Structured Thinking : Analysis, Exploration, Exploitation

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Structured Thinking : Analysis, Exploration, Exploitation

Postby General Patton » Thu Sep 27, 2012 11:36 am

The purpose of this thread is to expand on a comment I made on the Mars colonization thread:

viewtopic.php?f=8&t=29872&start=30#p478816
Most of the older models assume homogeneity and a fixed landscape. There was nothing that would balance exploration versus exploitation within a model. It assumed an equal level of risk aversion in consumer marketplaces, or assumed there wasn't a wide difference in adoption of technologies depending on social structure. The key is all of the implicit and explicit assumptions built into the model. You might expect that a prediction would have degrees of probability and also be focused as much on preventing you from being surprised about new developments.


So, a lot has changed in our ability to model situations since 1989 when the unified space plan was made.

*We no longer assume a Game-Theory style environment of homogeneous actors. This means that they are culturally, genetically, and epigenetically different.

*For more on genetics versus epigenetics in intelligence, see:
As we will see later, IQ doesn't grasp the full range of intelligent capabilities inherent in the exploration v. exploitation dynamic, e.g. resourcefulness. It also doesn't take into account cognitive biases that plague smart people:
http://thesciencepundit.blogspot.com/20 ... cians.html
http://www.psclipper.com/IntelligenceTraps.asp
(The above should also make it obvious that AGI researchers don't really want to make a "human" intelligence per se, they want to improve on the design. The diversity in thinking and intelligence between humans is a grain of sand compared to the beach of AGI. The same thing extends to synthetic biology and other developing sciences.)

*Game theory has now been expanded to include information advantages over time, however most attempts at modeling complex systems like Systems Dynamics have been doing that for decades

*A simple old-school model would be CARVER, which while very useful, isn't going to suit all of our potential needs
http://www.fas.org/irp/doddir/army/fm34-36/appd.htm

*System Dynamics thinking has stacks and flows, and kind of breaks down when modeling large complex systems. To fix that, modern Complex Systems thinking thinks in branches, with probabilities assigned to each given potential future.

*We now model the trade-off of exploration versus exploitation. Most under-grad decision making models assume a landscape in which things will stay relatively the same, you don't have a compounding bonus over time by exploring the landscape. Of course most real-world situations operate in a landscape that is moving very rapidly. This has brought a glut of business books touting the importance of innovation, without clearly defining the environment from which innovation comes from. I define innovation as the ability to explore, and then move into the exploitation area successfully. Most of these business books also ignore the "fast-follower advantage", you can call it imitation, or simply standing on the shoulders of giants:

http://articles.businessinsider.com/201 ... ilure-rate
In fact, a 1993 paper by Peter N. Golder and Gerard J. Tellis had a much more accurate description of what happens to startup companies entering new markets. [3] In their analysis Golder and Tellis found almost half of the market pioneers (First Movers) in their sample of 500 brands in 50 product categories failed. Even worse, the survivors’ mean market share was lower than found in other studies. Further, their study shows early market leaders (Fast Followers) have much greater long-term success; those in their sample entered the market an average of thirteen years later than the pioneers. What’s directly relevant from their work is a hierarchy showing what being first actually means for startups entering new or resegmented markets:


Innovator First to develop or patent an idea
Product Pioneer First to have a working model
First Mover First to sell the product 47% failure rate
Fast Follower Entered early but not first 8% failure rate


You must also consider that the costs of innovation are much less when you're imitating, 25% or more in most cases. The pioneers generally don't end up with a big marketshare. But innovation, whether or not it is a form of imitation, is usually a much better way to dealing with things versus relying on being smarter, having more capital or being harder working. With global competition it's guaranteed that there is someone already working on your problem and has more of all 3 factors.

On the subject on working hard, remember the productivity experiments so you don't burn out:
http://www.lostgarden.com/2008/09/rules ... ation.html

Getting back to innovation, Astro Teller did a good talk on how he structures his team to get innovation at Google's X Lab:



He does a lot of counter intuitive things here, the first thing is: What is the story? Any good exploitable product has a story, you have to obsess over your story. The second is, they show him 10 other bad ideas they found a long the way. This isn't a waste of time, it's to show that they have explored a lot of territory. After that they will do things like throw out the first 6 months of code, so that they aren't working on bad assumptions from the start of the project. This also gives you the ability to ruthlessly prune bad ideas. Assumptions, whether implicit or explicit in your model, will tend to cripple it over time.

The CIA Tradecraft manual makes this point loud and clear:
https://www.cia.gov/library/center-for- ... -apr09.pdf

So the next thing Astro has them do is list the tools they used to make the innovation, and have them improve on it. If you have a toolkit, improve your tools.

*This sort of exploration and improvement creates an exponential curve of growth, see Richard Hammond's You And Your Research:

http://www.cs.virginia.edu/~robins/YouAndYourResearch.html
Now for the matter of drive. You observe that most great scientists have tremendous drive. I worked for ten years with John Tukey at Bell Labs. He had tremendous drive. One day about three or four years after I joined, I discovered that John Tukey was slightly younger than I was. John was a genius and I clearly was not. Well I went storming into Bode’s office and said, “How can anybody my age know as much as John Tukey does?” He leaned back in his chair, put his hands behind his head, grinned slightly, and said, “You would be surprised Hamming, how much you would know if you worked as hard as he did that many years.” I simply slunk out of the office!
...
What Bode was saying was this: “Knowledge and productivity are like compound interest.” Given two people of approximately the same ability and one person who works ten percent more than the other, the latter will more than twice outproduce the former. The more you know, the more you learn; the more you learn, the more you can do; the more you can do, the more the opportunity – it is very much like compound interest. I don’t want to give you a rate, but it is a very high rate. Given two people with exactly the same ability, the one person who manages day in and day out to get in one more hour of thinking will be tremendously more productive over a lifetime. I took Bode’s remark to heart; I spent a good deal more of my time for some years trying to work a bit harder and I found, in fact, I could get more work done. I don’t like to say it in front of my wife, but I did sort of neglect her sometimes; I needed to study. You have to neglect things if you intend to get what you want done. There’s no question about this.


*An important concept none of these models cover is Resourcefulness, which is a talent every good entrepreneur has to have. The Prometheus Society, a high IQ society that accepts 1 out of 30,000 people, versus MENSA's loose 1 out of 50, has some very good articles about this:

http://216.224.180.96/~prom/oldsite/articles/changingface.html
The 2 basic premises, which I will first attempt to substantiate and will then use in furthering my argument, are:

1) Innate intelligence is unmeasurable; only overtly manifested ability can be evaluated—making intelligence tests, in many ways, akin to achievement tests.

2) The definition of “intelligence,” or of what component abilities should be most emphasized on an exam, is socially determined and thus may change in accordance with the needs of an era or culture.



The “classical” education highly valued by the erstwhile British aristocracy emphasized linguistic skills almost exclusively; youths were taught predominantly literature, philosophy, ancient Greek and history. In a more technologically oriented age, some of the most respected scholars of that time might have seemed profoundly inept or “one sided;” conversely, many modern scientists—esteemed for their acute mathematical and spatial reasoning—might have been judged “ill fit for higher learning” or “unintelligent” by an educational system which focused entirely on literary accomplishment.

In our own era, the ability to reason analytically is deemed vital to the advancement of technology and, thus, is stressed on many intelligence tests. Because all information is communicated through the verbal modes of speech and writing, vocabulary and linguistic reasoning are considered important indicators of “mental capacity.” The ability to “rote memorize”—vital to the retention of knowledge in a pre-literate era and important in the learning of complicated ecclesiastical rituals in the medieval period—has been de-emphasized; moreover, the focus on technologically useful forms of thinking has reduced the value placed on linguistic aptitude in isolation.

Thus, the mental abilities deserving emphasis—the criteria for assessing intelligence—may change with the times; men of extreme but one sided talent, deemed “brilliant” in one era, might be considered unremarkable in another. In evaluating intelligence, we measure how well an individual has assimilated the knowledge valued by his culture, how well he has learned to reason in conformity with the current styles of thinking, and how well he can adapt (on a cognitive level) to the conventions of his time.



“Resourcefulness” and the ability to think “broadly” (or “divergently”), to foresee how numerous factors might interact and to envision multiple possible solutions to any given problem, take priority. In an era when computers perform more and more of technology’s “analytical” work and when increasing numbers of people assume managerial roles, the incisive and narrowly-focused reasoning which considers data sequentially and ignores all ostensibly extraneous information may be superseded by the ability to consider heterogeneous pieces of information simultaneously.

The body of modern knowledge is enormous—too huge for one individual to master—even 5 lifetimes; continual advancement, especially in the technologies, assures that every man will always be “slightly ignorant” (even regarding the developments in his own specialty) and that, inevitably, he will often need to consult references for an explanation of new discoveries. The efficient use of such reference sources, necessary for adaptation to an ever-changing society, is of vital practical importance; gaining access to the facts of interest, when (abundant) information is stored in a complex manner, is facilitated by a divergent type of thinking called “resourcefulness.” This “resourcefulness,” as a key determinant of success in the modern world, may be a valid criterion by which to evaluate adult intelligence.


*Part of the reason Start-Up incubators in Silicon Valley give such good terms is because they have no idea who is going to be a winning bet, so they take lots of small ones and hope one pays off. After the initial stages, they aren't investing in an idea as much as the founders:

http://www.paulgraham.com/word.html
Like real world resourcefulness, conversational resourcefulness often means doing things you don’t want to. Chasing down all the implications of what’s said to you can sometimes lead to uncomfortable conclusions. The best word to describe the failure to do so is probably “denial,” though that seems a bit too narrow. A better way to describe the situation would be to say that the unsuccessful founders had the sort of conservatism that comes from weakness. They traversed idea space as gingerly as a very old person traverses the physical world. [1]

The unsuccessful founders weren’t stupid. Intellectually they were as capable as the successful founders of following all the implications of what one said to them. They just weren’t eager to


http://www.paulgraham.com/schlep.html
There are great startup ideas lying around unexploited right under our noses. One reason we don’t see them is a phenomenon I call schlep blindness. Schlep was originally a Yiddish word but has passed into general use in the US. It means a tedious, unpleasant task.

No one likes schleps, but hackers especially dislike them. Most hackers who start startups wish they could do it by just writing some clever software, putting it on a server somewhere, and watching the money roll in—without ever having to talk to users, or negotiate with other companies, or deal with other people’s broken code. Maybe that’s possible, but I haven’t seen it.

One of the many things we do at Y Combinator is teach hackers about the inevitability of schleps. No, you can’t start a startup by just writing code. I remember going through this realization myself. There was a point in 1995 when I was still trying to convince myself I could start a company by just writing code. But I soon learned from experience that schleps are not merely inevitable, but pretty much what business consists of. A company is defined by the schleps it will undertake. And schleps should be dealt with the same way you’d deal with a cold swimming pool: just jump in. Which is not to say you should seek out unpleasant work per se, but that you should never shrink from it if it’s on the path to something great.

The most dangerous thing about our dislike of schleps is that much of it is unconscious. Your unconscious won’t even let you see ideas that involve painful schleps. That’s schlep blindness.

How do you overcome schlep blindness? Frankly, the most valuable antidote to schlep blindness is probably ignorance. Most successful founders would probably say that if they’d known when they were starting their company about the obstacles they’d have to overcome, they might never have started it. Maybe that’s one reason the most successful startups of all so often have young founders.

In practice the founders grow with the problems. But no one seems able to foresee that, not even older, more experienced founders. So the reason younger founders have an advantage is that they make two mistakes that cancel each other out. They don’t know how much they can grow, but they also don’t know how much they’ll need to. Older founders only make the first mistake.

Ignorance can’t solve everything though. Some ideas so obviously entail alarming schleps that anyone can see them. How do you see ideas like that? The trick I recommend is to take yourself out of the picture. Instead of asking “what problem should I solve?” ask “what problem do I wish someone else would solve for me?” If someone who had to process payments before Stripe had tried asking that, Stripe would have been one of the first things they wished for.


http://www.paulgraham.com/relres.html
What would someone who was the opposite of hapless be like? They’d be relentlessly resourceful. Not merely relentless. That’s not enough to make things go your way except in a few mostly uninteresting domains. In any interesting domain, the difficulties will be novel. Which means you can’t simply plow through them, because you don’t know initially how hard they are; you don’t know whether you’re about to plow through a block of foam or granite. So you have to be resourceful. You have to keep trying new things.

Be relentlessly resourceful.

That sounds right, but is it simply a description of how to be successful in general? I don’t think so. This isn’t the recipe for success in writing or painting, for example. In that kind of work the recipe is more to be actively curious. Resourceful implies the obstacles are external, which they generally are in startups. But in writing and painting they’re mostly internal; the obstacle is your own obtuseness.


*Peter Thiel's excellent class on Start-Up's has a good way of modeling things from the position of a founder:
http://blakemasters.tumblr.com/peter-th ... 83-startup
markets
mimesis and competition
secrets
incrementalism
durability
teams
distribution
timing
financing
luck


*In my opinion, the two big ones are timing and distribution. It's been incredibly hard to get those in sync. Most ventures that need less than $1 billion can get funding, and there are lots of small payment options ready for ambitious founders today. But when you start working in a distribution system, or deal with timing, you start to see the order and chaos inherent in complex systems. When you work on those two you understand how small you really are.

*So, when you zoom out on this process, you move away from the simple individual inputs and start to view the entire, complex system that changes over time. You end up with a model where creations don't need creators because simple rules can create structured environments.

http://serendip.brynmawr.edu/complexity/complexity.html
Many (all?) interesting phenomena can usefully be described as "orderly ensemble properties" and productively understood in terms of the properties and interactions of sub-phenomena ("elements").
WHOLES ARE MADE OF PARTS

Ensemble properties are permitted by but not determined by element properties
WHOLES ARE MORE THAN THE SUM OF THEIR PARTS

The behavior of ensembles is both influenced by and influences the behavior of elements
THERE IS A RECIPROCAL CAUSAL RELATIONSHIP BETWEEN PARTS AND WHOLES

Orderly ensemble properties can and do arise in the absence of blueprints, plans, or discrete organizers.
INTERESTING WHOLES CAN ARISE SIMPLY FROM INTERACTING PARTS

Prove it? Try the Game of Life

Ensemble properties may be largely unaffected by variations in the properties and behavior of elements
HOLISTIC PROPERTIES MAY APPEAR RESISTANT TO CHANGES IN PARTS

Ensemble properties may be highly sensitive to variations in the properties and behavior of elements
HOLISTIC PROPERTIES MAY SUDDENLY AND APPARENTLY MYSTERIOUSLY CHANGE

An example? See chaos suddenly emerge from order

Ensemble properties can be dramatically changed by modifying the nature of the interaction among elements
ENUMERATION OF PARTS CANNOT ACCOUNT FOR WHOLES

Ensemble properties may be dynamic for reasons entirely internal to the ensemble
CHANGE DOES NOT NECESSARILY INDICATE THE EXISTENCE OF AN OUTSIDE AGENT OR FORCE

An example? See populations fluctuate for no external reason

The same change in element property or behavior may have a small effect on ensemble order at one time and a large effect at another time
THE RELATION BETWEEN PARTS AND WHOLE MAY ITSELF CHANGE FOR A GIVEN WHOLE.

Disorderly variations in element properties or behavior may be the driving force for ensemble order
INTERESTING WHOLES CAN ARISE FROM CHAOS OR RANDOMNESS

Really? Yep, look at fractals and at The Game of Life and at The Magic Sierpinski Triangle


Deterministic systems will not explore all possible ensemble states
RANDOMNESS PLAYS AN IMPORTANT ROLE IN THE EXPLORATION OF POSSIBLE WHOLES


I also recommend watching/listening to Scott Page's lecture on Understanding Complexity. You can find it on the TCC website or thepiratebay.
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby JackRiddler » Sun Oct 14, 2012 12:26 am

Oh for fuck's sake this is so goddamn smart and important, thank you, why are there no replies?
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby Nordic » Sun Oct 14, 2012 1:36 am

Uh .... maybe because almost nobody ever comes to a "dump"?

I know I don't.

But when I do, I'm always amazed at what's here.
"He who wounds the ecosphere literally wounds God" -- Philip K. Dick
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby Project Willow » Sun Oct 14, 2012 3:09 am

Oh for fuck's sake this is so goddamn smart and important, thank you, why are there no replies?


When you guys figure out how to stop someone from putting a gun to your head, then by all means, let me know.
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby JackRiddler » Sun Oct 14, 2012 6:15 am

Project Willow wrote:
Oh for fuck's sake this is so goddamn smart and important, thank you, why are there no replies?


When you guys figure out how to stop someone from putting a gun to your head, then by all means, let me know.


True there are also some nasties in there - Peter Thiel, and a lot of "How to Be A Hard-Working Killer." The part I like best is the last section, from brynmawr.edu, on complexity.
We meet at the borders of our being, we dream something of each others reality. - Harvey of R.I.

To Justice my maker from on high did incline:
I am by virtue of its might divine,
The highest Wisdom and the first Love.

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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby General Patton » Sun Oct 14, 2012 10:08 am

JackRiddler wrote:Oh for fuck's sake this is so goddamn smart and important, thank you, why are there no replies?


http://en.wikipedia.org/wiki/Warnock's_dilemma
The problem with no response is that there are five possible interpretations:
The post is correct, well-written information that needs no follow-up commentary. There's nothing more to say except "Yeah, what he said."
The post is complete and utter nonsense, and no one wants to waste the energy or bandwidth to even point this out.
No one read the post, for whatever reason.
No one understood the post, but won't ask for clarification, for whatever reason.
No one cares about the post, for whatever reason.

Image
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby JackRiddler » Sun Oct 14, 2012 11:53 am

General Patton wrote:Image


Upset?! Not at all.

Translated into SAE all I meant was, "Nice thread - read this."

It's like a joke about how to say "Hello" in Berlin dialect. (Sample: "What the fuck is going on here?!")
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby Wombaticus Rex » Sun Oct 14, 2012 4:42 pm

any excuse to bust out that particular .gif is good by me

Via: Class 17: Deep Thought

IV. Tackling AI

We have people from three different companies that are doing AI-related things here to talk with us today. Two of these companies—Vicarious and Prior Knowledge—are pretty early stage. The third, Palantir, is a bit later.

Vicarious is trying to build AI by develop algorithms that use the underlying principles of the human brain. They believe that higher-level concepts are derived from grounded experiences in the world, and thus creating AI requires first solving a human sensory modality. So their first step is building a vision system that understands images like humans do. That alone would have various commercial applications—e.g. image search, robotics, medical diagnostics—but the long-term plan is to go beyond vision and build generally intelligent machines.

Image

Prior Knowledge is taking a different approach to building AI. Their goal is less to emulate brain function and more to try to come up with different ways to process large amounts of data. They apply a variety of Bayesian probabilistic techniques to identifying patterns and ascertaining causation in large data sets. In a sense, it’s the opposite of simulating human brains; intelligent machines should process massive amounts of data in advanced mathematical ways that are quite different from how most people analyze things in everyday life.

Image

The big insight at Palantir is that the best way to stop terrorists isn’t regression analysis, where you look at what they’ve done in the past to try to predict what they’re going to do next. A better approach is more game theoretic. Palantir’s framework is not fundamentally about AI, but rather about intelligence augmentation.It falls very squarely within the Ricardo gains from trade paradigm. The key is to find the right balance between human and computer. This is a very similar to the anti-fraud techniques that PayPal developed. Humans couldn’t solve the fraud problem because there were millions of transactions going on. Computers couldn’t solve the problem because the fraud patterns changed. But having the computer do the hardcore computation and the humans do the final analysis, while a weaker form of AI, turns out to be optimal in these cases.


Later:

Peter Thiel: Let’s pose the secrecy questions. Are there other people who are working on this too? If so, how many, and if not, how do you know?

Eric Jonas: The community and class of algorithms we’re using is fairly well defined, so we think we have a good sense of the competitive and technological landscape. There are probably something like 200—so, to be conservative, let’s say 2000—people out there with the skills and enthusiasm to be able to execute what we’re going after. But are they all tackling the exact same problems we are, and in the same way? That seems really unlikely.

Certainly there is some value to the first mover advantage and defensible IP in AI contexts. But, looking ahead 20 years from now, there is no a priori reason to think that other countries around world will respect U.S. IP law as they develop and catch up. Once you know something is possible—once someone makes great headway in AI—the search space contracts dramatically. Competition is going to be a fact of life. The process angle that Scott mentioned is good. The thesis is that you can stay ahead if you build the best systems and understand them better than anyone else.

...

Question from the audience: Do we actually know enough about the brain to emulate it?

Eric Jonas: We understand surprisingly little about the brain. We know about how people solve problems. Humans are very good at intuiting patterns from small amount of data. Sometimes the process seems irrational, but it may actually be quite rational. But we don’t know much about the nuts and bolts of neural systems. We know that various functions are happening, just not how they work. So people take different approaches. We take a different approach, but maybe what we know is indeed enough to pursue an emulation strategy. That’s one coin to flip.

Scott Brown: Like I said earlier, we think emulation is the wrong approach. The Wright brothers didn’t need detailed models of bird physiology to build the airplane. Instead, we ask: what statistical irregularities would evolution have taken advantage of in designing the brain? If you look at me, you’ll notice that the pixels that make up my body are not moving at random over your visual field. They tend to stay together over time. There’s also a hierarchy, where when I move my face, my eyes and nose move with it. Seeing this spatial and temporal hierarchy to sensory data provides a good hint about what computations we should expect the brain to be doing. And lo and behold, when you look at the brain, you see a spatial and temporal hierarchy that mirrors the data of the world. Putting these ideas together in a rigorous mathematical way and testing how it applies to real-world data is how we’re trying to build AI. So the neurophysiology is very helpful, but in a general sense.
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby General Patton » Sun Oct 14, 2012 5:19 pm

Yes, I do like Palantir's approach. Over the next 10 years or so, it will be the most fruitful approach. I'm a mod in the Simulate reddit, which is a project to recreate a high fidelity simulation of the world:
http://www.reddit.com/r/simulate

There's a great deal of tangentially related material to dig into, as we have to try and simulate complex systems of all kinds. There is quite a bit going on there, more than I can digest at any given time. One of the bigger open questions we have right now is simulating technological progress with agents. Agents build tools so they can actualize desires, so we have behavior, motivators and as Tom puts it, emotions. There are a finite number of states a tool can go through to reach the ideal state to satisfy a desire. When you strengthen an agents curiosity, then in theory you open up a much larger range of technological possibilities. Right now the question is how we would represent it through levels of abstraction, because we only want to provide a framework. At least some of the behavior will have to merge on it's own for it to be dynamic. Eventually someone is going to have to open the hilarious barrel of monkeys that is Psi.

Have you looked at the CogPrime architecture?

http://wiki.opencog.org/w/CogPrime_Overview
Image

There are a few other interesting things, projects to the past like Simulpast and MASS:

http://simulpast.imf.csic.es/sp/
https://github.com/xrubio/pandora
https://www.bsc.es/computer-application ... -framework
http://oi.uchicago.edu/OI/PROJ/MASS/papers.htm

In terms of fun and immersion, I have no idea exactly how it would play out. You could probably take a much smaller slice of this and make it much more fun. We'll have 3d touch interfaces out on the market in 3-5 months from various manufacturers, and the 3d headset Oculus Rift will be out soon as well (John Carmack said you need a motion to photon latency of <20 miliseconds), so there is a lot of material to work with for next generation indie-devs. 1 answer leads to 10 more questions.
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby JackRiddler » Mon Oct 15, 2012 11:09 am

I'm guessing, and I don't know shit about AI science, but strictly from a historical outline POV I figure what it's going to look like in the first decades at least will be hybrids of computer and living brains - first animals, inevitably humans.
We meet at the borders of our being, we dream something of each others reality. - Harvey of R.I.

To Justice my maker from on high did incline:
I am by virtue of its might divine,
The highest Wisdom and the first Love.

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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby General Patton » Mon Oct 15, 2012 12:55 pm

JackRiddler wrote:I'm guessing, and I don't know shit about AI science, but strictly from a historical outline POV I figure what it's going to look like in the first decades at least will be hybrids of computer and living brains - first animals, inevitably humans.


We have some basic human/machine hybrids with different levels of integration right now. You have non-invasive stuff, brain sensors, 3d headsets, 3d gesture recogniton, stuff like that (which will be integrated into sensor nets for a variety of purposes). Then you have stuff like Parkinson's implants:
http://articles.cnn.com/2009-01-06/heal ... =PM:HEALTH

and remote controlled roaches:
http://www.nature.com/news/remote-contr ... ue-1.11403

One of the biggest holes right now is implant security, I know there are at least a few guys in Silicon Valley trying to create a field for this, because implants now include wifi technology. So you could shutdown or restart pacemakers (stop shocking yourself, stop shocking yourself) and similar implants if you are within a certain range of them. Whatever hardware you get in the beginning should ideally be open source and non-invasive. The framework for a secure structure is the same as always, restricted access for all users, built in hardware limitations and code audits.

At the same time, standard issue brains will become less secure over time:
http://www.wired.com/threatlevel/2012/08/brainwave-hacking/
A team of security researchers from Oxford, UC Berkeley, and the University of Geneva say that they were able to deduce digits of PIN numbers, birth months, areas of residence and other personal information by presenting 30 headset-wearing subjects with images of ATM machines, debit cards, maps, people, and random numbers in a series of experiments. The paper, titled “On the Feasibility of Side-Channel Attacks with Brain Computer Interfaces,” represents the first major attempt to uncover potential security risks in the use of the headsets.

“The correct answer was found by the first guess in 20% of the cases for the experiment with the PIN, the debit cards, people, and the ATM machine,” write the researchers. “The location was exactly guessed for 30% of users, month of birth for almost 60% and the bank based on the ATM machines for almost 30%.”


Right now, within NASA and the other brainy communities, biotech is given a bit more importance for the immediate future. We have a genome compiler and 3d printers to make strands of DNA or you can custom order them from labs:
http://www.genomecompiler.com/

Cost decreases are exponential:
http://www.genome.gov/sequencingcosts/

Glue it all together and enhance the human psychic functions exponentially over time by merging it with information technology then you have a recipe for fun.
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby Wombaticus Rex » Mon Oct 15, 2012 5:44 pm

First off, obviously, I cannot thank you enough for all the brainfood you've passed my way in the past three years.

This thread was especially great, and I've been unpacking it ever since it went up. What grabbed me the most was not even remotely related to AI horizons, though: it's the application of this kind of "structured thinking" to the meandering we do here at RI. We've seen some proto-projects, like Daniel Brandt's NNDB getting hooked up into the Flash-based mapping architecture of TheyRule.net, but the thought of applying Bayesian cartographic techniques to the social map of, say, elite pedo networks...surely there's some bleak gold to be mined there, no?

More soon, this is all very nebulous in my brainpan right now.
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby General Patton » Mon Oct 15, 2012 6:02 pm

http://littlesis.org/ is another resource

Use R, or RapidMiner (which can use R as an extension) or PANDA for Python. RapidMiner is probably the easiest to learn (has a GUI) and will have the best support, and has a lot of the algorithm's built in or addable as extensions.

edit: If the load is somewhat low, you could try just making something like a GoogleMap for it with an attached wiki or webpage.

Also it looks like RapidNet can work with Geodata too, it's a secondary program of RapidMiner.

http://rapid-i.com/content/view/281/225/

or

(Useful if you know Python)
http://www.python.org/download/
http://pandas.pydata.org/

or

http://www.r-project.org/
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby JackRiddler » Mon Oct 15, 2012 6:58 pm

Oof, just to be clear, the mind-reading experiment is not yet involving remote action or reception of actual thoughts. The subjects are strapped to a brainwave reader and shown random numbers.

To detect the first digit of the PIN, researchers presented the subjects with numbers from 0 to 9, flashing on the screen in random order, one by one. Each number was repeated 16 times, over a total duration of 90 seconds. The subjects’ brainwaves were monitored for telltale peaks that would rat them out.
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Re: Structured Thinking : Analysis, Exploration, Exploitatio

Postby General Patton » Mon Oct 15, 2012 7:19 pm

The closest thing to human level thought reading I've seen is this:
http://newscenter.berkeley.edu/2011/09/22/brain-movies/

Lots of work being done on worms, rats and the like though.
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