Tag Archives: #AI

The cost of AI

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A variant on Moore’s law is the observation that the financial capital required to launch a new business decreases exponentially as technology gets cheaper.

Pre-internet, and using a notional geometric scale for the purposes of illustration, you might have needed $5m to found and build a new venture. The World Wide Web probably reduced that to $500k, while cloud computing brought it down to $50k. With the expansion of SaaS and API solutions, that cost might have been $5k to get going. Now, vibe coding and $500 of AI prompts can probably launch a new website, build a back end database, implement an e-commerce solution and deploy agentic AI bots to go and find your first customers.

This is a great outcome if measured by a lower barrier to market entry. It also enables founders to “fail fast, fail cheap”, and incentivises innovation by financially de-risking the process.

But even though the cost of AI tools is extraordinarily cheap in terms of the computing and processing power they deliver, there is a huge cost to our rapid adoption of AI that needs to be accounted for.

First, we are seeing corporate lay-offs among tech firms and parts of the service industry that no longer need as many human bodies and minds to operate at scale. So there is a human, economic and societal cost of increased un(der)employment.

Second, traditional skills and expertise are being hugely reduced in perceived value – why pay a graphic artist to design an image when I can use dall-e for free?

Third, as more and more creative tasks are being outsourced or delegated to AI (“create a short story about an F1 race in the style of Ernest Hemingway”) we risk losing our own innate creativity (that comes with experimentation, curiosity, play and reflection). This in turn devalues the creative process itself (thanks to cheaper, AI-enabled production).

Fourth, AI (and the Large Language Models on which it is trained) has no great respect for intellectual property. It doesn’t recognise boundaries between copyright material, content that is subject to creative commons, content that is in the public domain, and content which is publicly available. Again, if copyright owners and original content creators are not recognised or compensated for their work, why would anyone aspire to creating anything original?

Finally, there is the cost of resources (energy, water, rare earth metals) needed to maintain huge AI processing plants and data centres. (But at least this demand is accelerating the development of renewable energy.)

A few years ago, I posted a blog about the importance of the human factor, in the face of technological progress brought by automation and AI. I still remain cautiously optimistic that AI will bring huge benefits, despite the rampant growth of AI in the three years since I wrote that piece. But we are currently in an awkward and comfortable transition phase. If more jobs are lost to AI, and if human-led output is increasingly devalued, perhaps we will need to revisit the debate about Universal Basic Income and other policies to facilitate this transition.

Next week: Music, music everywhere…. and none of it very memorable 

More on AI Hallucinations…

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The mainstream adoption of AI continues to reveal the precarious balance between the benefits and the pitfalls.

Yes, AI tools can reduce the time it takes to research information, to draft documents and to complete repetitive tasks.

But, AI is not so good at navigating subtle nuances, interpreting specific context or understanding satire or irony. In short, AI cannot “read the room” based on a few prompts and a collection of databases.

And then there is the issue of copyright licensing and other IP rights associated with the original content that large language models are trained on.

One of the biggest challenges to AI’s credibility is the frequent generation of “hallucinations” – false or misleading results that can populate even the most benign of search queries. I have commented previously on whether these errors are deliberate mistakes, an attempt at risk limitation (disclaimers), or a way of training AI tools on human users. (“Spot the deliberate mistake!) Or a get-out clause if we are stupid enough to rely on a dodgy AI summary!

With the proliferation of AI-generated results (“overviews”) in basic search queries, there is a tendency for AI tools to conflate or synthesize multiple sources and perspectives into a single “true” definition – often without authority or verified citations.

A recent example was a senior criminal barrister in Australia who submitted fake case citations and imaginary speeches in support of a client’s case.

Leaving aside the blatant dereliction of professional standards and the lapse in duty of care towards a client, this example of AI hallucinations within the context of legal proceedings is remarkable on a number of levels.

First, legal documents (statutes, law reports, secondary legislation, precedents, pleadings, contracts, witness statements, court transcripts, etc.) are highly structured and very specific as to their formal citations. (Having obtained an LLB degree, served as a paralegal for 5 years, and worked in legal publishing for more than 10 years, I am very aware of the risks of an incorrect citation or use of an inappropriate decision in support of a legal argument!!!)

Second, the legal profession has traditionally been at the forefront in the adoption and implementation of new technology. Whether this is the early use of on-line searches for case reports, database creation for managing document precedents, the use of practice and case management software, and the development of decision-trees to evaluate the potential success of client pleadings, lawyers have been at the vanguard of these innovations.

Third, a simple document review process (akin to a spell-check) should have exposed the erroneous case citations. The failure to do so reveals a level laziness or disregard that in another profession (e.g., medical, electrical, engineering) could give rise to a claim for negligence. (There are several established resources in this field, so this apparent omission or oversight is frankly embarrassing: https://libraryguides.griffith.edu.au/Law/case-citators, https://guides.sl.nsw.gov.au/case_law/case-citators, https://deakin.libguides.com/case-law/case-citators)

In short, as we continue to rely on AI tools, unless we apply due diligence to these applications or remain vigilant to their fallibility, we use them at our peril.

 

A postscript on AI

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AI tools and related search engines should know when a factual reference is incorrect, or indeed whether an individual (especially someone notable) is living or dead. In an interesting postscript to my recent series on AI, I came across this article – written by someone whom Google declared is no longer with us.

Glaring errors like these demand that tech companies (as well as publishers and media outlets who increasingly rely on these tools) take more seriously the individual’s right of reply, the right to correct or amend the record, as well as the right to privacy and to be forgotten on the internet.

As I commented in my series of articles, AI tools such as ChatGPT (and, it seems, Google Search) can easily conflate separate facts into false statements. Another reason to be on our guard as we embrace (and rely on) these new applications.

Next week: Bad Sports

 

 

Some final thoughts on AI

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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