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Content in Context helps companies to define the market for their products and services, to identify customers and build the business pipeline, and to develop their content marketing strategies. By working with our clients to design, build and grow their business, our primary focus is to extract commercial value from unique assets, including knowledge, data, know-how, processes and transactional information.

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

 

AI & Music

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In a recent episode of a TV detective show, an AI tech dude tries to outsmart an old school musicologist by re-creating the missing part of a vintage blues recording. The professor is asked to identify which is the “real” track, compared to the AI versions. The blues expert guesses correctly within a few beats – much to the frustration of the coder.

“How did you figure it out so quickly?”

“Easy – it’s not just what the AI added, but more importantly what it left out.”

The failure of AI to fully replicate the original song (by omitting a recording error that the AI has “corrected”) is another example showing how AI lacks the human touch, does not yet have intuition, and struggles to exercise informed judgement. Choices may often be a matter of taste, but innate human creativity cannot yet be replicated.

Soon, though, AI tools will displace a lot of work currently done by composers, lyricists, musicians, producers, arrangers and recording engineers. Already, digital audio workstation (DAW) software easily enables anyone with a computer or mobile device to create, record, sample and mix their own music, without needing to read a note of music and without having to strum a chord. Not only that, the software can emulate the acoustic properties of site-specific locations, and correct out-of-tune and out-of-time recordings. So anyone can pretend they are recording at Abbey Road.

I recently blogged about how AI is presenting fresh challenges (as well as opportunities) for the music industry. Expect to see “new” recordings released by (or attributed to) dead pop stars, especially if their back catalogue is out of copyright. This is about more than exhuming preexisting recordings, and enhancing them with today’s technology; this is deriving new content from a set of algorithms, trained on vast back catalogues, directed by specific prompts (“bass line in the style of Jon Entwistle”), and maybe given some core principles of musical composition.

And it’s the AI training that has prompted the major record companies to sue two AI software companies, a state of affairs which industry commentator, Rob Abelow says was inevitable, because:

“It’s been clear that Suno & Udio have trained on copyrighted material with no plan to license or compensate”.

But on the other hand, streaming and automated music are not new. Sound designer and artist Tero Parviainen recently quoted Curtis Roads’ “The Computer Music Tutorial” (2023):

“A new industry has emerged around artificial intelligence (AI) services for creating generic popular music, including Flow Machines, IBM Watson Beat, Google Magenta’s NSynth Super, OpenAI’s Jukebox, Jukedeck, Melodrive, Spotify’s Creator Technology Research Lab, and Amper Music. This is the latest incarnation of a trend that started in the 1920s called Muzak, to provide licensed background music in elevators, business and dental offices, hotels, shopping malls, supermarkets, and restaurants”

And even before the arrival of Muzak in the 1920s, the world’s first streaming service was launched in the late 1890s, using the world’s first synthesizer – the Teleharmonium. (Thanks to Mark Brend’s “The Sound of Tomorrow”, I learned that Mark Twain was the first subscriber.)

For music purists and snobs (among whom I would probably count myself), all this talk about the impact of AI on music raises questions of aesthetics as well as ethics. But I’m reminded of some comments made by Pink Floyd about 50 years ago, when asked about their use of synthesizers, during the making of “Live at Pompeii”. In short, they argue that such machines still need human input, and as long as the musicians are controlling the equipment (and not the other way around), then what’s the problem? It’s not like they are cheating, disguising what they are doing, or compensating for a lack of ability – and the technology doesn’t make them better musicians, it just allows them to do different things:

“It’s like saying, ‘Give a man a Les Paul guitar, and he becomes Eric Clapton… It’s not true.'”

(Well, not yet, but I’m sure AI is working on it…)

Next week: Some final thoughts on AI

Did you spot the “deliberate” mistake?

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A few days ago, I posted a blog that was largely written by AI. I simply entered a few links, suggested some themes, and added a prompt to “write a blog about AI and fakes” – ChatGPT did the rest.

Overall, ChatGPT does a pretty good job of summarising content, and synthesising logical conclusions based on what it has been fed. However, apart from the known risk of hallucinations, AI tools such as ChatGPT do not give specific citations for any external sources (neither those included in the prompts, nor any data on which their LLMs were trained); and they can leave out important information, but how such omissions are made is far from clear.

As promised, here are some clarifications that ChatGPT did not provide, in reverse order of their appearance in my earlier blog.

First, the “Conclusion” was 100% the work of ChatGPT. I wanted to remain as objective as possible, and did not prompt ChatGPT to come to any specific conclusion, and I did not include any implicit suggestion as to the sentiment to adopt. As such, this section is neutral, objective, balanced and logical. As was the introductory section ChatGPT generated.

For the comments on “AI and Copyright”, I entered the phrase “OpenAI and authors”, with links to articles from ABC News and Reuters. Again, apart from omitting specific citations to the source content, the ChatGPT output is balanced, even though those inputs might be construed as negative towards AI in general and OpenAI in particular. But this raises the question of ChatGPT’s own self-awareness, and whether it “knows” or understands that it is a product of OpenAI?

The section on “Legislative Actions on Deep Fakes” is reasonable, given the only guidance I provided was the phrase “Fake images”, and links to two Guardian articles (here and here). However, one of those articles details a specific legal case, and allegations of criminal activity – quite negative for AI. I doubt if ChatGPT fully understands the principles of sub judice, but maybe it used its discretion or bias to omit the details of this article?

I was reasonably impressed with how ChatGPT compiled the section on “Celebrity Persona Rights”, especially the summary it extracted from the Scientific American article. The phrase “persona rights” is lifted from the html link I supplied, and the only specific prompt I gave was “Scarlett Johansson”. Again, ChatGPT was happy to include potentially negative references to OpenAI. However, ChatGPT did not directly engage with an extract I had used from the source article, which provides more context:

“Most discussions about copyright and AI focus […] on whether and how copyrighted material can be used to train the technology, and whether new material that it produces can be copyrighted.”

But, you could argue that ChatGPT made an equivalent reference in the section on “AI and Copyright”.

More significantly, the original article (and dispute) is about AI-generated voice similarity, whereas ChatGPT refers to “likeness”, which I would usually interpret as visual similarity.

The references to “Mozilla’s Campaign Against Misinformation” and “AI in Indian Elections” are both much weaker by comparison. I used the prompt “Mozilla”, and links to the specific WhatsApp campaign, as well as a list of other Mozilla campaigns. I also used “Indian election” as a specific prompt, as well as a relevant news article. First, the Mozilla campaigns include one about AI transparency, which ChatGPT does not address here, or in the section on copyright – perhaps it decided it was too critical of OpenAI et al? Second, the ABC article includes mention of a deep fake video of a deceased Indian politician – which I would have thought merited a mention by ChatGPT.

Finally, the section on “Dylan and Rodin: A Fabricated Encounter” is probably the most problematic. I used the prompt, “Dylan and Rodin….”, and links to two recent articles by Dave Haslam – one that discusses an ongoing fake narrative about “Bob Dylan photographed playing chess in Paris”, and the other about a fabricated, AI-generated “photograph of Auguste Rodin and Camille Claudel”. (I also included a link to the latter fake, with the prompt “Image”.) Somehow, ChatGPT has confused and/or incorrectly conflated these two topics, and erroneously concluded that this was a reference to a false account of Dylan meeting Rodin in France. ChatGPT simply reproduced the fake photo (which I chose to omit from my published blog this week), and left out any mention of Claudel. I wonder whether this is perhaps because Haslam is not as well indexed in ChatGPT’s database compared to the incorrect/misleading social media posts, or his articles are too critical of AI (and those that replicate its errors and perpetuate its myths), and too nuanced in their arguments. And was the failure to mention Claudel an oversight, or something more insidious?

I know that ChatGPT and other AI tools are trying to protect themselves with caveat emptor-style disclaimers, and no-one should rely on any AI output unless they are confident of the results (or they are indifferent/negligent as to the potential for harm or mischief), but the Dylan/Rodin example illustrates the inherent risks we still face as end users.

AI and Deep (and not so deep…) Fakes

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The New York Times recently posted a quiz – “can you tell the difference between a photograph, and an image created by AI?”

Of the quiz examples, a mix of actual photos and AI-generated content, I was only able to correctly identify 8/10 as to which were which. My significant other claimed to have scored 10/10! In my defence, I correctly identified all of the AI images, but I mistook two authentic photos as being fakes. Of the latter, one featured a bunch of famous people, most of whom I did not recognise, and the photo had been significantly cropped, removing much of the visual context (I also suspect it had been subject to some additional photo-shopping, given it was a publicity shot). The other real photo had been taken at such an unusual angle that it distorted some of the natural perspective, so as to make some elements look wonky. (But maybe I’ve become more cynical or sceptical, and therefore I tend to disbelieve more of what I see, the more I know I am being exposed to AI-generated content?)

How can we remain alert to AI deceptions, while at the same time recognizing and embracing the potential that this amazing technology has to offer?

Taking my lead from the New York Times article, the following blog has been created using ChatGPT. All I did was enter some brief subject headings (including the title), a series of internet links, and a prompt to turn it into a blog on AI and fakes. Can you tell what is true, what is hallucination, and what important information has been left out?

AI and Deep (and not so deep…) Fakes

Artificial Intelligence (AI) has revolutionized numerous sectors, bringing forth innovations that were once unimaginable. However, one of the more controversial applications of AI is in the creation of deep fakes. These are hyper-realistic digital falsifications created using AI, capable of mimicking real people’s appearances and voices. This technology has sparked debates over ethics, security, and the future of digital media. Let’s delve into some recent examples and the implications they bring.

Dylan and Rodin: A Fabricated Encounter

A striking example of AI’s capacity to blur reality is the supposed encounter between Bob Dylan and the sculptor Auguste Rodin. In an article on Substack by Dave Haslam, the scenario of Dylan visiting Rodin’s Parisian studio is meticulously crafted, yet it’s a fabrication made plausible by AI tools. This fake story exemplifies how AI can create convincingly realistic yet entirely fictional narratives, challenging our ability to discern truth from fiction.

AI in Indian Elections

AI’s role extends beyond entertainment into more critical areas such as political campaigns. In the 2024 Indian elections, AI-generated deep fakes have been used to spread misinformation and manipulate public opinion. An article by ABC News details how AI is disrupting electoral processes, raising concerns about the integrity of democratic systems.

Mozilla’s Campaign Against Misinformation

In response to the growing threat of AI-generated misinformation, organizations like Mozilla have launched campaigns to protect the integrity of elections. Mozilla’s efforts aim to hold platforms like WhatsApp accountable for the spread of fake news and deep fakes, urging them to implement stricter controls to safeguard democratic processes.

Celebrity Persona Rights

AI’s ability to replicate real people has also sparked legal disputes, particularly concerning persona rights. Scarlett Johansson’s recent dispute with OpenAI highlights these issues. Johansson’s concerns about her likeness being used without consent underscore the broader debate about the ethical use of AI. As noted by Scientific American, the conflict brings to light questions about how AI-generated content can respect individual rights and privacy.

Legislative Actions on Deep Fakes

Governments are beginning to take action against the misuse of AI-generated content. In Australia, new laws are being proposed to combat the creation and distribution of deep fake pornography and other harmful images. The Guardian reports on legislative efforts to protect individuals from the malicious use of AI, reflecting a growing recognition of the need for regulation.

AI and Copyright

The intersection of AI and copyright law is another area of intense debate. A lawsuit involving famous authors against OpenAI raises questions about how AI uses copyrighted material for training and whether the content it generates can itself be copyrighted. This legal battle, reported by ABC News and Reuters, could set important precedents for how intellectual property laws adapt to the age of AI.

Conclusion

As AI technology continues to advance, its applications in creating deep fakes present both opportunities and challenges. While it can be used creatively and constructively, it also poses significant risks to privacy, security, and the authenticity of information. The ongoing developments in legislation, ethical guidelines, and technological safeguards will be crucial in shaping the future landscape of AI-generated content.

So, did you spot the “deliberate” error(s)? And what information was missing? Answers will be posted later this week.

Next week: AI & Music