Earlier quoted context omitted.
Thanks for putting this list together! I took some cog sci courses in college and have been meaning to dive more into the research around it and these papers seem like a good place to start. I expect to run into lots of jargon and concepts I don't understand. Would it be possible for me to reach out to you for questions when I'm unable to make sense of the content after having researched the unknown concepts online?
I could help if you are having trouble in animal cognition concepts. Let me know.
Ask HN: What's the best paper you've read in 2020?
91–100 of 197 posts
Re: Ask HN: What's the best paper you've read in 2020?
#92Earlier quoted context omitted.
Thanks for putting this list together! I took some cog sci courses in college and have been meaning to dive more into the research around it and these papers seem like a good place to start. I expect to run into lots of jargon and concepts I don't understand. Would it be possible for me to reach out to you for questions when I'm unable to make sense of the content after having researched the unknown concepts online?
sure, my email is in my profile. I'm not an expert though, just curious about "mind computations".
Re: Ask HN: What's the best paper you've read in 2020?
#93Three papers stick out for me in the IML / participatory machine learning space this year: 1) Michael, C. J., Acklin, D., & Scheuerman, J. (2020). On interactive machine learning and the potential of cognitive feedback. ArXiv:2003.10365 [Cs]. http://arxiv.org/abs/2003.10365 2) Denton, E., Hanna, A., Amironesei, R., Smart, A., Nicole, H., & Scheuerman, M. K. (2020). Bringing the people back in: Contesting benchmark ma…
Re: Ask HN: What's the best paper you've read in 2020?
#94Earlier quoted context omitted.
The primary reason why you should be using 3x or higher replication is the read throughput (which makes it only really relevant for magnetic storage). If the data is replicated 1.6x then there's only 1.6 magnetic disk heads per each file byte. If you replicate it 6x then there's 6 magnetic disk heads for each byte. At ~15x it becomes cheaper to store in SSD with ~1.5x reed-solomon/erasure code overhead since SSD has…
Everything you just said is on point, but I think that's an orthogonal thing to what the paper is going for. Hot data should absolutely have a fully-materialized copy at the node where operations are made, and an arbitrary number of readable copies can be materialized for added performance in systems that don't rely on strong consistency as much. However for cold-data, there really hasn't been (or at least I am unawa…
Also, these days the storage of the data doesn't have to be at the same machine that processes the data. A lot of datacenter setups have basically zero transfer cost (or, alternatively, all the within-DC transfer cost is in the CAPEX required to build the DC in the first place), ultra low latency, and essentially unlimited bandwidth for any within-datacenter communication. This doesn't hold for dc1->dc2 communication, in particular it is very very far from the truth in long distance lines.
One way to think about the above is that datacenters have become the new supercomputers of the IBM era - it's free and really fast to exchange data within a single DC.
Also2, this is completely independent of consistency guarantees. At best it relates to durability guarantees, but that I want from all storage solutions. And yes, properly done reed solomon has the same durability guarantees as plain old replicated setup.
Also to the above also2, single-DC solutions are never really durable as the DC can simply burn down or meet some other tragic end, you need geographic replication if your data cannot be accidentally lost without serious consequences (a lot of data actually can be lost, in particular if it is some kind of intermediate data that can be regenerated from the "source" with some engineering effort). This is not just a theoretical concern, I've seen "acts of God" destroy single-DC setups data, ay least partially. It is pretty rare, though.
Re: Ask HN: What's the best paper you've read in 2020?
#95Out of topic, but could you suggest a good resource for papers? I am interested in software mostly
https://github.com/papers-we-love/papers-we-love#other-good-...
Re: Ask HN: What's the best paper you've read in 2020?
#96One of my favorites is definitely A Unified Framework for Dopamine Signals across Timescales ( https://doi.org/10.1016/j.cell.2020.11.013 ), simply because of its experimental design. They 'teleported' rats in VR to see how their dopamine neurons responded, to determine whether TD learning explains dopamine signals on both short and long timescales. Short answer: it does.
Re: Ask HN: What's the best paper you've read in 2020?
#97Earlier quoted context omitted.
Yeah, exactly. If you wanted to know that your code was bug free, how could you do it? Set a team of experts to each independently scour for bugs. But when do you stop? The quick answer is that you should keep going until every bug you've found, has been found at least twice. I think of this as being that you "just barely" found a bug if only one person identified it, so there are probably still bugs you have "just b…
Is that a direct application of the paper or something else? (sorry I didn't read it) Just wondering because this rule of thumb sounds intuitively wrong to me. Depending on the difficulty of the bugs and the skill levels of the experts, it seems possible for them to find every "easy" bug at least twice while having none of them finding the hardest bug even once. (real world example would be some obscure zero-day secu…
The actual guarantee from the result is not that the number of unobserved "species" is small, but that the total population of all unobserved species is small. If you go back to the birds example, then you could say something like "at most 0.1% of all birds are from species that we haven't identified" but maybe those 0.1% of birds are from a million different species each with incredibly tiny populations. In the code bug example, the very rare species would be the bugs that are very unlikely to be found, i.e. it's more about estimating how many more bugs you will find if you continue to analyze it than how many are really there.
Re: Ask HN: What's the best paper you've read in 2020?
#98Even if a bit impractical in some regrards, I think an operating system/cloud that you interact with like a database is something we should aspirationally strive for. We're spending too much time gluing things together and not enough time being productive. Databases are great at tracking and describing resources (much better than YAML) and stored procedures that are like Lambdas would be neat.
Re: Ask HN: What's the best paper you've read in 2020?
#99Discovering Symbolic Models from Deep Learning with Inductive Biases [1] trains graph neural nets on astrophysical phenomena and then performs symbolic regression to generate algebraic formulae to elegantly model the phenomena in a classical physics framework. It's largely gone under the radar but has pretty interesting implications for NLP and language theory in my opinion. Direct Feedback Alignment Scales to Modern…
Re: Ask HN: What's the best paper you've read in 2020?
#100Keeping CALM: When Distributed Consistency Is Easy In computing theory, when do you actually need coordination to get consistency? They partition the space into two kinds of algorithm, and show that only one kinds needs coordination. CACM, 9/2020. https://cacm.acm.org/magazines/2020/9/246941-keeping-calm/fu...
Highly recommended reading.