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GENISAMA Announced Consumer Products toward Strong AI
GENISAMA Announced Consumer Products toward Strong AI

GENISAMA LLC is a startup based in East Lansing, Michigan, USA. It is a spin-off from AI research at Michigan State University. Nov. 3, 2017, It announced several consumer products that attract those who like to keep on top of current technology toward mobile 3D video recording and mobile viewing. The products are scheduled to arrive for the upcoming holiday shopping season. “They are also our first products toward strong AI," the company founder Dr. Juyang Weng said.

The product, called 3D Camera and Machine Learner, is a set of custom made gadgets that work together to expand the recording and reviewing power of smart phones. You can use the set to record 3D video and take 3D photos using its binocular cameras. The synchronization of the two binocular cameras makes the resulting 3D video more stable to watch, unlike some unsynchronized 3D cameras for mobile phones that do not take 3D photos. The provided 3D goggle is used to view 3D contents from smart phones, tablets, laptop computers, and 3D game consoles, hopefully more comfortable than anaglyph and methods that mask off about a half numbers of pixels for each eye. Its another function is machine learning, based on MSU patented technology Developmental Neworks. This represents a newly developed avenue of enjoyment for consumers: You can train the GENISAMA networks for your own personal likings.

3DTube is a video based Internet platform emphasizing 3D contents. You can share with your friends 3D movies that you take at your wedding, during your vacation, or when your child makes the first walking. The growing computing power of mobile phones will continue to make 3D video and 3D photos increasingly common in human lives.

AOS is a new kind of operating system based on the Developmental Network's capability to auto-program for general purposes. Companies and individuals may use the AOS to train AI systems, not through hand-crafted programs for task-specific machine learning, but instead interactions with the machine learners in a way similar to how parents and teacher interact with children: from simple to complex. “This relieved humans from handcrafting AI rules, as such rules are too many and too muddy for any human to program well," Weng predicts. For further detail, contact weng(at)

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