In this blog series, we’ve been exploring how Artificial Intelligence (AI) is used in industrial markets to improve quality and drive innovation. What exactly do we mean by “AI,” and how does it differ from machine learning and deep learning? Let’s take a deeper dive into these terms.
Artificial Intelligence AI is conceptual in nature. It is a term that is used in a general way to describe the capacity of machines to imitate some aspect of human intelligence. Accordingly, AI is a label often applied to software programs that perform cognitive tasks that humans can also do, like filtering junk email, interpreting handwriting, detecting fraud, translating speech, and recommending products. But these types of applications can perform their functions with or without the aid of advanced analytics and behavioral models. They can execute their functions with or without any capacity to “learn,” that is to say, to independently improve their performance over time. Certainly, however, their performance can be greatly enhanced with such attributes. Moreover, many would argue that the use of models and the capacity to “learn” are defining characteristics of “true” AI.
Part 2: Industrial applications of Artificial Intelligence and Machine Learning
Part 3: Differentiating Between Artificial Intelligence, Machine Learning and Deep Learning
Part 4: Benefits of Machine Learning in Industrial Contexts
Part 5: Key Challenges of Artificial Intelligence in Industrial Sectors
Part 6: Realizing the Value of Artificial Intelligence in Industrial Sectors
Part 7: Artificial Intelligence and Machine Learning at Dassault Systèmes
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