Technically not a help question. But crucial to be aware of, especially for
those of us in academia, or otherwise teaching R. I am not aware of a suitable
alternate forum. If this does not interest you, please simply ignore - I
already know that this may be somewhat OT.
Thanks.
------------------------------------------------------
You very likely have heard of ChatGPT, the conversation interface on top of the
GPT-3 large language model and that it can generate code. I thought it doesn't
do R - I was wrong. Here is a little experiment:
MY PROMPT:
==========
Please write me some R code that will read a tsv file, remove all rows that
have duplicates in the column "names", and store the median values of the
remaining rows in a variable called "Medians". Of course, that calculation
should only be done with columns that are numeric. Print the last variable in
order from largest to smallest.
AI RESPONSE:
============
Here is some R code that should accomplish what you've described:
(Note: the code is formatted in a little terminal window, complete with a
button to "copy code" at the top.)
----
# Read the tsv file
data <- read.table("file.tsv", header = TRUE, sep = "\t")
# Remove rows with duplicates in the "names" column
data <- data[!duplicated(data$names), ]
# Select only the numeric columns
num_columns <- sapply(data, is.numeric)
numeric_data <- data[, num_columns]
# Calculate the medians for each column
Medians <- apply(numeric_data, 1, median)
# Sort Medians in descending order
Medians <- sort(Medians, decreasing = TRUE)
# Print Medians
print(Medians)
----
This code assumes that the tsv file is called "file.tsv" and is stored in the
current working directory. It also assumes that the first row of the file
contains the column names.
MY PROMPT:
==========
Can you provide me with a bit of sample data that I can use to test this code?
AI RESPONSE:
============
Certainly! Here is some sample data that you can use to test the code I
provided:
----
name age height weight
Alice 23 5.6 120
Bob 32 6.2 180
Charlie 29 5.9 160
Alice 23 5.6 120
David 25 5.8 140
Eve 27 5.5 110
----
You can save this data as a tsv file and use it to test the code.
------------------------------------------------------
Notes:
1) The code, as written does not work with the test data. The reason is that
the test data specifies a column "name", but the code assumes "names" (which I
specified in the instructions). Once this is fixed in the test data, the code
executes.
2) The instructions are not accurately translated: I had asked for row means.
But changing the margin in the apply() statement from 2 to 1 does the correct
thing.
3) Note that the test data contains both numeric and non-numeric columns. Also,
the name "Alice" is duplicated in the test data, which is subtle, and the right
thing to do.
------------------------------------------------------
A similar experiment, run on the beta-site of OpenAI's Codex code-completion
tool gives the following code (complete with the comments.):
----
# Read in the data
data <- read.table("mydata.tsv", sep = "\t", header = TRUE)
# Remove all rows that have duplicates in the column "names"
unique_names <- unique(data$names)
data_unique <- data[data$names %in% unique_names,]
# Calculate medians
Medians <- sapply(data_unique[, sapply(data_unique, is.numeric)], median, na.rm
= TRUE)
# Print last variable in order form largest to smallest
rev(sort(Medians[length(Medians)]))
----
Note that the strategy is quite different (e.g using %in%, not duplicated() ),
the interpretation of "last variable" is technically correct but not what I had
in mind (ChatGPT got that right though).
Changing my prompts slightly resulted it going for a dplyr solution instead,
complete with %>% idioms etc ... again, syntactically correct but not giving me
the fully correct results.
------------------------------------------------------
Bottom line: The AI's ability to translate natural language instructions into
code is astounding. Errors the AI makes are subtle and probably not easy to fix
if you don't already know what you are doing. But the way that this can be
"confidently incorrect" and plausible makes it nearly impossible to detect
unless you actually run the code (you may have noticed that when you read the
code).
Will our students use it? Absolutely.
Will they successfully cheat with it? That depends on the assignment. We
probably need to _encourage_ them to use it rather than sanction - but require
them to attribute the AI, document prompts, and identify their own, additional
contributions.
Will it help them learn? When you are aware of the issues, it may be quite
useful. It may be especially useful to teach them to specify their code
carefully and completely, and to ask questions in the right way. Test cases are
crucial.
How will it affect what we do as instructors? I don't know. Really.
And the future? I am not pleased to extrapolate to a job market in which they
compete with knowledge workers who work 24/7 without benefits, vacation pay, or
even a salary. They'll need to rethink the value of their investment in an
academic education. We'll need to rethink what we do to provide value above and
beyond what AI's can do. (Nb. all of the arguments I hear about why humans will
always be better etc. are easily debunked, but that's even more OT :-)
--------------------------------------------------------
If you have thoughts to share how your institution is thinking about academic
integrity in this situation, or creative ideas how to integrate this into
teaching, I'd love to hear from you.
All the best!
Boris
--
Boris Steipe MD, PhD
University of Toronto
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