Finally, someone with a clue about biology tells it like it is about brain uploading
http://mathbabe.org/2015/10/20/guest-post-dirty-rant-about-the-human-brain-project/
In reading this, suggest being on guard against own impulse to find excuses to dismiss the arguments presented because they call into question some beliefs that seem to be deeply held by many in this community.
Programming is not a real time activity. Almost anything would be better than a real time test, maybe a provisional hire, or a take home, or asking people to code something in a few hours.
It depends. Writing a paper is not a realtime activity. Answering a free-response question can be. Proving a complex theorem is not a realtime activity, solving a basic math problem can be. It's a matter of calibrating the question difficulty so that is can be answered within the (soft) time-limits of an interview. Part of that calibration is letting the applicant "choose their weapon". Another part of it is letting them use the internet to look up anything they need to.
Our lead dev has passed this test, as has my summer grad student. There are two applicants being called back for second interviews (but the position is still open and it is not too late) who passed during their first interviews. Just to make sure, I first gave it to my 14 year old son and he nailed it in under half an hour.
Right. One of the other things that's possibly unclear from the description here is that this isn't a research scientist position; this is for a software developer supporting the research scientists. (If you think life extension is cool, it's probably a more fulfilling place to work than writing banking software.)
Correct, this is a staff programmer posting. Not faculty or post-doc (though when/if we do open a post-doc position, we'll be doing coding tests for that also, due to recent experiences).
Look at their body of work -- the code they wrote.
I work at a small but feisty research team whose focus is biomedical informatics, i.e. mining biomedical data. Especially anonymized hospital records pooled over multiple healthcare networks. My personal interest is ultimately life-extension, and my colleagues are warming up to the idea as well. But the short-term goal that will be useful many different research areas is building infrastructure to massively accelerate hypothesis testing on and modelling of retrospective human data.
We have a job posting here (permanent, non-faculty, full-time, benefits):
https://www.uthscsajobs.com/postings/3113
If you can program, want to work in an academic research setting, and can relocate to San Antonio, TX, I invite you to apply. Thanks.
Note: The first step of the recruitment process will be a coding challenge, which will include an arithmetical or string-manipulation problem to solve in real-time using a language and developer tools of your choice.
edit: If you tried applying and were unable to access the posting, it's because the link has changed, our HR has an automated process that periodically expires the links for some reason. I have now updated the job post link.
I'm a super-dummy when it comes to thinking about AI. I rightly leave it to people better equipped and more motivated than me.
But, can someone explain to me why a solution would not involve some form of "don't do things to people or their property without their permission"? Certainly, that would lead to a sub-optimal use of AI in some people's opinions. But it would completely respect the opinions of those who disagree.
Recognizing that I am probably the least AI-knowledgeable person to have posted a comment here, I ask, what am I missing?
it's not strictly an AI problem-- any sufficiently rapid optimization process bears the risk of irretrievably converging on an optimum nobody likes before anybody can intervene with an updated optimization target.
individual and property rights are not rigorously specified enough to be a sufficient safeguard against bad outcomes even in an economy moving at human speeds
in other words the science of getting what we ask for advances faster than the science of figuring out what to ask for
Let's suppose you start with $1000 to invest, and the only thing you can invest it in is stock ABC. You are only permitted to occupy two states:
* All assets in cash
* All assets in stock ABC
You incur a $2 transaction fee every time you buy or sell.
Kind of annoying limitations to operate under. But you have a powerful advantage as well. You have a perfect crystal ball that each day gives you the [probability density function](http://en.wikipedia.org/wiki/Probability_density_function) of ABC's closing price for the following day (but no further ahead in time).
What would be an optimal decision rule for when to buy and sell?
Research access to large amounts of anonymized patient data.
Take all the data you have, come up with some theory to describe it, build the scheme into a lossless data compressor, and invoke it on the data set. Write down the compression rate you achieve, and then try to do better. And better. And better. This goal will force you to systematically improve your understanding of the data.
(Note that transforming a sufficiently well specified statistical model into a lossless data compressor is a solved problem, and the solution is called arithmetic encoding - I can give you my implementation, or you can find one on the web. So what I'm really suggesting is just that you build statistical models of the raw data, and try systematically to improve those models).
(Note that transforming a sufficiently well specified statistical model into a lossless data compressor is a solved problem, and the solution is called arithmetic encoding - I can give you my implementation, or you can find one on the web.
The unsolved problems are the ones hiding behind the token "sufficiently well specified statistical model".
That said, thanks for the pointer to arithmetic encoding, that may be useful in the future.
Bayes is referenced a lot in LW, and for good reason but Bayes theorem is not all that difficult to understand particularly for someone with your education.
The point isn't understanding Bayes theorem. The point is methods that use Bayes theorem. My own statistics prof said that a lot of medical people don't use Bayes because it usually leads to more complicated math.
The most useful skill a knowledge of statistics can give you, arguably, is being able to objectively analyse and comprehend extremely large amounts of data.
That's not the skill that's taught in a statistics degree. Dealing with large amounts of biological data needs algorithms and quite often Bayes somewhere.
The point isn't understanding Bayes theorem. The point is methods that use Bayes theorem. My own statistics prof said that a lot of medical people don't use Bayes because it usually leads to more complicated math.
To me, the biggest problem with Bayes theorem or any other fundamental statistical concept, frequentist or not, is adapting it to specific, complex, real-life problems and finding ways to test its validity under real-world constraints. This tends to require a thorough understanding of both statistics and the problem domain.
That's not the skill that's taught in a statistics degree.
Not explicitly, no. My only evidence is anecdotal. The statisticians and programmers I've talked to appear to overall be more rigorous in their thinking than biologists. Or at least better able to rigorously articulate their ideas (the Achilles heel of statisticians and programmers is that they systematically underestimate the complexity of biological systems, but that's a different topic). I found that my own thinking became more organized and thorough over the course of my statistical training.
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If they never studied those things, they would never figure out the answers to those objections. If they already knew about all these things, new studies wouldn't be needed. What else is there to study if not things we don't understand?
I agree. My reason for posting the link here is as reality check-- LW seems to be full of people firmly convinced that brain-uploading is the only only viable path to preserving consciousness, as if the implementation "details" were an almost-solved problem.