As soon as the scientific subject Machine Learning comes up in a conversation, many people immediately abandon their interest. The cause is a technical jargon barrier which reflects in the ignorance of this world-changing subject. This is a short and uncomplicated introduction to the technology that today attracts the vast majority of venture capital: Machine Learning.
Natural Intelligence
In your first driving lesson, you observe a traffic jam ahead. You forget to brake and almost collide with the car in front of you. The next day you drive across a different highway in a completely different situation and see a traffic jam ahead again, you manage to slow down in time. A simple illustration of an effective learning process. People experience, then learn, and eventually, use this Natural Intelligence (NI) to effectively solve new problems in their daily lives.
Artificial Intelligence and Machine Learning
Machines use Artificial Intelligence (AI) to solve problems and make decisions. AI is the ability of machines to perform tasks that are generally associated with intelligent beings. But as the process of Natural Intelligence taught, a learning process must be preceded before obtaining problem-solving ability. For machines, this is called Machine Learning. People use experiences to learn from, machines obviously do not. Machines use big (large volume of) data.
Self-driving car
Google Driverless Car is a self-driving car and herewith one of the many applications of ML. The system of this car has been developed by means of Machine Learning and will itself ensure that you brake in time when approaching a traffic jam. The process starts with the input of a huge amount of varying real-life driving scenarios, the data. The system that will drive the car is then ‘trained’ with a complex mathematical model, an algorithm. In the training process, the algorithm learns to recognize each scenario on the road. Doing so, will the algorithm make logical connections in the data, this is Pattern Recognition. Together, this becomes an iterative process which by means of multiple car sensors will detect unprecedented situations on the road, such as traffic jams, which it effectively solves.
More and more sectors are confronted with influences of this technology. It helps to make decision-making processes many times more effective and will show much more feasibility and in the near future.
Robert van Gennep
