The Microsoft Data Science Virtual Machine (DSVM) now comes pre-configured with Microsoft R Server Developer Edition.
As you can scale the DSVM according to your needs, this is an easy way to get going with some heavy duty R computations.
Google’s self-driving car causes crash
The New Scientist reports that one of Google’s autonomous cars drove into a bus on 14 February 2016.
Apparently Google’s cars have been involved in 18 accidents in Mountain View since it started testing in 2010. All have been other vehicles striking a stationary or slow moving Google car. However, in the latest incident the AI decided to pull out into the path of the slow-moving bus—i.e. the AI was at fault.
What interested me was the explanation given by Google
Our car had detected the approaching bus, but predicted that it would yield to us because we were ahead of it
As the driver is software, it’s possible to to examine the logs and know exactly how and why the accident occurred. Obviously, things are rarely as clear-cut when human drivers are involved in accidents. This makes it a lot easier to determine where the problem lies and, more importantly, to reduce the chances of it happening again. A single incident produces a bunch of useful data.
The ability to audit the behavior of autonomous cars is going to be one of their big selling points.
Game over—computers can beat us at Space Invaders
It appears that the singularity is here.
Google Deepmind have created an adaptation of the DQN algorithm—which combines Q-learning with a deep neural network—to enable a computer to beat us at 31 Atari classics (including Space Invaders).
Any other task that AI could be applied to now seems so unimportant…
Apple steps up recruitment of machine learning experts
Apple is looking to recruit another 86 artificial intelligence experts, according to an article on VentureBeat.
The recruitment drive is due to concerns that they are falling behind Google, Amazon, Facebook and Microsoft in the area of machine learning. Competitors seem to be stealing a march on Apple by developing services that can anticipate users’ requirements—services that rely on sophisticated machine learning capabilities.
One of the challenges facing Apple is their privacy policy. Machine learning is data hungry and researchers crave lots of high quality data. As Apple leaves a substantial proportion of data on users’ devices—rather than storing it in the cloud—they have less data to play with than their peers. Less data means less effective machine learning solutions and, critically, also makes Apple a less attractive employer for ambitious PhDs.
A visual introduction to machine learning
Interesting first post in a planned series that uses visualization to explain machine learning.