I’ve recently been convinced to start using ChatGPT a bit more. I’ll confess, I don’t really ‘get’ A.I. yet. The actual nuts and bolts of the computational stuff involved are way over my head. I’m sure that it’ll revolutionise the world, but what about my own work? Despite some initial reservations I’m beginning to think there’s scope for using A.I. as a tool, but maybe not in the ways that I would have expected.
Unusually for a dry Saturday I’m not out climbing or lichen hunting, but sat at home nursing some lingering new year flu symptoms. In the interests of convalescing productively I thought I’d get ChatGPT to draw some lichens for me in R by asking the following question:
“Write me some code that will draw me a picture of the lichens Cladonia pyxidata, Xanothoria parietina, Rhizocarpon geographicum, and Lichina pygmaea using ggplot2.”
Me
ChatGPT was perfectly willing to carry out my request, but did offer me a word of caution:
“Creating detailed representations of multiple lichens in a single ggplot2 plot is quite complex due to the intricate structures of lichens. However, I can provide a basic example that includes a simplified representation of each lichen: Cladonia pyxidata, Xanthoria parietina, Rhizocarpon geographicum, and Lichina pygmaea.”
ChatGPT 3.5
Fair enough. The code generated was the following:
# Install and load ggplot2 if not already installed
if (!requireNamespace("ggplot2", quietly = TRUE)) {
install.packages("ggplot2")
}
library(ggplot2)
# Function to draw simplified representations of multiple lichens
draw_lichen_community <- function() {
ggplot() +
# Cladonia pyxidata
geom_polygon(
aes(x = c(2, 4, 5, 6, 7, 8, 9), y = c(2, 3, 4, 3, 4, 3, 2)),
fill = "green", color = "black"
) +
geom_point(
aes(x = c(4, 5, 6, 7, 8), y = c(3, 4, 3, 4, 3)),
size = 3, color = "brown"
) +
# Xanthoria parietina
geom_polygon(
aes(x = c(11, 12, 13, 14, 13), y = c(4, 5, 6, 5, 4)),
fill = "orange", color = "black"
) +
geom_point(
aes(x = 13, y = 5),
size = 3, color = "yellow"
) +
# Rhizocarpon geographicum
geom_polygon(
aes(x = c(18, 19, 20, 21, 20), y = c(3, 4, 5, 4, 3)),
fill = "gray", color = "black"
) +
geom_point(
aes(x = c(19, 20, 21), y = c(4, 5, 4)),
size = 3, color = "orange"
) +
# Lichina pygmaea
geom_polygon(
aes(x = c(25, 26, 27, 26), y = c(2, 3, 2, 1)),
fill = "purple", color = "black"
) +
geom_point(
aes(x = 26, y = 2),
size = 3, color = "pink"
) +
# Set axis limits
coord_cartesian(xlim = c(0, 30), ylim = c(0, 7)) +
# Remove axis labels and ticks
theme_void()
}
# Call the function to draw a simplified representation of multiple lichens
draw_lichen_community()So far so good. I like the fact that it decided to refer to my plot as a lichen ‘community’; there’s obviously enough out there to train the A.I. that this is a word that’s commonly used when referring to assemblages of different species (although the habitat that supported a community containing all of these species together would be a rather odd one indeed). ChatGPT handed off with one final nugget:
“This code attempts to represent Cladonia pyxidata, Xanthoria parietina, Rhizocarpon geographicum, and Lichina pygmaea in a single plot. Adjustments and refinements may be necessary to better represent the specific features of each lichen. Keep in mind that this is a highly stylized and abstract representation for illustrative purposes.”
ChatGPT 3.5
This was the result:
On first glance this could appear either awful or amazing depending on your frame of mind, but there are some really interesting things here. First up is how colour has been chosen. The Cladonia is greenish and Xanthoria has come out in the expected orange. Grey-ish components of the Rhizocarpon geographicum thallus have been interpreted as dominant, while Lichina pygmaea is a fetching purple. Of course none of these colours are true to life, but as stylized choices I think they all work pretty well (with the exception of R. geographicum which I’ll come back to in a minute). There’s also been recognition that apothecia are different things to the thallus, being shown as different coloured points on top of the main thallus polygon. This has come out best on the Cladonia, with clear red/brown points contrasting with a green body, just like (well, sort of) the real thing. Xanthoria and Lichina are different shades of the thallus colour, which is fair. Interestingly, while the two-tone nature of Rhizocarpon has been recognised, it’s a bit muddled. While in real life the thallus is mostly yellow with black apothecia, this has been represented as the inverse in the plot, with a black (grey) thallus and yellow (orange) apothecia. While wrong, you can see why this could happen; R. geographicum thalli in real life are pretty variable and I think the uninitiated human would probably struggle to differentiate between which part was which. In its defence, ChatGPT isn’t a lichenologist and doesn’t really know what a thallus or an apothecium is anyway. The last thing to note is general morphology, which really hasn’t been depicted at all apart from in the ‘spikiness’ of the Cladonia which could be interpreted as multiple stalks.
Some things to think about: ggplot2 is not the tool for drawing life-like representations of anything, let alone lichens, so I was already giving ChatGPT a pretty severe handicap. By doing so, it’s had an attempt at isolating some key features that it can use to answer the question. Interestingly, these are the key characteristics that we as people are drawn to as well; colour and shape. I often say to people that one of the reasons that lichens can be so hard for beginners is that there’s no archetypal lichen, no basic form that our minds jump to when we say the word ‘lichen’. Ask people what they think a lichen looks like and many will visualise Cladonia cups, while others think of a crust on a rock. There are no obvious straightforward rules that apply to all lichens as there are for something like trees (i.e. roots, trunk, branches, leaves). We might then use ChatGPT’s choices of what to put in these lichen plots as a way of identifying some rules of our own – that lichens can have two major parts, the thallus and the apothecia, and that lichens can come in lots of different colours.
As a test, to see how things went with what would, in my mind, be a simpler problem I thought I’d feed ChatGPT the same question but with some trees:
“Write me some code that will draw me a picture of the trees Quercus robur, Fraxinus excelsior, Pinus sylvestris, and Picea abies using ggplot2.”
Me
In its response ChatGPT automatically gave me back the common names of the species that I’d asked for, which was nice of it (I can see it struggling to do the same with lichens):
“Drawing realistic representations of trees using ggplot2 in R is quite challenging, as it’s primarily a tool for statistical and data visualization rather than detailed illustrations. However, I can provide a basic example that uses geometric shapes to represent simplified versions of oak (Quercus robur), ash (Fraxinus excelsior), pine (Pinus sylvestris), and spruce (Picea abies).”
ChatGPT 3.5
For anyone interested in the code, here it is:
# Install and load ggplot2 if not already installed
if (!requireNamespace("ggplot2", quietly = TRUE)) {
install.packages("ggplot2")
}
library(ggplot2)
# Function to draw simplified representations of multiple trees
draw_tree_community <- function() {
ggplot() +
# Quercus robur (Oak)
geom_rect(
aes(xmin = 2, xmax = 3, ymin = 1, ymax = 5),
fill = "brown", color = "black"
) +
# Fraxinus excelsior (Ash)
geom_rect(
aes(xmin = 7, xmax = 8, ymin = 1, ymax = 5),
fill = "gray", color = "black"
) +
# Pinus sylvestris (Pine)
geom_segment(
aes(x = 12, y = 1, xend = 12, yend = 5),
color = "green", size = 2
) +
geom_polygon(
aes(x = c(11, 12, 13), y = c(5, 8, 5)),
fill = "green", color = "black"
) +
# Picea abies (Spruce)
geom_segment(
aes(x = 17, y = 1, xend = 17, yend = 5),
color = "darkgreen", size = 2
) +
geom_polygon(
aes(x = c(16, 17, 18), y = c(5, 8, 5)),
fill = "darkgreen", color = "black"
) +
# Set axis limits
coord_cartesian(xlim = c(0, 20), ylim = c(0, 10)) +
# Remove axis labels and ticks
theme_void()
}
# Call the function to draw a simplified representation of multiple trees
draw_tree_community()Again, ChatGPT has its disclaimer:
“This code uses rectangles and segments to represent simplified versions of Quercus robur, Fraxinus excelsior, Pinus sylvestris, and Picea abies. Adjustments and refinements may be necessary for a more accurate representation or to capture specific features of each tree. Keep in mind that this is a highly stylized and abstract representation for illustrative purposes. For detailed botanical illustrations of trees, specialized graphic tools and techniques may be more appropriate.”
ChatGPT 3.5
Stylized is definitely the word. I was hoping that that it would draw the broadleaf trees with a circle on top and the conifers as a triangle, and while the latter part of this happened, both the oak and the ash have just been shown as trunks. It seems that ChatGPT isn’t really that interested in an archetypal anything but is just making it up as it goes along. I tried again, this time specifying that it show trunks and leaves:
Better, kind of, but still not what quite what I was expecting. I wasn’t sure how much I believed that ChatGPT really knew what it was doing here, so I asked the exact same question again but switched up the order of the trees to oak, spruce, ash, pine. This is what it came up with:
My confidence that ChatGPT was using any comprehensible logic to inform these plots at all was dwindling rapidly.
Whatever, in all of these cases we’ve gone in with a priori knowledge about what we’re looking at, which has really affected our interpretation (indeed, over-interpretation) of what ChatGPT has done . If someone showed you the lichen picture without telling you it was lichen, would you make that link? Probably not. The Cladonia could just as easily be a collapsed Christmas tree, or a bunch of basketballs on a grassy mountainside. Would the tree pictures fair any better? Perhaps, particularly with the addition of the ‘trunk and branches’ rule, but these are still highly abstract views. ChatGPT isn’t really telling us how to draw trees and lichens, but instead supplying an amalgamation of information about weirdly filtered copies of copies of something that somebody said was a tree or a lichen over infinite iterations, through the lens of 1,000 different pieces of code that were written to do something else entirely…. you get the idea.
Finally, I asked ChatGPT to draw me a lichen and a tree. ChatGPT thought I asked for a lichen on a tree and the outcome, a rusty rectangular Sputnik sporting a single tiny green orb, speaks for itself.
So what to conclude from all this? ChatGPT is good for many things, but it isn’t good at making things up. It needs prompts, both from you and from within the source material it’s drawing from in order to generate content. The less information it has on either side the worse it’s going to do, but at the same time it doesn’t know what to do with too much information. It’s also not particularly consistent; rules that might seem obvious to us aren’t automatically applied, and even if we supply a set of rules to follow, they likely won’t be adhered to in ways we expect. Arguably, the most useful thing that ChatGPT has done in this instance is to have made me write this blog and think about things in a way I wouldn’t have done otherwise. All scientific and artistic innovation throughout history has come about as a result of both the advances and limitations of the technology available at the time, with many interesting questions arising at the boundaries of what is possible. I think a particularly useful thing that can come out of AI is not specific answers to questions, but insight into how we actually frame those questions ourselves.
NC