Why Machine Learning is different? And will it help to drive the car?

The answer is simple, if you know the answer.

No, I don't want to fool you. This is really what I was thinking, while progressing on this subject.

There is a fundamental difference between the Machine Learning approach to the solving of a problem and a traditional approach.

I'll try to explain it with an example: imagine you enter in your Garage Box, that is also used as a deposit for (almost) everything you don't want at home.
You want to search something, an object that you probably remember, as form, colour, shape... you look around and magically you recognize it, partially hidden in a corner, with a different colour because the light is poor.

Ok, it is mostly Image Recognition.
Do you want to write an algorithm to tell to your computer how to do the same.

In the traditional approach, you write (on a paper if you're not so young...joking) the description of the steps you would use..

Wait a minute, after some time you realize that you actually don't know the exact steps your brain have used to recognize and identify that object, partially hidden, in a low light...

That's the problem... in many cases we don't (yet) know how our brain works, for example to recognize an object inside a photograph (a case simpler than the one I started from).

Machine Learning approach is different: you train a complex algorithm in such a way it can, by itself, calculate the best way to work, best parameters, from a large set of examples.
You show to your computer millions of photos telling him: this is a man, this is a cat, this is a car, this is a red car....
And it will learn how to recognize a red car, even if it is not exactly the same red...

Behind the scene:
- Multi-level Neural Networks
- Very smart algorithms to make the "training" faster and faster, and to enhance accuracy of the prediction
- A big Computational Capacity, to handle millions of examples and the calculation of a large set of parameters... for example innovation comes also by usage of GPU

The car... oh yes, well: have a look at the introduction, on Udacity site, of the Machine Learning Introductory Course.
You will see a video where the two instructors are in a Google Self Driving Car... and they'll explain that they teach to the car when it needs to hit the brake not writing a "long series of if... then.. else...", but.... letting the car to observe the behaviour of the instructor.

Sooner or later I'll come back on the subject.

(Credits: the initial example is inspired by some Videos in the Coursera Training on Neural Networks, by Toronto University).

Oracle IoT CS: BI Integration

IoT CS are mainly an easy way to manage a large set of devices and connect and acquire messages coming from them (Connect, Manage, Acquire).

But the Real Value comes from what you do on data coming from devices, on these messages.

Oracle IoT Cloud Service provides you an easy path to integrate with other Oracle Cloud Services.
For example, you can easily integrate with Oracle Business Intelligence Cloud Service.

In this video on YouTube, you can see a short demo on how to integrate with Oracle BICS and how to enable predictive mainteinance,

IoT CS - BICS Integration

Nespresso IoT, well I think there are better ways

I'm one of those who really love good coffe, and I'm a proud owner of a wonderful Red Nespresso Pixie.

I have always wondered how to integrate it in my Home (made) Automation System, and this is one way:

Nespresso Iot

But I think I'll find a better way, than a servo pushing the button.

Stay tuned.