Pushing Electrons

Following the enthusiastic reception of OpenAI's public ChatGPT release last year, there is now a gold rush by Big Tech to capitalize on it's capabilities. Microsoft recently announced the integration of ChatGPT with its Bing search engine. Google – who have published numerous papers on this tech previously, but never made the systems public – announced the introduction of their Bard system into Google Search. And, naturally, there's a host of start-ups building their own versions of ChatGPT, or leveraging integration with OpenAI's version to power a variety of activities.

What's fascinating about this explosion of applications is that the underlying tech – LLM's, or large-language models – is conceptually simple. These systems take a block of input text (called “tokens”), run them through a neural network, and output a block of text that (according to the system) “best matches” what should come next. It's a language pattern-matcher. That's it.

And yet, it's capabilities are surprisingly powerful. It can compose poems in a variety of styles over a range of subjects. It can summarize. It can assume personas. It can write computer code and (bad) jokes. It can offer advice. And the responses are tight, well-composed English. It's like chatting with another person.

Except, when it's not. As many have noted, it often returns incorrect, if confident, answers. It makes up data and citations. It's code is often buggy or just flat-out wrong. It has a gift for creating responses that sound correct, regardless of the actual truth.

It's language without intelligence.

Let's sit with that for a minute. Engineers have created a machine that can manipulate language with a gift rivalling great poets, and yet often fails simple math problems.

The implications of this are fascinating.

First, from a cognitive science perspective, it suggests that language skill and intelligence – definitely in a machine, possibly in humans, maybe as a general rule – are two completely separate things. Someone compared ChatGPT to “a confident white man” – which a) oof and b) may be more accurate than they realized. In an environment where performance is measured by verbal fluidity or writing skill, but not actual knowledge, ChatGPT would absolutely excel. There are many jobs in the world that fit this description (and unsurprisingly, they seem to be dominated by white men!) For these sorts of activities, an agent – human or machine – doesn't have to be good at any particular thing except for convincing others it is smart through verbal acuity and vague allusions to data, either actual or imagined. (Give it an opinion column in the New York Times!)

Second, technologically, it immediately suggests both the utility and the limits of the system. Need to write an email, an essay, a poem – any product that primarily requires high language skill? ChatGPT and it's successors can now do that with ease. If the ultimate outcome of the activity is influencing a human's opinion (a teacher, a client, a loved one), you're all set. However, if you require a result that is actually right and factual, it requires human intervention. ChatGPT has the human gift for reverse-engineering justifications for it's actions, no matter how outlandish, and so there's no circumstance where you should trust it, on it's own, to do or say the right thing. A person's judgment is still required.

You might ask “how useful is it's output if you still have to revise it?” To which you might also ask “what value is a writer to an editor?” You don't hammer with a chainsaw – all tools don't need to be fit for all purposes. But, if you need to quickly generate readable text with a certain style about a certain subject, it offers a great starting point without minimal labor. For knowledge workers, that offers an incredible potential for time savings.

Finally, these systems do suggest a path toward artificial general intelligence. These models essentially solve the “challenge” of language, but lack both 1) real, truthful information, as well as 2) the ability to sort and assemble that information into knowledge. The first of those is easily answered – hook it up to the Internet, or books or your email account, or any other source of meaningful reference data. Part of ChatGPT's limitations come from the fact that it is deliberately not connected to the Internet, both constraining it and (at this stage) enhancing it's safety.

And, as for the ability to manipulate knowledge – that is underway, with some working proofs-of-concept already developed. If engineers can develop a reasoning system to complement LLM's – enabling them to decompose questions into a connected set of simpler knowledge searches, and perhaps with the tools to integrate that data in various ways – these systems have the potential to facilitate a wide range of knowledge-based activities.

(In fact, some of the earliest AI systems were reasoning machines of exactly this genre, but based on discrete symbols instead of language. LLM's offer the potential to advance these systems by interpreting language-based information that's less clear-cut than mathematical symbols.)

Along with the technical aspects, we must also ask: what does this mean for society? From a business perspective, likely the same as what happens with all automation – the worst gets automated, the best gets accelerated, and humanity's relationship with production changes. Writers of low-quality or formulaic content may be out of a job. Better writers will no longer have to start from a blank page. The best writing will still be manual, bespoke, and rare. The tone of writing across all media will be homogenized, with the quality floor set to “confident white man” (potentially offering benefits toward diversity and inclusion). The quality of all professional communications will improve as LLM's are integrated into Word, Powerpoint, Outlook, and similar communication software. Knowledge management (think wiki's, CRM's, project management tools) becomes much faster and easier through becoming more automated. Software comments will be automatically generated, letting programmers focus on system development. Sales becomes more effective as follow-ups become automated and messages are tailored to the customer. And that's just the beginning.

From a social standpoint, the outlook is more complex. Personalizing content becomes dramatically easier – one could imagine a system where the author just releases prompts for interaction, and an LLM interprets it uniquely for each reader in the way the reader finds most engaging. Video games, especially narrative video games, become deeper and richer. Social media may have more posts but be less interesting. Misinformation production becomes accelerated, and likely becomes more effective as the feedback cycle also accelerates. These new systems magnify many of society's existing challenges, while also opening up exciting new modes of interaction.

This has been a long time coming in the artificial intelligence community. After years of limited results, the availability of Big Computing has enabled revolutions in image processing, art creation – and now, language-based tasks. These are exciting times, with many more developments assuredly coming soon.

Tags: #ChatGPT #LLM #AI #ArtificialIntelligence

Written by Dulany Weaver. Copyright 2022-2024. All rights reserved.