The first thing that comes to mind when you think about learning is a class full of children or students, trying to get a grasp of a new topic. But what if it is machine learning? Obviously, it has very little, if nothing at all, to do with schools and education. Many people hear this term in numerous different situations, but very few of them are trying to find out what is machine learning, and even fewer actually know exactly what it is.
Machine learning is a functional part of artificial intelligence, which implies the process of making computers take decisions and act without being specifically programmed. What it means and where is it applied in real life? In fact, machine learning is already quite widely spread and millions of people are enjoying its benefits on a daily basis: Facebook faces recognition, which is used for tagging the photos, mailbox automatic spam detecting software, targeted advertising in social networks, etc.
Many students and workers in IT fields consider machine learning as the future of technology, anticipating that with advanced algorithms and patterns it will greatly speed up the development of the semantic web. The aims of the semantic web (web 3.0), in turn, are to make the search more elaborate, able to distinguish the context and adjust to complex requests based on their meaning and gist.
Moreover, recent researchers have shown that there are reasons to think that advanced machine learning will replace people from thousands of jobs in the next few decades. Thereupon, IT specialists, as well as professionals of the sales industry, should have an eye on all up-to-date changes and tendencies in machine learning in order to keep their skills relevant and solicited, which, for the most part, means expanding knowledge in linguistics, management, visualization, statistics and Big Data.
While the potential impact of machine learning may be questionable, it is quite useful to know some important things about it and decide whether it is worth the fuss.
- Machine learning and artificial intelligence are not one and the same thing. Although for some people this fact is not even worth mentioning, these two definitions are often being confused. Experts claim that in most cases the usage of the term “artificial intelligence” is not justified and is often made to attract more attention.
- Unlike artificial intelligence, machine learning mostly depends on huge amounts of data and its analysis. Despite the fact that numerous IT specialists are constantly working in attempts to make algorithms of machine learning more sophisticated and accurate, the key factor which determines the success of this process is the amount of available data.
- The less data you have the simpler your algorithm should be. As machine learning develops a model depending on the patterns in provided data, investigating a space of possible models, which are determined by a set of parameters, when the parameter space is too big, the model is flawed.
- The effectiveness of machine learning is dependent on the quality of the data that was used to train it. Obviously, if the provided data is limited, machine learning can overlook some patterns, thus it benefits from the diversity of the given material.
- Machine learning works right only if the data which was used for its training is relevant and topical. In other words, the models should be renewed on regular basis in order to get the most of the machine learning process.
- The hardest work for machine learning is transforming the data. Despite the popular misconception, machine learning is not about finding and adjusting algorithms, it is about selecting data and characteristic development – transforming raw characteristics into the set of features that represent the gist of the data.
- Deep learning can be thought of as a breakthrough, but it is not a panacea. Deep learning did, in fact, contribute a lot into a significant number of machine learning application fields, but regardless of its innovation and obvious practical use, it still takes a considerable amount of effort and settings.
- There is still a high chance of operator error afflicting the machine learning systems. Although people have spent quite a considerable amount of time programming, they still can’t fully adjust to predicting the way machine will react to certain factors. In other words, machine learning systems are often made vulnerable by the human factor.
- Machine learning can unintentionally create self-fulfilling predictions. As the decisions that machine learning is making depend on previously collected data, once it develops a biased pattern, it can generate and process new data, which will support the obtained biases.
- Machine learning and artificial intelligence are not going to start a rise of the machines. People seem to be quite passionate about the possible ways we can extinct, but although the scenario where humanity is enslaved or destroyed by machines was repeatedly shown in science fiction books, movies, and video games, it is not going to happen.
To sum up, while the terrifying scenarios of the doomsday caused by uncontrolled machines and their artificial intelligence are better to be left to science fiction, the advancement of machine learning algorithms and applications is undoubted and real. This is why many professionals in IT field advise entrepreneurs to hire consultants and developers who will work out a strategy of implementing machine learning possibilities into a range of resources which a business operates with. Naturally, the companies, which prefer using the advantages of machine learning in their problem-solving operations and customer service, will outstrip those who avoid or hesitate in applying such novelties.
Oliver Swanson, aspiring content writer and IT enthusiast, dedicated contributor to pro-papers.com blog.