kkoncevicius

What does training data look like for a model like this?

> much of human interactions operates on rules that are not only unspoken but often unacknowledged or even outright denied.

This sounds interesting on it's own. I would be curious to hear more if you are willing to share.

If we could show the current models to someone like Alan Turing, I am sure he would conclude that we have AGI.

Looking at your ladder, made me think where I fit in and I mostly enjoy steps 1 and 2. The implementation and shipping are big chores to me. Once I know, half in my head, that the problem is solvable and I could see a path towards implementation I loose motivation to continue.

So possibly there are other classes of people, not just either enterpreneur or thinkerer. Thou not sure what this combination is, maybe something more like R&D.

And LLMs give a lot of value here. They allow to quickly do steps 2-3 - "hard and tedious" stuff - to confirm that the thing, in fact, became solvable.

The first entries are a bit weird to me. They are mostly about eugenics for people who got their Nobel prices in the early 1900s. The thing is that in those days even the state of California promoted eugenics [1]. And according to Wikipedia, so much so that they might have even played a role in influencing Germany [2].

[1]: https://en.wikipedia.org/wiki/Eugenics_in_California

[2]: https://en.wikipedia.org/wiki/Nazi_eugenics

This was true some time ago but nowadays I don't get this impression. Seems like whatever style I type in, the LLM is already pre-prompted to respond in "its" "own" "style".

Aha, missed it. But unable to delete the thread now.

I had a similar experience. I wanted to test it by asking it to summarise a scientific OMICs-related paper. It gave a warning about me potentially developing a bio-weapon or something like that. And switched back to Opus 4.8.

We can look at the same numbers in different way:

  Error with 91.3% = 8.7%
  Error with 94.5% = 5.5%

  Error reduction = 8.7% - 5.5% = 3.2%

So the improvement is 3.2% / 8.7% = 36.8%

A lot of art from the middle ages is anonymous. Painting itself is an extension of the artist, containing the intension of the person producing it and hence no name is necessary. This is a theoretical state of quality, where activity is not measured in numbers or on a scale but is seen as expression of a particular unique human being. Then comes the renaissance and painters begin to attach names to their works. Here starts a crucial shift - a turn from quality to quantity. Certain artists are better than others and hence quality itself is now measured (quantified) using a name of the person. After that the name becomes so prevalent that some works begin to be valuable only because a certain name was responsible in producing that work. Think - Picasso. Quantity starts to take over. Then comes film and comics and ads where the painter is expected to have no individuality, and he is praised for having a style and technique that is replaceable. Same is true for corporate software development by the way. Here the name (the intermediate state connecting quality and quantity) starts to disappear and is often replaced by a name of a "golem" - a corporation. Quantity dominates - more and faster is better, and the more "nameless" the better. Ten years ago one might think that this is the limit of dehumanisation and it cannot move any further. But now we have AI - where a work of art (or other kind of work) cannot be associated with any quality (cannot be given a name) in principle. And quantity (more, faster, cheaper) dominates. When you think in these terms, the "techno-optimism" is just a place somewhere in this arrow moving from quality to quantity. Or in other words moving from a qualitative anonymity (my work is an extension of my being) to quantitative anonymity (the work is not associated with any being). Hence, it is not a stable position.

Yes! I didn't understand why I always found the Little Prince story (and by extension "alchemist") so repulsive, until I read that book. Little prince is aimed at people who have lost their idealistic youth qualities and seek to get back in touch with that part of themselves. I had the opposite problem - I never fully left that place.

Even if they do not strike gold the second time, there can still be a multitude of reasons:

  1. The innovators will know a lot about the details, limitations and potential improvements concerning the thing they invented.
  2. Having a big name in your research team will attract other people to work with you.
  3. I assume the people who discovered something still have a higher chance to discover something big compared to "average" researchers.
  4. That person will not be hired by your competition.

I was dazzled with the drawing itself. Then by accident I discovered you can zoom in and out too. And on top of that - you can also rotate 360 degrees around the object.

Too far out of my field for me to understand how impressed I should be - but I am impressed.

The average life expectancy was low because of more deaths during childhood and wars. But the natural lifespan was more or less the same as it is today. For example, take a look at famous philosophers or politicians from Ancient Greece. Majority of them lived to about 70-80 years of age.

Such arguments go both ways. For example, if aging is accumulation of damage and not programmed, then why don't we see lucky people who live 5 times longer. Also how come the patterns of aging are so similar between individuals and even between different species (wrinkly skin, grey hair, fragile bones).

This would be an optimistic scenario and introduce a possibility for the "scheduled gene program" to be controlled or turned off. The current thinking in the field seems to favour the idea that aging is a complex combination of programmed changes, stochastic damage, as well as various adaptations to help cope with the damage.

> I use apple notes a lot but never thought of making it into a website. Is this homepage a notes app site?

Same question. Is there an example of how the final website might look like?

I am writing to add two other possibilities.

Within myself I notice that the project becomes boring when there is nothing new left to be learned from it. Depending on the project this could happen at 50% completion or 90% completion. Take scientific research for example. For me there is a lot of motivation to figure things out, to fill the gaps, to make sure everything is solid. But then there is the mundane part of putting it into text and publishing. And my energy is not in there. I already know what will go into that paper, I know getting it out will count as "success" and I know it should be shared. But my libido is not in it.

Another thing - the end of a big project signifies a big change. If you worked on something for a long time, what will you do once it's finished? Norman Finkelstein in one of his interview put it like that (paraphrasing): "I think some people genuinely don't want to end the conflict [between Israel and Palestine] because they built their whole life around it. In the past it was a problem for me as well. I have spent my whole academic career writing about this conflict. I read enough books to fill this room. Literally. If the conflict ends tomorrow - what am I going to do with my life?".

I think there is a general over-emphasis on DNA. There is no process in nature that builds a cell out of DNA. Instead DNA is just part of the cell and the cell can slice-and-dice its own DNA however it wants. Neutrophils even throw their DNA outside of the cell in order to build traps for bacteria.

https://www.thenewatlantis.com/publications/getting-over-the...

16-bit blends (i.e. 16-bit Portuguese) do not feel like proper 16-bit sound.

Right now it probably blends the styles together, taking elements from both, but not following the required restrictions.

I won't do a better job defending Jung's ideas than the man himself in his works, but just as a friendly reply: yes, but according to Jungs theories - these are archetypes - meaning all people have a combination of one _and_ the other. There might be people who refuse to grow up and stay in a kind of perpetual childhood with a failure-to-launch and never finishing their own projects (a good book about that is "The problem of puer aeternus"), and there are also children that grow up too quickly and start being cynical and sarcastic with a know-it-all attitude, and belief that nothing is worth doing and the world is going to shit. The actual human beings are a combination of those archetypes. So an astronaut is a cultural input, in the same way that saying that you have two hands is a cultural input, but what is different is the inner attitude - do you see a lot of potential to the point that you cannot make a decision between several choices, or do you see barriers and feel a loss of meaning. Again, a healthy combination is "best".

In a way this is a tension between an individual and society. Children dream of big impossible things, while archetypical grown-ups just want a paycheck and a house, with a few weeks of vacation. The extent to which we abandon childhood dreams is an indicator of how much we were crushed by society (or "real world").

Carl Jung investigated this with his "puer-aeternus" (the child that was promised) and "senex" (old man) archetypes. A really interesting read, if you have time for that. In essence I think he advocated a balance, where one starts at childhood, becomes a cynical grown-up and then re-integrates his childhood fantasies back into his character, but now in a less naive and wiser way.

Great suggestion, thank you. It's appropriately short and more fitting than I anticipated. Specially the part about brain atrophy.

For me posts like these go in the right direction but stop mid-way.

Sure, at first you will want an AI agent to draft emails that you review and approve before sending. But later you will get bored of approving AI drafts and want another agent to review them automatically. And then - you are no longer replying to your own emails.

Or to take another example where I've seen people excited about video-generation and thinking they will be using that for creating their own movies and video games. But if AI is advanced enough - why would someone go see a movie that you generated instead of generating a movie for himself. Just go with "AI - create an hour-long action movie that is set in ancient japan, has a love triangle between the main characters, contains some light horror elements, and a few unexpected twists in the story". And then watch that yourself.

Seems like many, if not all, AI applications, when taken to the limit, reduce the need of interaction between humans to 0.

Not sure why this comment was dead, but I vouched for it to appear again.

I also think the same - a to-do app will not help. Specially if you plan to add deadline information, difficulty and value scores. These will turn into distractions themselves which will require thinking and making decisions about.

There were always a big group of people who were obsessed about configs and plug-ins. My impression is that once NeoVim appeared all of them moved from Vim to NeoVim so that's why we have the impression about NeoVim being turned into Emacs.

Personally I use almost stock NeoVim (~30 lines config, with 2 tiny plugins, one of which I wrote myself). I find NeoVim to be very close to Vim, but with a few better corners. Things that I like are things like default `gc` operator for commenting and uncommenting portions of code.

From R side i think this is mainly because ggplot2 is really really slow.

Base R graphics would plot 100,000 points in about 100 milliseconds.

    x <- rnorm(100000)
    plot(x)

A quick benchmark with writing to a file:

    x <- rnorm(100000)
    system.time({
      png("file.png")
      plot(x)
      dev.off()
    })

     user  system elapsed
    0.179   0.002   0.180

I work with R and not python, so some things might not apply, but this:

> [...] it fixes the problem at the time, then it gets thrown away with the notebook only to be written again soon.

Is one of the reasons I stopped using notebooks.

One solution to your problem might be to create a simple executable script that, when called on the file of your dataset in a shell, would produce the visualisation you need. If it's an interactive visualisation then I would create a library or otherwise a re-usable piece of code that can be sourced. It takes some time but ends up saving more time in the end.

If you have custom-made things you have to check on your data tables, then likely no library will solve your problem without you doing some additional the work on top.

And for these:

> Or, especially for pandas, you want to separate functions to depend on the same expensive pre-calc. [...] Now you have to worry about the ordering of functions.

I save expensive outputs to intermediate files, and manage dependencies with a very simple build-system called redo [1][2].

[1]: http://www.goredo.cypherpunks.su

[2]: http://karolis.koncevicius.lt/posts/using_redo_to_manage_r_d...

As a whole, of course you have a point - big visualisations when done properly should help with data exploration. However, from my experience they rarely (but not never) do. I think it's specific to the type of data you work with and the visualisation you employ. Let me give an example.

Imagine we have some big data - like an OMIC dataset about chromatin modification differences between smokers and non-smokers. Genomes are large so one way to visualise might be to do a manhattan plot (mentioned here in another comment). Let's (hypothetically) say the pattern in the data is that chromatin in the vicinity of genes related to membrane functioning have more open chromatin marks in smokers compared to non smokers. A manhattan plot will not tell us that. And in order to be able to detect that in our visualisation we had to already know what we were looking for in the first place.

My point in this example is the following: in order to detect that we would have to know what to visualise first (i.e. visualise the genes related to membrane function separately from the rest). But then when we are looking for these kinds of associations - the visualisation becomes unnecessary. We can capture the comparison of interest with a single number (i.e. average difference between smokers vs non-smokers within this group of genes). And then we can test all kinds of associations by running a script with a for-loop in order to check all possible groups of genes we care about and return a number for each. It's much faster than visualisation. And then after this type of EDA is done, the picture would be produced as a result, displaying the effect and highlighting the insights.

I understand your point about visualisation being an indistinguishable part of EDA. But the example I provided above is much closer to my lived experience.