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Unless this article is leaving out some major points, the whole thing seems flawed. So their machine-learning models learn best if they fail 15% of the time. Fa
by geewee 7y ago
Unless this article is leaving out some major points, the whole thing seems flawed. So their machine-learning models learn best if they fail 15% of the time. Fair enough - but trying to discern anything meaningful about how often people should fail based on that, seems like quite a bit of a stretch
- kiba 7y agoYeah, the research reported mention machine learning experiment, but they hadn't validated that it also applies to people as well.
- Dumblydorr 7y agoAnecdotally, I'd say I fail most things 95% or more of the attempts. That's why we rewrite, debug, practice, drill, google, avoiding failure takes a lot of human effort.
- jstanley 7y agoIt depends what "failure" even means. If you mis-place a semicolon and have to go back and fix it, have you "failed"? I'd say no. I'd say a project has only failed if it never gets to a working state.
- newnewpdro 7y agoI think failure is defined by expectations. If you failed to succeed in the expected amount of time/effort, it's a failure. Maybe you add some tolerance of going past the estimate before classifying as a failure, but it's still rooted in the expectation. For example, if it took you ten years to pass kindergarten, you've failed.
- infogulch 7y agoHumans get way more information out of each failure than ML systems. When you or I fail just a few times we can analyse the failures and often discover huge classes of wrong behaviors, and never repeat any of them. We're also good at differentiating what parts of the failure caused it and can even learn what parts were successful. We might even test dozens of hypothesis at once in a single attempt, even if we're focusing on just one of them. A computer often only gets one bit of learning from a failure or a success: this single behavior in particular either did or did not work. My hypothesis is that we model the system we're studying and simulate many 'attempts' for every real world attempt. I.e. we grow a low-fidelity, but much faster, model of the system in our brain that we can use to make medium-low confidence predictions about the real system many times for each time we test against the real system. So when you say you fail 95% of the time, I'm saying each of those failures actually have 200 mini-successes embedded that you can still use to train your mental model.
- afarrell 7y agoUsually. Sometimes people go into a situation confused, fail, and don't know how to interpret why they failed or what parts caused the failure. I think it is worth distinguishing that type of failure from the type you're talking about. Why? Often when you tell someone you don't know how to do something or you think you'll fail at something, they have your type of failure in mind and they encourage you to just try again.
- pushrax 7y agoIt’s interesting to take this view and apply it to personal identity. A self seems to be a low fidelity model of one’s autonomous system. It seems necessary to include a model of self to reach the kind of predictive ability necessary to learn from few examples.
- mike_hock 7y ago> and often discover huge classes of wrong behaviors, and never repeat any of them Once burned, twice shy. And often that results in irrational aversion to huge classes of behaviors just because they appeared in the larger context of the failure of an endeavor as a whole, which I'd say is not a good way to learn from failures.