Shawn Riley on Artificial Intelligence
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The document discusses artificial intelligence (AI) in computer science, highlighting the distinction between machine intelligence and natural intelligence. It explains different types of AI, including data science based on inductive inference and knowledge engineering based on deductive reasoning, while stressing the importance of transparency in AI conclusions. The document also warns about the potential for misleading results from machine-learning techniques, citing a warning from Forrester.
Shawn Riley on Artificial Intelligence
- 1. SHAWN RILEY ON A.I. Chief Data Officer & Chief Information Security Officer DarkLight, Inc.
- 2. WHAT IS ARTIFICIAL INTELLIGENCE In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Intelligence is the ability to acquire and apply knowledge and skills. Artificial Intelligence uses inference which is a conclusion reached on the basis of evidence and reasoning. Data Science derived AI uses inductive inference and probabilistic reasoning. Knowledge Engineering derived AI uses deductive inference and contextual reasoning.
- 3. KNOWLEDGE ENGINEERING Sensor Human Skills DATA SCIENCE Focus on mimicking human domain experts experience in applying the knowledge Focus on patterns in the data set / data silo for classification, clustering, and making predictions Logical Top-Down Methods Statistical Bottom-Up Methods
- 4. KNOWLEDGE ENGINEERING Sensor Human Skills DATA SCIENCE The conclusion of reasoning is facts that are fully transparent and explainable The conclusion of reasoning is probable (conjecture) and not explainable (yet) Shareable W3C Ontologies and cognitive (skill) playbooks support both peer review & reproducibility of results In Zero-Trust Architecture the probable results from data science derived AI need to be verified - Forrester “Machine-learning techniques used by thousands of scientists to analyze data are producing results that are misleading and often completely wrong.” - BBC