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Sensors are cheap and abundant. They’re already in our devices, and soon enough, many of us may elect to carry sensors in and on our bodies, and embed them in our homes, offices, and cities. This terrifies people, Jason Silva says in a new video.
Who hasn’t heard of Big Brother or feared the rise of the surveillance state? But Silva says there’s an upside.
As the world is reduced to “algorithmic cascades of data” he thinks we’ll get what Steven Johnson calls the “long view,” like a microscope or telescope for previously invisible information and datasets.
Billions of sensors measuring location, motion, orientation, pressure, temperature, vital signs and more—each of these will be like a pixel. Seen up close, a modestly flashing primary color. But at a distance, individual pixels dissolve. Discrete points will smooth out into a contiguous image no one could have guessed by looking at each pixel alone.
Exactly what image will our sensors reveal?
HAL 9000 (credit: Warner Bros.) Is it possible to develop moral autonomous robots with a sense for right, wrong, and the consequences of
Via Claude Emond
Is it possible to develop “moral” autonomous robots with a sense for right, wrong, and the consequences of both?
Researchers from Tufts University, Brown University, and Rensselaer Polytechnic Institute think so, and are teaming with the U.S. Navy to explore technology that would pave the way to do exactly that.
“Moral competence can be roughly thought about as the ability to learn, reason with, act upon, and talk about the laws and societal conventions on which humans tend to agree,” says principal investigator Matthias Scheutz, professor of computer science at Tufts School of Engineering and director of the Human-Robot Interaction Laboratory (HRI Lab) at Tufts.
“The question is whether machines — or any other artificial system, for that matter — can emulate and exercise these abilities.”
But since there’s no universal agreement on the morality of laws and societal conventions, this raises some interesting questions. Was HAL 9000 (HAL = (Heuristically programmed ALgorithmic computer) moral? Who defines morality?
European scientists from six institutes and two universities have developed an online platform where robots can learn new skills from each other worldwide — a kind of “Wikipedia for robots.” The objective is to help develop robots better at helping elders with caring and household tasks. “The problem right now is that robots are often developed specifically for one task”, says René van de Molengraft, TU/e researcher and RoboEarth project leader.
“RoboEarth simply lets robots learn new tasks and situations from each other. All their knowledge and experience are shared worldwide on a central, online database.” In addition, some computing and “thinking” tasks can be carried out by the system’s “cloud engine,” he said, “so the robot doesn’t need to have as much computing or battery power on‑board.”
For example, a robot can image a hospital room and upload the resulting map to RoboEarth. Another robot, which doesn’t know the room, can use that map on RoboEarth to locate a glass of water immediately, without having to search for it endlessly. In the same way a task like opening a box of pills can be shared on RoboEarth, so other robots can also do it without having to be programmed for that specific type of box.
RoboEarth is based on four years of research by a team of scientists from six European research institutes (TU/e, Philips, ETH Zürich, TU München and the universities of Zaragoza and Stuttgart).
Robots learn from each other on 'Wiki for robots'
Via Dr. Stefan Gruenwald
Universal artificial intelligence
This scientific field is called universal artificial intelligence, with AIXI being the resulting super-intelligent agent.
The goal of AIXI is to maximise its reward over its lifetime – that's the planning part.
In summary, every interaction cycle consists of observation, learning, prediction, planning, decision, action and reward, followed by the next cycle.
If you're interested in exploring further, AIXI integrates numerous philosophical, computational and statistical principles:
Ockham's razor (simplicity) principle for model selectionEpicurus principle of multiple explanations as a justification of model averagingBayes rule for updating beliefsTuring machines as universal description languageKolmogorov complexity to quantify simplicitySolomonoff's universal prior andBellman equations for sequential decision making.
Chris Zhang is building emotionally-savvy “virtual partners” to help rehabilitate patients by teaching robots how to mimic and respond to human emotion.
Although the project started in 2005, Zhang, a professor of mechanical engineering at the University of Saskatchewan, has been in charge since 2007. The work is funded by the Natural Sciences and Engineering Research Council and there are currently four other members working on the project.
The goal of the project is to design machines that can analyze human emotion. Cameras and sensors track emotions in conjunction with other hardware such as a joystick and an ocular movement tracker. This hardware records information such as blood pressure, heartbeat, skin conductivity and eye movement.
This essay is a re-writing of “The Funeral Ceremonies of the Parsees,” by Jivanji Jamshedji Modi, originally read before the Anthropological Society of Bombay, on September 30, 1891, a fair-use of that content for creative literary aims. To those of the Zoroastrian faith I apologize for my shameless re-purposing of your time-honored traditions. My rationale for doing so is not to diminish or mock these ceremonies and beliefs, but to help contemporary people who are unfamiliar with these practices to look to the interesting and diverse history of human religion for ideas on how we can better understand and use new technology in a harmonious way.
(Credit: Neurowear) Keio University scientists have developed a neurocam --- a wearable camera system that detects emotions, based on an analysis of the
Keio University scientists have developed a “neurocam” — a wearable camera system that detects emotions, based on an analysis of the user’s brainwaves.
The hardware is a combination of Neurosky’s Mind Wave Mobile and a customized brainwave sensor.
The algorithm is based on measures of “interest” and “like” developed by Professor Mitsukura and the neurowear team.
The users interests are quantified on a range of 0 to 100. The camera automatically records five-second clips of scenes when the interest value exceeds 60, with timestamp and location, and can be replayed later and shared socially on Facebook.
Incomprehensible computer behaviors have evolved out of high-frequency stock trading, and humans aren't sure why. Eventually, it could start affecting high-tech warfare, too.
Via Laurent Vergnaud
Consequently, computer scientists are taking an ecological perspective by looking at the new environment in terms of a competitive population of adaptive trading agents.
“Even though each trading algorithm/robot is out to gain a profit at the expense of any other, and hence act as a predator, any algorithm which is trading has a market impact and hence can become noticeable to other algorithms,” said Neil Johnson, a professor of physics at the College of Arts and Sciences at the University of Miami (UM) and lead author of the new study. “So although they are all predators, some can then become the prey of other algorithms depending on the conditions. Just like animal predators can also fall prey to each other.”
When there’s a normal combination of prey and predators, he says, everything is in balance. But once predators are introduced that are too fast, they create extreme events.
"What we see with the new ultrafast computer algorithms is predatory trading,” he says. “In this case, the predator acts before the prey even knows it's there."
Johnson describes this new ecology as one consisting of mobs of ultrafast bots that frequently overwhelm the system. When events last less than a second, the financial world transitions to a new one inhabited by packs of aggressively trading algorithms.