experience


Can Machine Learning Turn Big Data into No Big Deal?

#artificialintelligence

With technology moving so fast, new ways to automate, and connected machines, how can managers and engineers simplify the complexity of that ecosystem? Is machine learning (ML) or artificial intelligence (AI) the key? This article will define some buzzwords, what they mean, and if they might help simplify these complex technologies so that you can move back into production. New technologies, such as Big Data and the Industrial Internet of Things, are gaining more traction. While security is a concern, some companies push ahead because the benefits are too great.


Banking's One-to-One Future is Finally Possible

#artificialintelligence

Almost a quarter century ago, a book was written about how organizations would focus on share of customer as opposed to share of market, building a personalized collaboration driven by big data. With advanced analytics, banking may finally getting close to realizing this vision. In 1993, a then revolutionary book, "The One to One Future: Building Relationships One Customer at a Time" was published, proposing the idea that as technology makes it affordable to track individual customers, marketing shifts from finding customers for products to finding products for customers. According to the authors, Don Peppers and Martha Rogers, Ph.D., a company could use technology to gather information about, and to communicate directly with, individuals to form a commercial bond. The book became a bestseller, and was on every marketer's bookshelf … almost a quarter century ago.


The Mind at AI: Horseless Carriage to Clock

AI Magazine

Commentators on AI converge on two goals they believe define the field: (1) to better understand the mind by specifying computational models and (2) to construct computer systems that perform actions traditionally regarded as mental. We should recognize that AI has a third, hidden, more basic aim; that the first two goals are special cases of the third; and that the actual technical substance of AI concerns only this more basic aim. This third aim is to establish new computation-based representational media, media in which human intellect can come to express itself with different clarity and force. This article articulates this proposal by showing how the intellectual activity we label AI can be likened in revealing ways to each of five familiar technologies. AI is not about building artificial intelligences, nor is it about understanding the human mind or any other kind of mind.


The 1994 AAAI Robot-Building Laboratory

AI Magazine

The 1994 AAAI Robot-Building Laboratory (RBL-94) was held during the Twelfth National Conference on Artificial Intelligence. The primary goal of RBL-94 was to provide those with little or no robotics experience the opportunity to acquire practical experience in a few days. Thirty persons, with backgrounds ranging from university professors to practitioners from industry, participated in the three-part lab. The event was meant to appeal to the hacker yearnings of participants to experience for themselves the joys and excitement of constructing a robot and to learn about the real problems of such an endeavor. RBL-94 was inspired by and shared a common history with a couple of robot-building laboratories.


Book Reviews

AI Magazine

Stephen Grossberg The expanded edition of Perceptrons (MIT Press, Cambridge, Mass, 1988, 292 pp, $12.50) by Marvin L. Minsky and Seymour A. Papert comes at a time of unprecedented interest in the biological and technological modeling of neural networks. The one-year-old International Neural Network Society (INNS) already has over 3500 members from 38 countries and 49 U.S. states, with members joining at the rate of more than 200 per month. The American Association for Artificial Intelligence was, in fact, a cooperating society at the INNS First Annual Meeting in Boston on 6-10 September 1988. Hardly a week goes by in which a scientific meeting or special journal issue does not feature recent neural network research. Thus, substantive technical reviews or informed general assessments of the broad sweep of neural network research are most welcome to help interested scientists find their way into this rapidly evolving technology.


Book Reviews

AI Magazine

Stephen Grossberg The expanded edition of Perceptrons (MIT Press, Cambridge, Mass, 1988, 292 pp, $12.50) by Marvin L. Minsky and Seymour A. Papert comes at a time of unprecedented interest in the biological and technological modeling of neural networks. The one-year-old International Neural Network Society (INNS) already has over 3500 members from 38 countries and 49 U.S. states, with members joining at the rate of more than 200 per month. The American Association for Artificial Intelligence was, in fact, a cooperating society at the INNS First Annual Meeting in Boston on 6-10 September 1988. Hardly a week goes by in which a scientific meeting or special journal issue does not feature recent neural network research. Thus, substantive technical reviews or informed general assessments of the broad sweep of neural network research are most welcome to help interested scientists find their way into this rapidly evolving technology.


Technoloev Transfer

AI Magazine

We use our experience with the Dipmeter Advisor system for well-log interpretation as a case study to examine the development of commercial expert systems. We discuss the nature of these systems as we see them in the coming decade, characteristics of the evolution process, development methods, and skills required in the development team. We argue that the tools and ideas of rapid prototyping and successive refinement accelerate the development process. We note that different types of people are required at different stages of expert system development: Those who are primarily knowledgeable in the domain, but who can use the framework to expand the domain knowledge; and those who can actually design and build expert system tools and components We also note that traditional programming skills continue to be required in the development of commercial expert systems Finally, we discuss the problem of technology transfer and compare our experience with some of the traditional wisdom of expert system development. We have observed during this effort that the development of a commercial expert system imposes a substantially different set of constraints and requirements in terms of characteristics and methods of development than those seen in the research environment.


Knowledge And Experience In Artificial Intelligence

AI Magazine

Via G. Galilei 5, 21027 Ispra (VA), Italy The period since the last conference in this series has been characterized by the explosive expansion of AI out of the confines of institutions of basic research like university departments into the worlds of industry, business, and government (a development I had long expected). But it seems to me that there are plenty-perhaps an overabundance-of other occasions, other conferences, other workshops, and the like, at which the applications of AI would appropriately be considered. In fact, it is ironic-though perhaps it may be understandable-that precisely now, when the outside world has discovered and started showing its appreciation of AI and its potential, there is a widespread malaise among research workers in the field about the health of their subject. This malaise has to do not only with logistic issues such as the drain of very good people from research into applications, or some of the gross inadequacies of structural and funding support by governments. It has to do also with the very heart and methodology of the subject.


Book Reviews

AI Magazine

Part of the Media Laboratory's heritage (its origins are in the School of Architecture) is a startling receptivity to the arts, especially music and the visual arts, and Brand repeatedly returns to this subject. Even here, intellectualism reigns: It is symptomatic that the lab members' interest in literature seems to be limited to science fiction. This lopsidedness echoes Turkle's complaint that hackers ignore the texture (emotion) of music in favor of its structure (intellect). Not an engineer himself, Brand is not always in a position to critically evaluate what he saw; I was reminded of persons who, on seeing ELIZA, concluded that computerized psychotherapy was just around the corner. As Brand points out, the Media Lab replaces the publish-orperish imperative with demo or die, and anyone who has produced a demo knows something about practical mendacity.


Selection of an Appropriate Domain for an Expert System

AI Magazine

This article discusses t,he selection of the domain for a knowledge-based expert system for a corporate application The selection of the domain is a critical task in an expert system development At the st,art of a project looking into the development of an expert, syst,em, the knowledge engineering project team must investigate one or several possible expert system domains They must decide whether the selected application(s) are best suited to solution by present expert system technology, or if there might he a hettel way (or, possibly, no way) to attack the problems. If there arc several possibilities, the team must also rank the potential applications and select the best availahlc To evaluate the potential of possible application domains, it has proved very useful to have a set of desired at,trihutes for a good expert system domain. This art,iclc presents such a set of attrihut,es The at,trihute set was developed as part of a major expert system development project at GTE Lahorat.ories. In particular, it focuses on selecting an expert system domain for a corporate application. One of the prime arcas of corporate interest is expert systems.