The history and the future of Artificial Intelligence could be summarized into three distinctive phases: embryonic, embedded and embodied. We briefly describe early efforts in AI aiming to mimic intelligent behavior, evolving later into a set of the useful, embedded and practical technologies. We project the possible future of embodied intelligent systems, able to model and understand the environment and learn from interactions, while learning and evolving in constantly changing circumstances. We conclude with the (heretical) thought that in the future, AI should re-emerge as research in complex systems. One particular embodiment of a complex system is the Intelligent Enterprise.

A number of researchers have proposed using digital marks to provide ownership (watermarking) identification for the property. One way of data hiding is digital signature, copyright label or digital watermark that completely characterizes the person who applies it and, therefore, marks it as being his property. Digital Watermarking is the process that embeds data called a watermark into an object such that watermark can be detected and extracted later to make an assertion about the object. Watermarking is either "visible" or "invisible". Although visible and invisible are visual terms watermarking is not limited to images, it can also be used to protect other types of multimedia object. Our research work is on watermarking techniques in particular.Many of these proposed techniques share three specific weaknesses: complexity of copy detection, vulnerability to mark removal after revelation for ownership verification, and mark integrity issues due to partial mark removal. This paper presents a method for watermarking Handwritten Signature that achieves robustness by responding to these three weaknesses. The key techniques involve using secure functions to generate and embed image marks that is more detectable, verifiable, and secure than existing protection and detection techniques.

With the advent of microarray technology it has been possible to measure thousands of expression values of genes in a single experiment. Biclustering or simultaneous clustering of both genes and conditions is challenging particularly for the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining. The objective here is to find sub-matrices, i.e., maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a range of conditions while maximizing the volume simultaneously. Since these two objectives are mutually conflicting, they become suitable candidates for multi-objective modeling. In this study, we will describe some recent literature on biclustering as well as a multi-objective evolutionary biclustering framework for gene expression data along with the experimental results.

There has been phenomenal and tremendous growth of PE's in India. They were established to attain the 'commanding heights' of the economy of the country and achieve rapid growth of industrialization and economic development. Some of these PEs later became 'white elephant' and started incurring losses. Several of them became chronically sick industries. The Govt. declared the disinvestment process, which began in 1991 with the sale of minority stakes in some PE's, shifted focus to strategic sales during 1999-2000 to 2003-04. The present UPA Govt. announced that, all disinvestment will be considered on a transparent and consultative case-by-case basis. The Govt. has approved the constitution of a "National Investment Fund" comprising of proceeds from disinvestment. The present paper is an attempt to discuss same important issues such as restructuring, valuation of equity, Mechanism of disinvestment, Application of disinvestment proceeds, Parliamentary approval and political issues.