possible minds
A set of notes based on the book possible minds
- 0 - intro (brockman)
- 1 - wrong but more relevant than ever (seth lloyd)
- 2 - the limitations of opaque learning machines (judea pearl)
- 3 - the purpose put into the machine (stuart russell)
- 4 - the third law (george dyson)
- 5 - what can we do? (daniel dennett)
- 7 - the unity of intelligence (frank wilczek)
- 8 - lets aspire to more than making ourselves obsolete (max tegmark)
- 9 - dissident messages (jaan taliin)
- 10 - tech prophecy and the underappreciated causal power of ideas (steven pinker)
- 11 - beyond reward and punishment (david deutsch)
- 12 - the artificial use of human beings (tom griffiths)
- 20 - making the invisible visible (hans ulrich obrist)
- 22 - algorists dream of objectivity (peter galison)
- 23 - the rights of machines (george church)
- 24 - the artistic use of cybernetic beings (caroline jones)
- David Kaiser: Information for wiener, Shannon, and for Us
- Neil Gershenfield: Scaling
- Alison Gopnik: AI’s versus Four-Year-Olds
- Rodney Brooks: The inhuman mess our machines have got us into
- Anca Dragan: Putting the human into the AI equation
- Chris Anderson: Gradient descent
- W. Daniel Hillis: The first machine intelligences
- Venki Ramakrishnan: Will computers become our overlords
- Alex Pentland: The human strategy
- Stephen Wolfram: Artificial intelligence and the future of civilization
0 - intro (brockman)
- new technologies = new perceptions
- we create tools and we mold ourselves through our use of them
- Wiener: “We must cease to kiss the whip that lashes us”
- initial book The human use of human beings
- he was mostly analog, fell out of fashion
- initially inspired the field
- ai has gone down and up for a while
- gofai - good old-fashioned ai
- things people thought would be hard, like chess, were easy
- lots of physicists in this book…
1 - wrong but more relevant than ever (seth lloyd)
- current AI is way worse than people think it is
- wiener was very pessimistic - wwII / cold war
- singularity is not coming…
2 - the limitations of opaque learning machines (judea pearl)
- 3 levels of reasoning
- statistical
- causal
- counterfactual - lots of counterfactuals but language is good and providing lots of them
- “explaining away” = “backwards blocking” in the conditioning literature
- starts causal inference, but doesn’t work for large systems
- dl is more about speed than learning
- dl is not interpretable
- example: ask someone why they are divorced?
- income, age, etc…
- something about relationship…
- correlations, causes, explanations (moral/rational) - biologically biased towards this?
- beliefs + desires cause actions
- randomly picking grants above some cutoff…
- pretty cool that different people do things because of norms (e.g. come to class at 4pm)
- could you do this with ai?
- facebook chatbot ex.
- paperclip machine, ads on social media
- states/companies are like ais
- equifinality - perturb behavior (like use grayscale images instead of color) and they can still do it (like stability)
3 - the purpose put into the machine (stuart russell)
- want safety in ai - need to specify right objective with no uncertainty
- value alignment - putting in the right purpose
- ai research studies the ability to achieve objectives, not the design of those objectives
- “better at making decisions - not making better decisions”
- want provable beneficial ai
- can’t just maximize rewards - optimal solution is to control human to give more rewards
- cooperative inverse-rl - robot learns reward function from human
- this way, uncertainty about rewards lets robot preserve its off-switch
- human actions don’t always reflect their true preferences
4 - the third law (george dyson)
- 2 eras: before/after digital computers
- before: thomas hobbes, gottfried wilhelm leibniz
- after:
- alan turing - intelligent machines
- john von neumann - reproducing machines
- claude shannon - communicate reliably
- norbert weiner - when would machines take control
- analog computing - all about error corrections
- nature uses digitial coding for proteins but analog for brain
- social graphs can use digital code for analog computing
- analog systems seem to control what they are mapping (e.g. decentralized traffic map)
- 3 laws of ai
- ashby’s law - any effective control system must be as complex as the system it controls
- von neumman’s law - defining characteristic of a complex system is that it constitutes its own simplest behavioral description
- 3rd law - any system simple enough to be understandable will not be complicated enough to behave intelligently and vice versa
5 - what can we do? (daniel dennett)
- dennett wrote from bacteria to bach & back
- praise: willingness to admit he is wrong / stay levelheaded
- rereading stuff opens new doors
- import to treat AI as tools - real danger is humans being slaves to the AI coming about naturally
- analogy to our dependence on fruit for vitamin C whereas other animals synthesize it
- tech has made it easy to tamper with evidence etc.
- Wiener: “In the long run, there is no distinction between arming ourselves and arming our enemies.”
- current AI is parasitic on human intelligence
- we are robots made of robots made of robots…with no magical ingredients thrown in along the way
- current humanoid embellishments are false advertising
- need a way to test safety/interpretability of systems, maybe with human judges
- people automatically personify things
- we need intelligent tools, not conscious ones - more like oracles
- very hard to build in morality into ais - even death might not seem bad
7 - the unity of intelligence (frank wilczek)
- can an ai be conscious/creative/evil?
- mind is emergent property of matter $\implies$ all intelligence is machine intelligence
- david hume: ‘reason is, and ought only to be, the slave of the passions’
- no sharp divide between natural and artificial intelligence: seem to work on the same physics
- intelligence seems to be an emergent behavior
- key differences between brains and computers: brains can self-repair, have higher connectivity, but lower efficiency overall
- most profound advantage of brain: connectivity and interactive development
- ais will be good at exploring
- defining general intelligence - maybe using language?
- earth’s environment not great for ais
- ai could control world w/ just info, not just physical means
- affective economy - sale of emotions (like talking to starbucks barista)
- people seem to like to live in human world
- ex. work in cafes, libraries, etc.
- future life institute - funded by elon…maybe just trying to make money
8 - lets aspire to more than making ourselves obsolete (max tegmark)
- sometimes listed as scaremonger
- maybe consciousness could be much more hype - like waking up from being drowsy
- survey of AI experts said 50% chance of general ai surpassing human intelligence by 2040-2050
- finding purpose if we aren’t needed for anything?
- importance of keeping ai beneficial
- possible AIs will replace all jobs
- curiosity is dangerous
- 3 reasons ai danger is downplayed
- people downplay danger because it makes their research seem good - “It is difficult to get a man to understand something, when his salary depends on his not understanding it” - Upton Sinclair - luddite - person opposoed to new technology or ways of working - stems from secret organization of english textile workers who protested
- it’s an abstract threat
- it feels hopeless to think about
- AI safety research must precede AI developments
- the real risk with AGI isn’t malice but competence
- intelligence = ability to accomplish complex goals
- how good are people at predicting the future of technology?
- joseph weizenbbam wrote psychotherapist bot that was pretty bad but scared him
9 - dissident messages (jaan taliin)
- voices that stand up slowly end up convincing people
- ai is different than tech that has come before - it can self-multiply
- human brain has caused lots of changes in the world - ai will be similar
- people seem to be tipping more towards the fact that the risk is large
- short-term risks: automation + bias
- one big risk: AI environmental risk: how to constrain ai to not render our environment uninhabitable for biological forms
- need to stop thinking of the world as a zero-sum game
- famous survery: katja grace at the future of humanity institute
10 - tech prophecy and the underappreciated causal power of ideas (steven pinker)
- “just as darwin made it possible for a thoughtful observer of the natural world to do without creationism, Turing and others made it possible for a thoughtful observer of the cognitive world to do without spiritualism”
- entropy view: ais is trying to stave off entropy by following specific goals
- ideas drive human history
- 2 possible demises
- surveillance state
- automatic speech recognition
- pinker thinks this isn’t a big deal because freedom of thought is driven by norms and institutions not tech
- tech’s biggest threat seems to be amplifying dubious voices not surpressing enlightened ones
- more tech has correlated w/ more democracy
- ai takes over
- seems too much like technological determinism
- intelligence is the ability to deploy novel means to attain a goal - doesn’t specify what the goal is
- knowledge are things we know - ours are mostly find food, mates, etc. machines will have other ones
- surveillance state
- if humans are smart enough to make ai, they are smart enough to test it
- “threat isn’t machine but what can be made of it”
11 - beyond reward and punishment (david deutsch)
- david deutsch - founder of quantum computing
- thinking - involves coming up w/ new hypotheses, not just being bayesian
- knowledge itself wasn’t hugely evolutionarily beneficial in the beginning, but retaining cultural knowledge was
- in the beginning, people didn’t really learn - just remembered cultural norms
- no one aspired to anything new
- so far, the way ais have been developed (e.g. chess-playing) is restricting a search space, but AGI wants them to come up with a new search space
- we usually don’t follow laws because of punishments - neither will AGIs
- open society is the only stable kind
- will be hard to test / optimize for directly
- AGI could still be deterministic
- tension between imitation and learning? (immitation/innovation)
- people falsely believe AGI should be able to learn on its own, like Nietzche’s causa sui, buy humans don’t do this
- culture might make you more model-free
12 - the artificial use of human beings (tom griffiths)
-
believes key to ml is human learning
-
we now have good models of images/text, but not of
-
value alignment
-
inverse rl: look at actions of intelligent agent, learn reward
- accuracy (heuristics) vs generalizability (often assumes rationality)
- however, people are often not rational - people follow simple heuristics
- ex. don’t calculate probabilities, just try to remember examples
-
people usually tradeoff time with how important a decision is - bounded optimality
- could ai actually produce more leisure?
20 - making the invisible visible (hans ulrich obrist)
- need to use art to better interpret visualizations, like deepdream
- ai as a tool, like photoshop
- tweaking simulations is art (again in a deep-dream like way)
- meta-objectives are important
- art - an early alarm system to think about the future, evocative
- design - has a clearer purpose, invisible
- fluxist movement - do it yourself, like flash mob, spontanous, not snobby
- this progress exhibit - guggenheim where they hand you off to people getting older
- art - tracks what people appreciate over time
- everything except museums + pixels are pixels
- marcel duchamp 1917 - urinal in art museum was worth a ton
22 - algorists dream of objectivity (peter galison)
- science historian
- stories of dangerous technologies have been repeated (e.g. nanoscience, recombinant DNA)
- review in psychology found objective models outperformed groups of human clinicians (“prediction procedures: the clinical-statistical controversy”)
- people initially started w/ drawing things
- then shifted to more objective measures (e.g. microscope)
- then slight shift away (e.g. humans outperformed algorithms at things)
- objectivity is not everything
- art w/ a nervous system
- animations with charcters that have goals
23 - the rights of machines (george church)
- machines should increasingly get rights as those of humans
- potential for AI to make humans smarter as well
24 - the artistic use of cybernetic beings (caroline jones)
- how to strech people beyond our simple, selfish parameters
- cybernetics seance art
- more grounded in hardware
- culture-based evolution
- uncanny valley - if things look too humanlike, we find them creepy
- this doesn’t happen for kids (until ~10 years)
- neil mendoza animal-based aft reflections
- is current ai more advanced than game of life?
David Kaiser: Information for wiener, Shannon, and for Us
- wiener: society can only be understood based on analyzing messages
- information = semantic information
- shannon: information = entropy (not reduction in entropy?)
- predictions
- information can not be conserved (effective level of info will be perpetually advancing)
- information is unsuited to being commodities
- can easily be replicated
- science started having citations in 17th century because before that people didn’t want to publish
- turned info into currency
- art world has struggled w/ this
- 80s: appropration art - only changed title
- literature for a long time had no copyrights
- algorithms hard to patent
- wiener’s warning: machines would dominate us only when individuals are the same
- style and such become more similar as we are more connected
- twitter would be the opposite of that
- amazon could make things more homogenous
- fashion changes consistently
- maybe arbitrary way to identify in/out groups
- comparison to markets
- cities seem to increase diversity - more people to interact with
- style and such become more similar as we are more connected
- dl should seek more semantic info not statistical info
Neil Gershenfield: Scaling
- ai is more about scaling laws rathern that fashions
- mania: success to limited domains
- depression: failure to ill-posed problems
- knowledge vs information: which is in the world, which is in your head?
- problem 1: communication - important that knowledge can be replicated w/ no loss (shannon)
- problem 2: computation - import knowledge can be stored (von Neumann)
- problem 3: generalization - how to come up w/ rules for reasoning?
- next: fabrication - how to make things?
- ex. body uses only 20 amino acids