{"id":1407,"date":"2022-05-05T15:50:12","date_gmt":"2022-05-05T19:50:12","guid":{"rendered":"https:\/\/courses.ideate.cmu.edu\/62-830\/s2022\/?p=1407"},"modified":"2022-05-05T15:50:12","modified_gmt":"2022-05-05T19:50:12","slug":"the-ai-stack","status":"publish","type":"post","link":"https:\/\/courses.ideate.cmu.edu\/62-830\/s2022\/?p=1407","title":{"rendered":"The AI Stack"},"content":{"rendered":"\n

What it means for the arts and how to implement it today<\/p>\n\n\n\n

Introduction<\/h2>\n\n\n\n

As I quickly approach my graduation from one of Carnegie Mellon University\u2019s data analytics programs, I\u2019m encouraged to reflect on the key lessons I will take into the work force. One consistent lesson is what it takes to incorporate Artificial Intelligence (AI) into an organization: Carnegie Mellon University (CMU) was ranked number one in 2022 for Artificial Intelligence Programs by U.S. News and World Report<\/a>. Incorporating CMU\u2019s AI lessons is critical for all industries but particularly those that are historically considered to be \u201cnon-technical fields.\u201d Within these fields, the ones who thrive will be those who incorporate at least the lower levels of the stack, namely data management, but an understanding of each layer of the stack will provide industries with a road map to understand how they can increase the efficiency and output of their organization\u2019s business processes.<\/p>\n\n\n\n

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Explanation of AI Stack<\/strong><\/h2>\n\n\n\n

As technology becomes an increasingly large part of the workplace, organizations are encouraged to engage in \u201cfull stack\u201d operations. At its heart, this means that organizations should work through developing each level as shown below:<\/p>\n\n\n\n

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Carnegie Mellon University’s AI Stack Diagram<\/a><\/figcaption><\/figure>\n\n\n\n

While there are MANY definitions and paths to achieving artificial intelligence within an organization, Carnegie Mellon defines AI<\/a> as understanding human needs and making \u201c\u2026smart decisions based on that understanding.\u201d Each of the levels in the diagram is achieved through iterative developments. Below is a brief definition of each layer of the stack and key considerations for the arts to consider within each.<\/p>\n\n\n\n

Computing & Devices<\/h5>\n\n\n\n

Defined as<\/a> \u201c\u2026the collection of computer machinery, data storage devices, and networks that support the processing and exchange of electronic information,\u201d the computing environment is likely the electronics within an organization that everyone will interact with and touch. If computers are too slow, data is strictly stored in old servers, etc, this can cause a shaky foundation for the rest of the stack. MIT <\/a>defines three areas that make computing and devices different with AI in mind:<\/p>\n\n\n\n

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  1. Carry out calculations in parallel<\/strong> \u2013 “speed” historically is how fast a computer could perform a single task (for example, loading a web browser). But now, computers are expected to load a web browser in the foreground while updating a database in the background. Talking to the organization\u2019s IT team about the capabilities of devices (particularly in terms of parallelization) is important in determining the technology expansion capabilities of the organization.<\/li><\/ol>\n<\/div>\n\n\n\n
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    Photo by ThisisEngineering RAEng<\/a> on Unsplash<\/a><\/figcaption><\/figure><\/div>\n<\/div>\n<\/div>\n\n\n\n
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    Photo by Omid Armin<\/a> on Unsplash<\/a><\/figcaption><\/figure><\/div>\n<\/div>\n\n\n\n
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    2. \u201cLearning\u201d rather than listening <\/strong>\u2013 Historically, computers took a single input from users and executed a task. When we think about devices in the context of the AI stack, however, devices are now expected to learn. In personal devices, this takes the form of voice recognition (Siri) and image recognition (unlocking an iPhone using facial recognition). The application of this in the art world is a greater application to recognize images and sounds. For example, with the proper devices and computing capabilities, museums can implement technology such as determining if a piece of artwork is a fake work or not.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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    Photo by Erik Mclean<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n<\/div>\n\n\n\n
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    3. Computerization of the commonplace<\/strong> \u2013 Think of this aspect of computing and devices in terms of a \u201csmart house.\u201d Modern infrastructures (i.e. storage of data on the cloud) enables things as simple as a doorbell to expand their capabilities to become \u201csmart\u201d technology. If smart technology has a place within an industry, it\u2019s important to first identify if the computing and devices infrastructure can support this change.<\/p>\n<\/div>\n<\/div>\n\n\n\n

    Data Management<\/h5>\n\n\n\n

    Data Management is critical to business success. Higher level processing and productivity within an organization cannot happen without proper data management, so in many ways, data management is the most critical level of the AI stack. Data management<\/a> is defined as \u201c\u2026 the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively.\u201dThe path to proper data management for different organizations can take many forms, but below is an example process for establishing data management:<\/p>\n\n\n\n

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    Data Management Process from TechTarget<\/a><\/figcaption><\/figure>\n\n\n\n

    The key to data management ultimately comes down to one word:<\/p>\n\n\n\n

    VALUE<\/strong><\/p>\n\n\n\n

    Data management has shown to bring organizations increased value by streamlining process and revealing where to focus business strategy. In the art industry, this could mean greater customer analytics within an art museum to see which populations visit most often, and therefore which populations to target with promotions and deals. For example, if detailed data management and analytics reveals that families with kids between the ages of 2 and 12 make up the majority of museum visitors over the summer, the museum could focus its displays and programs during those months on catering to that population.<\/p>\n\n\n\n

    Beyond the art industry, civic organizations and local governments have already shown how much value proper data management can generate: by creating the infrastructure for open source data management, the public sector<\/a> \u201c\u2026 has sparked innovation, driven efficiency, and fueled economic development.\u201d Imagine the possibilities for the art industry if more art organizations do the same.<\/p>\n\n\n\n

    Machine Learning and Modeling<\/h4>\n\n\n\n

    Machine Learning (ML) is an over-used and often misunderstood term. ML is defined as “…the systematic study of algorithms and systems that improve their knowledge or performance with experience” (Akoglu). In plain terms, ML and modeling are advanced math applied to data using computers. The most promising applications for ML in the art industry currently are the automation of business processes (see below), but there are also many examples for how ML is being used to GENERATE art. For more information on this, check out this Ted Talk on music and art generation using machine learning:<\/p>\n\n\n\n

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