This is my dumping ground for quotes and other stuff relating to the wonderful world of digital & communications.
“By way of analogy, imagine an intricate clockwork mechanism: if a single gear is faulty, the entire system grinds to a halt. However, once that gear is repaired, the clockwork springs to life, its hands gliding smoothly and its chimes echoing with precision. Similarly, AI models require a certain threshold of performance across multiple components before those emergent, unexpected abilities can truly shine”
“One way to understand backprop is to imagine a Kafkaesque judicial system. Picture an upper layer of a neural net as a jury that must try cases in perpetuity. The jury has just reached a verdict. In the dystopia in which backprop unfolds, the judge can tell the jurors that their verdict was wrong, and that they will be punished until they reform their ways. The jurors discover that three of them were especially influential in leading the group down the wrong path. This apportionment of blame is the first step in backpropagation.
In the next step, the three wrongheaded jurors determine how they themselves became misinformed. They consider their own influences—parents, teachers, pundits, and the like—and identify the individuals who misinformed them. Those blameworthy influencers, in turn, must identify their respective influences and apportion blame among them. Recursive rounds of finger-pointing ensue, as each layer of influencers calls its own influences to account, in a backward-sweeping cascade. Eventually, once it’s known who has misinformed whom and by how much, the network adjusts itself proportionately, so that individuals listen to their “bad” influences a little less and to their “good” influences a little more. The whole process repeats again and again, with mathematical precision, until verdicts—not just in this one case but in all cases—are collectively as “correct” as possible.”
“I enjoy the act of programming and I like to feel useful. The tools I’m familiar with, like the text editor I use to format and to browse code, serve both ends. They enhance my practice of the craft—and, though they allow me to deliver work faster, I still feel that I deserve the credit. But A.I., as it was being described, seemed different. It provided a lot of help. I worried that it would rob me of both the joy of working on puzzles and the satisfaction of being the one who solved them. I could be infinitely productive, and all I’d have to show for it would be the products themselves.”
“Summaries have been around for ages, most of them produced by people – and so they are not new in themselves. The time it takes to summarise a field has grown, however, and today there are several fields of research and knowledge that are impossible to summarise well before they change in fundamental ways. The limits of summarisation also limit how we can update our knowledge.”
“A theory of technology that places every informational product on a spectrum from Physician to Librarian:
The Physician’s primary aim is to protect you from context. In diagnosing or treating you, they draw on years of training, research, and personal experience, but rather than presenting that information to you in its raw form, they condense and synthesize. This is for good reason: When you go to a doctor’s office, your primary aim is not to have your curiosity sparked or to dive into primary sources; you want answers, in the form of diagnosis or treatment. The Physician saves you time and shelters you from information that might be misconstrued or unnecessarily anxiety-provoking.
In contrast, the Librarian’s primary aim is to point you toward context. In answering your questions, they draw on years of training, research, and personal experience, and they use that to pull you into a conversation with a knowledge system, and with the humans behind that knowledge system. The Librarian may save you time in the short term by getting you to a destination more quickly. But in the long term, their hope is that the destination will reveal itself to be a portal. They find thought enriching, rather than laborious, and understand their expertise to be in wayfinding rather than solutions. Sometimes, you ask a Librarian a question, and they point you to a book that is an answer to a question you didn’t even think to ask. Sometimes, you walk over to the stacks to retrieve the book, only for a different book to catch your eye instead. This too is success to the Librarian.”
“The step from one-prompt models to mission prompted or even unprompted or role-prompted agents is one that could introduce an interesting and qualitatively different set of question”
Some Wikipedians remark that their endeavor works in practice, but not in theory.
Wikipedia is no longer an encyclopedia, or at least not only an encyclopedia: Over the past decade it has become a kind of factual netting that holds the whole digital world together.
How actors are losing their voices to AI - https://on.ft.com/3CU465x via @FT
In 2005, Marston had signed a contract with IBM for a job he had recorded for a satnav system. In the 18-year-old contract, an industry standard, Marston had signed his voice rights away in perpetuity, at a time before generative AI even existed. Now, IBM is licensed to sell his voice to third parties who could clone it using AI and sell it for any commercial purpose. IBM said it was “aware of the concern raised by Mr Marston” and were “discussing it with him directly”.
“[Marston] is working in the same marketplace, he is still selling his voice for a living, and he is now competing with himself,” said Mathilde Pavis, the artist’s lawyer who specialises in digital cloning technologies. “He had signed a document but there was no agreement for him to be cloned by an unforeseen technology 20 years later.”
Thousands of other voiceover and performance artists face the same dilemma
He likens the latest AI technology to a Swiss army knife with 500 tools. “If you want to work out all the places you can use that in your organisation, you can either create a small team and put them in an ivory tower and they can come up with ideas, or you can give everyone a Swiss army knife and they’ll find their own use cases, as long as you’ve got guardrails around it.” Those guardrails include “first draft only; humans in the loop; use it for cases with a low cost of failure”
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At KPMG, global head of people Nhlamu Dlomu is more worried that work could intensify. “What is it that can help us not fall into that trap? What are the real guardrails we need to put around work so we can actually ensure we get the benefit [from AI], not just for organisations but for individuals as well?”
PwC’s Sharma has the same prediction. “What’s going to happen in a year’s time is that our clients . . . are going to expect us to deliver higher value insights in much much shorter timeframes.
AI shakes up way we work in three key industries - https://on.ft.com/3Pj60UR via @FT
He likens the latest AI technology to a Swiss army knife with 500 tools. “If you want to work out all the places you can use that in your organisation, you can either create a small team and put them in an ivory tower and they can come up with ideas, or you can give everyone a Swiss army knife and they’ll find their own use cases, as long as you’ve got guardrails around it.” Those guardrails include “first draft only; humans in the loop; use it for cases with a low cost of failure
He likens the latest AI technology to a Swiss army knife with 500 tools. “If you want to work out all the places you can use that in your organisation, you can either create a small team and put them in an ivory tower and they can come up with ideas, or you can give everyone a Swiss army knife and they’ll find their own use cases, as long as you’ve got guardrails around it.” Those guardrails include “first draft only; humans in the loop; use it for cases with a low cost of failure”
….
At KPMG, global head of people Nhlamu Dlomu is more worried that work could intensify. “What is it that can help us not fall into that trap? What are the real guardrails we need to put around work so we can actually ensure we get the benefit [from AI], not just for organisations but for individuals as well?”
PwC’s Sharma has the same prediction. “What’s going to happen in a year’s time is that our clients . . . are going to expect us to deliver higher value insights in much much shorter timeframes.
there are a million implications to outsourcing our first drafts to AI. We know people anchor on the first idea they see, influencing their future work, so even drafts that are completely rewritten will be AI-tinged. People may not be as thoughtful about what they write, or the lack of effort may mean they don’t think through problems as deeply. We may not learn how to write as well. We may be flooded with low-quality content.
For the tech giants, one solution to this is to better match advertisers with users through improved targeting. This is usually presented as a win-win-win situation: We get advertisements we’re more likely to appreciate, brands get a better result from their campaign, and both the website we visit and the ad network get more money. But the reality is very different. Targeting isn’t about making the user’s ad experience better; it’s about showing the highest-value advertisements to the users who match the advertiser’s criteria. In effect, this means that when you visit a site, it looks for the identifying information it has about you, and determines which detail has the highest value.
“If we have better tools for identifying which models are risky, companies and regulators can better ensure:
Responsible training: Responsible decisions are made about whether and how to train a new model that shows early signs of risk.
Responsible deployment: Responsible decisions are made about whether, when, and how to deploy potentially risky models.
Transparency: Useful and actionable information is reported to stakeholders, to help them prepare for or mitigate potential risks.
Appropriate security: Strong information security controls and systems are applied to models that might pose extreme risks.”
Most of us have inherited the belief that creativity—the result, we often think, of “being in the zone” or “achieving flow”—should feel easy, or “fluent.” And so we associate mental difficulty with futility.
But muddling through bad ideas is a necessary step in the creative process. The first solutions that come to mind tend to be either preexisting ideas or popular wisdom. These are the paths of least resistance. Though avoiding them requires some work, it’s the surest way to find original ideas that elude our default assumptions and strategies. Instead of interpreting difficulty as a sign of failure, we should see it as a harbinger of solutions.