[ACTIVITY]: Exploratory Visit to the Bay Area
In my opinion, living anywhere other than the center of your industry is a mistake. A lot of people — those who don’t live in that place — don’t want to hear it. But it’s true. Geographic locality is still — even in the age of the Internet — critically important if you want to maximize your access to the best companies, the best people, and the best opportunities. You can always cite exceptions, but that’s what they are: exceptions.
- Marc Andreessen
Like many people in the technology industry, I have been thinking seriously about moving to the Bay Area. However, before I decide to move, I want to do a lot of information gathering. Some basic pieces of information - employment prospects, cost of living statistics, and weather averages - can be found online. But I feel that one's quality of life is determined by a large number of very subtle factors - things like walkability, public transportation, housing quality/dollar of rent, lifestyle options, and so on. These kinds of things seem to require first-hand, in-person examination. For that reason, I'm planning to visit the Bay Area and do an in-depth exploration next month, August 20th-24th.
My guess is that a significant number of LWers are also thinking about moving to the Bay Area, and so I wanted to invite people to accompany me in this exploration. Here are some activities we might do:
- Travel around using public transportation. Which places are convenient to get from/to, and which places aren't?
- Visit the offices of the major tech companies like Google, Facebook, Apple, and Twitter. Ask some of their employees how they feel about being a software engineer in Silicon Valley.
- Eat at local restaurants - not so much the fancy/expensive ones, but the ones a person might go to for a typical, everyday lunch outing.
- See some of the sights. Again, the emphasis would be on the things that would affect our everyday lifestyle, should be decide to move, not so much on the tourist attractions. For example, the Golden Gate Bridge is an awesome structure, but I doubt it would improve my everyday life very much. In contrast, living near a good running trail would be a big boost to my lifestyle.
- Do some apartment viewing, to get a feel for how much rent a good/medium/student apartment costs in different areas and how good the amenities are.
- Go to some local LW meetups, if there are any scheduled for the time window.
- Visit the Stanford and UC Berkeley campuses and the surrounding areas.
- Interact with locals and ask them about their experience living in the region
- Visit a number of different neighborhoods, to try to get a sense of the pros and cons of each
- Discuss how to apply Bayesian decision theory to the problem of finding the optimal place to live ;)
Job Postings?
I often come across job postings that require a skill set - math, statistics, programming, etc - that might make them ideal for some LW readers. Since finding fun jobs for LWers seems like a good thing to do, I often think I should post these job profiles to the discussion section.
But nobody else has done this, and it seems like the kind of thing that people might get annoyed about. Certainly we don't want LW to be overrun with recruiters.
Any thoughts?
Significance of Compression Rate Method
Summary: The significance of the Compression Rate Method (CRM) is that it justifies a form of empirical inquiry into aspects of reality that have previously resisted systematic interrogation. Some examples of potential investigations are described. A key hypothesis is discussed, and the link between empirical science and lossless data compression is emphasized.
In my previous post, the protagonist Sophie developed a modified version of the scientific method. It consists of the following steps:
- Obtain a large database T related to a phenomenon of interest.
- Develop a theory of the phenomenon, and instantiate the theory as a compression program.
- Test the theory by invoking the compressor on T and measuring the net codelength achieved (encoded data plus length of compressor).
- Given two rival theories of the phenomenon, prefer the one that achieves a shorter net codelength.
This modified version preserves two of the essential attributes of the traditional method. First, it employs theoretical speculation, but guides and constrains that speculation using empirical observations. Second, it permits Strong Inference by allowing the field to make decisive comparisons between rival theories.
The key difference between the CRM and the traditional method is that the former does not depend on the use of controlled experiments. For that reason, it justifies inquiries into aspects of empirical reality that have never before been systematically interrogated. The kind of scientific theories that are tested by the CRM depend on the type of measurements in the database target T. If T contains measurements related to physical experiments, the theories of physics will be necessary to compress it. Other types of data lead to other types of science. Consider the following examples:
Link: Strong Inference
The paper "Strong Inference" by John R. Platt is a meta-analysis of scientific methodology published in Science in 1964. It starts off with a wonderfully aggressive claim:
Scientists these days tend to keep up a polite fiction that all science is equal.
The paper starts out by observing that some scientific fields progress much more rapidly than others. Why should this be?
Development of Compression Rate Method
Summary: This post provides a brief discussion of the traditional scientific method, and mentions some areas where the method cannot be directly applied. Then, through a series of thought experiments, a set of minor modifications to the traditional method are presented. The result is a refined version of the method, based on data compression.
Related to: Changing the Definition of Science, Einstein's Arrogance, The Dilemma: Science or Bayes?
ETA: For those who are familiar with notions such as Kolmogorov Complexity and MML, this piece may have a low ratio of novelty:words. The basic point is that one can compare scientific theories by instantiating them as compression programs, using them to compress a benchmark database of measurements related to a phenomenon of interest, and comparing the resulting codelengths (taking into account the length of the compressor itself).
Preface to a Proposal for a New Mode of Inquiry
Summary: The problem of AI has turned out to be a lot harder than was originally thought. One hypothesis is that the obstacle is not a shortcoming of mathematics or theory, but limitations in the philosophy of science. This article is a preview of a series of posts that will describe how, by making a minor revision in our understanding of the scientific method, further progress can be achieved by establishing AI as an empirical science.
The field of artificial intelligence has been around for more than fifty years. If one takes an optimistic view of things, its possible to believe that a lot of progress has been made. A chess program defeated the top-ranked human grandmaster. Robotic cars drove autonomously across 132 miles of Mojave desert. And Google seems to have made great strides in machine translation, apparently by feeding massive quantities of data to a statistical learning algorithm.
But even as the field has advanced, the horizon has seemed to recede. In some sense the field's successes make its failures all the more conspicuous. The best chess programs are better than any human, but go is still challenging for computers. Robotic cars can drive across the desert, but they're not ready to share the road with human drivers. And Google is pretty good at translating Spanish to English, but still produces howlers when translating Japanese to English. The failures indicate that, instead of being threads in a majestic general theory, the successes were just narrow, isolated solutions to problems that turned out to be easier than they originally appeared.
Two Challenges
Followup To: Play for a Cause, Singularity Institute $100k Challenge Grant
In the spirit of informal intellectual inquiry and friendly wagering, and with an eye toward raising a bit of money for SIAI, I offer the following two challenges to the LW community.
Challenge #1 - Bayes' Nets Skeptics' Challenge
Many LWers seem to be strong believers in the family of modeling methods variously called Bayes' Nets, belief networks, or graphical models. These methods are the topic of two SIAI-recommended books by Judea Pearl: "Probabilistic Reasoning in Intelligent Systems" and "Causality: Models, Reasoning and Inference".
The belief network paradigm has several attractive conceptual features. One feature is the ability of the networks to encode conditional independence relationships, which are intuitively natural and therefore attractive to humans. Often a naïve investigation of the statistical relationship between variables will produce nonsensical conclusions, and the idea of conditional independence can sometimes be used to unravel the mystery. A good example would be a data set relating to traffic accidents, which shows that red cars are more likely to be involved in accidents. But it's nearly absurd to believe that red cars are intrinsically more dangerous. Rather, red cars are preferred by young men, who tend to be reckless drivers. So the color of a car is not independent of the likelihood of a collision, but it is conditionally independent given the age and sex of the person driving the car. This relationship could be expressed by the following belief network:
Link: Interview with Vladimir Vapnik
I recently stumbled across this remarkable interview with Vladimir Vapnik, a leading light in statistical learning theory, one of the creators of the Support Vector Machine algorithm, and generally a cool guy. The interviewer obviously knows his stuff and asks probing questions. Vapnik describes his current research and also makes some interesting philosophical comments:
V-V: I believe that something drastic has happened in computer science and machine learning. Until recently, philosophy was based on the very simple idea that the world is simple. In machine learning, for the first time, we have examples where the world is not simple. For example, when we solve the "forest" problem (which is a low-dimensional problem) and use data of size 15,000 we get 85%-87% accuracy. However, when we use 500,000 training examples we achieve 98% of correct answers. This means that a good decision rule is not a simple one, it cannot be described by a very few parameters. This is actually a crucial point in approach to empirical inference.
Link: The Case for Working With Your Hands
The NYTimes recently publised a long semi-autobiographical article written by Michael Crawford, a University of Chicago Phd graduate who is currently employed as a motorcycle mechanic. The article is partially a somewhat standard lament about the alienation and drudgery of modern corporate work. But it is also very much about rationality. Here's an excerpt:
As it happened, in the spring I landed a job as executive director of a policy organization in Washington. This felt like a coup. But certain perversities became apparent as I settled into the job. It sometimes required me to reason backward, from desired conclusion to suitable premise. The organization had taken certain positions, and there were some facts it was more fond of than others. As its figurehead, I was making arguments I didn’t fully buy myself. Further, my boss seemed intent on retraining me according to a certain cognitive style — that of the corporate world, from which he had recently come. This style demanded that I project an image of rationality but not indulge too much in actual reasoning. As I sat in my K Street office, Fred’s life as an independent tradesman gave me an image that I kept coming back to: someone who really knows what he is doing, losing himself in work that is genuinely useful and has a certain integrity to it. He also seemed to be having a lot of fun.
I think this article will strike a chord with programmers. A large part of the satisfaction of motorcycle work that Crawford describes comes from the fact that such work requires one to confront reality, however harsh it may be. Reality cannot be placated by hand-waving, Powerpoint slides, excuses, or sweet talk. But the very harshness of the challenge means that when reality yields to the finesse of a craftsman, the reward is much greater. Programming has a similar aspect: a piece of software is basically either correct or incorrect. And programming, like mechanical work, allows one to interrogate and engage the system of interest through a very high-bandwidth channel: you write a test, run it, tweak it, re-run, etc.
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