The AI Index 2019 Report

[scottlocklin]

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-July...

[account73466]

True but I am not sure "nanotech" was used by top S&P 500 companies and their engineers on a daily basis.

[account73466]

>> 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.

[grammarxcore]

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.

[utopian3]

> 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)

[keithyjohnson]

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.

[ummonk]

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).

[cyorir]

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]

read the actual report instead of just the bullet points. the speed improvement is a function of cost on cloud hardware