![][ Stefano Maffulli](https://www.maffulli.net/wp-content/uploads/2020/12/cropped-yr4r1wl0rjs-scaled-1.jpg)
I always thought that crime per capita cannot be the only lens by which to read San Francisco crime issues. The city is considerably smaller than most other world class cities and there is really no way to avoid what in other places would be “sketchy neighborhoods.” If you take a walk in any of…
La banca è sempre aperta, ma non sempre. La comunicazione aziendale si fionda a capofitto nel ridicolo.
It was bound to happen sooner or later… It happened a lot sooner than I expected: now you can generate the weirdest designs in your head directly into Canva. Now Krita, Inkscape, LibreOffice have even more catching up to do.
In a nutshell, ELM is a way to combine evolutionary algorithms and large language models for generation of diverse data.
Evolutionary algorithms provide a way to generate diverse and novel data by making mutations and changes to candidates in the domain of interest, such as code. Language models provide a way to encode human knowledge to guide these mutations intelligently. Combining these two techniques therefore allows the search procedure to say on the “manifold of functionality” and lets the language model drive the evolutionary algorithm towards areas of the solution space that neither technique could find on their own.
To set the scene a little bit, there have been numerous papers in the past year or two using large language models (LLMs) for code generation and synthesis, including OpenAIs Codex and DeepMind’s AlphaCode. Several of these papers, such AlphaCode, have focused on ways to generate code for specific domains including programming puzzles and solutions. However, sometimes the domain we want to generate code for has limited data that is only rarely found or not found in the training distribution. In this case, attempting to generate high quality code with prompt engineering will usually be impractical.
ELM demonstrates that by incentivising diversity in program generation, we can create code in domains not in the training dataset using only a single seed program.
Did CarperAI just release an open source library that can generate code that can be used to train other AI/ML systems? Turtles, all the way down.
GitLab did not become Open Core to solve a sustainability problem that does not exist. It did it to maximize profit, as VC funding requires, and became crippleware over time. Gitea Ltd is following the same route for the same reasons and the shareholders merely try to hide their intent behind the smokescreen of Free Software sustainability. But anyone can see right through it.
Loic’s raises an important point: Sustainability of open source software is often confused with paying off investors. Those are not the same thing. He says “anyone can see right through it” but I think he’s underestimating the issue: I don’t think it’s that clear to everyone.
This is going to be interesting for all digital marketing managers. Reddit is vastly underestimated by marketers, because it requires a high level of direct engagement and personal involvement from the company. I always had good results there and I’m glad to see sophisticated tools like Sparktoro paying attention.
Firefox snap package is not ready for Wayland, the default environment for Ubuntu. I spent hours trying to find a workaround.
Welcome to Deep Dive: AI, an online event from the Open Source Initiative. We’ll be exploring how Artificial Intelligence impacts Open Source software, from developers to businesses to the rest of us.
I’ve been working on this event for the better part of 2022. I’m so stoked to see the first part, the podcast, on its launching ramp.
