Contemplations – Most recent

I'm incurably curious about many aspects of this journey of ours. Here are a few noteworthy items I've stumbled across that I want to remember so I can revisit them from time to time.

Appetizer:

"Although I am a typical loner in daily life, my consciousness of belonging to the invisible community of those who strive for truth, beauty, and justice has preserved me from feeling isolated."
– Albert Einstein, from a short speech he gave to the German League of Human Rights, Berlin, in 1932, which can be found in Einstein: A Life In Science by Michael White and John Gribbin, Dutton, 1994, pages 262 – 263: via Internet Archive⩘ .

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'There's this deep mystery of what, actually, is this thing?'

A colorful illustration of bearded man looking upward into a darkness with glowing white dots. The right half of his face is replaced by a black & white drawing that looks like a circuit board. Behind and bit to the right of his head is pale white outline of his head.Image credit: a cropped version of an illustration by Deena So'Oteh, The Guardian

This is an amazing long read article in The Guardian by Robert P Baird with the title 'There's this deep mystery of what, actually, is this thing?': the philosopher inside Google DeepMind AI⩘  and the subtitle "Since 2017, Iason Gabriel has worked at the tech giant, trying to anticipate – and think through – the impact of AI. But as commercial and geopolitical pressures escalate, can ethicists make any difference?"

It is in-depth articles like this that make paying for a subscription to The Guardian worthwhile!

As I was reading Baird's article, I kept coming across paragraphs that captured so well the ethical challenges underlying AI development. I'll share a few here so that I can easily revisit them, but really, just read the full article … it's truly worthwhile.

More generally, Gabriel has been a leading advocate for the idea that the current wave of AI development demands not just new technical vocabularies but also new ways of thinking about our relationship to technology, and even to ourselves. As he put it to me recently, in one of several long conversations we've had over the past few months, "I can take any technological artefact and ask: is it wise? Is it just? Is it caring? And the answer is no. But the depth of the question when it comes to AI – including what kind of ethics is appropriate to it – is hard to overstate. I sometimes feel like it's very hard to look at AI directly. There's this deep mystery there, which is: but what actually is this thing? We have a very literal answer, but the literal answer doesn't seem to necessarily provide a moral answer."
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Like the DeepMind founders, the AI safety contingent believed that human-grade machine intelligence was not only possible but imminent. The urgent task, as they saw things, was to make sure that AI systems didn't go awry. They took inspiration from a 1960 essay by Norbert Wiener, an American mathematician and computer scientist, who argued that humans and computers are "essentially foreign to each other". Because machines can operate much faster than people, Wiener said, "we had better be quite sure that the purpose put into the machine is the purpose which we really desire and not merely a colourful imitation of it".
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Gabriel's first major research project at DeepMind was a 2020 paper that straddled the concerns of both camps. The paper took the alignment problem seriously, but it also insisted that alignment had ethical and political implications that went beyond the technical challenges. As difficult as it might be to get a machine to act in accordance with some set of values, Gabriel argued, it was much harder to choose those values in the first place. "Given that we live in a pluralistic world that is full of competing conceptions of value," he asked, "how are we to decide which principles or objectives to encode in AI – and who has the right to make these decisions?"
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DeepMind's initial distrust of LLMs was not uncommon. In 2020, Timnit Gebru, a Google Research engineer who had worked with Buolamwini on Gender Shades, co-authored a broadside against the nascent technology titled On the Dangers of Stochastic Parrots. The paper, which eventually became a cornerstone of anti-AI advocacy, made the controversial claim that LLMs could only ever produce technically meaningless text and possessed no more understanding of human language than a parrot does. It also accused the models of wanton energy consumption, rampant and unaccountable bias, and "amplification of a hegemonic worldview".
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Gabriel and his co-authors argued that alignment was not merely a matter of making sure that AI systems acted in accordance with some stable set of preferences, values or principles. Instead, they argued, alignment should be seen as a four-way relationship involving the AI system, the user, developers and society. Framing the issue in this way made it possible to see all the ways in which a misaligned AI might go wrong. An AI trained to favour its developer might cause harm to its user, for example, by not reporting accurate information about the developer's competitors. Or an AI trained to follow its user's instructions too faithfully might cause harm to society, for instance, by helping the user hack into a bank. It was even possible, they argued, for AI systems to be misaligned in a way that harmed users or society without helping anyone.
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The questions Gabriel and his colleagues have raised about the design and deployment of AI are unquestionably good ones, and I got no sense that anyone I met was insincere about their feelings of moral responsibility. Yet it's also the case that the most ethically relevant fact about AI at the moment has less to do with a given model or even a given company than it does with the global situation: first, the fact that AI is the white-hot engine of an incipient arms race between the US and China, and second, that AI may be the fastest-growing industry the world has ever seen.
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At Google's annual developer conference, in May, the deployment of AI across the company’s product offerings was treated as cause for celebration. [Google CEO Sundar] Pichai said that the company sees qqAI as the most profound way to advance our mission and improve people's lives at scale". For many people, however, the sudden ubiquity of AI has been some combination of overwhelming, obnoxious and threatening. Nor is it reassuring to discover that the feeling that things are going too fast is shared even by people such as Hassabis, who, on a recent podcast, lamented the "ferocious commercial-pressure race that everyone's sort of locked into". What's happening now, he said, is not how he'd hoped the development of AI would go, "where we would be contemplating this philosophically and carefully considering each next step. We're not in that world."

At this point it seems likely that LLM-powered AI will be at least as consequential as the smartphone, and maybe the internet. But still I can't say that I'm pleased to see a "Write with Gemini" prompt appear whenever I stop for a few seconds to consider my next sentence in Google Docs. Still less am I eager to watch my children be used as guinea pigs for a dizzying new experiment in digital learning, or to discover what will happen to the global economy if the extravagant investments in AI can't generate the short-term returns the markets demand. And while it's not far-fetched to expect AI to enable breakthroughs that would justify the extreme amounts of energy it requires – better batteries, more efficient transmission grids, cures for serious diseases – I also don't think "hope for the best" is a reasonable answer to people concerned about the climate crisis.

'There's this deep mystery of what, actually, is this thing?': the philosopher inside Google DeepMind AI⩘  by Robert P Baird, The Guardian, Jun 30, 2026.

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