Machine Learning has computing power never seen before, but it cannot do magic by itself. It always requires the mediation of man, who is the only one capable of giving computers access to knowledge of the real world and making them capable of responding to our real needs. From this awareness, human-in-the-loop originates an approach that places people’s knowledge and experience at the center of machine learning processes.
Human In The Loop And Machine Learning: Computers Don’t Do Everything By Themselves
One of the most advanced applications of Machine Learning is giving birth to the next mobility revolution: smart cars. Thanks to this technological evolution, cars learn to drive autonomously and safely, monitor other cars’ movements, manage sudden maneuvers, and make decisions in fractions of a second. The car of the future will circulate autonomously for an even longer time. But there’s a problem: she must figure out where to go alone. The people at the center of the decision-making process always determine the destination to be reached.
Human-In-The-Loop For Business
The example of smart cars may seem trivial: we will always drive cars where we want. It’s the concept behind it that’s important: computers are incredibly accurate, super-fast, and can help us with an ever-growing number of tasks, but only we humans can teach machines how to achieve what we want, and set the limits of what they can (or can’t) do. This awareness has important consequences for how we think about machine learning as a business tool. It makes us understand that it is not a ready-to-use solution capable of solving problems once activated. It is always the people who shape it so that it can respond to the real needs of the business. Hence, human-in-the-loop.
The Discovery Phase Of A Machine Learning Project
The human-in-the-loop approach involves people in the virtuous circle – the loop – in which machine learning models are trained, perfected and monitored. Before entering this continuous cycle, it is necessary to understand how machine learning of the digital mind can answer our company’s needs. In the discovery phase, it is understood whether there is a raw material at the basis of this type of project: data. It is also necessary to understand what type of data these are and how they can be used to achieve our goals.
Why Does Machine Learning Require A Human-In-The-Loop?
Once the data set has been identified, the human-in-the-loop scenario places humans at the center of Machine Learning processes – so to speak, of the loop. But how does man fit into processes that are, by definition, highly automated? Wasn’t this technology a set of machine learning techniques and methodologies capable of improving itself without direct human intervention? Total automation cannot react to business changes, which now occur in ever shorter times.
As the business changes quickly, the Machine Learning models must also be able to adapt, from time to time, to the company’s needs. But computers need to learn how business changes: only we can adapt them to new goals. Only with our experience and knowledge can we know where we want to go and set the pace. Inserted in the loop, therefore, we have a decisive role in making the models increasingly precise, accurate and useful for solving business objectives.
Human-In-The-Loop For Companies
Many large global companies have made the human-in-the-loop an already established paradigm. For example, giants such as:
- Google to organize search engine results
- Facebook for automatic tagging in photos
- Pinterest filters images according to specific categories
India’s approach is not only technological but is based on the assumption that only human and artificial intelligence can guarantee business solutions shaped by the needs of companies.