Computer scientists

Computational issues that are pertinent in MD simulations are time complexity of the force calculations and the accuracy of the particle trajectories including other necessary quantitative measures. These two issues overwhelm computational scientists in several ways. MD simulations are done for long time periods and since numerical integration techniques involve discretization errors and stability restrictions which when not put in check, may corrupt the numerical solutions in such a way that they do not have any meaning and therefore, no useful inferences can be drawn from them. Different strategies such as globally stable numerical integrators and multiple time steps implementations have been used in this respect (see [27, 31]). 

In recent decades, computer scientists have tried to provide computers with the ability to learn. This area of research was summarized under the umbrella term "machine learning . Today machine learning is defined as "the study of computer algorithms that improve automatically through experience [1]. 

A variety of methods have been developed by mathematicians and computer scientists to address this task, which has become known as data mining (see Chapter 9, Section 9.8). Fayyad defined and described the term data mining as the nontrivial extraction of impHcit, previously unknown and potentially useful information from data, or the search for relationships and global patterns that exist in databases [16]. In order to extract information from huge quantities of data and to gain knowledge from this information, the analysis and exploration have to be performed by automatic or semi-automatic methods. Methods applicable for data analysis are presented in Chapter 9. 

With the realization that there are only a limited number of stable folds and many unrelated sequences that have the same fold, biologically oriented computer scientists started to address what is called the inverse folding problem namely, which sequence patterns are compatible with a specific fold If this question can be answered, such patterns could be used to search through the genome sequence databases and extract those sequences that have a specific fold, such as the cx/p barrel or the immunoglobulin fold. 

The relevance of the information to the process worker is another factor in design. This principle is often violated with the introduction of new VDU-based computer systems where information needed to assist computer scientists or production managers is mixed with information relevant for the safe operation of the plant. Clearly, some kind of structuring and prioritization will be necessary for the different users of the system. 

These trends will accelerate the shift of model building from computational scientists to experimental scientists. 

The days in which IP (intellectual property) strategists were separated into groups of pharmacologists (chemists or biologists) and other groups of computer scientists are slowly passing—in the same manner in which the tech-... 

To a computer scientist, VS is nothing but another text mining, only the bits and bytes stored that contain molecular information adopt a format quite different from natural language and without adequate warning cannot be quickly interpreted. It is not that modem day text does not contain text that is not natural language, but that they are adequately flagged and do not stop the NLP software. For example,... 

Computer scientists usually define AI in terms of what it can accomplish. They are especially interested in the creation of intelligent software that can reproduce the kind of behavior that humans would recognize as intelligent, such as understanding language or conducting a conversation. 

In the final chapter, Nawwaf Kharma looks ahead to some of the methods that experimental scientists may be using in the coming years. While the main aim of the book is to provide an introduction to AI methods, Kharma delves more deeply into some challenging new areas, opening a window on how a computer scientist views the use of AI to solve practical problems. 

Numerous books have been written on the topic of artificial neural networks most are written for, or from the point of view of, computer scientists and these are probably less suited to the needs of experimental scientists than those written with a more general audience in mind. 

In early work, GA strings were binary coded. Computer scientists are comfortable with binary representations and the problems tackled at that time could be easily expressed using this type of coding. Binary coding is sometimes appropriate in scientific applications, but it is less easy to interpret than alternative forms, as most scientific problems are naturally expressed using real numbers. 

SHRDLU was an example of a system that operated in a well-defined task domain and this software was an important steppingstone in the development of AI programs, enabling computer scientists to better understand how to construct expert systems and how to handle the user-computer interaction. However, the market for software that can rearrange children s blocks is limited and the development of the first ES in chemistry was a far more significant milestone in science. 

Computer scientists may use this book to gain a clearer picture of how experimental scientists use Artificial Intelligence tools. Chemists, biochemists, physicists, and others in the experimental sciences who have data to analyze or simulations to run will find tools within these pages that may speed up their work or make it more effective. For both groups, the aim of this book is to encourage a broader application of these methods. 

A decade ago, artificial intelligence (AI) was mainly of interest to computer scientists. Few researchers in the physical sciences were familiar with the area fewer still had tried to put its methods to practical use. However, in the past few years, AI has moved into the mainstream as a routine method for assessing data in the experimental sciences it promises to become one of the most important scientific tools for data analysis. 

In some respects, this is a strange state of affairs. The limits of the field are vague Even computer scientists sometimes find it difficult to pin down exactly what characterizes an AI application. Nevertheless, in a review that focuses on the use of AI in science, it would be cowardly to hide behind the excuse of vagueness, so we shall have a stab at defining AI ... 

Not all computer scientists would agree with this broad statement however, it does encompass virtually every AI method of interest to the chemist. As we shall see, learning is a key part of the definition. In each method discussed in this chapter, there is some means by which the algorithm learns, and then stores, knowledge as it attempts to solve a problem. 

For example, in distillation simulations the distillate and bottoms composition should be called XD(J) and XB(J) in the program. The tray compositions should be called X(NJ), where IV is the tray number starting from the bottom and J is the component number. Many computer scientists put all the compositions into one variable X(NJ) and index it so that the distillate is X(1J), the top tray is X(2J), etc. This gives a more compact program but makes it much more difficult to understand the code. 

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