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NANOTECHNOLOGY-THE FUTURE TECHNOLOGY

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NANOTECHNOLOGY-THE FUTURE TECHNOLOGY https://educationuniqueone.blogspot.com/  We can't imagine into how many fields this nanotechnology has entered.Nanotechnology has the vast diversity in many fields.   NANOTECHNOLOGY IS MAINLY CONCERNED ABOUT NANO PARICLES.   This nano particles are about the size of the molecules and can be incorporated in vast variety of products and fields.   The main reason for the development of nanotechnology is about nano particles  to make materials or things  increase  in strength,durability,stiffness,tensile strength,long losting ,high performance ,low cost manufacturing,light weight and lot more.  Few examples of this nano particles are titanium dioxide,aluminium particles,copper particles,carbon,silicon particles..... The main aim of this technolgy is even to make products as small as a few nanometers in length.  This nano field has many diversities and penetrated into even more fields. ...
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Machine Learning- Only course that dominates the future.

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Machine Learning-Only course that dominates the future.   NO DOUBT,Today's world is developing faster than ever. The skills required to survive in   the modern word is completely changing. People are adopting new strategies to  get hired in multi national companies like google,Facebook,IBM,Apple and so on.  But the task of those persons who get hired are completely changing due  to the advancement in technology and newly entering artificial intelligence injecting into the machines.   Machines are becoming so powerful that they can handle all the complicated tasks that human can only be able to do with ease.   Many scientist have discovered a lot of machines in order increase the productivity with decrease in human power resource.  Some scientists and mathematicians thought why don't machines can capable of handling intelligent thinking tasks which are performed by only humans in order to decrease the burden of human brain. ...
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why large language model (LLMS) hallucinate?

Large Language Models (LLMs) "hallucinate" when they generate information that is plausible-sounding but factually incorrect, nonsensical, or unfaithful to the input prompt. It's a key challenge in making LLMs reliable. Here's a breakdown of why this happens: 1. The Nature of LLMs: Pattern Recognizers, Not Knowledge Engines  * Statistical Prediction: Fundamentally, LLMs are trained to predict the next most probable word (or "token") in a sequence based on the vast amount of text data they've processed. They are excellent at identifying statistical patterns, grammar, and style.  * Lack of True Understanding/Reasoning: Unlike humans, LLMs don't "understand" concepts in the way we do, nor do they possess real-world common sense or consciousness. They don't have personal experiences or an internal model of reality. Their "knowledge" is embedded in the statistical relationships between words and phrases.  * Optimized for Fluency, N...
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