On Technique
How might Copilot’s descendants change the craft of programming?
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
How might Copilot’s descendants change the craft of programming?
And is artificial general intelligence what we really need?
What does it mean to say a computer model “understands”?
Here are some predictions for tech in 2022.
Machine Learning’s deployment stack is maturing
When Copilot writes your code, will you care whether it’s good or bad?
Where user-centric computing goes wrong
How do we build devices that are shared by default?
Risks of autoregressive language models and the future of prompt engineering
The InfoLandscape is fractured and shared reality is breaking down: who have become our Reality Brokers?
Is a new generation of AI systems arising from cross-fertilization between different AI disciplines?
Following O'Reilly online learning trends to see what's coming next.
Asking very simple questions often leads to discussions that give much more insight than more complex, technical questions.
The AI product manager’s job isn’t over when the product is released. PMs need to remain engaged after deployment.
Identifying AI opportunities and setting appropriate goals are critical to AI success, and yet can be difficult to do in practice. Some reasons for this include lack of AI literacy, maturity, and many other factors.
All-in-one platforms built from open source software make it easy to perform certain workflows, but make it hard to explore and grow beyond those boundaries.
Previous articles have gone through the basics of AI product management. Here we get to the meat: how do you bring a product to market?
Data is often biased. But that isn’t the real issue. Why is it biased? How do we build teams that are sensitive to that bias?