yigitdemirag

Every model is an approximation :) I don't work at HBP, therefore can't speak for their implementations. These are the SoTA of nm engineering. What do you mean by compartmental simulation?

Disclaimer: Working on neuromorphic chips.

Several options available but followings are superior to others in terms of capturing the real neuron/synapse behavior with a simple and compact VLSI implementation.

For neurons: Exponential adaptive integrate and fire models @ real-time.

For synapses: STD/STP + LTP/LTD circuits @ real-time.

I am surprised that nobody mentioned spacemacs[1] and its org-mode support. If you are used to vim shortcuts but want to give a chance to fully-supported org-mode, you should definitely try spacemacs.

Still, I don't think I am ever going to use spacemacs to write code, but for note-taking(with inline LaTeX preview)/todo/calendar, I am 100% happy with it.

[1] http://spacemacs.org/

This a very cool repository indeed! Have you compared the speed/accuracy of spikeflow with other SNN libraries like Brian2 on decent GPUs (or even TPUs)?

Is there any open-source -physically realistic- rocket flight simulation software that one can build/test landing algorithms on?

If you love emacs's functionality and prefer vim's key bindings, you may find http://spacemacs.org/ useful. Its org-mode support is amazing.

And don't forget inline LaTeX support. With this feature, listening technical conferences, lectures or noting mathematical ideas are really comfortable.

For books, PDFs : Zotero (if book/pdf requires to be sync have a copy at iCloud) - Because I usually need to cite these things.

For notes: First condense the thought and put it nicely to Dynalist - Because it is all easily searchable, linked and think-then-write tree-structure. - Mobile app is a must to capture ideas, thoughts on mobile.

For todo: Things/Todoist etc. would work. - Because you need deadlines, reminders.

It really depends on how you do note-taking. In my case, I take notes related to my research projects, meetings and things I find useful, which require me to find a software with Latex, MathJax support. I don't like latex code inside my note, I like the rendered view. Second requirement is that this software should be mobile support. On the bus, on waiting line etc, I should be able to edit/add notes.

Outliners are good for noting ideas. It makes you think more structural and organized. The less bullet you spend, the simple the idea becomes, that way reading it requires less energy.

I use Dynalist because of the reasons above and its mobile app is 10/10.

I am currently comparing OmniFocus 3 with Things 3 to decide which one better represents my working habits or preferably improve it.

- Things 3 is simple and captures everything I need from a todo-app. It is like writing down the things you need to do on a paper.

- OmniFocus 3 is complicated. There are ~5 buttons whenever you go in the app. No question, it is more customizable but does it really make a difference?

EDIT: Typo.

I wonder what could be added to this list from models explained in Eliezer Yudkowsky's Rationality: From AI to Zombies book (which is available online: https://intelligence.org/rationality-ai-zombies/)

PS: Created an OPML file for 113 models with explanations:

https://www.dropbox.com/s/ba4zvf626zpqz57/mental%20models.op...

I have tried to design a similar workflow using OmniOutliner, but miserably failed. I'm using it for basic todos and one line notes but it is nowhere close to this functionality.

They have VPN service as well. It could be critical for VPN.

“Most metrics are good until you start optimizing for any of them”

So sorry to find this thread on HN while intoxication. I need to write a blogpost about it in a couple of days. In short, these devices are more capable and realizable than most can imagine.

Disclaimer: Neuromorphic computing with PCRAM devices is my MSc and future PhD thesis topic.

I was using RescueTime for long time but then discover Timing[1] (which also works offline). Never tried Qbserve but I highly recommend Timing.

[1]https://timingapp.com/?lang=en

IBM's TrueNorth does the same, it is a common practice in neural network chips for inference. I think doing that kind of tricks for training is much more valuable. Doing training at hundreds of GPUs and inference with chips at mobile, can only be a temporary solution for deep learning market.

In addition to that great news, MuJoCo student licenses are now free: http://www.mujoco.org/index.html

I haven't read the article but wanted to comment on specifically for your question. As a person working on neuromorphic computing, I would like to make something clear:

Most of frontier neuromorphic research of today, neither focus on creating an "general artificial intelligence" by copying human brain nor think that neuromorphic computing is the most probable discipline to achieve it. Instead, optimizing hardware for neural networks is the focus. If we want to achieve high number of (>10^14) weights, low energy consumption and spatial shrinkage we would like to give up running 1000 GPU clusters which only a handful of companies have. Neuromorphic computing only suggests that we need a better hardware (which may or may not require working with spiking neural nets as a consequence) in order to make AI hardware scalable, nothing more.

(Another one) For DeepMind case, in order to be Research Scientists you have to have or about to have a PhD degree.

It was still there :) I was also summer student at 2014, how far summer studentship goes back I wonder..

These pictures are taken on the same floor, but just one floor below in this building there is another computer grid, similar to this one but a bit smaller. At that grid, there are, if I remember correctly, more advanced (Intel's xeon, AVX512 etc.) CPU architectures are operating. In the very same floor there are also robotic arms that access offline/long stored hard-drives per user request, which always seems pretty amazing to me. (Disclaimer: I was intern at CERN.)

This is also not triangulation but trilateration.

I wonder how hyped carbon nanotube technology is. In case of graphene, lots of groups had wonderful results on the theory, on simulations but when it comes to nanofabrication or mass-scale production no much progress is done.

What happens when payload is lost at such an event due to SpaceX related failure? Obviously they have to reschedule but does SpaceX cover partial losses etc?

Sorry for jumping like that before reading your blog post carefully but did you consider the fake user factor on your results? There are tons of accounts on Twitter posting 1-2 exactly the same or 2-3 character altered tweets from an unknown database per week. Those fake accounts mostly support AKP. I believe those are the ones that make your prediction inaccurate.

(not-related) I'm happy that you show my twitter post on your homepage. I do love using circuitlab.

fMRI I suppose. As far as I know, even if you have all neuron mapping of human brain, you could not achieve basic intelligence as brain constantly alter with these connections and their strengths. Even if you know all the connections, neuron firing types and representation of information over the network would create a big question for you.

For those who interested in the hardware implementation of neural networks, this project simulates neurons on the software level, however neuromorphic TrueNorth chip does the same job on hardware level which enable faster and more efficient applications.

What I wonder is that considering that computation is universal, would it be easier to achieve human-like understanding of the environment using hierarchical models without any use of neural networks?

Can we achieve such mathematical breakthrough without making use of neuroscience or related biological fields?