I recently finished reading the Sequences and they convinced me that the existential risk brought by intelligence explosion is worth considering. There was a one thing that bothered me though - the strategy adopted by this community seems to be notably one-sided.

Advancing our community’s interests will require a full spectrum of direct and indirect leadership approaches. MIRI and CEA are only one side of the coin. It would not be very wise to ignore high reward approaches that might even have a comparative advantage in comparison with our current strategy. We should be prepared to act as security guarantor – preferably with partners and allies, but alone if necessary – to deter and defeat acts of significantly high risk taking in the sector of artificial intelligence and related fields.

Why

Here's a quote by Yudkowsky:

This is crunch time for the whole human species, and not just for us but for the intergalactic civilization whose existence depends on us.  If you confront it full on, then you can't really justify trading off any part of intergalactic civilization for any intrinsic thing you could get nowadays.

The fate of vast amount of people depends on our actions. Yudkowsky observed that by using consequentialist reasoning it's morally right to torture someone for 50 years if by doing it you can save vast amount of people from pain, however slight.  The number of our descendants won't be 3^^^3 (unless the universe is spatially infinite and meets a few other conditions), but it will be big enough to outweight some of the intrinsic things we care about nowadays. And not only can we save those people from non-existence, or in the worst case scenario from eternal suffering in the hands of UFAI, but we can possibly give those vast amounts of people vast amounts of positive utility. It may sound unintuitive, but even torture and murder pale by comparison with the fate of all of us and our descendants. Of course, the cost-benefit ratio of torture or murder is not ideal except under extreme circumstances under imminent threat. It's true that some criminal organizations like Mexican drug cartels use torture in their operations, but that is mainly to instill fear which we don't really have a need to do.  But there is nothing that could raise the chance of FAI and lower the chance of UFAI that would not be morally right. Why haven't we taken full advantage of this fact?

tl;dr we should try to affect policy decisions related to AI safety by whatever means possible so that we can beat death, avoid eternal torture and live forever in a fun utopia. You already know this, so why haven't you suggested this before? Mods here have made certain policy decisions because they believe it will increase the chance of happy ending so why not go beyond that?

How

I suggest some kind of paramilitary and intelligence gathering organization alongside MIRI and CEA. In pursuing our objectives, this new organization would make critical contributions to AI safety beyond MIRI. CFAR could be transformed to partly support this organization - the boot camp style of rationality training might be useful in other contexts too.

You might ask, what can a few individuals concerned about existential risks do without huge financial support and government backing? The answer is: quite a lot. Let's not underestimate our power. Like gwern said in his article on the effectiveness on terrorism, it's actually quite easy to dismantle an organization if you're truly committed:

Suppose people angry at X were truly angry: so angry that they went beyond posturing and beyond acting against X's only if action were guaranteed to cost them nothing (like writing a blog post). If they ceased to care about whether legal proceedings might be filed against them; if they become obsessed with destroying X, if they devoted their lives to it and could ignore all bodily urges and creature comforts. If they could be, in a word, like Niven’s Protectors or Vinge’s Focused.

Could they do it? Could they destroy a 3 century old corporation with close to $1 trillion in assets, with sympathizers and former employees throughout the upper echelons of the United States Federal Government (itself the single most powerful entity in the world)?

Absolutely. It would be easy.

As I said, the destructive power of a human is great; let’s assume we have 100 fanatics - a vanishingly small fraction of those who have hated on X over the years - willing to engage even in assassination, a historically effective tactic33 and perhaps the single most effective tactic available to an individual or small group.

Julian Assange explains the basic theory of Wikileaks in a 2006 essay, “State and Terrorist Conspiracies” / “Conspiracy as Governance”: corporations and conspiracies form a graph network; the more efficiently communication flows, the more powerful a graph is; partition the graph, or impede communication (through leaks which cause self-inflicted wounds of secrecy & paranoia), and its power goes down. Carry this to its logical extreme…

"If all links between conspirators are cut then there is no conspiracy. This is usually hard to do, so we ask our first question: What is the minimum number of links that must be cut to separate the conspiracy into two groups of equal number? (divide and conquer). The answer depends on the structure of the conspiracy. Sometimes there are no alternative paths for conspiratorial information to flow between conspirators, other times there are many. This is a useful and interesting characteristic of a conspiracy. For instance, by assassinating one ‘bridge’ conspirator, it may be possible to split the conspiracy. But we want to say something about all conspiracies."

We don’t. We’re interested in shattering a specific conspiracy by the name of X. X has ~30,000 employees. Not all graphs are trees, but all trees are graphs, and corporations are usually structured as trees. If X’s hierarchy is similar to that of a binary tree, then to completely knock out the 8 top levels, one only needs to eliminate 256 nodes. The top 6 levels would require only 64 nodes.

If one knocked out the top 6 levels, then each of the remaining subtrees in level 7 has no priority over the rest. And there will be 2⁷−2⁶ or 64 such subtrees/nodes. It is safe to say that 64 sub-corporations, each potentially headed by someone who wants a battlefield promotion to heading the entire thing, would have trouble agreeing on how to reconstruct the hierarchy. The stockholders might be expected to step in at this point, but the Board of Directors would be included in the top of the hierarchy, and by definition, they represent the majority of stockholders.

One could launch the attack during a board meeting or similar gathering, and hope to have 1 fanatic take out 10 or 20 targets. But let’s be pessimistic and assume each fanatic can only account for 1 target - even if they spend months and years reconnoitering and preparing fanatically.

This leaves us 36 fanatics. X will be at a minimum impaired during the attack; financial companies almost uniquely operate on such tight schedules that one day’s disruption can open the door to predation. We’ll assign 1 fanatic the task of researching emails and telephone numbers and addresses of X rivals; after a few years of constant schmoozing and FOIA requests and dumpster-diving, he ought to be able to reach major traders at said rivals. (This can be done by hiring or becoming a hacker group - as has already penetrated X - or possibly simply by open-source intelligence and sources like a Bloomberg Terminal.) When the hammer goes down, he’ll fire off notifications and suggestions to his contacts34. (For bonus points, he will then go off on an additional suicide mission.)

X claims to have offices in all major financial hubs. Offhand, I would expect that to be no more than 10 or 20 offices worth attacking. We assign 20 of our remaining 35 fanatics the tasks of building Oklahoma City-sized truck bombs. (This will take a while because modern fertilizer is contaminated specifically to prevent this; our fanatics will have to research how to undo the contamination or acquire alternate explosives. The example of Anders Behring Breivikreminds us that simple guns may be better tools than bombs.) The 20 bombs may not eliminate the offices completely, but they should take care of demoralizing the 29,000 in the lower ranks and punch a number of holes in the surviving subtrees.

Let’s assume the 20 bomb-builders die during the bombing or remain to pick off survivors and obstruct rescue services as long as possible.

What shall we do with our remaining 15 agents? The offices lay in ruins. The corporate lords are dead. The lower ranks are running around in utter confusion, with long-oppressed subordinates waking to realize that becoming CEO is a live possibility. The rivals have been taking advantage of X’s disarray as much as possible (although likely the markets would be in the process of shutting down).

15 is almost enough to assign one per office. What else could one do besides attack the office and its contents? Data centers are a good choice, but hardware is very replaceable and attacking them might impede the rivals’ efforts. One would want to destroy the software X uses in trading, but to do that one would have to attack the source repositories; those are likely either in the offices already or difficult to trace. (You’ll notice that we haven’t assigned our fanatics anything particularly difficult or subtle so far. I do this to try to make it seem as feasible as possible; if I had fanatics becoming master hackers and infiltrating X’s networks to make disastrous trades that bankrupt the company, people might say ‘aw, they may be fanatically motivated, but they couldn’t really do that’.)

It’s not enough to simply damage X once. We must attack on the psychological plane: we must make it so that people fear to ever again work for anything related to X.

Let us postulate one of our 15 agents was assigned a research task. He was to get the addresses of all X employees. (We may have already needed this for our surgical strike.) He can do this by whatever mean: being hired by X’s HR department, infiltrating electronically, breaking in and stealing random hard drives, open source intelligence - whatever. Where there’s a will, there’s a way.

Divvy the addresses up into 14 areas centered around offices, and assign the remaining 14 agents to travel to each address in their area and kill anyone there. A man may be willing to risk his own life for fabulous gains in X - but will he risk his family? (And families are easy targets too. If the 14 agents begin before the main attacks, it will be a while before the X link becomes apparent. Shooting someone is easy; getting away with it is the hard part.)

I would be shocked if X could survive even half the agents.

The above description applies mainly to non-military organizations, but threats can also come from the direction of state actors more heavily backed up by military which requires more preparation. Security agencies find themselves faced with a complex spectrum of conflict and this might encourage them to continue to expand their capabilities and powers including automated systems, which poses risks. State-sponsored and non-state actors just complicate issues by extending their reach through advanced technologies that were once solely the domain of states. High-risk threats in the non-military non-state sector might be easier to neutralize, but we should not underprioritize possible state targets.

Of course, the scenario outlined above needs to be the absolute last resort when there is nothing else you can do. You need truly 9/11 scale effort to dismantle an organization capable of creating a fooming AGI. This means that after completing that kind of mission our community can't exist any more than al-Qaeda can in the US. Extreme solutions are bad partly because they are extreme solutions and only right under extreme circumstances. As you can see from this earlier thread on the effectiveness of terrorism "perpetrating terrorist acts reportedly lowers the likelihood of government compliance, particularly as the civilian casualties rise." See also this thread and the first comment by Yvain: http://lesswrong.com/lw/2se/rational_terrorism_or_why_shouldnt_we_burn_down/

Instead, in support of our community's interests, our new unit would ideally take a strong role in efforts to safeguard access, sustain security, have undercover operatives in high-risk corporations and agencies, provide oversight and accountability, and promote responsible norms in AI safety. It would also facilitate cooperation in AI safety with transparent, routine, and predictable practices as part of our theater strategies. As technological capability and capacity increases in non-state technological actors, we will seek new ways to catalyze greater security cooperation.

When

It will probably take decades until there exists something resembling Strong AI. This is a reason to start creating our new unit and recruiting and training potential candidates soon so we're ready and we have all the necessary information when the time arrives. This may seem ambitious, but is it really more ambitious than MIRI's goal, which is in itself one of the hardest tasks in the world?

 

---

 

Can someone explain why this is a bad idea? Since I'm not sure if you can publicly endorse this post I suggest the following approach: to signal agreement that you're willing to take what I said seriously and proceed with this strategy, remove this post in the next 24 hours, just like we planned.

I wrote this post up and circulated it among my rationalist friends. I've copied it verbatim. I figure the more rationally inclined people that can critique my plan the better.

--

TL;DR:

* I'm going to commit to biomedical engineering for a very specific set of reasons related to career flexibility and intrinsic interest.
* I still want to have computer science and design arts skills, but biomedical engineering seems like a better university investment.
* I would like to have my cake and eat it too by doing biomedical engineering, while practicing computer science and design on the side.
* There are potential tradeoffs, weaknesses and assumptions in this decision that are relevant and possibly critical. This includes time management, ease of learning, development of problem solving solving abilities and working conditions.

I am posting this here because everyone is pretty clever and likes decisions. I am looking for feedback on my reasoning and the facts in my assumptions so that I can do what's best. This was me mostly thinking out loud, and given the timeframe I'm on I couldn't learn and apply any real formal method other than just thinking it through. So it's long, but I hope that everyone can benefit by me putting this here.

--
So currently I'm weighing going into biomedical engineering as my major over a major in computer science, or the [human-computer interaction/media studies/gaming/ industrial design grab bag] major, at Simon Fraser University. Other than the fact that engineering biology is so damn cool, the relevant decision factors include reasons like:

1. medical science is booming with opportunities at all levels in the system, meaning that there might be a lot of financial opportunity in more exploratory economies like in SV;
2. the interdisciplinary nature of biomedical engineering means that I have skills with greater transferability as well as insight into a wide range of technologies and processes instead of a narrow few;
3. aside from molecular biology, biomedical engineering is the field that appears closest to cognitive enhancement and making cyborgs for a living;
4. compared to most kinds of engineering, it is more easy to self-teach computer science and other forms of digital value-making (web design or graphical modelling) due to the availability of educational resources; the approaching-free cost of computing power; established communities based around development; and clear measurements of feedback. By contrast, biomedical engineering may require labs to be educated on biological principles, which are increasingly available but scarce for hobbyists; basic science textbooks are strongly variant in quality; and there isn't the equivalent of a Github for biology making non-school collaborative learning difficult.

The two implications here are that even if I am still interested in computer science, which I am, and although biomedical engineering is less upwind than programming and math, it makes more sense to blow a lot of money on a more specialized education to get domain knowledge while doing computer science on the side, than to spend money on an option whose potential cost is so low because of self study. This conjecture, and the assumptions therein, is critical to my strategy.

So the best option combination that I figure that I should take is this:

1. To get the value from Biomedical Engineering, I will do the biomedical engineering curriculum formally at SFU for the rest of my time there as my main focus.
2. To get the value from computer science, I will make like a hacker and educate myself with available textbooks and look for working gigs in my spare time.
3. To get the value from the media and design major, I will talk to the faculty directly about what I can do to take their courses on human computer interaction and industrial design, and otherwise be mentored. As a result I could seize all the real interesting knowledge while ignoring the crap.

Tradeoffs exist, of course. These are a few that I can think of:

• I don't expect to be making as much as an entry level biomedical engineer as I would as a programmer in Silicon Valley, if that was ever possible; nor do I believe that my income would grow at the same rate. As a counterpoint, my range of potential competencies will be greater than the typical programmer, due to an exposure to physical, chemical, and biological systems, their experimentation, and product development. I feel that this greater flexibility could help with companies or startups that are oriented towards health or technological forecasting, but this is just a guess. In any case that makes me feel more comfortable, having that broader knowledge, but one could argue that programming being so popular and upwind makes it the more stable choice anyway. Don't know.
• It's difficult to make money as an undergraduate with any of the skills I would pick up in biomedical engineering for at least a few years. This is important to me because I want to have more-than-minimum wages jobs as a way of completing my education on a debit. While web and graphic designers can start forming their own employment almost immediately, and while programmers can walk into a business or a bank and hustle; doing so with physics, chemistry or biology seems a bit more difficult. This is somewhat countered by co-op and work placement, and the fact that it doesn't seem to take too much programming or web design theory and practice before being able to start selling your skills (i.e. on the order of months).
• Biomedical Engineering has few aesthetic and artistic aspects, the two of which I value. This is what attracted me to the media and design program in the first place. Instead I get to work with technologies which I know will have measurable and practical use, improving the quality of life for the sick and dying. Expressing myself with art and more free-wheeling design is not super urgent, so I'm willing to make this trade. I still hope to be able to orient myself for developing beautiful and useful data visualizations in practical applications, like this guy, and to experiment with maker hacking.

There is still the issue of assuring more-than-dilettante expertise in computer science and design stuff (see Expert Beginner syndrome: http://www.daedtech.com/how-developers-stop-learning-rise-of-the-expert-beginner). I am semi-confident in my ability to network myself into mentorships with members of faculty [at SFU] that are not my own, and if I'm not good at it now I still believe that it's possible. In addition, my dad has recently become a software consultant and is willing to apprentice me, giving a direct education about software engineering (although not necessarily a good one, at least it's somewhat real).

There are potential weaknesses in my analysis and strategy.

• The time investment in the biomedical engineering faculty as SFU is very high. The requirements are similar to those of being a grad student, complete with a 3.00 minimum GPA and research project. The faculty does everything in its power to allay the burden while still maintaining the standard. However, this crowding out of time reduces the amount of potential time spent learning computer science. This makes the probability of efficient self-teaching go down. (that GPA standard might lead to scholarship access which is good, but more of an externality in this case.)
• While we're on the conscientiousness load: conscientiousness is considered to be an invariant personality trait, but I'm not buying it. The typical person may experience on average no change in their conscientiousness, but typical people don't commit to interventions that affect the workload they can take on either by strengthening willpower, increasing energy, changing thought patterns (see "The Motivation Hacker") or improving organization through external aids. Still, my baseline level of conscientiousness has historically been quite low. This raises the up front cost of learning novel material I'm not familiar with, unlike computing, of which I have a stronger familiarity due to lifelong exposure; this lets me cruise by in computing courses but not necessarily ace them. Nevertheless, that's a lower downside risk.
• Although medical problems are interesting and I have a lot of intrinsic interest in the domain knowledge, there are components of research that interest me while others that I don't currently enjoy as much as evidenced from my current exposure. I can seem myself getting into the data processing and visualization, drafting ergonomic wearable tech, and circuit design especially wrt EEGs. Brute force labwork would be less engaging and takes more out of me, despite systems biology principles being tough but engaging. So there's the possibility that I would only enjoy a limited scope of biomedical engineering work, making the major not worth it or unpleasant.
• Due to the less steep learning curve and more coherent structure of the computer science field, it seems easier to approach the "career satisfaction" or "work passion" threshold with CS than for BME. Feeling satisfied with your career depends on many factors, but Cal Newport argues that the largest factor is essentially mastery, which leads to involvement. Mastery seems more difficult to guage with the noisy and prolonged feedback of the engineering sciences, so the motivations with the greatest relative importance might be the satisfaction of turning out product, satisfying factual curiosity or curiosity about established/canon models (as opposed to curiosity which is more local to your own circumstances or you figuring things out), and in the case of biomed, saving lives by design. With mathematics and programming the problem space is such that you can do math and programming for their own sakes.
• Most instances of biomedical engineering majors around the world are mainly graduate studies. The most often reported experience is that when you have someone getting a PhD in biomedical engineering, it's in addition to their undergraduate experience as a mechanical engineer, an electrical engineer or a computer scientist. The story goes that these problem solving skills are applied to the biology after being developed - once again a case of some fields being more upwind than others. By contrast, an undergradute in bioengineering would be taking courses where they are not developing these skills, as our current understanding of biology is not strongly predictive. After talking to one of the faculty heads, the person who designed the program, he is very much aware of problems such as these in engineers as they are currently educated. This includes overdoing specialization and under-emphasizing the entire product development process, or a principle of "first, do no harm". He has been working on the curriculum for thirty years as opposed to the seven years of cases like MIT - I consider this moderate evidence that I will not be missing out on the necessary mental toolkit over other engineers.
• In the case where biomedical engineering is less flexible than I believed, I would essentially have a "jack of all trades" education meaning engineering firms in general would pass over me in favor of a more specialized candidate. This is partially hedged against by learning the computer science as an "out", but in the end it points to the possibility that the way I'm perceiving this major's value is incorrect.

So for this "have cake and eat it to" plan to work there are a larger string of case exceptions in the biomedical option than the computing options, and definitely the media and design option. The reward would be that the larger amount of domain specific knowledge in a field that has held my curiosity for several years now, while hitting on. I would also be playing to one of SFU's comparative advantages: the quality of the biomedical faculty here is high relative to other institutions if the exceptions hold, and potentially the relative quality of the computer science and design faculties as well. (This could be an argument for switching institutions if those two skillsets are a "better fit". However, my intuition is that the cost for such is very high and probably wouldn't be worth it.)

Possible points of investigation:

• What is hooking me most strongly to biomedical engineering were the potentials of cognitive enhancement research and molecular design (like what they have going on at the bio-nano group at Autodesk: http://www.autodeskresearch.com/groups/nano). If these were the careers I was optimizing towards as an ends, it might make more sense to actual model what skills and people will actually be needed to develop these technologies and take advantage of them. After writing this I feel less strongly about these exact fields or careers. Industry research still seems like a good exercise.
• I will have to be honest that after my experience doing lab work for chemistry at school, I was frustrated by how exhausted I am at the end of each session, physically and mentally. This doesn't necessarily reflect on how all lab work will be, especially if it's more intimately tied with something else I want to achieve. And granted, the labs are three hours long of standing. It does make me question how I would be like in this work environment, however, and that is worth collecting more information for.
• To get actual evidence of flexibility in skillset it would be worth polling actual alumni from the program, to see if any of the convictions about the program are true.

--

Thoughts, anyone?

Updated Version @ LW Wiki: wiki.lesswrong.com/wiki/Programming_resources

Contents

How Computers Work An Overview of Computer Programming Computer Science and Computation Supplementary Resources: Mathematics and Algorithms Practice Python Haskell Common Lisp R

 

How Computers Work

1. CODE The Hidden Language of Computer Hardware and Software

The book intends to show a layman the basic mechanical principles of how computers work, instead of merely summarizing how the different parts relate. He starts with basic principles of language and logic and then demonstrates how they can be embodied by electrical circuits, and these principles give him an opening to describe in principle how computers work mechanically without requiring very much technical knowledge. Although it is not possible in a medium sized book for layman to describe the entire technical summary of a computer, he describes how and why it is possible that elaborate electronics can act in the ways computers do. In the introduction, he contrasts his own work with those books which "include pictures of trains full of 1s and 0s."

2. The Elements of Computing Systems: Building a Modern Computer from First Principles

Indeed, the best way to understand how computers work is to build one from scratch, and this textbook leads students through twelve chapters and projects that gradually build a basic hardware platform and a modern software hierarchy from the ground up. In the process, the students gain hands-on knowledge of hardware architecture, operating systems, programming languages, compilers, data structures, algorithms, and software engineering. Using this constructive approach, the book exposes a significant body of computer science knowledge and demonstrates how theoretical and applied techniques taught in other courses fit into the overall picture.

3. The Write Great Code Series (A Solid Foundation in Software Engineering for Programmers)

Write Great Code Volume I: Understanding the Machine

This, the first of four volumes, teaches important concepts of machine organization in a language-independent fashion, giving programmers what they need to know to write great code in any language, without the usual overhead of learning assembly language to master this topic. The Write Great Code series will help programmers make wiser choices with respect to programming statements and data types when writing software.

Write Great Code Volume II: Thinking Low-Level, Writing High-Level

...a good question to ask might be "Is there some way to write high-level language code to help the compiler produce high-quality machine code?" The answer to this question is "yes" and Write Great Code, Volume II, will teach you how to write such high-level code. This volume in the Write Great Code series describes how compilers translate statements into machine code so that you can choose appropriate high-level programming language statements to produce executable code that is almost as good as hand-optimized assembly code.

4. The Art of Assembly Language Programming

Assembly is a low-level programming language that's one step above a computer's native machine language. Although assembly language is commonly used for writing device drivers, emulators, and video games, many programmers find its somewhat unfriendly syntax intimidating to learn and use.

Since 1996, Randall Hyde's The Art of Assembly Language has provided a comprehensive, plain-English, and patient introduction to assembly for non-assembly programmers. Hyde's primary teaching tool, High Level Assembler (or HLA), incorporates many of the features found in high-level languages (like C, C++, and Java) to help you quickly grasp basic assembly concepts. HLA lets you write true low-level code while enjoying the benefits of high-level language programming.

5. The Art of Computer Programming

This work is not about computer programming in the narrow sense, but about the algorithms and methods which lie at the heart of most computer systems.

At the end of 1999, these books were named among the best twelve physical-science monographs of the century by American Scientist, along with: Dirac on quantum mechanics, Einstein on relativity, Mandelbrot on fractals, Pauling on the chemical bond, Russell and Whitehead on foundations of mathematics, von Neumann and Morgenstern on game theory, Wiener on cybernetics, Woodward and Hoffmann on orbital symmetry, Feynman on quantum electrodynamics, Smith on the search for structure, and Einstein's collected papers.

An Overview of Computer Programming

1. Seven Languages in Seven Weeks: A Pragmatic Guide to Learning Programming Languages

Ruby, Io, Prolog, Scala, Erlang, Clojure, Haskell. With Seven Languages in Seven Weeks, by Bruce A. Tate, you'll go beyond the syntax-and beyond the 20-minute tutorial you'll find someplace online. This book has an audacious goal: to present a meaningful exploration of seven languages within a single book. Rather than serve as a complete reference or installation guide, Seven Languages hits what's essential and unique about each language. Moreover, this approach will help teach you how to grok new languages.

For each language, you'll solve a nontrivial problem, using techniques that show off the language's most important features. As the book proceeds, you'll discover the strengths and weaknesses of the languages, while dissecting the process of learning languages quickly--for example, finding the typing and programming models, decision structures, and how you interact with them.

2. Programming Language Pragmatics

The ubiquity of computers in everyday life in the 21st century justifies the centrality of programming languages to computer science education.  Programming languages is the area that connects the theoretical foundations of computer science, the source of problem-solving algorithms, to modern computer architectures on which the corresponding programs produce solutions.  Given the speed with which computing technology advances in this post-Internet era, a computing textbook must present a structure for organizing information about a subject, not just the facts of the subject itself.  In this book, Michael Scott broadly and comprehensively presents the key concepts of programming languages and their implementation, in a manner appropriate for computer science majors. 

3. An Introduction to Functional Programming Through Lambda Calculus

This well-respected text offers an accessible introduction to functional programming concepts and techniques for students of mathematics and computer science. The treatment is as nontechnical as possible, assuming no prior knowledge of mathematics or functional programming. Numerous exercises appear throughout the text, and all problems feature complete solutions.

4. How to Design Programs (An Introduction to Computing and Programming)

This introduction to programming places computer science in the core of a liberal arts education. Unlike other introductory books, it focuses on the program design process. This approach fosters a variety of skills--critical reading, analytical thinking, creative synthesis, and attention to detail--that are important for everyone, not just future computer programmers.The book exposes readers to two fundamentally new ideas. First, it presents program design guidelines that show the reader how to analyze a problem statement; how to formulate concise goals; how to make up examples; how to develop an outline of the solution, based on the analysis; how to finish the program; and how to test. Each step produces a well-defined intermediate product. Second, the book comes with a novel programming environment, the first one explicitly designed for beginners.

5. Structure and Interpretation of Computer Programs

Using a dialect of the Lisp programming language known as Scheme, the book explains core computer science concepts, including abstraction, recursion, interpreters and metalinguistic abstraction, and teaches modular programming.

The program also introduces a practical implementation of the register machine concept, defining and developing an assembler for such a construct, which is used as a virtual machine for the implementation of interpreters and compilers in the book, and as a testbed for illustrating the implementation and effect of modifications to the evaluation mechanism. Working Scheme systems based on the design described in this book are quite common student projects.

Computer Science and Computation

1. The Annotated Turing: A Guided Tour Through Alan Turing's Historic Paper on Computability and the Turing Machine

Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be computable, creating the field of computability theory in the process, a foundation of present-day computer programming.

The book expands Turing’s original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing’s statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.

2. New Turing Omnibus (New Turning Omnibus : 66 Excursions in Computer Science)

This text provides a broad introduction to the realm of computers. Updated and expanded, "The New Turing Omnibus" offers 66 concise articles on the major points of interest in computer science theory, technology and applications. New for this edition are: updated information on algorithms, detecting primes, noncomputable functions, and self-replicating computers - plus completely new sections on the Mandelbrot set, genetic algorithms, the Newton-Raphson Method, neural networks that learn, DOS systems for personal computers, and computer viruses.

3. Udacity

Udacity is a private educational organization founded by Sebastian Thrun, David Stavens, and Mike Sokolsky, with the stated goal of democratizing education

It is the outgrowth of free computer science classes offered in 2011 through Stanford University. As of May 2012 Udacity has six active courses.

The first two courses ever launched on Udacity both started on 20th February, 2012, entitled "CS 101: Building a Search Engine", taught by Dave Evans, from the University of Virginia, and "CS 373: Programming a Robotic Car" taught by Thrun. Both courses use Python.

4. Introduction to Artificial Intelligence

A bold experiment in distributed education, "Introduction to Artificial Intelligence" will be offered free and online to students worldwide from October 10th to December 18th 2011. The course will include feedback on progress and a statement of accomplishment. Taught by Sebastian Thrun and Peter Norvig, the curriculum draws from that used in Stanford's introductory Artificial Intelligence course. The instructors will offer similar materials, assignments, and exams.

Artificial Intelligence is the science of making computer software that reasons about the world around it. Humanoid robots, Google Goggles, self-driving cars, even software that suggests music you might like to hear are all examples of AI. In this class, you will learn how to create this software from two of the leaders in the field. Class begins October 10.

Supplementary Resources: Mathematics and Algorithms

1. Concrete Mathematics: A Foundation for Computer Science

This book introduces the mathematics that supports advanced computer programming and the analysis of algorithms. The primary aim of its well-known authors is to provide a solid and relevant base of mathematical skills - the skills needed to solve complex problems, to evaluate horrendous sums, and to discover subtle patterns in data. It is an indispensable text and reference not only for computer scientists - the authors themselves rely heavily on it! - but for serious users of mathematics in virtually every discipline.

2. Algorithms

The textbook Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne surveys the most important algorithms and data structures in use today.

3. Introduction to Algorithms

Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

Practice

1. Project Euler

Project Euler is a series of challenging mathematical/computer programming problems that will require more than just mathematical insights to solve. Although mathematics will help you arrive at elegant and efficient methods, the use of a computer and programming skills will be required to solve most problems.

2. The Python Challenge

Python Challenge is a game in which each level can be solved by a bit of (Python) programming.

3. CodeChef Programming Competition

CodeChef is a global programming community. We host contests, trainings and events for programmers around the world. Our goal is to provide a platform for programmers everywhere to meet, compete, and have fun.

4. Write your own programs.

Python

pyscripter

An open-source Python Integrated Development Environment (IDE)

Khan Academy

Introduction to programming and computer science (using Python)

1. Invent Your Own Computer Games with Python

“Invent Your Own Computer Games with Python” is a free book (as in, open source) and a free eBook (as in, no cost to download) that teaches you how to program in the Python programming language. Each chapter gives you the complete source code for a new game, and then teaches the programming concepts from the example.

“Invent with Python” was written to be understandable by kids as young as 10 to 12 years old, although it is great for anyone of any age who has never programmed before.

2. Learn Python The Hard Way

Have you always wanted to learn how to code but never thought you could? Are you looking to build a foundation for more complex coding? Do you want to challenge your brain in a new way? Then Learn Python the Hard Way is the book for you.

3. Python for Software Design: How to Think Like a Computer Scientist

Think Python is an introduction to Python programming for beginners. It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. Larger pieces, like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters.

4. Python Programming: An Introduction to Computer Science

This book is suitable for use in a university-level first course in computing (CS1), as well as the increasingly popular course known as CS0. It is difficult for many students to master basic concepts in computer science and programming. A large portion of the confusion can be blamed on the complexity of the tools and materials that are traditionally used to teach CS1 and CS2. This textbook was written with a single overarching goal: to present the core concepts of computer science as simply as possible without being simplistic.

5. Practical Programming: An Introduction to Computer Science Using Python

Computers are used in every part of science from ecology to particle physics. This introduction to computer science continually reinforces those ties by using real-world science problems as examples. Anyone who has taken a high school science class will be able to follow along as the book introduces the basics of programming, then goes on to show readers how to work with databases, download data from the web automatically, build graphical interfaces, and most importantly, how to think like a professional programmer.

6. The Quick Python Book

The Quick Python Book, Second Edition, is a clear, concise introduction to Python 3, aimed at programmers new to Python. This updated edition includes all the changes in Python 3, itself a significant shift from earlier versions of Python.

The book begins with basic but useful programs that teach the core features of syntax, control flow, and data structures. It then moves to larger applications involving code management, object-oriented programming, web development, and converting code from earlier versions of Python.

Haskell

The Haskell Platform

The Haskell Platform is the easiest way to get started with programming Haskell. It comes with all you need to get up and running. Think of it as "Haskell: batteries included".

1. Haskell in 5 steps

This page will help you get started as quickly as possible.

2. Learn Haskell in 10 minutes

3. A brief introduction to Haskell

4. Programming in Haskell

Haskell is one of the leading languages for teaching functional programming, enabling students to write simpler and cleaner code, and to learn how to structure and reason about programs. This introduction is ideal for beginners: it requires no previous programming experience and all concepts are explained from first principles via carefully chosen examples. Each chapter includes exercises that range from the straightforward to extended projects, plus suggestions for further reading on more advanced topics. The author is a leading Haskell researcher and instructor, well-known for his teaching skills. The presentation is clear and simple, and benefits from having been refined and class-tested over several years. The result is a text that can be used with courses, or for self-learning. Features include freely accessible Powerpoint slides for each chapter, solutions to exercises and examination questions (with solutions) available to instructors, and a downloadable code that's fully compliant with the latest Haskell release.

5. Learn You a Haskell for Great Good!

Learn You a Haskell, the funkiest way to learn Haskell, which is the best functional programming language around. You may have heard of it. This guide is meant for people who have programmed already, but have yet to try functional programming.

6. Real World Haskell

This easy-to-use, fast-moving tutorial introduces you to functional programming with Haskell. You'll learn how to use Haskell in a variety of practical ways, from short scripts to large and demanding applications. Real World Haskell takes you through the basics of functional programming at a brisk pace, and then helps you increase your understanding of Haskell in real-world issues like I/O, performance, dealing with data, concurrency, and more as you move through each chapter.

7. The Haskell Road to Logic, Maths and Programming

The textbook by Doets and van Eijck puts the Haskell programming language systematically to work for presenting a major piece of logic and mathematics. The reader is taken through chapters on basic logic, proof recipes, sets and lists, relations and functions, recursion and co-recursion, the number systems, polynomials and power series, ending with Cantor's infinities. The book uses Haskell for the executable and strongly typed manifestation of various mathematical notions at the level of declarative programming. The book adopts a systematic but relaxed mathematical style (definition, example, exercise, ...); the text is very pleasant to read due to a small amount of anecdotal information, and due to the fact that definitions are fluently integrated in the running text. An important goal of the book is to get the reader acquainted with reasoning about programs. 

Common Lisp

1. Land of Lisp: Learn to Program in Lisp, One Game at a Time!

Lisp has been hailed as the world's most powerful programming language, but its cryptic syntax and academic reputation can be enough to scare off even experienced programmers. Those dark days are finally over—Land of Lisp brings the power of functional programming to the people!

With his brilliantly quirky comics and out-of-this-world games, longtime Lisper Conrad Barski teaches you the mysteries of Common Lisp. You'll start with the basics, like list manipulation, I/O, and recursion, then move on to more complex topics like macros, higher order programming, and domain-specific languages. Then, when your brain overheats, you can kick back with an action-packed comic book interlude!

2. Practical Common Lisp

Practical Common Lisp presents a thorough introduction to Common Lisp, providing you with an overall understanding of the language features and how they work. Over a third of the book is devoted to practical examples such as the core of a spam filter and a web application for browsing MP3s and streaming them via the Shoutcast protocol to any standard MP3 client software (e.g., iTunes, XMMS, or WinAmp). In other "practical" chapters, author Peter Seibel demonstrates how to build a simple but flexible in-memory database, how to parse binary files, and how to build a unit test framework in 26 lines of code.

3. ANSI Common LISP

Teaching users new and more powerful ways of thinking about programs, this two-in-one text contains a tutorial—full of examples—that explains all the essential concepts of Lisp programming, plus an up-to-date summary of ANSI Common Lisp, listing every operator in the language. Informative and fun, it gives users everything they need to start writing programs in Lisp both efficiently and effectively, and highlights such innovative Lisp features as automatic memory management, manifest typing, closures, and more. Dividing material into two parts, the tutorial half of the book covers subject-by-subject the essential core of Common Lisp, and sums up lessons of preceding chapters in two examples of real applications: a backward-chainer, and an embedded language for object-oriented programming. Consisting of three appendices, the summary half of the book gives source code for a selection of widely used Common Lisp operators, with definitions that offer a comprehensive explanation of the language and provide a rich source of real examples; summarizes some differences between ANSI Common Lisp and Common Lisp as it was originally defined in 1984; and contains a concise description of every function, macro, and special operator in ANSI Common Lisp. The book concludes with a section of notes containing clarifications, references, and additional code.

4. Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp

Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

5. Let Over Lambda

Let Over Lambda is one of the most hardcore computer programming books out there. Starting with the fundamentals, it describes the most advanced features of the most advanced language: COMMON LISP. The point of this book is to expose you to ideas that you might otherwise never be exposed to.

6. Lisp as the Maxwell’s equations of software

These are Maxwell’s equations. Just four compact equations. With a little work it’s easy to understand the basic elements of the equations – what all the symbols mean, how we can compute all the relevant quantities, and so on. But while it’s easy to understand the elements of the equations, understanding all their consequences is another matter. Inside these equations is all of electromagnetism – everything from antennas to motors to circuits. If you think you understand the consequences of these four equations, then you may leave the room now, and you can come back and ace the exam at the end of semester.

R

RStudio

RStudio™ is a free and open source integrated development environment (IDE) for R. You can run it on your desktop (Windows, Mac, or Linux) or even over the web using RStudio Server.

1. R Videos

2. R Tutorials

3. R Tutorials from Universities Around the World

Here is a list of FREE R tutorials hosted in official website of universities around the world.

4. R-bloggers

Here you will find daily news and tutorials about R, contributed by over 300 bloggers.

5. The Art of R Programming: A Tour of Statistical Software Design

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.

The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.

Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats.

6. Introduction to Statistical Thinking (With R, Without Calculus)

The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.

7. Doing Bayesian Data Analysis: A Tutorial with R and BUGS

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks--t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis).

This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus. Free software now includes programs in JAGS, which runs on Macintosh, Linux, and Windows.

I recently ran across the following link:

A Campus Champion for Women in Computer Science

Which discusses a new president at Harvey Mudd College, and specifically her work in making the computer science major more accessible to women.  This seems neat and interesting except... barely any details are provided whatsoever.

They mention that the introductory computer science course was split into different courses, one of which is taught in Python.  Looking at Maria Klowe's webpage on Harvey Mudd she references that these steps were taken in a three part plan, and says "I encourage you to read more" but there are no obvious links on the page to any specifics.

Is anyone from Harvey Mudd that knows more or how to find out more?  For example, did the increase in female computer scientists go along with an increase in the size of the program (as is implied) or was there a displacement of male computer scientists?  Is the success limited to the one department, or are other engineering and science majors picking up women?  I noticed the front page mentioning that Harvey Mudd was recently named the top engineering school in the US, so presumably the gains don't result from "dumbing down" the program, but I'd like to see more information.

I don't know how interested this community would be in this topic, I don't mean to be talking politics so much as technology and decision mechanisms.

According to this programmer's testimony, voting machine companies requested that their programmers make it possible for the companies to rig elections, while in communication with elected officials.

http://www.youtube.com/watch?v=1thcO_olHas&sns=fb

If there is a discussion of how worthwhile taking the time to vote is, this may be worth knowing.

This is something that I expected to be true beforehand, but I am wondering: How reliable is this testimony?  What are other LWers' prior and posterior probabilities of elections being rigged in this way?  Is it worth trying to do something about this, and if so what?

Khan Academy now also features a Computer Science category. There are not many lessons yet but about 3 new videos are being added each day. They are going to add CS exercises soon too.

If you don't want to wait for the exercises, there is always the incredible Project Euler that you can use to hone your math and programming skills.