IoT automation calls for new Artificial Intelligence tools

13 March 2017
Report type: Research Note
Author(s): Emil Berthelsen
Keywords: IoT Strategies, Industrial and Enterprise IoT, IoT, artificial intelligence (AI), machine learning (ML), deep learning, cognitive neural networks (CNN), recurrent neural networks (RNN), algorithms, remote monitoring and management, automation, Tensorflow
Companies: Google X Lab, IBM, Ersatz Labs, SIGRA Technologies, TeraDeep Inc., TensorFlow, Affectiva, Craftinity, MetaMind, Saffron Technology, Harvest.ai, Thinkbox Software Amazon, Raven Tech, Baidu, RealFace, Apple, Neokami, Relayr, Google, DeepMind, Turi, VocalIQ, Uber, Geometric Intelligence, Microsoft Ventures, IBM, and Intel
Number of Pages: 9

IoT applications include solutions which address (with increasingly levels of complexity) monitoring, remote management and control, efficiency and productivity, and ultimately full automation. As applications reach the automation stage, managing data becomes substantially more complex. This may involve different data sets and types, requiring completely new methods to process and optimize the data. For automation to achieve its loftier objectives, new approaches to ‘learning’ are being progressed in artificial intelligence fields such as deep learning.

This Research Note shares how IoT applications are beginning to mature towards fully automated environments, and what requirements this development places on technology service providers to provide advanced artificial intelligence tools when aiming to deliver complete end-to-end solutions.

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