The Singularity Summit was mind blowing. There were a LOT of very interesting talks – far too many to do justice to today. But here are a few that I was most taken by. Even that subset leaves off some I really enjoyed.
Transcript of "Singularity summit"
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 – 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
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
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