An interesting approach is the dual GPL and commercial license. This is used for example by the CGAL geometry library [1]. In this case, a user of the library has the choice of either paying for the library, or open sourcing the code of their software.
Some libraries let you borrow Playstation video games. I wonder if those libraries will have access to a system that allows people to borrow digital video games.
Beyond the technical aspects, this new technology also leads to fundamental social changes.
In particular, until now, mathematics were one of the rare sciences were great scientists could emerge from any country with a good education system.
With the raise of strong AI tools, only scientists in rich countries with access to those tools might be able to advance faster on the most difficult problems like the millennium problems.
Mathematics might become like experimental sciences were you need to build expensive machines to make further progress, such as nuclear fusion.
Actually, even now, the strongest models in mathematics are only available to a few engineers and a few mathematicians selected by Openai and Google.
> IDK how the custom hardware exploits this; would love to hear any ideas!
You might like this article [1], titled "FPGA-based CNN Acceleration using
Pattern-Aware Pruning". More context and details can be found in the PhD thesis of Léo Pradels [2].
Actually, in Europe, Gemini is officially not available for kids even at home [1]. In some countries like Germany, the restriction applies until 16 [2]. I find unsettling that even for supervised account, parents are forbidden to let their kids learn how to use Gemini, even between 14 and 16 yo.
Note that this restriction does not seem to appear from other AI company. So from outside, it looks like unsolicited interference from Google in the parental education choices.
This reminds me an interview of the author Patrick Modiano, just after he won the literature Nobel price. The presenter asked him if the money would help. His answer was something like: "well, I don't see how the money will help the next time I will be in front of a white page".
> How do we handle AI doing creative work? How do we treat AI creative work? How much creative work do we feel comfortable handing over to AI?
Just as a good for thought, looking back into history, during the late 1920s, mass production had a critical impact on Art Deco [1]. Artists were divided on the question if mass-produced art (using new industrial methods) could have a quality similar to hand-crafted art. It is clear that different people will have different opinion on the subject.
The technology is not there yet, but one example of mass production from AI would be book adaptation into movies. I'm sure that there are many other examples hard to predict that might: empower people, degrade art quality, improve art quality, divide people or maybe gather people.
You might be interested in a new experimental 3D scene learning and rendering approach called Radiant foam [1], which is supposed to be better suited for GPUs that don't have hardware ray tracing acceleration.
In this regard, the subreddit r/NeutralPolitics is interesting: it aims at evidence-based discussions on political issues. Threads are somehow in-between HN and Wikipedia. It is definitely interesting to read, and at the same time, participating in a discussion is quite daunting.
According to the book "A Convergence of Civilizations" from Youssef Courbage and Emmanuel Todd [1], the Iran revolution actually happened at the end of the 70s. And indeed, the political situation is not stable yet. The authors argue in the book that historically, it can take from 30 to more than 100 years before a country gets a stable democracy after a revolution.
Notably, the book was written before the Arab Spring revolutions, and yet, it predicted them rather accurately. The main thesis of the book is that a revolution arises when most of the men and most of the women in a country can read.
The flow of ideas goes both ways between AI and economy. Notably, the economist Friedrich Hayek [1] was a source of inspiration in the development of AI.
He wrote in 1945 on the idea that the price mechanism serves to share and synchronise local and personal knowledge [2]. In 1952, he described the brain as a self-ordering classification system based on a network of connections [3]. This last work was cited as a source of inspiration by Frank Rosenblatt in his 1958 paper on the perceptron [4], one of the pioneering studies in machine learning.
> They can get rid of 1/3-2/3s of their labor and make the same amount of money, why wouldn't they.
Competition may encourage companies to keep their labor. For example, in the video game industry, if the competitors of a company start shipping their games to all consoles at once, the company might want to do the same. Or if independent studios start shipping triple A games, a big studio may want to keep their labor to create quintuple A games.
On the other hand, even in an optimistic scenario where labor is still required, the skills required for the jobs might change. And since the AI tools are not mature yet, it is difficult to know which new skills will be useful in ten years from now, and it is even more difficult to start training for those new skills now.
With the help of AI tools, what would a quintuple A game look like? Maybe once we see some companies shipping quintuple A games that have commercial success, we might have some ideas on what new skills could be useful in the video game industry for example.
> You can use a language server perfectly easily with Vim, Emacs, Helix, Sublime, etc.
By the way, the language server protocol was originally developed for VSCode [1]. The popularity of LSP in other editors might have contributed to advertise VSCode.
Here is an overview of related restrictions in other countries [1]. Actually, in many European countries, Google does not grant access to Gemini for people under 16yo [2,3].
The best implementation I know of digital ID is the one in Estonia. It comes with a data tracker, such that each citizen can see who exactly has been looking at their data [1].
This reminds me of a hoax from the Yes Men [1]. They convinced temporarily the BBC that a company agreed to a compensation package for the victims of a chemical disaster, which resulted in a 4.23 percent decrease of the share price of the company. When it was revealed that it was a hoax, the share price returned to its initial price.
I wonder if it works better if we ask the LLM to produce a script that extract the resulting list, and then we run the script on the two input lists.
There is also the question of the two input lists: it's not clear if it is better to ask the LLM to extract the two input lists directly, or again to ask the LLM to write a script that extract the two input lists from the raw text data.
That's a very interesting question. When comparing wildly different computing machines, how to make a fair comparison?
At least two criteria comes in mind: the volume and the energy consumption.
Indeed we can safely assume that more volume and more energy leads to more computation power. For example, it is not fair to compare a 10m^3 room filled with computers with 10cm^3 computer. The same goes with the number of kilowhat-hours used.
Thinking further on those two criteria for GPUs and humans, we could also consider the access to energy and volume. First, energy access for machines has dramatically increased since the industrial revolution. Second, volume access for machines has also increased since the beginning of the mass production. In particular, creating one cube meter of new GPUs is faster than giving birth to a new human.
tldr: fair comparison of two machines should take into account their volume and their energy consumption. On the other hand, this might be mitigated by how fast a machine can increase its volume, and what is its bandwidth for energy consumption.
[1]: https://doc.cgal.org/latest/Manual/license.html