So, you oppose their desire to diversify their funding?
NPR gets 10% of their funding from tax dollars.
Who you are harming is small rural public radio stations in red areas.
As soon as you need to start leaning heavily on error correction, that is an indication that your architecture and solution is not correct. The final solution will need to be elegant and very close to a perfect solution immediately.
You must always keep close to the only known example we have of an intelligence which is the human brain. As soon as you start to wander away from the way the human brain does it, you are on your own and you are not relying on known examples of intelligence. Certainly that might be possible, but since there's only one known example in this universe of intelligence, it seems ridiculous to do anything but stick close to that example, which is the human brain.
This is actually a lazy approach as you describe it. Instead, what is needed is an elegant and simple approach that is 99% of the way there out of the gate. Soon as you start doing statistical tweaking and overfitting models, you are not approaching a solution.
When an architecture is based around world model building, then it is a casual outcome that similar concepts and things end up being stored in similar places. They overlap.
As soon as your solution starts to get mathematically complex, you are departing from what the human brain does. Not saying that in some universe it might be possible to make a statistical intelligence, but when you go that direction you are straying away from the only existing intelligences that we know about. The human brain. So the best solutions will closely echo neuroscience.
The alternative architectures must learn from streaming data, must be error tolerant and must have the characteristic that similar objects or concepts much naturally come near to each other. They must naturally overlap.
Any transformer based LLM will never achieve AGI because it's only trying to pick the next word. You need a larger amount of planning to achieve AGI.
Also, the characteristics of LLMs do not resemble any existing intelligence that we know of. Does a baby require 2 years of statistical analysis to become useful? No. Transformer architectures are parlor tricks. They are glorified Google but they're not doing anything or planning.
If you want that, then you have to base your architecture on the known examples of intelligence that we are aware of in the universe. And that's not a transformer. In fact, whatever AGI emerges will absolutely not contain a transformer.
The first thing you need to understand is that no current llm based, transformer architected AI is going to get to agi. The design in essence is not capable of that kind of creativity. In fact no AI that has at its root a statistical analysis or probabilistic correlation will get us past the glorified Google parlor trick that is the modern llm in every form.
A great leap in IP but unfortunately is too important to blab about widely, is the solution to this problem and the architecture that will be contained in the ultimate AGI solution that emerges.
You need to start looking for big problems to solve.
It was once said by a guy named Bruce Williams who knew what he was talking about that the way to get rich is to solve a problem.
So what you need to do is start looking for big problems. Not big problems that are only seen by developers, but big problems that are seen by the general public. People will pay for solutions.
You will be motivated by solutions, something that you need to research the hell out of. Something you need to make connections that no one else has made before to solve. That's it
What a bunch of petty sons of b***. Trump has aesthetics against electric cars, and that seems doubly bizarre since his partner is Elon Musk.
But this is just extreme and ridiculous. The right has always had a fetish about fighting against the environment. Trump will only be alive a few more years and he doesn't care what happens to the planet.
News for you. Elon Musk is not an engineer. Elon Musk does not know computer science. Yes, he's able to read books and learn subjects but he has never worked as a software engineer in any professional setting. You attribute competence to him in so many areas where he does not deserve it.
When a foreign key field is left empty, that implies that no foreign key relationship was claimed. By definition a primary key cannot be no. But a foreign key, which is a representation of another table's primary key, can be null. It merely means that no foreign key relationship is claimed.
For years I have been printing PDFs off on regular paper and then binding them into books.
1. Print it at work when no one is looking.
2. Get two rigid boards and squeeze the stack of paper together. I customarily use two wooden armrests that originally came from a garden-furniture lounger.
3. Squeeze the paper with just a 1/4-inch showing.
4. Use wood glue and with your finger working like a toothbrush, work the glue into the pages at the gluing end.
5. Get a 14-inch X 4-inch strip of canvas. I use cutoff painter's canvas.
6. Hang all this by the boards and put glue also on top of the canvas strip.
7. When it dries, remove the boards and glue down the sides.
You have a strong, bound book out of those printed pages.