Its Not About Sensor Making, its About Sense Making
On one hand...
On one hand.
But on the other.
Lets go back to the basics
Data as itself is not a fortune, but a problem. It becomes a fortune, when you drive it to Conclusions ---->Value
So: What is it a SMART product?Or: how can a product bring more value than its sensor?
It always starts with good people and their innate passion to solve difficult problems! The technology is just a tool to fulfil their quest.
The recent surge of data such as images, text, speech enabled by cellular phones and mobile devices has created a need to understand this complex data that was not machine understandable and searchable.
While the initial technology challenge in harnessing IoT is an infrastructural upgrade to address the data storage, integration, and analytic requirements, the end goal is to generate meaningful business insights from the ocean of data that can translate to strategic business advantages.
The idea is that: if like humans, Computers were to gather knowledge from experience, it avoids the need for human operators to formally specify all of the knowledge that the computer needs to solve a problem.
Deep Learning is used to address intuitive applications with high dimensionality.
Deep learning is often thought of as a set of algorithms that mimics the brain. A more accurate description would be an algorithm that learns in layers. Deep learning involves learning through layers which allows a computer to build a hierarchy of complex concepts out of simpler concepts.
Deep learning algorithms apply to many areas including Computer Vision, Image recognition, pattern recognition, speech recognition, behaviour recognition etc.
In this model, the algorithm must figure out for itself what the correct features are and how to compute themBefore, the System Engineer needed exhaustive information about the domain, in order to build a good system
In the rule-based system world (and even with traditional Machine Learning) the system engineer needed exhaustive information about the domain in order to build a good system. However, in this era of the IoT where new kinds of data are becoming available at a rapid clip, Deep Learning allows us to faster iterate on new data sources without requiring intimate knowledge of them.
In the Deep Learning domain, the engineers main focus is to define the architecture of the neural network. The network needs to be large enough to have the capacity to tune-up to a useful computation, but simple enough so that the computation time does not exceed the allocated time limit.
Optimizing the parameters of a neural network can take days and even weeks on the strongest machines, but the computation itself -- from raw inputs to output -- takes a fraction of a second, and it will take exactly the same amount of time at the end of the process as it did at the beginning
Real-time intelligence and greater control agility, while at the same time off-loading the heavy communications traffic.
Deep Learning is therefore a great advantage for a real-time system like a smart sensor, because it enables significantly enhanced scalability and flexibility. For any given time budget, we can tailor a neural network that fits this budget to the maximum threshold, and thus make sure we are fully utilizing our processing power.
If our computational budget increases and we have more time to run the calculation, we can assess a larger (and presumably better) network that will fully utilize the new budget.
Another advantage of using neural networks is that they are extremely portable. Software libraries make it very easy to build and customize a neural network, allowing us to run the same network on different types of devices -- just copy the parameters over and thats it
Dont build Sensors.
Build intelligence that augments human abilities and experiences
Helping computers interface with people and vise versa
THANKS!Any questions?You can find me at firstname.lastname@example.org Moriya Kassis
a unique guild for hardware and software enthusiasts