What is your [STATE] in the war for machine.AI

A straight forward question is how will you gain from Artificial Intelligence, Machine Learning, Embedded Solutions and or Distributing the intelligence at the EDGE in your industry? Le me provide me consume some content, in a human readable and understandable format, before you reply... 

Personally I have been working professionally about Intelligent systems for many decades and have demonstrated connected pumps to the (intra)internet, "smart" WAP phones already many years ago. No massive disruptive impact happened, other that we can predict failure and enabling operators to apply condition based with prescriptive maintenance, at scale.

ARM with The Economist published the Internet of Things Business Index and with less than a decade ago, when most people in business had no idea what we were talking about, now everybody has become an expert in (I)IoT, AI and ML. Many companies are (trying hard) to monetize from all the widely available tools and technologies and so-far it are the Amazones, MicroSofts and IBM's in the world of "cloud sharks" that are harvesting from the hype.

However “giving the world a digital nervous system” has turned out to be remarkably difficult. If corporate IP battles and geopolitics don’t bring you to your knees, the most basic technical issues have for example, achieving secure, reliable, customization, affordable and easy to deploy and scale, proven out it is hard to deliver with magnetizable business outcomes. Of course with exceptions, like any other emerging industry with first movers, who take it all in their specific niches.   

We have good news, it is all going to change, yes gain, for good and better. Very soon many of us will start to understand that the infrastructure of the Internet was not designed and implemented for low latency and high throughput. Data Gravity (in my next blog will explore that) is the problem that holds back the acceleration of the things we have been dreaming of. It is perfectly fine that the Law of Moore is dead by 2020 and we don't have to worry that autonomous systems will become history.

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.

At the stage we accept that intelligence (data analytics) has to be done as close as possible to where the action is (@ the EDGE) the boom of Artificial Intelligence will take off at a speed that will be beyond everybody's imagination.         

How do algorithms “know” which data points are useful for ACTION

Most of the time, the sensors which monitor industrial equipment are “dumb” in the sense that they are “unaware” of the data which they are forwarding to intelligent downstream systems. This means, in the case of traditional cloud-centric architectures, that all data generated by sensors is forwarded over the network for central processing. Because the sensors are dumb, they must forward all data data to the cloud, and only there will it be processed to remove redundant or irrelevant data.

However, in edge architectures, these insights are computed locally. Computing locally enables machines to instantly react to insights as they are computed, without incurring network latency. This ensures that operators are alerted to maintenance needs in milliseconds, instead of minutes or even hours. Furthermore, in cases of impending system failure, edge-based systems can shut down systems in advance of costly malfunctions. But how do “intelligent” edge-based systems “know” which data to process at the edge?

There is a common misconception that storing every bit of data generated is critical, as key insights may be buried in the mountains of data. But this ignores the fact that not all data is created equal. The vast majority of industrial sensor data is “noise” data comprising of duplicate, redundant, corrupt, or otherwise unuseful data. Machine Learning algorithms can trim this first layer of data at the edge, freeing up vast amounts of bandwidth in local networks and immense amounts of compute capacity in cloud and other database systems.

Let me know what is your STATE of AI/ML at the EDGE