4 ms·
What a grave misunderstanding! What he is demonstrating is remembering, or memory. It appears that the state of the mouse is [X, Y, Heading], where X and Y are
by donquichotte 6y ago
What a grave misunderstanding! What he is demonstrating is remembering, or memory.
It appears that the state of the mouse is [X, Y, Heading], where X and Y are discrete positions in the available cells.
Once the maze has been solved by "a rather involved strategy of trial and error", the solution is saved, and for any of the known states, the mouse "remembers" its way to the exit of the maze.
- optimuspaul 6y agosounds like learning to me. In the 1950s this was somewhat advanced machine learning.
- witherk 6y agoI kinda thought the point of the title was to show how what counts as AI is just the newest form a programming.
- DaniloDias 6y agoWhen standing on the shoulders of giants, it’s best to be compassionate in evaluating the implementations of the past.
- deleted 6y ago[deleted]
- jchallis 6y agoBy any reasonable definition of learning, the mouse adapts to a new situation.
- ARandomerDude 6y ago1952. After 70 years, millions of people, and billions of dollars, it makes sense that our algorithms are more sophisticated and our definitions more precise.
- deleted 6y ago[deleted]
- coldtea 6y agoAll learning amounts to remembering. A machine learning model is basically remembered weights. Everything we've learned is what we remember as solidified in our neurons, etc. Note that learning is not about discovering yourself. You also learn if you're capable of remembering (and optionally applying) something that somebody else taught you. Students e.g. learn a formula for X, the periodic table, etc. And this contraption not only remembers (that is, learns), but also has an algorithm to figure the solution in the first place (that is, tries and discovers). So the title "machine learning" or even "AI" is perfectly legitimate. It's not GAI, but it's also not 2020 (not that we have GAI in 2020).
- CodeGlitch 6y ago2021 :)
- coldtea 6y agoHeh, did 2020 really happen?
- Jugurtha 6y ago>All learning amounts to remembering. A machine learning model is basically remembered weights. Everything we've learned is what we remember as solidified in our neurons, etc. That's "not even wrong", as Pauli would say. Your paragraph suffers from using a shaky non inversible analogy: Machine learning often uses an analogy for the brain, neurons, activation functions, etc. Some accuracy about the real world is sacrificed in that analogy for the sake of being useful and to have something to reason with and shared taxonomy. We accept that loss for the sake of being productive and for lack of actual equivalents. What your first paragraph did is use that analogy of the brain used in machine learning, that is shaky to begin with, and use it to reason about the biological brain as if we did not have the actual thing. In other words, we had a biological brain that we clumsily modeled to get work done in ML, and the paragraph used that model to reason about the brain itself. Similar to how you translate from French --> English --> French and get a different output than the input. Remembering certainly plays a role in learning, though it is but one component. For it to be what learning is, everything has to be exactly the same with every instance. To use the analogy, a machine learning model returns predictions/output for instances it has not necessarily seen. Our brain produces outputs based on situations that had not yet happened, at least not in the 'anisotropic time' universe, before that. What do you think?
- recursivedoubts 6y agoI mean, what did Claude Shannon know about computers or anything else?
- throwmeaway112 6y agoI don’t know if you are being intentionally contrarian or are simply an idiot. Claude Shannon was among the most influential contributors to computer science and the modern world. Try doing some reading before posting idiotic comments. This species of comment is increasingly common and is really starting to turn me off to HN.
- deleted 6y ago[deleted]