Machine Learning, as the name suggests, machines (artificial intelligence) learn from data. For a data science strategy to work and to be able to perform genuinely accurate and efficient advanced analysis, it is essential to employ Machine Learning algorithms in the process.
After all, machine learning makes artificial intelligence refine and improve. And this is just one application for this technology; after all, the cases have only grown and diversified in recent years. The algorithms created can identify patterns and make predictions. Because of this, this technology is one of the most important and discussed when it comes to digital transformation.
Case Of Machine Learning: The Future Of Artificial Intelligence
In Artificial Intelligence and Machine Learning, the application that has generated several cases and moved the market internationally is GPT-3, a program developed by the American startup Open AI. In a simple explanation; it is an auto-complete text tool – similar to the search bar. Based on the first words you type, it predicts what the next ones will be, that is, what you are looking for.
Based on these texts, using deep learning (an advanced type of machine learning that generates knowledge based on neural networks) and without the need for human input, GPT-3 can identify patterns and regularities that are unknown to humans and that have already been used . in very diverse and complex applications. Between them:
- Code creation is only based on the description of elements and design
- Answering medical questions
- Style transfer to text
- Chatbot that allows you to talk to historical or fictional figures
As if it were not already significant just for the challenges it can solve, it is essential to point out that this program solves challenges that it was not explicitly programmed to solve. It is the most concrete step we have taken towards an AGI (Artificial General Intelligence), artificial intelligence with all the human mind’s capabilities.
Machine Learning: Trends
There is still much room to talk and study about Machine Learning, even with all these applications. It is a technology that transforms with discoveries routinely. For example, research from Carnegie Mellon University has found that teaching machines similar to how we educate children can be more effective than the more frequently used approach.
In practice, it means that, instead of presenting all the data and details to Artificial Intelligence, it would be the case to give the most general information first and then go into specifics. Any dog photo in an image recognition device would be classified only as a dog without specifying the breed.
More Applications And Cases For Machine Learning
As surprising as the applications of GPT-3 are, and the once distant AGI is approaching, we have other applications that use Machine Learning algorithms and are more concrete and known to technology professionals.
After that, the more specific information would be passed on to the AI, which would learn based on neural networks (deep learning). This would result in a much more efficient, faster and more accurate machine learning model. Consequently, making artificial intelligence learn in a more human-like way can revolutionize the market.
Regardless of the approach or how to train the algorithm, Machine Learning is an essential technology for digital transformation. These cases are just a few examples of what is possible. Different sectors of technology can benefit from both.
Predictive Analytics uses statistics to predict future scenarios. In other words, understand what will happen to your company or institution. It is essential to confirm hypotheses or answer complex questions that depend on several variables. Prescriptive Analytics provides suggestions for actions to be taken and gives advice based on future scenarios. It helps you understand what your company should do. It is possible to simulate different methods to obtain the best results in campaigns, processes or products.