Artificial Intelligence - Revision history

Tom: /* Creativity */

2026-03-23T00:46:53Z

‎Creativity

Tom: /* Truth from the AI */

2026-03-23T00:41:03Z

‎Truth from the AI

Tom: /* Truth from the AI */

2026-03-23T00:40:39Z

‎Truth from the AI

Tom: /* Creativity */

2026-03-23T00:37:08Z

‎Creativity

Tom: /* Paradox */

2026-03-23T00:32:42Z

‎Paradox

Tom: /* Origin */

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‎Origin

Tom: /* Origin */

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‎Origin

Tom: /* The Broad concepts */

2026-01-02T01:56:14Z

‎The Broad concepts

Tom: /* Vulnerabilities */

2026-01-02T01:50:48Z

‎Vulnerabilities

Tom: /* Regulation */

2026-01-02T01:45:07Z

‎Regulation

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 Sun explicitly reports examples from researchers and poets who experimented with GPT‑2, describing outputs that wandered, took risks, and made odd but evocative associations that newer models no longer produce. This is not nostalgia alone: Sun notes that AI executives themselves admit they have not released a model that writes well, despite enormous gains in capability.
-Jasmine Sun is currently The Atlantic’s clearest voice arguing that AI degrades creativity by eliminating stakes, judgment, and responsibility, even as it perfects surface‑level fluency.<ref>Sasmine Sun, ''The Human Skill That Eludes AI'' The Atlantic 2026-03-17</ref>
+Jasmine Sun is currently The Atlantic’s clearest voice arguing that AI degrades creativity by eliminating stakes, judgment, and responsibility, even as it perfects surface‑level fluency.<ref>Sasmine Sun, ''The Human Skill That Eludes AI'' The Atlantic 2026-03-17</ref><ref>Hard Fork ''AI-Washing Layoff? + Why LLMs Can't Write Well + Tokenmaxxing 2026-03-20</ref>
 ===Paradox===

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 ==Truth from the AI==
 Lying requires belief + intention.
 * Deception requires a goal to induce false belief.
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 * Not just to replace workers and cut wages - they know if we have no money, we can’t consume. The system collapses.
 * This is about reshaping access to knowledge itself. Controlling what you see. How you learn. Who you become. Who you obey. Who you trust.
 ===Effective Altruism===

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 <pre>
 Lying requires belief + intention.
-Deception requires a goal to induce false belief.
+* Deception requires a goal to induce false belief.
-AI has neither. So by definition, it can’t be “lying” or “deceiving” - at least not in the human sense.
+* AI has neither. So by definition, it can’t be “lying” or “deceiving” - at least not in the human sense.
 Yet the desire to interpret internal model states as belief-like is so strong, researchers are now building probes to "extract" them.
 Words like “belief,” “reasoning,” “understanding,” "thought", and “intelligence” are being borrowed from human cognition - without being earned, and devaluing them all in the process. Not just by journalists, or AI researchers, but especially the ones hyping the tech.
 Why?
+* Not just to replace workers and cut wages - they know if we have no money, we can’t consume. The system collapses.
-Not just to replace workers and cut wages - they know if we have no money, we can’t consume. The system collapses.
+* This is about reshaping access to knowledge itself. Controlling what you see. How you learn. Who you become. Who you obey. Who you trust.
 </pre>

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 ===Creativity===
-Jasmine Sun is currently The Atlantic’s clearest voice arguing that AI degrades creativity by eliminating stakes, judgment, and responsibility—even as it perfects surface‑level fluency.<ref>Sasmine Sun, ''The Human Skill That Eludes AI'' The Atlantic 2026-03-17</ref>
+the idea that early large language models (LLMs) showed more creative writing, and that this quality degraded as rules, alignment, and post‑training constraints were added. I’ll separate what is documented, what is strongly suggested by reporting, and what remains a hypothesis, and I’ll anchor claims to explicit sources; especially the recent work of Jasmine Sun reports a striking consensus among AI researchers and writers she interviewed:
 ===Paradox===
 Moravec’s Paradox describes our experience that it is hard to teach machines to do things that are easy for most humans (walking, for example) but comparatively easy to teach them things that are challenging for most humans (chess comes to mind).

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 Most users want to know '''WHY''' a decision was make or an action was performed. The article "Solving for Why"<ref> Marina Kraskovsky, ''Solving for Why'' '''CACM 65''' No 2 (2202-02) pp 11ff</ref> is a focus on [[Causality]] to help researchers overcome shortcomings that have bedeviled more traditional approaches to AI. It is not enough to predict things, a good AI will need to solves things and explain why the choice was make if the decision is to have any support by the human population. People will ask if the decision was fair and will second guess the choices make by the AI unless they understand and accept the decision. Today AI decision are fragile, a slight change in the environment may have drastic changes in outcomes. This may well be inexplicable to humans. It will be insufficient to convince experts in the field about the choices made. It is apparent in 2022 that expert opinions about COVID have not been very popular. Even more evidence will be required of a AI decision. This will be particularly true when the decision is costly in any way. Humans like to have choices, which may require the AI to offer choices. That will have the benefit of giving the humans some feeling that they are in control.  But beware of "Hobson's choice". That was a stable master at Oxford that was required to give the students a choice of horses, but selected the options so that there was no real choice.
 ===Paradox===
 Moravec’s Paradox describes our experience that it is hard to teach machines to do things that are easy for most humans (walking, for example) but comparatively easy to teach them things that are challenging for most humans (chess comes to mind).

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 During 1955 and 1956 computer scientist and cognitive psychologist Allen Newell, political scientist, economist and sociologist Herbert A. Simon, and systems programmer John Clifford Shaw, all working at the Rand Corporation in Santa Monica, California, developed the Logic Theorist, the first program deliberately engineered to mimic the problem solving skills of a human being. They decided to write a program that could prove theorems in the propositional calculus like those in Principia Mathematica by Alfred North Whitehead and Bertrand Russell.<ref>''Newell, Simon & Shaw Develop the First Artificial Intelligence Program'' https://www.historyofinformation.com/detail.php?id=742</ref>
-In 1956 John von Neumann agreed to present a discussion on "The Computer and the Brain" as the Yale Silliman lecture. He was too ill to complete the full gamet of lectures but did get enough presented to be published by Yale.<ref>John von Neumann, ''The Computer and the Brain'' (1958) Yale UP ISBN 03000244150</ref>
+In 1956 John von Neumann agreed to present a discussion on "The Computer and the Brain" as the Yale Silliman lecture. He was too ill to complete the full gamut of lectures but did get enough presented to be published by Yale.<ref>John von Neumann, ''The Computer and the Brain'' (1958) Yale UP ISBN 0300024150</ref> Perhaps the most interesting part is the discussion of the difference between arithmetic and logic. He noted that only a few bits of information were available to make a choice between options, but that the computer's arithmetic was growing to every more bits, now 64 in most machines, to give sufficient precision to make a choice. Only machines defined after 2020 used ultra-low precision (2–4 bits) primarily for inference, not training. IBM researchers presented techniques for 4-bit quantization at NeurIPS 2020, focusing on reducing memory and compute costs while maintaining accuracy for deep neural networks
 The first international conference was "Mechanisation of Thought Processes"<ref>National Physical Laboratory at Teddington,''Mechanisation of Thought Processes'' https://aitopics.org/download/aiclassics:98875BCD</ref> was held in 1958 with presentations by Minsky and John McCarthy.

← Older revision		Revision as of 02:02, 2 January 2026
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	During 1955 and 1956 computer scientist and cognitive psychologist Allen Newell, political scientist, economist and sociologist Herbert A. Simon, and systems programmer John Clifford Shaw, all working at the Rand Corporation in Santa Monica, California, developed the Logic Theorist, the first program deliberately engineered to mimic the problem solving skills of a human being. They decided to write a program that could prove theorems in the propositional calculus like those in Principia Mathematica by Alfred North Whitehead and Bertrand Russell.<ref>''Newell, Simon & Shaw Develop the First Artificial Intelligence Program'' https://www.historyofinformation.com/detail.php?id=742</ref>		During 1955 and 1956 computer scientist and cognitive psychologist Allen Newell, political scientist, economist and sociologist Herbert A. Simon, and systems programmer John Clifford Shaw, all working at the Rand Corporation in Santa Monica, California, developed the Logic Theorist, the first program deliberately engineered to mimic the problem solving skills of a human being. They decided to write a program that could prove theorems in the propositional calculus like those in Principia Mathematica by Alfred North Whitehead and Bertrand Russell.<ref>''Newell, Simon & Shaw Develop the First Artificial Intelligence Program'' https://www.historyofinformation.com/detail.php?id=742</ref>
		+
		+	In 1956 John von Neumann agreed to present a discussion on "The Computer and the Brain" as the Yale Silliman lecture. He was too ill to complete the full gamet of lectures but did get enough presented to be published by Yale.<ref>John von Neumann, ''The Computer and the Brain'' (1958) Yale UP ISBN 03000244150</ref>

	The first international conference was "Mechanisation of Thought Processes"<ref>National Physical Laboratory at Teddington,''Mechanisation of Thought Processes'' https://aitopics.org/download/aiclassics:98875BCD</ref> was held in 1958 with presentations by Minsky and John McCarthy.		The first international conference was "Mechanisation of Thought Processes"<ref>National Physical Laboratory at Teddington,''Mechanisation of Thought Processes'' https://aitopics.org/download/aiclassics:98875BCD</ref> was held in 1958 with presentations by Minsky and John McCarthy.

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 ===The Broad concepts===
 * There are two great forces both in physics and in human history
 ** The harnessing of energy which started with the use of animal power to human benefit.
 ** The harnessing of information which started with philosophy and then Gutenberg's press.
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 ** Early stage: When a technology is new, its future impact is unclear, making regulation difficult.
 ** Later stage: Once the technology is widely adopted, it becomes harder to control or modify.
 ===Why Bother?===
   Some jobs are too risky.

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 * Giving a AI a goal may result in unexpected and undesired solutions.
 ** Bruce Schneier described<ref>Bruce Schneier, ''Hackers Used to Be Humans. Soon, AIs Will Hack Humanity'' Wired (2021-04-18) https://www.wired.com/story/opinion-hackers-used-to-be-humans-soon-ais-will-hack-humanity/?bxid=5c5b250d24c17c67f8640083</ref> AI to be Like crafty genies. They will grant our wishes and then hack them, exploiting our social, political, and economic systems like never before.
 ===Uniquely Human===

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 ==Regulation==
-The big question is it safe? The simple answer is that new technology always comes with new risks. Antitrust law is the best-known method for controlling enterprises too large to be controlled by the government. Perhaps they can be used to control a super intelligent being of another sort. Perhaps that's all they ever are. Corporations with computers and imposed limitations.
+The big question is it safe? The simple answer is that new technology always comes with new risks. Antitrust law is the best-known method for controlling enterprises too large to be controlled by the government. Perhaps they can be used to control a super intelligent being of another sort. Perhaps that's all an AI ever is, corporations with computers and imposed limitations.
 * 2024-10-24 [https://www.whitehouse.gov/briefing-room/presidential-actions/2024/10/24/memorandum-on-advancing-the-united-states-leadership-in-artificial-intelligence-harnessing-artificial-intelligence-to-fulfill-national-security-objectives-and-fostering-the-safety-security/ Memorandum on Advancing the United States’ Leadership in Artificial Intelligence; Harnessing Artificial Intelligence to Fulfill National Security Objectives; and Fostering the Safety, Security, and Trustworthiness of Artificial Intelligence]
 * NIST issued a call for participation on [https://www.federalregister.gov/documents/2023/11/02/2023-24216/artificial-intelligence-safety-institute-consortium Artificial Intelligence Safety Institute Consortium] 2023-11-02 in the federal resister.