1. Singularity Summit
Selected Highlights

2. 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

3. 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.

4. 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.”

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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)

15. 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

16. Machine Learning Rapidly Discovering How Brain
Works – DemisHassabis
• 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

17. 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

18. 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.

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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