By: Carlos Almeida
September 11, 2020
Artificial Intelligence for Beginners
By: Carlos Almeida
September 11, 2020
People often become intrigued, or maybe even shellshocked, by the prospect of working with artificial intelligence. But where should one begin? This article will focus on defining and breaking down the various pieces that make up the subfields of artificial intelligence and provide insights into what further topics can be pursued in this learning journey.
What is AI?
Unlike comparing two temperature readings, intelligence is not one dimensional. What humans perceive as easy (picking up a piece of paper) or hard (playing chess) turns out to be the opposite when building AI. To illustrate the contrast, efforts being made by organizations such as Google with their Robotic Grasping Project could be compared to the famous chess match between IBM's Deep Blue and world champion, Garry Kasparov.
Today, there is no singular or universally agreed-upon definition for the term AI. To make things even more confusing, the term is loosely used in media and business to refer to statistics, business analytics, and if-then rules. The science fiction genre even portrays humanoid robots as the quintessential AI. Is there a simpler way to think of this nebulous term?
A simpler, general definition of AI that has circulated (author unknown) is Adaptive Autonomy. To further explain, it can perform tasks in complex environments without constant guidance by a user, accompanied by improving performance by learning from experience.
What are some common applications of AI?
- Autonomous machines (cars, trucks, drones, ships) AI techniques include determining the destination and what route should be taken (search and planning), combined with how objects are avoided throughout the route in a complex environment (computer vision). These must come together cohesively to allow for decisions to be made in an uncertain world.
- Content personalization Broadcasters, publishers, and search engines all use AI-powered algorithms to present content relevant to individual preferences with ill-conceived side effects such as filter bubbles, fake news, and propaganda.
- Image and video processing Face recognition is used for passport control, automatic tagging on social media, and autonomous vehicle object avoidance. Using AI to generate or manipulate imagery that can create animations based on real people and real movements, or transform photographs to imitate a painted portrait, is now beginning to challenge the long-held notion that seeing is believing.
How do the focus areas of AI fit together?
This section is meant to be a breakdown guide on how the various areas of interest are structured and help determine how to narrow them down into a specific niche.
Data Science - Is an umbrella term covering various areas, including machine learning, statistics, computer science (some aspects including algorithms and data storage), and a practical understanding of the context in which it is being applied, such as business or science.
Robotics - Combines all aspects of AI, including:
- Computer vision and speech recognition
- Natural language processing, information retrieval, and reasoning
- Cognitive modeling and affective computing
- Machine learning
This article won't cover the philosophy of AI, but it should be mentioned that this is a field of study all on its own. It's a field that will inevitably influence politics, policy, and how well society can adapt to the new work landscape. Interested in learning more? The question could be asked: does being human-like equate to being intelligent? Here are a few interesting links to help get started:
Alan Turing's Test https://en.wikipedia.org/wiki/Turing_test
Eugene Goostman chatbot https://en.wikipedia.org/wiki/Eugene_Goostman
John Searle's thought experiment: Chinese Room Argument https://www.iep.utm.edu/chineser/
Now that artificial intelligence's basic building blocks have been defined, it's time to take a deeper dive into further learning. It is always good to start with the field's high-level fundamentals and then narrow the focus as knowledge grows. Like everything else built, be it a house or an app or a data set, it is important to remember that security must be accounted for and built into all solutions. Further learning can be done by signing up at https://www.cybrary.it/ for the next step toward greater knowledge.