I was a sceptic about the impact of the new tranche of generative AI tools until this week when two of my friends demonstrated how they could be used in genuinely transformative ways that go far beyond faking essays or acting as a search interface with poor boundaries and a tendency to invent things that look plausible.
Last week in his regular Exponential View newsletter Azeem Azhar described in detail how he had used ChatGPT to design a new board game that combined the characteristics of Ticket to Ride and Azul, shaping it around the idea of discovering elements, and designing selection of game characters based around historical chemists.
It’s a subscriber-only post but you can get a sense of the dialogue.
Then another friend, Matt Webb, posted about how he’d used the code-generating capabilities of GPT-3, via GitHub Copilot, to write the code he needed to scrape a website as part of a project to build a podcast interface. As he wrote:
“Using GitHub Copilot to write code and calling out to GPT-3 programmatically to dodge days of graft actually brought tears to my eyes. I’ve coded, mostly as a hobby, my whole life – it’s a big creative outlet alongside writing – it’s so rarely felt like this. It feels like flying”.
These examples brought home to me the real power of these new tools, not as generators of random boilerplate text for business letters or marketing blurb, or as complex and potentially misleading interfaces to search engines, but as collaborators in our creative activity, supporting idea generating, doing some of the low-level heavy lifting, and sitting on our shoulders like supportive angels.
One of the things that also occurred to me reading Azeem’s piece was that ChatGPT didn’t get tired. For once, a line from the Terminator felt entirely appropriate to describe a current ML system: “It doesn’t feel pity, or remorse, or fear. And it absolutely will not stop… ever, until you have finished your project!”
That’s not to say that these tools live up to the exaggerated claims being made for them, as the rather embarrassing error about the capabilites of the James Webb Space Telescope that Google made at the announcement of their Bard LLM – and the consequent $100bn drop in Alphabet’s value – demonstrated.
Of course, Bard wasn’t being malicious, or even foolish, because these tools don’t have any capacity for feeling. They can’t lie because lying is saying something false with intent, and – we can’t say this strongly enough – they have no intent.
Pull the curtain away from GPT or Stable Diffusion and there’s no wizard, just a vast array of weightings running on a power-hungry set of GPUs. When ChatGPT engages with you it’s basically taking a drunkard’s walk through the forest of word frequencies, calling out the names of each tree as it leans on it before staggering onward. Like taking your own wine to an unlicensed restaurant with zero corkage, you bring the meaning – and because we are so good at projecting into the empty eyes of our machines (and pets.. but we can have that argument another time) we find all the profundity we’re looking for.
Perhaps one day we will develop general AI and the machine will both know what it is saying and – crucially – know that it is a thing that is saying something to us. When that happens we’ll look back on the current fuss over LLMs the way astronomers consider astrology – there was some good data collection and analysis but the fundamental model was so disconnected from reality that it was dangerous.
But even with the current limitations, it’s clear that these tools already have a real role as well-resourced, untiring support for creativity and ideation, with the ability to smash concepts together and produce fascinating results, and that may be enough to change the way we all work, especially in the creative industries. Just imagine what a hard-pressed producer looking for a new entertainment format could do with them.