31–40 of 189 posts
Contrary to applied physics or medicine, AI doesn't require massive capital investment like building a particle accelerator or running clinical trials over years.
So if we already suffer from a lack of diversity, why should we ape the organizational structure of those fields?
We are using the wrong computational paradigm. We have to abandon bits and go back to analog computing in the form of analog photonic computing that gives you fast Calculus. This is painfully obvious in the case of neural networks, which run faster on an analog computer and are also easier to program.
An agent can be intelligent without it learning how to read human language. Look around, most organisms in our world communicate using extremely simple binary language or don't communicate verbally at all. Yet, they are intelligent enough to do very complicated tasks which current robots fail to do. Intelligence is an easier problem than language, and thus should be solved before language. What a sigh of relief to re…
An agent can be intelligent without it learning how to read human language. Look around, most organisms in our world communicate using extremely simple binary language or don't communicate verbally at all. Yet, they are intelligent enough to do very complicated tasks which current robots fail to do. Intelligence is an easier problem than language, and thus should be solved before language. What a sigh of relief to re…
Yes.
> Yet, they are intelligent enough to do very complicated tasks which current robots fail to do.
True.
> Intelligence is an easier problem than language, and thus should be solved before language.
Wrong.
This is the classic mistake everybody makes, including people in Computer Science.
Because if that were so our robots would already be clambering backwards through chairs (per the metaphor in the article).
You have to think of deep evolutionary history. It took centuries to come up with advanced mathematics, so in some strange to humans sense, this isn't that hard. Same with language, it only took tens of thousands of years.
For Nature to learn how to develop a nervous system capable of flexibly interacting with the environment, culminating in our brains, took hundreds of millions of years.
This isn't an claim that we have to wait that long to re-engineer such powers, but it is to point out that if the possibility space for developing a nervous system was much larger than for the same organisms to learn language...
tldr; Walking is hard.
We have been conflating what is easy for us, with what is objectively easy, because we don't appreciate the Deep Time that Nature has been working with. I suspect we will develop EMs (brain emulations, a sort of short cut) before we understand what we are doing but I hope that is wrong.
Earlier quoted context omitted.
If there is a cliff, toddlers are scared of being nearby. Not sure if you are a parent, but this isn't the case at all!
https://youtu.be/WanGt1G6ScA Not sure what to think about it.
I'd say this proves the point completely. It shows that babies aren't scared of heights, and they don't acquire the fear from experience. Instead it seems likely it is that they either develop a mental model of how the world works, or they learn from watching other's behavior.
The article is calling us to teach AI to read words and phrases and look for the meaning, not just a statistical correlation. I put dictionary data into Pingtype English to try to parse phrases instead of just words. e.g. "pick [something] up". The purpose is to do word-for-word translation to Chinese as an educational tool. It's not perfect, but the dictionary is editable. You can contact me if you want to discuss n…
Most of the AI progress in last years is just tuning pattern recognition algorithms. We can not expect these algorithms to produce results like humans, because humans have a lot of information not just from percieving the world, but their patterns of thinking are also vastly dependend on the underlying structure of brain, that has developed over milions of years of evolution. If there is a cliff, toddlers are scared…
We are using the wrong computational paradigm. We have to abandon bits and go back to analog computing in the form of analog photonic computing that gives you fast Calculus. This is painfully obvious in the case of neural networks, which run faster on an analog computer and are also easier to program.
Can you provide any links for this, I'm interested but know nothing about it.
Check also real computation https://en.m.wikipedia.org/wiki/Real_computation and work of Lenore Blum.
Im super pumped about the research into computational metamaterials http://users.ece.utexas.edu/~aalu/research%20-%20page%203.ht...
We humans don't know how to learn. We don't know how learning works. We simply work work work work until we know whatever we set out to know, we don't learn how we learned it, but are happy that we simply know it and leave it at that. Therefore teaching someone/something else how to learn will be almost inherently impossible, because we don't understand it ourselves (yet?) And if we do learn how to learn, why would w…