Difference between AI and Machine Learning
Just like Cloud, AI is here with its shimmer and shine with the promise of an autonomous future that would make life easier on every realm. Some of the other technologies that speak of a promising future include robotics, IoT, etc.
But enterprises seem confused between AI and machine learning. They often the two and use it interchangeably many times to get across facts that are erroneous in nature. Therefore, CoreTechnologies Services, Inc, today, look at these two aspects – AI and Machine Learning in its basest forms.
Primarily, machine learning is an integral part of AI. It is a statistical and data-driven approach used in creating AI. Machine learning is dependent on data as for instance when a computer learns from data to improve the performance of a statistical approach is used to the data in a useful manner.
AI is a platform that mimics human intellect. Machine learning is a key subset of artificial intelligence code, but it is not artificially intelligent. In other words, AI is a neural network and machine learning is just another component that helps in extraction of data. AI evolves with teaching and learning and therefore it is necessary to understand this concept in any innovation.
Most data is domain-specific. To apply a model, for instance, trained in finance to a different field will cause an obvious deviation and model to perform differently. Therefore, the best approach is always to combine multiple methods of data extraction and train AI for the best outcome.
CoreIT observes that a combination of methods perform better and therefore the right combinations set an AI platform apart. A machine learning engine cannot provide all the capabilities on its own to deliver the results that businesses require and usually involves many technologies working in sync. Much of the confusion in the market is due to the depth of functionally in AI platforms that lead to real business transformation.
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