benjamin on ai and intelligence

benjamin bratton ..  ai.. on benjamin on ai.. intellect ness.. et al

via tweet [https://x.com/bratton/status/1910080872977699291]:

I highly recommend this new piece in @NoemaMag by @blaiseaguera and James Manyika on how AI is changing our understanding of what “intelligence” is. I hold that artificialization is one of the ways that we discover anew that which he artificialize. **Humans don’t think how humans think that they think, and this is becoming both more clear and more precise as “thinking” is instantiated in new substrates. https://noemamag.com/ai-is-evolving-and-changing-our-understanding-of-intelligence/

*intellectness as cancerous distraction

**perhaps legit free humans would see thinking/intellect ness as cancerous distraction.. humans don’t ‘think’ how whales think

notes/quotes from article:

note: he’s ref’d in it.. as is antikythera (benjamin on antikythera).. ie: 1\ The belief that we were at the center of the universe — bolstered by Ptolemy’s theory of epicycles, a major scientific achievement in its day — was both intuitive and compatible with religious traditions. Hence, Copernicus’s heliocentric paradigm wasn’t just a scientific advance but a hotly contested heresy and perhaps even, for some, as Benjamin Bratton notes, an existential trauma. . and 2\ Blaise Agüera y Arcas is a vice president and fellow at Google, where he is the chief technology officer of Technology & Society and founder of the Paradigms of Intelligence team. His book “What Is Intelligence?” will be released in September by Antikythera and MIT Press.

Life is computational because its stability depends on growth, healing or reproduction; and computation itself must evolve to support these essential functions.

perhaps whale life.. perhaps survival ness.. but not legit free people life.. ie: graeber unpredictability/surprise law; graeber violence/quantification law; of math and men; et al

Predictive Intelligence

For those of us who were involved in the early development of language models, the evident generality of AI based solely on next-word (or “next-token”) prediction has been paradigm-shifting. Even if we bought into the basic premise that brains are computational, most of us believed that true AI would require discovering some special algorithm, and that algorithm would help clear up the longstanding mysteries of intelligence and consciousness. So, it came as a shock when next-token prediction alone, applied at a massive scale, “solved” intelligence.

yeah.. oi.. again.. graeber unpredictability/surprise law et al..

Perhaps, then, the shock was unwarranted. *We already knew that the brain is computational and that whatever it does must be learnable, either by evolution or by experience — or else we would not exist. We have simply found ourselves in the odd position of reproducing something before fully understanding it. When Turing and von Neumann made their contributions to computer science, theory was ahead of practice. Today, practice is ahead of theory.

*? what?

*Being able to create intelligence in the lab gives us powerful new avenues for investigating its longstanding mysteries, because — despite claims to the contrary — artificial neural nets are not “black boxes.” We can not only examine their chains of thought but are also learning to probe them more deeply to conduct “artificial neuroscience.” And unlike biological brains, we can record and analyze every detail of their activity, run perfectly repeatable experiments at large scale, and turn on or off any part of the network to see what it does.

*oi.. statement is proof intell is something we made up.. not alive ness of human being ness..

Although we don’t yet fully understand the algorithms LLMs learn, we’re starting to grasp why learning to predict the next token works so well. The “predictive brain hypothesis” has a long history in neuroscience; it holds that brains evolved to continually model and predict the future..

Organisms incapable of predicting their need for water won’t survive long enough to pass on their faulty self-models.

perhaps whale brains could/would

At such times, we must prioritize not only technical advances, but knight moves that, as in chess, combine such advances with sideways steps into adjacent fields or paradigms to discover rich new intellectual territory, *rethink our assumptions and reimagine our foundations. **New paradigms will be needed to develop intelligence that will benefit humanity, advance science, and ultimately help us understand ourselves — as individuals, as ecologies of smaller intelligences and as constituents of larger wholes

*if still focused on intellectness.. still cancerous distraction.. not new/rich.. just same song

**none to date.. nothing legit new/diff.. because keep trying to tie everything we try to ie: intellect ness..

the thing we’ve not yet tried/seen: the unconditional part of left to own devices ness

[‘in an undisturbed ecosystem ..the individual left to its own devices.. serves the whole’ –dana meadows]

there’s a legit use of tech (nonjudgmental exponential labeling) to facil the seeming chaos of a global detox leap/dance.. the unconditional part of left-to-own-devices ness.. for (blank)’s sake.. and we’re missing it

ie: whatever for a year.. a legit sabbatical ish transition

______

_____

_____

_____

____

_____

_____