Creativity requires a new take on something. An original perspective. Something no one else has thought of. LLMs, by definition, produce the kinds of things they have already seen. The kinds of things that already exist. The kinds of things that reinforce the status quo, entrench bias, and emphasise the mundane.
Author: Dr Linda McIver
Dr Linda McIver pioneered authentic Data Science and Computational Science education with real impact for secondary students and founded the Australian Data Science Education Institute in 2018. Author of Raising Heretics: Teaching Kids to Change the World, Linda is an inspiring keynote speaker who has appeared on the ABC’s panel program Q&A, and regularly delivers engaging Professional Development for Primary, Secondary, and Tertiary Educators across all disciplines. A passionate educator, researcher and advocate for STEM, equity and inclusion, with a PhD in Computer Science Education and extensive teaching experience, Linda’s mission is to ensure that all Australian students have the opportunity to learn STEM and Data Science skills in the context of projects that empower them to solve problems and make a positive difference to the world.
Hallucinations all the way down
The thing is, it's not reasoning. It's not a reasoning machine. It's not even trying to give you correct answers. It's trying to give you statistically plausible text. The fact that the plausible text is occasionally correct is the surprise. The fact that regularly gets things wrong is exactly how these systems work.
Schrödinger’s AI
Pushing back against AI hype sometimes feels futile, but every person you reach matters. So how do we fight back?
Scaling scams
It's important, when you're making graphs, to think about the story you want to tell with the data, and what type of graph, and what features of the graph, will help you tell that story. Likewise, when you're looking at someone else's graph, we all need to apply that critical data literacy and look at the scale on the y axis, as well as checking the labels, finding out the origin of the data, and considering whether the graph is accurate, a valid way to display that data, and what story it might be trying to tell you.
Ethical uses of AI
There is no ethical way to use AI. At least not the Large Language Models (LLMs) we’re mostly talking about these days when we talk about AI. I’m going to unpack that in a moment, but first let me stress that I am not, for a moment, calling teachers who use AI unethical. The ethical responsibility here does not lie with users of LLMs. It lies firmly with the industry that has created and promoted the use of these machines in wholly unethical ways.