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Comment by tudorw | Hacker News Reader

I'd like to see more effort placed in the space between research and application, the output of academia seems largely a vast trove of unread and unimplemented ideas. When the research reveals something that is of value to us all, yet not exploitable in a capitalist system, if there is no financial gain from exploiting research then who else will look to benefit from it. Given the large amount spent on research, it seems we would benefit from additional spending on disseminating the results to those who can make use of the information to have real world impact.

As far as AI risks, I am more concerned of the risk of human error in believing they understand things enough to deliver systems that can really mimic intelligence when in reality I've seen nothing intelligent in anything that claims AI in it, high speed weighted pattern recognition, yes, intelligence, no.

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> When the research reveals something that is of value to us all, yet not exploitable in a capitalist system, if there is no financial gain from exploiting research then who else will look to benefit from it.

Moreover, if there is financial gain, the incentives in the current system are to grab an idea and keep the details of its realisation under tight wraps as far as possible: Further development is done outside of public academic circles and results are protected by trade secrets or patents.

Yes, the results of the research will eventually benefit society - when offered as a commercial service or product - but only in an opaque, "black box" kind of way as the details are not for public discussion. Even that offer is primarily driven by the desire to make money and consolidate power an only secondary to advance society.

I found anecdotal evidence of this when researching nutritional medicine, as I followed the path of who researched what most academics career had switched track to either start a nutritional medicine company practising the outcome of their research, whether for profit or protection, or had joined ranks with a traditional pharmaceutical company to work on their own product, regardless of the path no further work was published to the public.

> high speed weighted pattern recognition, yes, intelligence, no.

I'm starting to believe that this may still be sufficient to fulfill the 80 of the 80/20 rule. Properly directed, powerful pattern recognition can do a lot when tied into regular old programming logic.

Regular programs work well in low uncertainty environments, and some well trained ML networks can create that low uncertainty environment out of a relatively high uncertainty environment.

I don't disagree.

Hi, I'm the author of the post. I agree this is a very interesting area to look into. It would fall under "meta" in the main post. Here's some more info: http://blog.givewell.org/2015/04/21/science-policy-and-infra...

Agree, i wrote a little about it here https://medium.com/black-n-white/the-problem-with-problems-4...

The problem is to find the real problems to solve hiding under the obvious solutions.

I think it comes down to our limited capabilities in implementing things, so to see advancement here, we'll probably need better development and testing tools, and maybe better technological building blocks.

If it has value to us all then it is exploitable in a capitalist system.

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