Category Archives: Intellectual Property Rights

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

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

State of the Music Industry…

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Depending on your perspective, the music industry is in fine health. 2023 saw a record year for sales (physical, digital and streaming), and touring artists are generating more income from ticket sales and merchandising than the GDPs of many countries. Even vinyl records, CDs and cassettes are achieving better sales than in recent years!

On the other hand, only a small number of musicians are making huge bucks from touring; while smaller venues are closing down, meaning fewer opportunities for artists to perform.

And despite the growth in streaming, relatively few musicians are minting it from these subscription-based services, that typically pay very little in royalties to the vast majority of artists. (In fact, some content can be zero-rated unless it achieves a minimum number of plays.)

Aside from the impact of streaming services, there are two other related challenges that exercise the music industry: the growing use of Artificial Intelligence, and the need for musicians to be recognised and compensated more fairly for their work and their Intellectual Property.

With AI, a key issue is whether the software developers are being sufficiently transparent about the content sources used to train their models, and whether the authors and rights owners are being fairly recompensed in return for the use of their IP. Then there are questions of artistic “creativity”, authorial ownership, authenticity, fakes and passing-off when we are presented with AI-generated music. Generative music software has been around for some time, and anyone with a smart phone or laptop can access millions of tools and samples to compose, assemble and record their own music – and many people do just that, given the thousands of new songs that are being uploaded every day. Now, with the likes of Suno, it’s possible to “create” a 2-minute song (complete with lyrics) from just a short text prompt. Rolling Stone magazine recently did just that, and the result was both astonishing and dispiriting.

I played around with Suno myself (using the free version), and the brief prompt I submitted returned these two tracks, called “Midnight Shadows”:

Version 1

Version 2

The output is OK, not terrible, but displays very little in the way of compositional depth, melodic development, or harmonic structure. Both tracks sound as if a set of ready-made loops and samples had simply been cobbled together in the same key and tempo, and left to run for 2 minutes. Suno also generated two quite different compositions with lyrics, voiced by a male and a female singer/bot respectively. The lyrics were nonsensical attempts to verbally riff on the text prompt. The vocals sounded both disembodied (synthetic, auto-tuned and one-dimensional), and also exactly the sort of vocal stylings favoured by so many contemporary pop singers, and featured on karaoke talent shows like The Voice and Idol. As for Suno’s attempt to remix the tracks at my further prompting, the less said the better.

While content attribution can be addressed through IP rights and commercial licensing, the issue of “likeness” is harder to enforce. Artists can usually protect their image (and merchandising) against passing off, but can they protect the tone and timbre of their voice? A new law in Tennessee attempts to do just that, by protecting a singer’s a vocal likeness from unauthorised use. (I’m curious to know if this protection is going to be extended to Jimmy Page’s guitar sound and playing style, or an electronic musician’s computer processing and programming techniques?)

I follow a number of industry commentators who, very broadly speaking, represent the positive (Rob Abelow), negative (Damon Krukowski) and neutral (Shawn Reynaldo) stances on streaming, AI and musicians’ livelihood. For every positive opportunity that new technology presents, there is an equal (and sometimes greater) threat or challenge that musicians face. I was particularly struck by Shawn Reynaldo’s recent article on Rolling Stone’s Suno piece, entitled “A Music Industry That Doesn’t Sell Music”. The dystopian vision he presents is millions of consumers spending $10 a month to access music AI tools, so they can “create” and upload their content to streaming services, in the hope of covering their subscription fees….. Sounds ghastly, if you ask me.

Add to the mix the demise of music publications (for which AI and streaming are also to blame…), and it’s easy to see how the landscape for discovering, exploring and engaging with music has become highly concentrated via streaming platforms and their recommender engines (plus marketing budgets spent on behalf of major artists). In the 1970s and 1980s, I would hear about new music from the radio (John Peel), TV (OGWT, The Tube, Revolver, So It Goes, Something Else), the print weeklies (NME, Sounds, Melody Maker), as well as word of mouth from friends, and by going to see live music and turning up early enough to watch the support acts. Now, most of my music information comes from the few remaining print magazines such as Mojo and Uncut (which largely focus on legacy acts), The Wire (but probably too esoteric for its own good), and Electronic Sound (mainly because that’s the genre that most interests me); plus Bandcamp, BBC Radio 6’s “Freak Zone”, Twitter, and newsletters from artists, labels and retailers. The overall consequence of streaming and up/downloading is that there is too much music to listen to (but how much of it is worth the effort?), and multiple invitations to “follow”, “like”, “subscribe” and “sign up” for direct content (but again, how much of it is worth the effort?). For better or worse, the music media at least provided an editorial filter to help address quality vs quantity (even if much of it ended up being quite tribal).

In the past, the music industry operated as a network of vertically integrated businesses: they sourced the musical talent, they managed the recording, manufacturing and distribution of the content (including the hardware on which to play it), and they ran publishing and licensing divisions. When done well, this meant careful curation, the exercise of quality control, and a willingness to invest in nurturing new artists for several albums and for the duration of their career. But at times, record companies have self-sabotaged, by engaging in format wars (e.g., over CD, DCC and MiniDisc standards), by denying the existence of on-line and streaming platforms (until Apple and Spotify came along), and by becoming so bloated that by the mid-1980s, the major labels had to merge and consolidate to survive – largely because they almost abandoned the sustainable development of new talent. They also ignored their lucrative back catalogues, until specialist and independent labels and curators showed them how to do it properly. Now, they risk overloading the reissue market, because they lack proper curation and quality control.

The music industry really only does three things:

1) A&R (sourcing and developing new talent)

2) Marketing (promotion, media and public relations)

3) Distribution & Licensing (commercialisation).

Now, #1 and #2 have largely been outsourced to social media platforms (and inevitably, to AI and recommender algorithms), and #3 is going to be outsourced to web3 (micro-payments for streaming subscriptions, distribution of NFTs, and licensing via smart contracts). Whether we like it or not, and taking their lead from Apple and Spotify, the music businesses of the future will increasingly resemble tech companies. The problem is, tech rarely understands content from the perspective of aesthetics – so expect to hear increasingly bland AI-generated music from avatars and bots that only exist in the metaverse.

Meanwhile, I go to as many live gigs as I can justify, and brace my wallet for the next edition of Record Store Day later this month…

Next week: Reclaim The Night