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Combination of the radio occultation method plus change in the orbital plan in order to observe new target (the volcanic outgassing from Io satellite) by passing though the magnetosphere of Jupiter. It poses a challenge in the sensitivity of the electronics. Maybe it is accepted if the risk is low. Idea for the next GTOC challenge maybe? Optimising the cost on the electronics vs the frequency of passages?

rather impressive! and frightening

why are we not getting these type of startups in Europe .... btw: Will is british

I agree, the mentality is different, it's hard to find VC funding for hardware stuff, even more so if you wanna shoot your HW into space. but there is movement, e.g. pioneers.io, they are in vienna and are actively trying to get more VC funding (in europe) for HW and other engineering startups

thanks for the link ... just read this blog ... http://pioneers.io/blog/space-race-2-0-putting-satellites-into-the-hands-of-everyone a lot of selling talk but fundamentally I agree that they have a point ... and as ACT we will face the criticism in not so long that we have not managed (nor tried hard enough) to convince ESA about the need to embrace this "new space"

These annoying resonances... I want to kill Io after this!

encrypted phone calls on android and now also ios devices? here we go, from the programmer of PGP

Cool repository with python source code for GNC applications of AI technology.

something to watch on your 4K screen

1:37 - Europa and Io!

We are living in the future when live broadcasts are being censored by AI programs in real-time. I'm sure dictators everywhere are looking forward for these technologies to mature. Having a firewall over reality is so convenient.

Oh, and look - the copyright AI doesn't likes Democrats as well: http://www.wired.com/threatlevel/2012/09/youtube-flags-democrats-convention-video-on-copyright-grounds/

What this tells is that we should not take AI seriously until smart Luis's (or his son) managed to make something decent out of it ... "This was, of course, absurd. First of all, the clips had been provided by the studios to be shown during the award ceremony. The Hugo Awards had explicit permission to broadcast them. But even if they hadn't, it is absolutely fair use to broadcast clips of copyrighted material during an award ceremony. Unfortunately, the digital restriction management (DRM) robots on Ustream had not been programmed with these basic contours of copyright law. And then, it got worse. Amid more cries of dismay on Twitter, Reddit, and elsewhere, the official Worldcon Twitter announced: Chicon 7@chicon_7 We are sorry to report that #Ustream will not resume the video feed. #chicon7 #hugos #worldcon 3 Sep 12 ReplyRetweetFavorite And with that, the broadcast was officially cut off. Dumb robots, programmed to kill any broadcast containing copyrighted material, had destroyed the only live broadcast of the Hugo Awards. Sure, we could read what was happening on Twitter, or get the official winner announcement on the Hugo website, but that is hardly the same. We wanted to see our heroes and friends on that stage, and share the event with them. In the world of science fiction writing, the Hugo Awards are kind of like the Academy Awards. Careers are made; people get dressed up and give speeches; and celebrities rub shoulders with (admittedly geeky) paparazzi. You want to see and hear it if you can. But Ustream's incorrectly programmed copyright enforcement squad had destroyed our only access. It was like a Cory Doctorow story crossed with RoboCop 2, with DRM robots going crazy and shooting indiscriminately into a crowd of perfectly innocent broadcasts."

:-)

Enjoy....

our next gadget? fully open source it seems

Echoing this, in 2009 Google researchers Alon Halevy, Peter Norvig, and Fernando Pereira penned an article under the title The Unreasonable Effectiveness of Data. In it, they describe the surprising insight that given enough data, often the choice of mathematical model stops being as important - that particularly for their task of automated language translation, "simple models and a lot of data trump more elaborate models based on less data." If we make the leap and assume that this insight can be at least partially extended to fields beyond natural language processing, what we can expect is a situation in which domain knowledge is increasingly trumped by "mere" data-mining skills. I would argue that this prediction has already begun to pan-out: in a wide array of academic fields, the ability to effectively process data is superseding other more classical modes of research.

We made it to Slashdot's frontpage !!! :)

Congratulations, gentlemen!

What is the best (wisest) size for a group of individuals? Couzin and Kao put together a series of mathematical models that included correlation and several cues. In one model, for example, a group of animals had to choose between two options-think of two places to find food. But the cues for each choice were not equally reliable, nor were they equally correlated. The scientists found that in these models, a group was more likely to choose the superior option than an individual. Common experience will make us expect that the bigger the group got, the wiser it would become. But they found something very different. Small groups did better than individuals. But bigger groups did not do better than small groups. In fact, they did worse. A group of 5 to 20 individuals made better decisions than an infinitely large crowd. The problem with big groups is this: a faction of the group will follow correlated cues-in other words, the cues that look the same to many individuals. If a correlated cue is misleading, it may cause the whole faction to cast the wrong vote. Couzin and Kao found that this faction can drown out the diversity of information coming from the uncorrelated cue. And this problem only gets worse as the group gets bigger.

Couzin research was the starting point that co-inspired PaGMO from the very beginning. We invited him (and he came) at a formation flying conference for a plenary here in ESTEC. You can see PaGMO as a collective problem solving simulation. In that respect, we learned already that the size of the group and its internal structure (topology) counts and cannot be too large or too random. One of the project the ACT is running (and currently seeking for new ideas/actors) is briefly described here (http://esa.github.io/pygmo/examples/example2.html) and attempts answering the question :"How is collective decision making influenced by the information flow through the group?" by looking at complex simulations of large 'archipelagos'.

A nice GPU-based framework that is basically an emulator of the brain of the fruit fly. If you need a fruit fly brain - here it comes!

Let an AI build up your web page for just 96$ per year!

Amazing! Not even coding jobs are safe from AI anymore! AI programming will be the last bastion in the struggle against automation. Get ready :)

See title...

any papers or insights on methods available?

Couldn't find a paper for Alibaba's results but Microsoft Research's performance on this dataset was very close. The paper is here: https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf Btw the 'reading test' is a publicly available dataset called 'Stanford Question Answering Dataset (SQuAD)'. Their website shows a leaderboard: https://rajpurkar.github.io/SQuAD-explorer/

This has attracted a lot of attention in the DL community lately. Article: https://lilianweng.github.io/lil-log/2017/09/28/anatomize-deep-learning-with-information-theory.html Paper: https://arxiv.org/pdf/1703.00810.pdf