15.7 Artificial Intelligence
naked algorithms
AI starts with what are sometimes referred to as "naked algorithms" that need to be trained. These are starting point algorithms, and many are in the public domain or accessible from cloud provider services (e.g., Google TensorFlow) or through vendor APIs and inside software development kits (e.g., Apple Core ML).
Expert Systems
AI systems that leverage rules or examples to perform a task in a way that mimics applied human expertise. Expert systems are used in tasks ranging from medical diagnoses to product configuration.
AI with apple products
Data collected and constantly analyzed by Siri helps the voice assistant continually improve with better understanding of voices and accents worldwide, develop a greater understanding of context, and parse how devices are used so it can better service requests. AI fights fraud by improving analysis with each transaction. Maps? AI helps plot the best route by analyzing all sorts of traffic input. HealthKit? Machine learning can help keep cheats from climbing the leaderboard by identifying legitimate activity and filtering out bogus movement. Like your photos? AI recognizes faces and builds photo collages on what are determined to be your "best" pictures. Used FaceID or chatted with friends as a talking poop animoji
how do AI and machine learning remain challenging
Organizations require technical skills and must reeducate the workforce impacted by AI decision-making tools. Firms need a clean, deep, and accurate dataset. Predictions require a tight relationship between historical data and new data used for prediction. Legal issues may prohibit the use of machine learning and "black box" algorithms in industries requiring exposure of the decision-making process. Varying international laws may allow techniques in one region of the globe that are prohibited in others.
AI
Software that can mimic or improve upon functions that would otherwise require human intelligence.
deep learning
a subcategory of machine learning. The "deep" in deep learning typically refers to the layers of interconnections and analysis that are examined to arrive at results. Some technologists refer to "deep" as having more than one "hidden" layer between input and output. That might not mean much to most managers, but just as "Big Data" is "a lot," "Deep Learning" has more analytical complexity.
neural networks
category of AI used to identify patterns by testing multilayered relationships that humans can't detect on their own. Neural network techniques are often used in machine learning and are popular in data mining.
the factors leading to the explosion of machine learning
including open source tools; cloud computing for low-cost, high-volume data crunching; tools accessible via API or as part of software development kits; and new chips specifically designed for the simultaneous pattern hunting and relationship testing used in neural networks and other types of AI.
Genetic algorithms
model-building techniques where computers examine many potential solutions to a problem, iteratively modifying (mutating) various mathematical models, and comparing the mutated models to search for a best alternative function
AI and machine learning have exposed (bad stuff)
the possibility of unintended consequences, including the possibility of introducing unintended bias, discrimination, and violations of privacy.
goal of AI
to create computer programs that are able to mimic or improve upon functions that would otherwise require human intelligence.
machine learning
type of AI often broadly defined as software with the ability to learn or improve without being explicitly programmed. Many of the data mining techniques described in the prior section use machine learning.
supervised learning
where algorithms are trained by providing explicit examples of results sought, like defective vs. error-free, or stock price
Unsupervised Learning
where data are not explicitly labeled and don't have a predetermined result.
