Properly managing data is more essential than ever. Organizations now operate among a complicated web of business applications, ushering in extensive amounts of analytical information that can quickly become unwieldy. Without the right oversight, companies miss out on the chance to make the most of this data to drive smarter decision making, strategic planning and even reduce costs. The growing use of generative AI, machine learning and other emerging technologies are posed to transform data management but how can businesses best leverage these platforms to glean the most business value and risk assessment? In the upcoming Data Management: Navigating Opportunities for Success summit, leading experts in the field will discuss the latest data management strategies as well as what’s next in data analytics and architecture.
The adoption of zero trust has surged in recent years, driven by two main factors: 1). A wave of high-profile data breaches that highlighted the need for enhanced cybersecurity strategies and 2). The COVID-19 pandemic created the need for remote access technologies beyond VPN. While the zero trust model can be highly beneficial, it does have some challenges. That’s why making zero trust cybersecurity as effective as possible starts by understanding its challenges. In the upcoming The Zero Trust Journey: From Concept to Implementation summit, industry leaders, experts and practitioners provide resources and recommendations to help you build a zero trust framework.
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Implementing AI in K-12 education April 22, 2024 by Dan Wilson In the latest episode of the AI Think Tank Podcast, we ventured into the rapidly evolving intersection of artificial intelligence and K-12 education. I was fortunate to host a discussion that not only explored the transformative potentials of AI in educational settings but also tackled the complexities and ethical concerns that come with such technological integration. Joining me were friends Rebecca Bultsma and Ahmad Jawad, two notable experts who brought a wealth of knowledge and insight to our conversation.
Understanding GraphRAG – 3 Implementing a GraphRAG solution April 22, 2024 by Ajit Jaokar 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.
Quantization and LLMs – Condensing models to manageable sizes April 19, 2024 by Kevin Vu 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.
In-Depth
How to implement big data for your company April 23, 2024 by Yana Ihnatchyck 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.
Understanding GraphRAG – 2 addressing the limitations of RAG April 22, 2024 by Ajit Jaokar 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.
How predictive analytics improves payment fraud detection April 22, 2024 by Zachary Amos 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.
Diffusion and denoising – Explaining text-to-image generative AI April 19, 2024 by Kevin Vu 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.
How data impacts the digitalization of industries April 18, 2024 by Jane Marsh 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?
DSC Weekly 16 April 2024 April 16, 2024 by Scott Thompson Read more of the top articles from the Data Science Central community.
