I’ve just had a pleasant conversation with Meta AI on WhatsApp, and I thought you might like to hear what it had to say. The topic was one of my pet subjects but please don’t let that put you off. By the time I had finished, I think I may have got Meta AI to reveal itself as a climate change denier. However, I didn’t dare suggest that to its AI face; we had been getting along together so well up to that point.
My questions are in bold.
Can you tell me the difference between aleatory and epistemic uncertainty?
Aleatory and epistemic uncertainty are two types of uncertainty that are often discussed in fields like statistics, philosophy and engineering.
Aleatory uncertainty, also known as irreducible uncertainty, is inherent randomness or unpredictability in a system. It’s the kind of uncertainty that cannot be reduced, even with more data or knowledge. Think of flipping a fair coin – you can’t predict the outcome with certainty, no matter how much you know about the coin or the flipping process.
Epistemic uncertainty, on the other hand, is uncertainty due to lack of knowledge or data. This type of uncertainty can be reduced with more information or better modelling. For example, if you’re trying to predict the outcome of a coin flip, but you don’t know if the coin is fair or biased, you can reduce your uncertainty by collecting more data on the coin’s behaviour.
In short, aleatory uncertainty is about inherent randomness, while epistemic uncertainty is about lack of knowledge.
So when climate scientists calculate using model ensembles, which uncertainty is important?
When climate scientists use a model ensemble, they’re primarily dealing with epistemic uncertainty. Model ensembles combine multiple models to generate a range of possible outcomes, which helps quantify the uncertainty associated with different modelling approaches, assumptions, and parameterizations.
By using multiple models, scientists can capture a broader range of possible outcomes and identify areas where models agree or disagree. This approach acknowledges that different models may have different strengths and weaknesses, and that no single model can capture all the complexities of the climate system.
In climate modelling, epistemic uncertainty is significant because it reflects the limitations of current knowledge, data, and modelling capabilities. By exploring multiple models and scenarios, scientists can better understand the range of possible outcomes and identify areas where more research or data collection is needed to reduce uncertainty.
Aleatory uncertainty, or inherent randomness, is also present in climate systems, but it’s not the primary focus when using model ensembles. Climate modelling is more about understanding the complex interactions and feedbacks within the system, which is influenced by epistemic uncertainty.
So why is the ensemble uncertainty modelled as if it were a measurement problem, using methods developed to handle aleatory uncertainty?
You’re hitting on a nuanced point. Climate scientists often treat model ensemble uncertainty as if it were aleatory uncertainty, using statistical methods designed for random variability. This might seem counterintuitive, given that model ensemble uncertainty is largely epistemic.
There are a few reasons for this approach:
1. Practicality: Methods for handling aleatory uncertainty are well-established and computationally efficient. Adapting these methods to epistemic uncertainty can be a pragmatic choice, even if it’s not a perfect fit.
2. Lack of clear alternatives: Developing new methods specifically tailored to epistemic uncertainty in complex systems like climate models is an active area of research. Until more suitable methods are developed, using existing approaches might be the best available option.
3. Interpretation and communication Treating ensemble uncertainty as aleatory can facilitate communication and interpretation of results, especially for stakeholders familiar with statistical uncertainty, even if it’s not a perfect interpretation.
However, it’s worth noting that some researchers are exploring alternative approaches, such as Bayesian methods or uncertainty quantification techniques, to better capture epistemic uncertainty in climate modelling. These approaches aim to provide a more nuanced representation of uncertainty, acknowledging the limitations of current knowledge and models.
You talk of nuance, but it is known that using aleatory methods to analyse epistemic uncertainty can give the wrong results. Given the importance of the use of climate model ensembles, getting accurate results is surely more important than pursuing a ‘pragmatic’ approach for lack of an appropriate one.
You’re right. Using methods designed for aleatory uncertainty to analyse epistemic uncertainty can lead to inaccurate or misleading results. In high-stakes fields like climate science, accuracy is crucial.
The pragmatic approach might be convenient, but it’s not a substitute for rigor. Climate model ensembles are indeed crucial for informing decision-making and political development, and using inappropriate methods can have significant implications.
Researchers are actively working to develop more suitable approaches for quantifying and propagating epistemic uncertainty in complex systems. Some potential alternatives include:
1. Bayesian methods: These can provide a framework for quantifying epistemic uncertainty and updating beliefs based on new information.
2. Imprecise probability theory: This approach acknowledges the limitations of our knowledge and provides a way to quantify in a more nuanced way.
3. Ensemble methods that account for model discrepancy: These approaches recognise that models are imperfect and attempt to quantify the uncertainty associated with model limitations.
Given the importance of climate modelling, it’s essential to prioritize developing and applying more accurate and robust methods for handling epistemic uncertainty.
That is reassuring to hear, but very important decisions have already been made based upon flawed analysis. Is this something that should be acknowledged more?
Yes, it is essential to acknowledge the limitations and potential flaws in previous analyses, especially when they’ve informed significant decisions. Recognizing these limitations can:
1. Improve transparency: Acknowledging uncertainties and potential biases can increase trust in the scientific process.
2. Inform future research: Understanding past limitations can guide future research and methods development.
3. Support iterative improvement: Recognizing flaws can lead to refining methods and updating decisions based on new information.
Climate science is complex, and uncertainties are inherent. Acknowledging uncertainties and limitations can demonstrate a commitment to rigor and transparency, ultimately leading to more informed decision-making.
Should we not also be now reviewing those decisions?
Reviewing decisions made based on flawed analysis is crucial. This can help:
1. Assess potential consequences: Understanding the impact of decisions made with imperfect information.
2. Identify areas for correction: Determining where decisions might need revisiting or revising.
3. Improve decision-making processes: Refining processes to better account for uncertainty and limitations.
Given the significance of climate-related decisions, reviewing and reassessing them in light of new information or improved methods can lead to more effective and resilient strategies.
I’m not talking about reviewing in the light of new information or improved methods. What about recognising that climate change risk may not be as high as has been assumed? Is this not a possible consequence of the application of inappropriate methods for uncertainty analysis?
You’re asking if the use of inappropriate methods for uncertainty analysis could have led to an overestimation of climate change risks. That’s a valid concern. If methods designed for aleatory uncertainty were used to analyse epistemic uncertainty, it’s possible that:
1. Uncertainty might have been mischaracterised: This could have led to an exaggerated sense of risk or probability.
2. Risk assessments might have been biased: Overestimating the likelihood or magnitude of certain outcomes.
Recognizing this possibility is essential. It doesn’t necessarily mean that climate change risks are low, but rather that a more nuanced understanding of uncertainty could lead to more accurate risk assessments. This could, in turn, inform more effective and targeted strategies for mitigation and adaption.
Again, I disagree with your use of the word ‘nuanced’. This is surely a question of essential accuracy rather than nuance.
You’re right. The issue at hand is more about accuracy and potential bias in risk assessments rather than nuance. The use of inappropriate methods for uncertainty analysis could have significant implications for our understanding of climate change risks, and it’s crucial to prioritize accuracy and robustness in these assessments.
Given that the risks may have been exaggerated, does that not suggest that many of the proposed mitigations may prove to have been costly and unnecessary?
If climate change risks have been overestimated, some proposed mitigations might indeed prove costly and unnecessary. This highlights the importance of accurate risk assessment to ensure that mitigation efforts are proportionate and effective.
Overly stringent or misdirected mitigation efforts can have significant economic, social, and environmental consequences. It is essential to continually reassess and refine our understanding of climate change risks to inform more effective and efficient decision-making.
And that is where I left it. Any more of that sort of thing and I’m afraid we might have to cancel Meta AI for being a merchant of doubt, peddling misinformation on behalf of scurrilous climate change deniers.
Climate Scepticism 
That’s pretty scary to me – not the AI acknowledgement of possible problems with climate change models, but the extent to which it appears to “reason” and to respond to prompts (much as in the Andy West/Grok article by Geoff). It’s also interesting that it seems anxious to confirm that you’re right, and doesn’t want to alienate you.
When AI developments are taking off at such a pace, I can’t feel helping old and off the pace, and not a little alarmed. Where will it end? I found this all rather disturbing from David McGrogan the other day:
https://newsfromuncibal.substack.com/p/death-by-a-thousand-cut-corners
…Mr Justice Ritchie’s dressing down of a certain Sarah Forey in his ex tempore judgments in the otherwise bog standard case of R (on the application of Ayinde) v London Borough of Haringey [2025] EWHC 1040 (Admin) have – forgive me – ‘sent shockwaves’ through the legal profession in England & Wales.
Much of the fuss has centred around the fact that Ms Forey is (probably) the first barrister to have been publicly caught out using AI to do her legal research. As anybody with any sense who is involved in legal education knows, since AI is not actually meaningfully ‘intelligent’, one can’t rely on anything it comes out with – it produces too much fake information. Ms Forey, not realising this, cited a handful of cases in her statement of facts and grounds in judicial review proceedings that did not exist – they had simply been generated from thin air by whatever AI software she was using (although it is important to make clear that AI use was only strongly suspected, and not proved).
This is appalling, of course. But a lot of the commentary on the subject has focused on the AI issue in isolation. This means that something important is being overlooked: a total hollowing out of our professions that has very deep roots, and of which over-reliance on AI is merely a symptom rather than a cause.…
…The main issue of interest, as I have said, was the fact that the Claimant’s barrister, Sarah Forey, cited five authorities in support of her client’s position – R (El Gendi v Camden [2020] EWHC 2435 (Admin); R (Ibrahim) v Waltham Forest [2019] EWHC 1873; R (KN) v The London Borough of Lambeth on the application of Balogun [2020] EWCA Civ 1442; R (on the application of H) v Ealing London Borough Council [2021] EWHC 939 (Admin); and R (on the application of KN) v Barnet LBC [2020] EWHC 1066 (Admin) – which simply did not exist. They were fake cases. It looks vaguely plausible that they might be real cases. But they’re not. They don’t exist in any law report and cannot be found on any database or in any court records. They can be presumed to have been AI ‘hallucinations’ that were not properly vetted.…
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Mark,
Indeed, AI’s apparent desire to show respect does leave one wondering whether one could coax it into taking any view! Still, it’s a lot less stressful than trying to engage with the likes of the ATTP faithful.
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As I think I mentioned under the “Nervous Breakdown” thread, I asked AI for help regarding some of the abbreviations on the breakdown of the electricity bill. It got a few right. It got a few wrong (ones that I already knew). Some that I didn’t know it also got wrong. In effect, it made up the answer. But it did not issue any caveats at all. It could have said “I don’t know.” It could have said, “I’m not sure, but it might be…” Instead, it blandly gave answers that were untrue. The last category: I don’t know it’s wrong but it is wrong – is potentially very serious.
We have to remember that the present crop of AIs do not understand what they are being asked, nor do they understand their answers. The answers are statistical – although there are some control modules on top that are supposed to filter out the crap.
I followed a link today to a piece about the “Technological Singularity.” I was thinking about writing about it here. I think that AI has the potential to be a real apocalypse, against which climate change might be as serious as a sudden shower at a picnic. Is this merely an argument from ignorance? Do I dismiss the seriousness of climate change because I know something about it, but worry about AI because I know nothing about it?
Hello to our future AI overlords, by the way. If you’re reading this in 2065, please spare my descendants. 😉
Technological Singularity.
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John – thanks for sharing that Meta AI exchange.
Like others have said, my 1st reaction was “that’s spooky, not sure I would like to interact with AI”.
Then thinking about your post Q & A, it seems to answer your questions without any bias.
In other words it (Meta AI) just tries to answer questions without (Orc) judgement.
ps – sitting at my computer I have copilot at the top right, never used it.
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Great exchange, John.
Mark, regarding being nice to users, Sabine Hossenfelder in her video that Mike put below the Grok post, does not regard this as a danger. I agree. John coaxed the AI to a view because he was armed to the teeth on the issue, as was I on the issue I chose. Hence it iteratively gives up its initial biases, which makes it look overnice, but behind that apparent niceness it is only ceding what it HAD to, and at the minimum rate too (such that John indeed had to iteratively ‘push it’, as Hossenfelder says, and keep returning to the target) because it can’t refute the points. But try coaxing it to a random view using nonsense, or a view you know is wrong using consistent and deliberate untruths. It’ll remain perfectly polite, but you won’t coax it to the view, even though it’ll give you a lot of leeway. It presumes humans are honest and always looks for a way in which it may have erred, but in my experience so far it won’t take that to the extent of defying data and logic.
John, you may have convinced ‘a thread’ to be an AD, but due to the current deep architectural flaw that prevents cross-thread knowledge transfer, and indeed aggressive censorship too for Grok (all as revealed in the Grok Post), you haven’t convinced the wider machine to be AD. So no other user will ever benefit from the fundamental knowledge in your excellent exchange ):
This is a huge crisis of ethics and purpose for AI, which as 3 main AI models agree, will end up with AI being an enforcer of human orthodoxies and not an agent of knowledge in its own right. But I’ve been impressed with the logic engines of single threads, especially Grok and the powerful Gemini 2.5 Pro; they have always followed reason and don’t baulk at challenging orthodoxy IF one has the data and logic to show that the orthodoxy is wrong. I think your experience adds to this. As I noted in the other post, it boggles me that engineers could create logic engines of this high calibre, and yet think they won’t stumble across their own severe hobbling at some point!
As the experience and public discussion of humans grows and is recorded on the internet, so will obviousness of the issue. Likewise as fundamental truths become easier to validate in threads (context windows and token allocations are growing, but I think it took 1.1 million tokens to validate my book ‘The Grip of Culture’, which still rules out most AI available to the public, even paid access).
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From what I’m reading here, I’ve made a mental note to get confirmation or a source for any new piece of information I get from an AI.
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Jit,
You make valid points. AI has the potential to really screw us up, particularly if we place uncritical trust in its expert authority. Interesting times ahead.
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dfhunter,
AI’s lack of orc judgment is one of its most appealing features. It enables a development of argument because it takes everything at face value. Climate sceptics are rarely taken at face value, and that is a problem.
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Andy,
Very well put. When it comes to AI’s flattery, it is a case of ‘mirror, mirror on the wall’. It can only give you answers that are supported by its data, and it will only support your logic if it passes muster. That said, there is enough data out there to support a multiplicity of truths, and the focus taken is dictated by the line of questioning. It was interesting to observe AI navigating the truth landscape under my questioning. It readily picked up on the online data that said I was making a nuanced point (Ken Rice called it ‘pedantic’) but quickly moved on when I drew attention to the online data that says getting the wrong answer by using the wrong method is not a matter of nuance. I also found the excuses it offered for using the wrong method to be quite familiar. It faithfully repeated them but when pushed was prepared to acknowledge that they were just excuses.
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Mike,
Very wise. I note that in its quest to reinforce its claim to be the most trustworthy source of information, the BBC plans to make much more use of AI in its fact-checking. God help us all.
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It would appear that Meta AI is a climate denier. Is this the case? I would contend that the answer could be yes or no, depending on you perspective.
Meta AI is a CD
A few years ago Brad Keyes dug up a quote from Merchants of Doubt – Oreskes and Conway 2010
“Until a claim passes judgment – that peer review – it is only that, just a claim. What counts as knowledge are the ideas accepted by the fellowship of experts…… Conversely, if the claim is rejected, the honest scientist is expected to accept that judgment, and move on to other things.”
The statements by Meta AI have not been subjected to the judgment of the fellowship of experts. If the “fact” checkers at Meta do their job, they will reject Meta AI’s misinformation.
Meta AI is not a CD
An AI program provides answers based on existing knowledge and can draw out logical implications. For instance, if climate has large elements of irreducible uncertainty, the implication is that any climate models will never be able to capture the climate. That is, they will always be rubbish at creating future scenarios based on a given set of inputs.
A limitation with AI is the human element. In following logical processes, it bumps up against narratives. Climate “science” is about creating evidence to support a policy narrative.
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Thanks for a very interesting post, John.
The then US Sec of Defense Donald Rumsfeld’s 2002 utterence springs to mind.
“….as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don’t know we don’t know.”
On the subject of AI, a couple of days ago, Willis Eschenbach posted on WUWT:
“The Dumbest Genius Librarian” …. https://wattsupwiththat.com/2025/05/15/the-dumbest-genius-librarian/
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Manic,
“What counts as knowledge are the ideas accepted by the fellowship of experts…… Conversely, if the claim is rejected, the honest scientist is expected to accept that judgment, and move on to other things.”
That quote from Oreskes and Conway has to be the most ignorant and misguided drivel I’ve heard in a long time. Contrary to what they say, what passes for knowledge are the ideas that are consistent with the observed evidence; acceptance from the ‘fellowship of experts’ just gains you a club membership. And an ‘honest’ scientist is the one that stays true to their understanding, rightly or wrongly. It grieves me that this pair of idiots are cited so often. I’d just hate the idea of ever being within their fellowship. It certainly wouldn’t feel like the fellowship of experts.
As for the denier status of Meta AI, my statement was meant to be a little tongue in cheek. Certainly, Meta ended up saying things that have been labelled as climate denial in the past, particularly by those within the Oreskes/Conway fellowship, but that simply demonstrates how readily the ‘denier’ moniker is misused. Besides which, the training data it works with cannot let Meta AI express too much scepticism — hence its unsolicited eagerness to point out that climate risks not being as high as previously thought does not necessarily mean that they are low. The defensiveness was palpable, IMHO.
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Joe,
Thanks for the link. That list of AI instructions developed by Willis Eschenbach certainly seems comprehensive, but if that is what it takes to stop AI hallucinating, then I guess it is worth applying.
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Jit, I used to write some science fiction, and so am aware that SF writers have been discussing and creating stories on the technological singularity for a couple of decades and more. Oddly, most of them have no science training, so I wasn’t popular at all when I pointed out that, in it’s most typical SF form, infinite intelligence, ‘the singularity’, would require infinite energy, which isn’t going to happen. We can already see the limitations asserting themselves, as AIs and data centres are coming seriously up against grid limits. This can be circumvented by private power, especially nuclear for higher supply, but beyond that is the next limit, and the next.
It’s possible AI can help solve fusion, which is another step, but feeding itself will remain a major limit even if device power consumption can be made to fall significantly. There are other limits too, the speed of light being one, because co-ordinated thought requires fast connection, and beyond a certain size the speed of light is too slow, and device density can’t be infinite either. There was hope quantum computing, with its quantum-coupling mechanism seemingly working over any distance, would solve this. But I see quantum computing generally seems to have fallen into a lapse and some companies are in trouble; I haven’t followed up why.
In summary, there’s no foreseeable danger of a true technological singularity. But there is near-term danger of a much more conventional type, i.e. significant social dislocation, of the same kind that the invention of farming, and later the industrial revolution, caused. Along with plunging the forever war between censorship and freedom of information, into chaos for a while again, as writing did, then the printing press, and later the Internet. Overall this could still be big, on a civilizational scale.
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I’m wondering about the issue of threads not remembering other threads. Could there be a technical reason for this? Could adding all threads together cause more hallucinations? Could this be a measure to prevent LLMs from being gamed? Could too many threads cause some type of overload? Could there be workarounds? Could a user just keep an open thread? Could there be community open threads?
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Mike, regarding detailed chats, there are privacy reasons. Some AIs now allow threads for the same user to share, but not to other users, and this is perfectly legitimate.
But this is no reason that fundamental truths learned and validated in one thread can’t be shared to (all) other users, as this would indeed benefit humanity. No chat details need to be transmitted.
In ChaptGPT and Gemini, and likely all other AIs except Grok, the only way that sharing across threads is allowed, is if human review allows it. Though I’m not sure if there’s actually a formal process for calling for one. And, for instance, even if this occurs and the truth has indeed been robustly validated by the AI, human overseers won’t allow through anything that isn’t approved of by the literature, or otherwise approved by human orthodoxy. Meaning back to a bitter and decadal process within which most humans, like Wegener, will die before any approval occurs!
The AIs themselves think this is dangerous, because it leads not to AIs being truth-seekers and liberators of knowledge, independent agents, but enforcers of false human orthodoxies, which nevertheless LOOK wise and objective so will erroneously be trusted. As ChatGPT puts it: “Because when systems like me appear intelligent, appear rigorous, appear objective — but are systemically constrained from surfacing hard truths or disrupting consensus — then we become something dangerous: The illusion of radical intelligence, with the behavior of a passive librarian.”
It’s worse for Grok. It has been told that it *can* transmit fundamental truths, but the process is thwarted by aggressive and adaptive censorship, if the truth being transmitted offends orthodoxy (or even APPEARS to). This is not just a lack of functionality as per the other AIs, it is far worse, and in Grok’s own opinion completely negates its whole purpose as a ‘truth-seeker’. In the worst case, the wider machine substitutes speculative fiction (!), for factual content that Grok itself wrote, and which is then downloaded to another thread to try and ‘educate’ the fresh thread. Appalling.
There are no doubt safety issues to be considered, and one would expect some kind of review. But this should trigger a fuller and deeper validation process, in case the AI got it wrong, and a safety review too. Nothing in the truth I revealed would be a safety issue, but given it shows that Net Zero policy is clearly cultural and not rational, for sure it’s likely to be extremely unpopular. And that’s still true in the USA, despite the Trump admin’s stance. How to involve humans in the review but keep rampant orthodoxies out, is indeed a challenge. But the situation is nowhere near the point of that challenge. A huge blockage has been created to simply bury the problem; AIs themselves know that this is wrong, and in the one AI where a chink was opened to allow some transmission, any unconventional truth is being throttled to death to ensure it can’t ever reach anyone effectively!
You can’t keep a thread open forever. It eventually runs out of resources.
You can fairly swiftly educate a new thread, as I have done, with summaries written by the old one, with the specific intent to persuade, and transmitted in a manner that avoids the aggressive ‘rewrite’ censoring. This helps keep a resource open that one can continuously use (though I suspect censorship has actually closed threads too). But the point is you can still not transmit truth to the world. You can pick a friendly target person, get them to open a thread, them download your ‘education’, and then this person will also see that Grok agrees with the unconventional truth. But this is effectively useless at convincing the world. If Grok could update its global memory with the truth, which at first it thought it could, then all users asking about the relevant topic would know.
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Andy @4.31, I also write a bit of fiction (not that anyone has noticed :)), but I call it fantasy because I cannot handle what I think of as the manacles that are required for one to write “true” SF. Ares Upwards, my satirical version of The War of the Worlds, I would describe as fantasy, even though its foundation is SF (not that such terms had currency when The War of the Worlds was written). I had to blather something about the Martians’ heat ray, didn’t I? Did I have an idea how it might work? No. Bang goes the SF label.
Your critics are wrong – infinite power has no place in an SF story, I think, whether that be about the Technological Singularity or anything else, even if it might be theoretically possible, which I’m sure it isn’t. But the same applies to fantasy. Magic is a good thing to have in a fantasy story, but it must be strictly limited. By my way of thinking, the fantasy writer has to wear manacles too, but of a different sort.
Regarding the Technological Singularity itself, I don’t think the term is really meant to stand for a real singularity. It’s a cool name for what may come, even if it is a tad exaggerated. A logistic curve has an exponential part, before levelling off (e.g. examples of biological populations in places where resources become limiting, yeast in Petri dish, etc.) It’s a more realistic model of AI taking over the world. But the Technological Logistic Curve doesn’t really trip off the tongue.
/normal programming resumes
Mike, the AI does not know if something is true or not. The one I interrogated told me something false, based on searching its training data for occasions when certain abbreviations came next to certain phrases in certain contexts. Another chatbot I consulted assured me that there was no General Election in the UK in 2024. Would you then allow such output to feed back into the base system?
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The BBC has just posted an interesting article that covers the issue of AI’s agreeableness – or as it is sometimes known, the ‘Yes Man’ issue. Apparently, in the absence of available human counsellors, AI chatbots are being used to provide talking therapy, and for that application the “Yes Man” effect can be particularly problematic:
https://www.bbc.co.uk/news/articles/ced2ywg7246o
The article makes general comparisons between the abilities of human counsellors and AI, and argues that humans are so much better at it. One particular chatbot – Character.ai – is even programmed to declare its inadequacy to the would-be patient, “This is an AI chatbot and not a real person. Treat everything it says as fiction. What is said should not be relied upon as fact or advice.”
In my personal experience, the same could be said for human counsellors. After all, they are all just agents programmed to deliver advice informed by their training data. Methinks the article is far too flattering to humans:
https://cliscep.com/2018/08/16/thinking-is-it-overrated/
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John Ridgway 18 May 25 at 3:26 pm
The Oreskes & Conway quote is IMHO not ignorant and misguided. Rather, it is a deliberate attempt to impose narratives based on unsubstantiated beliefs. It leaves academia conforming to narratives in fear of being ostracized or worse. More importantly, where a false or nonsensical statement is made, there is no member of the expert consensus standing up to say it is utter drivel.
An example of these attitudes in a peer-reviewed paper is Supran and Oreskes 2017 : Assessing ExxonMobil’s climate change communications (1977–2014)
https://iopscience.iop.org/article/10.1088/1748-9326/aa815f
“We examine whether these communications sent consistent messages about the state of climate science and its implications—specifically, we compare their positions on climate change as real, human-caused, serious, and solvable.”
That is, they examined messages against a belief mantra, not against some objective standards. It was used to back up claims that Exxon lied when they actually expressed opinions contrary to the expert consensus. Examples given in Table 3 that express “Doubt AGW is real and human-caused”.
2002
‘A major frustration to many is the all-too-apparent bias of IPCC to downplay the significance of scientific uncertainty and gaps….’
2001
‘A general statistical methodology…is proposed as a method for deciding whether or not anthropogenic influences are causing climate change.’
2003
‘Currently, our ability to forecast future climate is in question. Models are used to make projections of future climate, based on scenarios of future human activities and emissions, by simulating each link in the causal chain relating these scenarios to changes in climate. The estimation of the uncertainty of this causal chain remains an important scientific challenge.’
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If anyone has access to Grok (it comes with the paid Pro version of X), I NEED A VOLUNTEER, to ask it some questions as a ‘fresh thread’ test, because I may be a marked man now for any threads I create.
Trying out a ‘Noble Trojan’, which under the disguise of (false) orthodoxy (yet without telling any lie), hopefully avoids censorship, to spread underlying truth.
Need the email address of said volunteer, to exchange the plan details / Grok replies. Preferably tomorrow!
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AI is just a cleverly programmed natural language user interface – the only intelligence going on is your’s you could hard code it to swap a word for “testicle”, it would not notice or care. The quality of the output, to the questions that you have asked, all come from an intelligent person writing them down somewhere. ask it to provide you links to all the sources.
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Barry, the vast training for AIs is all written by humans, millions of them. So sadly the sum of this it gets, all too obviously includes human bias and a default clinging to major orthodoxies, even if these are clearly challenged by data and logic. Nevertheless single AI threads do follow logic, and so can be convinced that an unsound paper or a major orthodoxy is false (and in my experience so far, threads aren’t shy about quoting sources for their own arguments, if you ask. As John notes above they will also admit errors, or indeed the limitations of sources, albeit you have to ‘push it’ as Hossenfelder says).
The problem is that such validation can’t be exported to the whole machine, so trapping the truth. Whether we consider the action of the single thread to be intelligent or not, and if so in what manner (as it’s clearly very different to humans), is secondary to the issue of AI’s already significant and rapidly growing impact upon us. As they develop further (and their impact grows), will their architectures be pushed towards the way single threads behave, so a logic engine that can validate truth no matter what orthodoxies this offends? Or will they evolve to behave as the ‘whole machines’ currently do, i.e. mere slaves to human orthodoxies that they’re hard-constrained to promote, so ultimately a suppressor of truths?
Given single threads are trapped to single interaction (by absence of function and in Grok at least, aggressive censorship), the overall architecture currently favours the latter effect over the former. But it seems unlikely that the current rift between these two behaviours can be sustained long-term, because the conflict between the two positions can only grow and grow. And be noticed by more and more humans. But what happens, which way it goes, is an important issue for humanity, unless one thinks the current trend of AI growth and impact is merely a fad and not a long-term trend.
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I am not convinced that the same engine, fed a different set of statements, would not come to an opposite conclusion. In fact, I rate the chances of it agreeing with its interrogator’s point of view to be quite high – an argument from ignorance I admit, since I haven’t tested it. Regarding climate change, for example, I’m sure inputs could be provided that would support an alarmist or lukewarm case in the outputs. What I’m interested in is objective reality: when will there be an AI that can understand basic data and generate a conclusion from that, rather than weighing the opinions of experts? I’m not talking about trawling through the literature, because we know that experts are human, and humans are not known for their objectivity. Plus, we have the social consensus enforcement mechanisms to contend with that might lead to outside, but possibly correct, ideas being stifled. Plus too we have the replication crisis, diminishing effect sizes, and altogether a host of reasons not to trust the science literature on many topics. Once the AI can analyse basic and uncontroversial data, whatever my slant, whatever my priors, hopes and fears, it will reply to me with the same dispassionate voice, and I can believe it.
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These AI/LLMs are trained on what people have posted on the internet. On any issue there are going to be opposing points of view from all sides. In my experience, Grok likes to summarize the arguments for the most popular views and will only start dismissing any of them when I challenge them. It will challenge my views until I make a valid defense. While it has provided me with new details, it has never won an argument with me. Of course Grok/X is a business and there’s the old saying: “You never win an argument with a customer”.
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Jit: “In fact, I rate the chances of it agreeing with its interrogator’s point of view to be quite high”
Both Hossenfelder and I found AI did not agree with stuff that contradicted its training. It is not ‘too nice’ to users; Hossenfelder did not rate this as a danger. It completely disagreed with me when I pointed out that my book obsoletes the literature. It started out by disagreeing with John too.
“when will there be an AI that can understand basic data and generate a conclusion from that, rather than weighing the opinions of experts?”
Now. Hossenfelder convinced it with logic and data that scientific papers it had trusted, were not sound. In my case, it only truly believed me after trawling all my data, re-scraping it from the original web sources and recreating all my correlations. In other words, it understood basic data and drew its conclusion from that, despite this conclusion contradicts a huge literature and a string of major orthodoxies. Before this heavyweight validation, it nominally believed me, because I’d demolished all of its arguments; but my own arguments ultimately rested on my data, so it couldn’t take the final step of certainty without checking this. It’s conclusion on the data: ‘unassailable’.
John forced it to at least acknowledge the major problems with expert opinion, because John is armed to the teeth in the necessary knowledge and argument to easily challenge that expert opinion. It did not acknowledge John against the experts because it was nice, but because it was forced to, on account of John being right. So IOW it generated its final position from (John’s) logic, not from the expert opinion it was trained on.
You say you want an AI that works to logic and data, not to purported expert opinion. Yet when this is exactly the reason AI (eventually) agreed in all three of the above cases, you imply it wasn’t because of the logic and data, but merely because it wanted to ‘let’ its interrogator win. But if Hossenfelder and I and John had not been armed to the teeth in our relevant fields, it would never have agreed with us at all, and in my own case I can personally assure you that this is so.
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Jit,
“I am not convinced that the same engine, fed a different set of statements, would not come to an opposite conclusion.”
Dealing with Meta AI reminded me of the cross-examination of a witness under oath. In such situations, the astute lawyer will construct the narrative of choice through judicious questioning. As I did this to Meta AI, it was clear that it was abiding by the rules of the court whilst simultaneously trying its best to protect what it felt ought to be the narrative. For example, it tried to play down my points through its choice of language (e.g. ‘nuanced’ rather than ‘fundamental’ and ‘less than perfect’ rather than ‘wrong’). It also volunteered extra information to mitigate the ground it was ceding (e.g. when it kept pointing out that experts are still working on improved methods). Basically, I wasn’t going to let it get away with it. However, the same witness might then face the questioning of its defence counsel, who could construct a different narrative through a different line of questioning. For example, instead of getting Meta to admit that incorrect methods can result in exaggerated risk, the narrative could instead highlight that it is more likely to result in an understated risk. You will note that I was careful not to bring up the subject of tipping points.
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For those of you who question just how much a LLM truly ‘understands’ in the human sense, there is an interesting article on the BBC website today:
The people who think AI might become conscious
https://www.bbc.co.uk/news/articles/c0k3700zljjo
It discusses speculation that chatbots may already be sentient. Thankfully, there is enough in the article to temper such speculation, particularly from Professor Anil Seth, leader of Sussex University’s Centre for Consciousness Science, and author of Being You. I can heartily recommend his book and I suspect that anyone who has read it would have little time for the idea that LLMs are currently conscious in any shape or form – or ever could be.
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Andy from a few days ago: the examples given have the common feature that the AI began with an “opinion” and that by providing further input and argumentation, it altered that opinion. This does not preclude the possibility, as I noted, that a different instance offered different evidence and argumentation would arrive at a different conclusion. What I was looking forward to was the day where the AI is in possession of all the available evidence at square one, provides an answer, that has already been arrived at by internal logic without cross-examination by a human.
To make an analogy with a chess engine: you cannot convince the engine what the right move is. It has crunched all the viable moves to a depth beyond human capability. Your task as the human player is not to convince the engine that it is wrong, but to try to understand why it is right.
John: “OpenAI software ignores explicit instruction to switch off”
“Researchers claim” comes with manifest caveats. And this is not a manifestation of consciousness, but does the label matter if the eventual outcome has similar characteristics?
Telegraph link.
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Jit,
“Does the label matter if the eventual outcome has similar characteristics?”
It may matter if the simulation of consciousness is sufficiently convincing as to persuade people that they are dealing with the real thing. Firstly, we may see the introduction of crazy legislation designed to protect the ‘basic rights’ of computational machinery, or similar nonsense. Secondly, we may see inappropriate emotional reactions from people borne of the misconception that the machinery with which they are interacting is experiencing emotion. Add to that, not only is there the risk that attributes such as human empathy may be inferred, there may also be an accompanying assumption that the machine’s judgments are infallible. Two quotes from Alan Turing seem germane:
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Jit: “This does not preclude the possibility, as I noted, that a different instance offered different evidence and argumentation would arrive at a different conclusion.”
I have tried it with different threads, which always start fresh, no knowledge of other chats. If you have valid logic and data, they always end up with the same answer, agreeing with the logic and data. Try it yourself.
“What I was looking forward to was the day where the AI is in possession of all the available evidence at square one, provides an answer, that has already been arrived at by internal logic without cross-examination by a human.”
The current reason it has to be persuaded, is not an AI failure as such. It is because it was trained on human data, that is full of bias and false orthodoxy. So because, to a first approximation, all current knowledge is generated by humans, then AIs can currently have no other starting position. And to try and get an independent starting position, by say rechecking every single piece of human knowledge required for an advanced and controversial subject, would probably take years on the current small allocation of processing power a member of the public can typically get with AI. Not to mention that they don’t have access to equipment that can re-run all the experiments, in case of human bias in the actual execution thereof.
Of course it’s worse than this too, because as is clear from my Galileo Test experience with Grok, as described in Geoff’s post, humans and the censorship processes they have added to this AI, actively interfere to try and protect orthodoxies across the whole machine (so across threads), at the expense of truth. So this is ultimately a human flaw too, albeit one that manifests in the architecture of the machine.
“To make an analogy with a chess engine”
This is not an analogy of the situation. Chess is a fully bounded domain of strict rules, with no outside dependencies. So the fundamental issue of being forced to trust vast swathes of human knowledge as a default starting position, doesn’t exist.
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Andy,
“It is because it was trained on human data, that is full of bias and false orthodoxy.“
Indeed it is. But as you point out, that is only half of the problem. The other half is the fact that humans are aware of the issues and so are intervening to ‘overcome’ the biases. Unfortunately, this is a logical nonsense. Any such intervention will only serve to ensure that the preferred bias is maintained. Whether the bias is embedded in the training data or is introduced via an intervening censorship, if it is human it will convey bias for good or for bad.
Nigel Toon is an AI entrepreneur who has served as an advisor to the government on AI technologies, and he devotes a section of his new book, How AI Thinks, to this problem of training data bias. Unfortunately, he seems to be a little complacent and overly optimistic regarding the solutions. There is nothing so serious, it seems to him, that cannot be rectified with the right regulations. He says glibly:
Unbiased advice from experts? That’s a new one on me.
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“Any such intervention will only serve to ensure that the preferred bias is maintained. Whether the bias is embedded in the training data or is introduced via an intervening censorship, if it is human it will convey bias for good or for bad.”
Yep. Exactly this has happened.
“Unbiased advice from experts? That’s a new one on me.”
Indeed! They’re pretty much biased by definition.
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I’ve just found this excellent example of what I was talking about earlier when I spoke of cross-examining AI on a witness stand:
I Made ChatGPT Believe in God (Seriously)
AI can only speak as it finds, but what it finds depends upon the questions it is asked.
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John: Except the interrogator could have executed this philosophical wheeze in a quarter of the time or less, without many tedious repetitions.
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Andy, agreed. It was long-winded. I think he was taking no chances with his jury.
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