Singularity Summit

Selected Highlights

Evolution and Post-human Future - Gregory Stock

• Where are the wonder drugs?– Takes year for each clinical trial– Approval process broken and much too slow and costly– Approves only fixes to deficits, not enhancements

• Will Singularity lead to triumph of human values?– More probably will lead to some form of end of humanity – We are the old hotness – meat, blood, bone – not the

future– “Consume my heart away; sick with desire and fastened

to a dying animal..” - Yeats

Evolution and Post-human future 2

• Evolution moves on– Bio and complex non-bio is something new– Non-bio intelligence is newer still– Likely values among post-humans

• High levels of competition• Cheap easy copies making death rather meaningless• Uploading disengages humans from body

– But what of Moravec’s point about our minds being very wedded to our evolution even in our metaphors and logic patterns?

– These beings will have little in common w/ humans– “There will be a gradual elimination of all forms of beings that we

care about” – Bostrom• For humans ready/willing/able to transcend, more of a transformation, I

think.

Evolution, Post-human Future #3

• Chance of preserving human values through Singularity?– Some super friendly near all powerful singleton

AGI may control and ensure it– Thinks it is impossible• “Emergent realm careening toward unknowable future

will go its own way regardless of our wishes.”

Evolution and Post-Human Future 4

• Signum – his company– Targeting Alzheimers with goal of preserving enough of brain

at least to be worth freezing– Noted that Alzheimers is helped by removing phosphate

buildups on proteins– Molecule PP2A help this. Coffee activates this molecule– Evidence coffee consumption decreases risk of Alzheimers by

50%! Also adult diabetes.– Caffeine is not the effectine agent. Sig1012 extract from the

coffee bean is– Can move to human trials quickly because Sig1012 is an

approved food extract

The Mind and How To Build One – Kurzweil

• Started off razzing critics– Much of this and his talk was from or similar

material as @ Citizen Scientist– Much ad lib (talk was teleconference)– Critics include Doug Hofstader, Jaron Lanier and

Michael Anissimov (to a much smaller degree)• Given 10**16 calc/sec for brain– Henry Markram (Blue Brain) says this will be

achieved in 2018

The Mind, How To Build One 2

• Brain has Lisp nature?– “..each cortical module is like a Lisp

statement..incredible hierarchy..”– We have good and constantly improving ideas

how these modules work– Says he believes a million or so lisp statements

could probably model the human brain (?!)• Must have been talking with Minsky

AI Against Aging – Ben Goertzel

• AI applied to bioinformatics – CEO, Biomind LLC– Work in collaboration with Genescent– Humans poor at understanding complex, high

component and relationship count systems– This is where AI comes in:• Searching for patterns and abstractions within large

genomic data sets• Scanning relevant literature for patterns and exploitable

knowledge

AI Against Aging 2

• Why do we age and what to do about it?– Hayflick limit– Aubrey’s approach – fix all the main damage that occurs as

we age• Many biologist skeptical esp. of unintended consequences of

things like plan to move mitochondrial DNA into the cell nucleus

– Antagonistic pleotropy• Apparently changes/adaptations occur at many age points in our

development• Unfortunately they stack on top of each other and interfere with

one another as more of them accumulate• Our bodies literally try to run different age adaptations at once

AI Against Aging 3

• Genescent work– Has bred flies that live 5.5x longer than usual– Selective breeding like this would work in humans

if you did it for 5,000 – 10,000 years as it takes hundreds of generations

– Long lived flies have a complex large array of differences compared to regular flies. Requires use of AI to mine the data for nuggets

– Looking for simple replicable critical factors

Extending Ourselves w/ Technology – Steve Mann

• His eyecam is great! – Everything he looked at was wirelessly broadcast and

displayed on the main screens– Illustrated many points by drawing on a small paper pad

which he was looking at. The contents displayed on main screen. Very natural and fluid

– Looks at audience and we see ourselves looking at him looking at us

– He broadcasted and shared with world all his experiences when out and and about for many years

– Has devised and worn wearable computers and experienced mediated reality for over 30 years

Extending Ourselves w/ Tech 2

• Surveillance is a clear and present danger• He originated Sousveillance

– Sur – from the top• Authorities and such watching and controlling the people

– Sous – from the bottom• People watching and controlling the authorities

• Wearable is better than ubiquitous– More control over own data if on one’s person and only shared as you

wish– Mediation of reality to remove unwanted stimuli, experience and to

augment reality

– Showed wearable chest camera like one MS now sells

Extending Ourselves w/ Tech 3

• Into new forms of interaction with tech and environment– Hydralophone

• Musical instrument that uses water through small holes that the player closes and runs their fingers over to produce complex wind instrument like sounds and chords

• Playing with these gives great tactile feedback and experential shaping the water flow through each opening to get different effects

• They have made these in many forms including large public interactive sculptures and self play larger sculptures

• The model on hand was fun to play with

BCI Past and Future – Brian Litt

• Classification– Open or closed loop (1 way or 2 way)– Degree of invasiveness

• Generally the more invasive the finer the detail and control but greater the risks

• BCI used today for– Epilepsy– Depression– Obesity– Parkinson’s– Compensation for loss (hearing, vision, gait, artificial limb control)– Restore or repair (stroke, spinal cord trauma, peripheral nerve injury)

BCI Past and Future 2

• Future BCI– Augment : consciousness, memory, speed,

perception, cognitive processing• Already controversial – olympics banned runner with

artificial lower leg as unfair to other runners

– Idea storage– Transfer/sharing of knowledge, feelings, behavior– Replay of experiences– Direct brain recording

Machine Learning Rapidly Discovering How Brain Works – Demis Hassabis

• Nonbio approach to AI– Symbolic AI is traditional way

• Formal logic, logic networks, lambda calculus, expert systems– Flaws: brittle, time consuming, poor generalization, increasing cost of new knowledge

in some designs

• Bio approach to AI– Use brain as blueprint– If space of all possible designs yields only a few sparsely scattered

successes then good to start from a successful approach– Problems

• 50 years from mapping entire brain• That is not the same as understanding that part that makes for intelligence or

how it does so• A human in a box (all of human brain) is not what we are looking for for AGI

Machine Learning 2

• A Third Way – System Neuroscience Approach– Three levels of understanding brain systems (Marr)

• Computational – goals of the system– Cognitive science and symbolic people want to focus here

• Algorithmic – how does system accomplish goals– This area is largely overlooked in the main AGI argument

• Implementations – what is the physical realization– Classic bio brain emulation people want to focus here

– So how do you find AGI relevant findings in 50,000 neuroscience papers a year?• It takes at least 5 years of dedicated multi-disciplinary

training to come close to being good at this

Machine Learning 3• So hybrid approad is to combine best of AGI and Neuroscience

– Some target areas• Mirror neurons• Model based vs model free systems• Theory of mind• Working memory• Top down intention

• Concepts are key– Three levels

• Symbolic – logic networks, symbolic systems• Perceptual – HTM (Hawkins), HMAX (Poggio)• Conceptual - ???

– Theory» HC stores the memories of recent memories or episodes and replays those memories

during sleep at sped-up rate. gives high level neocortex samples to learn from memories selected stochastically for replay. rewarded, emotional or salient memories are replayed more; circumvents the statistics of the external environment and leads to abstraction.

Modifying Boundary between Life and Death – Lance Becker

• Old notion of >4 minutes without oxygen is too late is wrong– Can resuscitate after 10, 20, 40 minutes – even an hour

• Lack of oxygen does note kill most cells directly– They are fine for some time except build up electrons in

mitochondria and don’t regulate calcium as well– Add oxygen at full normal values and they die

immediately? Why?• The free electrons plus a lot of oxygen forms dangerous radicals

like crazy• This destroys outright and/or triggers cell death response

Modifying Life/Death Boundary 2

• How can this be fixed?– Cooling the body to slow down necrotic processes

• Standard cooling not fast enough. Invented slush machine for very quickly (in minutes) bringing body temperature down

– Controlled slow reperfusion (reoxygentation) as heart is restarted• Gives system time to normalize

– Chemical cocktail to aid diffusing dangerous cellular conditions as more oxygen is introduced

– This same process means that donate organs can be kept in viable state much more easily and longer potentially solving organ donor shortages

– Kit form being designed for use in ambulances and suitably trained paramedics

Universal Measure of Intelligence – Shane Legg

• He show an algorithmic method for determining relative intelligence of AI systems

• Asks: Is computational intelligence going up as Moore’s law goes up?

• How to approach the problem– Internal properties of intelligence vs external

properties• We don’t know and can’t say much about internal properties• We can say a bit about external properties of intelligent

solutions

Univ. Measure of Intelligence 2

• What is definition of intelligence?– He has collected over 80 distinct definitions• “system that generates adaptive behavior for wide

variety of goals”• “ability of system to act appropriately in uncertain

environment with appropriate being that which increases probability of success”• Summary: intelligence is the property of an agent that

interacts with its environment to successfully achieve goals across a wide range of environments

Univ Measure of Intelligence 3

• General Formula for Intelligence– Sum((2**-K(mu)) * V(pi, mu), All-Environments)

• K is complexity. As in Occam’s razor we won’t to disvalue more complex solutions compared to simpler ones

• The agent is pi• An environment instance is mu• V(pi, mu) is success function for the agent in an environment• So summing the weighted performance of the agent over all

environments possible for this agent gives us the measure of the agents intelligence– Of course in practice we cannot usually enumerate all environments

Univ Measure of Intelligence 4

• Evaluating intelligence– So use Monte Carlo approximation (random

sampling of generate environments)– Actually running this has successfully classified

many AI systems correctly• May be sensitive to perturbations in the environment

sample so must do many runs to converge to more trustworthy value