TI to a 980TI), and learning algorithms keep improving / better learning algorithms become more widely used (e.g. Adam instead of stochastic gradient descent).
- antpls 7y ago
  And also, architectural search allowed for neural networks to use more efficient builtin blocks, using many less parameters, and achieving the same accuracy with smaller models (and lowering training cost)
cyorir 7y ago
The improvements in the report are mainly from improvements in cloud infrastructure, but that's not to say there haven't been improvements in developing small, efficient models as well. One notable model that was introduced in 2017 was MobileNet, which aimed to create a model that could function on a mobile device without much loss in accuracy. There have been many more attempts to shrink models for use on devices with limited resources since 2017. These smaller models tend to have lower training times as well.
hooande 7y ago
read the actual report instead of just the bullet points. the speed improvement is a function of cost on cloud hardware

deleted 7y ago

[deleted]

utopian3 7y ago

> In a year-and-a-half, the time required to train a large image classification system on cloud infrastructure has fallen from about three hours in October 2017 to about 88 seconds in July 2019. During the same period, the cost to train such a system has fallen similarly. 3 hours to 88 seconds? Wow... I wonder if that's been further reduced today (December 2019)