Singularity Summit 2010 – live blogging – day 2

Missed Eliezer Yudkowsky: Simplified  Humanism and Positive Futurism.

9:40 – Ramez Naam: The Digital Biome

Plenty of carrying capacity for the biome – 30-300 billion people with advanced biotech.  We are using only 1/1000 of the incident energy from the sun.  There’s no reason to crash due to lack of resources with advanced tech.  Population is predicted to level off at 10 billion.

Good points, but the Singularity is likely to happen on a shorter time scale.

10:40 – Lance Becker: Modifying the  Boundary between Life and Death

Cells that experience ischemia (lack of oxygen) die after re-perfusion with oxygen, rather than during the ischemia itself.  They die due to oxidative damage and apoptosis (cell suicide) related to mitochondria. How do we prevent the re-perfusion injury?

Current approaches – CPR, AED, Cooling.  Next step cardiovascular bypass, mitochondria meds, better cooling, controlled reperfusion.

Tried cocktail of meds and cooling on 6 people that failed CPR, etc., and 50% were resuscitated.

Might be applications to Cryonics.

11:15 – Ellen Heber-Katz: The MRL  mouse – how it regenerates and how we might do the same

Wound healing – scar formation (mammals) vs. epimorphic regeneration.

In regeneration – blastema – cell de-differentiation -> new limb. In mammals – regeneration in deer antlers.  Rabbit ears.

MRL mouse – they regenerate various tissues.  Inflammation response is necessary for regeneration.  The mice don’t use mitochondria (?!).  (during regeneration?)  DCA blocks glycolysis – blocks regeneration.

MRL mouse cells are lacking p21 molecule.  p21 protects against DNA damage, but blocks regeneration.  Selective blocking of p21 is currently investigated.  Removing p53 increases regeneration.

What about immortality?  MRL mouse variant lives to 3 years, die of autoimmune response.  p21 doesn’t get cancer until 16 months of age.  But they get an inflammatory autoimmune disease – die of tumors.

Separate tumors, inflammation and regeneration.

11:50 – Anita Goel: Information Processing & Physical  Intelligence in Nanomachines that Read/Write DNA

She is talking about controlling ribosomes and such from the macroscale.

Applications – includes “gene-radar” – portable diagnostics for DNA/RNA – point of care.  Also, energy transduction at the nanoscale, read/write DNA, store information, computing.

DNA Polymerase can go forward, backward (error correction), or pause.  This can be controlled from macro-scale.  Quality of output can thus be controlled.

These machines are very energy efficient.  How do they do that?

More stress on infrastructure-free diagnosis, developing world, etc. .

14:00 – Shane Legg: Universal  measures of intelligence

Yet another exponential computation graph. 🙂

2025 – 10^20 ops per second.  What does this translate to in meaningful intelligence?  He wants intelligence on the Y axis.

Human vs. Ideal.  Internal properties (“understanding”, “soul”, etc.) vs. External Properties. He is going to focus on External Ideal quadrant.

Definition of intelligence: “Intelligent system are expected to work, and work well, in many different environments.” – Gudwin

He shows an equation that takes into account success, agent, environment and Occam’s razor.


This measure is called Algorithmic Intelligence Quotient (AIQ).  It seems to order some relevant learning agents in a correct way.

I think it would be great if we had a general intelligence measure, so we can gauge our success in creating artificial systems.

14:40 – John Tooby: Can discovering the design principles governing natural intelligence unleash breakthroughs in artificial intelligence?

For scientific debates, history shows us that the truth typically lies incredibly far beyond the limits of what even the most radical scientists of an era propose (e.g. Galileo and the size of the universe).

All the pieces are in place for realizing the Enlightenment project of a rigorous natural science of human nature: reverse engineering the code.

Emphasize evolution – natural selection is the only known physical process that can push against entropy.  It only builds structures that solve evolutionarily recurrent adaptive problems.

Evolutionary psychology.  Rant against marginalization of evolution in psychology academia.

Example – avoid mating with genetically similar organisms – deleterious recessives.  But, care for genetic relatives.  An identification problem.  Computation of kinship index and adjustment of behavior based on it.

Implications for strong AI

– psychology map of humans so that it does what we mean

clues on how to achieve strong AI:

– dedicated intelligence
– pinpoint strategies
– “brilliant hacks”
– a mosaic of modules

Universe is too diverse to hold in our mind.  Minds need to reduce.

15:20 – Tooby, Goertzel, Yudkowsky & Legg panel: Narrow and General Intelligence

Goertzel – AIXI is “fictitious”

Legg – AIXI is a useful model, and MC AIXI is useful in practice for limited problem sizes.

Goertzel – if evolution can harness large amounts of computation with a simple engine, then maybe we can achieve general intelligence with a small number of more general modules.

Legg – need domain specific algorithms to be efficient.  But a general system can solve with unlimited power.

More discussion about how much domain specific algorithms you need.  Possibly start with a limited set and develop more over time.

Legg – classes of algorithms that are not exactly domain specific, but applicable to multiple domain areas.

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