How can deep learning change the evolution of machine learning?
Getting deep in the development of Machine Learning.
In the past few years, we have seen an incredible shift in new technologies. Big companies have improved their growth strategy and turned themselves into technologies such as artificial intelligence, deep learning, and machine learning. With the increase of attention around these technologies in recent years, they have been praised and showed promising innovations across different parts of business functions.
Machine learning and deep learning are forms of AI, but both have unique traits and use in terms of having benefits to the end-user and delivering services. Certainly, while machine learning had a long track record before deep learning, researchers and service provider companies were most likely to use ML algorithms to build a variety of models to improve statistics, simplify speech and envisage risk, and in other applications.
Machine learning simplifies a computer’s ability to learn and train itself in order to become an effective tool for new data landscape. Over the years it has significantly advanced its ability to evaluate complex, sophisticated and big data.
Arthur Samuel invented the first ML program and coined the phrase machine learning in 1952. After joining IBM’s Poughkeepsie Laboratory, Arthur built the first computer learning programs, which were developed to play the game of checkers. When each time checkers was played, the computer would always get better, fixing its mistakes, and finding better ways to improve from that data. This automatic learning is one of the first examples of machine learning.
Today, technology has become an integral part of processing data. When you compare it to deep learning, the main difference is that machine learning needs manual intervention in selecting which features to process, compare to deep learning who does it intuitively. It is believed by many experts that deep learning has increased interest in AI and stimulated the development of improved tools, processes, and infrastructure for all kinds of machine learning. Deep learning unique outcomes gained in applications such as computer vision, speech recognition, natural language understanding (NLU), threat detection, etc., it is increasingly becoming a big buzz among businesses.
Startups often see deep learning as an advanced, sophisticated subdivision of AI with predictive competences enthused by the brain’s ability to learn. The technology has the potential to identify objects in nearly milliseconds and with precision similar to a human brain does.
However, despite having a transformational impact of deep learning on business applications, software engineers still use the traditional statistical machine learning algorithms to capture information about training data. Most machine learning applications in corporations do not rely on neural networks and in its place capitalize on traditional machine learning models. Linear/logistic regression, random forests and boosted decision trees are the most common models. And these are the ones behind friend suggestions, ad targeting, user interest prediction, supply/demand simulation, and search result ranking, among other services technology companies use.
Despite the fact that deep neural networks are not utilized directly, they are indirectly driving fundamental changes in the field of machine learning. For example, predictive capabilities of deep learning have stirred data science professionals to contemplate distinct ways of framing problems that arise in other types of machine learning.