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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

I provide below my perspective on what was interesting, innovative, and influential in my watch list of the Top 10 data innovation trends during 2020. 1) Automated Narrative Text Generation tools became incredibly good in 2020, being able to create scary good “deep fake” articles.

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An AI Data Platform for All Seasons

Rocket-Powered Data Science

To see this, look no further than Pure Storage , whose core mission is to “ empower innovators by simplifying how people consume and interact with data.” See additional references and resources at the end of this article. In deep learning applications (including GenAI, LLMs, and computer vision), a data object (e.g.,

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Bringing an AI Product to Market

O'Reilly on Data

In this article, we turn our attention to the process itself: how do you bring a product to market? Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment.

Marketing 361
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The road to Software 2.0

O'Reilly on Data

Roughly a year ago, we wrote “ What machine learning means for software development.” In that article, we talked about Andrej Karpathy’s concept of Software 2.0. In short, we can use machine learning to automate software development itself. It’s time to evaluate what has happened in the year since we wrote that article.

Software 259
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The InnoGraph Artificial Intelligence Taxonomy

Ontotext

The official (first) repo is tensorflow/tensor2tensor that has topics: machine-learning reinforcement-learning deep-learning machine-translation tpu. By exploring the first topic machine-learning , we find 117k Github repos. will most likely have an article on Wikipedia that can serve as a starting point.

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Accelerating scope 3 emissions accounting: LLMs to the rescue

IBM Big Data Hub

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors.

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R vs Python: What’s the Best Language for Natural Language Processing?

Sisense

We’ll actually do this later in this article. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. Some standard Python libraries are Pandas, Numpy, Scikit-Learn, SciPy, and Matplotlib. R libraries. Every library has its own purpose and benefits.