In contrastive divergence the Kullback-Leibler divergence (KL-divergence) between the data distribution and the model distribution is minimized (here we assume to be discrete):
Here is the observed data distribution, is the model distribution and are the model parameters. A divergence (wikipedia) is a fancy term for something that resembles a metric distance. It is not an actual metric because the divergence of given can be different (and often is different) from the divergence of given . The Kullback-Leibler divergence exists only if implies .
The Yoga 900 is a beautiful machine that has a considerably long battery lifetime and can be folded such that it functions as a tablet. The Yoga arrived on Friday and the entire Crownstone team was enjoying how it came out of the box: it lifts up! If you’re creating your own hardware you suddenly appreciate how other people pay attention to packaging!
According to many the future is a socialist paradise. The autonomous car will change everything! We will be car sharing.
We can change parking lots into a lot of parks!
Blame the humans
Let us put aside the technical difficulties in developing autonomous cars. It might take many more years than currently
predicted by the new players in this old industry. For example, Sebastian Thrun recently told us in a
lecture at Delft
that his cars are more careful than humans by design and henceforth safer. However, there are grounds to expect that
being more aggressive is safer in certain circumstances! Going over the speed limit when you have to pass a car.
Speeding up considerably before merging into a fast moving lane on the highway. Can this combination of ``aggression’’ and
trust in other drivers be learnt? Or should humans be the ones blamed for slamming into unpredictable autonomous cars!? Anyway, let’s assume these are all minor
tweaks that don’t require any form of procedural and contextual intelligence that we possess as humans. We will have
these autonomous cars in 2020, everything is fancy, and humans can be blamed for all accidents.
Imagine one of the first AIs coming online. What is it gonna read about itself? How would it feel? Would it feel welcome? What is definitely the case is that it will learn a lot about humans. This is for example what Musk is saying about this alien life form:
“With artificial intelligence we are summoning the demon. In all those stories where there’s the guy with the pentagram and the holy water, it’s like – yeah, he’s sure he can control the demon. Doesn’t work out.”
We have put the Crownstone on Kickstarter, a smart power outlet with quite sophisticated technology. I think it’s nice for the general hacker community to get some more insight on the technology behind it.
A key problem (or challenge) within smart spaces is indoor localization: making estimates of users’ whereabouts. Without such information, systems are unable to react on the presence of users or, sometimes even more important, their absence. This can range from simply turning the lights on when someone enters a room to customizing the way devices interact with a specific user.
Even more important for a system to know where users exactly are, is to know where users are relative to the devices it can control or use to sense the environment. This relation between user and device location is an essential input to these systems.
At DoBots we have been working on robotics already for quite some time. One of the most well-known robotic algorithms
is SLAM (simultaneous localization and mapping). We have been porting these algorithms to the scenario in which we
have a human walking around, rather than a robot.
You can read more on the DoBots blog.
At first thought, it might seem that device recognition is not possible. There are devices that use the same amount
of power. However, after contemplating for a bit there are actually three ways in which more information can be
obtained. Firstly, by measuring voltage as well as current, we can measure reactive power. So, we can distinguish
motors from lamps quite easily. Secondly, we can observe the consumption pattern over the day. Thirdly, we can sample
at a very high frequency and detect disturbances on the current curve. A device leaves its signature on the grid. The
third option is something we keep for later, but which is of course quite interesting.
Observing a device over a longer time period leads to current curves such as these:
It is a fridge that turns on and off at regular time intervals. It is quite clear from this curve that the actual
power consumption value is not so relevant: the form is really telling!
We subsequently pool all kind of these features with boosting methods from machine learning. Boosting methods are collections of
weak classifiers. The particular classifier we have been testing is a random committee classifier. You can read more
on the DoBots blog again.
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