300 episodes
AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion AI & Data Today
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- Technology
Cognilytica's AI Today podcast focuses on relevant information about what's going on today in the world of artificial intelligence. Hosts Kathleen Walch and Ron Schmelzer discuss pressing topics around artificial intelligence with easy to digest content, interview guests and experts on the subject, and cut through the hype and noise to identify what is really happening with adoption and implementation of AI.
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Glossary Series: Regression, Linear Regression
Regression is a statistical and mathematical technique to find the relationship between two or more variables. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Regression and Linear Regression and explain how they relate to AI and why it's important to know about them.
Show Notes:
FREE Intro to CPMAI mini course
CPMAI Training and Certification
AI Glossary
AI Glossary Series - Machine Learning, Algorithm, Model
Glossary Series: Machine Learning Approaches: Supervised Learning, Unsupervised Learning, Reinforcement Learning
Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary
Glossary Series: Clustering, Cluster Analysis, K-Means, Gaussian Mixture Model -
Glossary Series: Clustering, Cluster Analysis, K-Means, Gaussian Mixture Model
The idea of grouping similar types of data together is the main idea behind clustering. Clustering supports the goals of Unsupervised Learning which is finding patterns in data without requiring labeled datasets. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Clustering, Cluster Analysis, K-Means, and Gaussian Mixture Model, and explain how they relate to AI and why it's important to know about them.
Show Notes:
FREE Intro to CPMAI mini course
CPMAI Training and Certification
AI Glossary
Glossary Series: Artificial Intelligence
AI Glossary Series - Machine Learning, Algorithm, Model
Glossary Series: Prediction, Inference, and Generalization
Glossary Series: Machine Learning Approaches: Supervised Learning, Unsupervised Learning, Reinforcement Learning
Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary
Glossary Series: Regression, Linear Regression -
Glossary Series: Deep Blue
In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define DeepBlue, including what it is and why it's notable for AI.
Show Notes:
FREE Intro to CPMAI mini courseCPMAI Training and CertificationAI GlossaryAI Glossary Series - Symbolic Systems & Expert SystemsAI Glossary Series: Cognitive Technology -
Glossary Series: Symbolic Systems & Expert Systems, Fuzzy Logic
Before this latest wave of AI where neural nets became the hottest algorithm of choice, an approach to machine learning that uses logic and constructs similar to the way that humans reason through problems called Symbolic Systems were actually the system of choice. Popularized in the late 1980s and early 1990s expert systems became the AI system of choice for organizations investing in cognitive technology. However, they proved to be overly complex and brittle and declined in popularity.
In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms symbolic systems, expert systems, and fuzzy logic.
Show Notes:
Free Intro to CPMAI mini courseCPMAI Training and CertificationAI GlossaryAI Glossary Series: Cognitive TechnologyAI Glossary Series: Neural Network -
Glossary Series: Random Forest, Boosted Trees
Sometimes for reasons such as improving performance or robustness it makes sense to create multiple decision trees and average the results to solve problems related to overfitting. Or, it makes sense to boost certain decision trees. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Random Forest and Boosted Trees, and explain how they relate to AI and why it's important to know about them.
Show Notes:
FREE Intro to CPMAI mini courseCPMAI Training and CertificationAI GlossaryGlossary Series: Artificial IntelligenceAI Glossary Series - Machine Learning, Algorithm, ModelGlossary Series: Prediction, Inference, and GeneralizationGlossary Series: Overfitting, Underfitting, Bias, Variance, Bias/Variance TradeoffAI Glossary Series: Ensemble ModelsAI Glossary Series: Decision TreesGlossary Series: Machine Learning Approaches: Supervised Learning, Unsupervised Learning, Reinforcement LearningGlossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision BoundaryGlossary Series: Regression, Linear Regression -
Glossary Series: Ensemble Model
Sometimes for reasons such as improving performance or robustness it makes sense to combine the results of multiple different models trained on the same data. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the term Ensemble Models, and explain how it relates to AI and why it's important to know about them.
Show Notes:
FREE Intro to CPMAI mini courseCPMAI Training and CertificationAI GlossaryGlossary Series: Artificial IntelligenceAI Glossary Series - Machine Learning, Algorithm, ModelGlossary Series: Prediction, Inference, and GeneralizationGlossary Series: Overfitting, Underfitting, Bias, Variance, Bias/Variance Tradeoff