It would actually be interesting for police agencies to use agents to go through unsolved crimes using all available data to look for clues. I think it's justified for some violent crimes at least.
Per capita. GDP is not meaningful for the average person most of the time. Norway has a very high GDP per capita, but a lower GDP than Canada. Would it make life better for them if they suddenly had double the GDP and double the population? Probably not really, might be more crowded.
Canadians might be able to produce more innovation if there wasn't so much inflation and tax burden due to high immigration.
Another interesting discrepancy is that people think current GPUs are maybe capable of running AGI but they still can barely manage photorealistic rendering of a single room in realtime, or simulate something like a shirt thrown into a pile of laundry. They can generate a video of it based on millions of existing videos, but not do a real simulation of light and physics in realtime.
An AGI doesn't need that level of detail to do most tasks effectively. To use the OP's example, a cat catching a bug out of the air does not need to run a fluid dynamics simulation of airflow over the bug's wings to be able to catch it. A cheap approximation of the flight path is sufficient. Perhaps some physical tasks will need that level of detail but many will not.
Is the human brain capable of doing real simulations of light and physics in realtime? Or does it hallucinate the details and represent some low-resolution mental image? You may be overestimating the capabilities of flesh-based neural networks and underestimating the capabilities of silicon-based neural networks.
That's a good point. It's just kind of surprising, there's been a lot of incremental progress towards rendering and simulation for a long time. If you'd asked 10 years ago if we'd have AGI or realistic realtime rendering and physics of a single room first I think most people would have answered the former.
The other challenge with real people is the emotional aspect. Easier and faster to tell ai the code it wrote is terrible and needs to be redone completely, or maybe you just end the session and use a new model. Trickier to do with a human.
Yes, it's that. You do probably end up wanting to store some "latest" denorm tables at some point, but it takes surprisingly long to reach that point, and isn't hard when you get there.
There are disadvantages to this, but it's a safe default. The alternative is possibly losing important data, finding out later you want historical records of things that are stored in kludgy separate tables, getting into more advanced locking situations, and having more complex DB migrations. Which I've had to pull teams out of many times.
I don’t have a lot of experience related to this so I’m just noting some things.
Some people in this thread don’t seem to think it’s that hard or overcomplicated.
When reading Designing Data-Intensive Applications my main takeaway was that event sourcing can make it easier to solve a lot of issues like performance, scaling, consistency, auditability, etc.
It would be interesting to look into what a low overhead way of implementing CRUD with event sourcing in Postgres would look like, then decide if it’s too complex.
Try making Hackernews lite. Users can post, comment on posts, vote/unvote posts, and delete their own posts and comments. CRUD tables might be post, comment, maybe vote. Event tables might be create_post, delete_post, create_comment, delete_comment, vote, unvote.
