The Project

Incognito

The Villain — Thu, 16 Jun 2011 13:22:09 +0000

Dr. David Eagleman, author of ‘Incognito: the secret lives of the brain’ did an interview on Fresh Air / NPR the other day:

Nothing earth-shattering here, but I like the point of view he takes concerning the brain, how it affects how each of us perceive reality–how that, in essence, makes us very different from one another–which is why not everyone should be treated as equals in, say, a court of law. I’m interested to see how this line of research progresses, particularly in the ability to treat and cure people’s mental states based on whatever illness they are suffering that is causing them to lose their human rationality or ‘moral fabric’ of society.

Dr. Eagleman also talks about his ‘possibilian’ religious views, which to me constitute a goofy, albeit positive take on agnosticism. Anyway, I would like to read the book.

Brain Link Dump, part 2

The Villain — Mon, 23 May 2011 10:44:36 +0000

There is so much happening with brain research. Here I’ll continue to share some advances, mostly from KurzweilAI.net, 20 brain-related links at a time:

Stem cell markers regulate synapse formation
Memory storage and reactivation is more complex than previous thought
Brain wiring revealed for adapting to the unexpected
Brain manipulation with electric stimulus
Learning causes structural changes in affected neurons
Mind’s circuit diagram to be revealed in giant map
Brain maps reveal clues to mental decline
Brain frequencies reveal clues about how it works
How the brain learns from mistakes
Learning about neuronal activity from fruit flies
Brain formation
Tangle of neurons that control aggression
Brains of humans, brains of worms
How the brain compresses visual information
Neurons operate in reverse
Brain imaging
Brain doesn’t need vision to ‘read’
Parts of the brain can switch functions
Human stem cells transformed into key neurons lost in Alzheimer’s
New light-sensing mechanism found in neurons

Our Daily Lives in the Progressive ‘Singularity’

The Villain — Wed, 02 Mar 2011 21:00:25 +0000

There is no question technology is advancing at a breakneck, never before seen pace. The historical course of technology's advances is perhaps best exemplified by today's Web, which has changed everything from how the economy is run, to finding knowledge, to our social lives, to privacy, etc. etc. Any sane person is aware of this. The signs of exponential growth in all kinds of computing power and information sharing are well described by Kurzweil's extrapolation of Moore's Law: the Law of Accelerating Returns. Despite social constraints, fluctuating economies, and despite only having recently discovered these long-term, antiquated trends, these laws seem to persist against all odds. This has led Kurzweil (and a growing list of others) to naturally conclude that at some point, our created technology's are going to surpass us in intelligence and start creating greater intelligences, and then who knows what? With all the facts at hand, the idea of this "technological singularity" holds some weight, and it is worth considering the possibility of it happening, and its ramifications. It is also worth considering some other points of view, including the idea that these ramped up exponential trends may have different results, that we may be experiencing already, day by day. This is a more subtle version of the 'Singularity,' but it, too holds weight and in my view is worth considering.

SINGULARITY: DEFINED
Before I begin a discussion about the Technological Singularity, it is worth taking time to define what this term actually describes. The Technological Singularity is considered inevitable by many, and ridiculous by others. There have been some proposing that perhaps the Singularity is already behind us. Maybe so, but that depends on your definition of the Singularity. I don't buy it.

One of the pioneers of the idea of a technological singularity, Vernor Vinge, wrote in 1993 that the singularity is "change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater than human intelligence." Basically, that means the point when machines surpass human intelligence, a.k.a. the creation of "Superintelligence," which goes on to create other superintelligences, essentially shutting humans out of the equation.

Before Vinge, I. G. Good wrote:

"Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an 'intelligence explosion,' and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control."

Then again, Good expected a superintelligence to be created some time in the 20th century.

I like the summation my friend makes: "I can't call it intelligence for something that is nothing without us. We left tomorrow and there would be a ton of idle computers, dead zoos and dead domesticated animals." I agree with that: if we humans were wiped off the face of the earth and robots / machines are operating everything without us, and to their own ends, we could call these super-human, or at least "human-like" intelligences.

If we go by any of these definitions, the Technological Singularity has indeed not yet happened.

But perhaps the Singularity happens, as predicted. Perhaps a super-human intelligence is created. Why would it then be automatically compelled to create other intelligences? Honestly, how can we even know that? An intelligence greater than our own will by its own definition have a different set of rules, and more than likely a different agenda altogether. Peter on consciousentities.com enters the Singularity conversation with a number of thought provoking criticisms along these same lines. I believe it may be a stretch to think that the simple creation of a super-human intelligence will result in an intelligence explosion that kicks us to the curb. There are others who agree with me on this. Maybe it will be more like a super-Google that is just really good at finding answers we didn't know existed and finding them in unprecedented ways we humans simply couldn't comprehend. On the flip-side of the coin, there are plenty of horror scenarios we can already imagine: Terminator, Eagle Eye, iRobot, and so on and so on--but of course we humans always like a good end-of-the-world story.

What would motivate the superintelligences to run things the way we do after we are gone? Common sense tells me that for a superintelligence to be motivated to run things in a way humans would run things, they would have to have the same human-like tendencies built on human-like hardware as well as software, and I don't see that happening.

As Kevin Kelly eloquently states:

The one kind of mind I doubt we'll make many of is an artificial mind just like a human. The only way to reconstruct a viable human species of mind is to use tissue and cells--and why bother when making human babies is so easy?

REAL VS. ARTIFICIAL INTELLIGENCE

Let's take the definition game a step closer to its core: what is AI, or intelligence for that matter? Our understanding of how to go about creating AI has changed as we have begun to redefine our understanding of our own intelligence. And it almost seems as if the more we try to define intelligence, the more it evades definition.

Considering our flawed definitions of our own intelligence, our exact definition of "artificial" intelligence likewise remains liquid as technology marches onward toward sentient machines. This has resulted in the "AI Effect."

The 'AI Effect,' simply stated, says: "AI is whatever hasn't been done yet." Much of what has already been created is AI by anyone's definition 50 years ago. We just don't call it AI because we accept these things as normal as they are integrated into our daily lives, and as our definitions of intelligence and Artificial Intelligence change. Other factors keeping us from calling these things true AI may include the type of change--gradual, incremental changes that have built up to what we have today as opposed to obvious revolutionary leaps--and furthermore it may simply be human nature, or fear, disallowing us to believe that anything close to AI has actually been created already.

From a Wired article: “If you told somebody in 1978, ‘You’re going to have this machine, and you’ll be able to type a few words and instantly get all of the world’s knowledge on that topic,’ they would probably consider that to be AI,” Google cofounder Larry Page says. “That seems routine now, but it’s a really big deal.” These kinds of things happen all the time as technology marches forward. Humans are learning from computers how to play better chess now, not the other way around.

Kevin Kelly blames our chauvinistic nature for this:

We are blind to this massive eruption of minds into [technology] because humans have a chauvinistic bias against any kind of intelligence that does not precisely mirror our own. Unless an artificial mind behaves exactly like a human one, we don't count it as intelligent. Sometimes we dismiss it by calling it "machine learning." So while we weren't watching, billions of tiny, insectlike artificial minds spawned deep into [technology], doing invisible, low-profile chores like reliably detecting credit-card fraud or filtering e-mail spam or reading text from documents. These proliferating microminds run speech recognition on the phone, assist in crucial medical diagnosis, and guide automatic gearshifts and brakes in cars. A few experimental minds can even drive a car autonomously for a hundred miles.

In Gödel, Escher, Bach, Hofstadter includes in his speculations about AI his answer to the question: "How would we even recognize AI?":

It is almost impossible to imagine that the "body" in which an AI program is housed would not affect it deeply. So unless it had an amazingly faithful replica of a human body--and why should it?--it would probably have enormously different perspectives on what is important, what is interesting, etc. ... My guess is that any AI program would, if comprehensible to us, seem pretty alien. For that reason, we will have a very hard time deciding when and if we really are dealing with an AI program, or just a "weird" program. ... Probably no one will ever understand the mysteries of intelligence and consciousness in an intuitive way. Each of us can understand people, and that is probably about as close as you can come.

In some respects, we have already created "super intelligences"--technologies that can easily outperform us at some things, like math and other specialized tasks. In some ways these machines are smarter than us, but they cannot live without us. They are capable of mass calculation etc. but not emotion. Not love, or hate, or happiness. If we're honest with ourselves, these are what we're looking for, and trying to produce.

Emotional intelligence, while ubiquitous for humans, is much more difficult to attain for the same machines that can easily outdo us in tasks we find downright tedious and boring. Getting machines to understand emotion may be a problem of hardware as much as software, as the two layers are tightly intertwined--both with living biological beings and with computers. Intelligence as we understand it emerges from these tightly interwoven complex layers.

If we do achieve this emotional intelligence, have we then reached the Singularity? The more I think about it, I don't know that there will be a definable 'moment' of reaching the Singularity. Technology keeps overtaking menial human tasks, tasks that once would have required what we considered intelligence to perform, such as beating us at chess, or, ahem, Jeopardy!... Then again, if a piece of technology can't be considered intelligence as long as it is something that is nothing without us, then all these advances so far cannot be considered any more than tools to supplement our own intelligence. After all, if all humans are suddenly wiped off the face of the earth, what good is Deep Blue, Watson, or Google Translate? For that matter, why would we even want to create human-like AI? Everything we've created thus far has been decide-ably nonhuman, but these tools have profoundly impacted our lives.

IMMERSED IN THE PUZZLE
So machines are now capable of teaching humans, while all technology still remains dependent upon its human creators. "In short, we are engaged in a permanent dance with machines, locked in an increasingly dependent embrace." This coevolution between humans and our technology is highlighted in two of Kevin Kelly's books, Out of Control and the recently released What Technology Wants. He makes many valid points about the history and likely future of technology, and our intimate relationship as humans with it.

Instead of the antiquated top-down approach of creating a smart robot, Kelly sees intelligence as being more likely to emerge from the Web and its infrastructure, the Internet. Time and time again he displays examples in nature and machines where intelligence emerges from the hive mind of the whole--something that can only be achieved from the bottom up. Something different, more sentient, more powerful, emerges from the combination of myriad dumb parts, which is exactly what the Web is. Furthermore, the Web is growing at an unprecedented pace, and it is easily becoming the largest database of information to have ever existed, at least in our corner of the universe.

Besides the web, he points to advances in GRIN technologies: Genetics, Robotics, Information and Nanotechnologies, as all being part of the forefront of technology's march toward achieving its "wants"--increasing: efficiency, opportunity, emergence, complexity, diversity, specialization, ubiquity, freedom, mutualism, beauty, sentience, structure, and evolvability.

The language recognition of Watson 5.0, the data set of the web, the yet to be created emotionally intelligent robot... Add all these facets together, and that super-intelligence becomes something more than we can imagine, but until these components are combined, they are each capable of becoming 'intelligent' in their own specific domain, whether or not we humans admit it. Deep Blue is already "chess intelligent," Calculators are "math intelligent," gene sequencers are "DNA intelligent"--all beyond human capabilities, just to name a few examples. Technology is our tool-set, and these tools continue to become smarter. Many have already surpassed our abilities. Combine all these fields of research, supplement them with the immense data set of the Web, and then, maybe, we'll have our super-intelligence--but it will likely be unrecognizable to us. Because we are, after all, only human.

All these things being considered, I think the "Singularity" may be a poor choice of word for an event that may not be singular at all. In fact, it may already be in progress, one event at a time--on many different fronts, incomprehensible to us at the present. Maybe 100 years from now we'll look back and say "Wow, how could we have missed the moment when the Web started outsmarting us?" There may be a pinpoint in time when a super-intelligent organism overtakes our intelligence and starts immediately spitting out more and more complex, intelligent machines which we are incapable of understanding. But maybe the "Technological Singularity" is more like a giant puzzle which is slowly being assembled from several directions. And even after it is all together, maybe no one will have a full view of it. Intelligent machines already outperform us in many areas, and other tasks such as understanding emotion and navigating obstacles will likely be achieved in the future as well. However, because of how tightly we are intertwined with our creations in this coevolutionary dance, I think machines will remain dependent on humans for advances just as we are now gleaning the benefits from our creations in technology. In other words, even if super-human-intelligence does attain human-like qualities such as love and hate, I think the super-intelligence would be ill-advised to just wipe us out. Call it naive optimism if you must, but I feel this scenario is as likely, if not more-so, than the prophesied human-machine war. In the meantime, let's just all sit back and enjoy the show.

Important References:

Peter. (2010, Oct. 2). "The Singularity". Conscious Entities. Retrieved from http://www.consciousentities.com/?p=620

Hofstadter, D. (1999). Gödel, Escher, Bach: An Eternal Golden Braid (20th Anniversary Ed.). Quote: p. 680. Basic Books.

Kelly, K. (1994). Out of Control. The New Biology of Machines, Social Systems, & the Economic World. Basic Books.

Kelly, K. (2010). What Technology Wants. Quotes: p. 329, p. 332. Viking Adult.

Kurzweil, R. (2001, Mar. 7). "Law of Accelerating Returns." Retrieved from http://www.kurzweilai.net/the-law-of-accelerating-returns

Levy, S. (2010, Dec. 27). "The AI Revolution is On." Wired Magazine. Retrieved from http://www.wired.com/magazine/2010/12/ff_ai_essay_airevolution/

Vinge, V. (1993). "What is the Singularity?" Retrieved from http://mindstalk.net/vinge/vinge-sing.html

Wikipedia. (n.d.). "AI Effect". Retrieved from http://en.wikipedia.org/wiki/AI_effect

Weighing Watson’s Win

The Villain — Tue, 22 Feb 2011 12:09:24 +0000

As everyone knows, Watson made the news last week by beating the two top human Jeopardy! champs of all time, but most people seem to appreciate the event as nothing more than an entertaining spectacle that will quickly be forgotten--like the latest celebrity break-up. After all, Deep Blue beat Kasparov at chess 14 years ago; we all know computers are 'smart' now, so what's the big deal?

Roboticist Hans Morovec answers this best, explaining how a seemingly-simple feat like winning at Jeopardy! is so much harder for computers than winning at chess, and vice-versa for humans:

Chess and natural-language question answering are two points on a scale of AI and robotics difficulty that runs in the opposite direction of difficulty for humans. Skills easy for humans are hard for computers, and vice versa.

We do effortlessly and effectively things that were long a matter of survival for our ancestors, the machinery for those skills having been ruthlessly optimized. Recent cultural tasks, on the other hand, often recruit ill-fitting survival skills in unnatural and often hugely inefficient ways.

Computers lack powerful specialized skills, they just perform simple operations in long sequences specified by programs. This general-purpose neutrality seems powerful when applied to tasks humans do inefficiently, but weak when imitating natural survival skills.

Watson does not represent the Technological Singularity or anything nearly as momentous as that, but I think it is realistic to view this event as a milestone toward general intelligence in computers--a very public milestone, specifically dealing with one facet of Artificial Intelligence research: Natural Language Processing. In scouring the web for relevant articles providing an accurate analysis of the importance of Watson's accomplishment, I found this article by Ben Goertzel, who I believe best explains what Watson truly represents. The achievement by IBM was not a be all and end all for AI, but it was definitely impressive. I look forward to seeing what more is in store for the Watson-like technologies as their development continues--especially when combined with the already well-established foundation of computational intelligence underlying all of today's technologies.

Unraveling intelligence, by unraveling the brain

The Villain — Mon, 31 Jan 2011 11:37:27 +0000

I apologize in advance that this is basically just one big link dump about brain research in recent years, but there is simply so much information that it is not worth me rehashing all of it in detail when you can see some of these cool advances through the articles themselves. In many ways, it is almost old-fashioned to think that unraveling the mysteries of the human brain will provide us with the answers we seek about intelligence and how to create AI. But since the human brain is among the most complex structures we ourselves have encountered in the universe, it is in the very least a matter of scientific curiosity to try to answer the questions about our most basic being:

So where can we start with our research? We begin as we do with much research that applies to humans; to simplify things, start with the animals:
Mapping fly brain
Mapping a mouse brain, sliver by sliver

There are the layers of the brain to consider, including the hardware and the software of it, and this layering problem is also being considered:

Research is being conducted on the small scale...
Each neuron its own computer
3-D firing of neurons recorded by microscrope
Tracking individual neurons for months
Neurons lose information at one bit per second
Time-lapse movie shot within the brain
Brain changes during development
Synapse map
"The Robopocalypse cometh"

... and the larger scale:
Smartphone chips emulating human brain
Facebook Neurons
Delicate balance of neurons in brain
What's missing from your cognitive toolkit?
Meditation changes brain state

... and still we have no complete answers for what it is that makes the brain function as our mind, so that we can understand the concepts of "red" and of "love". Qualia remains the elusive bond between the concrete and the abstract. Yet, as all these studies show, progress is being made, and, sooner or later, the pieces will come together.

Stepping Out of the System

The Villain — Wed, 08 Dec 2010 20:57:16 +0000

Ideas from GEB, part 1:

Understanding intelligence begins by thinking about the basics of problem-solving. GEB starts with formal systems--systems which operate by strict instructions to achieve different configurations. Chess, for instance, is a formal system which is capable of 13⁶⁴ different configurations with 32 pieces limited by rules of movement; a catalog of all these positions represents the realm of all possible configurations within the chess "problem space".

In Chapter 1 of GEB, Hofstadter introduces the "MIU-System," his example of a formal system, which is referred back to throughout the rest of the book. The goal of the "MU Puzzle" is to produce the letter string "MU" given a starting string of letters and 4 set rules from which the system cannot deviate. So how to get to "MU"? Randomly applying the 4 rules to the starting string is one thing to try, but that could go on forever with no solution. The key to finding a solution (or lack of solution) more quickly is to figure out methods for problem solving which can be applied to the problem set: find a way to get rid of obvious dead-ends, therefore narrowing the search space. How do we do this?

By stepping outside of the system through applying theorems to the data.

I like to think of theorems as cookie-cutter templates: patterns which provide organization to an otherwise chaotic jumble of data--which, in the MU Puzzle consists of strings and rules.

Page 37: "It is an inherent property of intelligence that it can jump out of the task which it is performing, and survey what it has done; it is always looking for, and often finding, patterns."

Hofstadter defines two modes of operation: Mechanical Mode and Intelligent Mode.

Mechanical Mode is simply when a list of instructions is repeated, as in computers. These instructions can be repeated ad infinitum because they never change. And because of that, these unchanging modules of knowledge can be built on top of, layer upon layer (resulting in complexity: such as the layers of understanding of the mind built upon the neurons and synapses of the brain, or the weather map mash-ups built on Google Maps made up of bits of data residing in the physical hardware of computer servers distributed by the network of the World Wide Web).

Intelligent Mode allows one to change the perspective, from being stuck within one of the layers to being able to have an outside view of the entire complexity: being able to step outside of the system; finding theorems for thought processes; thinking about thinking.

GEB goes on to ask "How well have computers been taught to jump out of the system?" Heuristic search methods such as Genetic Algorithms use natural selection, crossover and mutation to optimize searches within the problem space. This may not be "stepping outside of the system" per se, but they are involved in finding the correct procedural elements (i.e.: fitness functions) to tie into this declarative knowledge. Procedural versus Declarative is a topic discussed later in GEB, which I will write more about in future blogs. Also I look forward to talking more about layering and isomorphisms--the pattern recognition crucial to intelligence.

Life-Changing Books

The Villain — Sun, 05 Dec 2010 00:49:05 +0000

Ideas are powerful, and a well-written book is capable of everything from changing someone's mood to changing entire worldviews. I have read several books in the past couple of years which have made me think about the course of technology in different ways, so much so that a simple blog review of each will not do these works justice. Instead, I have decided to do a miniseries of reviews on each of these two books, to explain everything I have gotten out of reading through them.

The most notable book I would like to discuss is Kevin Kelly's "Out of Control," which can be read entirely online for free, here. The book's self-explanatory subtitle is "The New Biology of Machines," and though it was written in 1994, I believe this work explains some of the amazing implications the future of technology (ahem, biotechnology) holds for us.

Another book that has been less life-changing for me but appears on many others' "life-changers" lists and took me about 15 months to read through is Douglas Hofstadter's classic "Gödel, Escher, Bach: An Eternal Golden Braid" (A.K.A. "GEB"). This book deals heavily with the phenomenon of "strange loops" and the sources of meaning as we humans understand them. And when I say heavy, I mean heavy to the point that he seems to repeat things to a lower and lower level until the idea is almost exhausted onto itself. That being said, the book provides great insight into what is necessary for achieving Artificial Intelligence in machines, and how to understand our own intelligence to begin with. It is interesting to see, however, how AI has progressed in different ways than Hofstadter predicted it would back in 1979, such as a computer's ability to beat World Chess Champion Garry Kasparov in 1997.

I will be highlighting these two books in future blogs, comparing lessons I have gleaned from them with what is happening every day around us in the world of science, philosophy, and AI in particular. On a side note, check out the books that changed Kevin Kelly's Life. Yes, GEB is on that list, and yes, that is a coincidence--besides the fact that it is a really good book, of course.

Why AI?

The Villain — Wed, 10 Nov 2010 20:13:38 +0000

Why are humans so fascinated with being able to achieve Artificial Intelligence? I think the core of it is that feeling of inevitability, that it can be done, that it is a challenge to our own intelligence to be able to accomplish such a thing. It's like building a taller skyscraper or going to the moon. Still, if you stand back and take a look at it, AI seems like a strange goal. Why would we want to replace ourselves, or even potentially destroy ourselves, by achieving intelligence in machines, or even superior intelligence?

I can't answer for humanity as a whole, but here are 3 reasons I think are worthwhile:

1: Since 'Answers are in the air', AI can help us find answers we are incapable of... or too slow at finding ourselves--basically accelerating our own intelligence. This could include anything from more efficient fuel to a cure (or cures) for cancer. We already learn from and use nature's other intelligence resources, so why not create better intelligences to find those more elusive answers?

2: Artificial Intelligence could potentially reveal new facts and ideas about our own intelligence. Any artificial intelligence will likely be different from our own in many ways, and it by its own definition cannot be explicitly programmed by us humans. Just as we don't yet understand our own brains, the best AIs will most likely emerge from many parts in a complex pattern we won't truly anticipate or understand. So we will create AI without having all the facts ahead of time, and we will be wise to learn from those AIs that we do create, accidentally or otherwise. After all, it is easier to be objective when we are observing an intelligence other than our own, for obvious reasons. And this observation, over time, may reveal ways for us to improve upon our own human intelligence.

3: There are no physical time-lines with artificial intelligence. While all biological intelligence must die with the body, AI can live on and continue to improve upon itself indefinitely.

The primary problem AI development is facing is a fundamental lack of understanding about our own intelligence, both at the 'qualia' level of our consciousness, and the physical substrate of our brain. And even if we could suddenly reveal a blueprint to how all of it works, then what? Let's assume that each individual's consciousness in the biological realm emerges from a 'consciousness template' which is blank at birth, then filled in with the experiences of that individual throughout its lifetime, until its biological, physical end: death. The intelligence is inseparable from the physical body, and we have no other examples to work with outside of biology. How do we transfer the template of consciousness from the biological realm of DNA and neurons to the apparently immortal digital realm of circuitry based on ones and zeros?

Assuming all that is figured out, how can we even prove that we've created AI? Would we even recognize it, as it will likely be far different from our own? What makes us think we can replicate human-like intelligence, anyway? Will the Turing Test suffice to prove that human-like intelligence exists within a machine body? These are just a handful of the many subtle questions AI researchers are dealing with right now. And while not everyone may agree on why we want to create AI, or even if we want to pursue it at all, I believe there are a number of good arguments showing it is a worthwhile endeavor. Even if we don't achieve the ultimate goal, we are bound to learn some interesting things about ourselves along the way.

New Project Wave

Pariahpism — Sun, 07 Nov 2010 02:35:29 +0000

So the project became too huge for two people who subsequently went on tandem vacations to different parts of the world. The theory, while sound in theory, is just that. Looking at it from fresh eyes and mind, there was too much going on. We were planning on using a Genetic Program (Aforge) wrapped up in a Genetic Algorithm (Aforge) as the engine. Physics was to be handled by Physics Helper which was a tool wrapped up into XNA (C#) but utilized by Microsoft Blend and outputted onto Silverlight on a web-browser.

Now it all sounds good. It’s all within the .NET framework and Microsoft tools. It should all be compatible. But really, it’s so fucking complicated. How do you get a third party C# programming library to interface with Silverlight when you use Blend to do everything? How do you get Physics Helper to talk to anything when its sole utilization is through XNA? How do you use Aforge?

So now that I look and say that it could never be done with our staffing, expertise and timeframe, it’s cathartic. It could have been done as we saw it but it would have taken far too long for both our attention and the market we were selling to. It was a doomed project that would-of could-of been amazing if time stood still.

I look with clean eyes at the problems and hardships with solutions. The number one problem is abstraction. We were connecting several A to B solutions with our massive problem like a connect a dot picture into a patchwork A to B quilt that was our problem. It wasn’t going to work.

Now I have a singular focus, a singular bridge between it all. C#. I will learn it front and back. With it I will take Aforge into the fold, I will take XNA into the fold and if fortune favors I will catch Silverlight into the fold. Piece by piece the gravity of the problem will accumulate into something great.

Project: Remix

The Villain — Thu, 21 Oct 2010 00:13:59 +0000

Work in gaming is on hiatus. Getting back to the core of AI, more specifically the realm of Computational Intelligence and Artificial Evolution, I have taken interest in the basics of consciousness and how that can be emulated in software. I am not alone in this, of course. There is plenty of research going on in this arena, and websites I frequent on this subject include http://www.kurzweilai.net/, http://www.consciousentities.com/ and http://www.sciencedaily.com/. I'm almost done reading Gödel, Escher, Bach, and though the book is somewhat dated I have been drawing a lot out of it that I plan on sharing here. Kevin Kelly's book Out of Control got me started on all this, so I look forward to reading his newly released What Technology Wants. Why the gaming hiatus? Well, the main gaming guru of the team is onto a vagabonding stage of life, an experiment which I'm sure will be worth the blog in its own right. That is in the planning stages, and I have been out of the gaming loop for a while, so I am focusing more on research than implementation at the moment. My long blogging absence should soon be replaced with the explosion of ideas I've been processing from all of my readings, so watch out! You might soon be hit with some crazy AI/consciousness/Genetic Programming ramblings--HARD.