It has become impossible to tell managers mesmerised by artificial intelligence that the tools are not, in fact, helpful. So employees just play along with the fiction to keep their jobs, writes our tech columnist
Funny, that you even know about Nano!
…it’s one of the often overlooked projects because it doesn’t have a ton of fuck-off-money and instead tries to focus on a solid protocol.
Nano has a lot of interesting attributes, but I fail to see how what you describe would work in practice.
If both parties want to make sure there are no shenenigans at play, they need to know about the most recent state of the respective account chains, which essentially requires them to be online for agreeing on said transaction.
But overall Nano is very fast and efficient by design and only a failure in terms of “gainz for Lambo”.
As we’re here in a thread about AI I should remark that machine-to-machine-payments - in this case: agent-to-agent-payments - would work pretty well with Nano as currency because of the transaction finality (typically less than 1 second) and the feeless nature of transactions.
If AI agents are looking for the most viable way to transfer tiny amounts of value fast and without fees they might find Nano and use it - who knows…
…and just like Nano showed that efficient ways to create digital money are possible I’m hoping for efficient AI models that are economically and ecologically worthwhile.
What I understood then was that in person transactions could be done, as the local ledger of each user would authorize and record operations on and off the available balance and wait until network availability to syncronize with the global record.
Returning to the subject at hand: I can imagine very specialized “AI” being useful for scientifical research, where very knowledgeable people use it as a tool to facilitate processes but are nonetheless capable of reviewing whatever results it produces.
Not gigantic datacenters required for this but small, purpose made and perfected, locally run, even if on higher specifications hardware to do so, but machines built for a given task and purpose. The economic viability on it be damned; it’s a tool for research, it is not made to earn money.
To put it bluntly: if you do an offline transaction, you’re prone to fraud.
If you expect the senders account chain to have balance x (because that’s your offline record for that) and the sender has sent all funds to a different address after you synced that account chain, you receive money that isn’t there - kind of like an invalid cheque.
I have no clue how that would work in practice, because to know a random account in advance, you’d have to sync the whole amount of account chains there is (called block lattice in Nano’s case).
With a mobile device that’s hardly feasible and without a mobile device I don’t see how you’d get in contact with people to make such an offline transaction.
That kind of specialized AI is what I imagine to be a use case for locally run AI, too.
After all you don’t want to build processes on an AI, where you have zero control over what happens behind the curtains.
That includes feeding potentially sensitive data back to it as well as being unable to control the training data set, its learning, version numbers, etc.
Funny, that you even know about Nano!
…it’s one of the often overlooked projects because it doesn’t have a ton of fuck-off-money and instead tries to focus on a solid protocol.
Nano has a lot of interesting attributes, but I fail to see how what you describe would work in practice.
If both parties want to make sure there are no shenenigans at play, they need to know about the most recent state of the respective account chains, which essentially requires them to be online for agreeing on said transaction.
But overall Nano is very fast and efficient by design and only a failure in terms of “gainz for Lambo”.
As we’re here in a thread about AI I should remark that machine-to-machine-payments - in this case: agent-to-agent-payments - would work pretty well with Nano as currency because of the transaction finality (typically less than 1 second) and the feeless nature of transactions.
If AI agents are looking for the most viable way to transfer tiny amounts of value fast and without fees they might find Nano and use it - who knows…
…and just like Nano showed that efficient ways to create digital money are possible I’m hoping for efficient AI models that are economically and ecologically worthwhile.
What I understood then was that in person transactions could be done, as the local ledger of each user would authorize and record operations on and off the available balance and wait until network availability to syncronize with the global record.
Returning to the subject at hand: I can imagine very specialized “AI” being useful for scientifical research, where very knowledgeable people use it as a tool to facilitate processes but are nonetheless capable of reviewing whatever results it produces.
Not gigantic datacenters required for this but small, purpose made and perfected, locally run, even if on higher specifications hardware to do so, but machines built for a given task and purpose. The economic viability on it be damned; it’s a tool for research, it is not made to earn money.
To put it bluntly: if you do an offline transaction, you’re prone to fraud.
If you expect the senders account chain to have balance x (because that’s your offline record for that) and the sender has sent all funds to a different address after you synced that account chain, you receive money that isn’t there - kind of like an invalid cheque.
I have no clue how that would work in practice, because to know a random account in advance, you’d have to sync the whole amount of account chains there is (called block lattice in Nano’s case).
With a mobile device that’s hardly feasible and without a mobile device I don’t see how you’d get in contact with people to make such an offline transaction.
That kind of specialized AI is what I imagine to be a use case for locally run AI, too.
After all you don’t want to build processes on an AI, where you have zero control over what happens behind the curtains.
That includes feeding potentially sensitive data back to it as well as being unable to control the training data set, its learning, version numbers, etc.