[HN Gopher] Multimodal Neurons in Artificial Neural Networks ___________________________________________________________________ Multimodal Neurons in Artificial Neural Networks Author : todsacerdoti Score : 61 points Date : 2021-03-04 20:13 UTC (2 hours ago) (HTM) web link (openai.com) (TXT) w3m dump (openai.com) | gallerdude wrote: | I've always thought it's wild how we can apply one concept to so | many different types of things. For example, if I say something | is "soft," you probably think of the opposite of firmness. But at | the same time, I can describe a person as "soft," and the same | descriptor can say something meaningful about their character. | | Seeing the Spider-Man neuron work on multiple types (pictures, | drawings, text), makes it seem like we can teach AI to learn | these same type connections. | | And if we scale up the network size enough, what if we could see | these types through the equivalent of a being with 1000IQ? What | connection types are the most effective for a being like that? | Can we even understand them? Maybe they would be deep, and | archetypical in the way that Odysseus and Harry Potter are the | same, despite the fact that one is an ancient Greek king, and the | other is a modern British wizard. Even more interestingly, maybe | the connections would be completely inexplicable to us, with no | apparent rhyme or reason perceptible to humans. | colah3 wrote: | I'm really excited about the dream that we'll be able to learn | from neural networks. Shan Cater and Michael Nielsen wrote a | really inspiring article on this | (https://distill.pub/2017/aia/). I also wrote something about | this a while back | (http://colah.github.io/posts/2015-01-Visualizing- | Representat...). | | One of the amazing things about this project exploring CLIP was | seeing some hints of this. For example, one day I was studying | one of the Africa neurons and it generated the text "IMBEWU" -- | it turns out this is a popular TV show in South Africa | (https://en.wikipedia.org/wiki/Imbewu:_The_Seed). That's a | trivial example, but it begins to hint at something | interesting. | | I'd really love to see what a domain expert analyzing CLIP | would make of things. For example, I'd love to hear what | ethnographers think of the region neurons, or what historians | think of the time period neurons. Especially for future, larger | models. | biasdose wrote: | I'm impressed with OpenAI confronting this head on. | | "Our model, despite being trained on a curated subset of the | internet, still inherits its many unchecked biases and | associations." | | If these models find themselves into production environment - if | they are good enough and profitable enough - they will eventually | become legacy systems quietly perpetuating the biases of past | times. | kowlo wrote: | The typographic attacks are great fun. Labelling an apple as a | toaster is all it takes! | HPsquared wrote: | It needs a 'shenanigans' neuron. | iujjkfjdkkdkf wrote: | Can someone give more technical detail on what they are showing | with the "neurons"? | | They say "Each neuron is represented by a feature visualization | with a human-chosen concept labels to help quickly provide a | sense of each neuron", and these neurons are selected from the | final layer. I don't think I understand this. | the8472 wrote: | Start with random input, then incrementally optimize the input | to maximize the activation of one of the nodes in the graph, | the neuron. The visualization is one of those inputs that hit a | maximum. ___________________________________________________________________ (page generated 2021-03-04 23:00 UTC)