What are you working on?

I am writing a few papers and a book on machine ethics and superintelligence.

My goals with this work are to:

• Summarize the existing literature from machine ethics on how to design the motivational system of artificial moral agents (a surprisingly little-discussed problem so far; probably less than 5,000 pages in the academic press!) and apply it to the specific problem of superintelligence.

• Update and strengthen the Good / Chalmers argument for why a superintelligence is likely to arise within a few centuries if global catastrophe or active prevention do not occur.

• Explain in detail why a few dozen commonly proposed "solutions" to the problem of Friendly AI will not work. (Basically, catch everybody up to where Eliezer Yudkowsky was as of about 2004.)

• Translate the contributions of the SIAI community to machine ethics into the language of mainstream philosophy and science, to give SIAI more credibility and attract more elites to the cause of solving the Friendly AI problem.

I program by day, and program some more by night. I just finished Go Scoring Camera, an Android app that does computer vision to interpret Go boards, and I'm starting on Keyboard Builder, a tool for customizing the Android Phones' on-screen keyboard. I'll keep writing phone apps until they're sufficient to provide a passive income.

Making bayesian statistics easier and more accessible by coding advanced sampling algorithms for PyMC

Some background: I took statistics in high school because it seemed vaguely useful. Unfortunately, the material seemed very dry and involved mostly memorization and few general principles. It was boring and limited. College statistics was the same thing. I did some internships and statistics seemed very useful for figuring things out, but I didn't know how to do very much.

Later I started reading Overcoming Bias, and Yudkowsky kept mentioning this thing called "Bayes theorem" and how it was really powerful. I read a stats book on Bayesian Statistics and my mind was blown. The statistics that I had been taught was a collection of formulas that gave answers but not much insight, but Bayes theorem encapsulated not just all of the statistics I had learned but the very notion of "learning from data." I was hooked.

Later I figured out that the curse of dimensionality makes complex problems difficult (even though the simple statistics taught in stats classes now are still easy).

My project: Bayes theorem provides a simple coherent framework for learning from data. It massively clarifies how to think about data. It is something all engineers (and technical folk in general) could and should know. Not only is Bayesian stats very practical, but it turns a topic that even nerds find confusing and boring into something elegant and interesting.

I want to make fitting bayesian models as thought free as possible. Calculating the posterior distributions for your models is often very difficult and usually the most constraining issue. This is often true (though less so) even if you know a great deal about statistical computation.

As I have discussed here, I think the current lowest hanging fruit is the use of gradients and higher derivatives in algorithms for sampling from the posterior distribution. Thus my project for the last year + has been to work on improving PyMC, a Python package for doing Bayesian inference, adding gradient information, writing advanced general sampling algorithms found in the literature, improving the syntax of PyMC to make it simpler, more intuitive, and more powerful.

On my blog I linked to a package I built with a sampler that works using Langevin dynamics (uses gradient information), but more recently I have found that Hybrid (or Hamiltonian) Monte-Carlo is practically much simpler and works much better. This is my Hamiltonian MC implementation.

I am currently trying to improve my HMC sampler, and working on making PyMC faster and easier to maintain and extend.

If you know Bayesian stats and have some programming skills, I invite you help me improve statistical computation! just message me!

I'm writing the core of a system similar to Google Wave, but much simpler, more flexible, and mathematically elegant. It works peer-to-peer, too, which is nice. It's got some clever algorithms in it, and even cleverer data structures, and I've been through several iterations of the design and code. It's probably the most complex thing I've ever written, and it could be pretty darn handy in the future. This is a surprisingly difficult problem to solve well.

(I also did a quick detour one-off project: pwstore, for anybody wanting to store passwords in Haskell. I feel pretty good about this one; it's useful.)

I'm also writing a NaNoWriMo book, which I'd left half-finished back in November, because it actually was a lot of fun to write. I've got one wonderfully snarky character; if I give her first-person narrative, the story becomes extra fun. I don't even know how many words I'm up to now. It's a sprawling fantasy adventure thing.

I'm working on editing Shikamaru vs. the Logical Fallacies, a rationalist fanfic that I linked to here, as well as encouraging the author to create more. We recently finished Part 1 of Chapter 1.

I'm doing this to increase my and the author's rationality, to practice my editing skills, to help the author practice his writing skills, because it's fun for me and the author and the readers, and to raise the sanity waterline as much as possible, since this is a chance for readers to also increase their own rationality. Readers can assist in all of these goals by reading the fic, reviewing it, discussing it, and spreading it.

I'm writing a blog post every day in order to cultivate a writing habit, create a useful body of introductory material for the subfields of philosophy I find most worthwhile, and perhaps make a good impression on future employers (I'm still a college student and don't have the competence-demonstrating portfolio I'd like).

Edit: my blog is located at http://www.wrongbot.com

This is a great idea for a thread! We should have these at regular intervals.

I'm working on turning my PhD work into a product. The idea is to take logic-based specifications, expressed as structured english, and produce working information systems (such as web apps) from them. I have a prototype which got me the PhD but I need more work to get to a viable product. An intermediate step I am focusing on at the moment is using this technology to write validation checks for existing large datasets to pick out errors/outliers. So someone would apply the rule "Each patient with diabetes type I must be prescribed insulin" on a relevant dataset and the system would pick out cases where this does not hold. Normally this would need a complex SQL query that must be written by a developer and cannot be verified by the domain expert.

The other thing I'm working on is nurturing my addiction to the 17x17 challenge. This is my first forray into 'serious' math and I'm finding it extremely addictive. I usually dive into this when my motivation is low, and lo and behold, I'm motivated again! Having spent 7 of the last 14 months on this, still no solution, but I do think that if the filters I have work right, I will have trimmed the problem space I'm focusing on to about 52 cpu-days. Now I'm working on implementing these filters and speeding up my primary algorithms to reduce that further.

Finally, my perpetually on hold project is a social news community that provides a personalised experience, is spam-free and requires no moderation. Some of the ideas were expressed in my optimization by proxy articles. The conceptual framework is complete, but coding and productising is too much of an energy draw at the moment, so I'm leaving it aside or pushing it forward at a very slow pace.

If anyone wants to know more about any of the above, just ask.

I'm working on tightening the bounds on the Jordan-Schur theorem. I've improved the best known bounds but not by much. If this project does succeed it might end up turning into my PhD thesis.

1) Expanding my programming capabilities. This is because it's vital if I'm ever to develop one of my other big ideas. I can come up with algorithms and models pretty easily, but am not at the stage where I can put them into a user-friendly, non-command-line format, or build off of others' code (which rarely comes with easy compiling instructions).

I've tried various frameworks, but they're all hard to get started on -- more so than the inherent difficulty of programming would suggest. I follow the instructions just to get it set up, but they always leave out something vital so that I have to get an expert to look at why it doesn't set up right. I've tried Django+Python, the Android SDK, Matlab (incl. with Simulink), and .NET, becoming most proficient with the last two.

It's not all gloomy, though. Some successes: completing a useful internal development project at work that involved setting up a GUI to allow for easy database lookup and clean presentation of data. In technical languages, I've set up something similar but for signal processing and automated generation of relevant plots. I was able to modify a Firefox extension so that I could deftly browse websites from the keyboard alone with one hand (no, not for "that"). Could only figure out how to do it on my Linux box though.

2) "Infiltrating" an anti-rationalist group by purporting to be "one of them". I'm doing it to see what makes these people tick: to what extent they really believe this stuff, how well they achieve their goals (vs. failing in a roundabout way), what kind of useful signaling functions the group can serve, etc. (Obviously I can't go into more detail here, but feel free to PM/email me.)

3) Learning about cryptography (by reading Schneier's Applied Cryptography), hopefully to assimilate it at the 2+ Level. I find it important because it both has practical application, and bears directly on fundamental questions applicable to many circumstances: under what conditions you can make what inferences about a source or the meaning of something; where problem complexity/difficulty can originate, and how to increase or decrease it; what is randomness, etc.

It also trains you to truly understand and represent a system well enough to know its strong and weakest links. I've been impressed at seeing how people with experience in crypto are able to carry its concepts over to a fuzzy, real-world situation and justify counter-intuitive conclusions about why a given practice with actually increase or decrease vulnerability.

4) Experimenting with brain-machine interfaces (got an NIA and the Force trainer, don't laugh). This is because I have an interest in improving interfaces, and want to see what the potential is for removing a major bottleneck in interacting with a computer, or overcoming difficulties. (One idea is to combine NIA with Dasher.) I think there is great potential, both for brain exercise, and for extending expressive capabilities, though a direct brain link.

I am thinking about the design of quantum money, quantum copy-protected programs, and quantum program obfuscation. Basically, understanding whether and how quantum computers can implement the strongest possible cryptographic operations (all of which are known to be classically impossible and to have a wide range of applications).

I am working on the development of collaborative filtering / recommendation protocols for large communities. This includes, for example, a system for aggregating product reviews which is robust to the presence of large numbers of planted reviews, or a system for spam filtering based on trust which remains robust in the presence of a supermajority of sophisticated spammers. More generally, this work seems like an important first step in the development of automated and robust systems of trust.

I started working on these projects because they were interesting problems I was well equipped to work on which seemed likely to yield publications in time for graduate school applications (optimistically, these positions have been born out in both cases). I don't recommend this decision making process.

I think automating trust and designing better recommendation systems are more important than the overwhelming majority of theoretical problems currently studied, but I have realized more recently that there are more important issues to deal with. I am continuing to work on these problems now because of inertia, the fallacy of sunk costs, and a desire for status.

I'm in the early stages of my PhD research in metabolomics, which specifically means that I'm constructing what will end up being a library of tagged proteins to use in probing protein-small metabolite interactions. While the genomes and proteomes of quite a few model organisms are well-understood, cross-pathway regulatory interactions are another extremely important factor in metabolism, and these have had only minimal effort put to characterizing them. My work is therefore aimed at finding and characterizing these interactions on a broad scale, and incorporating that understanding into existing computational models in order to facilitate rational design in biochemical engineering.

I also have a fanfic project going that I started to 1) get better at writing; 2) challenge or avoid the most painfully prevalent tropes of the particular fandom (Labyrinth); and 3) to keep me somewhat sane. I found Eliezer's HPMoR not too terribly long ago (was my favorite method of procrastination while studying for quals, in fact...), and that's given me perhaps altogether too many ideas for other potential study distractions after I finish the story. http://www.fanfiction.net/s/6347718/1/bTalespinner_b

Investigating programmer productivity. Learning about the history of the programming profession in general, of the "software engineering" meme in particular, and detailed history of the "agile" meme. Learning some R and stats on the side.

Why - because there is a lot of leverage in looking at the question of why programming skills are what they are, and how they could generally be better, compared to just improving my own. There seem to be some low-hanging fruit, such as encouraging programmers to be more aware of cognitive biases affecting their work, or of the nature of probability and tools for working with it.

I am trying to understand personal interaction in a semi-technical fashion in order for me to have mental models which are intuitive enough to me which can guide me in casual (and, later, business) interaction. I've read through some of the OB posts on status and I'm currently reading the book Impro (which Robin Hanson recommends here). I've also partially made it through Dale Carnegie's How to Make Friends and Influence People. Book suggestions are very welcome.

I am working on a 2D adventure game that features some topics from lesswrong: rationality and recursive self-improvement.

I love making games and this seems like a good way to take the basic concepts we take for granted on LW (like AI going FOOM or cryonics) and present it in a different way, from a different angle, in a simpler fashion, in a different medium, to new audience.

Goal: make enough money that I can do this again. And again. And again. Continue making games about various lesswrong topics.

I am working on methods for control design in nonlinear stochastic systems. In other words, given some sort of robot or other mechanical system, how can I do some amount of precomputation to then allow the system to solve a wide range of tasks in real time?

The general strategy for solving this problem is to patch together many locally valid control policies into a global control policy. This involves verifying that a given feedback law will accomplish a given task in a given region, which usually reduces to solving a semi-definite program.

However, these semi-definite programs can be quite large, so I am also studying methods for approximately solving large semi-definite programs.

Finally, I am working on a method for verifying that a control policy will accomplish its goal even in the presence of noise (so far most of our work has focused on deterministic systems; I am adapting it to systems with randomness).

I chose these problems because they seemed like the most interesting and important problems that were being studied by the research group I work with. Similar to paulfc, I am working on them partially due to inertia; I will probably not continue to work on them after I go to graduate school. However, I do believe that the problem of nonlinear control is extremely important. I just believe that there are other problems that are even more important.

I'm working on a suite of R functions for remote dispatch of R batch jobs with a function-like syntax. The code assumes that the remote and local machines share directories using NFS) (or similar) and have the same path to the working directory. Right now the code is complete and bug-free (ha! haha! hahaha...eugh) -- I'm just trying to sort out a problem with NFS client-side file caching on my specific system.

I'm also working on contract, writing C code for a Bayesian clinical trial design company in Texas. I actually want to get involved in the statistics, not just the coding, but I'll take what I can get. Hopefully it will be a foot in the door.

At work, I've been working on motion detecting algorithms for video games, which involves a fair amount of statistics and machine learning. So I've also been learning a lot more about statistics, data mining, dealing with noisy data (we have some data that's reliably tags, and a lot of data whose tags are likely to be wrong), comparing different methods, explaining these things to non-technical people, visualizing the data (distributions, scatterplots), etc. I'm still feeling like an idiot a lot of the time, but the system (most of it implemented in Python) works quite well, I just keep having the nagging doubt that there's some algorithm out there that give better result, some analysis method that will allow me to see more in the data, etc. But with deadlines looming, I can't afford too much analysis paralysis either.

I've also been asking questions on Cross Validated, and browing back through the answers of one of my old questions, I saw the best one was answered by a certain "John Salvatier", and was thinking, "hmm, that name sounds familiar ..."

On the side (I'm another one of those "program by day, and program some more by night" guys), I'm working on a little web-based game that's not in a state worth showing right now, but is going along smoothly so far.

I'm about to start directing a community theatre production of Equus -- auditions this coming Monday and Tuesday. I've been wanting to do this show for years.

I'm working on making music that doesn't suck. Bleepy electropop, to be precise. Using LMMS, a cheap'n'cheerful open source clone of FruityLoops. I've been an avid record nerd for nearly thirty years now, it's time I saw what I could do.

Current state: First demo. Something almost done. A minor diversion. I'm getting to know the capabilities and limitations of LMMS and Audacity (which is basically the four-track we all wanted twenty years ago as a computer program). I'm accumulating a huge pile of fragments, which need putting together into actual pieces. I'm still looking for the sounds I'm after and learning more about sound and how to make what I want - a goal in mind. I'm learning how to do a mix on crappy speakers.

Hard parts: I have no musicianly ability whatsoever. My girlfriend (who is one of these people who can pick up pretty much any instrument and get something usable out of it) insists I learn a few chords on guitar. Also, for a writer, I'm finding it ridiculously difficult to come up with lyrics that I don't instantly think are awful.

Goals: Make something I think is good and other people think is good.

Why: because I feel like it and it amuses me.

Value to humanity: utterly minimal. Value to me: keeps me amused and out of trouble.

I've been working on being more sociable. I've been talking to people in my classes, and doing work in the lounge for my major. I'm not as productive as when I work on my own, but I'm getting involved in small talk. I think someone here mentioned that much of small-talk is just about being enthusiastic and friendly---once I started looking at it this way it became much easier---it is actually pleasant for me now.

I'm about halfway through reading Social Cognition by Gordon Moskowitz, which is helping me gain a better understanding of cognitive biases.

I've also been writing explanations of science topics for my grandmother, among other things like "what is an internet community?"

I'm working on an iPhone game I started when learning Objective-C. It's unknown (about 3000 free downloads/week) but loved (mostly rave App Store reviews), so I'm trying to make it go viral.

The version awaiting Apple review has incentives for introducing new players, and usage analytics to get a more accurate idea of what to fix and simplify. (Fan feedback is great, but they're already converted & over the learning curve). I think I'll still need to lower the learning curve/barrier to entry & boost the purple cow factor, but I'll bet I see something in the analytics data I would never have considered.

Also, after years of staring at keyboards I'm finally learning to type, hopefully from tonight, straight to Dvorak, no labels. Any advice for doing this, or software recommendations for a Mac, would be most welcome.

Aside from coursework and teaching, blah blah blah, I'm working on my master's thesis in statistics. I have data from a series of economic experiments that were originally analyzed using nonparametric methods that did not take the potential dependence structure in time into account - the data is several time series. I'm reanalyzing the data in a way that takes into account this dependence structure and including some model selection procedures. I'm considering several possible models, and at this stage I'm basically reading books and papers to learn about different potential models and Bayesian methods of analyzing those models, largely because I haven't had a course in time series yet.

I've had a little success with two books collecting some of my blog posts so now I'm trying actively to turn my blog writing into longer-form work suitable for book publication. I'm currently working on three series of blog posts simultaneously, all of which I hope to turn into books.

The first, How We Know What We Know is on a lot of the things people talk about on here, trying to explain Bayes' theorem, Kolmogrov Complexity and the scientific method to a lay audience. I'm doing this because a lot of my blog readers, especially those who enjoyed my book "Sci-Ence! Justice Leak!" (which tackled the same things more obliquely, examining comics and Doctor Who to make complementary points), are interested in this material but don't have any idea where to start.

The second, Cerebus reviewed part zero and part one, is a discussion of the 300-issue comic-book series Cerebus. I'm doing this because I consider Cerebus a great work of art, but one overshadowed by its creator's serious mental illness.

And the third, and most advanced, is a track-by-track review of every legitimately available Beach Boys song (top five results on that page). I'm doing this because there's no book available that covers the Beach Boys' whole career from a musical, rather than biographical, point of view, odd as that may sound.

I'm also writing a novel, but that's not online.

My goal with these generically is to try to earn enough money from the writing I would be doing anyway (these are all the kind of things I would be posting to my blog anyway, just organised better) to be able to quit my job and work primarily as a writer, with some time over for research, programming and music.

I'm working on (1) computer vision for augmented reality [my Phd], (2) machine learning for theorem provers, (3) building the Oxford Transhumanists, and (4) an iPhone app called relaxing stories

1) I build models of the wearer's environment that include meaningful labels like "floor", "wall", "table top". This will hopefully help us leverage 3D models for figuring out where objects, people, events, and interactions are happening and where they are likely to happen in the future.

2) Should theorem provers use training data? I think so. Theorem proving is provably impossible in general, humans are good at it because we learn to recognise patterns. I'm transforming the ATP problem from an abstract search problem over proofs to a black-box that searches greedily, asking at each point which direction to search next. The problem is then to learn which patterns indicate a fruitful search direction by examining a large corpos of previous proofs.

3) http://www.facebook.com/group.php?gid=265828309853

4) http://relaxingstories.com/

I am working on a new approach to epistemology -- essentially an extension of Frege's context principle -- that explains the nature of meaning and existence. I am finding that it provides traction on many problems in epistemology, linguistics, rationality and philosophy in general. I also believe that it provides traction on problems in more practical fields like physics and computer science.

I think that my approach to epistemology is pretty cool, that it is essentially a "Theory of Everything"; however epistemology is not my field of training or experience -- and it seems that nothing is really "new" -- so I am researching the literature. And yes I suppose that you can say there is related literature, in that essentially all literature in some sense explores meaning. I am reading related material in Buddhism, philosophy, ethics, epistemology, linguistics, semiotics, logic, theory of computation, mathematics, and physics. Much of this material is steeped in domain specific language; to even understand the abstract of many papers I need a deep background in the topic. My only real hope is to thread my way through this material looking for key concepts -- restricting my detailed analysis to sources that effectively add to my network of knowledge.

The research is paying off. My original topic of research was loosely "meaning is context dependent". Certainly this idea is well represented in the literature; there are forms of contextualism in ethics, logic, mathematics, epistemology, and linguistics. There are related philosophies like relativism, pluralism, perspectivism and nihilism. Although contextualism in these fields is clearly a contender for explaining the nature of truth and meaning, there are also valid criticisms of it. It is clear that the traditional approaches to contextualism are flawed; and yet I still believe that the core idea is valid.

I still have much more work to do, but I believe that I have found an approach to contextualism that fixes its traditional failings; but since this approach is potentially (relatively) novel I now need to worry about my intellectual property agreement with my employer. This is an unfortunate barrier to my progress. To verify and extend my ideas I need to expose them to public evaluation and criticism -- but my company has a broad definition of intellectual property and insists that I go through their invention disclosure process -- seeking permission to publish from them; permission that they will only grant if they are convinced it is in their interest.

I am working on an open translation memory web service. Instead of writing my thesis.

Our system takes a document and its translation, and it gives back a set of sentences, each together with its translation. Users can upload such document pairs, and other users can then search the resulting corpus for words and phrases in either of the two languages.

The core of the system is my hunalign sentence aligner algorithm. But the core is not that important actually. The trickiest parts of the system are all trivial from an algorithmic perspective (document processing, interfacing with 3rd party libraries, etc).

Why am I doing this? Because I feel that this is a useful service. I have some ideas about how to monetize it, but I will not be disappointed if these don't work out. Also, this work is more fun than writing a thesis. (My thesis is already at the phase where no new algorithms and experiments are needed, it really is just writing, but a huge amount of it.)

I'm working on developing a logically consistent and reality-based method of calculating pension liabilities. Currently in the US, pension liabilities are computed differently for public pension plans funded by taxpayers vs. private sector pension plans funded by corporations. I think neither current method makes sense.

Financial economists argue that pension liabilities should be discounted at risk-free rates based on the probability of their being paid, using a model that treats accrued pension liabilities like a security. I disagree, and I'm conducting an experiment to see whether a complete proof for the financial economics position exists - with no gaps. All the relevant articles I am aware of are basically hand-waving.

Coming out in opposition to the current financial economics argument is a necessary prerequisite to putting forth my model, which I claim is also based on financial economics.

My current project is Landwelded.

It is a reader-driven webcomic thing inspired by the MS Paint Adventures franchise by Andrew Hussie, in particular his latest installment, Homestuck.

It's about six young bayesians in a semi-weirdtopia, who may or may not at some point play a videogame together.

I'm working on

1) Synthesising biologically active and structurally challenging molecules for my PhD. See link.

2) Relaxing Stories iPhone app: The app is able to relax the user in under five minutes by listening to visually enhanced stories read by soothing voices.

3) Science communication: Writing about latest advances in chemistry for a wider audience. Also, reaching out other graduate students and institutes to get involved in communicating science.