If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Grimes has spent her career at Google, where she currently works on data-driven resource planning, cost analysis, and distributed cluster management software as part of the Technical Institute Group. Grimes holds a PhD in Statistics from Stanford University and an AB in Anthropology from Harvard University. Meta S. Brown is a consultant, speaker and writer who promotes the use of business analytics. A hands-on analyst who has tackled projects with up to $900 million at stake, she is a recognized expert in cutting-edge business analytics. Jennifer Chayes is a Distinguished Scientist and Managing Director at Microsoft Research.
The annual Workshop on the Validation and Verification of Knowledge-Based Systems is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). The 1994 workshop was significant in that there was a definitive move in the philosophical position of the workshop from a testing-and toolbased approach to KBS evaluation to that of a formal specification-based approach. This workshop included 12 full papers and 5 short papers and was attended by 35 researchers from government, industry, and academia. The workshop is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). It has influenced the evolution of the discipline from its origins in 1988; at this time, researchers were asking the questions, How can we evaluate the correctness of KBS? How is this process different from conventional system evolution?
Intelligent real time applications are a game changer in any industry. Deep Learning is one of the hottest buzzwords in this area. New technologies like GPUs combined with elastic cloud infrastructure enable the sophisticated usage of artificial neural networks to add business value in real world scenarios. Tech giants use it e.g. for image recognition and speech translation. This session discusses how any company can leverage deep learning in real time applications.
You probably did not hear it here first. Spark has been making waves in big data for a while now, and 2017 has not disappointed anyone who has bet on its meteoric rise. That was a pretty safe bet actually, as interpreting market signals, speaking with pundits and monitoring data all pointed to the same direction.
So we were excited when Confluent announced their inaugural Kafka Hackathon. It was a great opportunity to take our passion for data science and engineering, and apply it to neuroscience. We wondered, "Wouldn't it be cool to monitor our brain wave activity? And process those signals to control devices like home appliances, light switches, TV's, and drones?" We didn't end up having enough time to implement mind control of any IoT devices during the 2-hour hackathon.
The lex leader method for breaking symmetry in CSPs typically produces a large set of lexicographic ordering constraints. Several rules have been proposed to reduce such sets whilst preserving logical equivalence. These reduction rules are not generally confuent: they may reach more than one xpoint, depending on the order of application. These fixpoints vary in size. Smaller sets of lex constraints are desirable so ensuring reduction to a global minimum is essential. We characterise the systems of constraints for which the reduction rules are confluent in terms of a simple feature of the input, and define an algorithm to determine whether a set of lex constraints reduce confuently.
We study the combination of the following already known ideas for showing confluence of unconditional or conditional term rewriting systems into practically more useful confluence criteria for conditional systems: Our syntactical separation into constructor and non-constructor symbols, Huet's introduction and Toyama's generalization of parallel closedness for non-noetherian unconditional systems, the use of shallow confluence for proving confluence of noetherian and non-noetherian conditional systems, the idea that certain kinds of limited confluence can be assumed for checking the fulfilledness or infeasibility of the conditions of conditional critical pairs, and the idea that (when termination is given) only prime superpositions have to be considered and certain normalization restrictions can be applied for the substitutions fulfilling the conditions of conditional critical pairs. Besides combining and improving already known methods, we present the following new ideas and results: We strengthen the criterion for overlay joinable noetherian systems, and, by using the expressiveness of our syntactical separation into constructor and non-constructor symbols, we are able to present criteria for level confluence that are not criteria for shallow confluence actually and also able to weaken the severe requirement of normality (stiffened with left-linearity) in the criteria for shallow confluence of noetherian and non-noetherian conditional systems to the easily satisfied requirement of quasi-normality. Finally, the whole paper may also give a practically useful overview of the syntactical means for showing confluence of conditional term rewriting systems.