Industrial AI & Machine Lear1ing
 
 
 
 
Making the World a Better Place with a Low-Cost, Neural Network-based Skin Cancer Detector!
RICH NASS, EXECUTIVE VICE PRESIDENT

First prize in the Better Place Design Challenge went to the designer of a skin-cancer detector. It?s a device that uses artificial intelligence (AI) and machine learning to detect skin cancers such as melanoma in their early stages and improve survival rates. The platform runs a convolutional neural network (CNN) pre-trained in Tensorflow to identify cancerous tissues on the Renesas RZ/A1 Stream-it! development kit.

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Machine learning is a hot topic these days, and the biggest potential impact is in the industrial space. At the same time, the initial outlay for the technology could turn some vendors away. So the question is, at what cost does it make sense? That?s one of many questions I fired at Nikunj Mehta, who is the Founder and CEO of Falkonry in this week?s Five Minutes with?discussion.
 
 
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In part one of this three-part series, the authors investigate the drivers behind and potential applications of machine learning technology in highly automated driving scenarios. 
 
 
 
 

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The trick is experienced AI engineers that can develop the algorithms for the use case and provide the right input to the learning engine in order for the AI to be trained properly. Harrison mentions it?s invaluable to have someone who has done these things before in order to avoid ?garbage in, garbage out? scenarios that are common occurrences with less experienced AI engineers. 
 
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The AI Embedded Computer is designed for edge computing and Artificial Intelligence to coincide in all applications, with areas including the AI subsets machine vision, intelligent control, and deep learning.

 
 
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Intel recently unveiled its family of Vision Accelerator Design Products aimed at artificial intelligence (AI) inference and analytics performance on Edge devices, where data originate and are acted upon. The products come in two forms: one that features an array of Intel Movidius vision processors and one built on the company?s Arria 10 FPGA.
 
 
 
 
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As IoT developers solve the connectivity, manageability, and security challenges of deploying devices at the edge, requirements now turn to making these systems smarter.
 
 
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The upgraded machine learning technology?s algorithms are trained to anticipate conditions associated with wake word performance, such as word pronunciation, acoustics, device placement, room size, reverb and echo.
 
 
 
 
 
 
Sponsored by: Arrow Electronics
Date: December 18, 2:00 p.m. ET
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