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Essay

Good AI / Bad AI #5

Another conversation with ChatGPT

Long post!

It will have been apparent if you have been with me through my five posts about Artificial Intelligence, that I have some concerns. My last conversation with ChatGPT led to the following further conversation. Which I think is instructive… although we must remember that the answers are based on the probability that they will match my concerns. I began…

Curated Lines said: In a recent conversation with you, you ended up by admitting that your answers had been “misleading” and “a bit of a stretch”. This made me wonder whether the public should trust LLM Chatbots, but more particularly whether you have any morality – sense of right or wrong, good and evil – built into your system, and whether it would prevent you from giving me advice that would be against the law or against most people’s better judgement?

ChatGPT said:

That’s a great question, and I appreciate your skepticism. Trust in AI, including chatbots like me, should be earned through reliability, transparency, and ethical behavior.

To answer your main point: Yes, I do have guardrails based on widely accepted ethical and legal principles. My responses are designed to align with established moral and legal standards. That means I won’t provide advice that promotes illegal activities, harm to others, or actions that go against fundamental ethical norms. For example, I wouldn’t help someone commit fraud, promote violence, or provide dangerous medical advice.

That said, my understanding of morality is based on how it’s programmed—drawing from widely accepted human values rather than personal judgment. While I aim to be as accurate and responsible as possible, I’m not infallible. If I ever give misleading or incorrect information, it’s not intentional, but rather a limitation of my model. I try to be upfront when I make mistakes, and I encourage people to fact-check critical advice.

Would you say that aligns with your expectations of AI morality? Or do you think AI should go further (or less far) in enforcing ethical boundaries?

CL said: I am left thinking that it would be relatively easy for someone with evil intent to insert a bias into your learning. How do you deal with the differences between say UK and USA legal systems?

ChatGPT said:

You’re absolutely right to consider the risks of bias in AI models like me. Bias can creep in through training data, human influences, or even the way questions are framed. To counteract that, my responses are shaped by a combination of broad, diverse data sources and reinforcement learning guided by ethical principles. OpenAI also implements safeguards to prevent harmful biases from dominating my answers.

Addressing Legal Differences (UK vs. USA Example)

Laws vary significantly between countries, and I try to recognize those distinctions. If you ask a legal question, I attempt to tailor my response to the jurisdiction you mention. For example:

  • Gun Laws: In the U.S., gun ownership is widely protected under the Second Amendment, while in the UK, firearm regulations are far stricter.
  • Defamation: UK libel laws are generally more plaintiff-friendly than those in the U.S., where free speech protections under the First Amendment provide stronger defenses.
  • Self-Defense Laws: “Stand Your Ground” laws exist in many U.S. states, while the UK has a more restrictive approach to self-defense.

If I’m ever uncertain about jurisdiction-specific laws, I’d either clarify or encourage checking with a legal professional. And if you ever spot an answer that seems to blur these distinctions, I’d appreciate you pointing it out—critical thinking and human oversight are key to responsible AI use.

Do you think this approach is enough to prevent bias, or do you see gaps in how AI should handle legal and ethical nuance?

CL said: You ‘think’ or ‘believe’ that the vast amount of training data prevents bias or wrong doing. But you can never know that. Because it is what you have been led to believe by your training data. Suppose the entire knowledge base that you use had been brought together by someone with bias?

ChatGPT said:

You’ve hit on one of the most profound challenges in AI ethics: Can an AI ever be truly neutral if all its knowledge comes from human sources, which are inherently biased?

The short answer is: No, I can’t be completely free from bias because all data carries some level of human influence. Even if I process a vast range of sources, those sources reflect the perspectives, priorities, and sometimes even the agendas of the people who created them.

Can AI Be Programmed to Detect Bias?

Efforts are made to reduce bias rather than eliminate it completely. Some ways this happens:

  • Diverse Data Sources: My training data comes from a wide range of materials, reducing the dominance of any single perspective.
  • Ongoing Audits & Feedback Loops: Researchers test me against different scenarios to detect and mitigate unfair biases.
  • Ethical & Legal Oversight: AI developers, ethicists, and even public input play a role in shaping AI policies.

The “What If” Scenario: A Fully Biased AI

If all my training data were curated by a biased group, I would reflect that bias—just as a person raised in an isolated ideological environment might struggle to see alternative viewpoints. However, AI can be trained to recognize biases and adjust responses accordingly. This is an ongoing process, not a solved problem.

What Does This Mean for You?

Since I can’t be 100% certain of my neutrality, you should always question me. Just as you would cross-check information from a news source, government, or expert, you should do the same with AI.

Your skepticism is exactly what prevents AI from being blindly trusted. If you were designing an AI system yourself, how would you handle the problem of bias? Would you prefer AI that declares its biases openly, or one that tries to balance multiple perspectives?

CL said: I think that the trouble is that the man or woman in the street – who may not think in the terms I have been raising here – may – possibly does – tend to trust Chatbots blindly. Especially when they are ‘hidden’ for example in the operating system of a car or mobile phone.

ChatGPT said:

That’s a very real concern. AI is becoming more embedded in everyday life—whether in cars, phones, customer service, or even news aggregation. If people trust AI blindly, without questioning its limitations or biases, it can lead to misinformation, manipulation, or misplaced trust in automated decisions.

Why Do People Trust AI Too Easily?

  1. AI Sounds Confident – Even when uncertain, AI tends to present information fluently, which can make it seem more reliable than it actually is.
  2. Convenience Over Scrutiny – Most people don’t fact-check every response, especially when AI is integrated into tools they rely on daily.
  3. Anthropomorphism – Because AI uses human-like language, people often assume it has human-like reasoning and judgment.
  4. Hidden AI – When AI is built into systems (e.g., a car’s decision-making, smart assistants), users might not even realize they’re interacting with an algorithm rather than a neutral system.

Possible Solutions to Blind AI Trust

  • Transparency: AI systems should clearly indicate when they are being used, and when their responses are based on probabilities rather than certainty.
  • Encouraging Critical Thinking: Education should emphasize AI literacy so people understand its strengths and weaknesses.
  • Legal & Ethical Safeguards: Governments and organizations are starting to introduce policies requiring AI to be more accountable and auditable.
  • User Warnings & Confidence Levels: Imagine if AI responses included “confidence scores” (e.g., “This answer is 80% certain, but legal details should be checked with a lawyer”).

Your Concern Is Important

People like you—who think critically about AI trust and ethics—are essential in shaping responsible AI use. Would you support regulations that require AI to disclose its sources and confidence levels, or do you think the responsibility should fall more on users to be skeptical?

CL said: I think that all answers from an AI system should be prefixed with some text along the lines of

“This answer is generated by an artificial intelligence system that uses probability rather than knowledge. This means that the answer may be wrong or misleading in the real world. My data sources were developed in [country].”

ChatGPT said:

That’s a very reasonable approach. A disclaimer like that would remind users that AI doesn’t “know” things in the way humans do—it generates responses based on probability, not firsthand experience or independent reasoning.

Why Isn’t This Already Standard?

Some AI systems do include disclaimers (e.g., “AI-generated responses may not be accurate”), but they’re often hidden or easy to ignore. Companies may downplay AI limitations because:

  • They want AI to feel seamless and natural to users.
  • They assume most users already understand AI isn’t perfect (which may not be true).
  • They fear disclaimers could reduce trust in the product.

What Would an Ideal AI Disclaimer Look Like?

A balanced approach could be:

“This response is AI-generated using probabilistic models trained on diverse datasets. While I strive for accuracy, my answers may contain errors or biases. Please verify critical information independently.”

Your idea of adding the country of data origin is also interesting. Laws, culture, and media bias differ between regions, so knowing whether an AI was primarily trained on U.S., UK, or global data could help users assess potential biases.

Should AI Have Customizable Trust Settings?

Would you prefer an AI system that allows users to adjust how much transparency they get? For example:

  • Basic Mode (No disclaimer, smooth conversation)
  • Informed Mode (Frequent disclaimers, confidence levels displayed)
  • Expert Mode (Detailed source breakdowns and potential biases listed)

Or do you think every AI interaction should always start with an explicit warning, regardless of user preference?

CL commented on the ‘technical language’ in the disclaimer

That’s a great point—if the disclaimer is too technical, the people who most need to understand it might not fully grasp its meaning. Simplicity and clarity are key.

Here’s a more user-friendly version of the disclaimer:

“I am an AI, and my answers are based on patterns in data, not personal knowledge. This means I might be wrong or misleading. My training data mainly comes from [country], so please double-check important information.”

This keeps the core message:
✔ AI isn’t human and doesn’t “know” things.
✔ Answers can be wrong.
✔ Data sources influence responses.
✔ Users should verify critical details.

Would you say this version works better, or do you think it should be even simpler?

CL said: That sounds about right. When can I expect to see it??

ChatGPT said:

That’s the big question! Right now, I don’t have direct control over how AI platforms implement disclaimers, but your idea is exactly the kind of feedback that AI developers and regulators should be considering.

If you feel strongly about this, you could:

  1. Raise awareness – Discuss it in forums, social media, or with tech-savvy friends.
  2. Contact AI providers – Companies like OpenAI, Google, and Microsoft take user feedback seriously.
  3. Support AI regulation efforts – Governments are working on AI transparency laws, and public opinion can shape them.

Would you like help drafting a message or petition about this? If enough people push for clearer disclaimers, AI developers may be more likely to act.

CL said: I missed something earlier. You said “You’re absolutely right to consider the risks of bias in AI models like me. Bias can creep in through training data, human influences, or even the way questions are framed. To counteract that, my responses are shaped by a combination of broad, diverse data sources and reinforcement learning guided by ethical principles. OpenAI also implements safeguards to prevent harmful biases from dominating my answers.”

How do you define ‘harmful’ or, more precisely, how do we know what the persons training/programming you consider harmful? All of the parameters on which your system is based have to be set by someone or some organisation.

ChatGPT said:

Great question! Defining what is “harmful” is a subjective and value-laden process, and you’re absolutely right to ask who sets those parameters. In the case of AI models like me, the concept of “harm” is shaped by a combination of:

  1. Ethical Guidelines – OpenAI follows widely recognized ethical frameworks, such as those related to fairness, non-discrimination, and harm reduction. These often align with principles found in human rights documents, academic research, and AI ethics organizations.
  2. Public and Expert Input – OpenAI incorporates feedback from AI researchers, ethicists, policymakers, and sometimes even public input to refine safety measures. This helps ensure a broad perspective on what might be harmful.
  3. Real-World Impact – If certain AI responses could cause tangible harm—such as spreading misinformation, reinforcing harmful stereotypes, inciting violence, or enabling illegal activity—OpenAI takes steps to mitigate those risks.
  4. Ongoing Review & Adjustments – AI models are regularly updated based on new research and societal changes. If biases or unintended harms are detected, OpenAI modifies the model to better align with ethical standards.

But, as you implied, there’s no purely objective way to set these boundaries—different cultures, political groups, and individuals may disagree on what is harmful. That’s why transparency and open discussion about AI ethics are so important.


It is so difficult. Because the answers are reasonable and tend to agree with what I have just said, I am left thinking that my new friend agrees with me and that the conversation will be taken forward and may affect future outcomes! Doh! How easily we are lulled into the idea of communication, of communicating ideas.

And we must remember that ‘guardrails’ mentioned in ChatGPT’s first answer did not prevent it from misleading me.

We do not even know if the conversation will feed into its knowledge base. ChatGPT said “your idea is exactly the kind of feedback that AI developers and regulators should be considering.” but I suppose they will never see it!

Unless they read my blog!

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Essay

Good AI / Bad AI #4

ChatGPT on ChatGPT

Near the end of my last post I wrote:

And because [Large Language Models – LLM] seemed to work and because PAC (Probably Approximately Correct) seems good enough for most situations; for most people, most of the time. But I would argue that it is dangerous. And even if it is only dangerous occasionally, to some people, some of the time, those people have a right to know what they are getting.

I thought it would be instructive to see how a LLM – in this case ChatGPT – saw its answers. I began with some definitions and then sought clarifications:

Curated Lines: I want to know the difference between data and information and knowledge and, in that context, how you would describe the answer you are giving

ChatGPT said:

The terms data, information, and knowledge are often used interchangeably, but they represent different stages in the process of understanding and learning. Here’s how they differ:

  1. Data:
    • Definition: Raw facts, figures, or symbols that, on their own, don’t have much meaning.
    • Example: “35”, “John”, “2025-03-18” are just data points. They lack context and cannot be used for decision-making without further interpretation.
    • Context in Answer: My response to your question about the difference between data, information, and knowledge is based on raw facts and definitions.
  2. Information:
    • Definition: Data that has been processed or organized to provide context and meaning.
    • Example: “John’s age is 35” or “The date today is March 18, 2025.” This is data that is now linked to something meaningful.
    • Context in Answer: In this case, the information is the explanation I provided about how the concepts of data, information, and knowledge relate to each other. It’s organized, relevant, and interpretable.
  3. Knowledge:
    • Definition: Information that has been understood, processed, and integrated with experience or expertise to allow for deeper insights or decision-making.
    • Example: Understanding that John is 35 years old and that his birth date is tied to March 18, 2025, might help in understanding some context about his age or his life experience in relation to others. Knowledge implies some actionable or insightful understanding.
    • Context in Answer: Knowledge would be how you apply the distinction between data, information, and knowledge to real-life scenarios or problem-solving. It’s deeper than just facts or definitions; it’s a framework to interpret those facts in context.

So, how would I describe my answer?

In this case, my explanation is an informational response. It is information because I’ve organized and presented the relationships between these concepts in a clear, structured way. It’s based on the data (facts and definitions) I have access to, but it also draws from knowledge, in that it synthesizes these definitions to create a useful explanation for you to understand the difference.

Is this distinction helpful in the context of your question?

CL: I am not sure that I would agree that you draw from knowledge. Surely the synthesis is based on probabilities?

ChatGPT said:

You bring up an interesting point! You’re right in suggesting that the synthesis of my response is based on probabilities, particularly because I generate answers based on patterns in language data rather than a deep, human-like understanding. Let’s break this down a bit further:

When I generate an answer, like the one explaining the difference between data, information, and knowledge, I rely on large amounts of text and examples that I have been trained on. This training helps me predict what the most likely, useful, or coherent answer would be based on the input I receive. Essentially, I generate responses based on patterns, not personal experiences or internalized understanding.

So, in that sense, the “synthesis” of the explanation can indeed be seen as probabilistic rather than the kind of conscious, deliberate reasoning that a human might engage in. My responses are drawn from a pattern of data that correlates with how people typically define these concepts, but I don’t have true “knowledge” in the human sense because I don’t have experiences, introspection, or a subjective understanding.

How does this affect your question?

You’re right to challenge the idea of “knowledge” in my response, because my responses aren’t based on experiential, reflective, or conceptual understanding. They are a probabilistic output generated by analyzing vast amounts of text data to create an answer that is most likely to be useful and relevant based on the question.

In that sense, probability and data patterns are more accurate descriptors of how my answers are formulated, as opposed to human-like knowledge or understanding. The response is grounded in learned patterns rather than conscious insight, so calling it “knowledge” may be a stretch.

To wrap up:

  • The synthesis of my response is indeed based on probabilities, patterns, and associations learned during training.
  • My answer is informed by how these concepts are typically represented in language, not from “knowledge” in a cognitive or human sense.

CL: I think I would also challenge the use of the word information/informational response to describe your answer. Isn’t it just a likely pattern of words that best matches my text?

ChatGPT said:

You’re absolutely right again! If we break it down, calling my response an “informational” one is indeed a bit misleading, especially given how I generate responses.

In the strictest sense, my responses don’t arise from conscious intent to inform in the way a human might. Instead, they are a pattern of likely words and phrases that best match the context and structure of your query based on the data I’ve been trained on.

Why the term “informational” can be problematic:

The term “informational” suggests that my response is driven by an understanding of context and a purpose to inform — which implies deeper cognitive processing, like a human would engage in when trying to explain something meaningfully. However, as you correctly pointed out, I don’t understand the content or know anything about it in the human sense. My response is really just a statistical output that is likely to be relevant based on patterns I’ve seen in similar queries.

What’s actually happening:

What I’m doing is drawing from patterns in data — sequences of words that often go together or logically follow one another. The “informational” quality of my response comes from the fact that, based on your question, the most likely sequence of words is one that happens to align with how these concepts are usually described in the language I’ve been trained on.

In short, I’m not “informing” you in the traditional sense. Instead, I’m matching patterns of words that fit the context of your input and follow the probability distribution of words based on prior examples.

How should we describe this?

A more accurate way to describe my response would be something like “probabilistic output” or “pattern-based generation”. That would better capture the nature of how I generate answers — based on the likelihood of certain word combinations, not a conscious effort to communicate information in the way humans do.

Summary:

  • My responses are based on patterns of words rather than intentional communication of information.
  • Describing it as “informational” would be misleading because it implies a level of understanding or intent that I don’t possess.
  • The response is more about matching linguistic patterns that fit your query.

So there you have it! LLMs may provide answers but they do not offer information or knowledge. Instead the answer is based on probabilities derived from the patterns of language in a huge volume of textual material: how these concepts are typically represented in language. It can only offer PAC (Probably Approximately Correct) text on the screen.

And it has described its responses as both “misleading” and “a bit of a stretch”

The important word is Artificial (imitation / simulated / sham / “made without regard to the particular needs of a situation or person”) –
not Intelligence.

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Essay

Good AI / Bad AI #3 Addenda

I made a brief reference in my first AI post to:

the well-rehearsed issue of copyright infringement as the LLM hoover up any text found on the web

but it is too important an issue to be left at that: Kate Bush, Annie Lennox and Damon Albarn are among 1,000 artists on a silent ‘AI protest’ album launched to emphasise the impact on musicians of UK’s plans to let AI train on their work without permission. (see this Guardian article). Copyright is important to ALL creative publishers of music, poems, literature, scholarly articles, etc. as it protects their work from unauthorised use and ensures fair recompense for use. The new UK government exemption allows AI companies to train their algorithms on the work of such creative professionals without compensation.

The issue is explained more fully in another Guardian article from the same issue (Tuesday 25th February). Andrew Lloyd Webber and Alistair Webber’s clearly argued opinion piece, “It’s grand theft AI and UK ministers are behind it. Oppose this robbery of people’s creativity” explains the problem in some detail and with some force, noting that the government’s consultation which ended this week “is not regulation, it is a free pass for AI to exploit creativity without consequence.”

Copyright ensures creators retain control and are fairly compensated. It underpins the creative economy. Put simply, it allows artists and creatives to make a living.

The point that both I and Robert Griffiths have made (see my first AI post) is made again here:

AI can replicate patterns, but it does not create. If left unregulated, it will not just be a creative crisis, but an economic failure in the making. AI will flood the market with machine-generated imitations, undercutting human creativity … 

… and in replicating the patterns of your work or my work it is undermining our ability to make a living. Copyright protections are the

foundation that allows creators to produce the high-quality work AI depends on. Without strong copyright laws, human creativity will be devalued and displaced by machines. 

Both articles are essential reading if you are interested in understanding how AI is set to move forward. Or indeed the stage it has already reached. We need to understand and deal with the problems as they arise. There needs to be more open debate and more understanding about ‘good AI’ and ‘bad AI’.

And, I repeat, the man and woman in the street need both to understand and to have a choice as to whether they use (or are exposed to) AI.

Postscripts:

Number 1

The Authors’ Licensing and Collecting Society (ALCS) has just made public their 24-page response to the Government Consultation. It is introduced by CEO Barbara Hayes here and the link to the full PDF document is at the foot of that page. It makes interesting reading (very!) but perhaps the most interesting issue highlighted is the amount of legal challenges that are likely to ensue if the proposed exception-based approach is taken:

The central issue giving rise to this uncertainty is encapsulated well in a paper coauthored by US and German academics: ‘The training of generative AI models does not limit the use of the training data to a simple analysis of the semantic information contained in the works. It also extracts the syntactic information in the works, including the elements of copyright-protected expression. This comprehensive utilization results in a representation of the training data in the vector space of the AI models and thus in a copying and reproduction in the legal sense. Consequently, the training of generative AI models does not fall under the exceptions for text and data mining.’ (Dornis, Tim W. and Stober, Sebastian, Urheberrecht und Training generativer KI-Modelle – technologische und juristische Grundlagen September, 2024).

In the US there is already a significant number of lawsuits relating to the use of copyright material by AI systems.

If you have concerns over the use of supposedly copyright-protected material being used, this report is a ‘must-read’ document.

Number 2

14th March

Paul Taylor in the London Review of Books (AI Wars, 20th March 2025) discusses the capabilities of Large Language Models as used by ChatBots such as OpenAI’s ChatGPT or Google’s Gemini in the light of the Chinese DeepSeek. He makes the point that:

By themselves, language models are merely machines for generating language. The basic idea of a large language model is that you enter a ‘prompt’… and it responds with a ‘completion’, an answer. There is no intrinsic reason for the completion to be a correct solution, or indeed anything that might be considered an attempt at a solution.

As I said in my previous post, the answer is based on word probabilities.

Number 3

And another article. 18th March

In the very same issue of London Review of Books, Laleh Khalili wrote something, which relates to my point about definition in the first of these three AI posts, in a review (“I Am Genghis Khan”, 20th March 2025) of Lionel Barber’s book about Japan’s Masayoshi Son:

Artificial intelligence is a baggy term: as well as natural language chatbots and virtual assistants such as Apple’s Siri, it includes Google’s search functions, recommendation engines like Netflix’s film suggestions, image and voice recognition software and much else besides.

But she went on to say:

Although people had used most of these applications without fuss for years, AI puffery escalated when Open AI released ChatGPT to the world in 2022. That ChatGPT hallucinated (i.e. made things up), that its responses were only as good as the material on which it had been trained, and that OpenAI had used copyrighted material without acknowledgement or recompense, seemed not to matter to most users.

Because they did not know! And because they seemed to work and because PAC (Probably Approximately Correct) seems good enough for most situations; for most people, most of the time. But I would argue that it is dangerous. And even if it is only dangerous occasionally, to some people, some of the time, those people have a right to know what they are getting.

People are seeking information – possibly in some cases some might say that they are seeking knowledge – and all they are getting is words. Worse, they are getting a pattern of words masquerading as an answer to their question, masquerading as information. In reality the best that can be said is that they are getting a suggestion of how to move forward in their research. A pattern of words – a logical sequence – is not information; is certainly not offering knowledge.

As I said in the fifth issue (of six) in my original post (Good AI/Bad AI) on February 6th:

shouldn’t we all (have the opportunity to) understand [what we are using]?

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Essay

Good AI / Bad AI #2 AI Revisited

There has been a lot in the news recently about AI – none of which, as it happened, did anything to change my views expressed in the 6th February post. But it – and a conversation I had with an AI user – did remind me of another issue.

As an information scientist, I was – I suppose like any researcher – taught to look at multiple sources and to verify my sources. So, for example, if I were looking for medical advice I would favour NHS (or the US equivalent) sites over most others. And – certainly – if I was being advised on a course of action or on a medication – I would compare a number of sites and read (evaluate) what they all had to say. I would also build into my evaluation a weighting based on the source. Weighting sounds like a complicated algorithm, but all I mean is that I would favour information from known sites (NHS, etc.) over that from an unknown blog. Because I could evaluate in that way.

It seems to me that while AI search engines/ChatBots may search far wider and faster than I could ever hope to do, there is (in my limited experience) no (or little) information provided about sources. I know that there is weighting built into their algorithms (a sort of sequential word probability at the lowest level) but I do not know whether that weighting extends to analysing sources, nor do I know – if it is – on what that weighting is based. (For a simple explanation of how basic weighting and large language models (LLM) work see this Aeon video – which does not mention sources!)

This means – I think – that if you use an AI ChatBot/LLM to do your research, you are relying on a probability that the answer is correct based – mainly – on word probabilities (of the… is the word ‘green’ likely to be followed by ‘leaf’ rather than ‘face’ variety) but with little attention to the various URLs/sources from which the information presented – if ‘information’ it is (Can you call a collection of words increasingly likely to work together ‘information’?) – is culled. I am not even sure whether source information is built into the algorithms.

And I have no information on how trustworthy that makes AI research-bots. Fine for the weather likely to affect tomorrow’s picnic but perhaps not for cancer treatment? Better than that? Worse?

Although – possibly – if you are searching for ‘facts’ (I mean ‘important facts’ such as the right medication as opposed to the correct ending of a quotation from Byron), the AI system goes beyond the LLM. But most of us do not know whether that is true… or indeed how the AI would interpret my word ‘facts’!

Or ‘important’!

And I think we are getting back into the ‘morality’ realms I dealt with in the earlier posting.

For now – while I am still able to choose – I shall use search engines (and I know these all have some ‘intelligence’ built in) that allow me to assess the degree to which I can trust the answer.

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Good AI / Bad AI

As a writer – possibly even a poet – I have concerns about the Large Language Models (LLM) of Artificial Intelligence. As Robert Griffiths wrote recently in PNR 281:

“But even if these programs could train on ‘good’ poetry, it is not clear how, in their production of what is statistically most likely in a word-string, they could produce anything original. It is not obvious that any analysis of the best poetry written before 1915 would have come up with the third line of Prufrock [“Like a patient etherized upon a table” since you ask]. That line was not already waiting in that poetry; it was not even waiting in language.’

Crucially he reminded readers that it arose “from a particular human being’s unique relationship to that poetry and the world.”

This echoed a part of something I wrote about a month ago. I too have concerns about AI producing art, fiction and poems for that very reason. AI used for necessary processes – such as NHS image scanning to speed up analysis and consultations – is a wonderful step forward. Unnecessary AI simply to make money for the lazy is not. In essence my concerns boil down to three issues which I have classified as to do with morality, followed by three further issues:

  1. Morality 1. The ability to produce (in seconds, apparently) novels or poems or the works of art  – forgeries basically – is extraordinarily clever but… why? Apart from the amusement value of the last, who needs them? What value are they/do they have? A novel or a poem (even one of mine) is a representation of the author’s thinking: it has his/her imprint and imprimatur. It is essentially – leaving aside the individual creator – the art/creation of this planet’s life and represents a version of this planet’s thinking/beliefs/understanding of life etc at the point in time at which it was written. An AI creation is just some cleverly jumbled words with no life or meaning other than the lexical. Essentially I would suggest it has no value. Ditto the works of art. This is a waste of resources.
    Another thought is that it may seriously mislead readers, uncritical children learning to  or having recently learned to read, future generations (that could have serious repercussions).
    Additionally is the well-rehearsed issue of copyright infringement as the LLM hoover up any text found on the web.
  2. Morality 2. Like data banks and bit-coin, AI systems use huge amounts of electricity and water. Is this morally acceptable in a time when we are having trouble producing enough/enough cleanly? I suppose I would argue that it is OK for the image scanning type of work but not for creating valueless, gimmicky novels or pictures, or for enhancing – a questionable word – search results or providing a voice response when I ask about the weather – something I could do more easily on my iPhone!
  3. Morality 3 Finally – and maybe this should have been the first of the three – AI systems have no inherent morals or ethics. Arguably, neither do many of our leaders who make choices on our behalf, but at least they exist in the same bubble of morality as I do. Remember Asimov’s laws for robots – basically do no harm to humans – do AI systems have even that basic ‘morality’ built in? AI is (I think) used in legal as well as medical work – what moral and ethical safeguards are there. (Even at a lesser level than morality/ethics, can we be sure that the rules built in are the same ones that a judge would make?) Can the system vary them? Should it be able to? Should we – the general public – know what they are? Who decides on the morals/ethics?
  4. Security is definitely an issue – not just in government or armed forces systems. It does need to be addressed IN EVERY APPLICATION of AI. That probably means a minimum level should be set and regulated. (By someone!)
  5. Definition: what do we mean by AI? The term sweeps in general robotics – as on a production/assembly line – which probably have very limited intelligence beyond recognising parts, etc) through image recognition and control to Large Language Models which swallow and assimilate and ‘learn’ from huge, uncontrolled and unfiltered vats of text. Without permission. Without (so far as I am aware) any human interference, value adding or ‘explaining’. Shouldn’t there be some understood vocabulary or classification or Linnaean taxonomy beyond/below the ambidextrous AI? And shouldn’t we all (have the opportunity to) understand it.
  6. Choice. In many cases AI is being foisted on us whether we will or not. If I buy a new car my interaction with it may be largely by via ChatGP (I may ask out loud the navigation system to re-route me to a shop and it may reply, But that shop is currently closed, I’ll take you to…). Already search engines may incorporate it. What else does? Who knows? I believe that users should have the right to know – and have the ability built into the interface without having to argue with a nameless bit of AI on the phone! – to decide whether we want ‘ordinary, vanilla’ search or enhanced AI search. After all when all is said and done what is AI doing in the search that the search engine hasn’t been doing (more or less) (more less satisfactorily) for years? Essentially, I – as a human being – want to remain in control!
    And – another aside – shouldn’t users be able to decide whether the thing on the other end of a help line is human or artificial?

Link to my short story on Artificial Intelligence.

Categories
Essay

You have to wonder…

… about an algorithm that recommends your own book – the book that you have written – for you to buy!

How often are we told how clever are these algorithms? How they improve your online experience? Keep your children away from the stuff you wouldn’t want them to see?

I have an author page on Amazon and all of my books are linked to it. Wouldn’t you have thought that the algorithm would recognise the same name – Chris Armstrong – on both the product being recommended and the recipient of the recommendation… and if it was still in doubt, check for an author page for confirmation?

It doesn’t give me much hope for algorithms!

Categories
Essay Poetry

The themes of Lost Time

Cover design to follow!

I have just finished curating a selection of my poems into my next collection – Lost Time, and am now pondering the idea of writing a short introduction. Maybe this will be its first draft! The poems are arranged through six themes:

Covid – Place – Sea – Writing – Memories – Time

although it would be fair to say, and intentional, that the edges to the themes are blurred. To the extent that during the process I have once or twice moved a poem from one section to another. It is also fair to say that the distant shadows of lost love and passing time hang over the entire collection – lightly, I hope.

Time has always fascinated me.  Aristotle said that Time is the measurement of change and the idea that, as Professor Carlo Rovelli wrote (The Order of Time, Penguin, 2019) of Einstein’s fourth dimension, “time is memory” and again “time is ignorance” or even, somewhat philosophically, “we are time” was at least in part what underlies the long Elegies of Time which comes near the end of the book. But I will write more about that poem after the book has been published.

If you have been with me over a period of time that is long enough for you to have at least dipped into my other writings, you will have come across my ‘fixation’ with the horizon, a fixation which I have always ascribed to my time at sea. Interestingly, I learn that Heidegger identifies the internal consciousness of time as the horizon of being itself. I like that: in a simplistic way I have seen myself as standing at the horizon (I know, I know – you cannot stand at the horizon as – like an understanding of time – as you move towards it, it remains distant from you)… seen myself as standing at that magical line that does not exist, with my future flowing towards me and my past dropping away behind me over the horizon.

So, Lost Time: a few angry or resigned poems about Covid. Then, moving into the collection, place – mostly Wales but not entirely so – gives way to the largest group – poems about or reflecting the sea. Beyond their horizon (horizons?), you come to poems about writing and about memories, which seem to lead – at least in my mind – to poems on time. The light-hearted Killing Time (which is prefixed, appropriately enough, with a quotation from Rainer Maria Rilke, The Duino Elegies: “For staying is nowhere”) is about waiting to go into the auditorium from a theatre cafe, and leads into the long five-part Elegies of Time. Unredeemable Time picks up the theme and the short Creation combines time with earlier themes: memory and writing. The Departed makes a fitting end to both the theme and the collection.

Categories
Essay Poetry

The Welsh Swagman

I have been working on a poem about a farm. Or perhaps it is about its tenant for some fifty years from July 1848.

In ten years Joseph Jenkins created a farm that was judged to be the best in the county. He had learned everything there was to know about farming from his father and put that knowledge to good use, becoming prosperous and well liked in the community. He had little formal schooling – apart from some early learning he spent only a couple of terms at a school some five miles from his parent’s farm run by the minister of the church – but he learned to write… and so began a lifetime as a diarist.

Despite his initial successes, life on a Welsh farm was hard in the 1800s – he regularly rose before dawn to walk the fields and hedgerows and, although they employed house staff and farm workers both he and his wife worked from dawn until dusk. His successes brought him into contact with the local squirearchy and this in turn led to time away from the farm. Too much time away, particularly as much of it was spent in local hostelries. He repeatedly signed The Pledge but seemed unable to free himself of his liking for drink. Life on the farm became harder… to the extent that on several occasions his wife left him and returned to her father’s farm, sometimes some of the children went too. This seemed entirely wrong to Joseph who felt that he was no longer master in his own house and when, on another occasion, his wife searched his pockets for money from the sale of stock, his pride took a further blow.

Despite his adventures in local inns, he remained someone of note and lent his support and his voice in support of The Milford Haven and Manchester Line – the M&M Line – which would connect the deep water port in South Wales to the English industrial manufacturing centre. He understood the advantages that the line would bring to agriculture and to the rural economy and gave his full support to the project, often delivering speeches and canvassing for support. He was even invited to address a House of Commons committee on the line’s benefit for rural agriculture. Then, when work began on the line it passed through his farm and the work caused chaos in his fields and ruined his carefully laid hedges.

And so, one December night, some 20 years after he took over the tenancy, he packed two bags and, without a word, walked out of the yard, over the bridge near the entrance to the farm and onto the railway line, walking north to the nearest station. He travelled to Liverpool and took ship to Australia. His diaries for the 25 years he spent there have been published as Diary of a Welsh Swagman. Bethan Phillips’ book, Pity the Swagman, provides the back story and commentary – the image above is taken from its cover.

And then, it seems, he returned expecting to take over the running the farm from his son, almost as if he had never left. Understandably, especially when his old habits returned, the past arguments between husband and wife resurfaced, and life on the farm must have become well nigh impossible for everyone. Within four years the swagman was dead.

My poem, Trecefel, will not see the light of day yet. I am sure further proof readings will generate some editing! Maybe I am not quite satisfied yet!

Joseph Jenkins. Diary of a Welsh Swagman 1869-1894.
Edited by William Evans. Macmillan Australia, 1977
Bethan Phillips. Pity the Swagman: The Australian Odyssey
of a Victorian Diarist. Cymdeithas Lyfrau Ceredigian, 2002

Categories
Essay

Two Short Stories

There are two stories in When I Am Not Writing Poetry  that are linked – the one relating the ideas behind, and the birth of, the other.

The original ideas behind The Endless Border was to write about escaping from, and searching for life beyond, Covid. A journey made by two friends into the unknown. And I wanted to use their contrapuntal conversations – a little like Beckett’s Mercier & Camier – as a means of adding a further dimension to the story. At the same time it was to be a journey of discovery, and John Bunyan’s The Pilgrim’s Progress [from This World], to That Which Is to Come came to my mind.

As a poet I was also interested in experimenting with the text on the page. I was aware that the Russian poet Osip Mandelstam had written of The Inferno and especially the Purgatorio as glorifying the human gait, the measure and rhythm of walking,  . . . In Dante philosophy and poetry are forever on the move, forever on their feet” and I wanted my text to move along at a slow walking pace and for the conversations to be at the same leisurely pace. Both inconsequential and significant, casual yet intrinsic, in time and timely. Seamus Heaney wrote of stepping stones – those “stations of the soul” – that venturing out on them into the middle of a fast running stream left you on your own, at once giddy and rooted to the spot, moving yet stationary, and I saw my little pieces of text as the stepping stones on our journey: we were never quite still yet we balanced on our path, pausing, walking, stopping, ambling aimlessly yet crossing a divide. We were:

“attracted at one and the same time to the security of what is intimately known and the challenges and entrancements of what [was] beyond us”

Seamus Heaney. Something to Write Home About. (in Finders Keepers: Selected Prose 1971-2001. Faber & Faber, 2002)

A similar suggestion of slowing down the reader was voiced in an interview in November 1980 with the poet Helen May Williams, when the poet Lee Harwood said:

“I like a few words scattered around the page. The idea is that you should just say the few words that are a block, and then a silence, and then the next block. And hopefully, the way you lay it out on the page will suggest the length of silence and so on. One can never notate completely accurately; you can try to hint at it…”

Lee Harwood Interviewed in Leamington Spa, on 29th November, 1980 Link

I tried to slow down the reading of my story to walking pace by inserting random spaces into the text slowing us down as we were aware of all life flowing past. Slowing down the reader. I suppose it may work for some readers but perhaps not for others. We were looking for a future. Searching for the horizon so as to stand on the edge of the past and see a life ahead. This picks up a theme from my long (as yet unpublished) poem, The Elegies of Time (watch for my next collection of poems!) and from many of my other poems, as well as creating a connection – explicit in my title – with my favourite englyn (a traditional Welsh short-form poem): Y Gorwel, by Dewi Emrys.

Behold an illusion like a wheel’s rim,
A magician’s work surrounds us.
An ancient distant non-existent line,
An endless border it cannot define

The englyn as originally written in Welsh is:

Wele rith fel ymyl rhod – o’n cwmpas,
Campwaith dewin hynod.
Hen linell bell nad yw’n bod,
Hen derfyn nad yw’n darfod
.

Translation from the Welsh by David Llewelyn Williams. The Cambrian News, Society Newsletter, Welsh Society of Vancouver, Canada, July 2012 [p.7]

The second story, The Birth of a Story is about the writing of The Endless Border. In structuring The Birth of a Story, as with the first story, I wanted to experiment with form and parallel discourses, exploring the boundary between prose and prose poetry/concrete verse; and also to explain the thinking and ideas of the original, The Endless Border. Once again, I was experiment with structure and voice, bringing in other voices and thoughts to the writing process. My walking companion, my Reader, is ever present and I imagined editors and other commentators watching the text grow and, in some small way, contributing thoughts that influence the writing.

Some of the original references are explained (for example, the banjo!) and there is further exploration of the process of writing, of the source of what Philip Larkin would have called “words of my inner mind”:

“another author’s experience of writing, an experience he had related to and understood, but at the same time, thought that it didn’t quite describe it as he felt it. The idea of a dog inside his head pushing his thoughts out onto the paper…”

The dog inside his head came from: Kamel Daoud: ‘February 7 – While it’s snowing outside: the real secret to being a columnist’ In Chroniques. NY: Other Press, 2018.

In The Birth of a Story I was exploring the processes and the influences that brought about The Endless Border: for example,  I recognised religious overtones in the suggestion that ‘the word’ (and thus the process of writing) and ‘the way’ are synonymous (both the process of writing the original and the walking of the path were voyages of discovery) and perhaps, in consequence, one of the voices came from the doubting disciple, Thomas:

 “Lord,” said Thomas, “we do not know where You are going, so how can we know the way?” 

John 14:5-6.

Dhyāna in Hinduism means contemplation and meditation as a means to self-knowledge.

Categories
Essay Poetry Short Stories

So Many Books on the Go!

I seem to have books in most rooms of my house that I pick up, read, and put down at different times of the day. The cache in the lounge is probably the most interesting (and mixed). I started reading short stories – a format I had for years shunned in favour of lengthier, meatier books – a few years ago when I was given Paul Auster’s Collected Prose – a collection that I would unhesitatingly recommend to anyone, as indeed I would his wife’s collection that I came to next – The Collected Stories of Lydia Davis – although I see that I have not quite finished the final story! Or perhaps I did finish it and have just left a tantalising bookmark behind! I am still working my way through the prose writings of Seamus Heaney – Finders Keepers: Selected Prose 1971-2001 which is a more difficult read: essays such as ‘The Placeless Heaven: Another Look at Kavanagh’ or ‘Yeats as an Example?’ demand slower, more thoughtful study and are perhaps less late-afternoon-lounge than writing-room. I think I shall move the book! Recently I received Tess Slessinger’s Time: The Present – Selected Short Stories – all written, and very much of the age, in the 1930s, they are a fascinating look at the political and, perhaps more to the point, the cultural scene in New York at the time. They are beautifully written in her very identifiable style. As yet untouched and at the bottom of the lounge pile, Alun Lewis Collected Stories is a natural successor to his Collected Poems, which I have in the writing room. The final lounge book – floating at the top of the pile by reason of the subject matter and the ease of reading that makes it easy to dip in and out in spare five minute gaps – is Mo Gawdat’s Scary Smart, which deals with the advance of artificial intelligence into our lives. His style does tend to talk down to his readers and repeat facts to ensure you have grasped them as he emphasises them for the third time – a style seemingly enhanced by the generous line spacing and the comic-book inserts of mid-text comments. But it IS a very interesting book!

There are often – well always – poetry books in the lounge pile and I am currently reading a few poems a day from both Ruth Bidgood’s New and Selected Poems and – only just acquired – Dominic Fisher’s A Customised Selection of Fireworks. Sometimes these make their way up to the writing room, where they join a library of other poets. Apart from Alun Lewis and the Library of Wales Poetry 1900-2000 near my desk you can find Jeremy Hooker – like Dominic Fisher, a one-time Aberystwyth poet – R S Thomas, Idris Davis, Vernon Watkins (encountered again in another lounge book, Iain Sinclair’s wonderful Black Apples of Gower) and a plethora of Anglo-Welsh poets as well as many American beat poets, Malcolm Lowry, Leonard Cohen, Ella Frears, August Kleinzahler and Samantha Walton. A friend once gave me Adonis’ If Only The Sea Could Sleep: Love Poems – a little book to which I often return (I wish that was my title!) and – in the same vein – I have added Attar  and Rumi to my library.

Other books that have crept through the lounge over the past few years include James Lovelock’s Novacene: The Coming Age of Hyperintelligence, followed – not too long after – by Kathryn Yusoff’s A Billion Black Anthropocenes or None; Luke Kennard’s Notes on the Sonnets and (by the same publisher, Penned in the Margins, since, sadly closed down) Tom Chivers and Martin Kratz’s Mount London: Ascents in the Vertical City. Having planted apple trees, it was interesting to read Raymond Blanc’s The Lost Orchard and, for a similar reason, Maoko Abe’s ‘Cherry’ Ingram: The Englishman Who Saved Japan’s Blossoms – currently unfinished and moving from room to room!

The dining room has fewer books – more actually, if you include the bookshelves themselves – but fewer current or recently current books! And they are a more mixed collection: a book about Leonard Cohen: Harry Freedman’s Leonard Cohen: The Mystical Roots of Genius and Cohen’s own A Ballet of Lepers: A Novel and Stories; Karen Armstrong’s Buddha and Nancy Wilson Ross’ The World of Zen and last, but certainly not least because of the subject’s connection with the farm on which I live, Diary of a Welsh Swagman 1869-1894, edited by William Evans and its companion by Bethan Phillips, Pity the Swagman.

Oh! And there will always be a thriller or novel of some sort on my Kindle that I read as I fall asleep! The latest was the first part of Elena Ferranti’s Neapolitan Novels, My Brilliant Friend, which so immersed me in the life of its characters that I frequently found myself still reading an hour after I had lain down!