The Instant Rise of Machine Intelligence?

December 28, 2016
By

(This article was first published on Florian Teschner, and kindly contributed to R-bloggers)

Currently the news are filled with articles about the rise of machine intelligence, artificial intelligence and deep learning.
For the average reader it seems that there was this single technical breakthrough that made AI possible. While I strongly believe in the fascinating opportunities around deep learning for image recognition, natural language processing and even end-to-end “intelligent” systems (e.g. chat bots), I wanted to get a better feeling of the recent technological progress.

First I read about tensorflow (for R) and watched a number of great talks about it. Do not miss Nuts and Bolts of Applying Deep Learning (Andrew Ng) and Tensorflow and deep learning – without at PhD by Martin Görner. Second I started to look at publications and error improvements on public datasets.
There is surprisingly little information about the improvement rate of machine learning on public datasets. I found one great resource I would like to analyse in the following post.
All datasets (“MINST”,”CIFAR-10”, “CIFAR-100”, “STL-10”, “SVHN”) are image classification tasks and results are published in academic (peer-reviewed) outlets.
In order to better aggregate the results, I report the trimmed (10 percent) mean error rate per year per dataset.

plot of chunk unnamed-chunk-2

We see that the mean reported error drops in all datasets per year. Each panel has it’s own x,y-scales, however inspected closely, we see that there is no apparent drop in the error rate in one particular year. Rather, it seems that the improvement rate per dataset is a linear function of the time.
To get a better look at the best performer, let’s do the same plot with just the lowest reported error rates per year.

plot of chunk unnamed-chunk-3

Again, there is not a single year that appears to mark the rise of machines but it looks like a continuous process.
If it is a continuous process, let’s quickly summarise the learning rate per dataset.

Dataset Improvement Years PP. Improvement per Year
CIFAR-10 3% 6 0.4%
CIFAR-100 29% 5 5.9%
MINST 63% 13 4.8%
STL-10 36% 5 7.1%
SVHN 16% 6 2.6%

The improvement column lists the percent improvement from the first year best publication to the current best publication. The dataset have been around for various timeframes (indicated in column 2). Finally we get the percentage point increase per year. While the improvement varies, across the board it seem that 5% improvement is reasonable.

So if there is not a single year that marks instant spike in improvement, what is the hype about? I assume that with the steady process in the recent years AI seems to approach or even surpass human-level performance on some tasks. Basically the news is not a technology breakthrough but rather a passing of an important threshold.

In case you want to have a look at the data yourself:

To leave a comment for the author, please follow the link and comment on their blog: Florian Teschner.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)