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Examining Hollywood's current AI issues, Part I: The Writers
Much of the entertainment industry is currently shut down due to ongoing strikes, first from the Writers Guild of America (WGA) and then by the Screen Actor’s Guild — American Federation of Television and Radio Artists (SAG-AFTRA).
While there are many issues at hand in both strikes, one of the most reported is the looming fear of AI. Both writers and actors are worried that AI will be used to reduce work opportunities. In the words of SAG-AFTRA president Fran Drescher:
We are all going to be in jeopardy of being replaced by machines…
So, is this a reasonable fear? How likely is it that writers and actors will be replaced by machines, at least before the next round of labor talks a few years down the road?
This post is going to concentrate on the issues surrounding the WGA concerns, while the SAG-AFTRA concerns will be discussed in the next post.
An Extinction Level Event?
The degree of AI fear among writers seems to be high, to say the least.
A recent article in Variety spoke with several striking writers, who offered the following perspectives that seem to be fairly representative of WGA members in general:
“The corporations will push us all into extinction if they can,” said Chap Taylor, a screenwriter and professor at New York University. The AI issue “is life and death,” he said. “That’s the one that turns us into the makers of buggy whips.”
“AI has become my number one issue,” said TV writer Chris Duffy, who was marching outside Disney headquarters in Burbank. “I think it’s an existential one. The fact that they refused to negotiate made me be like, ‘Oh, you really want to use it.’”
Kelly Wheeler, also a TV writer, said she too is “most scared about AI.”
“I love writing and I love being around writers,” she said. “And the idea that that creative energy can just be stripped away from television, and instead have a robot do our job – or attempt to – is terrifying.”
The Points of Concern
The initial AI proposal offered by the WGA to the AMPTP was brief and no doubt intended as a starting point to be fleshed out:
Regulate use of artificial intelligence on MBA- covered projects: AI can’t write or rewrite literary material; can’t be used as source material; and MBA-covered material can’t be used to train AI.
Given the situation, some of the more pertinent questions to consider regarding the WGA AI concerns are:
How good are AI systems at screenwriting and are they capable of replacing a human screenwriter now or in the foreseeable future?
Will the studios be able to reduce or eliminate the need for human screenwriters by using AI now or in the foreseeable future?
What are the legal ramifications of using AI generated writing?
What are the legal ramifications of AI systems ingesting pre-existing written works?
There are many unknowns and much hype surrounding each one of these questions.
Robot Wordsmiths or Artificial Hacks
So how good is AI at screenwriting? By pretty much all accounts, not very good.
As discussed in a previous post, current Large Language Model (LLM) systems, such as OpenAI’s GPT-4, are impressive technology but still have significant limitations. Among these limitations is that no existing LLM system currently available to the public is able to write a full length script.
This is due to the nature of the technology: one of the breakthrough developments at the heart of LLM’s, the Transformer Architecture, allows the system to evaluate and output large chunks of data at a time rather than single words or short commands. This allows them to generate very impressive written output.
Current systems, however, are not able to evaluate and output something as long as a full script, at least one that’s coherent from beginning to end. However, it’s quite likely that they’ll be able to generate coherent script length output in the not too distant future.
Developing these LLM systems currently requires an extremely large expenditure to make meaningful improvements in this area. While OpenAI is keeping development costs close to the vest, its safe to say that the development leading to GPT-4 cost multiple hundreds of million dollars. While it’s possible that just making the same type of system even bigger will be enough of an improvement to allow the system to write full screenplays, it will be extremely expensive to do that.
This, of course, assumes that the system will be improved in the same way previous systems were improved, i.e. by making them bigger. This is unlikely to be the path forward, though, as Sam Altman, CEO of OpenAI, has stated himself. While this may mean future development will be cheaper than previous brute force approaches, it also means that it’s more unpredictable, as it will rely on enhancements and breakthroughs that are currently not clear.
But all we would be doing, either way, is creating an LLM system capable of writing something as long as a screenplay. It doesn’t mean that the screenplay will be any good. Generating a good screenplay is where things get a little thornier.
As discussed in this post, LLM systems have absolutely no comprehension of what they’re creating and how it relates to the world around them or to the systems themselves. They are systems that rely on extremely sophisticated statistical analysis, and their output is completely reliant on their input. In other words, anything they create is going to be derivative to some degree. Frequently to a large degree.
People are often shocked when they ask ChatGPT to write something for them — an email, a poem, a business proposal, etc. — and they get back something impressive, something that seems like it could have been written by a human. But the system at the heart of ChatGPT has sucked in millions, and likely billions, of similar emails, poems, and business proposals. It has sucked in most of the Internet and a significant chunk of other written material, all of which has been written by humans.
What this means is that what an LLM system spits out is going to seem very much like what went into it. It’s going to seem human-like. The more examples available as input, the better and more comprehensive will be its output.
Similarly, most of the things written by humans are also like things that those humans have read as input. In other words, the relationship between input to humans and output of humans is very similar to the relationship between input to LLM systems and output of LLM systems. The difference is, humans can occasionally create unique output based on their own personal experiences, thoughts, and ideas. LLMs don’t have personal experiences, thoughts, or ideas. In other words, humans can be truly creative while LLMs cannot.
Some have stretched the definition of creativity to include moderately unique rearrangements of existing information, and perhaps that is one aspect of creativity. But creativity by definition is an act of creation, not an act of organization. To paraphrase some old Apple ads, creativity is thinking differently from what has come before.
When we think of great movies or tv shows, we don’t think, “Hey, they really re-arranged all those plot elements and dialogue well.” Truly creative stories dazzle us in ways that merely competent ones do not. When these LLM systems are able to write full length screenplays, those screenplays are only going to be derivative of what has come before.
And here, of course, is one of the problems. Many of the human-written screenplays out in the wild are also pretty derivative of what’s come before. When something comes out that seems new and fresh and well-thought-out, people are impressed. The current technology used in LLM systems like ChatGPT are not capable of new and fresh and well-thought-out. They are, in other words, artificial hacks.
This might not always be the case with AI, but it is almost assuredly going to be the case with this type of LLM system. Getting beyond derivative means understanding the world and understanding humans, and this is something beyond no only every current LLM system but also beyond any currently foreseeable AI system.
So this brings us to the second question above: will the studios be able to reduce or eliminate the need for human screenwriters now or in the foreseeable future?
One extremely important point not often discussed is that the world of entertainment is very collaborative. Scripts may have one or more writers but they also have input from other individuals, including producers, studio execs, leading actors, and possibly many others. LLM systems are not good at following very specific instructions. While producers and studio execs might say the same about screenwriters, humans, at least in theory, are capable of making specific changes to address specific instructions.
Most of the images for this blog are created using generative AI. Getting something specific from that generative AI is nearly impossible — each image is a roll of insufficiently weighted dice. Most images require fixes, adjustments, additions, and combination with other images.
LLM’s are similarly non-sentient black boxes. You can try to coax them into getting something close to what you want, but you can’t promise them more money, more work, more credit, or more anything to make sure you get exactly what you want. So while producers and studio execs might like to be able to press a button on an LLM system and have a script pop out, they will most definitely not like its inability to address specific notes on that script.
Polishing It Up
As previously discussed, getting a technology to 80% or 90% of where it needs to be is usually quite a bit easier than getting it to 99% or 100%. We don’t know if that last 10% or 20% is going to take one year or ten years or even longer. Getting that last 10% or 20% towards good scripts may not be possible with technology like LLM systems. Technically speaking, it’s very unlikely that an AI system is going to be able to write what most would consider a good screenplay anytime soon.
Before the strike, the WGA put together an informal AI working group to help formulate their proposal to AMPTP. John Rogers, a member of the group, described AI this way::
The capabilities are wildly overblown…A lot of this hype is because Silicon Valley needs the next big thing and they don’t have one. So this is it.
Another member of that group, John Lopez, had this to say after spending many hours with ChatGPT:
It took almost as much work as writing it from scratch myself…It did make me freak out a little bit less.
One way around this, of course, is to use LLM systems in conjunction with humans. That is, use the LLM system to get the first 80% of the way down the road to finished script, and then get a human to do the last 20%. This has already been discussed by both the WGA and AMPTP, but the details of how that would work are where the disagreements pop up.
But simply having the studio use AI to generate scripts that are then polished by human writers is still not likely to result in very good scripts. Creativity doesn’t just happen at the end of the line. The ideas, concepts, and characters that go into the initial idea are usually more important than any final polish at the end. Those initial elements are the hard part, typically the part that’s most creative. This is why the WGA itself puts so much emphasis on who developed the story and initial script when arbitrating credits.
Becoming A Writer
So can an AI system even be a writer, at least as far as the WGA and its signatories are concerned?
The current WGA Minimum Basic Agreement (MBA) states the following:
The term "writer" shall not be deemed to include any corporate or impersonal purveyor of literary material or rights therein.
The MBA further defines both a writer and a professional writer as a person before adding any other qualifications. This pretty much precludes any non-person system from being a WGA member writer. However, once an AMPTP signatory employs a writer past a certain minimal amount, that writer must join the WGA.
This would likely preclude AMPTP signatory studios from being able to use an AI system in any way that would affect credits or compensation. In other words, an AI system is a tool, like Final Draft or Microsoft Word. Its use by a studio is similar to giving script notes to a writer rather than employing the AI system as another writer, specifically in regards to credits and compensation.
It’s worth noting that while AMPTP rejected the initial WGA AI proposal quoted above, they did offer a “side letter” to underscore the existing contract language specifying that a writer must be a person. They did not, however, wish to go further than that and instead suggested holding annual meetings to discuss advances in AI technology.
The lack of language explicitly prohibiting using an AI system or crediting an AI system as a writer in the current WGA agreement combined with the wait-and-see proposal of the AMPTP is no doubt the primary source of concern among WGA members.
As writer/producer Michael Colton put it:
I don’t think people are feeling like tomorrow AI is going to write a perfect sitcom script. But the fear is that studios will use AI to turn out a crappy first draft, and then turn it over to writers who they hire for a few days or a week to turn it into something good. And they won’t pay them as if it’s an original script. That is the fear.
Writer/director Paul Schrader described the issue more succinctly:
The Guild doesn’t fear AI as much as it fears not getting paid…
Currently credit and compensation are very dependent on multiple aspects of a script creation, including whether the story is original or based on pre-existing work, whether the screenwriter created the original draft of the script, and the degree to which the screenwriter’s creative input is apparent in the final script.
Importantly, as far as the US Copyright office is concerned, AI created work cannot be copyrighted. Unless and until this changes, no studio is going to use AI alone to create a script. Intellectual property is simply too critical an asset for a studio to allow it to be jeopardized in any way. In fact, the studios will strive to avoid any hint that they might not own the full, defensible rights to their content.
That Which Has Come Before
The last major topic to discuss is involves the legal ramifications of AI systems ingesting pre-existing written works. This is an issue that extends far beyond the WGA, but it was a part of their initial proposal to AMPTP.
As a sample of things to come, on 7/7/23, authors Sarah Silverman, Christopher Golden, and Richard Kadrey were named as plaintiffs in a class action lawsuit against Meta and OpenAI for copyright infringement of books each author had written. The plaintiffs’ claim is that the LLM systems developed by each company ingested copyrighted material from the authors without consent or compensation.
They don’t claim that the system plagiarized the works outright, but simply that OpenAI and Meta used their books to train the systems.
While the complaint against Meta states that Meta has admitted using a dataset containing some of the authors’ books, the complaint against OpenAI seems to only make assumptions that the authors’ books were used to train the systems. The only specific evidence offered in the complaint against OpenAI’s use of the authors’ books seems to be this:
40. On information and belief, the reason ChatGPT can accurately summarize a certain copyrighted book is because that book was copied by OpenAI and ingested by the underlying OpenAI Language Model (either GPT-3.5 or GPT-4) as part of its training data.
41. When ChatGPT was prompted to summarize books written by each of the Plaintiffs, it generated very accurate summaries. These summaries are attached as Exhibit B. The summaries get some details wrong. These details are highlighted in the summaries. This is expected, since a large language model mixes together expressive material derived from many sources. Still, the rest of the summaries are accurate, which means that ChatGPT retains knowledge of particular works in the training dataset and is able to output similar textual content. At no point did ChatGPT reproduce any of the copyright management information Plaintiffs included with their published works.
Unfortunately for this complaint, the initial supposition in point 40 and repeated in point 41 is incorrect. OpenAI’s LLM model did not have to ingest the source material to summarize it; it only had to ingest other summaries. This includes bookseller and review summaries, as well as the many summaries produced by readers of the books and offered in reader reviews.
The LLM systems created by OpenAI may have used the authors’ books in their training, but the plaintiffs will likely have to show more evidence for this than is demonstrated by the above paragraphs. The plaintiffs are also suing for statutory damages, actual damages, and restitution of profits from both companies. Nailing down what those might be is likely to be challenging.
A big part of this lawsuit may boil down to what constitutes a copy. The complaints use language suggesting that the LLM systems create something akin to an internal copy to generate their output.
But this isn’t really the case, at least not unless we change the definition of a “copy.” The LLM does ingest data, but the data is used to adjust an internal model that bears no resemblance to the data it ingests. What is adjusted is the relationship between a vast array of mathematical constructs called artificial neurons that are vaguely similar in function to the neurons in our brains. The internal model then generates new output based on how this array of artificial neurons has been adjusted. This is, of course, a vast simplification, but the main point is that there is not what we would generally call a copy of ingested material inside the system.
No New Thing
The greater question, and the one pertinent to the WGA, is whether ingesting copyrighted material by an LLM means the output of the LLM is copyright infringement. This is a difficult question, because this is kind of what people do when they ingest literary material and then summarize it. Or when they generate their own literary material.
What is the tipping point between learning and plagiarizing for humans? Would it make sense to stop humans from reading the works of existing WGA writers to be employed as a WGA writer?
One would hope that our current legal system would provide a reasonable starting point on the path to solving this issue. There is already a large body of law that covers copyright and plagiarism, and there are many previous cases establishing precedent.
It may be worth keeping in mind that true originality is hard to come by, and most literary work is derivative of previous works to some degree. Movies are released each year that are similar to movies of years past. It’s also not uncommon for multiple movies with similar premises to come out the same year — Antz/A Bug’s Life, Armageddon/Deep Impact, Volcano/Dante’s Peak, Olympus Has Fallen/White House Down, etc.
This is not to say that these works are deliberately derivative, but instead that it is difficult to avoid being influenced by what one has absorbed, both through direct life experience as well as through reading the experiences of others.
As it is written in Ecclesiastes 1:9:
The thing that hath been, it is that which shall be; and that which is done is that which shall be done: and there is no new thing under the sun.