Palimpsest

Evolving notes, images and sounds by Luis Apiolaza

Page 5 of 72

Existential Comics: Turing Test with a dog.

The AI House of Cards

2024-06-20 /

Many of the people pushing today for AI to be used in all organisations, particularly of the ChatGPT/LLM persuasion, were pushing NFTs and, before that, cryptocurrencies. And the blockchain, of course. How many meetings we had in our organisations (including universities) asking us to think of blockchain applications for research projects or teaching? A few.

Before all that, 2012 was the year of the MOOC (Massive open online course), when Coursera, Udacity and edX were going to disrupt and break universities. Everyone had to spend, sorry, invest on developing online courses because of the fear of missing out (FOMO). MOOCs are popular, but did not affect university attendance at the predicted scale and universities didn’t make money with most courses.

Now “prompt engineers” insist that we are missing out the LLM revolution, that our students are falling behind, as we do not allow our students to use ChatGPT in assignments. The same ChatGPT that uses fake citations (hallucinations, my bad) to inexistent journal articles to write the requested introduction. What are we supposed to teach? How do we infuse critical thinking, ethical dilemmas, discussion, etc in a probabilistic parrot trained violating the copyright of half of the internet?

Can there be real experts on a technology that appeared year and a half ago? If I look at what I do for work, how long did it take me to achieve my current level of understanding? Way more than 1.5 years.

You could think of this text as a rant; yes, I am tired of people pushing AI. There are piles of money being burnt by large companies right now, a field full of grifters, huge environmental issues as these things use as much power as a small country, and plenty of other distractions. Everyone wants to make money before the whole thing collapses.

Now, if you want to read someone much more tired than me, a scathing post, then I Will [expletive] Piledrive You If You Mention AI Again is a great piece.

PS. I crossposted this text in LinkedIn and my WordPress blog. Both of them offered AI to write my post. Sigh. Featured Image: Existential Comics 15

Counter of La Picada de Pedro, showing lots of yummy food.

Breeding: simple interfaces, complex strategies

2024-06-14 /

I found this text I wrote 20 years ago(*), part of a discussion document I prepared for a review of the radiata pine breeding strategy. Fixed a couple of typos, but I guess we still need pretty much the same thing. 🤔

“Different breeders value different things or, better put, they emphasise different values when developing breeding strategies. One of the reasons why many breeding programs struggle to achieve results is that they face an extremely complex list of activities, which are almost impossible to complete.

“A knee-jerk reaction from some breeders has been to recur to the KISS principle when developing breeding strategies. Unfortunately, the typical reaction has been “let’s create this dumb down strategy because it is simple to apply”. Bzzz. Wrong answer! What they have often done is to create a glorified “deployment strategy” that has almost no chance of surviving in the long term: that is, short term gain based on long term disappointment.

“Breeders need to realise that what needs to be simple is the _interface_ of the strategy. This means that we need a smooth interaction between the “theoretical animal” and the people that will be implementing it. This does not mean that the strategy is theoretically simple, but that the day-to-day activities are a breeze to complete.

“This type of interface requires the development—either in-house or through contracting the service—of tools that make life easy. For example:

  • Easy access to predicted breeding values, including desktop and online access. In addition, there needs to be an idea of the reliability of those predicted values if we are going to use them for deployment purposes.
  • Tools that make easy deciding what to select and which trees should be mated with each other (mate selection and allocation).
  • Protocols for deployment and tools for keeping control of the availability of genetic material.
  • Easy management of the interaction between improvement and deployment objectives.

“In summary, breeders need tools for dealing with the huge amount of data created by breeding and deployment activities, so it can be transformed into information.”

(*) Well, 19 years ago, this was 2005, but twenty sounds much better.

Grafitti in Valdivia.

Are PhDs a pyramid scheme?

2024-06-06 /

If you are a professor in academia you are supposed to form/train new PhDs. In the past, those new PhDs would go to other universities and then train new PhDs, and their PhD students would go to other universities repeating this process ad infinitum. At some point, 20 years ago, 30 years ago—I don’t really know—we reached PhD saturation in academia. There were way too many PhDs for the number of available positions in universities.

It should be obvious by now that thinking of the package [getting a PhD + job in academia] is close to believing in the Tooth Fairy, Santa Claus or whoever is your favourite imaginary character. This doesn’t mean that you should not study a PhD, but that there should be a clear consideration that you will (most likely) have to work outside academia.

I struggle with this as a supervisor. I cannot in good conscience supervise a project *unless* I can see that the project/topic will set the student with skills to work for industry or some other avenue that is not a university.

In my personal opinion, PhDs for future academics are indeed a pyramid scheme.

Airplane row without a window

Why is this trait I like getting worse in the breeding programme?

2024-06-05 /

The short answer: because the trait you like is not part of the breeding objective and, therefore, has not an economic weight assigned to it. And if it doesn’t have an economic weight it has 0 (zero) economic importance.

A longer answer: in breeding there is a distinction between objective traits (which have an impact on profit), and the selection criteria (variables that are easy and cheap to assess, and that are correlated with the objective traits). They may even happen at different ages. For example, in forestry stem volume and wood stiffness at rotation age (say 25 years) can be objective traits for the production of structural timber. Stem diameter, wood density and standing tree velocity at age 8 can be selection criteria.

Some of the confusion may come from when people like a selection criterion (like wood density) and think the breeding programme is trying to improve that. In this example, we weren’t (at least at that time). We cared about volume and stiffness. Sacrificing levels of some selection criterion while pursuing the objective traits is perfectly fine if I am maximising value. And in a modern breeding programme you are pretty much always looking at value, not at a single trait.

If you find this interesting, you may also like Why did my breeding values go down?

Home-made Tom yum with fishmeat balls on rice noodles.

Last day of teaching

2024-05-31 /

…for this semester. I was really trying to keep my head above water, but gulp, glug, I kept on taking water in. There is a pile of marking and two exams coming my way in a few weeks. Anyone that could invite me to their home in Rarotonga? I need to recover from the always brutal semester steam roller.

Despite all the teaching, ideas keep on living for free in my head. Where to next? This is a common question when working in research: at some point the project has to be completed. Perhaps everything went well and the objectives were achieved, the findings were published, the student completed their PhD, etc. Or, perhaps, the whole thing was messy, or unattainable, or the experiment didn’t work out, or we run out of money.

Last weekend one of our students submitted his PhD, with chapters either published or somewhere in the publication pipeline. There is a sense of Where to next? From an implementation point of view, it is a matter of using the results, perhaps tweaking things here and there, but now it is an operational breeding programme issue. That topic will have to wait before I revisit it.

In conversations with a colleague in Chile (A) we talk ideas. Another colleague (B)informs us that our frontrunner was “too applied” for funding. It could make a significant practical difference, at least in my opinion, but the funding body has a strong preference for more “fundamental” research. The same funding body that does not like forestry too much, because it is “too slow”. When you put fundamental + forestry is hard to get results in 3 years of funding. Go figure.

B suggested another idea in which I am still getting my head around. Not quite my topic BUT I am a sucker for interesting problems and learning. Now reading about stuff that’s new for me, and see if I can connect it in a meaningful way to #breeding and #woodquality, and I don’t have to go all the way to Kevin Bacon’s degrees of separation.

I dislike (or should I say hate?) the push of Large Language Models (LLM) for writing. I can’t see the point, because Where is the terapeutic value of asking ‘write 300 words in Luis’ style’? I can, pardon, I need to write this because I can’t stop writing. I have to empty my head: it is 4:30 pm, Friday afternoon, the last day of teaching of this semester. Phew! And that’s how #academia feels today, ladies and gentlemen.

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