By Sam Piha
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Almost every day there is a piece in the news about the opportunities and dangers of Artificial Intelligence (AI). Many young people, even those who are very tech savvy, know little about this topic. Adults, including those in afterschool programs, know even less. Yet, AI is all around us and is being used by companies more and more.
We believe that youth need to understand more about AI, and afterschool is a perfect place to do this. But are afterschool leaders equipped for this? See our briefing paper on AI and afterschool here.
“It’s important for educators to understand AI so they can help their youth make sense of a technological development that is predicted to be a huge force in the world, experts say. It’s crucial for educators to be AI literate, to be able to explain what it is, and to understand its powers and limitations.” [1]
Below are some common and more esoteric AI terms and definitions that may be helpful.
Artificial Intelligence (AI) - AI is a machine’s ability to perform the cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with an environment, problem solving, and even exercising creativity. You’ve probably interacted with AI even if you didn’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are some customer service chatbots that pop up to help you navigate websites. [2]
Algorithm - a procedure used for solving a problem or performing a computation. Algorithms act as an exact list of instructions that conduct specified actions step by step in either hardware- or software-based routines. Algorithms are widely used throughout all areas of IT. In mathematics and computer science, an algorithm usually refers to a small procedure that solves a recurrent problem. Algorithms are also used as specifications for performing data processing and play a major role in automated systems. [3]
Deepfake - A deepfake is an image, or a video or audio recording, that has been edited using an algorithm to replace the person in the original with someone else (especially a public figure) in a way that makes it look authentic. [4]
Strong AI - also known as artificial general intelligence, is a machine that can solve problems it’s never been trained to work on — much like a human can. This is the kind of AI we see in movies, like the robots from Westworld or the character Data from Star Trek: The Next Generation. This type of AI doesn’t actually exist yet. [5]
Weak AI - sometimes referred to as narrow AI or specialized AI, operates within a limited context and is a simulation of human intelligence applied to a narrowly defined problem (like driving a car, transcribing human speech or curating content on a website). Weak AI is often focused on performing a single task extremely well. While these machines may seem intelligent, they operate under far more constraints and limitations than even the most basic human intelligence. Weak AI examples include: Siri, Alexa and other smart assistants, self-driving cars, Google search, email spam filters, and Netflix’s recommendations. [6]
Machine Learning (ML) - A machine learning algorithm is fed data by a computer and uses statistical techniques to help it “learn” how to get progressively better at a task, without necessarily having been specifically programmed for that task. Instead, ML algorithms use historical data as input to predict new output values. [7]
Deep Learning - a type of machine learning that runs inputs through a biologically inspired neural network architecture. The neural networks contain a number of hidden layers through which the data is processed, allowing the machine to go “deep” in its learning, making connections and weighing input for the best results. [8]
The Four Types of AI - AI can be divided into four categories, based on the type and complexity of the tasks a system is able to perform. They are reactive machines, limited memory, theory of mind, and self-awareness. [9]
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Reactive Machines - A reactive machine follows the most basic of AI principles and, as its name implies, is capable of only using its intelligence to perceive and react to the world in front of it. A reactive machine cannot store a memory and, as a result, cannot rely on past experiences to inform decision making in real time. Perceiving the world directly means that reactive machines are designed to complete only a limited number of specialized duties. Reactive Machine examples include Deep Blue, which was designed by IBM in the 1990s as a chess-playing supercomputer and defeated international grandmaster Gary Kasparov in a game. [10]
Limited Memory - Limited memory AI has the ability to store previous data and predictions when gathering information and weighing potential decisions — essentially looking into the past for clues on what may come next. Limited memory AI is more complex and presents greater possibilities than reactive machines. [11]
Theory of Mind - Theory of mind is just that — theoretical. We have not yet achieved the technological and scientific capabilities necessary to reach this next level of AI. The concept is based on the psychological premise of understanding that other living things have thoughts and emotions that affect the behavior of one’s self. In terms of AI machines, this would mean that AI could comprehend how humans, animals and other machines feel and make decisions through self-reflection and determination, and then utilize that information to make decisions of their own. Essentially, machines would have to be able to grasp and process the concept of “mind,” the fluctuations of emotions in decision-making and a litany of other psychological concepts in real time, creating a two-way relationship between people and AI. [12]
Self-Awareness - Once theory of mind can be established, sometime well into the future of AI, the final step will be for AI to become self-aware. This kind of AI possesses human-level consciousness and understands its own existence in the world, as well as the presence and emotional state of others. It would be able to understand what others may need based on not just what they communicate to them but how they communicate it. Self-awareness in AI relies both on human researchers understanding the premise of consciousness and then learning how to replicate that so it can be built into machines. [13]
Stay tuned to the LIAS Blog for more on the topic of AI.
END NOTES
[1] Inspirit Scholars, What is AI for Kids? An Introduction to Artificial Intelligence for Kids, https://www.inspiritscholars.com/blog/what-is-ai-for-kids/
[2] McKinsey & Company, What is AI?, https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-ai
[3] Alexander S. Gillis, algorithm, https://www.techtarget.com/whatis/definition/algorithm
[4] Mirriam- Webster, deepfake, https://www.merriam-webster.com/dictionary/deepfake#h1
[5] Alyssa Schroer, What is Artificial Intelligence?, https://builtin.com/artificial-intelligence
[6]-[13] IBID.
Lights On Afterschool
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Lights On Afterschool! This nationwide event, organized by the Afterschool Alliance, calls attention to the importance of afterschool programs and the resources required to keep the lights on and the doors open. Everything you need to plan a successful event, from case studies to sample materials, is available in the
Lights On Afterschool Planning Kit.
To learn more about Lights On Afterschool, register an event, access event planning tools, or to find out what’s going on in your area, visit afterschoolalliance.org.
