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Artificial Intelligence (AI) and Machine Learning Case Study
Intro New Media (NMIX 2020)
University of Georgia
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Tuesday, October 15, 2019
Artificial Intelligence (AI)/Machine Learning (ML) Case
Study
• Bots are really just a subset of AI.
• Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated
by machines, in contrast to the natural intelligence(NI) displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents“: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.
• The scope of AI is disputed: as machines become increasingly capable, tasks
considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology. Capabilities generally classified as AI as of 2017 include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery network and military simulations.
• Artificial intelligence was founded as an academic discipline in 1956, and in the years
since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter“), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e. “robotics” or “machine learning”), the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).
• The traditional problems (or goals) of AI research include reasoning, knowledge
representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field’s long- term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on
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Tuesday, October 15, 2019 statistics, probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy and many others.
The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”. This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabatedly. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science. AI is a booming career field (at least until machines take over).
“Turing Test” — Alan Turing in 1950: “test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses.” He was highly influential in theoretical computer science. “Father of theoretical computer science and AI.”
Issac Asimov’s Three Laws of Robotics — went from robots to AI in general.
“Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.” Machine learning >> shown through MarI/O, a video game player simulator, via Kottke. Shows simulations and neuro-networks.
The type of bots getting the most coverage use conversational UI and interface with an existing messaging application. They help the user of the app find information or get something done in a seamless, automated way through text-based commands (think typing out: “Hey, Pizza Hut bot! Send me a large pepperoni pizza” and the pizza shows up at your door 30 minutes later). The concept of conversational UI is very important in many devices.
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Artificial Intelligence (AI) and Machine Learning Case Study
Course: Intro New Media (NMIX 2020)
University: University of Georgia
