The words artificial intelligence o AI is everywhere, everyone repeats it with almost every sentence and it has gotten to the point where it has become so tainted by continued misuse that unrelated solutions are being sold in relation to this technology to achieve better sales. That is why we are going to briefly remind you what AI is and what it is not.
What differentiates artificial intelligence from other solutions is the ability to learn as data is given or corrected. This requires an amount of storage and bandwidth that is not possible on our home PCs. On the contrary, a good part of the AI solutions that are sold are the result of their application, but not by themselves. In other words, they are the end that has been achieved through the medium of artificial intelligence.
What triggered the AI boom?
We must start from the fact that the AI concept is today as multimedia was in the 90s. That is to say a word used by component manufacturers to designate not the inclusion of a new technology in chips by selling it through its biggest real-world players. application. In other words, if units capable of performing vector calculations in a few clock cycles gave us the ability to view and edit audio and video on our high-speed computers, as well as 3D games, the units in charge of doing mathematical calculations with the matrices of numbers are those that pushed the birth of AI.
Without embargo, hemos to leave from the hecho that ha habito an exponential increase in the information necessary to provide sufficient information to this unidades y esto es lo que nos debería hacer levantar la ceja ante ciertas aplicaciones que venden como soluciones de IA y que realmente They are not. It must be taken into account that for something to be considered artificial intelligence, it must have the ability to learn on the fly.
What shouldn’t we consider artificial intelligence?
There are several examples, but we will give you three that are quite illustrative:
- The first case is the automating, the fact that a device or program has a series of pre-programmed behaviors that mark its behavior according to a series of input parameters and reaction to the environment is not artificial intelligence. We can create a robot that comes out of a maze, but doesn’t have the ability to learn it.
- Second, we have the case of machine learning or machine learningas well as deep learning. Both are AI disciplines, but you can have such an algorithm that does not learn from data, but instead applies a previously learned algorithm. For example, NVIDIA’s DLSS doesn’t get polished over time on our graphics card and they have to release new versions with every patch. On the contrary, the training is done on company supercomputers, but not on people’s PCs.
- Then we have other cases like recommendation systems and data analysis, which is not about interpreting the information and coming to a conclusion, but about knowing how to classify it. In other words, we call AI the ability of a system to draw a conclusion through certain data.
Can you give us examples?
Of course, that’s why we have to give you a negative and a positive example so that you know to differentiate more precisely when we are in front of an AI and when we are not.
- For example, many audio-oriented AI systems provide the ability to cancel noise generated in recordings. This doesn’t require a Tensor unit and is simply based on adding a channel with the audio reversed so that both are canceled. It’s a case of what an AI is not.
- On the other hand, I can collect a huge amount of images in JPEG format and train the AI to detect common image artifacts in this format and correct them for me. The more information you give it, the more accurate your prediction will be. From there, I can obtain an algorithm, which would not be artificial intelligence, if not an automatism to eliminate these artefacts.
The second example, we can apply it with the example of the robot and the maze, where we can have an AI with the ability to study the maze that dictates the orders to get out of it in the shortest possible time. The first would be an artificial intelligence algorithm, the second a derivative solution.
