We did our special show on Saturday from the Singularity Summit. We’ll be back next Wednesday at the usual time.

Meanwhile if you need a futuristic fix, may I recommend this blast from a year ago?

I was telling a friend about Anita Goel’s rather provocative conclusion to the talk she gave Sunday morning, in which she asked:

What if information, consciousness, and mind are something pervasive, more primal, and even more fundamental than matter, energy or even space time?

Not a question that a lot of people would be comfortable asking with the likes of Michael Vassar and Eliezer Yudkowsky in the room. What I wanted to make clear to my friend was that a lot of Singularity Summit types would reject this sort of speculation because of the hint of mysticism, not because Singularity Summit types shy away from radical cosmological ideas.

For example…are we living in the Matrix? (And BTW, if we are, Anita’s question could be answered in the affirmative without a smidge of mysticism.)

Anyhow, I gave the show a listen myself and something occured to me — modesty aside, this is some awesome radio (thanks to the guests, of course.). Our new special series will not be a repeat of The World Transformed — there’s currently no reason to repeat it. The series we did still stands up.

On to new topics this fall.

6:15 James Randi

A few angles to consider in your pursuit of the singularity.

Talking about the Singularity is like Aristotle talking about interstellar travel.

How do we know things?

Human beings are good at tricking, fooling, and bamboozling each other.

Randi shows that the “microphone” he is holding is really an electric razor and his “eyeglasses” are actually empty frames. And yet we have to make assumptions. We would be catatonic otherwise.

Think critically about the world as it is presented. We don’t debunk. We start not assuming that the thing is bunk. We take a scientific approach assuming that we don’t know.

Great story about scientists at Livermore labs fooled by a simple magic trick.

Another story: MIT physicist takes David Copperfield’s “flying” illusion as being done with superconductivity.

Fooled by his own education.

We are often not as bright as we think we are. If we still can’t distingish between fantasy and reality, how can we undertake creating greater than human intelligence?

Favorite terms — vibrations and quantum. Watch for Woo Woos to adopt the terminology of the singularity.

He does a mentalist trick — shows how easily we are fooled. It’s not that people are stupid, they just aren’t informed.

Two great clips from the Tonight Show, exposing a tv faith healer and “psychic surgeons.”

5:35 PM Irene Pepperberg

Nonhuman Intelligence: Where we are and where we’re headedwq

Animals, a very complex relationship: friends, property, competitors, metaphors /icons.

Animal robots: people would feel guilty when turning them off.

Key question: why is Pluto a dog while Goofy is a humanoid? Americans spend $46 billion per year on pets.

We don’t yet really understand how intelligent animals are.

Selective breeding causes a host of problems: thoroughbred race horses with heart problems, dog breeds with hip displasia.

Amazing videos of a crow modifying a tool to get a treat, a parrot counting objects and identifying colors.

We don’t yet know how intelligent animals are. Should we really be augmenting animal intelligence until we have a better handle on that?

Currently we use animals on a lot of ways. Making them more intelligent puts all of those into question. Or do we really want to takeon super-intelligent squirrels?

5:00 PM Jose Cordeiro

The Future of Energy and the Energy of the Future

Interesting background — political exile from Venezuala. Part of the UN MIllenium Council. Teaching fellow at Singularity University.

Energy is the biggest industry in the world. It is the industry that rules the world. Biggest challenge / opportunity facing us — how to make solar energy affordable for humanity.

Peak Oil, if it happens, is not relavent. We had Peak Whale Oil in the 19th century and yet managed to push on.

90% correlation between temperature fluctuations on Mars and on Earth. Should be taken into consideration in climate models.

Club of Rome: Limits to Growth. Was wrong because it did not take technological change into consideration.

We have gone through energy waves from lumber to coal to oil. At each stage we produce less carbon and use more hydrogen.

Buckminster Fuller talked about creating a global energy network. Still a long way off, but wireless electricity and other developments will bring it closer.

Will have space elevator in the next 30 years.

Craig Venter’s bacteria will eat CO2 and excrete 99 octane gasoline. From fossil hydrocarbons to living carbohydrates. A major phase shift!

Two major experiments currently goin on with nuclear fusion. One in France, one at Livermore labs. Right now fusion works, but it’s not economical. It’s a matter of time before we get a handle on the basic process that powers the universe.

We currently use about 16 Terawatts. If we used only 1% of 1% of the energy available from the sun we would solve all the world’s energy problems.Right now solar energy is growing exponentially.

Japan plans by the year 2030 to power Tokyo with space-based solar.

We need cheap energy to solve the world’s problems. In 30 years, we may have hundreds of times as much energy as we currently do — for free.

Old Chinese saying: “Do not blame the darkness. Light up the world.”

4:25 PM David Hanson

Why Characters Are Key to Friendly A.I.

We are hardwired to form relationships. We are hardwired to learn and grow via relationships.

Part of our intelligence is knowing how to respond to persons. Modeling people through robots and simulation helps us to understand the dynamics of nonverbal communication, which at the neuroscience level is not yet thoroughly understood. Having these characters among us as our friends will build trust with these emerging intelligences.

Defining character. Has to have:

Agency
The illusion of reality
Values / empathy

Currently character robots don’t have as much intelligence as we would like.

Creating these robots is a mixture of engineering, art, and cognitive and neuroscience. Still driven largely by intuition because we don’t yet fully understand the dynamics of the relationships people form.

Consumer demand will drive the development of good machines — machines that are part of the human family.

David has developed a material called “Frubber” that makes the faces of these robots much more realistic.

3:20 PM Tooby, Goertzel, Yudkowsky & Legg panel

Narrow and General Intelligence

Missed this session.

2:40 PM John Tooby

Can discovering the design principles governing natural intelligence unleash breakthroughs in artificial intelligence?

Opening comment — “It’s a pleasure to be among the intellectually adventerous.”

All the pieces in place for realizing the enlightenment goal of establishing a rigorous natural science of human nature.

There are two ways of studying the brain — one as a physical system. It’s an amzaingly vast, daunting system from that perspective. The other approach is to map it at a computational level — look at it from the standpoint of its functions.

All better than random capabilities in the brain reflect problem-solving strategies developed through selection. We need to do research into how these strategies became brain functions as a guide to developing AI.

2:00 PM Shane Legg

Universal measures of intelligence

Intelligence — are we concerned with what’s going on inside or can we accept intelligence strictly through observing external behavior. Plus, are we interested in human or ideal intelligence?

Shane takes the perspective of ideal intelligence as viewed from external behavior.

Definitions of Intelligence

– Intelligent systems are expected to work and work well in many different environments.

–A cluster of cognitive abilites that lead to successfull adaptation in a wide range of environments

– Act appropriately in an uncertain environment

–Generates adaptive behavior

Shane’s synthesis:

Property of an agent that interacts with its environment so as to successfully achieve goals across a wide range of environments.

+ Occam’s Razor

Together you get a formula for intelligence.

Shane shows a mathematical formula for intelligence.

It is formally defined, captures the essence of many informal definitioons, orders simple agents correctly, it is continuous, and it is non anthropocentric.

The goal is to use the formula to measure intelligence in the real world.

Shane is now taking the equation and implementing it to acsertain AIQ — algorithmic intelligence quotient.Initial tests are encouraging — providing exactly the results he would expect to see on evaluations of algorithms.

If you can’t measure it, it’s not science. Need solid measures in place in order to track progress towards artifiicial intelligence.

11:50 AM Anita Goel

Information Processing & Physical Intelligence in Nanomachines that Read/Write DNA

Convergence of fundamental physics, nanotechnology, and biotechnology.

About 15 years ago got interested in the reading and writing machines in DNA. There are many mysteries about this process — how is it modulated by the environment? Is it a complex adaptive system? Got interested in the physics of this machine and this process. How can we control that machine? Could we introduce precision control?

A divergence. Medicine is practiced at the level of chemistry and microbiology. Physicists don’t know much about physics. How can these two worlds be brought together?

Can we develop a conceptual framework for developing nano-tools — knobs and controls to harness these nanomachines for a number of practical applications?

Precision controls have been introduced. Applications:
Converting energy efficiently at the nanoscale
Storing information in DNA
Computing

Gene RADAR — enables real-time point of care diagnosis. Handheld pathology lab — take a sample and get a result.

Several nanomachines within DNA. One of these uses a template to replicate the sequence.

Tools for single molecule detection and manipulation.

Anita closes her talk with some audience provocation / interaction.

Are nanomachines intelligent?

Audience answer: No.

Nanomotor might be thought of as a Maxwell Demon? Second law of thermodynamics — we go from order to disorder. Can we go the other way?

On the nanoscale, you can’t get a free lunch but you can get a cheaper lunch.

Feyman calculated in 1999 — if you stored all of human information into DNA you could store it in a box less than a millimeter thick. These nanomotors make 10/15th computational steps to take one step forward.

Intelligence = Information extracted / Information Present

Need a new framework that brings matter, energy, and consciousness into one framework.

Life, mind, and consciousness are emergent phenomena — get enough complexity and you get those things.

What if information, consciousness, and mind are something pervasive, more primal, and even more fundamental than matter, energy or even space time?

Refernces to John Archibald Wheeler — It from Bit.

11:15 AM Ellen Heber-Katz

The MRL mouse – how it regenerates and how we might do the same

Some mice can grow back more parts than others. Have found some mice that can grow back limbs, restore holes punched in ears.

Have looked at genetics. Have looked at the biology. Inflmation is key. Some part of the anti inflmatory reponse is related to the regeneration process. Anti-inflammatory drugs block the regeneration process

MRL mouse has mitochondria sitting on top of the nucleus. Resembles a stem cell.

Ear cells are very rapidly growing.

Have identified what may be the regeneration gene. Does regenration lead to immortality? Not currently, the MRL mouse dies of tumors related to an inflammatory autoimmune disease. Now working on separating tumors and inflammation from regeneration.

Question: why did we evolve away from regeneration — went with scar tissue instead? It is incorrectly believed that scarring is faster. May have to do with evolving away from the autoimmune / inflammatory problems.

10:40 AM Lance Becker

Modifying the Boundary between Life and Death

We used to have a clear bright line bwetween life and death. What we see now is an increasingly flexible boundary between the two.

We are “death” phobic. People don’t like the word — don’t like to talk about it. But if we’re going to prolong life, we need to understand death better.

What is death?

We don’t know much about it. We don’t understand its processes very well.

A more fundamental question — when is death? Did research some time ago on heart cells. Deprived them of oxygen for an hour — looking for when they died. Cells deprived of oxygen did not die. Cells died when oxygen was restored to them.

This occurs with brain cells and many other types of cells in the body.

This is intentional death — it’s a very active process. Mitochondria are key to this. We know that their function is energy. But apparently they also have a cell death switch built in. When you have no oxygen in your body, electrons build up in the mitochondria. When oxygen is reintroduced, the presence of those electrons is the signal to throw the death switch.

Cooling helps inhibit that process — “A cold heart can save your brain.” But cooling is a very time-dependent process. And our coolants aren’t good.

Working on developing a “slurry” to cool patients in a matter of minutes. Normally take about 8 hours to cool a person down.

“Mostly dead is slightly alive.”

That’s a good summary of what we know about the boundary between life and death.

Uncontrolled reperfusion — no oxygen for an hour. Lethal for animals. Controlled reperfusion with a cocktail — 6/6 test subjects survive.

Emergency room doctors trying to save lives are all about restoring oxygen to all parts of the body that aren’t getting it.

The border between life and death can be modified. We don’t know what the time window is. It could be that there is no point of death. We ned to understand and monitor mitochondria better — key to preventing death from many dieases (Alzheimer’s, Parkinson’s, cancer).

9:00 AM Eliezer Yudkowsky

Simplified Humanism and Positive Futurism

Opposition to life extension has a long pedigree. From pious clergymen saying it was wrong to cure smallpox because it’s God’s perogative to smite whom He wishes to a recent (tragic) case about a British couple delayed from having a second child who would be a bone marrow match and would have offered hope for their terminal son.

Death doesn’t make life meaningful. Life makes life meaningful.

“You know what makes this sunset beautiful? The fact that one day I will no longer exist.”

Yeah, right.

If no one had ever heard of old age or death, would anybody buy the “benefits” of it? Absolutely not.

Life doesn’t have to complicated. Sometimes the obvious answer is right.We need simplified Humanism.

Typical: Curing disease is good, unless genes are involved
Simplified: Curing disease is good.

Shape matters not: fishes and chickens are non-persons because they’re differently brained, not differently shaped. (We wouldn’t eat Yoda, for example.)

Simplified humanism — embrace the goal of success rather than making excuses for failure.

In the hunter-gatherer age, 15-65% of men died violently. 100 million people died in wars in the 20th century, if we were still killing each other at the hunter-gatherer rate, it would have been 2 billion. (Stepehn Pinker.)

Futurism: Rational first, then positive.

1. What you want doesn’t control how the world is.
2. There’s no destiny that helps you.
3. Magic doesn’t work.
4. You can’t just make stuff up.

Positive futurism doesn’t mean foretelling a golden age. It means that “a much nicer place to live” is still on the table as a stake.

Technophile — technology is good
Technophobe — technology is bad
Technonormal — talk of golden age and catastrophe is childish
Technovolatile — most likely scenario is a godlen age or a catastrophe (not “normailty’)

Eliezer is a technovolatile. Most serious thinkers on these subjects are. They are the heirs of the Enlightenment.

9:40 AM Ramez Naam

The Digital Biome

Massive climate change is a possibility. Not the projections of the IPCC, but consequences of enough warming to release huge amounts of methane into the atmosphere. We don’t know it will happen, but it is a possibility and one that we should take precautions against.

Fish catch is leveling off. The amount of effort required to bring fish in is going up dramatically. Boats are going ten times the distance they used to go and bringing back fewer fish.

Fresh water. Major North American aquifer dropping fast.

Food yields have been mostly good news. In 08 there was a major spike in food prices (due partly to push to biofuels.)

Endangered species. Each species is a source of information — we’re losing data.

Peak oil. We’ll run out eventually. May be occuring now, may not be until mid century.

Bio science is now digital. Shows us a picture of a bio sequencing center — looks like a data center.

George Church wants to sequence 100,000 genomes by 2020 — thinking too small. It should cost about ten bucks by then. It will be possible to do millions.

If the genome is digital, can we edit it? Short answer — yes.

Benefits:

Energy — current biofeuls are not efficient. Craig venter is creating designer organisms that will produce highly efficient and clean fuels. Turning alage into ethanol or hydrogen. Algae doesn’t compete with food crops — uses waste water. By 2013, DARPA plans to produce biofuels on site for the entire US armed forces fleet. Tobacco virus has been modified to create photovoltaice cells via tobacco.

Increase the photosynthetic capabilites of the planet by 6%, offsets our Co2 increases.

Genetically modified salmon — grows to maturity much faster.

Pathogens are moving more quickly, but we’re moving more quickly as well.

Direct solar energy looking more and more promising. Unlocking more energy is the key to solving the water problem — we can desalinate the ocean to get more fresh water.

There is no guarantee of success. How do we make it more likely that we get a good future, not one of the many bad ones? US energy R&D spending $1.8 billion; defense R&D $78 billion (some of which does trickle down to energy.)

Meanwhile, we spend a trillion a year on energy.

5:20 Terry Sejnowski

Reverse-engineering brains is within reach

Fascinating description of how a software program taught itself to play backgammon using a very simple learning heuristic. The program went from rudimentary skills to grand master status — similar to IBM’s Deep Blue, but without teams of programmers hard-coding game strategies into it. It taught itself.

This learning ability has broad application across a host of technologies: soon we will have a cognitive power grid, cognitive cars, congitive homes, etc.

5:50 Dennis Bray

What Cells Can Do That Robots Can’t

Bray argues that the information encoding capability of RNA and other proteins far surpasses what is typically found in even very advanced robotic systems. We have a vast, he argues “almost infinite” (but that seems excessive) capability to store data.

If true, it still seems to me that Kurzweil’s exponential processing growth eventually closes this gap.

6:15 Sejnowski/Bray debate

Will we soon realistically emulate biological systems?

I had to miss this one in order to do me Steven mann interview and video. I’m guessing they disagreed.

2:00 Steven Mann

Humanistic Intelligence Augmentation and Mediation

Steven Mann is a cyborg. As we watch him speak we’re getting a live feed from the video camera mounted on his eyeglasses. He’s been wearing computers for 30 years.

He is demoing the Eyetap (one of his many inventions), a camera that enables him to continuosly broadcast more or less excatly what he is seeing at all times.

He describes what he does as “Glogging” (abbreviated from “cyborg logging.”) Unlike a blog which provides digital, discrete data a Glog presents continuous, streaming data.

Now a mini-concert played on the hydrolophone, a musical instrument invented by Mann:

“World’s first musical instrument that produces sound from vibrations in in water itself, and also uses water as the user interface.”

This kind of pure streaming experience is a brief glimps into the world of the undigital singularity — which I don’t get, but it sounds kind of cool. In any case, it’s a fascinating merger of science, engineering, and art.

3:00 Mandayam Srinivasan

Enhancing our bodies and evolving our brains

Haptic interfaces — touch interfaces. Showed a video of deaf/blin individuals trained via a methodology called TADOMA. Amazing, seeing a person with no eyesight or hearing having a conversation with someone, able to “listen” to what the other person is saying just by touching her face.

Haptics applications:

Virtual reality — using real touch to operate in artificial environments

Teleoperation — using real touch to operate in real envionments via computer interfaces

3:25 Brian Litt

The past, present and future of brain machine interfaces.

I sat this and the next one out as I was putting some audio together for tonight’s podcast. But From Brian’s abstract:

Brain-computer interfaces (aka Brain-Machine Interfaces or Neuroprosthetics), long of interest to science fiction writers and creative thinkers, became a government funded research discipline in the United States beginning in the 1970s. The vision of its architects at DARPA and the National Science Foundation was to restore motor control to soldiers with brain, spinal cord and limb injuries, programs that continue to flourish today. Early devices sampled a variety of neural signals, including scalp EEG and evoked potentials, though the first dramatic successes arose ~ 20 years later from more modern technologies that allowed completely paralyzed (or “locked in”) patients to operate computers or move robotic arms using nothing but their thoughts. These systems record multi-unit neuronal activity from small, targeted brain regions, compute transfer functions to transduce this activity into movement control signals, and conduct it to “effectors,” such as computer cursors or robotic limbs. What has followed is an explosion of innovation in hardware (materials, batteries, computation speed and miniaturization), software (e.g. machine learning), and systems neuroscience that is producing a growing array of implantable neural recording and activation devices to treat disease, restore and potentially augment human function.

BCIs are now universally accepted in a variety of forms. Brain stimulation devices for movement disorders and pain are implanted in patients on almost every continent. New successes, such as recent reports of treating depression with brain stimulation, are world news. Auditory prostheses such as cochlear implants are now commonplace, visual prostheses have reached early milestones to restore low resolution sight, and haptics research holds promise to restore sensation in the setting of limb loss, brain and peripheral nerve injury. Early areas of emphasis, such as prosthetic limb research, have made the most progress, using both real-time feedback to improve responsiveness of artificial arms and legs, and transplanted peripheral nerves to drive sensors. BCIs for speech work slowly but they function enough to be gaining users, and those for cognition, particularly for memory, are being tested in early forms, with great promise. Underlying all of these implementations are an understanding that “neuroplasticity,” the brain’s ability to adapt and interpret regular and logical signals when taught, can take even low levels of information and interpret it logically. This is the case, for example, in cochlear implants where patients can learn to interpret crude electrical stimulations through a handful of macroelectrodes as intelligible speech.

The major hurdles to better BCIs are both technical and rooted in neuroscience. Materials science researcher must deliver more durable and better-tolerated implantable materials to prevent failure and rejection. Engineers must craft smaller, higher resolution devices with more contacts, higher density but that can also cover larger regions, to be able to record and activate the large neuronal networks involved in brain functions. Better machine learning techniques to extract pertinent information from neural signals without relying on human experts to identify them are required. Finally, ways of dramatically increasing information transfer rates, and to optimize neuroplasticity are required to get fast enough bandwidth from humans to devices to make their speed useful. Challenges on the neuroscience side are equally important, most crucially determining on what scale to record neural activity (e.g. single neurons, cortical columns, broad brain regions etc.), how much activity, and over how large a region. We also need better techniques to map the diverse regions in the brain that work together in cognition and other functions, both invasively and non-invasively in humans, in order to unlock how they work.

The future of BCI research is extremely bright. The scientific community worldwide is making rapid progress in each of the above challenge areas, as demonstrated by the number of devices being invented, tested, deployed for human use, and the dramatically increasing research literature in the area of BCI. Most crucially, the rate of information transfer from human brain to computers is rapidly increasing, though in part by using more invasive technologies. Taking the step from repairing damage and restoring function to augmenting our abilities to see, hear, move or think is a dramatic one, and one with major ethical and moral implications. Devices to restore and enhance memory are already being tested, and our growing understanding of how memories are encoded and retrieved give dim glimpses of how information might be transferred from computer storage to human consciousness, though this type of application seems far off now. Augmentation of strength, perhaps reducible to mechanical design once appropriate control is established, seems much less challenging by comparison. What seems most clear is that the pace of advancement in these areas is accelerating. That BCI research will eventually transition from plasticity and repair to augmentation is not in doubt. It is imperative that we think carefully about how and where, scientifically, this shift should take place, and how we might best guide this process.

 

4:15 Demis Hassabis

The past, present and future of brain machine interfaces.

Also missed this one, but  Brian Wang writes:

Neuroscience is rapidly teasing apart the functional roles of the brain’s components, and in some cases even the types of algorithms that they use. Machine learning, meanwhile, is producing a growing collection of techniques for specific kinds of problems, but as yet no general purpose algorithm for artificial intelligence. By bringing these two fields together, we can have both a high level architecture for an artificial general intelligence, and working algorithms for implementing many of the required components. In this talk I will outline the case for pursuing this approach, some current work in progress, and some of the challenges we face going forward.