Category Archives: Blogging

ChatGPT as an essay-writing assistant – Part III

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The Challenge of a Satisfying Conclusion

When I published Part II of this series back in February 2025, I had a plan for Part III. Show the prompts I used, analyze the time investment, evaluate the result against Part I’s GPT-3.5 baseline, maybe try one more iteration with even newer tools. Straightforward. Methodical.

But I never finished it. To be honest, I lost interest. Another marginally better AI-generated essay wasn’t going to cut it—not for me, and probably not for you readers either. Another iteration showing GPT-4.5 writes slightly better than GPT-4? That’s predictable, uninspiring… so I dropped it.

But the unfinished series sat there in the back of my mind. I wasn’t actively working on it, but I also couldn’t quite let it go. It created a kind of block—I found myself not writing about anything at all, partly because this felt incomplete, partly because my interests had genuinely shifted elsewhere.

Recently though, I came back to this question. Not because I wanted to complete the series for completeness sake, but because I wanted to understand what would actually make Part III worthwhile.

So I asked for help. I brainstormed with Claude (Anthropic’s AI) about what Part III should actually be about—what would make it worth writing and worth reading. And something clicked.

What Was the Question Really Asking For?

Looking back now, with decades between me and that moment in Professoressa Carbone’s classroom, I think I understand what she was asking for. She wasn’t looking for recitation of Plato’s philosophy mechanically applied to medieval warfare. She wanted to see if I could reason using philosophical frameworks in unfamiliar territory. Synthesis, not facts. Thinking, not performing memorization.

At 15, I wasn’t ready for that. I had volunteered for the oral examination thinking I could rely on prepared material about Plato’s recent lessons. Instead, she cut through my preparation with a single question that required genuine philosophical thinking: “What would Plato have thought about the Hundred Years’ War?”

It was a brilliant pedagogical move. It required understanding Plato’s ideas deeply enough to apply them to a completely different context—a context Plato never encountered, in a historical period he never knew. It required the kind of intellectual flexibility and reasoning that, honestly, I didn’t have yet.

The humiliation I felt wasn’t really about not knowing facts. It was about being exposed as someone trying to get by on memorization rather than understanding. And I think she knew it. She saw through my bluff.

So What Would Satisfy?

This brings me back to the problem of Part III. Showing that AI can now generate a more sophisticated-sounding essay than my 15-year-old self could produce doesn’t prove anything interesting. AI is very good at generating sophisticated-sounding content. That’s almost the problem.

What would actually satisfy—both as closure for this series and as something worth your time reading—is demonstrating the kind of reasoning Professoressa Carbone was asking for. Can I, now, with the benefit of intellectual maturity and AI assistance, actually think through what Plato might have thought about prolonged warfare between nations? Not just string together plausible-sounding paragraphs with proper citations, but engage in genuine philosophical reasoning?

What Would That Actually Look Like?

If I were to actually write that essay—the one demonstrating real philosophical reasoning rather than AI-generated content—what would it need?

Looking back at the GPT-4 essay from Part II, it has proper citations and coherent structure, but it’s superficial. It lists Platonic concepts (philosopher-kings, guardians, ideal states) and applies them mechanically to medieval warfare. That’s exactly the kind of recitation Professoressa Carbone was testing me against.

Real reasoning would require:

  • Connecting Plato’s specific ideas to specific events or decisions during the Hundred Years’ War—not just general principles applied generally
  • Exploring how Plato’s concepts might actually illuminate something about prolonged conflict between nations that we wouldn’t see otherwise
  • Considering contemporary interpretations or modern applications—what do we learn about conflict, governance, or political philosophy from this exercise?
  • Drawing genuine insights about both Plato and warfare, not just restating both

That’s the essay I’d want to write someday. Not as an academic exercise, but as personal closure—proving to myself I can do the kind of thinking she was asking for.

Closure for Now

But that’s not this post. This post is about giving you, the readers, closure on this series. About acknowledging honestly what I learned about AI as a writing assistant, and why simple iteration wasn’t the answer.

Here’s what I’ve learned:

AI is excellent at generating plausible content. GPT-4 produced an essay that looks credible—proper structure, citations, coherent arguments. For many purposes, that’s enough.

But AI doesn’t reason, it recognizes patterns. The essay from Part II strings together familiar ideas in familiar ways. It’s sophisticated pattern matching, not thinking. It can’t do what Professoressa Carbone was asking for: genuine synthesis that produces new insight.

The real value of AI as a writing assistant isn’t in replacing thinking—it’s in supporting it. AI can help with research, organization, articulation. It can reduce cognitive load so you can focus on the hard part: the actual reasoning. But you still have to do the reasoning.

Writing with AI requires clarity about what you’re trying to accomplish. If you want content generation, AI does that well. If you want thinking support, you need to know what thinking you’re trying to do. The tool can’t figure that out for you.

This series started with a simple question: can AI help me write an essay? The answer turned out to be more nuanced than I expected. It depends entirely on what kind of essay, and what role you want AI to play. For the essay I’d need to write to truly answer Professoressa Carbone’s question—the one that demonstrates reasoning rather than recitation—AI could help, but it couldn’t do the essential work.

Maybe someday I’ll write that essay. For now, I’m moving on to other projects where I’m excited about what AI can do: document extraction in geoscience, agentic workflows, problems where AI’s strengths align better with what I’m trying to accomplish.

Thank you for following this journey with me. Even if it didn’t end where I originally planned, I learned something worth sharing.

A Final Thought: Rigor Without Brutality

I started this series partly because of concerns about AI in education—concerns rooted in my own experience.

ChatGPT has educators calling for more in-class writing and oral examinations. I agree we need assessment that can’t be faked by AI. But I’m deeply opposed to the brutality that often came with those older systems.

Here’s the thing: the brutality was never necessary for the educational value. Professoressa Carbone’s question was pedagogically brilliant. The public humiliation didn’t make it more effective; it just made it traumatic.

We need assessment methods that demand genuine reasoning, in environments that support both students and teachers. It’s possible to have rigorous evaluation without breaking people in the process.

AI forces us to confront what we actually value in education: not the appearance of learning, but the development of genuine understanding and reasoning. The question is whether we can build systems that nurture that without the cruelty.

AI/HI Transparency Statement Modified from Brewin http://www.theguardian.com/books/2024/apr/04/why-i-wrote-an-ai-transparency-statement-for-my-book-and-think-other-authors-should-too

Has any text been generated using AI?Yes
Has any text been improved or corrected using HI?Yes

Additional context: This post was collaboratively written through an iterative conversation with Claude (Anthropic). The human author provided the direction, constraints, personal context, and decisions about what to include/exclude. The AI assistant drafted text, which was then reviewed and revised based on feedback. Sections were rewritten multiple times to match the author’s voice and intentions. The final editorial decisions, including what content made it to publication, were made by the human author.

10-year anniversary

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July 15th, 2022

Dear readers:

I started writing this blog 10 years ago and it is to date one of the things I am the most proud of.

This is a big milestone for me, so I would like to begin with thanking all the people that encouraged me at the beginning, and in particular, for their valiant support and feedback: Matt Hall, Evan Bianco, Oliver Kuhn, Steve Lynch, and last but not least my life partner Rebecca.

A lot of the work I did in the first couple of years was on furthering my understanding, and sharing that with others, of the use of colours in scientific visualization, and how to use better colormaps, for example Perceptual rainbow palette, part a and part b.

I am grateful I achieved those knowledge-sharing goals:

  • The material is referenced in the matplotlib documentation
  • The blog has often been used as reference in talks and other publications on colormaps, beginning with this classic matplotlib talk given by Kristen Thyng at scipy 2014
  • I was thrilled to have received positive feedback on my work by Bernice Rogovitz, someone I hold in very high esteem
  • Some of that work on the blog resulted in being invited by Matt Hall to write a tutorial for The Leading Edge (SEG). The tutorial came with a Jupyter notebook demonstrating how to evaluate default colour maps and favour more perceptual alternatives
  • I am particularly proud to see that the article is still ranking in the top 20 most downloaded papers from The Leading Edge (between 2010-2020)
  • Additionally, the two blog post are to date top results for the #google search “perceptual rainbow” and “perceptual palette images”

Ultimately, I am very happy to have created a space for sharing and exchanging ideas freely.

So, to celebrate these 10 years of MyCarta, I treated it to a new domain, mycartablog.com (but the old domain, and links still work) and a brand new look (it took me a while to get there but I like it a lot) with a theme that should now be responsive for all devices (welcome to the new era Matteo!).

I will also soon publish a short series of short but sweet new posts on colormaps and visualization (and republish on linkedin).

Thank you all for sharing this journey!

Looking for opportunities

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As of yesterday,  I no longer have a full-time day job.

sorry

I am looking for opportunities.

I’d love to hear about projects in geophysics, computational geoscience, data science, machine learning. Feel free to get in touch with me at matteo@mycarta.ca.

Thanks,

Matteo

sketch2model

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This guest post (first published here) is by Elwyn Galloway, author of Scibbatical on WordPress. It is the first in our series of collaborative articles about sketch2model, a project from the 2015 Calgary Geoscience Hackathon organized by Agile Geoscience. Happy reading.

Collaboration in action. Evan, Matteo, and Elwyn (foreground, L to R) work on sketch2model at the 2015 Calgary Geoscience Hackathon. Photo courtesy of Penny Colton.

Welcome to an epic blog crossover event. Two authors collaborating to tell a single story over the course of several articles.

We’ve each mentioned the sketch2model project on our respective blogs, MyCarta and scibbatical, without giving much detail about it. Apologies if you’ve been waiting anxiously for more. Through the next while, you’ll get to know sketch2model as well as we do.

The sketch2model team came together at the 2015 Geoscience Hackathon (Calgary), hosted by Agile Geoscience. Elwyn and Evan Saltman (epsalt on twitter and GitHub) knew each other from a previous employer, but neither had met Matteo before. All were intrigued by the project idea, and the individual skill sets were diverse enough to combine into a well-rounded group. Ben Bougher, part of the Agile Geoscience team, assisted with the original web interface at the hackathon. Agile’s take on this hackathon can be found on their blog.

Conception

The idea behind sketch2model is that a user should be able to easily create forward seismic models. Modelling at the speed of imagination, allowing seamless transition from idea to synthetic seismic section. It should happen quickly enough to be incorporated into a conversation. It should happen where collaboration happens.

The skech2model concept: modelling at the speed of imagination. Take a sketch (a), turn it into an earth model (b), create a forward seismic model (c). Our hack takes you from a to b.

Geophysicists like to model wedges, and for good reasons. However, wedge logic can get lost on colleagues. It may not effectively demonstrate the capability of seismic data in a given situation. The idea is not to supplant that kind of modeling, but to enable a new, lighter kind of modeling. Modeling that can easily produce results for twelve different depositional scenarios as quickly as they can be sketched on a whiteboard.

The Hack

Building something mobile to turn a sketch into a synthetic seismic section is a pretty tall order for a weekend. We decided to take a shortcut by leveraging an existing project: Agile’s online seismic modelling package, modelr. The fact that modelr works through any web browser (including a smartphone) kept things mobile. In addition, modelr’s existing functionality allows a user to upload a png image and use it as a rock property model. We chose to use a web API to interface our code with the web application (as a bonus, our approach conveniently fit with the hackathon’s theme of Web). Using modelr’s capabilities, our hack was left with the task of turning a photo of a sketched geologic section into a png image where each geologic body is identified as a different color. An image processing project!

Agile is a strong proponent for Python in geophysics (for reasons nicely articulated in their blog post), and the team was familiar with the language to one extent or another. There was no question that it was the language of choice for this project. And no regrets!

We aimed to create an algorithm robust enough to handle any image of anything a user might sketch while accurately reproducing their intent. Marker on whiteboard presents different challenges than pencil on paper. Light conditions can be highly variable. Sketches can be simple or complex, tidy or messy. When a user leaves a small gap between two lines of the sketch, should the algorithm take the sketch as-is and interpret a single body? Or fill the small gap and interpret two separate bodies?

Our algorithm needs to be robust enough to handle a variety of source images: simple, complex, pencil, marker, paper, white board (check out the glare on the bottom left image). These are some of the test images we used.

Matteo has used image processing for geoscience before, so he landed on an approach for our hack almost instantly: binarize the image to distinguish sketch from background (turn color image into a binary image via thresholding); identify and segregate geobodies; create output image with each body colored uniquely.

Taking the image of the original sketch (left) and creating a binary image (right) is an integral part of the sketch2model process.

Python has functions to binarize a color image, but for our applications, the results were very inconsistent. We needed a tool that would work for a variety of media in various lighting conditions. Fortunately, Matteo had some tricks up his sleeve to precondition the images before binarization. We landed on a robust flow that can binarize whatever we throw at it. Matteo will be crafting a blog post on this topic to explain what we’ve implemented.

Once the image is binarized, each geological body must be automatically identified as a closed polygon. If the sketch were reproduced exactly as imagined, a segmentation function would do a good job. The trouble is that the sketch captured is rarely the same as the one intended — an artist may accidentally leave small gaps between sketch lines, or the sketch medium can cause unintentional effects (for example, whiteboard markers can erase a little when sketch lines cross, see example below). We applied some morphological filtering to compensate for the sketch imperfections. If applied too liberally, this type of filtering causes unwanted side effects. Elwyn will explore how we struck a balance between filling unintentional gaps and accurate sketch reproduction in an upcoming blog post.

Morphological filtering can compensate for imperfections in a sketch, as demonstrated in this example. The original sketch (left) was done with a marker on white board. Notice how the vertical stroke erased a small part of the horizontal one. The binarized version of the sketch (middle) shows an unintentional gap between the strokes, but morphological filtering successfully closes the small gap (right).

Compared to the binarization and segmentation, generating the output is a snap. With this final step, we’ve transformed a sketch into a png image where each geologic body is a different color. It’s ready to become a synthetic seismic section in modelr.

Into the Wild

“This is so cool. Draw something on a whiteboard and have a synthetic seismogram right on your iPad five seconds later. I mean, that’s magical.”

Sketch2model was a working prototype by the end of the hackathon. It wasn’t the most robust algorithm, but it worked on a good proportion of our test images. The results were promising enough to continue development after the hackathon. Evidently, we weren’t the only ones interested in further development because sketch2model came up on the February 17th episode of Undersampled Radio. Host Matt Hall: “This is so cool. Draw something on a whiteboard and have a synthetic seismogram right on your iPad five seconds later. I mean, that’s magical.”

Since the hackathon, the algorithm and web interface have progressed to the point that you can use it on your own images at sketch2model.com. To integrate this functionality directly into the forward modelling process, sketch2model will become an option in modelr. The team has made this an open-source project, so you’ll also find it on GitHub. Check out the sketch2model repository if you’re interested in the nuts and bolts of the algorithm. Information posted on these sites is scant right now, but we are working to add more information and documentation.

Sketch2model is designed to enable a new kind of collaboration and creativity in subsurface modelling. By applying image processing techniques, our team built a path to an unconventional kind of forward seismic modelling. Development has progressed to the point that we’ve released it into the wild to see how you’ll use it.

Welcome to MyCarta, part II

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I started this blog in 2012; in these 3 1/2 years it has been a wonderful way to channel some of my interests in image processing, geophysics, and visualization (in particular colour), and more recently Python.

During this time, among other things, I learned how to build and maintain a blog, I packaged a popular Matlab function, wrote an essay for Agile Geoscience’s first book on Geophysics, presented at the 2012 CSEG Geoconvention, and wrote two tutorials for The Leading Edge. Last, but not least, I made many new friends and professional connections.

Starting with 2016 I would like to concentrate my efforts on building useful (hopefully) and fun (for me at least) open source (this one is for sure) tools in Python. This is going to be my modus operandi:

  • do some work, get to some milestones
  • upload the relevant IPython/Jupiter Notebooks on GitHub
  • post about it on this blog, on Twitter, and LinkedIn

Here are a couple of examples of ongoing projects:

rainbowbot

The idea for this project was inspired by Matt Hall of Agile Geoscience. The plan is to eventually build a web app that will:

sketch2model

This is a project started at the 2015 Calgary Geoscience Hackathon organized by Agile Geoscience with Elwyn Galloway, Evan Saltman, and Ben Bougher. The original idea, proposed by Elwyn at the Hackathon, was to make an app that would turn an image of geological sketch into a model, as in the figure below.

The implementation of the finished app involves using morphological filtering and other image processing methods to enhance the sketch image and convert it into a model with discrete bodies, then pass it on to Agile’s modelr.io to create a synthetic.

Happy 2016!!

Hacker Roots

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In Welcome to MyCarta, part II, I mentioned sketch2model a fun App project started at the 2015 Calgary Geoscience Hackathon organized by Agile Geoscience.

One of my team mates from the hackathon, Elwyn Galloway, just started writing at scibbatical a new science blog on WordPress.

In the next little while, Elwyn and I will be authoring together and crosspost a short series on the hackathon project.

For now, welcome Elwyn!

Why I replaced my Creative Commons license with Konomark

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Why I share

I think openness in geoscience is very important, and I feel we all have a duty to be open with our work, data, ideas when possible and practical. I certainly do believe in sharing a good deal of the work I do in my spare time. So much so that when I started this blog there was no doubt in my mind I would include an agreement for people to use and modify freely what I published. Indeed, I venture to say I conceived the blog primarily as a vehicle for sharing.

Some of the reasons for sharing are also selfish (in its best sense): doing so gives me a sense of fulfillment, and pleasure, as Matt Hall writes in Five things I wish I’d known (one of the essays in 52 You Should Know About Geophysics), you can find incredible opportunities for growth in writing, talking, and teaching.  There is also the professional advantage of maintaining visibility in the marketplace, or as Sven Treitel puts it, Publish or perish, industrial style (again in 52 You Should Know About Geophysics).

How I used to share

At the beginning I choose an Attribution-NonCommercial-ShareAlike license (CC BY-NC-SA) but soon removed the non-commercial limitation in favour of  an Attribution-ShareAlike license (CC BY-SA).

A (very) cold shower

Unfortunately, one day last year I ‘woke up’ to an umpleasant surprise: in two days an online magazine had reposted all my content – literally, A to Z! I found this out easily because I received pingback approval requests for each of them (thank you WP!). Quite shocked, I confess, the first thing I did was to check the site: indeed all my posts were there. The published included an attribution with my name at the top of each post but I was not convinced this would be fair use. Quite the contrary, to me this was a clear example of content scraping, and the reason why I say that is because they republished even my Welcome post and my On a short blogging sabbatical post – in the science category! – please see the two screen captures below (I removed their information) of the pingbacks:

If this was a legitimate endeavour, I reasoned, a magazine with thoughtful editing,  I was sure those two posts would have not been republished. Also, I saw that posts from many other blogs were republished en masse daily.

Limitations of Creative Commons licenses

I asked for advice/help from my twitter followers, and on WordPress Forums, while at the same time started doing some research.  That is when I learned this is very common, however being in good company (google returned about 9,310,000 results when searching ‘blog scraping’) did not feel like much consolation: I read that sites may get away with scraping content, or at least try. I will quote directly from the Plagiarism Today article Creative Commons: License to Splog?: “They can scrape an entire feed, offer token attribution to each full post lifted (often linking just to the original post) and rest comfortably knowing that they are within the bounds of the law. After all, they had permission …Though clearly there is a difference between taking and reposting a single work and reposting an entire site, the license offers a blanket protection that covers both behaviors”.

Fight or flight?

Yes, Creative Commons have mechanisms that allow fighting of this abuse, but their effectiveness is yet to be proved (read for example this other article by Plagiarism Today, Using Creative Commons to Stop Scraping). Notice these articles are a bit out of date but as far as I could see things have not improved much. The way is still the hard way of tracking down the culprit and fighting through  legal action, although social media support helps.

It is possible to switch to a more restrictive Creative Commons license like the Attribution-NonCommercial-NoDerivs (perhaps modified as a CC+), but that only allows to cut your losses, not to fight the abuse,  as it is only on a going-forward basis (I read this in an article, and jotted down a note, but  I unfortunately cannot track down the source – you may be luckier, or cleverer).

Then I was contacted by the site administrator through my blog contact form (again I removed their information), who had read my question on the WordPress forum:

Your Name: ______
Your Email Address: ______
Your Website: ______
Message: Hello.
Your site is under a CC license. What’s the trouble in republishing your content?
Regards.
Subject: Your license

Time: Thursday July 26, 2012 at 12:26 am
IP Address: ________
Contact Form URL: http://mycartablog.com/contact/
Sent by an unverified visitor to your site.

I responded with a polite letter, as suggested by @punkish on twitter. I explained why I thought they were exceeding what was warranted under the Creative Commons license, that republishing the About page and Sabbatical posts  was to me proof of scraping, and I threatened to pursue legal recourse, starting with DMCA Notice of Copyright Infringement. Following my email  they removed all my posts from their site, and notified me.

Two alternatives

I think I was fortunate in this case, and decided to take matters into my own hands to prevent it from happening again. Following my research I saw two good, viable ways to better protect my blog from scraping whole content, while continuing to share my work. The first one involved switching to WordPress.org. This would allow more customization of the blog, and use of such tools as the WP RSS footer plugin, which allows to Get credit for scraped posts, and WP DMCA website protection. Another benefit of switching to WordPress.org is that – if you are of belligerent inclination – you can try to actively fight content scraping with cloacking. Currently, although it is one of my goals for the future of this blog, I am not prepared to switch WordPress.com due to time constraints.

Having decided to stick to WordPress.com  the alternative I camu up with was to remove the CC license, replacing it with a Copyright notice with what I would call a liberal attitude.  A simple way to do that was to add the Konomark logo accompanied by a statement that encourages sharing, but without surrendering any rights upfront. Addtionally you can prevent content theft from your WordPress.com (or at least reduce the risk) by configuring the RRS feed so that it displays post summaries only, not full posts.

How I share now

I customized my statement to reduce as much as possible the need for readers to ask for permission by allowing WorPress reblogging and by allowing completely open use of my published code and media.  Below is a screen capture of my statement, which it is located in the blog footer:

I hope this will be helpful for those that may have the same problem. Let me know what you think.

Additional reading

Whine Journalism and how to bring the splashback – a great story and a great step-by-step guide, to fight content theft

Content Scrapers – How to Find Out Who is Stealing Your Content & What to Do About It

Copysquare and Konomark

Useful tools to detect stolen content

Copyscape – Search for copies of your page on the web

Google Alerts – (for example read this article)

Google Report scraper pages

TinEye – reverse image search engine. ‘It finds out where an image came from, how it is being used, if modified versions of the image exist…’

MyCarta – 2012 in review

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The WordPress.com stats helper monkeys prepared a 2012 annual report for this blog.

Here’s an excerpt:

4,329 films were submitted to the 2012 Cannes Film Festival. This blog had 19,000 views in 2012. If each view were a film, this blog would power 4 Film Festivals

Click here to see the complete report.