Who is Responsible for Climate Change? — A Graphical Approach

Who is Responsible for Climate Change? — A Graphical Approach.

Overview

Author(s): Prashant Mudgal

Originally published on Towards AI.

A Data-driven approach to the global warming issue

So, here I was, minding my own business and teaching myself the basics of quantum computing when I enrolled in IBM’s global quantum summer school this August, it’s an intensive course, so one has to really immerse oneself to understand the basics, I was reading various blogs and articles written on the relevant articles by many people, and that’s when I stumbled upon one guy whose repository was quite rich in not only quantum related articles but also contained articles on philosophy, spirituality, and a few other topics that weren’t that evolved in my opinion but one that caught my… Read the full blog for free on Medium.

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Published via Towards AI

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AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.