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Understanding GraphRAG - 3 Implementing a GraphRAG solution - DataScienceCentral.com
In this third part of the solution, we discuss how to implement a GraphRAG. This implementation needs an understanding of Langchain which we shall also discuss. As we have discussed, the combination of Knowledge Graphs and vector databases brings the ability to manage both structured and unstructured information. To implement the end to end solution,… Read More »Understanding GraphRAG – 3 Implementing a GraphRAG solution
Understanding GraphRAG - 2 addressing the limitations of RAG - DataScienceCentral.com
Background We follow on from the last post and explore the limitations of RAG and how you can overcome these limitations using the idea of a GRAPHRAG. The GRAPHRAG combines a knowledge graph with a RAG. Thus, the primary construct of the GRAPHRAG is a knowledge graph. A Knowledge Graph represents knowledge and its relationships… Read More »Understanding GraphRAG – 2 addressing the limitations of RAG
Why is AI different? It Can Guide Our Societal Aspirations - DataScienceCentral.com
Traditional analytics optimize based on existing data, reflecting past realities, limitations, and biases. In contrast, AI focuses on future aspirations, identifying the learning needed to achieve aspirational outcomes and guiding your evolution toward these outcomes. When I talk to my students, the question I keep getting is, “Is AI really different from traditional analytics?” My… Read More »Why is AI different? It Can Guide Our Societal Aspirations
Cybersecurity practices and AI deployments - DataScienceCentral.com
Explore the convergence of AI and cybersecurity with expert Tim Rohrbaugh on the AI Think Tank Podcast Show #4. Learn about deploying open-source AI models locally using tools like WSL, FlowiseAI, ollama, and OpenWebUI for enhanced data security and privacy. Gain insights into the benefits of AI in cybersecurity and how to leverage technology to protect digital assets.
How predictive analytics improves payment fraud detection - DataScienceCentral.com
Payment fraud is a significant issue for banks, customers, government agencies and others. However, advanced predictive analytics tools can reduce or eliminate it. Minimizing false alarms Many people have had the embarrassing experience of trying to pay for something and having the transaction flagged. It can be understandably frustrating whether this happens at an e-commerce… Read More »How predictive analytics improves payment fraud detection
Retrieval augmented fine-tuning and data integrations - DataScienceCentral.com
Join us on the AI Think Tank Podcast as host Dan Wilson delves into RAFT technology with AI experts Suman Aluru and Caleb Stevens. Discover how Retrieval Augmented Fine Tuning is revolutionizing AI applications by enhancing model accuracy and streamlining data integration. Learn about its practical impact in reducing AI errors and its superiority over traditional training methods. Don't miss this insightful discussion on the future of AI technology.
GenAI, RAG, LLM… Oh My! - DataScienceCentral.com
I am pleased to announce the release of my updated “Thinking Like a Data Scientist” workbook. As a next step, I plan to work on a supplemental workbook incorporating GenAI tools like OpenAI ChatGPT, Google Gemini, and Microsoft Copilot with the Thinking Like a Data Scientist approach. We need to improve our prompt engineering skills… Read More »GenAI, RAG, LLM… Oh My!
Diffusion and denoising - Explaining text-to-image generative AI - DataScienceCentral.com
The concept of diffusion Denoising diffusion models are trained to pull patterns out of noise, to generate a desirable image. The training process involves showing model examples of images (or other data) with varying levels of noise determined according to a noise scheduling algorithm, intending to predict what parts of the data are noise. If… Read More »Diffusion and denoising – Explaining text-to-image generative AI
Understanding GraphRAG - 1: The challenges of RAG - DataScienceCentral.com
Background Retrieval Augmented Generation(RAG) is an approach for enhancing existing LLMs with external knowledge sources, to provide more relevant and contextual answers. In a RAG, the retrieval component fetches additional information that grounds the response to specific sources and the information is then fed to the LLM prompt to ground the response from the LLM(the… Read More »Understanding GraphRAG – 1: The challenges of RAG
Quantization and LLMs - Condensing models to manageable sizes - DataScienceCentral.com
The scale and complexity of LLMs The incredible abilities of LLMs are powered by their vast neural networks which are made up of billions of parameters. These parameters are the result of training on extensive text corpora and are fine-tuned to make the models as accurate and versatile as possible. This level of complexity requires… Read More »Quantization and LLMs – Condensing models to manageable sizes
Implementing AI in K-12 education - DataScienceCentral.com
Explore the future of education with the AI Think Tank Podcast, host Dan Wilson and guests Rebecca Bultsma and Ahmad Jawad discuss the integration of AI in K-12 education. Dive into the potential benefits, ethical considerations, and practical applications of AI technologies in enhancing learning experiences and addressing the challenges of modern education. Join us for insightful analysis on how artificial intelligence is transforming educational practices and shaping responsible adoption strategies in schools.
Losing control of your company's data? You're not alone - DataScienceCentral.com
Photo by Hermann Traub on Pixabay Losing control of your company’s data? You’re not alone To survive and thrive, data likes to be richly and logically connected across a single, virtually contiguous, N-dimensionally extensible space. In this sense, data is ecosystemic and highly interdependent, just as the elements of the natural world are. Rich connectivity… Read More »Losing control of your company’s data? You’re not alone
How data impacts the digitalization of industries - DataScienceCentral.com
Since data varies from industry to industry, its impact on digitalization efforts differs widely — a utilization strategy that works in one may be ineffective in another. How does the variety and availability of information impact the digital transformation process in various fields? The connection between data and digitalization Data is the core transformative tool… Read More »How data impacts the digitalization of industries
Data fitness and its impact potential on enterprise agility - DataScienceCentral.com
Illustration by Dirk Wouters on PIxabay I had the opportunity to attend Enterprise Agility University’s prompt engineering course in April. The course provided a helpful agility lens through which to view efforts on how to make large language models useful. An LLM in this current enterprise context is like a herd of unruly sheep—you need… Read More »Data fitness and its impact potential on enterprise agility
How to implement big data for your company - DataScienceCentral.com
Big data analytics empowers organizations to get valuable insights from vast and intricate data sets, offering a pathway to improved decision-making, excellent performance, and competitive advantage. As the volume of global data surges, exemplified by the expected 167 exabytes of monthly mobile traffic by 2024, the rise of analytics offers immense potential. In this article… Read More »How to implement big data for your company
How data impacts the digitalization of industries - DataScienceCentral.com
Since data varies from industry to industry, its impact on digitalization efforts differs widely — a utilization strategy that works in one may be ineffective in another. How does the variety and availability of information impact the digital transformation process in various fields? The connection between data and digitalization Data is the core transformative tool… Read More »How data impacts the digitalization of industries
1, Data Sentience: 0, Digital Consciousness - DataScienceCentral.com
The conjecture of consciousness for generative AI is not of its equality to human consciousness. It one of data storage, where, in comparison to human memory, if the feature [vector] interactions of large language models [to digital memory] are similar to how the human memory is conscious of its contents. Consciousness is defined as subjective… Read More »1, Data Sentience: 0, Digital Consciousness
Question answering tutorial with Hugging Face BERT - DataScienceCentral.com
What is Question Answering AI Question answering AI refers to systems and models designed to understand natural language questions posed by users and provide relevant and accurate answers. These systems leverage techniques from natural language processing (NLP), machine learning, and sometimes deep learning to comprehend the meaning of questions and generate appropriate responses. The goal… Read More »Question answering tutorial with Hugging Face BERT
Precision Prediction: AI Forecasting Crop Yields & Weathering Market Volatility
The world's agricultural sector faces a dual challenge: the unpredictability of crop yields and the volatility of agricultural markets. These uncertainties pose significant obstacles to farmers, businesses, and consumers alike.
LLMs, Safety and Sentience: Would AI Consciousness Surpass Humans'? - DataScienceCentral.com
There is a general expectation—from several quarters—that AI would someday surpass human intelligence. There is, however, little agreement on when, how or if ever, AI might become conscious. There is hardly any discussion on if AI becomes conscious, at what point it would surpass human consciousness. A central definition of consciousness is having subjective experience.… Read More »LLMs, Safety and Sentience: Would AI Consciousness Surpass Humans’?