Last week, I attended a talk by musical polymath, Jim O’Rourke, on the Serge Paperface modular synthesizer. It was part memoir, part demonstration, and part philosophy tutorial. At its heart, the Serge is an outwardly human-controlled electronic instrument, incorporating any number and combination of processors, switches, circuits, rheostats, filters, voltage controllers and patch cables. These circuits take their lead from the operator’s initial instructions, but then use that data (voltage values) to generate and manipulate sound. As the sound evolves, the “composition” takes on the appearance of a neural network as the signal is re-patched to and from each component, sometimes with random and unexpected results – rather like our own thought patterns.

But the Serge is not an example of Artificial Intelligence, despite its ability to process multiple data points (sequentially, in parallel, and simultaneously) and notwithstanding the level of unpredictability. On the other hand, that unpredictability may make it more “human” than AI.

My reasons for using the Serge as the beginning of this concluding blog on AI are three-fold:

First, these modular synthesizers only became viable with the availability of transistors and integrated circuits that replaced the valves of old, just as today’s portable computers rely on silicon chips and microprocessors. Likewise, although some elements of AI have been around for decades, the exponential rise of mobile devices, the internet, cloud computing and social media has allowed AI to ride on the back of their growth and into our lives.

Second, O’Rourke referred to the Serge as being “a way of life”, in that it leads users to think differently about music, to adopt an open mind towards the notion of composition, and to experiment knowing the results will be unpredictable, even unstable. In other words, suspend all pre-conception and embrace its whims (even surrender to its charms). Which is what many optimists would have us do with AI – although I think that there are still too many current concerns (and the potential for great harm) before we can get fully comfortable with what AI is doing, even if much of may actually be positive and beneficial. At least the Serge can be turned off with the flick of a switch if things get out of hand.

Third, as part of his presentation O’Rourke made reference to Stephen Levy’s book, “Artificial Life”, published 30 years ago. In fact, he cited it almost as a counterfoil to AI, in that Levy was exploring the interface between biological life and digital DNA in a pre-internet era, yet his thesis is even more relevant as AI neural nets become a reality.

So, where do I think we are in the evolution of AI? A number of cliches come to mind – the Genie is already out of the bottle, and like King Canute we can’t turn back the tide, but like the Sorceror’s Apprentice maybe we shouldn’t meddle with something we don’t understand. I still believe the risks associated with deep fakes, AI hallucinations and other factual errors that will inevitably be repeated and replicated without a thought to correct the record represent a major concern. I also think more transparency is needed as to how LLMs are built, and the way AI is trained on them, as well as disclosures when AI is actually being deployed, and what content has been used to generate the results. Issues of copyright theft and IP infringements are probably manageable with a combination of technology, industry goodwill and legal common sense. Subject to those legal clarifications, questions about what is “real” or original and what is “fake” or artificial in terms of creativity will probably come down to personal taste and aesthetics. But expect to see lots of disputes in the field of arts and entertainment when it comes to annual awards and recognition for creativity and originality!

At times, I can see AI is simply a combination of mega databases, powerful search engines, predictive tools, programmable logic, smart decision trees, pattern recognition on steroids, all aided by hi-speed computer processing and widespread data distribution. At other times, it feels like we are all being made the subject matter or inputs of AI (it is happening “to” us, rather than working for us), and in return we get a mix of computer-generated outputs with a high dose of AI “dramatic license”.

My over-arching conclusion at this point in the AI journey is that it resembles GMO crops – unless you live off grid and all your computers are air-locked, then every device, network and database you interact with has been trained on, touched by or tainted with AI. It’s inevitable and unavoidable.

Next week: RWAs and the next phase of tokenisation