AI and the Human Factor

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Earlier this month, I went to the Melbourne premiere of “Eno”, a documentary by Gary Hustwit, which is described as the world’s first generative feature film. Each time the film is shown, the choice and sequencing of scenes is different – no two versions are ever the same. Some content may never be screened at all.

I’ll leave readers to explore the director’s rationale for this approach (and the implications for film-making, cinema and streaming). But during a Q&A following the screening, Hustwit was at pains to explain that this is NOT a film generated by AI. He was also guarded and refrained from revealing too much about the proprietary software and hardware system he co-developed to compile and present the film.

However, the director did want to stress that he didn’t simply tell an AI bot to scour the internet, scrape any content by, about or featuring Brian Eno, and then assemble it into a compilation of clips. This documentary is presented according to a series of rules-based algorithms, and is a content-led venture curated by its creator. Yes, he had to review hours and hours of archive footage from which to draw key themes, but he also had to shoot new interview footage of Eno, that would help to frame the context and support the narrative, while avoiding a banal biopic or series of talking heads. The result is a skillful balance between linear story telling, intriguing juxtaposition, traditional interviews, critical analysis, and deep exploration of the subject. The point is, for all its powerful capabilities, AI could not have created this film. It needed to start with human elements: innate curiosity on the part of the director; intelligent and empathetic interaction between film maker and subject; and expert judgement in editing the content – as a well as an element of risk-taking in allowing the algorithm to make the final choices when it comes to each screened version.

That the subject of this documentary is Eno should not be surprising, either. He has a reputation for being a modern polymath, interested in science and technology as well as art. His use of Oblique Strategies in his creative work, his fascination with systems, his development of generative music, and his adoption of technology all point to someone who resists categorisation, and for whom work is play (and vice versa). In fact, imagination and play are the two key activities that define what it is to be human, as Eno explored in an essay for the BBC a few years ago. Again, AI does not yet have the power of imagination (and probably has no sense of play).

Sure, AI can conjure up all sorts of text, images, video, sound, music and other outputs. But in truth, it can only regurgitate what it has been trained on, even when extrapolating from data with which it has been supplied, and the human prompts it is given. This process of creation is more akin to plagiarism – taking source materials created by other people, blending and configuring them into some sort of “new” artefact, and passing the results off as the AI’s own work.

Plagiarism is neither new, nor is it exclusive to AI, of course. In fact, it’s a very natural human response to our environment: we all copy and transform images and sounds around us, as a form of tribute, hommage, mimicry, creative engagement, pastiche, parody, satire, criticism, acknowledgement or denouncement. Leaving aside issues of attribution, permitted use, fair comment, IP rights, (mis)appropriation and deep fakes, some would argue that it is inevitable (and even a duty) for artists and creatives to “steal” ideas from their sources of inspiration. Notably, Robert Shore in his book about “originality”. The music industry is especially adept at all forms of “copying” – sampling, interpolation, remixes, mash-ups, cover versions – something that AI has been capable of for many years. See for example this (limited) app from Google released a few years ago. Whether the results could be regarded as the works of J.S.Bach or the creation of Google’s algorithm trained on Bach’s music would be a question for Bach scholars, musicologists, IP lawyers and software analysts.

Finally, for the last word on AI and the human condition, I refer you to the closing scene from John Carpenter’s cult SciFi film, “Dark Star”, where an “intelligent” bomb outsmarts its human interlocutor. Enjoy!

Next week: AI hallucinations and the law

 

 

Whose side is AI on?

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At the risk of coming across as some sort of Luddite, recent commentary on Artificial Intelligence suggests that it is only natural to have concerns and misgivings about its rapid development and widespread deployment. Of course, at its heart, it’s just another technology at our disposal – but by its very definition, generative AI is not passive, and is likely to impact all areas of our life, whether we invite it in or not.

Over the next few weeks, I will be discussing some non-technical themes relating to AI – creativity and AI, legal implications of AI, and form over substance when it comes to AI itself.

To start with, these are a few of the questions that I have been mulling over:

– Is AI working for us, as a tool that we control and manage?  Or is AI working with us, in a partnership of equals? Or, more likely, is AI working against us, in the sense that it is happening to us, whether we like it or not, let alone whether we are actually aware of it?

– Is AI being wielded by a bunch of tech bros, who feed it with all their own prejudices, unconscious bias and cognitive limitations?

– Who decides what the Large Language Models (LLMs) that power AI are trained on?

– How does AI get permission to create derived content from our own Intellectual Property? Even if our content is on the web, being “publicly available” is not the same as “in the public domain”

– Who is responsible for what AI publishes, and are AI agents accountable for their actions? In the event of false, incorrect, misleading or inappropriate content created by AI, how do we get to clarify the record, or seek a right of reply?

– Why are AI tools adding increased caveats? (“This is not financial advice, this is not to be relied on in a court of law, this is only based on information available as at a certain point in time, this is not a recommendation, etc.”) And is this only going to increase, as in the recent example of changes to Google’s AI-generated search results? (But really, do we need to be told that eating rocks or adding glue to pizza are bad ideas?)

– From my own experience, tools like Chat GPT return “deliberate” factual errors. Why? Is it to keep us on our toes (“Gotcha!”)? Is it to use our responses (or lack thereof) to train the model to be more accurate? Is it to underline the caveat emptor principle (“What, you relied on Otter to write your college essay? What were you thinking?”). Or is it to counter plagiarism (“You could only have got that false information from our AI engine”). If you think the latter is far-fetched, I refer you to the notion of “trap streets” in maps and directories.

– Should AI tools contain better attribution (sources and acknowledgments) in their results? Should they disclose the list of “ingredients” used (like food labelling?) Should they provide verifiable citations for their references? (It’s an idea that is gaining some attention.)

– Finally, the increased use of cloud-based services and crowd-sourced content (not just in AI tools) means that there is the potential for overreach when it comes to end user licensing agreements by ChatGPT, Otter, Adobe Firefly, Gemini, Midjourney etc. Only recently, Adobe had to clarify latest changes to their service agreement, in response to some social media criticism.

Next week: AI and the Human Factor

The Law of Diminishing Returns….

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After blogging about false economies last week, a similar topic came to mind – the law of diminishing returns. This is the principle that suggests the longer you keep doing the same thing, the less benefit you derive from it.

I know from personal experience that continuing to work at something for any length of time can lead to a decline in results – fatigue sets in, I’m more prone to making mistakes, and I end up spending more time double-checking and correcting my work. So, in way, it’s another false economy – spending more hours on a task (the input) does not justify the amount of effort, or guarantee the quality of the end results (the output). Part of it is down to efficiencies, but it’s also to do with losing focus, being distracted, or being so intent on the “doing” and not the “achieving”.

Politicians, writers, musicians, artists, athletes are all susceptible to the law of diminishing returns. Doggedly repeating the same old slogans in pursuit of the same old policies (or trying to hold on to power for its own sake) lead political parties into stagnation and electoral dead ends. Successful athletes who don’t know when to hang up their boots rarely get to choose the timing of their retirement. Creatives who keep recording the “same” album, writing the “same” novel, making the “same” film, or painting the “same” picture come across as stale, formulaic, tired, boring and bereft of ideas – it’s clear that they have nothing new to say, so why should we keep paying attention?

Next week: Whose side is AI on?

 

 

False Economies – if it’s cheap, there must be a reason!

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When I was 7 or 8 years old, I asked my parents to buy me a specific brand of toy as a birthday or Christmas present. With the best of intentions, they chose instead a close approximation of the real thing – presumably because it was cheaper, and to them it was exactly the same. Of course, being cheaper, it was badly designed, poorly made, and was nothing like the toy I had asked for. From memory, it only lasted only a few months before falling apart.

This was my first lesson in false economies – cheap and cheerful can quickly become cheap and nasty, rather like some cheaper brands of peanut butter, which are bulked out with sugar, oils, fats and other additives (instead of containing 100% peanuts).

Many years ago I had some shirts made in Shenzhen, because they seemed like a bargain. Sadly, another false economy – after I got them home, I realised the cut was all wrong, and I’m sure they had substituted a cheaper fabric to the one I had chosen. They were unwearable. On the other hand, some jackets I had made in Hong Kong lasted nearly twenty years, because I had paid a bit more to go to an established tailor.

I’m not saying that more expensive branded goods and so-called luxury items are always “better” – but as a general rule, when doing like-for-like comparisons, you get what you pay for. When an item costs more to buy, it invariably lasts longer because of the materials used, the better design, the superior manufacturing and the overall higher quality.

I appreciate that in the current economic environment, consumers are even more cost conscious, and are looking for value for money, if not actual bargains. But just because something is cheap, doesn’t mean it’s the better option. Look at the true cost of fast fashion, fast food, fast money…

Next week: The Law of Diminishing Returns….