Where I write down what I am reading and thinking about. Mostly AI at work.
A model can be 95% right on day one, but if nobody opens it, that figure produces no value at all.
This is a reminder I hold on to: right today does not mean right forever.
This is the gap where I see a great many AI projects stop.
This is a lesson in cost discipline that I see many teams learn a little late.
This is a principle I believe holds for AI and for operations alike: what you cannot see, you cannot manage.
This is the step that turns AI from something that only talks into something that actually does the work.
This is a habit I learned: to find out how solid something is, try breaking it yourself before anyone outside gets the chance.
This is what I believe separates an AI system worth using from one that will cause trouble.
This is the aspect I think will matter more and more as AI moves into tightly regulated industries.
This is what I say whenever someone tells me proudly that their company collects a great deal of data.
Google opened free AI training aimed at every school and university teacher in the US. The striking part is not the number, but the rhythm of one lesson a month.
A survey of 1,550 leaders across six countries. The figure that caught me was not 73%, but the 42% who said AI is already good enough and their organisation cannot absorb it.
Mỗi sáng tôi gửi một việc nhỏ bạn làm được ngay hôm đó với AI. Đọc hết trong hai phút.