The last six months has seen a rising flood of publicity about “killer robots” and autonomy in weapons systems. On November 19, 2012, Human Rights Watch (HRW) issued a 50-page report “Losing Humanity: The Case against Killer Robots” outlining concerns about “fully autonomous weapons that could select and engage targets without human intervention” and claiming that a “preemptive prohibition on their development and use is needed”.
The Department of Defense has found a new way to create an artificially intelligent being: jazz music.
Kelland Thomas, a jazz musician and associate professor at the University of Arizona School of Music, was recently granted funding to build a system capable of musically improvising with a human player. Thomas' program, called MUSICA, for Music Improvising Collaborative Agent, will be far more advanced than the othervirtual musicians out there.
This paper is a part of a series covering Deep Learning applications for Smart cities/IoT with an emphasis on Security (human activity detection, surveillance etc). It also relates to my teaching at Oxford and UPM (Madrid) on Data Science and Internet of Things.
Nineteen stories up in a Brooklyn office tower, the view from Manuela Veloso’s office—azure skies, New York Harbor, the Statue of Liberty—is exhilarating. But right now we only have eyes for the nondescript windows below us in the tower across the street.
Superintelligence: Paths, Dangers, Strategies is an astonishing book with an alarming thesis: Intelligent machines are “quite possibly the most important and most daunting challenge humanity has ever faced.” In it, Oxford University philosopher Nick Bostrom, who has built his reputation on the study of “existential risk,” argues forcefully that artificial intelligence might be the most apocalyptic technology of all.
We spent the past few months asking some of the field’s most renowned researchers and entrepreneurs what inspired them to pour their intellectual life into something that once seemed so unlikely and ominous.
ets take a close look at three related terms (Deep Learning vs Machine Learning vs Pattern Recognition), and see how they relate to some of the hottest tech-themes in 2015 (namely Robotics and Artificial Intelligence).
Enterprise software is about to undergo radical transformation — a substantial change that will make the shift to software as a service (SaaS) look like a simple facelift. This transformation is being powered by machine learning.
Few people in the tech world can truly be said to “need no introduction.” Stephen Wolfram is certainly one of them. But while he may not need one, the breadth and magnitude of his accomplishments over the past four decades invite a brief review: Stephen Wolfram is a distinguished scientist, technologist and entrepreneur. He has devoted…
Non seulement il y aura de moins en moins d’emplois pour les gens qui font un travail manuels, mais les emplois intellectuels seront également remplacés par des ordinateurs. Presque toute les industrie et les professions seront touchées et cela va créer un ensemble de problèmes sociaux parce que la plupart des gens ne pourront pas s’adapter à un tel changement dramatique.
Instead of focusing on the how of IoT, customers need to be focused on the what of IoT—namely the data. All of the strategy and shiny objects in the world won’t help if the data isn’t accurate, secure, and actionable. The data should always drive the strategy...
We're all familiar with the likes of Siri, GoogleNow, Cortana, and, most recently, Amazon Echo. These supposedly intelligent software apps can lead us to the nearest coffee shop, remind us of upcoming meetings, and even give us a good laugh when we foolishly ask what 0 is divided by 0.
The fundamental question to keep in mind is the following, "Are the learned weights of a neural network derivate works of the input images?" In other words, when deep learning touches your data, who owns what?
While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century.
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