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I'm glad people are working on better approaches to AI, but I don't think articles like this, based on sources like Marcus, contribute much. Mostly because Mar

by vonnik 11y ago

I'm glad people are working on better approaches to AI, but I don't think articles like this, based on sources like Marcus, contribute much. Mostly because Marcus isn't willing to talk about what he's working on right now. Hand-wavy stuff about how children learn, a tired analogy that's been trotted out many times before. This article was too early, and it doesn't contain real news. Reproducible results, or it didn't happen... Fwiw, the statement below is simply untrue. > “If you want to get a robot to learn to walk, or an autonomous vehicle to learn to drive, you can’t present it with a data set of a million examples of it falling over and breaking or having accidents—that just doesn’t work.” The entire premise of Deepmind's work combining DL and reinforcement learning shows that autonomous agents learn from millions of examples. And in fact, data is not the limiting parameter here. Trying to create an AI that doesn't need big data will be great for edge computing -- but we're actually swimming in data and using it to set new records every year -- http://deeplearning4j.org/accuracy.html http://deeplearning4j.org/accuracy.html -- so we might as well rely on it. Ignoring big data in the pursuit of AI is like ignoring sunlight in the pursuit of clean energy. Deep learning is one of the best ways we have to use that data. In a sense, deep is the new normal.

_dps 11y ago
> but we're actually swimming in data ... I think this is a "looking under the lamppost for your keys because that's where the light is" situation. We hear about tons-of-data and deep learning because teams like Google/FB have problems that fit those situations well. This is not representative of the vast sea of learning applications most organizations face. I work on learning applications day-in-day-out. In a typical year I work with over 50 different organizations/projects. Almost none of them have data anywhere near what deep learning requires. Here are just a few examples from the past year: 1) optimizing recruiting pipeline. Maybe you have ~1000s of data points per year 2) medical billing applications: typically hundreds of data points per year 3) novel but slow-moving financial instruments, maybe 1 data point per day 4) automated sensor calibration where each run of the hardware costs you a few hundred dollars: depends on your budget, but thousands of data points in a year are representative I think Google/FB and the "web+mobile is everything hivemind" (I'm not saying that applies to you) have deeply distorted people's expectations for how much data is available to solve a typical problem.
justifier 11y ago
Ignoring big data in the pursuit of AI is like ignoring sunlight in the pursuit of clean energy. It's everywhere. Why not use it? I'm persuing clean energy without sunlight My argument in response would be we are swimming in a lot of things Data and sunlight are only two of them

by vonnik 11y ago

_dps 11y ago

> but we're actually swimming in data ... I think this is a "looking under the lamppost for your keys because that's where the light is" situation. We hear about tons-of-data and deep learning because teams like Google/FB have problems that fit those situations well. This is not representative of the vast sea of learning applications most organizations face. I work on learning applications day-in-day-out. In a typical year I work with over 50 different organizations/projects. Almost none of them have data anywhere near what deep learning requires. Here are just a few examples from the past year: 1) optimizing recruiting pipeline. Maybe you have ~1000s of data points per year 2) medical billing applications: typically hundreds of data points per year 3) novel but slow-moving financial instruments, maybe 1 data point per day 4) automated sensor calibration where each run of the hardware costs you a few hundred dollars: depends on your budget, but thousands of data points in a year are representative I think Google/FB and the "web+mobile is everything hivemind" (I'm not saying that applies to you) have deeply distorted people's expectations for how much data is available to solve a typical problem.

justifier 11y ago

Ignoring big data in the pursuit of AI is like ignoring sunlight in the pursuit of clean energy. It's everywhere. Why not use it? I'm persuing clean energy without sunlight My argument in response would be we are swimming in a lot of things Data and sunlight are only two of them