First of all, If you try to put any data that you have into the predictive algorithm, it is going to predict some results, but they’re not going to be what you need. Instead, the algorithm is going to deliver you a set of very low-quality predictions. In other words, it follows the principle “garbage in, garbage out”, in which the decision-making might be flawed due to incomplete, or imprecise data. Improving the quality of the historical data is extremely difficult but it is a must before you even start thinking about predictive optimization in logistics operations.
AI is currently one of the most overhyped topics. There is this misguided belief that one day, computers are going to be smarter than people. That they are going to take many jobs from us and that they are going to start a war against people. In actuality, computers cannot be people because they lack creativity, imagination, and they cannot think abstractly. Artificial intelligence is a set of algorithms which can provide very complex outputs and decisions based on incoming data. Therefore when you have an individual stream of data such as anything from transport shipments to video and audio data coming from a self-driving car, AI is the software that can make decisions that are much more complicated compared to the decisions the traditional software makes.
Today, we are happy to present you the newest format of our blog – “Transmetrics CEO Comments.” In this set of articles, our CEO will share his professional opinion on the concepts which emerge at the interception of logistics and innovation. The goal of the format is to unravel the misconceptions about the new technologies and to highlight the power that they can bring to logistics.