Knowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules, objects, and frame-based ontologies. Expert systems were one of the first successful applications of artificial intelligence technology to real world business problems. Researchers at Stanford and other AI laboratories worked with doctors and other highly skilled experts to develop systems that could automate complex tasks such as medical diagnosis. Until this point computers had mostly been used to automate highly data intensive tasks but not for complex reasoning. Technologies such as inference engines allowed developers for the first time to tackle more complex problems. As expert systems scaled up from demonstration prototypes to industrial strength applications it was soon realized that the acquisition of domain expert knowledge was one of if not the most critical task in the knowledge engineering process. This knowledge acquisition process became an intense area of research on its own. One of the earlier works on the topic used Batesonian theories of learning to guide the process. One approach to knowledge acquisition investigated was to use natural language parsing and generation to facilitate knowledge acquisition. Natural language parsing could be performed on manuals and other expert documents and an initial first pass at the rules and objects could be developed automatically. Text generation was also extremely useful in generating explanations for system behavior. This greatly facilitated the development and maintenance of expert systems. A more recent approach to knowledge acquisition is a re-use based approach. Knowledge can be developed in ontologies that conform to standards such as the Web Ontology Language (OWL). In this way knowledge can be standardized and shared across a broad community of knowledge workers. One example domain where this approach has been successful is bioinformatics. (Wikipedia).
Deep learning is a machine learning technique that learns features and tasks directly from data. This data can include images, text, or sound. The video uses an example image recognition problem to illustrate how deep learning algorithms learn to classify input images into appropriate ca
From playlist Introduction to Deep Learning
Machine learning describes computer systems that are able to automatically perform tasks based on data. A machine learning system takes data as input and produces an approach or solution to a task as output, without the need for human intervention. Machine learning is closely tied to th
From playlist Data Science Dictionary
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Machine Learning
1.1 Machine Learning: an introduction
Deep Learning Course Purdue University Fall 2016
From playlist Deep-Learning-Course
Continuous multi-fidelity optimization
This video is #8 in the Adaptive Experimentation series presented at the 18th IEEE Conference on eScience in Salt Lake City, UT (October 10-14, 2022). In this video, Sterling Baird @sterling-baird presents on continuous multifidelity optimization. Continuous multi-fidelity optimization is
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RubyConf 2019 - What happens when a linguist learns to code? by Erica Sosa
What happens when a linguist learns to code? by Erica Sosa When people find out about my former career as a linguist and language teacher, they often ask if my background helped me learn how to code. I started to wonder if there was some overlap between learning a natural language and a
From playlist RubyConf 2019
Does 3D immersion enhance nursing students' pharmacology knowledge acquisition and satisfaction?
Presentation from The Campus Alliance for Advanced Visualization 2019 (CAAV19) held at Indiana University (IU) in Bloomington, Indiana. Abstract: Virtual reality has the unique potential to make healthcare education more readily available to students who have been previously marginalized
From playlist CAAV19 - The Campus Alliance for Advanced Visualization 2019
Knowing Ourselves Intellectually vs. Knowing Ourselves Emotionally
It's obviously a great idea to try to understand ourselves, but one of the further distinctions we need to make is between knowing ourselves intellectually and knowing ourselves emotionally. The latter variety of knowledge is rather harder but a good deal more valuable as well. For gifts a
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From playlist COMP0168 (2020/21)
NLP for scaling healthcare access to everyone I Healthcare NLP Summit 2021
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/ Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/ Watch all Healthcare NLP Summit 2021 sessions: https://www.nlpsummit.org/ Telemedicine is a rapidly growing medium o
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The New Naturalism II: Evolutionary Riddles
Dwight H. Terry Lectureship October 19, 2006 The New Naturalism II: Evolutionary Riddles Barbara Herrnstein Smith is Braxton Craven Professor of Comparative Literature and English and director of the Center for Interdisciplinary Studies in Science and Cultural Theory at Duke University
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Machine Learning with scikit learn Part Two | SciPy 2017 Tutorial | Andreas Mueller & Alexandre Gram
Tutorial materials found here: https://scipy2017.scipy.org/ehome/220975/493423/ Machine learning is the task of extracting knowledge from data, often with the goal of generalizing to new and unseen data. Applications of machine learning now touch nearly every aspect of everyday life, fro
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5G Explained: Initial Acquisition Procedures in 5G NR
This video discusses initial acquisition procedures starting with cell search. By acquiring the primary and secondary synchronization signals (PSS and SSS), you can see the timing and the knowledge of the physical cell identity. The video then discusses broadcast channel (BCH) decoding, ho
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🔥Machine Learning Question And Answers - Interactive Quiz | Machine Learning Quiz 2022 | Simplilearn
🔥Join the Quiz - at https://www.menti.com with code "46337129 " and share with us your screenshot of results on youtubecontest@simplilearn.net to win exciting prizes This YouTube live quiz on Machine Learning will touch upon the basics of Data Science and Machine Learning to enlighten you
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While recent advances in natural language processing are widely publicized, deployment in critical applications like healthcare and defense is lagging behind. Why? Because people don’t trust AI agents. Why? Because the agents can’t explain their decisions in normal human terms. Why? Bec
From playlist NLP Summit 2021