In middle 2014 I started thinking about a data-gathering tool for quantified-self info, with the explicit purpose of treating life as a machine learning problem. I am happy to say that for the past year, Joseph Lewis and I have been working on something I think is quite incredible.
We are very close to having a first version of the software we wrote available. I just wanted to say that I can’t wait to show off what we have accomplished. Our hope is to have a nice demo running by January!
In the last week of September I started on a very interesting personal project. I was finished with homework for the week, and decided that it couldn’t hurt to play around with an idea that was bothering me for a while.
“It should take me no more than a couple days to get something up and running”, I thought.
One month later…
Nothing is up and running yet.
For the past 4 weeks I have spent every second of my free time (and some not-so-free time stolen from other obligations) to work on this project.
It wasn’t all for nothing, though. I have finally reached the first milestone. What does that mean? Well, I have the code that underlies the thing that will underlie the actual code that does stuff. In effect, I have finished a very convoluted and complex boilerplate. So about 500 more milestones, and I might have the equivalent of “hello world”.
Nevertheless, I am very proud of myself, despite not having much to show.
Now it is time for a day or two of break, so that I can catch up with my obligations, and then on to milestone 2!
The Great Balancing Act
There are several methods for reading papers. There is the “read-on-a-laptop” method, which gives a good idea of what is going on, and then there is the “print-the-paper-and-spend-6-hours-going-through-it-word-by-word” method. The second method seems to inevitably... continue »
The king is dead, long live the king!
The time has come for me to say goodbye to the Kwiat Group, whose members have for the past 3 years been incessantly a source of friendship and inspiration. It was there that I first tasted the joys of research. I am extremely grateful to them for helping me discover one of the greatest sources of joy and satisfaction in my life. I will definitely bring them many cookies next time I am in town.
In leaving Kwiat Group, I am leaving the world of Physics research, and moving on to Machine Learning. I’m spending the summer working with Dan Gauthier’s group at Duke University, focusing on Reservoir Computing.
Imagine throwing a pebble into a pond which has some nonlinear properties. It turns out that it might be possible to more easily extract information about the pebble’s properties through the excitations of the waves and movements of the pond upon the pebble’s impact, than by looking at the original pebble! These Liquid State Networks, which are called Reservoir Computers when in the form of a Neural Network, can achieve good performance in machine learning tasks, despite having only one hidden layer, which is both generated randomly and fixed!
Sounds awesome, so I will see where this track of research leads me.