Thoughtful, informed discussion of the future of AI and Machine Learning
Anyone who’s worked in a manufacturing space can tell you that one of the most significant challenges they face is managing supply chains of the materials and speciality items used to fabricate products. Without clear foresight into demand, how, when and from where they’ll get these vital items, the manufacturing process can quickly become untenable.
Abacus.AI’s service empowers all our developers and data scientists to rapidly create powerful deep learning models at scale, in production. We can optimize all aspects of our user experience including personalizing emails, predictive churn and providing contextual real-time recommendations. This translates a lift in both user-engagement and revenue.
Today, we at Abacus.AI are thrilled to announce our Series A funding and Mike Volpi and Ram Shriram as Board Directors. In addition, we are open-sourcing our debiasing module. In this post, we give an introduction to bias in computer vision models and discuss our new research on debiasing models.
Most of the machine learning applications are concerned with processing data such as images or databases – their key characteristic is that they can be “taken in” by a learning model all at once. They don’t have any temporal properties. Today we’ll be talking about a different case – models that deal with data that is sequential by nature, text, and voice being several examples.
Neural architecture search (NAS) is a popular area of machine learning, with the goal of automating the development of the best neural network for a given dataset. In this post, we summarize our recent paper, which suggests that existing NAS benchmarks may be too small to effectively evaluate NAS algorithms.
When reading about Machine Learning, the majority of the material you’ve encountered is likely concerned with classification problems. What about the other way around when you want to create data with predefined features? Today we’ll be breaking down VAEs and understanding the intuition behind them.
Pattern recognition is a crucial aspect of modern data analytics. These patterns can be studied to better understand the underlying structure of data and monitor behavior over time. However, there are often rare items or observations that seem to differ significantly from these patterns. These items are called anomalies (or outliers).
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- Solving A Hard AI Problem: Generating Forecasts In Sparse Data Environments! - July 30, 2020
- Becoming An AI First Organization: The 1-800-Flowers Journey - July 23, 2020
- Debiasing Facial Prediction Models with Adversarial Fine-Tuning - July 14, 2020