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This article seeks to understand the encounter between Ayurveda and the modern market through an analysis of decisions regarding the product profiling, positioning and packaging of Ayurvedic medicines by its leading manufacturer, Dabur. These seemingly mundane, economic decisions are seen here as expressions of a deep operation of power, mediated through culture. The analysis takes us beyond the simplified picture of the rise of modern biomedicine as the inevitable and onward march of rationality, or that of Ayurveda as the helpless victim of modernity. It argues that the multiple strategies adopted by the Ayurvedic pharmaceutical companies, in response to the changing conditions of the market, can be viewed in larger terms as its response to the changing nature of the field of power. This identifies the 'moment of confrontation', the 'moment of withdrawal' and the 'moment of diversion' as some of the strategic responses. While these strategies did succeed in creating and retaining a foothold for Ayurvedic medicines in the modern market, this success came at a heavy cost: Ayurvedic medicine had to be cast in the mould of modern medicine and disconnected from its relationship to the knowledge system. The analysis brings out some of the ironies and dilemmas of this encounter.

The parameters of modern knowledge systems are clearly showing fault lines—that if there is a continuation of the technological systems at the heart of development, neglecting the twin issues of ecology and equity—there is a serious threat to human existence. This article seeks to answer a specific question: in the context of the twenty-first century search of offering alternatives to the hegemonic development paradigm, what kind of knowledges of production in society could possibly be best developed at this point in history? It argues that the answer lies in ‘already existing knowledge systems ( AEKS)’, accompanied by critical thinking on production, distribution and consumption systems. Locating the production of knowledge in five spaces—historical context, policy formulation, political economic structures, forms of collective action and articulation of contested epistemologies—it argues that when AEKS are understood both in form and transformation in these spaces, that the possibilities they offer for substantial alternatives can be explored.

by itself through learning using some of the sense and thinking mechanisms of people. 4 To understand the concept of AI, one must be familiar with the following terms 2 : Machine learning (ML): Machine learning is a branch of computer science that builds algorithms guided by data. Deep learning: Specific form of learning based on algorithms of neural networks. Representation learning: Representation learning is a subtype of ML in which the computer algorithm learns the features required to classify the provided data. This does not require a hand labeled data like ML. Artificial neural networks (ANNs): This involves networks of highly interconnected computer processors that has the ability to learn from past examples, analyze nonlinear data, handle imprecise information, and generalize enabling application of the model to independent data thus making it a very attractive analytical tool in the field of medicine. The greatest advantage of these systems is that they have the capability to solve the problems that are too complex to be solved by conventional methods. They are useful in various areas of medicinal science like diagnosis of diseases, biomedical identification, image analysis, and data analysis. Clinical decision support system (CDSS):A CDSS is a system between a broad dynamic (medical) knowledge database and an inferencing output mechanism that are a set of algorithms derived from evidence-based medical practice executed through medical logic modules. Currently, the intuitive interphase with