The home WiFi network is a sacred place; your own local neighborhood of cyberspace. There we connect our phones, laptops, and “smart” devices to each other and to the Internet and in turn we improve our lives, or so we are told. By the late twenty teens, our local networks have become populated by a growing number of devices. From 📺 smart TVs and media players to 🗣 home assistants, 📹 security cameras, refrigerators, 🔒 door locks and🌡thermostats, our home networks are a haven for trusted personal and domestic devices.
Many of these devices offer limited or non-existent authentication to access and control their services. They inherently trust other machines on the network in the same way that you would inherently trust someone you’ve allowed into your home. They use protocols like Universal Plug and Play (UPnP) and HTTP to communicate freely between one another but are inherently protected from inbound connections from the Internet by means of their router’s firewall 🚫. They operate in a sort of walled garden, safe from external threat. Or so their developers probably thought.
The problems are huge, not least because the EU will implement an automated content filter, which means that memes will die, but also, if you have the money to spam the system with requests, you can basically kill any content you want with the actual content holder only having a marginal chance of navigating EU burocracy in order to regain ownership of their rights.
Among our exciting announcements at //Build, one of the things I was thrilled to launch is the AI Lab – a collection of AI projects designed to help developers explore, experience, learn about and code with the latest Microsoft AI Platform technologies.
What is AI Lab?
AI Lab helps our large fast-growing community of developers get started on AI. It currently houses five projects that showcase the latest in custom vision, attnGAN (more below), Visual Studio tools for AI, Cognitive Search, machine reading comprehension and more. Each lab gives you access to the experimentation playground, source code on GitHub, a crisp developer-friendly video, and insights into the underlying business problem and solution. One of the projects we highlighted at //Build was the search and rescue challenge which gave the opportunity to developers worldwide to use AI School resources to build and deploy their first AI model for a problem involving aerial drones.
a team of engineers at the Johns Hopkins University that has created an electronic skin. When layered on top of prosthetic hands, this e-dermis brings back a real sense of touch through the fingertips.
“After many years, I felt my hand, as if a hollow shell got filled with life again,” says the anonymous amputee who served as the team’s principal volunteer tester.
Made of fabric and rubber laced with sensors to mimic nerve endings, e-dermis recreates a sense of touch as well as pain by sensing stimuli and relaying the impulses back to the peripheral nerves.
“We’ve made a sensor that goes over the fingertips of a prosthetic hand and acts like your own skin would,” says Luke Osborn, a graduate student in biomedical engineering. “It’s inspired by what is happening in human biology, with receptors for both touch and pain.
“This is interesting and new,” Osborn said, “because now we can have a prosthetic hand that is already on the market and fit it with an e-dermis that can tell the wearer whether he or she is picking up something that is round or whether it has sharp points.”
00:00Current time01:31Engineers at the Johns Hopkins University have created an electronic skin and aim to restore the sense of touch through the fingertips of prosthetic hands. Credit: Science Robotics/AAAS
The work—published June 20 in the journal Science Robotics – shows it is possible to restore a range of natural, touch-based feelings to amputees who use prosthetic limbs. The ability to detect pain could be useful, for instance, not only in prosthetic hands but also in lower limb prostheses, alerting the user to potential damage to the device.
A group of scientists have built a neural network to sniff out any unusual nuclear activity. Researchers from the Pacific Northwest National Laboratory (PNNL), one of the United States Department of Energy national laboratories, decided to see if they could use deep learning to sort through the different nuclear decay events to identify any suspicious behavior.
The lab, buried beneath 81 feet of concrete, rock and earth, is blocked out from energy from cosmic rays, electronics and other sources. It means that the data collected is less noisy, making it easier to pinpoint unusual activity.
The system looks for electrons emitted and scattered from radioactive particles decaying, and monitor the abundance of argon-37, a radioactive isotope of argon-39 that is created synthetically through nuclear explosions.
Argon-37 which has a half-life of 35 days, is emitted when calcium captures excess neutrons and decays by emitting an alpha particle. Emily Mace, a scientist at PNNL, said she looks for the energy, timing, duration and other features of the decay events to see if it’s from nuclear testing.
“Some pulse shapes are difficult to interpret,” said Mace. “It can be challenging to differentiate between good and bad data.”
Deep learning makes that process easier. Computer scientists collected 32,000 pulses and annotated their properties, teaching the system to spot any odd features that might classify a signal as ‘good’ or ‘bad’.
“Signals can be well behaved or they can be poorly behaved,” said Jesse Ward. “For the network to learn about the good signals, it needs a decent amount of bad signals for comparison.” When the researchers tested their system with 50,000 pulses and asked human experts to differentiate signals, the neural network agreed with them 100 per cent of the time.
It also correctly identified 99.9 per cent of the pulses compared to 96.1 per cent from more conventional techniques.
Red shell is a Spyware that tracks data of your PC and shares it with 3rd parties. On their website they formulate it all in very harmless language, but the fact is that this is software from someone i don’t trust and whom i never invited, which is looking at my data and running on my pc against my will. This should have no place in a full price PC game, and in no games if it were up to me.
I make this thread to raise awareness of these user unfriendly marketing practices and data mining software that are common on the mobile market, and which are flooding over to our PC Games market. As a person and a gamer i refuse to be data mined. My data is my own and you have no business making money of it.
The announcement yesterday was only from “Holy Potatoes! We’re in Space?!”, but i would consider all their games as on risk to contain that spyware if they choose to include it again, with or without announcement. Also the Publisher of this one title is Daedalic Entertainment, while the others are self published. I would think it could be interesting to check if other Daedalic Entertainment Games have that spyware in it as well. I had no time to do that.
