LinkedIn on the ‘Great Reshuffle’: Green skills, green jobs, and blind spots
LinkedIn embarked on an ambitious analysis of data from its nearly 800 million members worldwide to derive insights on the green transition. But while useful, the analysis comes with its blind spots, too.
Read More →Massaging AI language models for fun, profit and ethics
Do AI language models really demonstrate intelligence? What about morality? Is it ok to tweak them, and if yes, who gets to do this, and how do the rest of us know?
Read More →Trustworthy AI: How to ensure trust and ethics in AI
A pragmatic and direct approach to ethics and trust in artificial intelligence (AI) — who would not want that? This is how Beena Ammanath describes her new book, Trustworthy AI.
Read More →The state of AI ethics: The principles, the tools, the regulations
What do we talk about when we talk about AI ethics? Just like AI itself, definitions for AI ethics seem to abound. A definition that seems to have garnered some consensus is that AI ethics is a system of moral principles and techniques intended to inform the development and responsible use of artificial intelligence technologies.
Read More →Working from home means working different hours, but not necessarily more
The National Bureau of Economic Research analyzed the side effects of remote work using data from GitHub.
Read More →AI ethics in the real world: FTC commissioner shows a path toward economic justice
FTC Commissioner Rebecca Kelly Slaughter lays out machine learning and AI's potential for harm and outlines some ways for the FTC to counter it.
Read More →Open-source software economics and community health analytics: Enter CHAOSS
Trying to capture the value open-source software generates can be a bit chaotic. The CHAOSS project may lend a helping hand.
Read More →Open source AI stack is ready for its moment
Open source stacks enabled software to eat the world. Now several innovative companies are working to build a similar open source software stack for AI development.
Read More →Data meets science: Open access, code, datasets, and knowledge graphs for machine learning research and beyond
A new interconnected ecosystem for research is shaping up, and machine learning is just the tip of the iceberg.
Read More →Getting there: Structured data, semantics, robotics, and the future of AI
Leveraging structure in data is key to making progress in AI, says AI prodigy Gary Marcus. A forward-looking view on Software 2.0, AI chips, robotics, and the future of AI
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