4 ms·
There’s going to be people plugging their ears and shouting “but it’s just nonlinear function approximation!” all the way into the singularity.
by laretluval 8y ago
There’s going to be people plugging their ears and shouting “but it’s just nonlinear function approximation!” all the way into the singularity.
- ggm 8y agoSingularity be like fusion... Always far enough away to be hinted at from thin evidence
- perl4ever 8y agoHuman intelligence involves choosing which context to use at any given point. So far, so called AI appears to take an external source of context for granted, which is why it seems to me fundamentally different from "real" intelligence, just like your eyes are different from your entire nervous system.
- wetpaws 8y agoEvery time there is some progress in AI people would yell "but this is not a real AI 'cause humans can..." ( play go/understand pictures/language/semantics/etc, goalpost is always in motion)
- pdimitar 8y agoStop playing the victim. Just make a driverless car that never makes mistakes already -- and I promise you the goalpost won't move for a while. Go and chess mean nothing out there. The world has a lot of very practical and concrete needs that this magical "AI" is nowhere near solving yet. I find the constant defensiveness of the ML proponents tiring and cringe-worthy. Somebody should remind you that what you are doing is not a backyard hobby. The people who fund you make a lot of promises and I don't think any of them have been delivered.
- testrun 8y agoAs if humans in cars does not make mistakes. Last time I checked humans have intelligence.
- dang 8y agoThis comment crosses into incivility. Please don't do that on HN, regardless of how right you think you are compared to someone else. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- pdimitar 8y agoUnderstood and point taken. Thank you.
- mtrimpe 8y agoAI carves away what it means to be human one trivial slice at a time. That's how it always has been and always will be until perhaps one day there is truly nothing 'truly human' left to slice off.
- goatlover 8y agoIf it happens and doesn’t involve more than the current DL approach.
- jsinai 8y agoClassic regression is only intelligible because there were only a few parameters and people could use ANOVA to try and interpret them. IMO ANOVA is alchemy as well and most people trained to use it, don’t fully understand it. Should we leave decisions to those kinds of models? Not to mention that part of what makes NN such a step forward is precisely the high nonlinearity. When you have millions of parameters, the contribution of each is unimportant and so any kind of analog to ANOVA would be barking up the wrong tree. In a NN all those parameters work in concert to learn a decision boundary to separate data. It’s not intelligible at the minute level but at least we know what it’s doing in the end. The problem of course lies with the outliers and that’s not so much a problem with NN being a black box as it is a problem with the nature of large datasets and our own inability to rationalize each and every datapoint. I’m going to defend NN as the natural evolution of regression. It’s precisely their high nonlinearity which makes them better. The problem is not that they “are” alchemy but that we treat them “as” magic. Society as no place leaving important decisions to algorithms unintended, NN or not. If major insurance companies left their decisions to logistic regression (which was and is still the case), then would we be making the same arguments? Probably not because someone paid by the insurance company will pull out that other kind of alchemy called ANOVA ...
- RA_Fisher 8y agoANOVA is grounded by theory that has stood the test of time for decades. Statisticians that question ANOVA aren't a thing (despite such an accurate criticism being valuable career-wise to an academic). Gelman points out that multilevel models are easier to use than ANOVA and I tend to agree.
- vharuck 8y agoClassic regression's potential for intelligibility relies on the modeler choosing terms which are based on observed relations and improve performance. For example, population forecasts can be modeled as the current population plus forecasted changes through immigration, emigration, birth, and death. Yes, somebody can produce "magical" regression models with terms divorced from reality. But unsupervised learning is never guaranteed to produce a reality-grounded model, no matter the user's skill. It goes through steps, tries different transformations, and chooses the algorithm which led to the best result. Logic and understanding played no part. That sounds like alchemy. It definitely works, but alchemy also stumbled onto theories later explained by chemistry. And, yes, anyone who doesn't consider the implications of the linear model behind ANOVA would also be a "magician."