I see, I see what you don't see

If you look at all the (technological) developments over the past two centuries, AI is nothing more than the next step in evolution. The culmination of the digital revolution, as some claim. Over time, there has been resistance to change and the introduction of innovations. Anyway, you could say that humans are naturally resistant to change. Why change? After all, things are fine the way they are now. Okay, it might be a little easier.....

See, and this is now one of the reasons to change, to make an innovation. For the previous great revolution, we have to go back in time almost 200 years. In 1839, the first steam train ran in the Netherlands, between Amsterdam and Haarlem. And everyone was afraid of that big black monster, the locomotive that had to drive the train. The advantages of the train, namely that the vehicle would make people's lives easier, be able to transport goods and promote mobility, did not occur to anyone at that time.

Innovations make life easier

But back to this time, to the benefits that contemporary innovations have for society. Almost all innovations are based on smart technology. Or on artificial intelligence, artificial intelligence. Using a computer to mimic human thinking, as the Dictionary defines artificial intelligence. And almost without exception, these innovations serve to make life easier. If you look around carefully, you will see that AI is everywhere on the streets. In security cameras, at automatic barriers with license plate recognition in parking garages, but also at self-scanning checkouts in the supermarket that register whether you have a discount on a particular product.

Human and AI better together to do better work

Looking well, we've already talked about it. And good looking, or perceiving is at the heart of the roadmap that Datacadabra has developed to successfully implement AI in a digital intelligence platform. Such a platform is an integrated set of technologies and tools that enable us to effectively develop, implement and manage digital intelligence, such as artificial intelligence (AI) and machine learning (ML). All with the intention of enabling humans and AI to do a better job together, to make work easier.

The information we observed in step 1, we are going to collect and further process in step 2, in order to run a calculation on it and make predictions about the outcome in step 3. Step 4 is the implementation phase, or the phase in which we will actually test and implement the model in practice.

The dark side of AI

In this blog, let's focus further on Step 1 of our roadmap, perceiving. 

With microphones, cameras, sensors, infrared or logarithms. The important thing is to be sure of what you are observing, because it sometimes happens that your perception is a loop on reality. Take for example the deep fake videos that pop up on the Internet. Of these videos, image and sound have been manipulated with the help of AI in such a way that you think you see or hear a certain person based on the typical characteristics that particular person has. Face, voice, intonation. You think you see darter Michael van Gerwen, but in reality you see and hear former minister Ferdinand Grapperhaus in Michael van Gerwen's dart outfit.

Black with white stripes does not make a zebra

So perception listens closely. A picture showing only black and white stripes does not make a picture of a zebra. It could also be a crosswalk or a horse wearing a blanket with zebra stripes. There are several factors that come into play. All of these must be considered before we can make a judgment about it. The important thing is to focus on what is relevant. Only when we zoom deeper into the information and further analyze and structure it (step 2) and learn to understand it (step 3), can we perhaps draw the conclusion that it is a zebra. To then turn that observation into actionable information and implement it in an overview, dashboard or report or integrate it into an API. 

So much for our blog on perceiving, step 1 within our roadmap to implement AI in a DIF (Data Integrated Framework) model. Our next blog will focus on Step 2 (Structuring Information). Can't wait and want to know now what AI can do for your organization? Then schedule a meeting with us.