I also got experience with the following (5 = a lot, 1 = a little) :

#machinelearning #ml (3) (I have implemented some ML models myself in the past, for learning purposes.)
#guix (3) (Using it for reproducible setups of projects.)
#functionalprogramming #fp (5) (Doing it in my own projects.)
#objectorientedprogramming #oop (4) (last job and past 😜 in my own projects.)
#CI / #CD (3) (Last job)
#make (4) (using it for my own project setups and convenience)
#testing (4) (last job, own projects)

@alex_p_roe Speaking of fruit fly research, you'd be amused or surprised to learn that the original U-net architecture (which today powers stable diffusion, among many other machine learning techniques) introduced in a paper by Ronneberger et al. (2015; https://arxiv.org/abs/1505.04597 ) was developed to perform image segmentation of fly neural tissue as imaged with electron microscopy, to reconstruct neurons and therefore map the brain connectome.

So all those "wasteful" research funding grants to fruit fly research motivated and led to the biggest discovery fueling the whole of the modern "AI" boom. One never knows where basic research will lead, it's impossible to predict. Hence basic research is not at all wasteful, on the contrary, it's essential, it's the foundation of a rich, wealthy, creative society. And also very cheap, comparatively: https://albert.rierol.net/tell/20160601_Unintended_consequences_of_untimely_research.html

Search also for the returns on the human genome project, or on the humble origins of DNA sequencing, to name just two among many.

#Drosophila#StableDiffusion#MachineLearning #academia

"What if I told you that one of the most well-capitalized AI companies on the planet is asking volunteers to help them uncover “lost cities” in the #Amazonia—by feeding #machinelearning models with open satellite data, #lidar, “colonial” text and map records, and #indigenous oral histories? This is the premise of the #OpenAItoZChallenge, a #Kaggle-hosted hackathon framed as a platform to "push the limits" of #AI through global knowledge cooperation.

In practice, this is a product development experiment cloaked as public participation. The contributions of users, the mapping of biocultural data, and the modeling of ancestral landscapes all feed into the refinement of OpenAI’s proprietary systems. The task itself may appear novel. The logic is not. This is the familiar playbook of Big Tech firms—capture public knowledge, reframe it as open input, and channel it into infrastructure that serves commercial, rather than communal goals."

https://www.techpolicy.press/unpacking-openais-amazonian-archaeology-initiative/