Greetings from the team at Alectio! This month’s newsletter centers around a series we published about responsible AI: what we as a community have gotten right so far, what we’ve gotten wrong, and how best to fix our mistakes. We really enjoyed writing it and hope you enjoy reading it too!
As always, we’re also sharing the most interesting research and blogs we’ve stumbled upon in the past month. Thanks and have a great week!
A FEW THINGS WE WROTE
How We Got Responsible AI All Wrong
The introduction to our series looks at the difference between fairness and responsibility in AI and why that distinction matters.
Impact, Bias, and Sustainability in AI
Our second installment covers ways machine learning teams can take tangible steps to combat bias and reduce their carbon footprint.
Increasing Accessibility to AI
In our third part of the series, we tackle democratizing AI so state-of-the-art machine learning is available to everyone, not just Fortune 100 companies with massive R&D budgets.
We conclude with a post centered around making the opportunities in AI global and how each of us can do our part to make this a reality.
A FEW THINGS WE LIKED
A free download of a 500 page PyTorch development, direct from the source.
National Cloud Computing Project
A welcome news story that dives into a Stanford initiative aimed at democratizing access to powerful compute resources.
Reddit’s machine learning community compiles notable papers published in the field every week. A nice resource for new research.
When Not to Use Machine Learning
We enjoyed this smart and interactive Towards Data Science post by Cassie Kozyrkov and thought you might enjoy it too.
That’s it for this month. Please don’t hesitate to reach out to this email or email@example.com if we can help you with anything. Stay safe and stay healthy.