this post was submitted on 12 Jul 2023
62 points (100.0% liked)
Technology
19 readers
2 users here now
This magazine is dedicated to discussions on the latest developments, trends, and innovations in the world of technology. Whether you are a tech enthusiast, a developer, or simply curious about the latest gadgets and software, this is the place for you. Here you can share your knowledge, ask questions, and engage in discussions on topics such as artificial intelligence, robotics, cloud computing, cybersecurity, and more. From the impact of technology on society to the ethical considerations of new technologies, this category covers a wide range of topics related to technology. Join the conversation and let's explore the ever-evolving world of technology together!
founded 1 year ago
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Not defending musk, but it is really difficult to find women in AI. The few that are around are very valuable for tech companies that will pay higher salaries to have "token women", better if they are from some minority group, to show better diversity statistics. Therefore retention is also difficult. Finding good people in AI is already difficult and expansive, finding women is a real challenge.
I strongly believe that diversity brings a lot of value, and women are important in any team. But the solution unfortunately is not in the current market, but it is at school levels. Culture must change.
Retention is indeed a problem. I don't think I qualify as a "woman in AI," but I am a cis woman who has trained (well, fine-tuned) my own models on my gaming PC at home as a hobby. Several years ago, I fucked off and became a professional photographer after working for a Fortune 50 for a decade; I loved my job but hated the sexism. There's almost no amount of money that could get me back to working in tech.
(Incidentally, a bunch of my images were scraped and used in the training data set for Stable Diffusion. I'm mad about this and have no desire to help corporations profit off others' art.)
From my experience ML and data science in general are very welcoming to women and people with very different backgrounds. Also the way of working is very different. Agile doesn't really work, because is a non-deterministic world, you have relatively "long" projects, no PM chasing burndown (burnout) story points (or whatever those silly metrics are called), curious and interesting colleagues that are there for passion. You can give it a try. As said, in many industries, women in data science and ML are highly valued and unfortunately there are not enough of them.