devlovstad

5 dni temudiscuss

I've worked for a year in a lab doing cancer genomics and had to learn everything from scratch, since my background is in computer science.

It's definitely possible to learn enough to be productive within a few months, but to actually comprehend and understand the underlying biology takes much, much longer. I still don't understand much of what is presented by people from other labs outside of my specialty.

devlovstad

w zeszłym miesiącudiscuss

I have AkademikerPension as my pension fund through work and this move suits me quite well. They've already excluded Tesla as well as a variety of companies that profit of weapon production, fossil fuel production or are suspected for human rights violations.

https://akademikerpension.dk/ansvarlighed/ekskluderede-selsk...

devlovstad

2 miesiące temudiscuss

I work with genomics pipelines in my day job. This repo does not seem quite ready for serious usage until a comparison is made with existing tools such as Bowtie 2/samtools/Strelka or similar. For cancer genomes, it's also a bit limiting that it does not call structural variants instead of just SNVs/indels.

devlovstad

2 miesiące temudiscuss

While it seems cool, I am still waiting for native Jupyter Notebook support for it to be useful to me. When that happens, I'll give it a spin, but it seems like they recently took it off the roadmap.

devlovstad

6 miesięcy temudiscuss

AkademikerPension is a quite unusual pension fund, even by Danish standards due to them being quite political in their holdings. This stance is based on surveys made from their members, where many have indicated that placing their money ethically is as important as getting a good return/minimizing risk. In March 2025, they chose to drop their Tesla shares due to union-busting, lack of independence in the board and due to Elon Musk's actions[1].

Last year, AkademikerPension had a return between 3 and 6 percent, which is lower than other Danish pension funds[2].

[1] https://akademikerpension.dk/nyheder/vi-ekskluderer-tesla/ (Danish)

[2] https://akademikerpension.dk/nyheder/afkast-mellem-3-og-6-pr... (Danish)

devlovstad

6 miesięcy temudiscuss

I've read through most of the first paper mentioned.

Here, the authors have taken set up two synthetic experiments where transformers have to learn the probability of observing events from a sampled from a "ground truth" Bayesian model. If the probability assigned by the transformers to the event space matches the Bayesian posterior predictive distribution, then the authors infer that the model is performing Bayesian inference for these tasks. Furthermore, they use this to argue that transformers are performing Bayesian inference in general (belief-propagation throughout layers).

The transformers are trained on thousands of different "ground truth" Bayesian models, each randomly initialized which means that there's no underlying signal to be learned besides the belief propagation mechanism itself. This makes me wonder if any sufficiently powerful maximum likelihood-based model would meet this criteria of "doing Bayesian inference" in this scenario.

The transformers in this paper do not intrinsically know to perform inference due to the fact that they're transformers. They perform inference because the optimal solution to the problems in the experiments is specifically to do inference, and transformers are powerful enough to model belief propagation. I find it hard to extrapolate that this is what is happening for LLMs, for example.

devlovstad

8 miesięcy temudiscuss

uv has made working with different python versions and environments much, much nicer for me. Most of my colleagues in computational genomics use conda, but I've yet to encounter a scenario where I've been unable to just use uv instead.

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