6.4. ai futures

6.4.1. human compatible

A set of notes based on the book human compatible, by Stuart Russell 2019

6.4.1.1. what if we succeed?

  • candidates for biggest event in the future of humanity

    • we all die

    • we all live forever

    • we conquer the universe

    • we are visited by a superior alien civilization

    • we invent superintelligent AI

  • defn: humans are intelligent to the extent that our actions can be expected to achieve our objectives (given what we perceive)

    • machines are beneficial to the extent that their actions can be expected to achieve our objectives

  • Baldwin effect - learning can make evolution easier

  • utility for things like money is diminishing

    • rational agents maximize expected utility

  • McCarthy helped usher in knowledge-based systems, which use first-order logic

    • however, these didn’t incorporate uncertainty

    • modern AI uses utilities and probabilities instead of goals and logic

    • bayesian networks are like probabilistic propositional logic, along with bayesian logic, probabilistic programming languages

  • language already encodes a great deal about what we know

  • inductive logic programming - propose new concepts and definitions in order to identify theories that are both accurate and concise

  • want to be able to learn many useful abstractions

  • a superhuman ai could do a lot

    • e.g. help with evacuating by individually guiding every person/vehicle

    • carry out experiments and compare against all existing results easily

    • high-level goal: raise the standard of living for everyone everywhere?

    • AI tutoring

  • EU GDPR’s “right to an explanation” wording is actually much weaker: “meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject.”

  • whataboutery - a method for deflecting questions where one always asks “what about X?” rather than engaging

6.4.1.2. harms of ai

  • ex. surveillance, persuasion, and control

  • ex. lethal autonomous weapons (these are scalable)

  • ex. automated blackmail

  • ex. deepfakes / fake media

  • ex. automation - how to solve this? Universal basic income?

6.4.1.3. value alignment

  • ex. king midas

  • ex. driving dangerously

  • ex. in optimizing sea oxygen levels, takes them out of the air

  • ex. in curing cancer, gives everyone tumors

  • note: for an AI, it might be easier to convince of a different objective than actually solve the objective

  • basically any optimization objective will lead AI to disable its own off-switch

6.4.1.4. possible solns

  • Oracle AI - can only answer yes/no/probabilistic questions, otherwise no output to the real world

  • inverse RL

    • ai should be uncertain about utitilies

    • utilties should be inferred from human preferences

    • in systems that interact, need to express preferences in terms of game theory

  • complications

    • can be difficult to parse human instruction into preferences

    • people are different

    • AI loyal to one person might harm others

    • ai ethics

      • consequentalism - choices should be judged according to expected consequences

      • deontological ethics, vritue ethics - concerned with the moral character of actions + individuals

      • hard to compare utilties across people

      • utilitarianism has issues when there is negative utility

    • preferences can change

  • AI should be regulated

  • deep learning is a lot like our sensory systems - logic is still need to act on these abstractions

6.4.2. possible minds

edited by John Brockman, 2019)

6.4.2.1. 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…

6.4.2.2. 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…

6.4.2.3. 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)

6.4.2.4. 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

6.4.2.5. 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

6.4.2.6. 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

6.4.2.7. 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

6.4.2.8. 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

    1. 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

    2. it’s an abstract threat

    3. 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

6.4.2.9. 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

6.4.2.10. 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

  • 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”

6.4.2.11. 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

6.4.2.12. 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?

6.4.2.13. 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

6.4.2.14. 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

6.4.2.15. the rights of machines (george church)

  • machines should increasingly get rights as those of humans

  • potential for AI to make humans smarter as well

6.4.2.16. 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?


6.4.2.17. 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

  • dl should seek more semantic info not statistical info

6.4.2.18. 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