Chapter 3: What is AI? likely follow the text written so far; AI chatbots use a very large statistical model to add one likely word at a time, thereby writing surprisingly coherent essays. When we ask about the model at the heart of AI, we begin to get answers about “what aspects of reality does the model approximate well?” and “how appropriate is it to the decision to be made?” One could similarly ask about algorithms—the specific decisionmaking processes that an AI model uses to go from inputs to outputs. One could also ask about the quality of the data used to build the model—for example, how representative is that data? Switching among three terms—models, algorithms, and data—will become confusing. Because the terms are closely related, we’ve chosen to focus on the concept of AI models. We want to bring to the fore the idea that every AI model is incomplete, and it's important to know how well the AI model fits the reality we care about, where the model will break down, and how. Sometimes people avoid talking about the specifics of models to create a mystique. Talking as though AI is unbounded in its potential capabilities and a nearly perfect approximation to reality can convey an excitement about the possibilities of the future. The future, however, can be oversold. Similarly, sometimes people stop calling a model AI when its use becomes commonplace, yet such systems are still AI models with all of the risks discussed here. We need to know exactly when and where AI models fail to align to visions for teaching and learning. 3.5. Insight: AI Systems Enable New Forms of Interaction AI models allow computational processes to make recommendations or plans and also enable them to support forms P a g e | 25
36 Publizr Home