Curt Doolittle

Theme: AI

  • Explain why you think that a government can’t stop crypto. 😉

    Explain why you think that a government can’t stop crypto. 😉

    Source date (UTC): 2021-05-12 03:39:45 UTC

    Original post: https://twitter.com/i/web/status/1392323585230921730

    Reply addressees: @bitcornfarmer1

    Replying to: https://twitter.com/i/web/status/1392323127439511556

  • RT @LukeWeinhagen: @WorMartiN @curtdoolittle @Lord__Sousa @bryanbrey @ThruTheHay

    RT @LukeWeinhagen: @WorMartiN @curtdoolittle @Lord__Sousa @bryanbrey @ThruTheHayes App connecting your wallet to 3D printer. Print out enco…

    Source date (UTC): 2021-05-09 15:01:41 UTC

    Original post: https://twitter.com/i/web/status/1391408036183515138

  • I can post a dozen articles in the past month that say the state will clamp down

    I can post a dozen articles in the past month that say the state will clamp down on crypto. Like I said. It’s not clear that we are doing anything other than free R&D for “State coins”

    Source date (UTC): 2021-05-08 10:49:09 UTC

    Original post: https://twitter.com/i/web/status/1390982095930109957

    Reply addressees: @bryanbrey @ThruTheHayes @WorMartiN @LukeWeinhagen

    Replying to: https://twitter.com/i/web/status/1390891953861009414

  • Of course, just as women need men less because of the technologies men invented

    Of course, just as women need men less because of the technologies men invented – rather soon, on the visible horizon, probably via the Chinese – men will no longer need women, because artificial wombs will be superior, and exceptional women’s eggs can easily be cloned.

    Source date (UTC): 2021-05-06 19:27:31 UTC

    Original post: https://twitter.com/i/web/status/1390387769868967941

  • JFG: I think he’s stating that technology needs a means of decidability. Human (

    JFG: I think he’s stating that technology needs a means of decidability. Human (life) decidability is provided by need for acquisition. A machine or genetically created equivalent needs a means of decidability otherwise ‘shutting down’ is the cheapest (logical) alternative.

    Source date (UTC): 2021-05-03 14:20:49 UTC

    Original post: https://twitter.com/i/web/status/1389223422677422087

    Reply addressees: @JFGariepy

    Replying to: https://twitter.com/i/web/status/1389222292736155650

  • Another example: Babbage fails to generalize his discovery of computation, which

    Another example: Babbage fails to generalize his discovery of computation, which is why Darwin didn’t solve his open ‘puzzle’ of the digital nature of DNA.

    Source date (UTC): 2021-04-27 01:59:03 UTC

    Original post: https://twitter.com/i/web/status/1386862424515559431

  • (Update on presentation: 1. Method: terms thru grammars are ready. 2. Brain, Per

    (Update on presentation: 1. Method: terms thru grammars are ready. 2. Brain, Perception, Mind, Consciousness, Qualia, Biases, Agency etc I think ready for testing. 3. Intelligence, personality, morals, and political bias have been ready. 4. Rest of 1st principles need run-thru. )

    Source date (UTC): 2021-04-24 04:17:14 UTC

    Original post: https://twitter.com/i/web/status/1385810035385765888

  • COMMENT ONT HE PRESENT CONDITION OF AI (and the possibility of the next AI winte

    COMMENT ONT HE PRESENT CONDITION OF AI (and the possibility of the next AI winter … or not )

    https://propertarianinstitute.com/2021/04/19/comment-on-the-present-condition-of-ai/

    Source date (UTC): 2021-04-19 20:05:57 UTC

    Original post: https://twitter.com/i/web/status/1384236850647162884

  • Comment on the present condition of AI

    Good. Accurate. Would say that I think at least some of us who are aware of the three shortcomings that confirm your opinion. hardware, world model, self-training, sufficient recursion of prediction.
     
    (a) neural nets today can categorize and predict within a trained domain. in other words, they aren’t ai’s their robots (machines)
    (b) adversarial neural nets can only improve that process
    (c) the hardware is inverted from the brain which has many millions of tiny processors (columns) working in parallel vs serial or batches of serial processing.
    (d) the brain works on sequences in time that test for coherence of prediction between ‘nodes’ (groups of neurons, columns, macro columns)
    (e) the coherent predictions across these subsystems survive competition with one another for integration,
    (f) integration of relatively simultaneous predictions produces our experience of a moment.
    (g) the brain creates an index of coherence producing an episodic memory out of location, place, borders, landmarks, objects, head direction, eye direction, the direction of movement, rate of turn, and rate of movement.
    (h) it is these episodes that survive the test of coherence over time in a continuous stream of input that we auto-associate with one another, producing predictions.
    (i) we ‘wayfind’ by recursion.
    (j) we develop a hierarchy of recursion, and eventually what we call consciousness if enough recursion is possible, across enoug neurons, with enough biological economy to maintain that neural activity.
     
    So thats a more precise manner of explainin the authors correct assessment that all we have done is produce hardware cheap enough to accomplish what all of us working on AI in the 80s knew already. And thanfully tools that make development cheap enough. But really, Baysian systems are just another form of database for the categorization of stimuli.

  • Comment on the present condition of AI

    Good. Accurate. Would say that I think at least some of us who are aware of the three shortcomings that confirm your opinion. hardware, world model, self-training, sufficient recursion of prediction.
     
    (a) neural nets today can categorize and predict within a trained domain. in other words, they aren’t ai’s their robots (machines)
    (b) adversarial neural nets can only improve that process
    (c) the hardware is inverted from the brain which has many millions of tiny processors (columns) working in parallel vs serial or batches of serial processing.
    (d) the brain works on sequences in time that test for coherence of prediction between ‘nodes’ (groups of neurons, columns, macro columns)
    (e) the coherent predictions across these subsystems survive competition with one another for integration,
    (f) integration of relatively simultaneous predictions produces our experience of a moment.
    (g) the brain creates an index of coherence producing an episodic memory out of location, place, borders, landmarks, objects, head direction, eye direction, the direction of movement, rate of turn, and rate of movement.
    (h) it is these episodes that survive the test of coherence over time in a continuous stream of input that we auto-associate with one another, producing predictions.
    (i) we ‘wayfind’ by recursion.
    (j) we develop a hierarchy of recursion, and eventually what we call consciousness if enough recursion is possible, across enoug neurons, with enough biological economy to maintain that neural activity.
     
    So thats a more precise manner of explainin the authors correct assessment that all we have done is produce hardware cheap enough to accomplish what all of us working on AI in the 80s knew already. And thanfully tools that make development cheap enough. But really, Baysian systems are just another form of database for the categorization of stimuli.