AI and Machine Learning are expanding in companies, as they provide new ways to leverage the value of data. But in many cases the organizations that implement these technologies are not taking advantage of the true potential of AI, because of many different reasons. And this is especially noticeable in projects that combine data science with AI and machine learning techniques, as they introduce more complexity and are much more difficult to design.
But experts say that the combined use of these technologies is not only capturing attention because of its possibilities, but in the future it will be a way for companies to boost their digital applications and services. Although for now the developers are not able to know how to maximize their potential, and experts offer a series of recommendations to achieve it.
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For example, Aaron Edell, director of applied AI in the software firm Veritone, who in a recent interview with Siliconrepublic.com offered four main recommendations for companies that want to use artificial intelligence, data science and machine learning in our digital projects and the first thing is that in the field of machine learning is to focus on solving problems, instead of diverting attention and trying to constantly refine the things that already work. Because on the road to improving technology, it is common to forget what this technology is being developed in the first place.
The second piece of advice is that when it comes to overcoming challenges using data science, it is essential to focus on the customer, especially in those industries that are more customer-oriented and consumer oriented. Thus, they perceive that they are offered solutions to their problems, something that offers better commercial results and manages to retain the customer. Thirdly, Edell recommends not diverting attention from the primary objectives until they have been achieved, since the possibilities of these three technologies together are enormous, and there is a risk of getting lost along the way. Therefore, he guarantees that the problems that affect people must be resolved first, and that hinder or hinder the development of their tasks.
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Finally, it highlights the classic idea that is the “perfection is the enemy of efficiency”, a truth applicable to most cases. With regard to AI and ML technologies in combination with data science, it highlights that most problems do not require solutions with total accuracy. For example, in certain cases it is not necessary to accurately account for a certain magnitude to understand the situation, but an approximate figure is sufficient and developing a technology which is capable of performing this type of analysis with absolute precision could take a lot of time and money, something that could be unnecessary.
