Machine learning is a topic of interest in a wide range of fields at the moment. This is because it involves machines becoming better and better at their intended functions by performing them, which is something that promises to provide incredible gains in not just efficacy but also efficiency. As a result, it is no wonder that interested parties are rushing to implement it in their own operations, though it should be noted that machine learning still possesses significant issues that are preventing it from fulfilling its full potential.
What Is Pienso Planning In Regards to Machine Learning?
Of course, so long as a problem is serious enough, there will be people who are interested in solving it. One such example when it comes to machine learning is a start-up called Pienso, which is interested in what is sometimes called the human-in-the-loop problem. So far, it has managed to raise $2.1 million in seed funding for this purpose, meaning that there are interested parties that see potential in its projects.
For those who are curious, Pienso is planning to come up with products that will help people without the technical expertise and experience to interact with machine learning algorithms on a fundamental level. This is important because machine learning needs human input to ensure that the machines improve in the right manner. However, there is a limited number of people with the right expertise and experience when it comes to machine learning. Even worse, there is an even more limited number of people with the right expertise and experience needed to not just train machines using machine learning but also train machines using machine learning for a particular field such as climate science and forensic accounting, which is becoming more and more problematic as machine learning starts seeing use in more and more fields. Should Pienso succeed in its plans, it would enable people without technical expertise and experience to train machines using machine learning, thus making them that much more accessible.
Why Is This Important?
There are a number of reasons that human involvement remains critical in the machine learning process. Common examples include but not limited to labeling, tuning, and testing, without which the process could not proceed.
For those who are curious, labeling is when a human assign labels to data so that it can be used as training data by the machine. Suppose that a machine has been tasked with determining whether there is a person in a picture or not. In this case, labeling means that a human trainer would need to provide the right answers for a set of pictures that the machine can use to guide its own decisions. However, it is important to note that the human trainer would have to provide the right answers in a manner that will help the machine learn from them, which can be much more complicated than it sounds on initial consideration.
Meanwhile, tuning is when human trainers make small adjustments in order to get better results. One example is when a machine gets stuck on a particularly challenging case that it cannot solve on its own, which is when the human trainer steps in to provide the correct answer. In this manner, the machine can learn more about such edge cases, which should help it make better decisions in the future. For comparison, testing is much more interested in whether the machine is coming up with the right decisions or not, which tends to see human trainers scoring its results for accuracy.
Given this information, it should be clear why Pienso’s plans are so important. Simply put, as machine learning is put to use in more and more complicated tasks in more and more specialized fields, its trainers need more and more specialized expertise and experience of their own. For example, if a machine is going to be trained to find particular patterns in climate data, the human trainer is going to the need the climate science-related expertise and experience needed to train it in the right manner. However, said individual is also going to need an in-depth understanding of computers as well as other machine learning-related fields such as statistics to work with machine learning, which tends to be a rather rare combination of competencies to say the least. By making machine learning algorithms more accessible to interested individuals, Pienso could do much to empower the training of machines using machine learning, provided that it can live up to its promises.