Group items tagged

Machine learning yields detection of 72 new fast radio bursts from distant galaxy. SETI folks are getting excited.

A simple, but neat helper for coding: ML gives idiomatic usage patterns to semi-automate the daily development work.

Machine learning to write better machine learning code...count me in haha

A nice merger of different AI technologies. System teaches itself to derive concepts from images and some Q/A-pairs.

Blockchains, now with more buzzwords!

Time travel! Finally. Can we also use it for antigravity?

Related paper at: http://journals.aps.org/prl/pdf/10.1103/PhysRevLett.118.080501

System developed at MIT CSAIL aims to help linguists decipher languages that have been lost to history.

... related to the new mathematical proof of emergence of structures in random graphs.

EXACTLY what I wanted to investigate as a rapid constuction tool for shape memory alloy structures!

created by a Dutch artist :)

Any idea how?

It seems like he just recreates in the room the right combination of temperature and humidity and than with a fog machine he generates the cloud ... I thought he could be a nice candidate for the artist in residence :)

The human brain both stores and processes information at the same time. Now computer scientists say they can do the same thing The human brain is an extraordinary computing machine. Nobody understands exactly how it works its magic but part of the trick is the ability to store and process information at the same time.

Let's all start up some crazy space companies together: harvest hydrogen on Jupiter, trap black holes as unlimited energy supplies, use high temperatures close to the sun to bake bread! Apparently it is really easy to do just about anything and Deep Space Industries is really good at it. Plus: in their video they show Mars One concepts while referring to ESA and NASA.

hmm aside from the technological feasibility, their approach still should be taken as an example, and deserve a little support. By tackling such difficult problems, they will devise innovative stuffs. Plus, even if this doom-to-fail endeavour may still seem you useless, it creates jobs and make people think... it is already a positive! Final word: how is that different from what Planetary Resources plan to do? It is founded by a bunch of so-called "nuts" ... (http://www.planetaryresources.com/team/) ! a little thought: "We must never be afraid to go too far, for success lies just beyond" - Proust

I don't think that this proposal is very different from the one by Planetary Resources. My scepticism is rooted in the fact that - at least to my knowledge - fully autonomous mining technology has not even been demonstrated on Earth. I am sure that their proposition is in principle (technically) feasible but at the same time I do not believe that a privately funded company will find enough people to finance a multi-billion dollar R&D project that may or may not lead to an economically sensible outcome, i.e. generate profit (not income - you have to pay back the R&D cost first) within the next 25 years. And on that timescale anything can happen - for all we know we will all be slaves to the singularity by the time they start mining. I do think that people who tackle difficult problems deserve support - and lots of it. It seems however that up till now they have only tackled making a promotional video... About job creation (sorry for the sarcasm): if usefulness is not so important my proposal would be to give shovels to two people - person A digs a hole and person B fills up the same hole at the same time. The good thing about this is that you can increase the number of jobs created simply by handing out more shovels.

Toward Autonomous Humanoids check out our new video with the iCub in the IM-CLeVeR project

Admit it ... You have fallen in love ....

you dont' know how often we had to shoot that scene :) but it is an adorable baby robot (if it works :))

google paper on quantum computers ... anybody with further insight on how realistic this is

Not an answer to Leopold's question but here is a little primer on quantum computers for those that are (like me) still confused about what they actually do: http://www.dwavesys.com/tutorials/background-reading-series/quantum-computing-primer It give a good intuitive idea of the kinds of problems that an adiabatic quantum computer can tackle, an easy analogy of the computation and an explanation of how this get set up in the computer. Also, there is emphasis on how and why quantum computers lend themselves to machine learning (and maybe trajectory optimization??? - ;-) ).

from one of the reddit threads discussing this: "bit fishy, crazy if real". "Incredible claims: - Train only using about 10% of imagenet-12, i.e. around 120k images (i.e. they use 6k images per arm) - get to the same or better accuracy as the equivalent VGG net - Training is not via backprop but more simpler PCA + Sparsity regime (see section 4.1), shouldn't take more than 10 hours just on CPU probably "

clicking the link says the manuscript was withdrawn :))

This "one-shot learning" paper by Googe Deepmind also claims to be able to learn from very few training data. Thought it might be interesting for you guys: https://arxiv.org/pdf/1605.06065v1.pdf

Cute info-graphics on different machine learning architectures

Maybe of interest for the ever lasting discussion around the coffee machines of our meeting room... :) paper at http://epubs.siam.org/doi/pdf/10.1137/15M1036658

Implementation of the deep learning-based image classifier (online). Try making a picture with your phone and upload it there. Pretty impressive results. EDIT: Okay, it works the best with well exposed simple objects (pen, mug).

There has been much recent interest in accelerating materials discovery. High-throughput calculations and combinatorial experiments have been the approaches of choice to narrow the search space. The emphasis has largely been on feature or descriptor selection or the use of regression tools, such as least squares, to predict properties. The regression studies have been hampered by small data sets, large model or prediction uncertainties and extrapolation to a vast unexplored chemical space with little or no experimental feedback to validate the predictions. Thus, they are prone to be suboptimal. Here an adaptive design approach is used that provides a robust, guided basis for the selection of the next material for experimental measurements by using uncertainties and maximizing the 'expected improvement' from the best-so-far material in an iterative loop with feedback from experiments. It balances the goal of searching materials likely to have the best property (exploitation) with the need to explore parts of the search space with fewer sampling points and greater uncertainty.

Can AI create the next wonder material? http://www.nature.com/news/can-artificial-intelligence-create-the-next-wonder-material-1.19850?WT.ec_id=NATURE-20160505&spMailingID=51301208&spUserID=MTEzODM0NjYzMzgS1&spJobID=920498312&spReportId=OTIwNDk4MzEyS0

Sounds like Microsoft is developing a full Linux binary compatibility layer for Win 10 using syscall translation (like for example FreeBSD has for Linux binaries). In simpler terms: You can run any Linux binary (from the Ubuntu base or any of the packages) directly from Windows. No virtual machine. No emulation layer. No recompiling like SUA or Cygwin.

AutoDraw is a new kind of drawing tool. It pairs machine learning with drawings from talented artists to help everyone create anything visual, fast. There's nothing to download. Nothing to pay for. And it works anywhere: smartphone, tablet, laptop, desktop, etc. AutoDraw's suggestion tool uses the same technology used in QuickDraw, to guess what you're trying to draw. Right now, it can guess hundreds of drawings and we look forward to adding more over time. If you are interested in creating drawings for others to use with AutoDraw, contact us here. We hope AutoDraw will help make drawing and creating a little more accessible and fun for everyone.