Intro to Artificial Intelligence (AI)
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From smart speakers to self-driving cars, you’ve probably heard a lot about Artificial Intelligence (AI). In this introductory guide, let’s take a closer look and learn more about what AI is all about!
What is AI?
In computer science, the term artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machine. In popular usage, artificial intelligence refers to the ability of a computer or machine to mimic the capabilities of the human mind—learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems—and combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.
Types of AI
Artificial Narrow Intelligence (ANI)
Narrow intelligence refers to the use of AI algorithms to perform specific tasks. Most of the AI used today falls under this category. Examples of this type of AI include face recognition, Apple’s Siri, Amazon’s Alexa, self-driving cars, and quality control processes in modern factory assembly lines.
Artificial General Intelligence (AGI)
General Intelligence refers to a type of AI that more closely replicates the way the human brain works. It can learn, discover and solve problems on its own, without any human intervention. This type of AI does not yet exist, however it is the subject of many science fiction books & movies and raises many philosophical and ethical questions.
How does AI work?
Most AI today is built using machine learning algorithms. These types of algorithms are based on neural networks, which mimic the way neurons in a human brain work. The advantage of these algorithms is that, once trained, they are very efficient at recognizing patterns.
How is AI trained?
AI is trained by feeding neural networks large amounts of data.
In the case of supervised learning, training data is prepared and labeled by an AI engineer in advance, so that the algorithm can learn the correct relationship between inputs and outputs. Given enough data, the AI will eventually be able to correctly recognize and identify desired patterns with a high degree of confidence. This is how things like face recognition and speech detection work.
In the case of unsupervised learning, an AI engineer doesn’t know in advance what they want the algorithm to do. They feed the AI algorithm lots of data and then allow it to try to find patterns on its own. Data scientists and researchers then examine the output of the AI to see if there are any insights or meaningful relationships to be found. This type of AI can be unpredictable, and not always productive.
Examples of AI (interactive)
- Google AI Experiments
- AI Face Generator
- Text Generator
- Runway ML (cloud-based machine learning platform)
- AI for Everyone (4 week introductory course on Coursera)
- Deeplearning.ai (beginner, intermediate, and advanced Coursera courses, career oriented)
- Google’s Machine Learning Crash Course (software-oriented crash course)
- But What is a Neural Network? (math-oriented video series on Youtube)
Software engineering resources
- Open AI (creators of the powerful GPT-3 language model)
- Open Source Speech Recognition (TTS tools + resources)
- AI Artists (community of artists exploring Artifical Intelligence)
- ml5js.org (approachable, friendly machine learning for the web)
- Unfolding AI (MIT Symposium on Art, Computation and AI)
AI Reading List
- Alpaydin, Ethem. “Introduction to Machine Learning.” 4th Edition, MIT Press, 2020. A recently published textbook focusing on machine learning, including developments in deep learning and neural networks.
- Jennings, Charles “Artificial Intelligence: Rise of the Lightspeed Learners.” Rowman and Littlefield, 2019. An opinionated and anecdotal account of AI with coverage of AI cybersecurity and comparison of AI development in China and the USA.
- Kaplan, Jerry. “Artificial Intelligence: What Everyone Needs To Know.” Oxford University Press, 2016. A thorough primer covering the definitions, history and potential social impacts of AI.
- Kaplan, Jerry. “Humans Need Not Apply.” Yale University Press 2015. A critical look at possible economic and social consequences of AI, and consideration of policy proposals to promote equity.
- Lee, Kai-Fu. “AI Superpowers: China, Silicon Valley, and the New World Order.” Houghton Mifflin Harcourt, 2018.
- Pickover, Clifford A. “Artificial Intelligence: An Illustrated History.” Sterling Publishing, 2019. A lavishly illustrated survey of the technical and cultural history of AI.
- Rahman, Was. “AI and Machine Learning.” Sage Publishing, 2020. Includes a non-technical but detailed explanation of how AI works, as well as information on the history and implications of AI.
- Shane, Janelle. “You Look Like A Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World A Weirder Place.” Voracious/Little, Brown and Company, 2019.
- Warwick, Kevin. “Artificial Intelligence: The Basics.” Routledge, 2012. In addition to chapters on the definitions, history and philosophy of AI, this book includes chapters on robots and sensory systems, such as computer vision.
- “In the Age of AI.” PBS, 2020. DVD with a two hour program covering the perils and promises of AI.