https://upskill.temple.edu/ai-automation
Learn how to strategically use AI and no-code tools to build automations for your business. Understand when AI agents and agentic AI can help–and how they fit into your workflows.
https://cis.temple.edu/~pwang/
Artificial Intelligence (AI): basic questions, A General Theory of Intelligence, materials in Chinese（中文资料） Artificial General Intelligence (AGI): introduction, journal, society, education
https://noncredit.temple.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=196883538
Explore what AI can do to reduce costs, gather more and better business data, automate time-consuming tasks, improve efficiency, lower human error, reallocate staff time for higher priority functions, and more. Then master the concepts and fundamental techniques of implementing AI.
https://cis.temple.edu/~pwang/5603-AI/5603-index.htm
CIS 5603. Artificial Intelligence Section 001, Fall 2025 Syllabus Instructor: Dr. Pei Wang Schedule ... Sample Projects 2024 Fall 2023 Fall 2022 Spring 2021 Spring 2016 Spring Relevant Links Canvas AI Topics URL: http://www.cis.temple.edu/~pwang/5603-AI/5603-index.htm
https://guides.temple.edu/ai-research-tools
This guide offers advice on AI-powered tools and functionality created for or used in academic research.
https://guides.temple.edu/ai-research-tools/assess
This guide offers advice on AI-powered tools and functionality created for or used in academic research.
https://www.fox.temple.edu/faculty-research/institutes-centers/global-institute-artificial-intelligence-business-analytics
Internally, AIBA will empower faculty and doctoral students with a library of cutting-edge AI/ML models and algorithms via hands-on coding exercises, real-world business problems, and top journal publication for higher research productivity and contributions to their respective disciplines.
https://cis.temple.edu/~pwang/AGI-Intro.html
To many AGI researchers, "AGI" is simply the original "AI", with the "G" added to differentiate it from the problem-specific works that have co-opted the "AI" label in an undesirable way.
https://cis.temple.edu/~pwang/5603-AI/Lecture/05-Reasoning-uncertain.htm
To directly use statistical inference in AI, a major problem is the requirement for a joint distribution function on all the random variables involved. To solve this problem, Judea Pearl created Bayesian Networks, also known as Belief Networks.
https://cis.temple.edu/~pwang/Publication/AI_Definitions.pdf
Limited by length, this paper concentrates on the major types of working definitions of AI, without analyzing every proposed definition in detail. For the same reason, the paper will not address how to build an AI system according to a given working definition.