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Is the human brain analog or digital? - Quora
The brain is neither analog nor digital, but works using a signal processing paradigm that has some properties in common with both.
Unlike a digital computer, the brain does not use binary logic or binary addressable memory, and it does not perform binary arithmetic. Information in the brain is represented in terms of statistical approximations and estimations rather than exact values. The brain is also non-deterministic and cannot replay instruction sequences with error-free precision. So in all these ways, the brain is definitely not "digital".
At the same time, the signals sent around the brain are "either-or" states that are similar to binary. A neuron fires or it does not. These all-or-nothing pulses are the basic language of the brain. So in this sense, the brain is computing using something like binary signals. Instead of 1s and 0s, or "on" and "off", the brain uses "spike" or "no spike" (referring to the firing of a neuron).
q-n-a  qra  expert-experience  neuro  neuro-nitgrit  analogy  deep-learning  nature  discrete  smoothness  IEEE  bits  coding-theory  communication  trivia  bio  volo-avolo  causation  random  order-disorder  ems  models  methodology  abstraction  nitty-gritty  computation  physics  electromag  scale  coarse-fine 
5 weeks ago by nhaliday
The Hanson-Yudkowsky AI-Foom Debate - Machine Intelligence Research Institute
How Deviant Recent AI Progress Lumpiness?:
I seem to disagree with most people working on artificial intelligence (AI) risk. While with them I expect rapid change once AI is powerful enough to replace most all human workers, I expect this change to be spread across the world, not concentrated in one main localized AI system. The efforts of AI risk folks to design AI systems whose values won’t drift might stop global AI value drift if there is just one main AI system. But doing so in a world of many AI systems at similar abilities levels requires strong global governance of AI systems, which is a tall order anytime soon. Their continued focus on preventing single system drift suggests that they expect a single main AI system.

The main reason that I understand to expect relatively local AI progress is if AI progress is unusually lumpy, i.e., arriving in unusually fewer larger packages rather than in the usual many smaller packages. If one AI team finds a big lump, it might jump way ahead of the other teams.

However, we have a vast literature on the lumpiness of research and innovation more generally, which clearly says that usually most of the value in innovation is found in many small innovations. We have also so far seen this in computer science (CS) and AI. Even if there have been historical examples where much value was found in particular big innovations, such as nuclear weapons or the origin of humans.

Apparently many people associated with AI risk, including the star machine learning (ML) researchers that they often idolize, find it intuitively plausible that AI and ML progress is exceptionally lumpy. Such researchers often say, “My project is ‘huge’, and will soon do it all!” A decade ago my ex-co-blogger Eliezer Yudkowsky and I argued here on this blog about our differing estimates of AI progress lumpiness. He recently offered Alpha Go Zero as evidence of AI lumpiness:


In this post, let me give another example (beyond two big lumps in a row) of what could change my mind. I offer a clear observable indicator, for which data should have available now: deviant citation lumpiness in recent ML research. One standard measure of research impact is citations; bigger lumpier developments gain more citations that smaller ones. And it turns out that the lumpiness of citations is remarkably constant across research fields! See this March 3 paper in Science:

I Still Don’t Get Foom:
All of which makes it look like I’m the one with the problem; everyone else gets it. Even so, I’m gonna try to explain my problem again, in the hope that someone can explain where I’m going wrong. Here goes.

“Intelligence” just means an ability to do mental/calculation tasks, averaged over many tasks. I’ve always found it plausible that machines will continue to do more kinds of mental tasks better, and eventually be better at pretty much all of them. But what I’ve found it hard to accept is a “local explosion.” This is where a single machine, built by a single project using only a tiny fraction of world resources, goes in a short time (e.g., weeks) from being so weak that it is usually beat by a single human with the usual tools, to so powerful that it easily takes over the entire world. Yes, smarter machines may greatly increase overall economic growth rates, and yes such growth may be uneven. But this degree of unevenness seems implausibly extreme. Let me explain.

If we count by economic value, humans now do most of the mental tasks worth doing. Evolution has given us a brain chock-full of useful well-honed modules. And the fact that most mental tasks require the use of many modules is enough to explain why some of us are smarter than others. (There’d be a common “g” factor in task performance even with independent module variation.) Our modules aren’t that different from those of other primates, but because ours are different enough to allow lots of cultural transmission of innovation, we’ve out-competed other primates handily.

We’ve had computers for over seventy years, and have slowly build up libraries of software modules for them. Like brains, computers do mental tasks by combining modules. An important mental task is software innovation: improving these modules, adding new ones, and finding new ways to combine them. Ideas for new modules are sometimes inspired by the modules we see in our brains. When an innovation team finds an improvement, they usually sell access to it, which gives them resources for new projects, and lets others take advantage of their innovation.


In Bostrom’s graph above the line for an initially small project and system has a much higher slope, which means that it becomes in a short time vastly better at software innovation. Better than the entire rest of the world put together. And my key question is: how could it plausibly do that? Since the rest of the world is already trying the best it can to usefully innovate, and to abstract to promote such innovation, what exactly gives one small project such a huge advantage to let it innovate so much faster?


In fact, most software innovation seems to be driven by hardware advances, instead of innovator creativity. Apparently, good ideas are available but must usually wait until hardware is cheap enough to support them.

Yes, sometimes architectural choices have wider impacts. But I was an artificial intelligence researcher for nine years, ending twenty years ago, and I never saw an architecture choice make a huge difference, relative to other reasonable architecture choices. For most big systems, overall architecture matters a lot less than getting lots of detail right. Researchers have long wandered the space of architectures, mostly rediscovering variations on what others found before.

Some hope that a small project could be much better at innovation because it specializes in that topic, and much better understands new theoretical insights into the basic nature of innovation or intelligence. But I don’t think those are actually topics where one can usefully specialize much, or where we’ll find much useful new theory. To be much better at learning, the project would instead have to be much better at hundreds of specific kinds of learning. Which is very hard to do in a small project.

What does Bostrom say? Alas, not much. He distinguishes several advantages of digital over human minds, but all software shares those advantages. Bostrom also distinguishes five paths: better software, brain emulation (i.e., ems), biological enhancement of humans, brain-computer interfaces, and better human organizations. He doesn’t think interfaces would work, and sees organizations and better biology as only playing supporting roles.


Similarly, while you might imagine someday standing in awe in front of a super intelligence that embodies all the power of a new age, superintelligence just isn’t the sort of thing that one project could invent. As “intelligence” is just the name we give to being better at many mental tasks by using many good mental modules, there’s no one place to improve it. So I can’t see a plausible way one project could increase its intelligence vastly faster than could the rest of the world.

Takeoff speeds:
Futurists have argued for years about whether the development of AGI will look more like a breakthrough within a small group (“fast takeoff”), or a continuous acceleration distributed across the broader economy or a large firm (“slow takeoff”).

I currently think a slow takeoff is significantly more likely. This post explains some of my reasoning and why I think it matters. Mostly the post lists arguments I often hear for a fast takeoff and explains why I don’t find them compelling.

(Note: this is not a post about whether an intelligence explosion will occur. That seems very likely to me. Quantitatively I expect it to go along these lines. So e.g. while I disagree with many of the claims and assumptions in Intelligence Explosion Microeconomics, I don’t disagree with the central thesis or with most of the arguments.)
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7 weeks ago by nhaliday
The Space Trilogy - Wikipedia
Out of the Silent Planet:

Weston makes a long speech justifying his proposed invasion of Malacandra on "progressive" and evolutionary grounds, which Ransom attempts to translate into Malacandrian, thus laying bare the brutality and crudity of Weston's ambitions.

Oyarsa listens carefully to Weston's speech and acknowledges that the scientist is acting out of a sense of duty to his species, and not mere greed. This renders him more mercifully disposed towards the scientist, who accepts that he may die while giving Man the means to continue. However, on closer examination Oyarsa points out that Weston's loyalty is not to Man's mind – or he would equally value the intelligent alien minds already inhabiting Malacandra, instead of seeking to displace them in favour of humanity; nor to Man's body – since, as Weston is well aware of and at ease with, Man's physical form will alter over time, and indeed would have to in order to adapt to Weston's programme of space exploration and colonisation. It seems then that Weston is loyal only to "the seed" – Man's genome – which he seeks to propagate. When Oyarsa questions why this is an intelligible motivation for action, Weston's eloquence fails him and he can only articulate that if Oyarsa does not understand Man's basic loyalty to Man then he, Weston, cannot possibly instruct him.



The rafts or floating islands are indeed Paradise, not only in the sense that they provide a pleasant and care-free life (until the arrival of Weston) but also in the sense that Ransom is for weeks and months naked in the presence of a beautiful naked woman without once lusting after her or being tempted to seduce her. This is because of the perfection in that world.

The plot thickens when Professor Weston arrives in a spaceship and lands in a part of the ocean quite close to the Fixed Land. He at first announces to Ransom that he is a reformed man, but appears to still be in search of power. Instead of the strictly materialist attitude he displayed when first meeting Ransom, he asserts he had become aware of the existence of spiritual beings and pledges allegiance to what he calls the "Life-Force." Ransom, however, disagrees with Weston's position that the spiritual is inherently good, and indeed Weston soon shows signs of demonic possession.

In this state, the possessed Weston finds the Queen and tries to tempt her into defying Maleldil's orders by spending a night on the Fixed Land. Ransom, perceiving this, believes that he must act as a counter-tempter. Well versed in the Bible and Christian theology, Ransom realises that if the pristine Queen, who has never heard of Evil, succumbs to the tempter's arguments, the Fall of Man will be re-enacted on Perelandra. He struggles through day after day of lengthy arguments illustrating various approaches to temptation, but the demonic Weston shows super-human brilliance in debate (though when "off-duty" he displays moronic, asinine behaviour and small-minded viciousness) and moreover appears never to need sleep.

With the demonic Weston on the verge of winning, the desperate Ransom hears in the night what he gradually realises is a Divine voice, commanding him to physically attack the Tempter. Ransom is reluctant, and debates with the divine (inner) voice for the entire duration of the night. A curious twist is introduced here; whereas the name "Ransom" is said to be derived from the title "Ranolf's Son", it can also refer to a reward given in exchange for a treasured life. Recalling this, and recalling that his God would (and has) sacrificed Himself in a similar situation, Ransom decides to confront the Tempter outright.

Ransom attacks his opponent bare-handed, using only physical force. Weston's body is unable to withstand this despite the Tempter's superior abilities of rhetoric, and so the Tempter flees. Ultimately Ransom chases him over the ocean, Weston fleeing and Ransom chasing on the backs of giant and friendly fish. During a fleeting truce, the "real" Weston appears to momentarily re-inhabit his body, and recount his experience of Hell, wherein the damned soul is not consigned to pain or fire, as supposed by popular eschatology, but is absorbed into the Devil, losing all independent existence.
fiction  scifi-fantasy  tip-of-tongue  literature  big-peeps  religion  christianity  theos  space  xenobio  analogy  myth  eden  deep-materialism  new-religion  sanctity-degradation  civil-liberty  exit-voice  speaking  truth  realness  embodied  fighting  old-anglo  group-selection  war  paying-rent  counter-revolution  morality  parable  competition  the-basilisk  gnosis-logos  individualism-collectivism  language  physics  science  evolution  conquest-empire  self-interest  hmm  intricacy  analytical-holistic  tradeoffs  paradox  heterodox  narrative  philosophy  expansionism  genetics  duty  us-them  interests  nietzschean  parallax  the-devil  the-self 
january 2018 by nhaliday
Sacred text as cultural genome: an inheritance mechanism and method for studying cultural evolution: Religion, Brain & Behavior: Vol 7, No 3
Yasha M. Hartberg & David Sloan Wilson

Any process of evolution requires a mechanism of inheritance for the transmission of information across generations and the expression of phenotypes during each generation. Genetic inheritance mechanisms have been studied for over a century but mechanisms of inheritance for human cultural evolution are far less well understood. Sacred religious texts have the properties required for an inheritance system. They are replicated across generations with high fidelity and are transcribed into action every generation by the invocation and interpretation of selected passages. In this article we borrow concepts and methods from genetics and epigenetics to study the “expressed phenotypes” of six Christian churches that differ along a conservative–progressive axis. Their phenotypic differences, despite drawing upon the same sacred text, can be explained in part by differential expression of the sacred text. Since the invocation and interpretation of sacred texts are often well preserved, our methods allow the expressed phenotypes of religious groups to be studied at any time and place in history.
study  interdisciplinary  bio  sociology  cultural-dynamics  anthropology  religion  christianity  theos  protestant-catholic  politics  ideology  correlation  organizing  institutions  analogy  genetics  genomics  epigenetics  comparison  culture  pdf  piracy  density  flexibility  noble-lie  deep-materialism  new-religion  universalism-particularism  homo-hetero  hypocrisy  group-selection  models  coordination  info-dynamics  evolution  impact  left-wing  right-wing  time  tradition  spreading  sanctity-degradation  coalitions  trees  usa  social-capital  hari-seldon  wisdom  the-basilisk  frequency 
january 2018 by nhaliday

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