The battle of folds

On 20th February 2019, Samsung announced a whole bunch of new products – Fold, S10, S10+, S10e, wireless charging and earbuds, and a prototype for a 5G phone, together with a set of technological achievements – Adobe Premiere Rush video editing, enhanced camera capabilities and increased storage space. Everything looks slick and elegant. You can see the highlights of their conference in this video.

The center of their presentation was obviously the Galaxy Fold which is the first foldable phone. Or so we thought. On 24th February 2019, Huawei announced their own take on the foldable screen, but this time it is on the outside of the phone. This solves the problem of Samsung’s approach, but on the other hand, it comes with the danger of scratching or breaking the folded screen at the hinge. Here are some informations on Huawei Mate X.

Who can fold it better? Samsung or Huawei?

Toughening up graphene

Laser-induced graphene (LIG) is a promising component that can be added to a variety of materials in order to enhance them and create tough, conductive parts for wearable electronics, anti-icing, antimicrobial applications, sensors and water treatment.

Credit: Tour Group/Rice University

The researchers at Rice University and Ben-Gurios University have infused LIG with plastic, rubber, cement, wax and other materials to create composites for a wide range of applications. LIG is obtained by having a commercial laser burn the surface of a thin sheet of polymide (a common plastic), which in turn is transformed into flakes of interconnected graphene. This is a fast and inexpensive process, but the material on its own is not mechanically robust. It can be bent and flexed, but it peels off, so it is better off as a component of other materials.

LIG can be used by pouring a thin layer of another material on top of it, and as its creator has said “You just pour it in, and now you transfer all the beautiful aspects of LIG into a material that’s highly robust”.

Source (Rice University. “Laser-induced graphene gets tough, with help.” ScienceDaily. ScienceDaily, 12 February 2019.)

Original paper: Luong, D.X., Yang, K., Yoon, J., Singh, S.P., Wang, T., Arnusch, C.J. and Tour, J.M., 2019. Laser-induced graphene composites as multifunctional surfaces. ACS nano13(2), pp.2579-2586.

Atari master

Atari is a new AI which, as the name suggest, is related to arcade games. The creators claim that is is 10 times faster than Google DeepMind AI. What sets it apart is the capability of solving problems in environments where actions to achieve a goal are not immediately obvious.

It uses a reinforcement learning technique, which relies on increasing the number of points when performing actions that bring the agent closer towards the goal, and decreasing the points otherwise. As an example, it rewards actions such as ‘climb the ladder’ or ‘jump over that pit’.

Associate Professor Fabio Zambetta from RMIT University had said “We’ve shown that the right kind of algorithms can improve results using a smarter approach rather than purely brute forcing a problem end-to-end on very powerful computers. Our results show how much closer we’re getting to autonomous AI and could be a key line of inquiry if we want to keep making substantial progress in this field.”


However, their findings are not new in the domain, as other similar methods already exist. For example, Unity’s ML Agents use reinforcement learning for multiple types of games, with various tasks, such as: balancing objects on flat surfaces, playing tennis, controlling double-jointed arms, learning to walk, and navigating in a labyrinth to complete a task. The method is well-documented and can be found on GitHub.

Details about how Atari achieves its performance, in terms of technology and algorithms have not been made public yet. An oral presentation will take place at the 33rd AAAI Conference on Artificial Intelligence in Honolulu, which will hopefully shed light on these implementation issues.

Source (Atari master: New AI smashes Google DeepMind in video game challenge, RMIT Australia, 31.01.2019)

Original paper: Dann, M., Zambetta, F. and Thangarajah, J., 2019, July. Deriving subgoals autonomously to accelerate learning in sparse reward domains. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 881-889).