Category: Technology and its malcontents

The limits of generative AI

Whilst cruising the interwebs I can across the follow, which nicely captures the limits of large language (LLMs) models.

In the persuasive practice of Derrida, Paul de Man and others, it took language not as a reminder of secret structure but as the home of a recurring crisis of meaning, a place where interpretation learned that it could never end. It did not hold, as many of its detractors thought it did, that there was no reality apart from language, and it’s wrong to translate Derrida’s famous ‘Il n’y a pas de hors-texte’ as ‘there is nothing outside the text.’ A hors-texte is an unnumbered page in a printed book. Derrida is saying that even the unnumbered pages count, just as an outlaw, in French an hors-la-loi, has everything to do with the law, since it makes him what he is. More crudely, we might say that interpretation is theoretically endless, but this claim itself needs interpretation. Endless is not the same as pointless; and what is endless in theory is often stopped easily enough in practice. We may think – I do think – that the reasons for stopping are usually more interesting than the empty possibility of going on for ever, although then it would be worth asking whether those reasons are practical or theoretical.
Wood, Michael. 2016. “We Do It All the Time.” London Review of Books, February 4. https://www.lrb.co.uk/the-paper/v38/n03/michael-wood/we-do-it-all-the-time.

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Even the effects already discovered are due to chance and experiment rather than to the sciences; for our present sciences are nothing more than peculiar arrangements of matters already discovered, and not methods for discovery or plans for new operations.

Aphorism VIII. Francis Bacon, Novum Organum, Book 1, 1620

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Where will LLMs take us?

Not a week seems to pass by without some surprising news concerning large-language models (LLMs). Most recently it was when an LLM trained for other purposes played chess at a reasonable level. This seemingly constant stream of surprising news has led to talk that LLMs are the next general-purpose technology—a technology that affects an entire economy—and will usher in new era of rapid productivity growth. They might even accelerate global economic growth by an order of magnitude, as the Industrial Revolution did, providing us with a Fifth Industrial Revolution.

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The coming wave

This book describes itself as the work of ‘the ultimate insider’. This seems rather apt as it provides us with a glimpse of what the technocratic chattering class are saying about the current AI moment. Unfortunately it doesn’t provide us with insight into how this current moment will play out as the view from inside appears to be is quite poor, lacking the perspective need to really grapple with this question.

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Forever ten years away

Why do some the technologies always seem to be ten years away? We’re not talking about the science fiction dreaming of faster than light travel or general AI and the singularity. Those ten years apply to technologies that forever seem to be just out of reach, just beyond our current technical capabilities, like nuclear fusion (as opposed to fission) or quantum computing. Researchers make incremental progress and we’re told that (once the technology works) its going to change everything, but despite this incremental progress estimates of when the technology will be commercialised and so available to the public always seem to be in the ballpark of ‘ten years’.

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Gen AI’s other use

The hype for generative AI doesn’t seem to be dying off. This is unsurprising as—unlike the metaverse, blockchain, and crypto—the technology is providing demonstrable benefits. We’re clearly in the installation phase where mad experimentation is the rule rather than the exception.

A lot of the mad experimentation we’re seeing is focused on either integrating new things into a LLM, or on jamming a LLM into some existing solution to ‘revolutionise’ it. There’s some great stuff in there—a wealth of new LLM-powered creative tools is enabling us to unleash our artistic urgers. On the other hand, integrating a LLM with an online learning platform is useful, but unlikely to be revolutionary.

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On our new robot overlords

One might be convinced that our robot overlords have finally arrived, with all the noise in the news and social media about the new generation of generative AI tools. Tools such as GPT-3 & GPT-4, Midjourney, and Stable Diffusion, have resulted in a wave of creativity as we experiment with them, discovering what they can do, the new opportunities they represent, how to trick them, and where they fail. It’s now possible to turn a rough drawing into a functioning web sitecreate a recipe from a picture of potential ingredients, or develop a Seinfield-spoof streaming show. Conversations with these tools have even led some users to believe that the technology is conscious.

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A new narrative for digital data

We have a new essay published on Deloitte InsightsA new narrative for digital data, a collaboration between the Centre for the Edge, Deloitte Integrity, and the Australian Data Standards Body that picks apart some of the continuing challenges with data privacy. It seems that every week the is a new announcement where the personal information for millions of individuals leaked to some fraudster. Indeed, if data privacy were a country then we would consider it a fail state. This essay compares Western and an Indigenous Australian framings of this problem to argue that our Western obsession with property rights might be the problem, rather than the solution.

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Why hasn’t AI delivered on it’s promise?

We have a new essay published on Deloitte Insights, Why hasn’t AI delivered on its promise?, a collaboration between the Centre for the Edge and and the AI Institute. This time we’re smashing together the ideas from The real landscape of technology-enabled opportunity and Reconstructing jobs to see if they can help us understand why, despite recent advances in AI, adoption seems to be lacking.

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