This is an excerpt from an article published in Fab Shop Magazine written by Len Zapalowski and Amin Ghasemazar.
To understand AI’s impact on manufacturing, it’s key to learn the different approaches and the lingo behind them.
The manufacturing industry is experiencing another transformation driven by the recent rise of Generative AI (GAI), which joins the more established Discriminative AI (DAI). Understanding these two fundamental approaches to AI is crucial for business owners and manufacturing professionals. While DAI focuses on making decisions, GAI creates new content.
DAI uses data to make decisions, such as: is this a flower, if so, what kind? Or, given these past trends, what will happen next? These two classes of decisions are known as classification and regression and have accounted for most applications of AI to date. For instance, they help us read car license plates or detect banking fraud.
The newer GAI has a different purpose. GAI creates new content. Famously, ChatGPT allows a user to seek narrative answers to complex questions. You want an essay on Oppenheimer? You got it. Or, if you want a picture of a subject that has a particular likeness – such as, what would you look like if you were the offspring of John F. Kennedy? – just ask.
AI solutions consist of an AI model that processes the external incoming data to be optimized and then makes a decision. It knows how to do this because the AI model has been trained on a training data set beforehand. But where did this training data set come from and how much of it is out there? The answer to that depends on the application.
It’s worthwhile comprehending the size of the data currently being generated in databases and what it means to industry and society at large as seen through the AI lens. A recent study by UBS Warburg estimates that the amount of data available globally is expected to grow more than 10 times from 2020 to 2030, reaching 660 zettabytes. A zettabyte is a billion terabytes or a trillion gigabytes.
This new data is generated by the existing population of humans and machines with an additional two billion more internet users and 30 billion IoT devices expected to come on line by the end of the decade. This is while data storage costs go down by 25 to 30 percent every year. Thus, we have many more devices and people adding data to ever lower digital storage costs.
Two countries lead the data accumulation: The United States and China. At the moment, China’s store is about the same as the United States’, but it is growing much faster. Most Organization for Economic Co-operation and Development (OECD) countries have a slower growth of data than emerging economies.
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