I guess it makes sense if they will hit about 100 level in 2029, and then goes up from there.
Masayoshi Son says singularity, the moment when artificial intelligence surpasses the human brain, will happen in “in this century, for sure.”
I guess it makes sense if they will hit about 100 level in 2029, and then goes up from there.
Masayoshi Son says singularity, the moment when artificial intelligence surpasses the human brain, will happen in “in this century, for sure.”
In what promises to be one small step for space travel, and one giant leap for the next generation of manufacturing, an Israeli startup is planning to land a vehicle on the moon that has crucial parts made using 3D printing technology.
SpaceIL is among five teams vying for Google’s $30 million in prize money to get a spacecraft to the moon by the end of March. One of the startup’s suppliers, Zurich-based RUAG Space, advised turning to 3D printing to manufacture the legs of its unmanned lunar lander. With financial stakes high and a tight deadline, SpaceIL engineers were at first deeply skeptical, according to RUAG executive Franck Mouriaux. They finally acquiesced after a lot of convincing.
Driverless cars need superhuman senses. And for the most part they seem to have them, in the form of lidar, radar, ultrasound, near-infrared, and other sensors. But regular cameras, often forgotten about in favor of more exotic technologies, are incredibly important given they’re used to collect data that’s used to, say, read the messages on road signs. So Sony’s new image sensor is designed to give regular camera vision a boost, too.
The new $90 IMX324 has an effective resolution of only 7.42 megapixels, which sounds small compared to your smartphone camera. But with about three times the vertical resolution of most car camera sensors, it packs a punch. It can see road signs from 160 meters away, has low-light sensitivity that allows it to see pedestrians in dark situations, and offers a trick that captures dark sections at high sensitivity but bright sections at high resolution in order to max out image recognition. The image above shows how much sharper the new tech than its predecessor from the same distance.
Don’t expect a beefed-up camera to eliminate the need for other sensors, though: even with strong low-light performance, cameras don’t work well in the dark, and they can’t offer the precise ranging abilities of other sensors. That means lidar and radar will remain crucial complements to humble optical cameras, however fancy they get.
Amid the tumult, there’s one clear winner: the $50 billion company that controls most of the world’s market for factory automation and industrial robotics. In fact, Fanuc might just be the single most important manufacturing company in the world right now, because everything Fanuc does is designed to make it part of what every other manufacturing company is doing.
Fanuc, a secretive Japanese factory-automation business, might be the planet’s most important manufacturer.
A lobbying group representing top artificial-intelligence companies including Amazon.com Inc., Facebook Inc. and Google issued a warning to lawmakers on Tuesday: hands off our algorithms.
Robots are watching us. Literally.
Google has curated a set of YouTube clips to help machines learn how humans exist in the world. The AVAs, or “atomic visual actions,” are three-second clips of people doing everyday things like drinking water, taking a photo, playing an instrument, hugging, standing or cooking.
Each clip labels the person the AI should focus on, along with a description of their pose and whether they’re interacting with an object or another human.
DeepMind’s Professor David Silver describes AlphaGo Zero, the latest evolution of AlphaGo, the first computer program to defeat a world champion at the ancient Chinese game of Go. Zero is even more powerful and is arguably the strongest Go player in history.
Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0.
If similar techniques can be applied to other structured problems, such as protein folding, reducing energy consumption or searching for revolutionary new materials, the resulting breakthroughs have the potential to positively impact society.
Find out more here: https://deepmind.com/blog/alphago-zero-learning-scratch