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Category: robotics/AI

LAPEER COUNTY, MI – U.S. Army convoys are set to roll down the interstate in Lapeer and St. Clair counties as part of a first-time testing of driverless military vehicle equipment on public roadways in the state.

If successful, officials say the technology that may save the lives of soldiers thousands of miles away.

Representatives from the U.S. Army Tank Automotive Research Development and Engineering Center, or TARDEC, and Michigan Department of Transportation met with residents in Imlay City and Capac on May 23 to discuss the testing scheduled for late June along Interstate 69.

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One misstep after another for Boston Dynamics. First they get bought by the bunglers at Google, who dont want to do business with the Pentagon, so they buy a company who almost exclusively works for the Pentagon, and now getting sold to a foreign electronics company.

Pretty sad treatment for the company making some of the best robotics in the world. It should be sold to a US defense contractor.

Google is selling off robotics company Boston Dynamics, and Toyota is a serious contender.

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Shutterstock.

Can we really have a conversation with a bot? Voice assistants like Siri, Cortana and Google Now make a good attempt at it, but these are still clearly machines. Their level of artificial intelligence is far behind human intellect. But you can bet Google is working on improving AI.

Renown author Ray Kurzweil has revealed him and his team have been working with Google to create chatbots. These are said to be advanced bots with which you can have ‘interesting conversations’.

Not much else was revealed, but Kurzweeil did specify one of these would be based off one of his book’s characters — Danielle. But these chatbots won’t be limited to specific personalities.

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Fun stuff.

What are the fundamental limitations inherent in machine learning systems?

That’s the central question of a potential new DARPA program known as the Fundamental Limits of Learning (Fun LoL) which according to the researchers will address how the quest for the ultimate learning machine can be measured and tracked in a systematic and principled way.

+More on Network World: Not dead yet: 7 of the oldest federal IT systems still wheezing away +

“It’s not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren’t easily generalized to solve related problems or that can’t be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently—but how much so? To date, very little is known about the limits of what could be achieved for a given learning problem or even how such limits might be determined,” DARPA stated.

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Ok; a little different. Not sure why we need a robot to flinch.

It uses a ‘robot-tissue’ patch modelled on human skin to decide how much pain should be felt and thus what action to take. But now, a group of German researchers is embarking upon a new mission to gift robots with pain sensors — or their mechanical equivalents.

It’s only a matter of time before robots are practically human-like. This is why robots that work in extreme, unsafe environments might need an analog to pain to help them make decisions in chaotic workspaces, thereby acting to protect themselves or the humans and equipment around them.

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The recent Google I/O developer conference at which the company reveals its new products and directions brought with it several surprising announcements that mark significant changes for the way the company approaches its online business.

The first was the admission by Google chief executive, Sundar Pichai, that Amazon had taken the lead in voice-activated devices when it launched Echo last year when he announced the company’s own Google Home, a similar table-top, voice-controlled AI assistant. These devices have been made possible by the rapid improvement of voice-recognition technology and AI fast enough to respond in real time to questions and answers. But under the surface, the devices are deeply integrated with the cloud, and in the case of Echo, Amazon’s online marketplace.

This is significant because Echo now boasts more than 400 different “skills” and connected suppliers through which users can order food, look up calendar appointments, pay credit card bills, search for information and many other things – just by asking with their voice. The ecosystem of companion products around these intelligent assistants is growing too: just by asking, you can switch on the lights in any room, control the thermosat, security system and fire detectors, automate perimeter doors or fences.

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Like anything else there are fundamentals around when, what, and why to use certain technologies and methods to achieve real value and return out of an investment; and bot technology is no different.

Bots are not only cheaper and faster to build than apps, they let companies engage consumers where they spend most of their mobile time: messaging platforms. Join us to understand why Facebook is going all in on chatbots for Messenger and brands as diverse as Staples, Bank of America, and Taco Bell are leading the bot charge — and bots are literally changing the conversation.

Register here for free.

The robot invasion has begun! Or, to put it more accurately, the bot invasion has begun.

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Perhaps it’s serendipitous, then, that the machines have finally arrived. Truly smart, truly impressive robots and machine learning algorithms that may help usher in a new Green Revolution to keep humans fed on an increasingly mercurial planet. Think satellites that automatically detect drought patterns, tractors that eyeball plants and kill the sick ones, and an AI-powered smartphone app that can tell a farmer what disease has crippled their crop.

Forget scarecrows. The future of agriculture is in the hands of the machines.

A Digital Green Thumb

Deep learning is a powerful method of computing in which programmers don’t explicitly tell a computer what to do, but instead train it to recognize certain patterns. You could feed a computer photos of diseased and healthy plant leaves, labeled as such. From these it will learn what diseased and healthy leaves look like, and determine the health of new leaves on its own.

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