We'll start with classification, whose objective is to predict an outcome by creating separate classes in a data set. Uses for classification algorithms include junk email detection and healthcare risk analysis. In the former, after scanning the text of an email and tagging recognized words and phrases, the email's "signature" can be fed into a classification algorithm to determine whether it qualifies as spam. In the latter, a patient's vital statistics, health history, activity levels and demographic data can be run through an algorithm to assign a risk score for particular diseases. One method of classification is a decision tree, which is similar to a flow chart in providing a hierarchical sequence of information -- in this case, "tests" of different parameters in the data entities being classified.
We study the empirical strategies that humans follow as they teach a target concept with a simple 1D threshold to a robot. Previous studies of computational teaching, particularly the teaching dimension model and the curriculum learning principle, offer contradictory predictions on what optimal strategy the teacher should follow in this teaching task. We show through behavioral studies that humans employ three distinct teaching strategies, one of which is consistent with the curriculum learning principle, and propose a novel theoretical framework as a potential explanation for this strategy. This framework, which assumes a teaching goal of minimizing the learner's expected generalization error at each iteration, extends the standard teaching dimension model and offers a theoretical justification for curriculum learning.
Two Cornell experts in artificial intelligence (AI) have joined a nationwide team setting out to ensure that when computers are running the world, they will make decisions compatible with human values. "We are in a period in history when we start using these machines to make judgments," said Bart Selman, professor of computer science. "If decisions are properly structured, the horrors we've seen in the movies won't happen." Selman and Joseph Halpern, professor of computer science, have become co-principal investigators for the Center for Human-Compatible Artificial Intelligence, a nationwide research effort based at the University of California, Berkeley. Initially they will collaborate with scientists at Berkeley and the University of Michigan.
Explicit, analytical thought is powerful; it moves a person to make personal and professional decisions based on gathering, assessing, and using evidence garnered through research. Not a day goes by without reading about advances in digital analytics or new ways to analyze big data. Making personal and business decisions based on an implicit, intuitive perspective, however, is just as powerful and profound -- though enigmatically inexplicable. While both decision-making strategies can be argued by steadfast advocates of each to be the best (or only), way to make choices, being flexible and open-minded to explore, consider, and embrace both is ultimately the best strategy. Listening to your intuition about a decision you are about to make has its benefits.
As part of the continued strategy and growth of Decision Point AI it has today incorporated it's professional services consulting business Decision Point AI UK as Decision Point AI Limited in Edinburgh, Scotland. We have embarked upon a major strategic engagement in the UK, USA and the Europe, using Scotland as a launch pad. Today we formally launch Decision Point AI UK our UK Professional Services Consulting capability with the'problem solved' attitude to servicing clients issues. As with our UK consulting launches 3 month lead period, we also involved in soft launches in the USA from NYC and Europe from Dublin. The company continues to gain clients and markets based on the prior'BigFour' relationships from its leadership team and new clients from the thriving Scottish technology and innovation scene.