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Artificial Intelligence

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Artificial Intelligence

Shaping the Future of Innovation

De Gruyter,

15 mins. de lectura
8 ideas fundamentales
Audio y Texto

¿De qué se trata?

AI is reshaping business and society. Here’s how it actually works.

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Artificial intelligence is reshaping work and society in a host of ways. But do you understand how AI works? If you’re like most people, probably not. Enter this guide by computer scientist Alfred Essa, which offers a practical breakdown of AI fundamentals. Systems are modeled on the human brain, he explains, with its tens of billions of neurons. Essa details the design of neural networks and large language models clearly and patiently. If you’re interested in understanding the nuts and bolts of AI systems, from algorithms and machine learning to deep learning and generative AI, this text is a good starting point.

Summary

Algorithms are the building blocks of computing.

An algorithm is a set of clear instructions that tells a computer what output it should produce. While humans can adapt to uncertainty, computers can’t act in the face of ambiguity. Each step the computer must follow must be clearly defined. For example, while a human can easily follow a recipe for a peanut butter sandwich that instructs the cook to spread the peanut butter evenly, a machine must know exactly what “evenly” means before it can proceed. An algorithm requires not just step-by-step directions but also input data. If the instructions and input are sound, the algorithm yields a clear output.

Algorithms operate in loops — a series of repeating steps — and branches — decision-making moments. Together, these mechanisms ensure the program can handle repetitive tasks and adapt when something changes, without needing brand-new instructions for every possible scenario. A well-designed algorithm is efficient, meaning that the number of potential loops is limited. Programmers seek not just an algorithm that will solve a problem but one that will solve the problem in the ...

About the Author

Alfred Essa has led AI and advanced analytics teams across academia and industry. He has been a Simon Fellow at Carnegie Mellon University, Vice President of Analytics and R&D at McGraw Hill Education, and CIO at MIT’s Sloan School of Management.

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