(2019) by David Weinberger
intro – everything all at once
as we will see, machine learning is just one of many tools and strategies that have been increasingly bringing us face to face w the incomprehensible intricacy of our every day world. but this benefit comes at a price: we need to give up our insistence on always understanding our world and how things happen in it..t
but not what our new tools, esp machine learning and the internet, are bringing home to us the immensity of the data and info around us, we’re beginning to accept that the true complexity of the world far outstrips the laws and models we devise to explain it
this literal disillusionment is something for us to embrace – and not only because it’s happening whether we embrace it or not. we are at the beginning of a great leap forward in our powers of understanding and managing the future: rather than always having to wrestle our world down to a size we can predict, control and feel comfortable with, we are starting to build strategies that take our world’s complexity into account
a/b testing (compare two versions.. determining which is more effective.. so what’s next) works w/o needing, or generating, a hypothesis about why it works..
a/b testing is just one ie of a technique that inconspicuously shows us that principles, laws, and generalization aren’t as important as we thought.. maybe.. maybe .. principles are what we use when we can’t handle the fine grains of reality
these 2 techs (programming-technique/machine-learning and global-space/internet) also have at least 3 things in common that have been teaching us about how the world works: both are huge/connected/complex
the importance of the hugeness of both machine learning and the internet is the level of detail they enable. rather than having to get rid of detail be generalizing or suppressing ‘marginal’ info and ideas, both thrive on details and uniqueness..
ginorm small ness
our success w these techs – rather than the techs themselves – is showing us the world as more complex and chaotic than we thought, which in turn, is encouraging us tot explore new approaches and strategies, challenging our assumptions about the nature and importance of understanding and explanation, and ultimately leading us to a new sense of how things happen
important predictions like the ones made by deep patient are being made more accurately than ever before by machine learning systems that we may never be able to understand..t
not accurate if non-legit data (ie from whales in sea world)
chaos theory isn’t crazy, but it can seem that way because it describes nonlinear system – system that work differently as they scale up
our vision has been clarified because at last we have tools that extract value from vast and chaotic details. we have tools that let us get everyday value out of the theory. the internet has plunged us into world that does not hide its wildness but rather revels in it..ai in the form of machine learning, and esp deep learning, is letting us benefit from data we used to exclude as too vast, messy, and trivial..t
you might think we’e spent the past 20 yrs or so coming up w ways to avoid having to anticipate what’s going to happen next.. that’s exactly what we’ve been doing
oddness of book.. each ch ends w an essay about how these changes are affecting some of the most basic formation of our understanding.. i’m calling these brief essays ‘codas’ although a musical coda closes a piece, whereas i hope that these essay will open the chapters up, giving an indication of how deep and far reaching these changes are likely to be in our lives
i’ve been driven toward the questions this book approaches ever since i was a philosophy major in college..most of all, what have we sacrificed in our attempt to make the world understandable and controllable..
letting go of that control.. like you say p 17..would change everything
i became fascinated by the internet in th e80s and then by the early web precisely because they seemed to me to tear down institutions and ways of knowing that maintained control by *narrowing our possibilities; that is the subtext of the four books i’ve written about the internet.. starting w cluetrain manifesto and most recently too big to know
*finite set of choices ness
1 – the evolution of prediction
it is no mere curiosity, for as we will see, how we predict shows us how we think the future happens and thus how the world works
predictions live in a sweet spot between surprise and certainty
let’s look briefly at 3 early cultures that understood the world in ways that kept them from making what we would toady recognize as predictions: egyptian, hebrew & greek
ray kurzweil thinks that simple rules instantiated as computer programs will give rise to machines that not only think but think better than we do. the game of life influenced .. stephen wolfram’s development of a ‘new kind of science’ which explains the universe as a vast computer.. wolfram uses this approach – simple rules w complex results – to explain everything form the patterns in shattered glass to the placement of branches circling in a sapling’s trunk
the game of life shows that a universe w simple rules doesn’t itself have to be as predictable as a clock, where each tick is followed by a tock..
when small changes can have giant effects, even when we know the rules, we may not be able to predict the future. to know it, we have to live thru it
our new tech is both further enlightening us and removing the requirement that we understand the how of our world in order to be able to predict what happens next
wait.. the goal isn’t to predict what happens next.. it’s for 7bn alive people
and no matter how seemingly enlightening w/o understanding.. these predictions are again non-legit because they are from data of ie: whales in sea world
the normal is a poorly paved road running thru the endless territory belonging to the kingdom of accidents. our plans are low beams that point wherever we look and leave the rest in the dark..t
2 – inexplicable models
a spreadsheet thus is a simple ie of a working model based on a fully understandable conceptual model – levy: reality by the numbers
george ep box: all models are wrong but some are useful
accuracy of models (tides, planets) based on fictitious model.. error makes working model quite beautiful
this working model can deal w more complexity because it doesn’t have a conceptual model.. it puts the actual forces to use in a controlled and adjustable way.. because this model is not merely a symbolic one – real water is rolling past a real, scaled down boulder – the results aren’t limited by what we know to factor in..
machine learning is making clear a problem w the very idea of conceptual models.. suppose our concepts and the world they model aren’t nearly as alike as we’ve thought..t
huge but beyond machine learning.. ie: we’re getting data from whales in sea world
in all these cases (spreadsheets to rivers) models stand in for the real thing: the armillary si not the heavenly domain, the spreadsheet is not the business, the tubes filled w colored water are not the economy. they do so by simplifying the real world version.. models simplify systems until they yield *acceptably accurate predictions
*acceptable to who?.. thinking we’ve gone overboard on predict\able ness
models thereby assume that we humans can id the elements that are relevant to the thing we are modeling.. models assume some degree of regularity
because the simplification process is done by human beings, models reflect our strengths/weaknesses.. the strengths include our ability to see the order beneath the apparent flux of change. but we are also inevitably prone to using unexamined assumptions have limited memories and inherent biases, and are willing to simplify our world to the point where we can understand it
perhaps most extreme/invasive/cancerous we’ve done.. predicting off the assumption of whales in sea world
despite models’ inescapable weaknesses due to our won flawed natures, they have been essential to how we understand and control our world
but we don’t understand it.. because we are trying to control it
the have become the stable frameworks that enable us to predict and explain the ever changing and overwhelming world in process all around us
? yeah.. i don’t think so.. rather the stable cancer
we are transitioning to a new type of working model, on that does not require knowing how a system works and that does no require simplifying it, at least not to the degree we have in the past. this makes the rise of machine learning one of the most significant disruptions in our history
in intro talked about deep patient .. able to predict onset of diseases that have defied human diagnostic abilities
imagining disease shouldn’t be our focus (because imagining us not being us created most if not all of them in the first place)
deep patient unusually good at telling which patients are at risk fo developing schizophrenia..
better to not label that in the first place.. ie: crazywise.. but even better to us ai/machine-learning/tech (whatever you want to call it) to get us all back/to our whole selves.. (ie: almaas holes law)
another ie w credit scoring et al
(on another ie as figuring out why a plane crashed and killed 505 of 509 passengers) – 4th there is some reason why we want an explanation.. boeing wanted to know to fix the problem.. relatives wanted to know in order to know whether to sue
to sue.. oi
in short, explaining is a social act that has social motives and is performed according or rules and norms that serve social purposes
but do they really? – do we even have a say in purposes..? imagining that whole concept would be irrelevant – or really diff – if 7b people were truly free
we have developed the rules of these explanation games over many centuries. they are exquisitely well worked out, and we inhabit them as if they were as obvious as using a spoon to drink hot soup
huge problem here – assuming et al
another ie.. how to explain what causes war.. this would be a tricky model for us to build using our old assumptions. but this is the sort of world that deep learning assumes
we know the world is complex, but we desperately want it to be simple enough for us to understand and to manage
huge problem here – managing.. et al
a traditional computer can tell us about all the data it’s dealing with, and the computer does nothing to that data that a human didn’t program it to do. but a deep learning program that has constructed its model out of the data we’ve given it can’t always be interrogated about its ‘decisions’.. while there are still element humans control – which data is put in, how that data is preprocessed, …
let’s just stop there.. let’s reset to diff data – ie: self-talk as data
if for this book the question is ‘how is our engagement w our new tech changing our ideas about how things happen’.. then perhaps first thing we should learn is that the very difficulty of removing (or sometimes even mitigating) the biases in our data makes it clear that thing happen unfairly..
by treating the governance of ai as a question of optimizations, we can focus the necessary argument about them on what truly matter: what is it that we want from a system, and what are we willing to give up to get it
most.. if not all.. need detox.. before they could answer that.. ie: krishnamurti free-will law.. that’s why we’re where we’re at today.. we’re not taking detox first.. freedom first.. seriously .. chomsky serious things law
3 – beyond preparation – unanticipation
anticipation that leads to some preparatory action is the fundamental way we engage w our world.. if we were to stop anticipating and preparing, we literally would not dip a spoon into a bowl of soup or look ahead at where we’re walking.it’s at the heart of our strategies, as well as the way we navigate the most mundane of our every day activities..
this is how we prep for an unknown future .. but it’s not w/o its costs..
we americans throw out 40% of our food.. equiv to $165 b a year.. because we cooked too much or bought supplies that outran their use by dates..
there’s a simple reason none of this goes onto the scales when we assess our reliance on our prehistoric strategy of anticipating and prep ing for a future most marked by its unpredictability: we have no scales and we do no weighing because we have had no alternative..
now we do.. we can adopt strategies of unanticipation
why anticipate when you can launch, learn and iterate (on eric ries lean startup and slack and dropbox et al)
rather.. imagine if we weren’t all trying to sell something.. and 7b were launching something everyday.. just via their daily curiosity
because.. what is our goal.. better products..? or better/alive people?
they are against the idea that quality is best achieved the way ford id’d: by knowing beforehand exactly what you want and then planning the perfect procedures that will get it right every time
crazy.. but i think that’s what we’ve got here.. something that is exactly what 7b people want with perfect procedures that will get it right (as close to right as possible.. well.. there is no right) every time.. (p84) .. ‘that takes advantage of what bits and networks allow.. agility’ ..et al
if only you could hear me.. as it could be..
as the success of all these techniques (fb, code for america, github, et al) shows, the true price we paid for the old way of doing things became apparent once we had tools and tech that let things happen differently
yes.. all have plusses.. but we’re missing the really different thing (ie: all are still based in old operating system).. ie: a nother way
Jason Rohrer (@jasonrohrer) tweeted at 8:09 PM – 4 Jun 2019 :
If you’re thinking about using @github for your life’s work, FYI, they may remove it without any warning or notice, based on some user “report” made out of spite. That happened today for the 5+ years of One Hour One Life work that I’m hosting there. They didn’t even email me. (http://twitter.com/jasonrohrer/status/1136092621149880320?s=17)
begs gershenfeld something else law (everyone too busy doing the thing they can’t not do to spend their days being inspectors of inspectors)
in short, foo (o reilly) was an even carefully planned to enable unplanned discussions
perhaps unplanned.. but not unprogrammed.. ie: as whales in sea world .. can’t really/truly get discussions/convos/whatever .. from the gut (what’s sorely missing.. causing us to spin our wheels.. even unanticipated/unprepped wheels.. spinning)
seely and hagel power of pull vs push
every time we touch the net, we relearn the same lesson: unanticipation creates possibilities
yeah.. if we could only listen to that
we are now building to meet needs that a connected world of users might invent for one another
yeah.. we need to stop inventing for others.. that’s a waste/excess.. as well
browsing (defined by *relative lack of anticipation) is to search and the weekend is to the week
not *relative lack enough today.. we have the means for better. ie: curiosity and decision making et al
libraries are in the business of being over prepared.. because they cannot finely predict all the works that their communities might want.. only about 3-4% of harvard uni library’ magnificent collection is check ed out every year.. it takes almost 1000 libraries to run harvard’s library system.. w budget of over 150 m..
being over prepared is expensive, but when it comes to the ingredients of creation, not of consumption, it is a necessary gift
not necessary anymore. dang.. wish we could talk
4 – beyond causality (interoperability)
interoperability is the ability to use element designed for one system in another system the designers may never have hear of, and in ways that they did no anticipate..
beyond leggos interop.. because that’s in same system
john palfrey and urs gasser give another type of ie in their book interop (2012)
5 – strategy and possibility
if how we predict tells us how we think about the future, how we strategize tells us how we think about possibility
possibility looks diff in a universe characterized by interoperability
in an interoperable world in which everything affects everything else, the strategic path forward may be to open as many paths as possible and enable everyone to charge down them all at once, together and apart..t
obscurity has some advantages that clarity just can’t match: creates empowerment and enables creativity and engagement..t
obscurity has these powers because clarity is not a natural state for humans.. our lives are uncertain..clarity is a helpful tool, but there’s often more truth in obscurity..t
6 – progress and creativity
1967 charles van doren on progress: 1\ pattern of change exists in history 2\ pattern is known 3\ is irreversible 4\ is toward the better 5\ something causes pattern to occur/persevere
on tools not seen as worthy of progress (w/o human tool is useless).. tools have purpose.. a computer does not have a purpose or 87 purposes.. it has whatever purpose one of us programs into it.. that makes computers special in our history.. computers can do whatever can be done by representing the world as bits.. this has cause the straight line of progress to sprout exponential curves.. and if computers bent the line of progress upward in field after field, connecting those computers to one another is twisting it into knots..
lessig on non interop in the future of ideas
the shapeless shape is the new shape of progress – which is to say, of the future..
the diff between progress’s old, sloping line and its new shape is the diff between incrementally improving a clock by coming up w a new way to fasten a balance wheel, and smashing the mech, throwing it in the air and never knowing all diff ways the scattered parts have been picked up and reused.. except in digi world.. don’t have to smash..
if had to draw sloping line for internet.. many of biggest tick marks would be for what was given away..
the perpetually startling fact of the internet its most distinctive characteristic is not its openness to every purpose.. for disconnected computers are open in the same way.. rather, ie’s the interoperability it enables among everyone privilege a a connection to it..
overall.. the internet is generative to a degree we have until not only experience w language
beyond though .. right?.. language isn’t interoperable.. that’s why we need to take this opp to leap toward idio-jargon as global/common language (yet we’re missing it)
jonathan zittrains’ future of internet and generativity in his book future of net and how to stop it.. how easily we can use a tool for own purpose despite what designed for.. ie: laptop; apis; open standards/protocols; open libraries of code; open licenses content.. all generative.. generative of what? we can’t know. that’s what makes them generative..
while interop refers to degree to which elements from diff systems can work together, generativity is the ability of a tool/system to be used in unanticipated ways.. interoperable systems are generative.. generavitiy is the degree to which interoperability enables unanticipation
generativity has been supercharged by the constant availability of the ever interoperable internet
for those whom net is our where for much of the day.. we expect overall, this new environ to enable reuse, plasticity, reframing and sharing at levels never before experienced
the most important tick marks are the generative ones that lead in 1000s of other direction
traditional progress is a line drawn between tick marks. generativity understands that straight lines are in denial..
what drives generative progress is not a final destination dragging us along.. but rather the lowering of barriers to invention.. by interop, gen, and an open network of collaborators – so that human ingenuity can be applied to needs/desires/whims that otherwise would have gone unnoticed and unaddressed..t
begs a mech that listens to every voice/curiosity.. everyday.. tech as it could be..
where once there were lonely geniuses standing on the shoulders of giants all looking up and to the right, now there are networks of people alive w ideas keeping one another up late at night..t
just not yet all of us.. has to be all of us
why have we so insisted on turning complex histories into simple stories? marshal mcluhan was right: the medium is the message. we shrank our ideas to fit on pages sewn in a sequence that we then glued between cardboard stops. books are good at telling stories and bad at guiding us thru knowledge that bursts out in every conceivable direction, as all knowledge does when we let it
but now the medium of our daily experience – the internet – has the capacity, the connections, and the engine needed to express the richly chaotic nature of the world..t
indeed.. and we’re missing it..
this comes at the price of the comforting illusion of comprehension, as ai has been teaching us
those models (machine learning’s model of world) may be impenetrable to our will to understand, but they are nevertheless enabling us to see that the world, its people, its things, and its history are like those models but ever so much more so
again.. and we’re missing it.. (ie: using wrong data.. et al)
what drives this type of progress does not compel it to move in a particular direction. their is no perfection pulling it forward. interoperability isn’t directional.. t
the motive force of this new type of progress may be commercial or social, but more often is perhaps simultaneously – someone feeling in her heart that playing w some thing or system will reveal more of what it is, what she is, what we are, and what we could be..
we have a word for the shape formed when movement rapidly emanates from a single point in myriad directions. it’s not an inclined line.. it’s an explosion
re:wire: the internet has offered us an opp and we haven’t taken it (weinberger)
perhaps most humiliating for me (in being wrong about the inevitability of the net changing us) is the extent to which my vision was blurred by my privileged position as a middle class wester white man.. ie: not everyone had leisure time to browse or freedom/confidence to blog their views..
it took me an embarrassingly long time to realize the source of my assumption of the inevitability of the nets’ triumph.. it wasn’t tech that was the driver but rather my idealistic conviction that given an opp, people would rush to satisfy their human yearning to connect/create/ speak in their own voices about what matters to them..
dang man.. that part is true.. we’ve just been using tech wrong.. using wrong data.. and we’ve never given people the opp.. there’s always been strings.. so .. intoxication
the determinacy i sensed was coming not from the tech but from our deep human need to connect and to create
rather: a and a – maté basic needs
but that is too simple an answer..
has to be simple
7 – make. more. meaning.
ai is going to force us to make decisions about fairness at levels of precision that we previously could ignore or gloss over..t
precision comes at the cost of meaning. messiness is the root of all..
in this way, the internet’s collaborate, cacophonous chaos of links and machine learning’s model of models unrestrained by complexity are far more representative of what things are than aristotle’s or linnaeus’s attempt to clarify meaning w the edge of a scalpel..
yeah .. not enough.. not for syria.. homeless.. prisoners.. et al
now at last, our tools are complicit in our awe
perhaps Tim Berners-Lee geeked out when he realized a way to ground the chaos of perhaps David Weinberger‘s too big to know ness. perhaps he should have. www ness is huge. berners-lee everyone law et al
perhaps why we haven’t yet gotten to equity.. over a decade later.. is less about the grounding ability and more about the which chaos. [i’m thinking a specific chaos matters now because of the shell-less quagmire we’ve gotten ourselves into from all the perpetuate\ing not us ness. perhaps in a more natural state.. everything is misc.]
perhaps the chaos we focus on grounding/facilitating first.. is 7 billion plus curiosities. everyday.
and.. perhaps this begs we deal-with/listen-to alive people. ie: people who are freed up/back to their innate/insatiable curiosities/imaginations/wonderings/wanderings/ponderings/whimsy.
and so.. perhaps..
let’s do this first: free art-ists.