3 ms·

I feel that Numenta will break the ceiling that we're coming to now. I have quite a few upcoming personal projects that I plan to use there neural network(HTM)

by ghthor 9y ago

I feel that Numenta will break the ceiling that we're coming to now. I have quite a few upcoming personal projects that I plan to use there neural network(HTM) structure to enhance, the key being enhance. The mindset that I feel is lacking in the ML/AI community is that we should be creating systems that can be augmented and improved using AI, the product itself should stand alone without using any AI at all. This is similar to how JIT compilation can improve the machine code as the program runs. Part of how our own brains work is that the cerebral cortex doesn't really have any direct control over the body at all, it can only watch and identify patterns and project intentions to the the rest of the brain/body. This is a passive structure initially, it observes both the environment and how the lower structure interact with that environment. Only after it's observed and learned the "commands" can it begin to influence how the body moves around and interacts with the world. You could look at the CLI terminal as an example, on it's own I can interact and use it perfectly fine. But lets say I added some patterning matching and prediction AI that could make really good suggestions about what command I will type next, at some point you could promote these suggestions so that they always execute when the AI predicts you would use it. This is exactly how the brain works. Most of my ideas of applied AI revolve around improving our HID interfaces to computers themselves. For example, I'd like to reduce our chances of developing RSI related to keyboard/mouse/trackpad usage and I feel that chorded keyboards are more ergonomic. But chorded keyboards are slower to use and harder to learn. The mapping of the chords is where I plan to apply some AI, initially I'm going to randomize how chords are bound to character inputs, these random values will use many fingers to input 1 character. As the user learns and uses the chords to input characters the AI can identify common patterns and promote those patterns into chords that use less and less fingers to input, thus improving the input speed potential of the user. The next place to use AI in this project would be autocorrection and using the user's input context(program, filetype, time, surrounding text) to continue improving these bindings and making completion suggestions.

davidwihl 9y ago
From Yann LeCunn. --- Numenta, Vicarious, NuPic, HTM, CLA, etc. Jeff Hawkins has the right intuition and the right philosophy. Some of us have had similar ideas for several decades. Certainly, we all agree that AI systems of the future will be hierarchical (it’s the very idea of deep learning) and will use temporal prediction. But the difficulty is to instantiate these concepts and reduce them to practice. Another difficulty is grounding them on sound mathematical principles (is this algorithm minimizing an objective function?). I think Jeff Hawkins, Dileep George and others greatly underestimated the difficulty of reducing these conceptual ideas to practice. As far as I can tell, HTM has not been demonstrated to get anywhere close to state of the art on any serious task. I still think [Vicarious] is mostly hype. There is no public information about the underlying technology. The principals don’t have a particularly good track record of success. And the only demo is way behind what you can do with “plain vanilla” convolutional nets (see this Google blog post and this ICLR 2014 paper and this video of the ICLR talk). HTM, NuPIC, and Numenta received a lot more publicity than they deserved because of the Internet millionaire / Silocon Valley celebrity status of Jeff Hawkins. But I haven’t seen any result that would give substance to the hype. Don’t get fooled by people who claim to have a solution to Artificial General Intelligence, who claim to have AI systems that work “just like the human brain”, or who claim to have figured out how the brain works (well, except if it’s Geoff Hinton making the claim). Ask them what error rate they get on MNIST or ImageNet. ---- Reference: http://fastml.com/yann-lecuns-answers-from-the-reddit-ama/ http://fastml.com/yann-lecuns-answers-from-the-reddit-ama/
- cityhall 9y ago
  MNIST error rates are noise these days, but otherwise agree. These guys have been around for the entire deep learning boom of the last decade, and have never produced results to match their handwaving predictions. They're the Moller Skycar of the field.

by ghthor 9y ago

davidwihl 9y ago

From Yann LeCunn. --- Numenta, Vicarious, NuPic, HTM, CLA, etc. Jeff Hawkins has the right intuition and the right philosophy. Some of us have had similar ideas for several decades. Certainly, we all agree that AI systems of the future will be hierarchical (it’s the very idea of deep learning) and will use temporal prediction. But the difficulty is to instantiate these concepts and reduce them to practice. Another difficulty is grounding them on sound mathematical principles (is this algorithm minimizing an objective function?). I think Jeff Hawkins, Dileep George and others greatly underestimated the difficulty of reducing these conceptual ideas to practice. As far as I can tell, HTM has not been demonstrated to get anywhere close to state of the art on any serious task. I still think [Vicarious] is mostly hype. There is no public information about the underlying technology. The principals don’t have a particularly good track record of success. And the only demo is way behind what you can do with “plain vanilla” convolutional nets (see this Google blog post and this ICLR 2014 paper and this video of the ICLR talk). HTM, NuPIC, and Numenta received a lot more publicity than they deserved because of the Internet millionaire / Silocon Valley celebrity status of Jeff Hawkins. But I haven’t seen any result that would give substance to the hype. Don’t get fooled by people who claim to have a solution to Artificial General Intelligence, who claim to have AI systems that work “just like the human brain”, or who claim to have figured out how the brain works (well, except if it’s Geoff Hinton making the claim). Ask them what error rate they get on MNIST or ImageNet. ---- Reference: http://fastml.com/yann-lecuns-answers-from-the-reddit-ama/ http://fastml.com/yann-lecuns-answers-from-the-reddit-ama/

cityhall 9y ago
MNIST error rates are noise these days, but otherwise agree. These guys have been around for the entire deep learning boom of the last decade, and have never produced results to match their handwaving predictions. They're the Moller Skycar of the field.