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Introduction to Artificial Intelligence, Machine Learning and Neural Networks


Distinction between AI, machine learning, deep learning and classic AI;; Explanation of rule based systems and limitations;; Representing data as …

Survey on Evaluation Methods for Dialogue Systems Artificial Intelligence

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class.

Symbolic AI vs Neural Networks • r/artificial


This reminds me a bit of what /u/sixwings used to say. I think the idea was that (most) neural networks were still basically just rule-based systems and that they all used supervised learning (even the reinforcement/unsupervised learning ones). I will also note that often the network's inputs and outputs are symbolic in the sense that we associate them with local and (somewhat) interpretable meanings (although this is a bit more debatable for things like pixels). Under all of this lies a question of what "a GOFAI approach" is. Neural networks have certainly been around for a very long time, so someone could say they're old(-fashioned), good and AI...

Getting up to speed on AI - Banking Exchange


These terms--just now coming into the banking vernacular--can be confusing, especially since they are all subsets of the equally dense term artificial intelligence or AI. It behooves bankers to make the effort to get a handle on what they mean, how they are interrelated, and, most important, what potential they offer to improve customer relationships, reduce fraud, beef up operational efficiency, reduce costs, and, ultimately, add to revenues. Through a number of interviews with Banking Exchange, bankers, analysts, and a prominent futurist paint a picture of what AI and its various subsets will mean to the banking industry. "AI is making it possible for customers to engage with companies in more ways than ever before through voice, gesture recognition, video, and chat, while opening up possibilities to serve customers beyond their own digital properties," says Brad Stewart, senior vice-president, head of product, AI Enterprise Solutions, at Wells Fargo. "We think these will continue to get even more sophisticated.