Smart Insight

We all know AI is helping us to give several steps ahead in some differentiated sectors. Many scientists point out that AI is leading us to a new industrial revolution. I have the same belief. AI is increasingly improving our world in all aspects. Today's topic is how AI is helping us in the mobility scope. Mobility - VCI, Portugal We are seeing a very significant amount of investments in the automotive industry to apply AI-based algorithms. The self-driving cars are becoming more reliable due to those investments. However, AI is not strictly applied to drive the vehicle. Mobility - VCI, Portugal We see lots of other car features using AI. Some examples are  the emergency brake systems, adaptative cruise control, read traffic signals to identify the allowed velocity, driving monitoring systems to check if the driver is distracted, systems to adjust the vehicle settings such as temperature, mirrors and seat position , and so forth. These features are improving global mobilit...

Being a farmer is not only making plants grow. Apart from all the care applied to the plants, they also need to collect what has been produced. The harvesting of agricultural products is one of the most difficult, and delicate stages in the food production chain. Harvesting strawberries - Photo by Farsai Chaikulngamdee on Unsplash Millions of people are employed in this process. It is a seasoned process what means temporary jobs. These jobs are mostly available in the summer season, where we reach the harvest phase for the majority of the plants. Many farmers dedicate themselves to fruit growth. In this agriculture's field, we have to take care of plants and then pick the fruits. As you may think, this is a hard and repetitive task. Image a farm with several acres of land. G etting all the fruits from the plants can take months and involve lots of people.  Hiring so many people is very expensive for farmers. Besides, people can harvest fruits inappropriately, reducing the fruit...

These days, the most used branch in AI is Deep Learning.  We can consider Deep Learning as a subset of Machine Learning, where the used algorithms should "learn" by themselves.  Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) are the State of the Art in terms of development and business implementation.  In Machine Learning (ML) the programming paradigm is a little bit different from the conventional. A "usual" programmer analyzes the inputs and develops the program to get the expected outputs.  For instance, we want to create a program that sums two numbers. The programmer evaluates the type of inputs and outputs and then writes the code to transform the input values into the desired outputs. When working on ML, we give the inputs and outputs to the system, and the algorithm "builds" the program itself. Thus, in ML programming paradigm is the algorithm that creates the program for us.  We represent the data as features and i...