WOW! Extremely Awesome!
One pilot, four drones, and a whole lot of firepower.
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Category: robotics/AI – Page 2,469
Because of a plethora of data from sensor networks, Internet of Things devices and big data resources combined with a dearth of data scientists to effectively mold that data, we are leaving many important applications – from intelligence to science and workforce management – on the table.
It is a situation the researchers at DARPA want to remedy with a new program called Data-Driven Discovery of Models (D3M). The goal of D3M is to develop algorithms and software to help overcome the data-science expertise gap by facilitating non-experts to construct complex empirical models through automation of large parts of the model-creation process. If successful, researchers using D3M tools will effectively have access to an army of “virtual data scientists,” DARPA stated.
+More on Network World: Feeling jammed? Not like this I bet+
This army of virtual data scientists is needed because some experts project deficits of 140,000 to 190,000 data scientists worldwide in 2016 alone, and increasing shortfalls in coming years. Also, because the process to build empirical models is so manual, their relative sophistication and value is often limited, DARPA stated.
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Computer chips have stopped getting faster. For the past 10 years, chips’ performance improvements have come from the addition of processing units known as cores.
In theory, a program on a 64-core machine would be 64 times as fast as it would be on a single-core machine. But it rarely works out that way. Most computer programs are sequential, and splitting them up so that chunks of them can run in parallel causes all kinds of complications.
In the May/June issue of the Institute of Electrical and Electronics Engineers’ journal Micro, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new chip design they call Swarm, which should make parallel programs not only much more efficient but easier to write, too.
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OpenAI technical goals
Posted in governance, neuroscience, robotics/AI
OpenAI’s mission is to build safe AI, and ensure AI’s benefits are as widely and evenly distributed as possible. We’re trying to build AI as part of a larger community, and we want to share our plans and capabilities along the way. We’re also working to solidify our organization’s governance structure and will share our thoughts on that later this year.
Our metric
Defining a metric for intelligence is tricky, but we need one to measure our progress and focus our research. We’re thus building a living metric which measures how well an agent can achieve its user’s intended goal in a wide range of environments.
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This Robot Is A Security Guard
Posted in robotics/AI, security
Will the good bots finish last in the war of bots? Dark bots are definitely not that easily stopped by AI in companies.
The bot era is here, and the world has already begun to see its transformative potential. But like any technology, there will be bad bots as predictably as good ones. With every advancement, there are people looking to exploit it. Anticipating what they might do is key so that builders, developers, and users can prevent, preempt, and prepare.
Here are the “dark bots” we’re likely to see:
The Stealthy Bot
This is a bot whose ownership is unknown, giving it the freedom to cheat with impunity. Trust and verification is essential for security on any platform. On the Web, services like Truste, VeriSign, and others have provided the trust infrastructure needed. For apps, Apple and Google do the same. Credit card issuers and payment platforms do as much for merchants offering paid services. But who will provide the trust infrastructure for bots? Will messaging channels certify bot developers? Each channel has a different approval process now. It will be crucial to arrive at a shared certification process.
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