From steam power and electricity to computers and the internet, technological advancements have always disrupted labor markets, pushing out some jobs while creating others. Artificial intelligence remains something of a misnomer – the smartest computer systems still don’t actually know anything – but the technology has reached an inflection point where it’s poised to affect new classes of jobs: artists and knowledge workers.
Specifically, the emergence of large language models – AI systems that are trained on vast amounts of text – means computers can now produce human-sounding written language and convert descriptive phrases into realistic images. The Conversation asked five artificial intelligence researchers to discuss how large language models are likely to affect artists and knowledge workers. And, as our experts noted, the technology is far from perfect, which raises a host of issues – from misinformation to plagiarism – that affect human workers.
To jump ahead to each response, here’s a list of each:
Lynne Parker, Associate Vice Chancellor, University of Tennessee
Large language models are making creativity and knowledge work accessible to all. Everyone with an internet connection can now use tools like ChatGPT or DALL-E 2 to express themselves and make sense of huge stores of information by, for example, producing text summaries.
These new AI tools can’t read minds, of course. A new, yet simpler, kind of human creativity is needed in the form of text prompts to get the results the human user is seeking. Through iterative prompting – an example of human-AI collaboration – the AI system generates successive rounds of outputs until the human writing the prompts is satisfied with the results. For example, the (human) winner of the recent Colorado State Fair competition in the digital artist category, who used an AI-powered tool, demonstrated creativity, but not of the sort that requires brushes and an eye for color and texture.
While there are significant benefits to opening the world of creativity and knowledge work to everyone, these new AI tools also have downsides. First, they could accelerate the loss of important human skills that will remain important in the coming years, especially writing skills. Educational institutes need to craft and enforce policies on allowable uses of large language models to ensure fair play and desirable learning outcomes.
Educators are preparing for a world where students have ready access to AI-powered text generators.
Second, these AI tools raise questions around intellectual property protections. While human creators are regularly inspired by existing artifacts in the world, including architecture and the writings, music and paintings of others, there are unanswered questions on the proper and fair use by large language models of copyrighted or open-source training examples. Ongoing lawsuits are now debating this issue, which may have implications for the future design and use of large language models.
As society navigates the implications of these new AI tools, the public seems ready to embrace them. The chatbot ChatGPT went viral quickly, as did image generator Dall-E mini and others. This suggests a huge untapped potential for creativity, and the importance of making creative and knowledge work accessible to all.
Potential inaccuracies, biases and plagiarism
Daniel Acuña, Associate Professor of Computer Science, University of Colorado Boulder
I am a regular user of GitHub Copilot, a tool for helping people write computer code, and I’ve spent countless hours playing with ChatGPT and similar tools for AI-generated text. In my experience, these tools are good at exploring ideas that I haven’t thought about before.
I’ve been impressed by the models’ capacity to translate my instructions into coherent text or code. They are useful for discovering new ways to improve the flow of my ideas, or creating solutions with software packages that I didn’t know existed. Once I see what these tools generate, I can evaluate their quality and edit heavily. Overall, I think they raise the bar on what is considered creative.
But I have several reservations.
One set of problems is their inaccuracies – small and big. With Copilot and ChatGPT, I am constantly looking for whether ideas are too shallow – for example, text without much substance or inefficient code, or output that is just plain wrong, such as wrong analogies or conclusions, or code that doesn’t run. If users are not critical of what these tools produce, the tools are potentially harmful.