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Photocredits: https://www.semanticscholar.org/paper/GNNExplainer%3A-Generating-Explanations-for-Graph-Ying-Bourgeois/00358a3f17821476d93461192b9229fe7d92bb3f Graph neural networks (GNNs) are a powerful type of machine learning model that can be used to learn from and make predictions on graph-structured data. GNNs are used in a wide variety of applications, including social network analysis, fraud detection, and drug discovery. However, GNNs can also be complex and difficult to understand.
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Photocredits: https://www.researchgate.net/figure/Explaining-predictions-of-an-AI-system-using-SA-and-LRP-Image-courtesy-of-W-Samek-15_fig7_336131051 Deep learning models have become increasingly powerful and successful in recent years, but they can also be complex and difficult to understand. This can make it challenging to trust their decisions, especially in critical applications where it is important to know why a model made a particular prediction. The increasing complexity of deep
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Photocredits: https://realkm.com/2023/03/06/introduction-to-knowledge-graphs-part-2-history-of-knowledge-graphs/ Data representation and organization has witnessed significant evolution, especially in the last few decades. Central to this evolution is the concept of the Knowledge Graph (KG). But where did it all start? And how did we progress from simple linked data structures known as semantic webs to today’s sophisticated AI-integrated knowledge graphs? The
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Photocredits: https://www.tigergraph.com/blog/understanding-graph-embeddings/ Knowledge graphs (KGs) provide a structured and semantically rich way to represent knowledge by capturing entities and their relationships. While KGs are valuable in capturing relational information, many machine learning models require data in vector form. That’s where embeddings come into play. What are Embeddings? Embeddings are dense vector representations of data, which
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Photo credits: https://paperswithcode.com/method/multi-head-attention Multi-head attention is a mechanism that allows a model to focus on different parts of its input sequence, from different perspectives. It is a key component of the Transformer architecture, which is a state-of-the-art model for natural language processing (NLP) tasks such as machine translation, text summarization, and question answering. How multi-head
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Photo Credits: https://www.kaggle.com/code/residentmario/transformer-architecture-self-attention Self-attention is a mechanism that allows a model to focus on different parts of its input sequence. It is a key component of the Transformer architecture, which is a state-of-the-art model for natural language processing (NLP) tasks such as machine translation, text summarization, and question answering. How self-attention works Self-attention works by
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Photo Credits: https://ai-techpark.com/navigating-ethics-in-the-era-of-generative-ai/ Generative AI is a rapidly evolving field with the potential to revolutionize many industries. This technology can be used to create realistic and convincing content, such as images, videos, and text. This has the potential to be used for good, such as creating educational materials or generating realistic medical images. However, it
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Photocredits:https://www.opensourceforu.com/2018/03/all-about-edge-computing-architecture-open-source-frameworks-and-iot-solutions/ Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data, i.e., at the edge of the network. This enables faster responses and reduced latency for applications that require real-time data processing and decision-making. Apache Kafka is a distributed streaming platform that can be used to
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PhotoCredits: https://venturebeat.com/ai/4-trends-shaping-the-future-of-practical-generative-ai/ Generative AI is a rapidly developing field with the potential to revolutionize many industries and aspects of our lives. In the next 5-10 years, we can expect to see generative AI become even more powerful and sophisticated, with new and innovative applications emerging all the time. One of the most exciting areas of
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Photo Credits: https://aibusiness.com/nlp/meta-offers-companies-free-use-of-llama-2-language-model Llama 2 is a large language model (LLM) developed by Meta AI and Microsoft. It was released in April 2023, and is the successor to Llama 1. Llama 2 is an open-source model, which means that it is freely available for anyone to use, modify, and improve. This makes it a powerful
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