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Applied Scientist Core Maching Learning Intern/siliconarmada.com

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Superior verbal and written communication skills. PREFERRED QUALIFICATIONS Ability to convey rigorous mathematical concepts and considerations to non-experts. Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives.


Neuroscience and Machine Learning Restore Movement in Paralyzed Man's Hand » Behind the Headlines

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Last week, the New York Times reported the first successful "limb reanimation" in a person with quadriplegia. Ian Burkhart, 24, had broken his neck as a teen in a diving accident. His spine was damaged at the fifth cervical vertebra, leaving him paralyzed from the shoulders down. Using nerve bypass technology that transmits his thoughts directly to his hand muscles, he has regained control over his right hand and fingers. This is the first time a brain-computer interface has been used to help an individual move his own hands.


Death Without Emotion - Artificial Intelligence, Drones & Morality - Machine Philosopher

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Drones seem to be a hot topic for debate at the moment in terms of their morality. Drones currently are controlled from control rooms at a distance, there is no pilot within the vehicle allowing no harm to come to him. However, what happens when there isn't even a man or woman holding the trigger? This is where modern warfare seems to be progressing, and though its a controversial topic I don't see any halt of advancements in the area coming any time soon. A computer's'mind' is far from similar to ours.


Artificial Intelligence in Agriculture. Part 2: How Farming is Going Automated with AI Technologies – AI.Business

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As you may read from our first article farming robots are shaping agriculture and will feed humans of the future. Economics will demand a leap from theoretical concept of artificial intelligence to its practical application in agriculture, many experts suggest. But this process has already begun and is irreversible. Automated irrigation systems, crop health monitoring, face recognition systems for domestic cattle, CBR systems for fishing industry and many others are clear examples of how AI can be the Holy Grail for the farming industry. Irrigation systems are as old as man itself since agriculture is the foremost occupation of civilized humanity.


Perpetuuiti

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Av3ar is the next generation Cognitive Computing and Machine Learning system from Perpetuuiti. Av3ar is the next generation Cognitive Computing and Machine Learning system from Perpetuuiti. It is aimed to deliver end-to-end Interactive solutions that dramatically improves the operational efficiencies of customers in the global marketplace. It understands, learns and responds back to customers with emotions like humans. The product gets plugged into the customer place and absorbs all the data to learn the pattern of the day-to-day tasks and processes.


I'm Taking Your Job!

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I met Mr. Lee in Taiping, China, circa 1993. He managed "Warehouse B," a massive structure that stored over 3,000 reusable tools and dies. A humble man, he sat quietly at his high-top desk in a simple button-down shirt and black slacks. His workers treated him with the respect you might reserve for a beloved grandfather, each literally running to do his bidding before he would even finish a gently delivered request. His ledgers were meticulously neat.


Korean IBM Watson to launch in 2017 ZDNet

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IBM will launch a Korean version of its AI platform Watson next year in cooperation with local IT service vendor SK C&C, the companies have announced. SK announced Monday that it signed a cooperation agreement with Big Blue on May 4 and will together build an integrated system to market Watson in South Korea. They will develop Korean data analysis solutions based on machine learning and natural language semantic analysis technology for Watson within this year, and will commercialise it sometime in the first half of 2017, SK said. IBM and SK will also build a "Watson Cloud Platform" at the Korean company's datacentre in Pangyo -- the local version of Silicon Valley -- that IT developers and managers can access to make their own applications. For example, an open market business can apply the Watson solution to its product search features to make a personalized contents recommendation solution.


Innovation In 2016: Driving The Customer Experience

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As 2016 gets underway, retailers must be well past the point of retail-as-usual. We're in an industry in transition, and, as such, I'd like to share some transformative trends that innovators will leverage to shape customer engagement in 2016. Predictive Analytics And Machine Learning Predictive analytics aren't new -- remember a few years back when Target used predictive analytics to promote pregnancy products to a young teenage woman whose parents didn't know she was pregnant? Predictive data -- and the means retailers use to analyze it -- has matured, and retailers can rely on the analytics to avoid markdowns and out-of-stocks as well as optimize prices and purchase quantities.


Shutterstock shows machine learning smarts with reverse image search for stock photos

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Shutterstock is flexing its AI muscles with the news that the stock photo giant is introducing new computer-vision search smarts to its platform. The company, which is headquartered in New York's Empire State Building, went public back in 2012 and now offers more than 70 million images for bloggers and media outlets -- which can make searching for specific assets challenging. Of course, the trusty old keyword search tool is effective to an extent, but what if you want to find images that are similar to one you have in your possession? Or what if you want alternative images based on color schemes, mood, or shapes? This is where Shutterstock's new reverse image search comes into play.


This is The Machine Learning Age – These Examples Show Why

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"A breakthrough in Machine Learning would be worth ten Microsofts." – Bill Gates Machine Learning has been defined, by Arthur Samuel, as "A Field of study that gives computers the ability to learn without being explicitly programmed." In essence, the approach draws upon the tremendous computing power at the disposal of today's "machines" to compare vast amounts of data and iteratively improve decision making from instance to instance as more and more data gets available, and analysed. Clearly data is not in short supply today – there are more than enough scarily large numbers floating around to drive home that point adequately. This availability of data and a desire to leverage it is driving the market for Machine Learning northwards. BCC Research estimated that by 2019 this would reach 15.3 Billion, growing at close to 20% annually on average.