Senate set to approve self-driving cars for US roadways

Senate set to approve self-driving cars for US roadways

  • The US Senate today announced it had reached an agreement internally concerning self-driving car technology.
  • The Senate is expected to pass legislation on October 4th that would clear regulations and restrictions for manufacturers, in essence providing a clear path to putting driverless cars on the road.
  • The House passed legislation this summer in a bipartisan effort to ensure the US remains at the cutting-edge of driverless car development.
  • Earlier this year the the US Department of Transportation, in conjunction with the National Highway Traffic Safety Administration, released its updated guidelines for self-driving cars.
  • In the document, a series of safety considerations are provided to manufacturers with explicit mention that State governments should not regulate self-driving cars: – – NHTSA strongly encourages States not to codify this Voluntary Guidance (that is, incorporate it into State statutes) as a legal requirement for any phases of…

US Senators today announced and agreement to pass legislation that approves driverless cars on US roadways, to be voted on in October.
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The Internet of Things cannot be defined

#IoT cannot be defined... or can it? 📶 #AI #smartcities #IoTcouncil

  • As Internet of Things becomes a household term, it is logical and natural that people want to know what it is.
  • The Internet of Things can be the best possible feedback on my physical and mental health, the best possible deals based on real time monitoring for resource allocation, the best possible decision making based on real time data and information from open sources and the best possible alignments of my local providers with the global potential of wider communities.
  • Internet of Things is in its essence the seamless flow between the

    ¥ VWAN (very wide area network): the ‘wise’ city as e-gov services everywhere no longer tied to physical locations

    Whoever ensures traceability, sustainability and security linking up the gateways is de facto and de jure the new power.

  • In short; Internet of Things will define your future everyday.
  • Council hosts the Policy and Regulation Track at The 4th IEEE World Forum on The Internet of Things (WF-IoT 2018).

As Internet of Things becomes a household term, it is logical and natural that people want to know what it is. Definitions spring up.
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Google Deep Learning system diagnoses cancer better than a pathologist with unlimited time

Google Deep Learning system diagnoses cancer better than a pathologist with unlimited time

  • Google has been working on advanced image-recognition systems for several years through its GoogLeNet projects.
  • The project was, in part, aimed at the company’s autonomous car project, teaching self-driving cars to recognize everything from road layouts to stop signs.
  • The company has now applied GoogLeNet tech to cancer diagnosis, and reports that the system was already delivering good results straight out of the box, but says that tweaking the system has delivered stunning performance.
  • Pathologists have always faced a huge data problem in order to obtain an accurate diagnosis.

Google Deep Learning system diagnoses cancer better than a pathologist with unlimited time
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Google’s prototype machine learning software lets you enhance low-res photos

Google's prototype machine learning software lets you enhance low-res photos

  • Now compare that to the composite image below, which shows the low-res image on the left, and the traditionally upsampled version on the right.
  • In the composite image from Google below, the top section is the original, low-res picture, and the bottom is the RAISR-enhanced version.
  • The adaptive upsampling means the resulting, zoomed images are less blurry.
  • But while traditional upsampling methods make these images bigger by filling in new pixel values using fixed rules, RAISR adapts its methods to the type of image its looking at.
  • Google PhotoScan turns your prints into high-quality digital images

Pulling up a low-quality image and telling the computer to “enhance” the resolution has long been the stuff of TV fantasy. But, thanks to machine learning, we are actually getting much better at…
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Calling All Dancing Queens: ABBA Plans to Reunite — but There’s a Catch

Calling all dancing queens: ABBA plans to reunite — but there’s a catch

  • Reproduction in whole or in part without permission is prohibited.
  • ABBA fans: dust off your platforms and prepare to step into a time machine.
  • In June, all four members sung on stage for the first time in 30 years during a special 50th anniversary party in Stockholm.
  • There are few details about the performance, but the new show aims to blend virtual reality and artificial intelligence in an entirely new way to enable ABBA fans of all ages to interact with the band’s unique disco sound.
  • “We are exploring a new technological world, with virtual reality and artificial intelligence at the forefront, that will allow us to create new forms of entertainment and content we couldn’t have previously imagined.”

The Swedish super-group is returning for an “entertainment experience” that will blend cutting-edge virtual reality and artificial intelligence
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srez/README.md at master · david-gpu/srez · GitHub

This is CSI-level: facial reconstruction via machine learning DCGA networks
 HT @diogomonica

  • The generator network relies on ResNet modules as we’ve found them to train substantially faster than more old-fashioned architectures.
  • ‘s an random, non cherry-picked, example of what this network can do.
  • The adversarial term of the loss function ensures the generator produces plausible faces, while the L1 term ensures that those faces resemble the low-res input data.
  • In addition to that the loss function of the generator has a term that measures the L1 difference between the 16×16 input and downscaled version of the image produced by the generator.
  • Extract all images to a subfolder named dataset .

srez – Image super-resolution through deep learning
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GitHub

Code for super-resolution of faces, model is DCGAN, implementation TensorFlow:

  • The generator network relies on ResNet modules as we’ve found them to train substantially faster than more old-fashioned architectures.
  • The resulting 64×64 images display sharp features that are plausible based on the dataset that was used to train the neural net.
  • We have found that this L1 term greatly accelerates the convergence of the network during the first batches and also appears to prevent the generator from getting stuck in a poor local solution.
  • ‘s an random, non cherry-picked, example of what this network can do.
  • Download zip file titled Align&Cropped Images and extract all images to a subfolder named dataset .

Read the full article, click here.


@Reza_Zadeh: “Code for super-resolution of faces, model is DCGAN, implementation TensorFlow:”


srez – Image super-resolution through deep learning


GitHub