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My recommendation for anyone getting into programming is to watch the SICP videos. If you're completely new to programming, this subject matter may be a little deep. But if you are anxious to get past the scripting level of programming and into the methods for organising, planning, and thinking about programming, there is no better introduction.

Structure and Interpretation of Computer Programs

I'll bet the computations could have been finished a lot faster (development time excluded ;) on a PS3 http://cag.csail.mit.edu/ps3/

Every engineering professor I had (yes, I went to a respectable university) graded on a curve. Trust me, if they didn't, MIT's graduation rate would be around 10%.

And engineering is not just about knowing algorithms and properties, it's about making assumptions and choosing from different valid approaches. There's not always a "right" answer and my professors were smart enough to recognize that.

Ask any two engineers for a solution to a complex problem and you'll get five different solutions - hence the use of safety factors in practice.

No, they aren't screwed.

They still have all the original RoofNet code. And they should, if they aren't complete idiots, have the modifications that they themselves made in source control.

The software is still as free as ever.

I've been following the development of mesh wifi technology for several years now. From the moment I first grokked what was going on with it, it struck me as a great disruptive technology. One of the most successful early projects, and one that I followed with a great deal of interest was MIT's Roofnet project - an implementation of commodity hardware and open source software, built on Linux, which provides wifi coverage for MIT's campus.

In 2006 a spin-off company named Meraki was formed to develop and commercialize the MIT Roofnet technology. At the time I was on the board of the Vancouver Community Network and had been championing more development of wireless technology. We immediately ordered 9 of the first beta units to try out. The technology was cheap ($50/unit) and it worked but what prevented us from going any further with it was the pricing model that they decided to adopt - $5/node/month for access to the "dashboard" - the real-time monitoring software that they were developing for managing the networks. We decided that this cost was prohibitive for our purposes and the Merakis were shelved.

In September of 2007 I heard about a group of Vancouver community wifi enthusiasts who were getting together with the goal of setting up community wifi in Canada's poorest neighbourhood. I came out to a meeting and invited along some people whom I know are interested in any project that is about bridging the digital divide. The technology that was trumpeted at that meeting was Meraki. Since my previous brush with them they had changed their pricing structure and now they would let you run a free network (with free access to their dashboard) or a subscription (paid) network for 10% of your charges. We (the group, which came to call itself " FreeTheNet ") were unanimous that the free option was what we wanted to do and we quickly began building out a public network.

In October Meraki announced that they were changing their pricing model (yet again) and that they would be vastly raising the costs of their hardware (tripling, in fact). I remember going to their website to learn more about what they were doing and their new marketing slogan was something like "Build your business using exciting new technology where the rules of the game keep changing " How ironic; I wish I'd kept a screenshot of that! Under their new system there was no way that we could build out the network we envisioned. At roughly that point, one of our most experienced hackers said "forget Meraki", we're going to write our own firmware and dashboard and promptly started researching that. By late Novermber he was able to demostrate an open routing firmware called B.A.T.M.A.N. running with a mesh helper inside called Robin, that provided the same functionality as the Meraki firmware. This could be installed in the commodity Meraki hardware which greeted you with a friendly and encouraging "happy hacking" when you logged into it via the console.

Over December and January he worked on adding features that we wanted to our network to have (and that we had previously been encouraging Meraki to build to improve their system - things like per node custom splash screen, enhancements to the dashboard to improve scalability, etc.) All of this was being tested on Meraki hardware because this is what we had spent our money on back when they supported and encouraged the kind of work we were doing.

Then in February Meraki announced a change to their EULA (End User Licence Agreement) which precluded anyone from changing any of the software that they install on t

Ruby and Erlang are the two I've spent the most time with so far. I like Ruby enough so far, that I've decided to write the initial
batch of install scripts for OpenQabal in Ruby.

Outside of that wish-list, I also harbor some vague hope of one day finding time to dabble with Forth, Fortran, Perl, and maybe Dylan.

This probably won't get read, but I need a way to procrastinate my thesis :) I can't say much about the financial aid policies of lesser institutions, but I do know my college's financial aid policies pretty well, and the other elite institutions have been catching up to us lately. Just some rough figures for Princeton, the median income for a family on financial aid here is $90,000/year, and the average family on financial aid only pays $10,000/year to Princeton. Our aid policies are all need-based, so it depends on your exact financial situation, but I'm guessing that "upper-middle class" will be somewhere in that range. Also, education is an investment, so you might also want to consider student loans. Princeton doesn't make you take out a loan as part of the aid package either, but if you need a loan to help cover the family contribution, they are available.

The best advice I can give you is, for the most part, apply to colleges without looking at the price tag. Then, when you get your acceptances, look at the aid awards and make a decision then.

Oh, and if you're interested in CS, I just have to say that I'm currently taking Brian Kernighan's class, and it's awesome :)

Colleges compete with each other. If you get accepted to MIT, Stanford will take that into account when determining your financial aid. If you get a great financial aid package from Stanford, CMU will adjust their offer accordingly. Harvard recently announced that they would eliminate tuition for students whose parents make less than $60,000/year. Other schools are following suit to stay competitive. Some schools seem rather upset because they can't afford to compete with that offer.

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On another note I would like to add my voice to the choir that thinks you need to learn some humility. When I graduated from high school I knew C, BASIC, Pascal, LOGO, and Assembly. Does that somehow make me special? As someone else mentioned Ivy League schools tend to favor functional languages like Scheme and LISP.

Regardless, Computer Science is about programming languages in the same way writing a great novel is about speaking English, French, Chinese, and Russian. In other words it's not about programming languages. All Turing-complete languages are equally powerful. Some just happen to be better suited than others to certain tasks.

If you study Computer Science at any school you won't spend much time learning C, Java, OCaml, or any other language. You will instead study data structures, computational complexity, databases, networking, artificial intelligence, operating systems, and software engineering. If you study languages it will likely be types of languages and the characteristics that define them.

My own impression judging from the story submission and your two posts is that MIT will politely decline your application. Most students at top-notch schools have perfect or nearly perfect SAT scores, not just on the math section. They also have Type-A personalities and would never give up because "my mother brought up the argument 'don't even try, we won't have the money for that'".

MIT and similar caliber schools WILL give you financial aid packages that are tailored to your financial situation. I'm graduating from MIT this year, I only have about $3,000 in loans. Guess what I paid for tuition this year? Nothing. Due to financial aid, MIT was actually cheaper than UC Berkeley for me, despite being an in-state California resident and a Regents scholarship recipient. The basic point is, the hard part is getting into the school--once you have that, the university will make it as easy as possible for you attend. Those massive endowments are thankfully occasionally used for something good. If you've got the brains, don't let money be an impediment to your education. Check out a recent Tech article from MIT--if your parents make less than $75k/year, tuition is free: http://www-tech.mit.edu/V128/N11/endowment.html

You may want to look into MIT again. They just announced a couple of weeks ago that students from families that earn less than $75k/yr. will not have to pay tuition. They've also changed the factors they look at to determine financial aid for other income levels:

Fin. Aid Boosted; No Tuition For Families Earning Under $75K

MIT has also always had a policy of basically, "You get in, and we'll help you figure out how to afford it."

A couple more things:

• Students loans are *not* as bad as everyone makes them out to be. Especially graduating from a place like MIT, where you can expect $50+k/yr at your first job. It's also the "good" kind of debt - low interest rates, and interest payments that can be deducted on taxes.
• Don't believe the anti-college (or anti-prestige) hype. It is absolutely worth it to spend four years at a place like MIT. It is true that you can gimp your way through and get nothing more out of it than any other school (or "real-world" experience) would give you. But, if you really want to do something exciting/amazing/etc., there's no easier place to make it happen than a place where you're surrounded by other bright/smart/energetic people.

Disclaimer: I graduated from MIT, and would not trade that experience for anything.

DO NOT let lack of money dissuade you from what you want to do. That breads resentment and bitterness. Do it, do it well, and the money will come.

I went to MIT. I hate it when people assume that you have to be rich to go there, or make comments like "my parents couldn't afford that." That isn't a reason to not even try. I'm not sure about the original poster's financial status (upper middle class can be a big range), but MIT recently announced it will be tuition free for those families making $75000 or less.

And the name does make a difference. I got my first job due to it (poor match in the end, but that is another story). Many employers see it as a short cut to the type of person you are. You *will* get a good job if you went to another school, especially if you are good (goodness will always override name in the end), but as other posters have mentioned the fact that you are surrounded by smart and clever people kicks your own performance up a notch. Being able to see exactly what you are capable of and find and notice your limits is an amazing experience. I wouldn't trade my time at MIT for the world, despite 4 years of complaining about the workload, the pressure and the frosh.

Here is one significant quote from it:

For those receiving an MIT scholarship, which is six out of every 10 MIT undergraduates, net tuition is $8,100--an amount that approximates the in-state cost of many public universities

However, the key is whether you can afford it. They have sophisticated metrics for figuring out what your family can afford to pay without undue hardship

MIT to be tuition-free for families earning less than $75,000 a year: http://web.mit.edu/newsoffice/2008/tuition-0307.html

The problem is, my upper-middle class family had more down to Earth plans for me and my college choices (about $30,000/year more down to Earth, actually), so financial aid and college savings won't come anywhere near MIT's price tag.

MIT's website says financial aid is guaranteed for admitted students.

http://web.mit.edu/sfs/financial_aid/mitgo_undergrad.html

I suppose I don't have an answer to the original question, but get their financial aid folks on the horn and see what they have in the way of work study, internships, etc. Whatever you got back on your FAFSA probably isn't the last word in the matter.

Re: "I want to be clear that in no way are we exploring any camera devices that would monitor customer behavior."

MIT certainly is looking into it, with a project called:

Development of Longitudinal Home Activity Datasets as a Shared Resource

"This project will create a new community information infrastructure resource that consists of datasets of dense, multi-modal sensor records of poeple living in their homes, where the homes have been instrumented with a house-wide portable sensing infrastructure. The sensing system itself, developed based on past work using a live-in laboratory and using cameras, microphones, wireless sensors, and mobile devices, will permit easy replication so that researchers interested in collecting additional datasets on their own will have a mechanism by which they might do so."

Project Abstract:

http://www.mit.edu/~intille/AbstractNSF.html
http://www.mit.edu/~intille/ResearchPositionNSF.html

Re: "I want to be clear that in no way are we exploring any camera devices that would monitor customer behavior."

MIT certainly is looking into it, with a project called:

Development of Longitudinal Home Activity Datasets as a Shared Resource

"This project will create a new community information infrastructure resource that consists of datasets of dense, multi-modal sensor records of poeple living in their homes, where the homes have been instrumented with a house-wide portable sensing infrastructure. The sensing system itself, developed based on past work using a live-in laboratory and using cameras, microphones, wireless sensors, and mobile devices, will permit easy replication so that researchers interested in collecting additional datasets on their own will have a mechanism by which they might do so."

Project Abstract:

http://www.mit.edu/~intille/AbstractNSF.html
http://www.mit.edu/~intille/ResearchPositionNSF.html

Free resources for Scheme.

The classic, Structure and Interpretation of Computer Programs:
http://mitpress.mit.edu/sicp/full-text/book/book.html

SICP videos:
http://swiss.csail.mit.edu/classes/6.001/abelson-sussman-lectures/

How To Design Programs (somewhat PLT Scheme specific):
http://www.htdp.org/2003-09-26/Book/

The Scheme Programming Language (somewhat Chez Scheme specific):
http://www.scheme.com/tspl3/

Teach Yourself Scheme in Fixnum Days (also PLT to an extent):
http://www.ccs.neu.edu/home/dorai/t-y-scheme/t-y-scheme.html

Of course there are others, and most of these can be bought in dead-tree form.

Free resources for Scheme.

The classic, Structure and Interpretation of Computer Programs:
http://mitpress.mit.edu/sicp/full-text/book/book.html

SICP videos:
http://swiss.csail.mit.edu/classes/6.001/abelson-sussman-lectures/

How To Design Programs (somewhat PLT Scheme specific):
http://www.htdp.org/2003-09-26/Book/

The Scheme Programming Language (somewhat Chez Scheme specific):
http://www.scheme.com/tspl3/

Teach Yourself Scheme in Fixnum Days (also PLT to an extent):
http://www.ccs.neu.edu/home/dorai/t-y-scheme/t-y-scheme.html

Of course there are others, and most of these can be bought in dead-tree form.

I would recommend Scheme. Not because I recommend you program in it, but because it will change the way you think about programming and programming languages, especially if you also read Structure and Interpretation of Computer Programs (affectionately known as SICP).

Scheme is a small programming language, so you can learn it relatively quickly. This allows you time to read some of the literature about Scheme. And SICP. This will give you insight in the design of programming languages, and how features (like loops, class systems, etc.) can be implemented in terms of simpler constructs, given the right primitives and powerful enough abstractions.

And, in the end, it's the primitives and abstractions that matter. Given the right primitives and abstractions, you can mold your programming language to be anything you want. Scheme, and other languages in the larger Lisp family, are very good at this. That's probably why they are still around, despite tracing their roots back to the 1950s. Other languages are not so good at it, and you will end up writing lots of boilerplate and repetitive code if you program in them.

I could go on and on and list all the insights Scheme has provided me with, but I'd say you go see for yourself. SICP is a good place to start, because it starts at the basics (so you don't need to know Scheme before you start), but gets up to speed really quickly (so you won't get bored). You could also try my Scheme tutorial.

Note that all this is about the 5th revision of Scheme (known as R5RS). R5RS lacks many primitives that would make it useful for Real World applications (just to name one thing, there is nothing about networking). Many implementations of Scheme (and there are many) provide various such primitives - but that's not the point here. The point is about the fundamentals of programming.

As a final remark, if you are used to Java's "everything is part of a class" curfew, Scheme will provide a refreshing change of perspective. R5RS doesn't even have classes. Instead, the focus is on procedures. So you will be decomposing your system in terms of what it does, more than in terms of what real, imaginary, or "forced on you by the programming language" objects it acts on. In my experience, this leads to many small functions, each doing a simple thing, which can then be composed to build your system - or perhaps a very different system that happens to re-use part of the functionality. Unlike Java's classes, each containing a bunch of method, each of which contains gobs of repetitive code...try to adapt _that_ to do something similar, but slightly different. Again, it's all about primitives and abstractions.

I'm very confused as to why you got modded -1, but thanks for the explanation of the saturation. I'm still confused as to how a CCD seems to be able to take violet and magically interpret it as magenta -- is there special software that does this somehow? I know that when I use real violet LEDs, they frequently look blue in the CCD; perhaps most of the "purple" flowers we see are actually magenta?

Neat work by Bruce, my personal inspiration was a good friend of mine: http://sub-zero.mit.edu/fbyte/ledart/

The only news source I've found that's providing any real coverage any more is The Tech.

Star's next court date is March 21st. Her attorney has moved to have all charges dismissed.
http://www-tech.mit.edu/V128/N1/simpson.html

Actually, IIRC much of the foundation of Color Kinetics, as well as Brian's work, was done by another MIT student, frostbyte (now deceased), and much of it probably qualifies public domain or prior art. Therefore it's likely, Phillips only has a patent on the implementation of a specific controller.

I'm surprised to see Brian made the front of slashdot for his artwork when his academic work, converting ethanol to H2 by creating Rh and CeO2 nanowires using a genetically engineered T9 capsid as the wire template seems far cooler. Unfortunately, is lab's home page seems to be down at the moment.

http://diamond.businessobjects.com/node/18669 http://www.haystack.mit.edu/edu/undergrad/srt/forum/calendar.php?s=&action=getinfo&eventid=1 http://sci.rutgers.edu/forum/calendar.php?do=getinfo&e=31&day=2000-3-15 https://www.naropa.edu/forum/calendar.php?do=getinfo&e=2&day=2008-3-15

What facility does this unstoppable robot force have for creating more of its self? Did you read the article? Even a quick skimming mentions using swarm technology to solve problems, not to replicate. Just because there are thousands of problem solving robots doesn't imply that they will suddenly decide to begin to evolve and replicate.

Solving problems en mass is one thing, spontaneously developing the ability to replicate is completely another. Even if a snake robot swarm, unleashed into a collapsed building to find and help survivors, spontaneously decided to start replicating, where would it find the materials to do so? I'm pretty sure most collapsed buildings are short on snake robot parts.

This idea is related to Rodney Brooks "Fast Cheap and Out of Control" idea. Instead of having one super expensive robot that symbolically processes the world around it and then interacts with it, you have thousands of fast, cheap and barely controlled robots that do the same task as one big by working together and each supplying one small piece of functionality such as sensing, moving or manipulating. Nothing about this implies that they will suddenly begin to replicate.

If, at some point in the future, we develop the ability build robots that can use raw materials to create more of themselves, unleashing thousands of them with no direct control mechanism would probably be a bad idea. Until then, there's not much to worry about unless you work for FOX news and need a SCARY and SENSATIONAL headline for the hour.

I might be a bit old for anonymous, but I do remember alt.fuck.the.skull.of.jesus.

sorry, can't find the ascii art

Are you trying to tell me that anonymous is politically correct?

Jesus Christ. I thought I'd grown up but am I going to have to come down there and teach you a lesson in trolling?

The EPR (European Pressurised Water Reactor) developed by AREVA is a new nuclear reactor designed to achieve greater output (1600 MW) and longer plant life (60 years) than conventional nuclear reactors. The first one is currently under construction in Finland at Olkiluoto. For this new design, an integrated forging was applied for the nozzle shell, including an integral flange (Fig. 1). A 500 t ingot is necessary to manufacture this part, which was the first large part manufactured on a new 14 kt press installed at JSW in 2003. The part was completed 11 months after pouring. The technologies of each manufacturing step and the properties of the part are described.

According to this, Russia can produce two reactor pressure vessel forgings per year, with plans to double by 2011.

But all this delay in "evolutionary" boiling water reactors could be good news for pebble bed reactors. This Blog has a handy summary of the advantages and disadvantages of pebble beds. Last November, Westinghouse bought a pebble bed company called IST Nuclear. Some nice diagrams.

if i RTFA correctly, by 'limited turing test' they mean to see if any second-lifers fail to notice that there's a bot in their midst; that's been happening for a long while now (e.g. Barry's fateful encounter with Julia on TinyMUD).

if on the other hand the occupants know there is a bot in their midst, determination will be trivial to achieve and impossible to prevent:

"hope you don't mind if i start typing everything backwards... ?eman rouy s'tahw os"
"c4n u r3ad this @nd r35p0nd |n p|g l@t|n?"

etc.

You got modded Funny, but I thought it worth noting a paper on male/female disparity in higher education (within Computer Science) that was written by a women about ten years ago. Apparently, men make it sucky for women to go through a higher education and they are allegedly not treated fairly.

I'm going to go out on a limb and say MIT uses Scheme, or some variant of Common LISP. My intro CS courses at Berkeley did the same. It was a real eye opener to build OOP and metaprogramming from scratch using the really quite simple constructs afforded to you in Scheme. Later on we used Java for data structures, but after a semester spent marveling at SICP and the elegance & power of functional programming, that was a painful experience indeed.

It can be used to land planes, too!

(Obligatory Dilbert)

The article leaves out a few important details. Here is what I have to add to the debate, also informed from seeing Jack Gallant speak at this year's Scene Understanding Symposium (SUnS) about a month ago.

Let's look at the actual paper from MIT:

Ultracapacitors or double layer capacitors (DLCs) are energy storage devices whose operation is based on the double layer effect [1]. By utilizing highly porous carbon material with a surface area up to 2000m2/g as electrodes (as in Fig. 3) commercial DLCs can achieve a energy density (6Wh/kg) much greater than the energy density of a conventional capacitor. However, this figure is much lower than the energy density reached by Lithium-Ion batteries (120Wh/kg).

Project Goals
Design and Implement an Ultracapacitor cell (see Figs. 1 and 3) based on Carbon nanotubes (CNTs) that can enhance the performance achievable by batteries. Our analysis shows that the utilization of a matrix of vertically aligned CNTs (see Fig. 2 - right) as electrode structure, can lead to an ultracapacitor characterized by a power density greater than 100kW/kg (three orders of magnitude higher than batteries), a lifetime longer than 300,000 cycles, and an energy density higher than 60Wh/kg.

So they're trying to make a capacitor with half the energy density of lithium-ion batteries. That's an achievement, but it won't replace batteries.

Great power supply for a dragster, at 100kW/kg. You only need a quarter mile of range, and you can get a few megawatts for a few seconds from a modest ultracapacitor bank.

(When posting Slashdot articles, please try to get better sources. And link the original paper, not some blog, please.)

Last week we discussed Popular Mechanics' reporting from MIT, but missed one of the coolest breakthrough of all, something scientists have been working on quietly as Detroit spends money elsewhere.

Link directly to the cities.media.mit.edu info/scoot photo...

Bypassing the ever-silly: /.Soulskill/anonymous(again /.)/PM biz ...enjoy.
-=-=-= -=-=-=

Scooter with ITRI and Sanyang Motors

RoboScooter - Clean, Green Mobility for Today's Crowded Cities

The RoboScooter is a lightweight, folding, electric motor scooter. It is designed to provide convenient, inexpensive mobility in urban areas while radically reducing the negative effects of extensive vehicle use - road congestion, excessive consumption of space for parking, traffic noise, air pollution, carbon emissions that exacerbate global warming, and energy use. It is clean, green, silent, and compact.

People Ryan Chin, PhD Candidate, Smart Cities, Media Lab Yaniv Fain, Sloan School Michael Chia-Liang Lin, MSc Candidate, Smart Cities, Media Lab Arthur Petron, Mechanical Engineering Raul-David "Retro" Poblano, MSc Candidate, Smart Cities, Media Lab Andres Sevtsuk, PhD Candidate, Dept. of Urban Studies & Planning

SYM/Sanyang Motors Grand Wu Wan Ching Chang

ITRI Wen-Jean Hsueh Eugene Hsiao Ying-Tzu Lin Barbara Yeh

Not a transcript, but...

First, this is not about some single fabriacation device, but about personal fabrication in general. I had to post that in bold, because judging by the comments so far, everybody thinks this is about stereolithography or machines from the Diamond Age. It is not about the machines per se. It is about the convergence of computing and manufacturing. It is about the social possibilities (they made a fabrication lab, not a single machine, and were surprised at the enthusiasm with which it was received by normal people and the ingenuity of the results - three people at MIT are doing PhDs on something invented by an 8 year-old in one of the labs). It is about self-organising and replicating systems.

The exciting bit for me was the comparison of the current state of personal fabrication to the minicomputer era in computing. The transition from mainframes (factories, expensive machines shops with skilled staff) to PCs (Diamond Age) is under way.

Stereolithography machines aren't magic. They're a useful way of making plastic shapes in small quantities, expensively. But that's about it.

Evidently you didn't watch the video, but there's no mention of stereolithography in the summary either, so I'm not sure why you went off on that tangent. The video is about personal fabrication, the technologies used for it are almost an aside - he gives examples of everything from CNC to proteins. They do, however, have a collection of technologies, called a Fab Lab. It's not a stereolithography machine or indeed any single machine or technology, but a $25 000 lab of kit which is sufficient to make stuff that does stuff. Think machine shop + electronics lab + design tools.

You should watch the video, it's pretty interesting.

So the way that this works to explore the so called "Dark ages" of the universe is by looking at the 21-cm line from neutral hydrogen. According to our best knowledge of the history of the universe, after the big bang, the universe was pretty boring, consisting of neutral hydrogen gas and not much else. Neutral hydrogen emits 1.4 GHz radio waves from the hyperfine spin-flip of the electron, but when the first stars/galaxies turned on the light they emitted ionized bubbles of the surrounding hydrogen, which then ceased to emit this radio emission (since only neutral hydrogen emits in radio). The universe is pretty much totally ionized now, so we are targeting the era in the universe when it was transitioning from all neutral to all ionized. This is called the epoch of reionization (EoR for short).

The expansion of the universe means that any emission from neutral hydrogen in the early universe is actually "redshifted" to lower frequencies, so our best guess from the known data of Gunn-Petersen troughs in quasars and HI density from WMAP is that the interesting period for studying the reionization of the universe is at redshifts of 6-7 and greater, which means that the 1.4 GHz radio emission gets shifted down to ~200 MHz or less.

The advantages of putting this on the moon is that you really have a tough time doing radio astronomy below say 50 MHz (which would let us see the early stages of reionization) from earth because at these frequencies the ionosphere starts becoming really opaque to radio emission, so we can't see anything. Also, these arrays will literally consist of thousands of antennae, so it would be really hard to put something that big / with that many independent elements in an orbiting configuration. Also, the earth emits a fair bit of low frequency radio emission so putting it on the lunar far side dramatically reduces the background.

Hope that was informative. Check out the MWA project for my personal favorite array being built to look for the higher frequency pieces of this signal.

1. Make solar energy affordable - Done
2. Provide energy from fusion - This is something I don't know anything about.
3. Develop carbon sequestration methods - More information
4. Manage the nitrogen cycle - More information. I feel like on a basic, local level this can already be accomplished easily. On an advanced/global level though... Manage it? In the next 100 years maybe we can gather some data points so we can UNDERSTAND it. Until then, any attempts to "manage" it would be foolish
5. Provide access to clean water - Tried and true method and 1, 2, 3 Orgs doing it.
6. Restore and improve urban infrastructure - And run on-time and build more parks - but who will fund it?
7. Advance health informatics - This "engineering goal" is too general to discuss. It's like, make it easier to get useful data on our health. Duh!
8. Engineer better medicines - I think "Engineer better robots" would be a more worthwhile engineering goal... but that's just me.
9. Reverse-engineer the brain - Teaching it, and studying it
10. Prevent nuclear terror - This is a political bombshell that I won't go near, but from what I see the strategy is (a) deterrence, and (b) threaten anybody with a nuclear project.
11. Secure cyberspace - Ha!
12. Enhance virtual reality - In a practical way or just enough so that my brain can be tricked into thinking that an incredibly hot women is going down on me?
13. Advance personalized learning - Not sure what this is...
14. Engineer the tools for scientific discovery - Another overly general one, but I'd like to think "discovery" is a misspelling of "exploration". Lately I've been thinking that our satellites are similar to the Triremes of Greece times (which are bound to stay close to our shores), the Apollo/Space Shuttle is like Viking ships (which couldn't (or weren't) be used to setup a new settlement), and then this would be the equivalent of the Nina, Pinta, and Santa Maria (except they will be called Washington, Jefferson, Franklin, and Lincoln).

I am going to be fair... this is really a list of things that can be completed in the next 25 years. These are not "100 year" goals. They are simply to generalized, for the most part. A real engineer knows that goals should be Specific, Measurable, and ARTistic. These goals don't qualify.

1. Make solar energy affordable - Done
2. Provide energy from fusion - This is something I don't know anything about.
3. Develop carbon sequestration methods - More information
4. Manage the nitrogen cycle - More information. I feel like on a basic, local level this can already be accomplished easily. On an advanced/global level though... Manage it? In the next 100 years maybe we can gather some data points so we can UNDERSTAND it. Until then, any attempts to "manage" it would be foolish
5. Provide access to clean water - Tried and true method and 1, 2, 3 Orgs doing it.
6. Restore and improve urban infrastructure - And run on-time and build more parks - but who will fund it?
7. Advance health informatics - This "engineering goal" is too general to discuss. It's like, make it easier to get useful data on our health. Duh!
8. Engineer better medicines - I think "Engineer better robots" would be a more worthwhile engineering goal... but that's just me.
9. Reverse-engineer the brain - Teaching it, and studying it
10. Prevent nuclear terror - This is a political bombshell that I won't go near, but from what I see the strategy is (a) deterrence, and (b) threaten anybody with a nuclear project.
11. Secure cyberspace - Ha!
12. Enhance virtual reality - In a practical way or just enough so that my brain can be tricked into thinking that an incredibly hot women is going down on me?
13. Advance personalized learning - Not sure what this is...
14. Engineer the tools for scientific discovery - Another overly general one, but I'd like to think "discovery" is a misspelling of "exploration". Lately I've been thinking that our satellites are similar to the Triremes of Greece times (which are bound to stay close to our shores), the Apollo/Space Shuttle is like Viking ships (which couldn't (or weren't) be used to setup a new settlement), and then this would be the equivalent of the Nina, Pinta, and Santa Maria (except they will be called Washington, Jefferson, Franklin, and Lincoln).

I am going to be fair... this is really a list of things that can be completed in the next 25 years. These are not "100 year" goals. They are simply to generalized, for the most part. A real engineer knows that goals should be Specific, Measurable, and ARTistic. These goals don't qualify.

... a revolutionary new way of protecting secret, spacebased, black, dull colored facilities with attached heatresistant ceramics to become ultimately overheated by using semiconductor based heat-to-electric-energy-converter-technology to power the boardsystems with electric energy as well as an laser generator which beams odd energy to the outer space were it is not longer a harm for the satellite.

(Hey, that could be a real way: http://web.mit.edu/newsoffice/2001/electricity-1205.html)

Moore's law is an observation about the cost per transistor in a circuit. Making faster computation is all about transistor density and the distance signals must travel. Even after the 2-D transistor density levels off, the race will be on to make cheaper 3-D chips using wafer-bonding methods, giving us a new dimension to increase density and thus speed up computation:
http://mtlweb.mit.edu/researchgroups/icsystems/3dcsg/

And we'll still see the same exponential benefits to GOPs/$ for a long time after 3-D transistor density maxes out. The economics that drive the exponential cost-per-computation trend are more related to volume of demand which offsets high fixed production costs and less related to our ability to actually cram more transistors on a chip.

Are you familiar with the fact that patent litigation takes YEARS, and millions of dollars? Sometimes a legitimate company may only be able to afford a single lawsuit at one time. The patent office can already reject your application on the ground of prosecution laches, and the court can rule an issued patent unenforceable due to laches for unreasonable delay. Actively suing someone else isn't unreasonable delay, so it can take many years before a patent holder gets around to suing you. I don't have a cite for that exact proposition, but I recall reading it while doing legitimate legal research. Also, read Symbol II and Symbol IV.

Warning: rambling post ahead.

My gut feeling is that, from strictly a hardware perspective, we're already capable of building a human-level AI. The problem is that, from a software perspective, we've focused too much on approaches that will never work.

As far as I'm concerned, the #1 problem is the Big Damn Database approach, which is basically a cargo cult in disguise. Though expert systems are useful in their niches, "1. Expert system 2. ??? 3. AI!" is not a workable roadmap to the future. I'm certain that it's far easier to start with an ignorant AI and teach it a pile of facts than it is to start with a pile of facts and teach it to develop a personality.

The #2 problem is the Down To The Synapse approach. This, unlike BDD, could quite possibly create "A"I if given enough hardware. But I think that, while DTTS will lead to a better understanding of medicine, it won't advance the AI field. It won't lead to an improved understanding of how human cognition works — it certainly won't teach us anything we didn't already know from Phineas Gage and company.

Even if we go to all the trouble of developing a supercomputer capable of DTTS emulation of a human brain — so what? If we ask this emulated AI to compute 2+2, millions of simulated synapses will fire, trillions of transistors will flip states, phenomenal amounts of electricity will pour into the supercomputer, just for the AI to give the very same answer that a simple circuit consisting of a few dozen transistors could've answered in a tiny fraction of the time, using the amount of electricity stored on your fingertip when you rub your shoes on the carpet during winter. And that's not even a Strong AI question. That's not to say that working DTTS won't be profound in some sense, but we know we can build it better, yet we won't have the faintest idea of where to go next.

That brings me to my core idea — goals first, emotions close behind. Anyone who's pondered the "is/ought" problem in philosophy already knows the truth of this, even if they don't know they know the truth of it. The people building cockroach robots were on the right track all along; they're just thinking too small. MIT's Kismet, for instance, gives an idea of where AI needs to head.

That said, I think building a full-on robot like Kismet is premature. A robot requires an enormous number of systems to process sensory data, and those processing systems are largely peripheral to the core idea of AI. If we had an AI already, we could put the AI in the robot, try a few things, and ask the AI what works best. So, ideally, I think we need to look at a pure software approach to AI before we go off building robot bodies for them to inhabit.

And how to do that? I think Electric Funstuff's Sim-hilarities captures the essence of that. If we give AIs a virtual world to live in — say, an MMO — then that removes a lot of the need for divining meaning from sensory input, allowing a sharper focus on the "intelligence" aspect of AI. Start with that, grow from there, and I can definitely see human-level AI by 2029.

Warning: rambling post ahead.

My gut feeling is that, from strictly a hardware perspective, we're already capable of building a human-level AI. The problem is that, from a software perspective, we've focused too much on approaches that will never work.

As far as I'm concerned, the #1 problem is the Big Damn Database approach, which is basically a cargo cult in disguise. Though expert systems are useful in their niches, "1. Expert system 2. ??? 3. AI!" is not a workable roadmap to the future. I'm certain that it's far easier to start with an ignorant AI and teach it a pile of facts than it is to start with a pile of facts and teach it to develop a personality.

The #2 problem is the Down To The Synapse approach. This, unlike BDD, could quite possibly create "A"I if given enough hardware. But I think that, while DTTS will lead to a better understanding of medicine, it won't advance the AI field. It won't lead to an improved understanding of how human cognition works — it certainly won't teach us anything we didn't already know from Phineas Gage and company.

Even if we go to all the trouble of developing a supercomputer capable of DTTS emulation of a human brain — so what? If we ask this emulated AI to compute 2+2, millions of simulated synapses will fire, trillions of transistors will flip states, phenomenal amounts of electricity will pour into the supercomputer, just for the AI to give the very same answer that a simple circuit consisting of a few dozen transistors could've answered in a tiny fraction of the time, using the amount of electricity stored on your fingertip when you rub your shoes on the carpet during winter. And that's not even a Strong AI question. That's not to say that working DTTS won't be profound in some sense, but we know we can build it better, yet we won't have the faintest idea of where to go next.

That brings me to my core idea — goals first, emotions close behind. Anyone who's pondered the "is/ought" problem in philosophy already knows the truth of this, even if they don't know they know the truth of it. The people building cockroach robots were on the right track all along; they're just thinking too small. MIT's Kismet, for instance, gives an idea of where AI needs to head.

That said, I think building a full-on robot like Kismet is premature. A robot requires an enormous number of systems to process sensory data, and those processing systems are largely peripheral to the core idea of AI. If we had an AI already, we could put the AI in the robot, try a few things, and ask the AI what works best. So, ideally, I think we need to look at a pure software approach to AI before we go off building robot bodies for them to inhabit.

And how to do that? I think Electric Funstuff's Sim-hilarities captures the essence of that. If we give AIs a virtual world to live in — say, an MMO — then that removes a lot of the need for divining meaning from sensory input, allowing a sharper focus on the "intelligence" aspect of AI. Start with that, grow from there, and I can definitely see human-level AI by 2029.