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AI

Are Gates, Musk Being 'Too Aggressive' With AI Concerns? (xconomy.com) 311

gthuang88 reports on a talk titled "Will Robots Eat Your Job?" Bill Gates and Elon Musk are sounding the alarm "too aggressively" over artificial intelligence's potential negative consequences for society, says MIT professor Erik Brynjolfsson. The co-author of The Second Machine Age argues it will take at least 30 to 50 years for robots and software to eliminate the need for human laborers. In the meantime, he says, we should be investing in education so that people are prepared for the jobs of the future, and are focused on where they still have an advantage over machines -- creativity, empathy, leadership, and teamwork.
The professor acknowledges "there are some legitimate concerns" about robots taking jobs away from humans, but "I don't think it's a problem we have to face today... It can be counterproductive to overestimate what machines can do right now." Eventually humankind will reach a world where robots do practically everything, the professor believes, but with a universal basic income this could simply leave us humans with more leisure time.

Comment Router? (Score 1, Informative) 173

When did we start calling wireless access points "routers"? Oh, sure, I know lots of consumer routers have access points built in, and maybe I'm just being pedantic, but come on already. We already use the word "router" for something and we already had a perfectly good word for "access point". I had to dig through three articles before I learned what the actual problem was.

Comment Re:Better idea (Score 1, Insightful) 74

If you have access to it and can know who has it and where it is you can probably get an officer to come with you and knock on a door.

I think you would be astonished at how difficult it is to get the police to react or respond to petty theft calls. Even if you hand them everything they need to make an arrest.

Comment "Missed a lot of e-mails" (Score 4, Insightful) 66

She also said that NSID shifted attention to other projects and basically forgot that it had promised to build a canine mind-reader. “We missed a lot of emails, so we’re really sorry about that,” Mazetti says. “We had a restructuring at the company, and we had an absent-minded engineer in charge.”

No, you ignored a lot of e-mails. You had people trying to contact you for two years. "Missing e-mails" is believable when it's less than five. It's really not necessary to continue lying to your backers.

Comment Re:FP16 isn't even meant for computation (Score 1) 55

So, one problem is that there is not always more data. In my field, we have a surplus of some sorts of data, but other data requires hundreds of thousands of hours of human input, and we only have so much of that to go around. Processing all of that is easy enough, getting more is not.

Also, by "effective", I should have made it clear that I meant "an effective overall solution to the problem", which includes all costs of training a wider, lower-precision network. This includes input data collection, storage and processing, all of the custom software to handle this odd floating point format, including FP16-specific test code and documentation, run time server costs and latency, any increased risks introduced by using code paths in training and , etc.

I'm not saying that I don't believe it's possible, I've just seen absolutely no evidence that this is a significant win in most or even a sizable fraction of cases, or that it represents a "best practice" in the field. Our own experiments have shown a severe degradation in performance when using these nets w/out a complete retraining, the software engineering costs will be nontrivial, and much of the hardware we are forced to run on does not even support this functionality.

As an analog, when we use integer based nets and switch between 16-bit and 8-bit integers, we see an unacceptable level of degradation, even though there is a modest speedup and we can use slightly larger neural nets. I'm very wary of anything with a mantissa much smaller than 16 bits for that reason--those few bits seem to make a significant difference, at least for what we're doing. We're solving a very difficult constrained optimization problem using markov chains in real time, and if the observational features are lower fidelity, the optimization search will run out of time to explore the search space effectively before the result is returned to the rest of the system. It's possible that the sensitivity of our optimization algorithm to input quality is the issue here, not the fundamental usefulness of FP16, but I'm still quite skeptical. If this were a "slam dunk", I'd expect to see it move through the literature in a wave like the Restricted Boltzmann Machine did.

Oh, and thank you for the like (great reading) and the thoughtful reply. Not always easy to find on niche topics online.

Comment Re:Exploitative by design? (Score 1) 153

It seems like these systems are exploitative by design, even if exploitation wasn't explicitly the goal. They're designed with every possible algorithm and available data to maximize labor output at the lowest possible cost. Individual workers are operating at extreme information asymmetry and against a system which does not negotiate and only offers a take it or leave it choice.

This is by far the best comment I've ever seen regarding this sort of algorithmic labor management.

Normally I'm all for this sort of thing--my company is a client and uses it to handle large bursts of data processing quickly--but the information symmetry argument is a powerful one. Also, there doesn't seem to be a lot of competition in this space, which might otherwise ameliorate a lot of the problems induced by the "take it or leave it" bargaining approach.

The analysis provided by the article is absurd, but yours seems to lead to the inescapable conclusion that some kind of regulation is necessary to prevent blatant exploitation. Maybe just reducing information asymmetry in some way, or requiring transparency in reports available to the public on the website regarding effective wages paid to workers as a fraction of the minimum and average wages of employees in their respective countries. Surely someone can find an answer to this.

Comment Re:FP16 isn't even meant for computation (Score 1) 55

Accidentally posted as anonymous coward, reposting under my actual name.

So they're all excited about the lowest-precision, smallest-size floating point math in IEEE 754?

FP16 is good enough for neural nets. Do you really think the output voltage of a biological neurons has 32 bits of precision and range? For any given speed, FP16 allows you to run NNs that are wider and deeper, and/or to use bigger datasets That is way more important than the precision of individual operations.

There's a lot of rounding error with FP16. The neural networks I use are 16-bit integers, which work much, much better, at least for the work I'm doing. Also, do you have a good citation that FP16 neural networks are, overall, more effective than FP32 networks, as you've described?

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