Voici les 2 news dont vous trouverez le résumé au format bullet points plus bas :
- La marine américaine veut pouvoir abattre les missiles ennemis avec des canons laser
- Découvrez le robot à tout faire que développe Google Moonshots, déjà capable de trier les ordures avec 3.5% d'erreur (contre 20% pour le staff humain)
- Les avantages des canons laser :
- solid-state laser weapon can be fired for a marginal cost of less than one dollar per shot, the report said. The cost corresponds to the fuel needed to generate the electricity used for each shot;
- as long as the ship has fuel, the laser can fire, so it will never run out of ammunition.
- Lasers also have the advantage of versatility: unlike SAMs (Surface-to-Air Missile), laser weapons reach their target at the speed of light, can be quickly redirected towards another target, keep track of a radically maneuvering missile, and power down to jam enemy sensors.
- En comparaison : SAMs aboard destroyers can cost anywhere from $787,000 to $972,000 to shoot each
- The US Navy is scared to death that rival countries like China, Russia and Iran might sink its multibillion dollar surface ships with powerful cruise missiles and waves of cheap drones. But while ship-mounted lasers could be the Navy's most effective response to these threats, a new Congressional Research Service report on directed energy weapons indicates many of the Navy's newest destroyers might not have enough power to fire them.
- Unfortunately, this power management issue will only become more pronounced as the Navy develops even more powerful and power-hungry lasers over the next several years. The current HELIOS system is only 60 kW, and the Navy wants to ramp up the power to 300 kW on future lasers so that they can burn through the nose cones of enemy cruise missiles, the report said.
Découvrez le robot à tout faire que développe Google Moonshots, déjà capable de trier les ordures avec 3.5% d'erreur (contre 20% pour le staff humain)
- The robots they’ve built combine a wheeled base with a single arm and a head full of sensors (including LIDAR) for 3D scanning, borrowed from Alphabet’s self-driving car division, Waymo.
- For now, they’re largely restricted to sorting trash for recycling
- While that might sound mundane, identifying different kinds of trash, grasping it, and moving it to the correct bin is still a difficult thing for a robot to do consistently. Some of the robots also have to navigate around the office to sort trash at various recycling stations.
- Alphabet says even its human staff were getting it wrong 20% of the time, but after several months of training the robots have managed to get that down to 3.5%
- Every day, 30 robots toil away in what’s been dubbed the “playpen” sorting trash, and then every night thousands of virtual robots continue to practice in a simulation.
- This experience is then used to update the robots’ control algorithms each night. All the robots also share their experiences with the others through a process called collaborative learning.
- the hope is that creating robots that are able to learn from little more than experience in complex environments like an office should be a first step towards general-purpose robots that can pick up a variety of useful skills to assist humans.