Читать книгу Smarter Data Science - Cole Stryker - Страница 27

“We don't just pass along our DNA, we pass along our ideas.”

—Lisa Seacat DeLuca

TEDBlog

The use of artificial intelligence (AI) is not exclusively about technology, though AI cannot exist without it. Organizational motivation to adopt digital transformation is, in large part, being driven by AI. Arguably, the rate of successful AI initiatives is far less than the number of AI initiatives that are started. The gap is not centered on the choice of which AI algorithm to use. This is why AI is not just about the tech.

AI does not force its own organizational agenda. AI augments how an organization works, driving how people think and participate in the organization. Through tying together organizational goals with AI tools, organizations can align strategies that guide business models in the right direction. An organization augmented as a coherent unit is likely to achieve its digital goals and experience a positive impact from using AI.

As organizations realize value from the use of AI, business processes will see further remediation to operate efficiently with data as a direct result of AI-generated predictions, solutions, and augmented human decision-making.

From pressures that emanate from within the organization as well as those from the outside, the need to develop a balanced tactical and strategic approach to AI is required for addressing options and trade-offs. AI is a revolutionary capability, and during its incorporation, organizational action must not be seen as remaining conventional.

As a data scientist, you'll determine what types of inputs, or features, will be of benefit to your models. Whether determining which features to include (feature engineering) or which features to exclude (feature selection), this chapter will help you determine which features you'll need for the models that you develop. You'll also learn about the importance of organizing data and the purpose of democratizing data.