Accelerating Future

The InnerSpace Foundation and The IF Prize

“The IF takes the position that the most rapid timelines to solving humanity’s most serious problems — including providing complete and lasting cures for the most diseased and disabled — will be accomplished through widespread improvement of memory and mind, rather than through the best efforts of people who are well-meaning but of naturally limited abilities.” - Dr. Pete Estep

Apr 30th, 2008 (Palo Alto): Dr. Pete Estep will discuss the InnerSpace Foundation (IF), a new nonprofit being developed to promote and support neuroengineering approaches for the enhancement of memory and learning – biomedical goals that have the potential to improve not only the lives of those suffering from a specific malady, but everyone’s life.

This new organization is pursuing human intelligence enhancement as a humanitarian goal.

Looking at their website, Theodore Berger is involved. You may remember Berger as the team leader of the prosthetic hippocampus project that we mention here at Accelerating Future so often. On the IF website, Berger says, “Given sufficient funding, the development of a functional memory prosthetic device is as good as done.” Berger is Director of the Center for Neural Engineering at the University of Southern California. The rest of the advisers page is a list of world-class neuroscientists, many of which I’ve never heard of. The organization was founded by Preston Estep and James Clement.

The organization is offering two prizes: The IF Prize for Learning and The IF Prize for Memory. From the site’s FAQ:

“The IF Prize for Learning will be awarded for the successful development and demonstration of a device similar in function to a flash drive (a.k.a. thumb drive) for computers. This device will store standardized information that can be accessed by the brain (sometimes referred to as “downloading”) by thought alone (volitional access). This will allow someone to “learn” information in a completely revolutionary way. The other device will also be similar to a flash drive but will write or store a person’s memory information (sometimes referred to as “uploading”), which can be subsequently retrieved by thought.”

One more question from the FAQ:

Q: Are these technologies extremely futuristic, maybe even science fiction?

A: No. Nearly all of the technologies we use daily and take for granted, such as cell phones, airplanes, submarines, microwave ovens, and digital computers, once existed only as scientific possibilities and fiction. Ten years ago, thought-driven brain-computer interfaces were science fiction. But, recently, neuroengineers have made dramatic advances in interfacing electronic devices with the brain, and have demonstrated thought-controlled prosthetic limbs, computer desktop functions and gameplaying, and even basic speech synthesis.

Is this the beginning of a true intelligence augmentation effort?

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Currently existing:

• writing systems
• digital communication
• caffeine, maybe sugar
• nootropics, maybe (need more data)
• education, hands-on learning
• other intellectual stimulation
• enriched environments
• good nutrition

Cutting-edge:

• inference games
• prediction markets
• AI software

Future:

• increasing neuron density and connectivity
• gene therapy, which has been successful in mice
• brain-computer interfaces, brain implants
• cognition enhancing drugs
• developing strong AI

See Ben Goertzel on why the Internet may not be making us smarter.

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There needs to be a central resource of online papers containing evolutionary explanations for the various facets of human psychology. I began one on the SL4 wiki in 2002 or thereabouts, and thought I would call attention to it by reposting it here. If you want to make your own additions, the link is here.

Good places to go digging for papers:

[Psycholoquy]
[CogPrints]
[CiteSeer]
[Google Directory]
[Open Directory]
[BBS Online]

Overviews
[Evolutionary Psychology Primer] by Leda Cosmides and John Tooby
[Evolutionary Psychology FAQ] by Edward Hagen
[Study and Learning Materials Online for Evolutionary Psychology] by Paul Kenyon
[Evolutionary Psychology] (book chapter, technical) by Russil Durrant and Bruce J. Ellis

Aestheticism and Culture
[Aestheticism in the Theory of Custom] by Ekkehart Schlicht

Altruism
[Varieties of altruism - and the common ground between them] by Nicholas Humphrey
[Pro-community altruism and social status in a Shuar village] by M.E. Price

Brains
[Developmental structure in brain evolution] by Finlay, Darlington & Nicastro

Children, behavior of
[Descent versus design in children’s reasoning about animals] by H.C. Barrett

Committment Signalling
[Are there nonverbal cues to commitment?] by Brown & Moore

Consciousness
[The Uses of Consciousness] by Nicholas Humphrey
[On a Confusion About a Function of Consciousness] by Ned Block

Cults
[Sex, Drugs, and Cults] by Keith Henson

Emotions
[Evolutionary Psychology and the Emotions] by Cosmides & Tooby

Essentialism
[On the Functional Origins of Essentialism] by H. Clark Barrett

Depression and Grief
[Depression as bargaining: The case postpartum] by Edward H. Hagen
[The Bargaining Model of Depression] by Edward H. Hagen
[The Functions of Postpartum Depression] by Edward H. Hagen

Dreaming and Sleep
[The Reinterpretation of Dreams: An evolutionary hypothesis of the function of dreaming] by Antti Revonsuo
[Dreaming and the Brain: Toward a Cognitive Neuroscience of Conscious States] by Hobson, Pace-Schott & Stickgold
[A Review of Mentation in REM and NREM Sleep] by Tore A. Nielsen, Ph.D
[Dreaming and REM Sleep are Controlled by Different Brain Mechanisms] by Mark Solms
[The Case Against Memory Consolidation in REM Sleep] by Robert P. Vertes, Ph.D.

Evolutionarily Internalized Regularities
[Regularities of the Physical World and the Absence of their Internalization] by Heiko Hecht
[Evolutionary Internalized Regularities] by Robert Schwartz

Folk Biology
[Folk Biology and the Anthropology of Science: Cognitive Universals and Cultural Particulars] by Scott Atran

Food Sharing
[To Give and to Give Not: The behavioral ecology of human food transfers] by Michael Gurven

Game Playing
[Game theory and reciprocity in some extensive form experimental games] by Cabe, Rassenti, & Smith

General Intelligence
[Levels of Organization in General Intelligence] by Eliezer S. Yudkowsky

Gossip
[Informational Warfare] by Hess Hagen

Infants, behavior of
[The Signal Functions of Early Infant Crying] by Joseph Soltis

Linguistics
[Natural Language and Natural Selection] by Steven Pinker and Paul Bloom
[Brain Evolution and Neurolinguistic Preconditions] by Wendy K. Wilkins
[The Frame/Content Theory of Evolution and Speech Production] by Peter F. Neilage
[Anticipatory Semantics Processes] by Frédéric and Pascal Lavigne
[From Hand to Mouth: Some Critical Stages in the Evolution of Language] by Steklis & Harnad
[Evolution, Communication, and the Proper Function of Language] by Gloria Origgi and Dan Sperber

Literary Representation
[The Deep Structure of Literary Representations] by Joseph Carroll

Mathematical Ability
[Sexual Selection and Sex Differences in Mathematical Abilities] by David C. Geary

Mating
[The Evolution of Human Mating: Trade-Offs and Strategic Pluralism] by Steven W. Gangestad

Metarepresentation
[Consider the Source: The evolution of adaptations for decoupling and metarepresentation] by Cosmides & Tooby
[Metarepresentations in an Evolutionary Perspective] by Dan Sperber

Memory
[Decisions and the Evolution of Memory] by Klein, Cosmides, Tooby

Music
[Music and Dance as a Coalitional Signaling System] by Hagen Bryant

Obsessive-Compulsive Disorder (OCD)
[An Evolutionary Hypothesis For Obsessive Compulsive Disorder: A Psychological Immune System?] by Abed & Pauw

Pain
[Sex Differences in Pain] by Karen J. Berkley

Phobias
[Preparedness and Phobias: Specific Evolved Associations or a Generalized Expectancy Bias?] by Graham Davey

Punishment
[Punitive Sentiment as an Anti-Free Rider Psychological Device] by Price, Cosmides & Tooby

Rationality
[Nonconsequentialist decisions] by Jonathan Baron
[Individual Differences in Reasoning:
Implications for the Rationality Debate?] by Keith E. Stanovich
[Simple Heuristics That Make Us Smart] by Gigerenzer & Todd
[Generalization, Similarity, and Bayesian Inference] by Tenenbaum & Griffiths
[The Base Rate Fallacy Reconsidered: Descriptive, Normative and Methodological Challenges] by Jonathan J. Koehler

Sexual Selection
[Mating Mind Precis] by Geoffrey Miller

Social Relations and Behavior, Machiavellian Intelligence
[Meeting One’s Twin: Perceived Social Closeness and Familiarity] by Segal, Hershberger & Arad
[Intentional Relations and Social Understanding] by Barresi & Moore
[Pavlovian Feed-Forward Mechanisms in the Control of Social Behavior] by Domjan, Cusato, & Villarreal
[Co-Evolution of Cortex Size, Group Size, and Language in Humans] by R.I.M. Dunbar

Sociopathy
[The Sociobiology of Sociopath: an Integrated Evolutionary Model] by Linda Mealey

Symbol Systems
[Perceptual Symbol Systems] by Lawrence W. Barsalou

Women, behavior of
[Staying alive: Evolution, culture and women’s intra-sexual aggression] by Anne Campbell

Other
[Was Cypher Right?: Why We Stay In Our Matrix] by Robin Hanson

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“Human universals” is a term used in anthropology and evolutionary psychology to refer to behavioral or cognitive traits common to all neurologically normal humans. The notion of human universals was partially formulated as a challenge to cultural relativism, a predominant view of human nature in the late 20th century, which some psychologists and anthropologists see as greatly exaggerating the variance among members of the human species.

In a book of the same name published in 1991, professor of anthropology Donald Brown listed hundreds of human universals in an effort to emphasize the fundamental cognitive commonality between members of the human species. Some of these human universals include incest avoidance, territoriality, fear of death, rituals, childcare, pretend play, mourning, food sharing, kin groups, social structure, collective decision making, etiquette, envy, weapons, aesthetics, and many more. Wider recognition of human universals has led to a sort of mini-revolution in psychology, which has begun to take more input from the harder sciences of anthropology and biology, and less from the ubiquitous pop-psychology of the 20th century.

One of the greatest popularizers of the notion of human universals in recent years has been from Steven Pinker, a cognitive scientist at Harvard and author of four widely read books on the human mind. As a champion of the rising science of evolutionary psychology, Pinker argues that, in the same way we all have ten fingers, ten toes, two eyes, two ears, and a mouth, all with the same basic biological features from person to person, we should expect our cognitive features to have similar commonality. The psychological differences between human beings are then differences of degree, not in kind.

The existence of an experimentally verifiable set of human universals has two key consequences. The first is that it makes further psychological experimentation and research more valuable than some may have thought. If we can identify the common cognitive features between us and their characteristics, we learn not only about every human culture and individual on earth today, but of those into the indefinite future, as long as their genomes stay essentially human. The second is that the human species has more in common than conventional psychology would have us think - that conflicts arise in spite of our fundamental cognitive similarities, rather than from them.

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Using experiments in which people were asked to read text, look at pictures, and hear words, short passages of music, sentences, and nonsense syllables, then asked between minutes or days later what they remembered, (using binary yes-or-no answers, some of which could be answered merely based on vague recollections) then comparing those answers to that of a control group, Bell Labs scientist Thomas K. Landauer (pictured above) determined in 1984 that human beings can retain about 2 bits of memory per second. This holds under all experimental conditions whether the information is visual, verbal, musical, etc. You can read more on this at “How Many Bytes in Human Memory?” by Ralph Merkle.

Over the course of a species-averaged 30-year lifespan, with 15 waking hours per day, this rounds to about 150MB of memory per lifetime. That means that a 30-year old human would be able to make approximately 1,200,000,000 binary distinctions based on memories until their ability to make distinctions based on memory reaches breaking point. Consider that a typical courtroom hearing probably extracts no more than a few thousand bits (perhaps a KB at most) from witnesses based on testimony. Using that as a reference, this number seems reasonable, if not a bit high. The estimate also assumes that people are exposed to novel information content every second of their waking lives.

Of course, some prodigies, such as Daniel Tammet, probably are capable of retaining significantly more information than 2 bits per second, but this is a unusual case.

Given that approximately 107 billion people have ever lived on this planet (”people” meaning members of the species Homo sapiens since 50,000 BC), we can derive a rough estimate of the total information content of our species’ entire memory, present and past:

1.07 x 10¹¹ x 1.5 x 10⁸ bytes = 1.6 x 10¹⁹ bytes.

This works out to approximately 10⁷ terabytes, around 20 times the information generated by the entire Internet in 2002. Another way of putting it is approximately 16 exabytes. According to Roy Williams of Caltech, all the words ever spoken by human beings sum to about 5 exabytes. So the adage that we pay attention and remember less than half of what other people say apparently holds true.

According to The Social Life of Information, and the prior linked source, the world generated 2-3 exabytes of unique information in 1999, and the number is increasing. Given that the memory capacity of present-day humanity is about 31 petabytes, (31 thousandths of an exabyte), we certainly rely on artificial storage media to record whatever information we want to archive.

The beauty of electronic storage is that we can generate as much information as we want, certain that we can always build new hard drives to store it as long as it is digitized. Without electronic storage, we’d only be able to remember about a hundreth of all novel information generated in a given modern year.

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From the University of Rochester:

Mysterious ‘Neural Noise’ Actually Primes Brain for Peak Performance

UPI (November 15, 2006)
Study: Neural Noise Primes Our Brain
Alex Pouget, Associate Professor of Brain and Cognitive Sciences

Researchers at the University of Rochester may have answered one of neuroscience’s most vexing questions—how can it be that our neurons, which are responsible for our crystal-clear thoughts, seem to fire in utterly random ways?

In the November issue of Nature Neuroscience, the Rochester study shows that the brain’s cortex uses seemingly chaotic, or “noisy,” signals to represent the ambiguities of the real world—and that this noise dramatically enhances the brain’s processing, enabling us to make decisions in an uncertain world.

“You’d think this is crazy because engineers are always fighting to reduce the noise in their circuits, and yet here’s the best computing machine in the universe—and it looks utterly random,” says Alex Pouget, associate professor of brain and cognitive sciences at the University of Rochester.

Pouget’s work for the first time connects two of the brain’s biggest mysteries; why it’s so noisy, and how it can perform such complex calculations. As counter-intuitive as it sounds, the noise seems integral to making those calculations possible.

In the last decade, Pouget and his colleagues in the University of Rochester’s Department of Brain and Cognitive Sciences have blazed a new path to understanding our gray matter. The traditional approach has assumed the brain uses the same method computation in general had used up until the mid-80s: You see an image and you relate that image to one stored in your head. But the reality of the cranial world seems to be a confusing array of possibilities and probabilities, all of which are somehow, mysteriously, properly calculated.

The science of drawing answers from such a variety of probabilities is called Bayesian computing, after minister Thomas Bayes who founded the unusual branch of math 150 years ago. Pouget says that when we seem to be struck by an idea from out of the blue, our brain has actually just resolved many probabilities its been fervently calculating.

“We’ve known for several years that at the behavioral level, we’re ‘Bayes optimal,’ meaning we are excellent at taking various bits of probability information, weighing their relative worth, and coming to a good conclusion quickly,” says Pouget. “But we’ve always been at a loss to explain how our brains are able to conduct such complex Bayesian computations so easily.”

Two years ago, while talking with a physics friend, some probabilities in Pouget’s own head suddenly resolved.

“One day I had a drink with some machine-learning researchers, and we suddenly said, ‘Oh, it’s not noise,’ because noise implies something’s wrong,” says Pouget. “We started to realize then that what looked like noise may actually be the brain’s way of running at optimal performance.”

Bayesian computing can be done most efficiently when data is formatted in what’s called “Poisson distribution.”

And the neural noise, Pouget noticed, looked suspiciously like this optimal distribution.

This idea set Pouget and his team into investigating whether our neurons’ noise really fits this Poisson distribution, and in his current Nature Neuroscience paper he found that it fit extremely well.

“The cortex appears wired at its foundation to run Bayesian computations as efficiently as can be possible,” says Pouget. His paper says the uncertainty of the real world is represented by this noise, and the noise itself is in a format that reduces the resources needed to compute it. Anyone familiar with log tables and slide rules knows that while multiplying large numbers is difficult, adding them with log tables is relatively undemanding.

The brain is apparently designed in a similar manner—”coding” the possibilities it encounters into a format that makes it tremendously easier to compute an answer.

Pouget now prefers to call the noise “variability.” Our neurons are responding to the light, sounds, and other sensory information from the world around us. But if we want to do something, such as jump over a stream, we need to extract data that is not inherently part of that information. We need to process all the variables we see, including how wide the stream appears, what the consequences of falling in might be, and how far we know we can jump. Each neuron responds to a particular variable and the brain will decide on a conclusion about the whole set of variables using Bayesian inference.

As you reach your decision, you’d have a lot of trouble articulating most of the variables your brain just processed for you. Similarly, intuition may be less a burst of insight than a rough consensus among your neurons.

Pouget and his team are now expanding their findings across the entire cortex, because every part of our highly developed cortex displays a similar underlying Bayes-optimal structure.

“If the structure is the same, that means there must be something fundamentally similar among vision, movement, reasoning, loving—anything that takes place in the human cortex,” says Pouget. “The way you learn language must be essentially the same as the way a doctor reasons out a diagnosis, and right now our lab is pushing hard to find out exactly how that noise makes all these different aspects of being human possible.”

Pouget’s work still has its skeptics, but this, his fourth paper in Nature Neuroscience on the topic, is starting to win converts.

“If you ask me, this is the coming revolution,” says Pouget. “It hit machine learning and cognitive science, and I think it’s just hitting neuroscience. In 10 or 20 years, I think the way everybody thinks about the brain is going to be in these terms.”

Not all of Pouget’s neurons are in agreement, however.

“…but I’ve been wrong before,” he shrugs.

Definitive evidence that the brain is a Bayesian computer. Fascinating!

Here is a good blog on machine learning.

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Via Pink Tentacle:

Hitachi has successfully tested a brain-machine interface that allows users to turn power switches on and off with their mind. Relying on optical topography, a neuroimaging technique that uses near-infrared light to map blood concentration in the brain, the system can recognize the changes in brain blood flow associated with mental activity and translate those changes into voltage signals for controlling external devices. In the experiments, test subjects were able to activate the power switch of a model train by performing mental arithmetic and reciting items from memory.

The prototype brain-machine interface allows only simple control of switches, but with a better understanding of the subtle variations in blood concentrations associated with various brain activities, the signals can be refined and used to control more complex mechanical operations.

In the long term, brain-machine interface technology may help paralyzed patients become independent by empowering them to carry out actions with their minds. In the short term, Hitachi sees potential applications for this brain-machine interface in the field of cognitive rehabilitation, where it can be used as an entertaining tool for demonstrating a patient’s progress.

The company hopes to make this technology commercially available in five years.

The article writer forgot to mention the application where you use the system to control a cloud of utility fog 100m on a side that you pumped out of your home nanofactory over the course of a week.

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