With tap-to-pay, you can store multiple cards in your digital wallet, and you don't have to remember any of their PINs. You can use your fingerprint to sign transactions. I believe this makes it faster.
Asynchronous and parallel programming are concepts I've never really learned, and admittedly scared to use, because I never really understand what the code is doing or even the right way of writing such code. Does anyone have any good resources that helped you learn this and have a good mental model of the concepts involved?
I have a beginner question - Can WebRTC be used as an alternative to sending base64-encoded images to a backend server for image processing? Is this approach recommended?
Is it currently possible to reliably limit the cut-off knowledge of an LLM (either during training or inference)? An interesting experiment would be to feed an LLM mathematical knowledge only up to the year of proving a theorem, and then see if it can actually come up with the novel techniques used in the proof. For example, having only access to papers prior to 1993, can an LLM come up with Wiles' proof of FLT?
I don't see how the authorship by Christopher Manning shifts favour towards the other paper; this paper has Antonio Torralba as a co-author, who's also one of the big shots in AI.
This also reminds of how an employee single-handedly brought down UK's second oldest merchant bank at the time, by speculative trading: https://en.m.wikipedia.org/wiki/Nick_Leeson
How is it subjective? Can't terms like "replacing" and "fast" be quantified by metrics related to rates of unemployment and adoption of AI systems for tasks previously manned by humans? I'm not saying the data is readily available, but I do see a route to objectively measuring this.