Monthly Archives: February 2023

“Above the Trend Line” – Your Industry Rumor Central for 2/28/2023

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

Typeface Emerges From Stealth With a Generative AI Application For Enterprise Content Creation and $65 Million in Funding 

Typeface, the generative AI application for enterprise content creation, is emerging from stealth to empower businesses to securely create exceptional, brand-personalized content faster and easier than ever before.

Data Science 101: The Data Science Process

Welcome to insideBIGDATA’s Data Science 101 channel brining you perspectives for the topics of the day in data science, machine learning, AI and deep learning. Many of the video presentations come from my lectures for my Introduction to Data Science class I teach at UCLA Extension. In today’s slide-based video presentation I discuss The Data […]

Level AI Introduces the Future of Customer Service With Generative AI Solution, AgentGPT

Level AI, a leader in advanced conversation intelligence solutions for the contact center, announced a game-changing generative AI product. AgentGPT is a secure, omniscient generative AI system for customer service teams, trained on a client’s proprietary customer conversational data. It helps agents successfully handle even the most complex questions, stepping in to answer what can’t be found in the help section or other publicly available resources.

Research Highlights: A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

The Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A pretrained foundation model, such as BERT, GPT-3, MAE, DALLE-E, and ChatGPT, is trained on large-scale data which provides a reasonable parameter initialization for a wide range of downstream applications.

The Strength of America’s Data Will Determine the Impact of the CHIPS and Science Act

In this special guest feature, Robert Lowe, CEO of Wellspring Worldwide, looks into how data strength needs to be the key focal area as the government begins to act on the CHIPS Act and future innovation efforts.

Book Review: Tree-based Methods for Statistical Learning in R

Here’s a new title that is a “must have” for any data scientist who uses the R language. It’s a wonderful learning resource for tree-based techniques in statistical learning, one that’s become my go-to text when I find the need to do a deep dive into various ML topic areas for my work. The methods […]

Google Cloud Unveils Its 2023 Data and AI Trends Report

Google Cloud worked with IDC on multiple studies involving global organizations across industries in order to explore how data leaders are successfully addressing key data and AI challenges. The company compiled the results in its 2023 Data and AI Trends report. In it, you’ll find the metrics-rich research behind the top five data and AI trends, along with tips and customer examples for incorporating them into your plans. 

Heard on the Street – 2/21/2023

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

Research Highlights: MIT Develops First Generative Model for Anomaly Detection that Combines both Reconstruction-based and Prediction-based Models

Kalyan Veeramachaneni and his team at the MIT Data-to-AI (DAI) Lab have developed the first generative model, the AutoEncoder with Regression (AER) for time series anomaly detection, that combines both reconstruction-based and prediction-based models. They’ve been building it for three years—AER has been learning and extracting intelligence for signals and has reached maturity to outperform the market’s leading models significantly.