He also taught the biochemistry course that I finally took, two years after finishing my Ph.D. David J. States deserves much of the credit as well. In the three years we have been working together, he greatly extended my understanding of not only what biologists know, but how they think. He has read several drafts of this chapter and made helpful suggestions. David Landsman, Mark Boguski, Kalí Tal and Jill Shirmer have also read the chapter and made suggestions. Angel Lee graciously supplied the gel used in Figure 4. Of course, all remaining mistakes are my responsibility.

When making a decision a trust officer in a bank is confronted with a large assortment of information. In keeping with the postulates of this theory, the main postulates for the analysis of the investment decision process are that there exist: 1. A memory that contains lists of industries each of which has a list of companies associated with it. The memory also contains information associated with the general economy, industries, and individual companies. The set of rules constitutes the structure of the decision processes for an individual investor. It might be compared to the "rules of thumb" of the traditional "expert," but there is an important difference In common with other problem-solving programs, the processes are used iteratively and recursively. Lists of industries and companies are searched for particular attributes; sublists are created, searched and divided again. For example, to obtain a high growth portfolio, the list of companies stored in memory is searched to obtain securities with the desired pand) characteristics.

He then solves the simpler problem and uses the information obtained in the solution of the simpler problem in the solution of the original complex problem.

Methods of Approach At the outset it might be well to distinguish sharply between two general approaches to the problem of machine learning.

The editors have provided him with special introductory sections as a ing guide to a thorough understand of the reports.

By studying biological systems, Several definitions for the term robot have been proposed principles may be discovered that can be used, perhaps by (Jablonowski and Posey, 1985). None of these definitions analogy, to improve the functional components of a robot are adequate because they exclude robot intelligence of as well as their cooperation.

It is no pure coincidence that an organic chemist rather than a computer specialist is writing this foreword.

The nature of the business doesn't matter; in every business computers have made numerous changes in record keeping, process control, and decision-making. And there will be more. One of the most important trends in computing is making computers behave intelligently. The software underneath this intelligent behavior is called an expert system, sometimes also called a knowledgebased system, or knowledge system. An expert system is a computer program that reasons about a problem in much the same way, and with about the same performance, as specialists. This chapter is about the trend toward using expert systems: what it means, how it's possible, and how to think about it. There have been lead articles about this in Fortune, Business Week, and Newsweek; most Fortune-SOO companies are using expert systems; many are establishing research and development groups for them; even staid IBM is marketing expert systems tools and using them internally. Bruce G. Buchanan I 129 There are many reasons why companies want to build an expert system. Most of them are based on the premise that: Expertise is a scarce resource. And the corollary (by Murphy's Law): Even when there is enough expertise, it is never close enough to the person who needs it in a hurry. Because this is true, almost by definition, an expert system containing some of the knowledge of a company's specialists may have several benefits.. There are several examples of expert systems working in various problem areas. At present, they are used more as "intelligent assistants" than as replacements for technicians or experts. That is, they help people think through difficult problems and may provide suggestions about what to do, without taking over every aspect of the task. Although the problems are quite different they can be categorized into two major classes problems of interpretation and problems of construction. Interpretive problem examples include Schlumberger's Dipmeter Advisor, which replicates the expertise of some of their company-wide specialists who interpret data from clients' oil wells and then sell the expert system's interpretations around the world.

We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models. Using the matrix factorization approach to collaborative filtering, the CKF accounts for time evolution by modeling each low-dimensional latent embedding as a multidimensional Brownian motion. Each observation is a random variable whose distribution is parameterized by the dot product of the relevant Brownian motions at that moment in time. This is naturally interpreted as a Kalman filter with multiple interacting state space vectors. We also present a method for learning a dynamically evolving drift parameter for each location by modeling it as a geometric Brownian motion. We handle posterior intractability via a mean-field variational approximation, which also preserves tractability for downstream calculations in a manner similar to the Kalman filter. We evaluate the model on several large datasets, providing quantitative evaluation on the 10 million Movielens and 100 million Netflix datasets and qualitative evaluation on a set of 39 million stock returns divided across roughly 6,500 companies from the years 1962-2014.

N RECENT years, wind has made an increasing contribution to the world's energy supply mix. However, there is still much to be done in all areas of the technology for it to reach its full potential. Currently, horizontal-axis wind turbines (HAWTs) are the most commonly used form. However, "modern wind farms comprised of HAWTs require significant land resources to separate each wind turbine from the adjacent turbine wakes. This aerodynamic constraint limits the amount of power that can be extracted from a given wind farm footprint. The resulting inefficiency of HAWT farms is currently compensated by using taller wind turbines to access greater wind resources at high altitudes, but this solution comes at the expense of higher engineering costs and greater visual, acoustic, radar and environmental impact" [1]. This has forced wind energy systems away from high energy demand population centres and towards remote locations with higher distribution costs. In contrast, vertical-axis wind turbines (VAWTs) do not need to be oriented to wind direction and can be positioned closely together, potentially resulting in much higher efficiency. VAWT can also be easier to manufacture, may scale more easily, are typically inherently lightweight with little or no noise pollution, and are more able to tolerate extreme weather conditions [2].