Google’s self-driving car most likely uses mapping of traffic signs using google street view images for roadway inventory management. If traffic signs are not in its database, it can still “see” and detect moving objects which can be distinguished from the presence of certain stationary objects, like traffic lights. So its software can classify objects based ...
In the standard Turing test (or imitation game), the interrogator can ask multiple arbitrary questions, while, in the case of captchas, usually, there's only one question or problem. Additionally, in the Turing test, the interrogator interactively communicates with both the human and the machine. Furthermore, captchas do not test the conversational skills ...
The AI of the car uses sensor data to process all the data and classifies objects based on the size, shape and movement patterns. It can recognize surroundings from a 360 degree perspective by making predictions about vehicles, people and objects around it will move.
It can detect pedestrians, but as moving, column-shaped blurs of pixels, so it really ...
The question is based on a false fact: in Michigan, it is currently legal (under certain conditions described here ) for an autonomous car to operate without a driver.
The reason that the federal government has not enacted any direct legislation (although they have enacted guidelines) on autonomous cars is because it is still a developing technology (as ...
Google has not released the manufacturing details for their TPUs. However, it's suspected that they're produced by either Taiwan Semiconductor Manufacturing or GlobalFoundries, as these are some of the largest companies in the industry.
##Why Some Investors and Researchers Prefer Radar Over Radar, and Thereof Recent Developments in Radar
Direct Answer to Your Question / What This Answer is About
" ... Why does Google use radar? Doesn't LIDAR do everything radar can do? In particular, are there technical advantages with radar regarding object detection and tracking? ... " ~ ...
I'm not sure what Google is using to perform that task, but most companies use region based convolutional neural nets to locate traffic signs and other objects.
But other companies use a deep neural network + bag of words approach to find objects.
See: Bag-of-Words Based Deep Neural Network for Image Retrieval which shows a general approach, to get the ...
are the LSTM cells on a given row all the same cell, with time flowing forward from left to right?
Yes this is correct
The x-axis on this figure is basically the time axis. Essentially all pink boxes in the same row are the same LSTM cell, with different inputs from the same sequence. At each timestep, the cell takes an input and produces an output which is ...
Here are a few websites that help run the deep dream generator:
This blog by Google AI explains the concept and here is the GitHub repo for the same. It has a python notebook for reproducing the results.
Google's Quick Draw became five things.
A pilot application
A market test
An experiment in massively data intensive reinforcement learning 1
A data segue into handwriting recognition 1
A data collection device for other branches of research (not necessarily an intentional purpose during conception) 2
Google's team on the project wished to delve into ...
Artificial Intelligence at Google — Our Principles
Objectives for AI Applications
Be socially beneficial.
Avoid creating or reinforcing unfair bias.
Be built and tested for safety.
Be accountable to people.
Incorporate privacy design principles.
Uphold high standards of scientific excellence.
Be made available for uses that accord with these ...
Google is just one of the vendors for AI and their initial AI self-service is limited to image analysis. Their are other AI vendors (Microsoft, Amazon) that have other services also. AI is definitely capable of continuous video feed analysis. The self-driving car technology uses live video feed analysis. Are you exploring AI in general or specific use cases? ...
This answer addresses the incorrect assumption that the US government prohibits it (that is, here's no federal law against it, but state laws vary), so I will address the incorrect assumption that there now exists "well-designed AI".
If you look at the actual reports submitted by the autonomous car companies who have tested in California, you'll see that ...
LIDARs, especially cheap LIDARs, have problems with reflective surfaces (like metallic paint on cars), strong lights like car headlights, weather(rain, snow, hail, fog), and have a considerably shorter range than comparable in price radars. Of course, they have much better precision, so some hardware stacks for cars are using both.