Fri.Apr 26, 2024

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New Chinese Model Outperforms GPT-4 Turbo!

Analytics Vidhya

SenseTime, a leading AI company based in China, has launched its latest model, SenseNova 5.0, marking a significant advancement in artificial intelligence. This new model has been shown to outperform many powerful large language models, including GPT-4 Turbo. Despite the lack of buzz surrounding its release, it looks promising in revolutionizing various industries with its […] The post New Chinese Model Outperforms GPT-4 Turbo!

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TransUnion transforms its business model with IT

CIO Business Intelligence

Count TransUnion among the rising tide of enterprises evolving their identities thanks to IT. “We are thinking like a software company and transforming ourselves like a software company,” says Venkat Achanta, chief technology, data, and analytics officer of the $4 billion credit bureau, which is recasting itself into a customer data services provider intent on parlaying its reputation for trust and ample data assets to drive analytics, machine learning (ML), and AI development on the cloud.

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Mastering Decoder-Only Transformer: A Comprehensive Guide

Analytics Vidhya

Introduction In this blog post, we will explore the Decoder-Only Transformer architecture, which is a variation of the Transformer model primarily used for tasks like language translation and text generation. The Decoder-Only Transformer consists of several blocks stacked together, each containing key components such as masked multi-head self-attention and feed-forward transformations.

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Is Data Science a Bubble Waiting to Burst?

KDnuggets

The need for data science has not decreased or been replaced; instead, it’s the field of data science maturing, with a greater demand for specialized skills and practical experience.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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How Data Efficient GANs Generate Images of Cats and Dogs?

Analytics Vidhya

Introduction Generative adversarial networks are a popular framework for Image generation. In this article we’ll train Data-efficient GANs with Adaptive Discriminator Augmentation that addresses the challenge of limited training data. Adaptive Discriminator Augmentation dynamically adjusts data augmentation during GAN training, preventing discriminator overfitting and enhancing model generalization.

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Free Google Cloud Learning Path for Gemini

KDnuggets

Find out all about Google Cloud's latest learning path, and learn how to use the Gemini language model in the Google Cloud.

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#ClouderaLife Allyship April Q&A with Antoine Burrell

Cloudera

This month is Allyship April—a time dedicated to deepening our understanding of allyship and its profound impact on fostering inclusive cultures. Allyship isn’t merely a buzzword; it’s a fundamental commitment to actively support and advocate for marginalized individuals and communities within our organization. This month, we’ve engaged in meaningful conversations, challenged our assumptions, and committed to tangible actions that drive positive change.

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From Ego-centric To Eco-centric: Future-Proofing Energy and Utilites Operations

Data Virtualization

Reading Time: 5 minutes How we generate, manage, and consume energy is changing, especially in this volatile post-pandemic world. A sustainable utility future requires improved business resilience with the ability to quickly address new energy-transition requirements as well as increasing regulatory and political demands. The post From Ego-centric To Eco-centric: Future-Proofing Energy and Utilites Operations appeared first on Data Management Blog - Data Integration and Modern Data Management Ar

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Use your corporate identities for analytics with Amazon EMR and AWS IAM Identity Center

AWS Big Data

To enable your workforce users for analytics with fine-grained data access controls and audit data access, you might have to create multiple AWS Identity and Access Management (IAM) roles with different data permissions and map the workforce users to one of those roles. Multiple users are often mapped to the same role where they need similar privileges to enable data access controls at the corporate user or group level and audit data access.

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Bigger isn’t always better: How hybrid AI pattern enables smaller language models

IBM Big Data Hub

As large language models (LLMs) have entered the common vernacular, people have discovered how to use apps that access them. Modern AI tools can generate, create, summarize, translate, classify and even converse. Tools in the generative AI domain allow us to generate responses to prompts after learning from existing artifacts. One area that has not seen much innovation is at the far edge and on constrained devices.

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Understanding User Needs and Satisfying Them

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

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Operationalizing Data Quality: The Key to Successful Modern Analytics

Dataiku

Trash in: Trash out. The old mantra for data quality holds as true today as it did years ago. With the rise of Generative AI and the push for self-service analytics, good data is in more demand than ever before. However, with this increase in demand and democratization, ensuring good data quality has become increasingly challenging. More and more of the governance framework is increasingly automated, and ensuring data quality across all data sources and data products becomes more difficult to ha