The history of computing is full of generational battles in which innovators and companies fought to shape the future. In the early era of computing, debates over the ideal architecture of systems with less power than most modern watches established the standards that exist today. From the late 1970s to early 1990s, the recognition of the personal computing market saw early computers like the Radio Shack TRS-80 and Commodore 64 transition into more widespread PC platforms like the Apple Macintosh and Windows-powered PCs. The end of Moore's Law is ushering in another such upheaval, though one which is seeing a renewed interest in entirely new approaches to computing. These new approaches include quantum computing and neural-inspired computing, both which have significantly risen in popularity in recent years.
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 is becoming increasingly used throughout the world of technology, and there are now endless blogs, books, courses and other resources available for those to use. If that's still not quite good enough for you and you don't want to implement deep learning yourself, there are now several machine learning API services available that will do it for you. But, where has this rise in deep learning stemmed from you may be wondering? Big companies use deep learning techniques in various practices throughout their businesses. They generate lots of data, and this is mega important to them as they can learn from this data which will ultimately lead to increased revenue.
This event highlights the changes that NVIDIA GPU-accelerated computing has brought to the analytics, machine learning, and deep learning segments. This new computing paradigm is enabling customers to extend the benefits beyond big data with the power of deep learning and accelerated analytics. The leaders from this growing ecosystem of solutions and technologies that are delivering on this promise will lead an active discussion in the keynote and panels.