Music services, parking assistants, navigation devices: all these services work on the basis of artificial intelligence and cognitive systems. To work optimally, they need human feedback from algorithm trainers.
Cognitive systems are invisible to humans. Nevertheless, they support, synchronise and plan everyday life. They tell you the fastest route from A to B and ensure that all lifts in large office buildings have the ground floor as their default position during morning rush hour to save valuable time.
Algorithms and AI: the subtle difference
If, for example, an algorithm for an intelligent lift system is put into action in an office block, it initially ‘only’ comprises a calculation rule for the computer. The formula includes factors like the number of lifts, floors and employees. The system is also programmed so that it optimises itself with every new piece of information. This process is also called machine learning.
It is thus only after the feeding of information and in combination with machine learning that an algorithm becomes artificial intelligence, AI for short.
The basis of AI: machine learning
In the example of the lift system, the algorithm is fed the data of all the users after the start of operation. It recognises that there is a big rush to all lifts between 8 and 9 a.m. The program adjusts to this behaviour. It ensures that lifts stop on the ground floor in the morning in order to be able to quickly pick up new passengers there.
Self-teaching room-booking systems work on a similar basis. The Job Wizards feature ‘the Smart Room Booking System – when meeting rooms learn and think’ explains how that works.
Definitions: what is what?
Algorithm - A formula, calculation rule or program for a computer.
Machine learning - Machine learning is another name for self-teaching systems. Most cognitive systems learn with the help of training data and human feedback.
Algorithm trainer - A person who provides a self-teaching system with helpful feedback.
Programmer - A person who develops programs for algorithms, AI and cognitive systems
Self-teaching and cognitive systems
Machine learning is primarily based on two methods: in unsupervised learning, systems search for certain patterns in large databases and filter them out. This is how recommended purchases on Amazon (customers who bought A also bought B) or music recommendations work, for example.
Analyses of the stock market and recognition of credit card fraud also work using that method. The composition of the data is very important for unsupervised learning: if it is incorrect or outdated, it influences the effectiveness and the quality of the artificial intelligence and cognitive systems.
How algorithms are trained
In supervised learning, algorithms are fed with training data. At the same time, the algorithms receive categorising feedback from people accompanying the training. In this context, people almost function as algorithm trainers for the self-teaching systems, for example in the classification of images and news or the weighting of data.
If, for example, a system is to learn how to correctly file publicly accessible information in a company database, it initially needs feedback, monitoring and supervision from a human.
Does every SME need an algorithm trainer?
Most algorithm trainers work behind the scenes. The job is only interesting to an SME if its own AI system is to be specially developed for a specific task. The program for AI or a cognitive system is written by programmers.
However, no specific training is required for the algorithm feedback and training jobs, which are precisely defined in the program in advance. It is often a case of simple sorting tasks, which are carried out by algorithm trainers. The sorting tasks are usually divided into masses of small jobs, carried out by crowdworkers. Amazon Mechanical Turk is a platform that organises this training feedback worldwide
