For two weeks last July, I cocooned myself in a hotel in Portland, OR, living and breathing probabilistic programming as a “student” in the probabilistic programming summer school run by DARPA. The school is part of the broader DARPA program on Probabilistic Programming for Advanced Machine Learning (PPAML), which has resulted in a great infusion of energy (and funding) into the probabilistic programming space. Last year was the inaugural one for the summer school, one that is meant to introduce and disseminate the languages and tools being developed to the broader scientific and technology communities. The school was graciously hosted by Galois Inc., which did a terrific job of organizing the event. Thankfully, they’re hosting the summer school again this year (there’s still time to apply!), which made me think that now is a good time to reflect on last year’s program and provide a snapshot of the state of the field. I will also take some liberty in prognosticating on the future of this space. Note that I am by no means a probabilistic programming expert, merely a curious outsider with a problem or two to solve.
Yesterday’s news about the horrific massacre in Paris shook me really hard. I spent the day very upset, and the night puzzled by my extreme reaction. Terrorism attacks have virtually become fixtures of the daily news, with yesterday alone over a dozen killed in Iraq. Why did this bother me so much?
I previously blogged on my adventures in self quantification (QS). In that post I wrote about the general system but did not delve into specific projects. Ultimately however the utility of self quantification is in the detailed insights it gives, and so I’m going to dive deeper into a project that passed a major milestone earlier today: publication of a paper. If you’re interested in the science behind this project, see my other post, A New Way to Read the Genome. Here I will focus on the application and utility of QS as applied to individual projects.
I am pleased to announce that earlier today the embargo was lifted on our most recent paper. This work represents the culmination of over two years of effort by my collaborators and I. You can find the official version on the Nature Genetics website here, and the freely available ReadCube version here. In this post, I will focus on making the science accessible to the lay reader. I have also written another post, The Quantified Anatomy of a Paper, which delves into the quantified-self analytics of this project.
There has been a lot of renewed interest lately in neural networks (NNs) due to their popularity as a model for deep learning architectures (there are non-NN based deep learning approaches based on sum-products networks and support vector machines with deep kernels, among others). Perhaps due to their loose analogy with biological brains, the behavior of neural networks has acquired an almost mystical status. This is compounded by the fact that theoretical analysis of multilayer perceptrons (one of the most common architectures) remains very limited, although the situation is gradually improving. To gain an intuitive understanding of what a learning algorithm does, I usually like to think about its representational power, as this provides insight into what can, if not necessarily what does, happen inside the algorithm to solve a given problem. I will do this here for the case of multilayer perceptrons. By the end of this informal discussion I hope to provide an intuitive picture of the surprisingly simple representations that NNs encode.
It is tempting to assume that with the appropriate choice of weights for the edges connecting the second and third layers of the NN discussed in this post, it would be possible to create classifiers that output over any composite region defined by unions and intersections of the 7 regions shown below.
This post will be a question to you dear reader. Consider the following scenario: Death is no longer at everyone’s doorstep. Any person can choose to live healthily for as long as they wish, with the caveat that no new person can be born unless someone already living decides to “exit”, a euphemism for a completely painless death, something as easy as walking through a door. Thus while one can go on living forever, it would hypothetically deprive some other person from experiencing the joys (and pains) of life and growth.
If you lived in such a world, would you ever exit? If so, what would you first want to do/accomplish before freeing up your spot for someone else? Feel free to comment below.
I recently came across an art installation online by Yang Liu, a Chinese-born artist who lives in Germany. Her series of visual designs contrast the cultural norms and values of China and Germany, and their broader respective civilizations. Being a product of the West and East myself, I was constantly nodding at her images, as they captured much of the cultural differences between my adopted and birth country, a topic on which I have previously written. As I continued scrolling, I found myself “choosing” between which side I preferred best, depending on the topic. These choices, in the form of picking the blue (Germany) or red (China) tile and trivial though they are, in fact summarize one of my life’s larger struggles; the straddling of two different and often incongruous ways of being, and the striving to define an identity that is at once consistent with and is a synthesis of both.
It has now been over a year since my move to Boston from Palo Alto, which seems like a fitting time to take a retrospective look at the two places. My sampling will be far from unbiased, having lived close to 20 years in the Bay Area. As a result this will be more like “Boston through the eyes of a Northern Californian”. There is no specific order to the comparisons below; I will vacillate between the substantive and the frivolous. And there will be no declared winner; both places are far too different and offer far too much for one to dominate the other in the Pareto optimal sense. At times, this will be more about Stanford vs. Harvard than the Bay Area vs. Boston, as much of my experience is ultimately grounded by my local environment.