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Memristors power quick-learning neural network

#artificialintelligence

A new type of neural network made with memristors can dramatically improve the efficiency of teaching machines to think like humans.


IoT analytics, Edge Computing and Smart Objects

@machinelearnbot

In this post, I propose that IoT analytics should be a part of'Smart objects' and discuss the implications of doing so The term'Smart objects' has been around from the times of Ubiquitous Computing. However, as we have started building Smart objects, I believe that the meaning and definition has evolved. Some of these analytics could be performed on the device itself ex computing at the edge concept from Intel, Cisco and others. To manage multiple sensor feeds, we need to understand concepts like sensor fusion (pdf) (source freescale). In addition, the rise of CPU capacity leads to greater intelligence on the device – for example Qualcomm Zeroth platform which enables Deep learning algorithms on the device.


Deep Learning on the Edge

#artificialintelligence

Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases. Deep Learning on the edge alleviates the above issues, and provides other benefits. Edge here refers to the computation that is performed locally on the consumer's products.


IoT analytics, Edge Computing and Smart Objects

@machinelearnbot

In this post, I propose that IoT analytics should be a part of'Smart objects' and discuss the implications of doing so The term'Smart objects' has been around from the times of Ubiquitous Computing. However, as we have started building Smart objects, I believe that the meaning and definition has evolved. Some of these analytics could be performed on the device itself ex computing at the edge concept from Intel, Cisco and others. To manage multiple sensor feeds, we need to understand concepts like sensor fusion (pdf) (source freescale). In addition, the rise of CPU capacity leads to greater intelligence on the device – for example Qualcomm Zeroth platform which enables Deep learning algorithms on ... So, in a nutshell, its a evolving concept especially if we include IoT analytics in the definition of Smart objects (and that some of these analytics could be performed at the Edge) ..


Deep Learning on the Edge – Towards Data Science

#artificialintelligence

Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases. Computing on the edge alleviates the above issues, and provides other benefits. Edge here refers to the computation that is performed locally on the consumer's products.