Hi Stuart, I am about to complete a PhD in Machine Learning and would be interested in collaborations like these but only October onwards.
I have written and presented papers at Machine Learning conferences, and am quite interested in contributing to concrete AI safety research. My work so far has been on issues in supervised ranking tasks, but I have read a fair bit on reinforcement learning.
I am not close to Oxford. I am current in Austin, TX and will be in the bay area October onwards.
Are there any newbies wandering these parts? Leave a comment here! I want to know if there's any interest in a weekly/fortnightly/monthly newbie thread?
By newbie, I mean that you've found LessWrong somewhat recently and are getting exposed to many ideas like those in the Sequences but you don't post because you wonder if the things you would talk about get discussed later or might have already been discussed somewhere.
LW has a reputation of being very harsh on newbies, so maybe a newbie thread where we can discuss things without annoying those critical people would give people a place to hang out.
I've been having digestive trouble recently and have started wondering if I've developed a new allergy/intolerance (Known: milk, cashewnuts, chocolate). Does anyone have a recommendation for tests to check for these?
Apparently, "Eight foods account for 90% of all food-allergic reactions: milk, eggs, peanuts, tree nuts (e.g.,
walnuts, almonds, cashews, pistachios, pecans), wheat, soy, fish, and shellfish." (source: http://www.foodallergy.org/file/facts-stats.pdf). However, nuts are good for you (eg. https://examine.com/faq/how-can-i-best-ensure-cardiovascular-health-and-longevity/). So what do you do?
I imagine allergies are bad for your body, even apart from the digestive issues. So, do you take any supplements for nuts?
The only one I am aware of here is Omega-3, which is common to various nuts.
Sidenote: If you are willing to put a "nuts without allergies" supplement together, I might buy it from you. See https://www.reddit.com/r/Supplements/comments/3ptsz8/i_am_a_supplement_caffeineenergy_pill_company/cw9gwht for business advice.
I've been preparing for coding interviews, and I realized that the skill had gotten "rusty" from disuse. A specific example is coding a binary search, which is a little nontrivial because you have to think carefully to avoid off-by-one errors.
When people talk about old skills they talk about them in two ways: some skills you can supposedly never forget, like riding a bike, Some others can get rusty, so you need to keep brushing them up over and over again.
Neither of these seems actually true. I think it's more like the exponential forgetting curve we have for (verbal) memory. The neurons for the skill still exist but you can't access them after a while, and when you recall the skill, you get to the same level as before. If you keep reinforcing it from time to time, say according to the spaced repetition schedule, the skills become permanent. (I've made the exponential analogy because it would be cool if motor memory and verbal memory had similar mechanisms, but it's just a model that I'm familiar with)
Has anyone heard of something like this in the psych literature?
What are your experiences with skills like these that you don't use as often? Have you made a skill "permanent" through repeated practice.
There is an opinion expressed here, that I agree with: http://smerity.com/articles/2016/tayandyou.html TL;dr: No "learning" from interactions on twitter happened. The bot was parroting old training data, because it does not really generate text. The researchers didn't apply an offensiveness filter at all.
I think this chat bot was performing badly right from the start. It would not make sense to give too much importance to the users it was chatting with, and they did not change its mind. That bit of media sensationalism is BS.
Natural language generation is an open problem and almost every method I have seen (not an expert in NLP, but would call myself one in Machine Learning) ends up parroting some of its training text, implying that it is overfitting.
Given this, we should learn nothing about AI from this experiment, only about people's reaction to it, mainly the media reaction to it. Users' reaction while talking to AI is well documented.
I took it.
I just attended one too! I am composing a post on this, about halfway done.
I'd be interested in a collaboration where we both talk about our experiences, though I would like to see what you think. My post is laden with my own interpretations. Send me a message if you want to discuss once you have your outline down
Hi, I'm an AI PhD student and I just signed up for the Udacity Deep Learning course. Lets do this!
I'm interested in setting up the dev environment. But I'm running into technical issues setting up the VM etc. I expect more such questions will come up. What is the right place to discuss these? Perhaps a channel on the slack? Or do we want something more permanent to help new contributors?
It depends on why I'm making the list.
If I'm making a todo list for a project I'm working on, Workflowy is good because its simple and supports hierarchical lists.
For longer lived stuff where I add and delete stuff like grocery/shopping lists or books to read, I use wunderlist because they have an android app, a standalone windows app and it looks pretty. Browser-based apps annoy me so I like the windows app and the android app is nice to have when I'm actually in the grocery store.
When I'm making a list because I need to be productive and not as a way to plan, I use a paper todolist: http://www.amazon.com/gp/product/B0006HWLW2/ref=oh_aui_detailpage_o08_s00?ie=UTF8&psc=1. Checking things off on paper does wonders for productivity and having the printed thing helps set the mood.