Tag Archives: #chess

EPFL Moves Boldly Into Space With Its CHESS Satellites (Astronomy)

The EPFL Spacecraft Team has set itself the ambitious goal of launching two satellites by 2023. With this bold initiative, this student team hopes to gain further insight into the chemical composition of the outermost layers of our atmosphere.

Designing a satellite and launching it into space is no run-of-the-mill project. Rather, it’s one that forever marks the early careers of the students who take part – just ask the EPFL students who designed the SwissCube, a 1U CubeSat (a small standardized unit measuring 10 cm x 10 cm) launched in 2009. Today, a new group of students, the EPFL Spacecraft Team, is taking on a new challenge. With the support of the EPFL Space Center (eSpace), they are developing a constellation of two satellites, called CHESS, that will be launched in two years. The team is currently seeking additional members and sponsors.

This ambitious project has already signed on six universities, three companies, 15 professors and 53 students.* The two satellites will work in concert; each one will be a 3U CubeSat bearing primary and secondary payloads. They will orbit at different altitudes – one will travel in a circular orbit at the low altitude of around 550 km, and the other will travel in an elliptic orbit at an altitude oscillating between 400 km and 1,000 km. The constellation will be launched in March 2023 and remain in flight for at least two years.

This project will give the students who participate each year a chance to learn about complicated space technology and gain experience working on a cross-disciplinary team. “It’s a way to learn the real-world skills required in our industry, like team management, coordination, communication and fundraising,” says Emmanuelle David, the deputy director of eSpace. “These are skills you can’t learn only from a book. And they will let the students become operational as soon as they start their first job or when and if they decide to start their own business.”

One satellite will be placed in a circular orbit at around 550 km, while the other will travel in an elliptic orbit between 400 km and 1,000 km. © EPFL

Understanding the exosphere’s chemistry

In addition to giving students valuable experience, the CHESS mission will have several other objectives. The first is scientific. As the two satellites orbit around the Earth, they will collect detailed information about the exosphere – the outermost layer of the atmosphere, starting at 400 km above the planet’s surface. Since the satellites will follow separate orbits, they will collect complementary spatial and temporal datasets.

“The last time this layer of the atmosphere was analyzed in detail was nearly 40 years ago,” says Dr. Rico Fausch, a physicist at the University of Bern’s Space Research & Planetary Sciences Department. “So these updated data will be very useful in helping us better understand the exosphere’s chemistry and how it has changed over time, especially in light of global warming. They will also let us check whether the upper layers of the atmosphere are indeed cooling as recent studies have suggested – which would be another direct consequence of the accumulation of greenhouse gases around our planet.”


The satellites will collect detailed data on the various gases in the exosphere – nitrogen, oxygen, ozone, carbon dioxide, hydrogen, helium, etc. – and their isotopes. The overall goal is to study temperature fluctuations, the processes and mechanisms by which atmospheric gases escape into space, and how many free-floating electrons and ions there are in the exosphere. Molecules are much harder to find at high altitudes than low ones, since individual particles collide and bind together much more slowly.

Technological advancements

The CHESS mission also aims to spur technological innovation. The satellites will be equipped with state-of-the-art instruments, such as the new mass spectrometers being developed jointly by the University of Bern and the company Spacetek – two pioneers in this field. The spectrometers will be used to identify specific compounds and their chemical structures by taking mass measurements, and will be lighter and more efficient than the ones used back in the 1980s.

New technologies will be tested thanks to the CHESS mission © EPFL

The satellites will also feature next-generation Global Navigation Satellite System (GNSS) receivers that are lighter, less expensive, and more accurate than existing models. These receivers are being developed jointly by ETH Zurich and u-blox. They will not only record extremely precise data on the satellites’ positions (on the order of ±1 cm), but also measure air density and the number of free-floating electrons. In addition, the CHESS mission will perform in situ tests of a new kind of solar panel designed specifically for spacecraft, based on technology developed at RUAG. And because the two satellites will send radio communications to ground stations, they will help create a Swiss-wide X-band network – a communications network using an ultra-high-frequency radio band.

Switzerland’s growing space industry

“I was lucky enough to work with the team that designed, built and launched SwissCube – which is still operational today, some 11 years later,” says Muriel Richard, an aerospace engineer and the co-founder & CTO of ClearSpace. “I’ve been following the CHESS project closely for the past two years, and I see that these students are just as skilled, smart and motivated – and are worth believing in and investing in. They’ll send these satellites into orbit, that’s for sure!”

CHESS forms part of a broader trend of growing interest in space-industry R&D, both at EPFL and in Switzerland as a whole. The project will help train the next generation of aerospace engineers, pull together skills from a wide range of fields, expand the existing space-industry network and forge ties among the scientific community.

*Partner organizations: University of Bern, Hochschule Luzern, HES-SO Valais-Wallis, ETH Zurich, Haute École ARC, Spacetek, u-blox and RUAG


Illustrations: One out of two CHESS Satellites ©E.Nardini /EPFL Spacecraft Team

Provided by EPFL

Artificial Intelligence Beats Us in Chess, But Not in Memory (Engineering)

The brain strategy for storing memories is more efficient than AI’s one, a new study reveals.

In the last decades, Artificial Intelligence has shown to be very good at achieving exceptional goals in several fields. Chess is one of them: in 1996, for the first time, the computer Deep Blue beat a human player, chess champion Garry Kasparov. A new piece of research shows now that the brain strategy for storing memories may lead to imperfect memories, but in turn, allows it to store more memories, and with less hassle than AI. The new study, carried out by SISSA scientists in collaboration with Kavli Institute for Systems Neuroscience & Centre for Neural Computation, Trondheim, Norway, has just been published in Physical Review Letters.

The brain strategy for storing memories may lead to imperfect memories, but in turn, allows it to store more memories, and with less hassle than AI. © Shahab Mohsenin

Neural networks, real or artificial, learn by tweaking the connections between neurons. Making them stronger or weaker, some neurons become more active, some less, until a pattern of activity emerges. This pattern is what we call “a memory”. The AI strategy is to use complex long algorithms, which iteratively tune and optimize the connections. The brain does it much simpler: each connection between neurons changes just based on how active the two neurons are at the same time. When compared to the AI algorithm, this had long been thought to permit the storage of fewer memories. But, in terms of memory capacity and retrieval, this wisdom is largely based on analysing networks assuming a fundamental simplification: that neurons can be considered as binary units.

The new research, however, shows otherwise: the fewer number of memories stored using the brain strategy depends on such unrealistic assumption. When the simple strategy used by the brain to change the connections is combined with biologically plausible models for single neurons response, that strategy performs as well as, or even better, than AI algorithms. How could this be the case? Paradoxically, the answer is in introducing errors: when a memory is effectively retrieved this can be identical to the original input-to-be-memorized or correlated to it. The brain strategy leads to the retrieval of memories which are not identical to the original input, silencing the activity of those neurons that are only barely active in each pattern. Those silenced neurons, indeed, do not play a crucial role in distinguishing among the different memories stored within a same network. By ignoring them, neural resources can be focused on those neurons that do matter in an input-to-be-memorized and enable a higher capacity.

Overall, this research highlights how biologically plausible self-organized learning procedures can be just as efficient as slow and neurally implausible training algorithms.

Reference: Schönsberg, Francesca, Yasser Roudi, and Alessandro Treves, “Efficiency of Local Learning Rules in Threshold-Linear Associative Networks”, Physical Review Letters 126.1 (2021): 018301 https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.018301

Provided by SISSA

There Are More Games Of Chess Possible Than Atoms In The Universe (Maths)

To anyone who has ever complained that a game of chess is boring, we can at least guarantee you this: Every game you play will be different. How do we know? Well, it’s estimated that there are more possible iterations of a game of chess than there are atoms in the known universe. In fact, the number of possible moves is so vast that no one has ever been able to calculate it exactly.

In the 1950s, mathematician Claude Shannon wrote a paper about how one could program a computer to play chess. In it, he made a quick calculation to determine how many different games of chess were possible, and came up with the number 10^120. This is a very, very large number — the number of atoms in the observable universe, by comparison, is only estimated to be around 10^80.

Shannon’s number came from a rough calculation that used averages instead of exact figures. It assumed that at any point in the game you’d have an average of 30 legal moves, for example, and that every game has an average of 80 total moves. But that’s not how chess works. You have many fewer legal moves at the beginning of a game than the end, and games can go much shorter or longer than 80 moves.

There are other complications as well: even if you have 30 possible moves, only a few will make sense strategically. This is why it’s such a challenge to calculate the number of possible games of chess, and why to this day, no one has landed on an exact figure.

References: (1) https://www.popsci.com/science/article/2010-12/fyi-how-many-different-ways-can-chess-game-unfold/ (2) https://www.scientificamerican.com/article/claude-e-shannon-founder/