@babe On a similar note:
alcinnz
boosted
Hey y'all. My wife recently graduated with a masters in CS (main focus areas being data science and AI/ML) and is still looking for a job as the market is utter shit. We're in the Bay Area for the foreseeable future, so she's currently looking for job opportunities either in the area or fully remote!
#GetFediHired#SFBayArea#ComputerScience#MachineLearning#DataScience
Hey y'all. My wife recently graduated with a masters in CS (main focus areas being data science and AI/ML) and is still looking for a job as the market is utter shit. We're in the Bay Area for the foreseeable future, so she's currently looking for job opportunities either in the area or fully remote!
#GetFediHired#SFBayArea#ComputerScience#MachineLearning#DataScience
Ulrike Hahn
boosted
Next week our ECR group are starting a #MachineLearning sprint to speed up the development of our new #IceSheet surface mass budget emulation models.
I'm looking for hints, ideas tips and inspiration to help smooth and accelerate the process.
If you're used to working in #Sprint mode, what in your opinion makes it work or otherwise hinders it?
What can I as supervisor do to make it run better?
And how should we celebrate it's conclusion?
Also, should me do an #AMA on the process?
Next week our ECR group are starting a #MachineLearning sprint to speed up the development of our new #IceSheet surface mass budget emulation models.
I'm looking for hints, ideas tips and inspiration to help smooth and accelerate the process.
If you're used to working in #Sprint mode, what in your opinion makes it work or otherwise hinders it?
What can I as supervisor do to make it run better?
And how should we celebrate it's conclusion?
Also, should me do an #AMA on the process?
I am currently looking for a #job . I am an experienced (senior) software developer/engineer with 7y of experience. If someone is looking for a capable software engineer or knows someone looking for engineers, please let me know.
I do #python #scheme #django #docker and aim for reproducible software. I learned some #devops and #ansible and can manage servers. Used to do #fullstack dev work, before everything needed to be an SPA.
I am looking for #remote work or work in #berlin or #potsdam .
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)
Björn Brembs
and 1 other
boosted
@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.
@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.
hamish campbell
boosted
"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/
"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/