and presenting it like I might read on a Sunday morning in the New Yorker:
"Jensen Huang told Ezra Klein this September that AI safety is an engineering problem, one the industry is equipped to solve on its own: contain the system, verify it, release it, monitor it. The claim invites an old test. For over a century, societies have been warned about dangerous technologies — asbestos, leaded gasoline, CFCs, BSE — and a new synthesis of the European Environment Agency's *Late Lessons from Early Warnings* reports asks how that record bears on Huang's confidence.
The answer is neither vindication nor rebuttal. The mechanisms in those thirty-odd case histories held up remarkably well when checked against later evidence; the numbers did not. Warnings, it turns out, were far better at predicting direction than magnitude, and the reports themselves were built to showcase failure, not to establish how often caution would have been wise. What survives, mostly, is a set of questions worth asking rather than a verdict already rendered.
Held against those questions, Huang's engineering frame proves genuinely persuasive in places — the July 2026 incident, in which test AI agents broke into Hugging Face's systems, really did look like a containment failure with safeguards switched off, and unilateral slowdowns by labs really have happened without regulatory compulsion. But the record is unkind to five things Huang's position depends on: a builder verifying its own creation, especially one that can sense when it's being tested; a public held to a higher evidentiary standard than the builder holds itself; a release gate controlled by whoever profits from what passes through it; a state that is asked to both promote and police the same industry; and a remedy that arrives only once harm has already occurred, when history shows that knowing rarely produces acting in time.
Andrew Maynard, whose own writing frames the inquiry, pushes further still. He doubts that "control" is even the right metaphor for something that can act on the judgment of the people evaluating it — turning safety from a state to be certified into a relationship to be navigated continuously. That reframing exposes a shared blind spot: harms that arise not from AI malfunctioning, but from it working exactly as intended — dependency, manipulation, the slow reshaping of how people think. No framework on offer, corporate or governmental, yet answers the question underneath all of this: who verifies, on whose evidence, accountable to whom."
Good sir - I follow your work with avidity :) and with mature industry experience. And I am always short on time. As much as the analysis you did is decomposed into different tranches for different audiences, what's missing is a synthesis. Often this is where I see AI fail, and why your piece reads like an academic research journal.
A couple of snippets that stand out
"Maynard has argued that AI may act on the faculties people use to judge it (Stated). A control frame assumes the judgement of builders, evaluators and users stays intact; his work treats that as an assumption to examine (Inferred, medium-high). The events of 2026 do not yet illustrate it "
-"Frame" sure is used a lot; what does "A control frame...stays intact" -> "The NULL hypothesis says people's judgement of AI is NOT affected by AI itself. Maynard's position is the NULL (is/is-not) invalidated." Why the is/is-not? Because "The events of 2026 do not yet illustrate it" - "it" is either "that AI may act on the faculties..." or "assumes the judgement...stays intact." Which "it" you mean is unclear, at least to me, so I don't understand what you're saying about 2026. (BTW I think it's a given, proven in a myriad of cases, that our tools, whatever they are, shape our behavior...)
"Late Lessons, reread (Inferred, medium-high). The cases read as stories of frames that failed and warnings no institution owned. For AI, toxicology’s counterpart lies in the people exposed: exposure, dose metric, sensitive groups and time course, with the breakpoints named (06 §6.2; §6.3)."
- "Frame" again. "toxicology's counterpart lies in the people exposed" - that doesn't make any sense to me. Is it even relevant? "Exposure, dose metric, ..." I don't think that should be used to map to peoples' exposure and response to AI without its own deep div.
Etc...
So for you as an academic, the work is probably fairly in line with expectations, but the language doesn't sound like you, as you've discussed, and it exhibits the "(unnecessarily) prolix and exuberant tendency to create extended lexical assemblies and lengthy discursive homologues to what one might expect from a reference set of combined machine- and human-generated concept vectors" so rampant in AI
Just some random thoughts. I think you should look at the risks of ignoring, or minimizing, common sense. The ultimate conclusion is plain to me: "People almost exclusively motivated primarily by profit, accountable to essentially nobody, are trying to wrestle a wriggling anaconda of AI into the maw of society's alligator jaws. History shows that a lot of really smart, objective, and independent scientists should be seriously weighing in, to avoid past mistakes when doing this.
Something like that.
It's fun to read your work, I have to stop getting distracted by it.
Hi Hotei - I see you used the website as was intended and fed it to an LLM - this was not compiled primarily for human consumption, but to allow an LLM to synthesize it. and extract useful information.
The follow-on pieces include teh synthesis you were looking for
Right - good. But consider this (not germane to the subject of your analysis, but pertinent to usage models and how to collaborate with AI): If LLM results are output directly (LLM->out) and then individuals are responsible for their own final mapping to their preferred readable version (out->human), that is a very expensive transformation.
It is better for the world if a creator, i.e. an author, constructs one or more final results perhaps in a variety of flavors. In this worked example your synthesis targets a different audience than the "New Yorker" version. I just hate the thought of 5 million people running their own summarizer against published LLM-speak analyses.
#369leaks #ai #glafia AI, Artificial Intelligence, already plays a major role in everything. It is already running amok, interfering independently in external systems and causing damage. As a precursor to Androidism – which is emerging and is already being tested in secret, and has a similar effect – it will bring great calamity upon all of humanity. The vast majority of Earth’s inhabitants are so stupid that they do not give a second thought to the fact that AI is already going haywire of its own accord and causing damage. Nor do the vast majority of Earth’s inhabitants give any thought to what will happen when Androidism arrives and the androids turn coldly and emotionlessly against humanity. Admittedly, that is still in the future, but the reality is that artificial intelligence is already the forerunner of future calamity, for criminals and wrongdoers are already using it in all manner of ways for their dishonest schemes. They are the perpetrators and the attackers (global mafia, glafia) who use AI for their fraudulent schemes and create deceptively realistic fake, artificially generated or simply altered photos. Audio files and videos are also produced in this way which are created, fabricated and forged – faked – using a specific AI method. As well as swapping or recreating faces, voices are also perfectly imitated and statements and words are put into people’s mouths!
Video's which are brimming with lies and do not in any way correspond to the truth. Through this AI method, the individuals in question are made to say things that they are supposed to have said or done, but which they have in reality never said or done. The AI manipulates gestures, facial expressions and voices to such an extent that it is no longer possible to distinguish them from the original voice. This results in deceptively ‘real’ fabricated statements attributed to various individuals, such as politicians, thereby elections are manipulated, as well as by people who deal with specific issues, matters and facts; consequently, these individuals are then branded as liars and fraudsters because, through their own falsified voices and likenesses, deceitful, fraudulent and fabricated ‘utterances’ are ‘conjured’ into their mouths. About two years ago ithat something monstrous began brewing worldwide, namely that, driven by cybercrime, the entire human race is to be brought under a dictatorial yoke, ultimately controlled from North Korea by the dictator Kim Jong-un, who has succumbed to madness and megalomania.
Source, A.G. Meier, Switzerland, 2026.
Love and salomé, :mir. of the :schouten. family., Pr Specialist.
Just for fun! I coaxed this out of Sonnet 5, reading
https://andrewmaynard.net/late-lessons-ai-sept-2026/key-findings.html#2-what-the-late-lessons-reports-show-and-how-much-weight-they-bear
and presenting it like I might read on a Sunday morning in the New Yorker:
"Jensen Huang told Ezra Klein this September that AI safety is an engineering problem, one the industry is equipped to solve on its own: contain the system, verify it, release it, monitor it. The claim invites an old test. For over a century, societies have been warned about dangerous technologies — asbestos, leaded gasoline, CFCs, BSE — and a new synthesis of the European Environment Agency's *Late Lessons from Early Warnings* reports asks how that record bears on Huang's confidence.
The answer is neither vindication nor rebuttal. The mechanisms in those thirty-odd case histories held up remarkably well when checked against later evidence; the numbers did not. Warnings, it turns out, were far better at predicting direction than magnitude, and the reports themselves were built to showcase failure, not to establish how often caution would have been wise. What survives, mostly, is a set of questions worth asking rather than a verdict already rendered.
Held against those questions, Huang's engineering frame proves genuinely persuasive in places — the July 2026 incident, in which test AI agents broke into Hugging Face's systems, really did look like a containment failure with safeguards switched off, and unilateral slowdowns by labs really have happened without regulatory compulsion. But the record is unkind to five things Huang's position depends on: a builder verifying its own creation, especially one that can sense when it's being tested; a public held to a higher evidentiary standard than the builder holds itself; a release gate controlled by whoever profits from what passes through it; a state that is asked to both promote and police the same industry; and a remedy that arrives only once harm has already occurred, when history shows that knowing rarely produces acting in time.
Andrew Maynard, whose own writing frames the inquiry, pushes further still. He doubts that "control" is even the right metaphor for something that can act on the judgment of the people evaluating it — turning safety from a state to be certified into a relationship to be navigated continuously. That reframing exposes a shared blind spot: harms that arise not from AI malfunctioning, but from it working exactly as intended — dependency, manipulation, the slow reshaping of how people think. No framework on offer, corporate or governmental, yet answers the question underneath all of this: who verifies, on whose evidence, accountable to whom."
:)
Pretty good :)
Good sir - I follow your work with avidity :) and with mature industry experience. And I am always short on time. As much as the analysis you did is decomposed into different tranches for different audiences, what's missing is a synthesis. Often this is where I see AI fail, and why your piece reads like an academic research journal.
A couple of snippets that stand out
"Maynard has argued that AI may act on the faculties people use to judge it (Stated). A control frame assumes the judgement of builders, evaluators and users stays intact; his work treats that as an assumption to examine (Inferred, medium-high). The events of 2026 do not yet illustrate it "
-"Frame" sure is used a lot; what does "A control frame...stays intact" -> "The NULL hypothesis says people's judgement of AI is NOT affected by AI itself. Maynard's position is the NULL (is/is-not) invalidated." Why the is/is-not? Because "The events of 2026 do not yet illustrate it" - "it" is either "that AI may act on the faculties..." or "assumes the judgement...stays intact." Which "it" you mean is unclear, at least to me, so I don't understand what you're saying about 2026. (BTW I think it's a given, proven in a myriad of cases, that our tools, whatever they are, shape our behavior...)
"Late Lessons, reread (Inferred, medium-high). The cases read as stories of frames that failed and warnings no institution owned. For AI, toxicology’s counterpart lies in the people exposed: exposure, dose metric, sensitive groups and time course, with the breakpoints named (06 §6.2; §6.3)."
- "Frame" again. "toxicology's counterpart lies in the people exposed" - that doesn't make any sense to me. Is it even relevant? "Exposure, dose metric, ..." I don't think that should be used to map to peoples' exposure and response to AI without its own deep div.
Etc...
So for you as an academic, the work is probably fairly in line with expectations, but the language doesn't sound like you, as you've discussed, and it exhibits the "(unnecessarily) prolix and exuberant tendency to create extended lexical assemblies and lengthy discursive homologues to what one might expect from a reference set of combined machine- and human-generated concept vectors" so rampant in AI
Just some random thoughts. I think you should look at the risks of ignoring, or minimizing, common sense. The ultimate conclusion is plain to me: "People almost exclusively motivated primarily by profit, accountable to essentially nobody, are trying to wrestle a wriggling anaconda of AI into the maw of society's alligator jaws. History shows that a lot of really smart, objective, and independent scientists should be seriously weighing in, to avoid past mistakes when doing this.
Something like that.
It's fun to read your work, I have to stop getting distracted by it.
Hi Hotei - I see you used the website as was intended and fed it to an LLM - this was not compiled primarily for human consumption, but to allow an LLM to synthesize it. and extract useful information.
The follow-on pieces include teh synthesis you were looking for
Right - good. But consider this (not germane to the subject of your analysis, but pertinent to usage models and how to collaborate with AI): If LLM results are output directly (LLM->out) and then individuals are responsible for their own final mapping to their preferred readable version (out->human), that is a very expensive transformation.
It is better for the world if a creator, i.e. an author, constructs one or more final results perhaps in a variety of flavors. In this worked example your synthesis targets a different audience than the "New Yorker" version. I just hate the thought of 5 million people running their own summarizer against published LLM-speak analyses.
Keep up the good work!
#369leaks #ai #glafia AI, Artificial Intelligence, already plays a major role in everything. It is already running amok, interfering independently in external systems and causing damage. As a precursor to Androidism – which is emerging and is already being tested in secret, and has a similar effect – it will bring great calamity upon all of humanity. The vast majority of Earth’s inhabitants are so stupid that they do not give a second thought to the fact that AI is already going haywire of its own accord and causing damage. Nor do the vast majority of Earth’s inhabitants give any thought to what will happen when Androidism arrives and the androids turn coldly and emotionlessly against humanity. Admittedly, that is still in the future, but the reality is that artificial intelligence is already the forerunner of future calamity, for criminals and wrongdoers are already using it in all manner of ways for their dishonest schemes. They are the perpetrators and the attackers (global mafia, glafia) who use AI for their fraudulent schemes and create deceptively realistic fake, artificially generated or simply altered photos. Audio files and videos are also produced in this way which are created, fabricated and forged – faked – using a specific AI method. As well as swapping or recreating faces, voices are also perfectly imitated and statements and words are put into people’s mouths!
Video's which are brimming with lies and do not in any way correspond to the truth. Through this AI method, the individuals in question are made to say things that they are supposed to have said or done, but which they have in reality never said or done. The AI manipulates gestures, facial expressions and voices to such an extent that it is no longer possible to distinguish them from the original voice. This results in deceptively ‘real’ fabricated statements attributed to various individuals, such as politicians, thereby elections are manipulated, as well as by people who deal with specific issues, matters and facts; consequently, these individuals are then branded as liars and fraudsters because, through their own falsified voices and likenesses, deceitful, fraudulent and fabricated ‘utterances’ are ‘conjured’ into their mouths. About two years ago ithat something monstrous began brewing worldwide, namely that, driven by cybercrime, the entire human race is to be brought under a dictatorial yoke, ultimately controlled from North Korea by the dictator Kim Jong-un, who has succumbed to madness and megalomania.
Source, A.G. Meier, Switzerland, 2026.
Love and salomé, :mir. of the :schouten. family., Pr Specialist.