En ik maar zoeken naar verschillen... blijkt de grap te zijn dat er geen verschillen zijn.quote:
Echt niet? Op de rechterfoto lijkt de weg veel meer naar rechts te gaan alsof de foto van een ander standpunt genomen is.quote:
Mja, ik zie het gewoon echt niet.quote:Op donderdag 8 februari 2018 15:55 schreef Kijkertje het volgende:
Echt niet? Op de rechterfoto lijkt de weg veel meer naar rechts te gaan alsof de foto van een ander standpunt genomen is.
Misschien wat minder op de details letten op zoek naar verschillen en gewoon naar beide foto's tegelijkertijd kijken als geheel?
quote:Why even a moth’s brain is smarter than an AI
One of the curious features of the deep neural networks behind machine learning is that they are surprisingly different from the neural networks in biological systems. While there are similarities, some critical machine-learning mechanisms have no analogue in the natural world, where learning seems to occur in a different way.
These differences probably account for why machine-learning systems lag so far behind natural ones in some aspects of performance. Insects, for example, can recognize odors after just a handful of exposures. Machines, on the other hand, need huge training data sets to learn. Computer scientists hope that understanding more about natural forms of learning will help them close the gap.
quote:Driverless cars: mapping the trouble ahead
When a self-driving car looks at the world, there are many things it sees. It has radars that measure distance to the next car, it has cameras that take in colour images of the street and its Lidar sensors send out laser pulses that gauge the surroundings. For any robot-driven car, one of the most important components of the journey is not just what it sees but what it knows beforehand about the area it is travelling through.
The robot needs a map, but not just any map — these cars need a three-dimensional representation of the environment around them, one that is updated continuously and is accurate down to the centimetre. As it cruises through the streets, a self-driving car collects more than a terabyte of data a day, enough to fill 1,400 CDs. With that much detailed information coming from the car’s many sensors, however, it is uneconomic to send it through a network like the internet.
Instead, companies have to physically move the data from one hard drive to another, a process sometimes called the “sneakernet” because engineers joke that the hard drives move at the pace of their own footwear.
The data collection is part of a great race to amass knowledge about the physical world that can be used to train the new generation of cars. Researchers hope that eventually the base layer of information will have applications not just for transport and logistics, but also for the development of augmented reality technologies — becoming like a simulation of the real world that can be used by any robot, drone or car.