Singularity Summit 2010 – live blogging – day 1

9:30 – Missed Michael Vassar‘s talk.

9:50 – Gregory Stock is talking.  He is skeptical about progress in the bio realm.  He says that the FDA is a damper on progress, but he also says that there are difficult problems.  He brings up Alzheimer’s as an example.  I think he is underestimating the power of info tech to change the way we do bio-science.  Having read/write access to DNA, plus “in-silico” simulations will change the game.

Now he is talking about Silicon and saying that the complexity of computers rivals that of life.  And now he is talking about the rapid exponential progress in DNA technology.  As far as I understand, he is worried that we will create new life forms that will supersede humans.  He is saying that human evolution is “not exponential”. I think he means that it’s a very slow exponential compared to tech.

He is talking about a “planetary super-organism” – merging of the biosphere with technological artifacts (cities, Internet).  He says it’s beyond a metaphor.  However, it’s not a replicator and does not have a boundary, so I am doubtful that it’s useful to think about what’s happening on earth in this way.

He is saying that the biosphere will be subsumed but still exist in the new merged organism.  However, it seems to me that biology is not competitive because of the orders of magnitude in performance difference.  He thinks that timescales will enlarge, but the characteristic frequencies of mechanical systems are much higher, so this doesn’t make sense to me.

He says we are in the midst of the Singularity.

Cyberspace – boundaries are weak (but what about encryption?), copies are easy (yes!).  Uploading will lead to the disappearance/detachment of humans that take this route.  Ways to stop evolution to a post-human future – singleton rule, super-organism communities.  But it seems he thinks that stopping is not possible.  People can’t affect because they are cells, and can’t affect the organism.

He says that people that follow regulation will not be the ones to make progress.  The ones that do make progress are the nimble ones.  Agreed.

11:00 – Ray Kurzweil – The Mind and how to build one

Singularity worry – is it feasible to emulate the brain.

We derived narrow AI capabilities from reverse engineering the auditor and visual systems.  5th computing paradigm – Moore’s law – planar silicon.  6th paradigm – 3d molecular computing.

He is complaining that critics set up straw-man arguments.  More about exponential progress…

Why some people accept, and some people have emotional issues with accepting exponential change and it’s implications?  Not because of sophistication, intelligence, etc. .

Quick recap of exponential progress.

Exponential progress in brain scanning.  Resolution doubling every year.  Showing a image of a simulation of a cortical column.  Mentions blue brain project – Markram.  Markram expects to reverse engineer the brain by 2018, Kurzweil thinks it will take until the 2020’s.  Design of the brain is in the genome, which puts an upper bound on the code complexity strictly required – 25MB.  ~10K code for cerebellum.

How to create a mind?  Cortical columns are the basic modules.  He equates these to LISP atoms.  I think that’s too simplistic, because concepts are fluidly created.  Nevertheless, I agree that these are the building blocks of the neocortex and it would be a good research direction for AI/AGI.  It seems he is talking about de-novo mind design rather than uploading.

He says consciousness is a matter of faith and is not scientifically falsifiable.  It is necessary to believe in it for morality.  I’m not so sure – I agree with Dennet that it’s to do with having a self-model, which could be a scientific concept.

Wasn’t very satisfying as a roadmap to h+ AI.


* Something about the collapse of the wave function related to consciousness.  But the MWI doesn’t really have any wave function collapse, so I think that direction isn’t useful.  Ray is saying the same thing.

* What level of detail for whole brain emulation?  Ray says the point of WBE is to understand the principle of operation of the brain (i.e. reverse engineering).  He says once we understand how cortical columns at a low level we can recode them at a higher level and have a much more computationally efficient simulation.

* Similar question – is molecular level simulation necessary?  Ray responds that we don’t need to scan the whole brain at that resolution.  Distinct types of neurons can be scanned with high resolution once.  It seems he is again not talking about uploading.

12:00 – Ben Goertzel – AI for increased human healthspan

Human body as a machine.

The material in this talk is available on the h+ magazine site.

14:00 – Steven Mann: Humanistic Intelligence Augmentation and Mediation

Steven has glasses on which include a camera (I call this a speccam).  The image from the camera is up on the left screen. He is concerned about surveillance. He is practicing sousveillance.  He is drawing a diagram on a piece of paper instead of showing a slide.  The diagram is visible through his speccam.

He is showing another speccam using his speccam.  It re-renders the visible field on a 45 degree piece of glass, basically interposing a processing layer on vision – a heads up display.

A milling machine can replicate itself.

He talks about some patents he got on high dynamic range image stitching.  Seems obvious – and kind of disappointing to see a software patent from him.

Cyborg-log.  Continuous.  He is talking about getting to a resolution below the perception granularity of a human.  “Un-digital”.

He brought another person on stage that is talking about user-interfaces that keep humans in the loop as we get “un-digital”.  Using high bandwidth interaction devices – tactile, visual.  Musical instruments.  They are demoing one on stage.

More about musical instruments and patents.  This doesn’t seem very relevant.  That’s about it for this talk.

14:50 – Mandayam Srinivasan: Enhancing our bodies and evolving our brains

Skipped this one.

15:25 – Brian Litt: The past, present and future of brain machine interfaces

Long review of existing tech – small number of electrodes, poor biocompatibility.

His new work – implant 2.5 micron thick flexible surfaces with 720 electrodes for capturing neuronal signals.  Biocompatible, partly bioabsorbable (?).

Someone else’s work in rats.  Memories played back during sleep at 6x original waking acquisition speed.

Speculates about self-assembling injectable electrodes.

16:15 – Demis Hassabis: Combining systems neuroscience and machine learning: a new approach to AGI

How to create meaning for symbols?  CYC is brittle and cumbersome.

Search space of possible AGI solutions: regime 1 – small and dense search space.  regime 2 – large sparse search space.  In regime #2 it makes sense to use the brain as reference.  He thinks regime #2 is the case.  Arguments: evolution only produced it once, failure of AI field to date.  I somewhat agree, except there might be a more dense area that we might be overlooking.

Abstract at one end of spectrum, whole brain emulation as other end.  He thinks the abstract end is ad-hoc and unprincipled and WBE is 50 year in the future.

He advocates the middle way “systems neuroscience”.  Extract algorithm from brain function.  E.g. vision algorithm from the way vision areas work in the brain.  Then you have a component for narrow AI or AGI.  Another example is space perception (“grid cells”).

Reinforcement learning is another example.  HNN is another.

This approach still seems somewhat ad-hoc to me.  It seems likely that there are aspects of the way the brain works that cannot be identified as a separate module.  These kind of aspects would be too intertwined with other modules and aspects to tease apart into a separate algorithm.

However, we might be able to achieve a different type of intellect with the modular reverse engineering approach that he advocates.

17:20 – Terry Sejnowski: Reverse-engineering brains is within reach

Should look at evolution when trying to understand the brain.  A lot of conservation of genes even from “primitive” life forms.

Scale of the brain: 1e15 synapses, 1e11 neurons, 1e16 bits/s.

Temporal difference learning = dynamic programming.  Dopamine system.

Use of TD for backgammon in 1994. Radio, radar, car and power grid applications in next 5 years.

Not very exciting.

17:50 – Dennis Bray: What Cells Can Do That Robots Can’t

The main thrust of this talk is that a lot of computation is done by cells.  It seems to me that this computation is not relevant to the functioning of the mind.  Most of it is keeping homeostasis in the cell and with the environment.  When a person loses a limb, they maintain their mind and identity, so the cells in their limb could not have been “mission critical” to their identity and intelligence.  Therefore the speaker’s argument seems irrelevant to the prospect of creating machine intelligence.

18:20 – Terry and Dennis are debating

Terry is saying that they will bite the bullet and do a “complete” molecular simulation.  They did a monte-carlo simulation of a synapse over 100ms.  Cool video of the simulation showing about 100 synapses – “MCELL”.

Dennis rebuts.  He compares the brain to a neural network.  He says that neurons are all different.  I don’t see how that is related to the problem at hand – obviously you want to simulate the diversity of neurons present in a real brain by scanning one or by generating using embryo development simulation.

Terry: simulated a network of 300 generic neurons, and they learned useful things.

They seem to be “agreeing” and taking questions from the audience.

Dennis – there is nothing impossible in principle.  He is now asking about details of Terry’s simulation.  Terry – different proteins and molecules important at different times.

Dennis – there are so many different things to simulate.  Terry – exponential progress.

I think they are mostly in agreement about only some molecular aspects being relevant and we need to simulate to find out.  The main difference between them is probably related to intuition about exponential progress and the tools and resources it will make available in a short amount of time.  Terry can see that.

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