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The Best Machine Learning Research of Summer 2019
Academic institutions, AI labs, and research departments of other organizations are constantly generating novel insights into data science, whether it’s machine learning, deep learning, NLP, or other disciplines. Summer 2019 generated some interesting machine learning research, and here are a few of our top picks. [Related Article: The Best... Read more
Interoperable AI: High-Performance Inferencing of ML and DNN Models Using Open-Source Tools
TensorFlow? PyTorch? Keras? There are many popular frameworks to choose from when working with deep learning and machine learning models, each with its own pros and cons for practical usability in product development or research. Once you decide which to use to train your model, you need to figure... Read more
FSGAN: Subject Agnostic Face Swapping and Reenactment
Generative adversarial networks are getting a lot of press as the general public raises their fears on the forgery techniques, but the data science community has been tracking their development, precision, benefits, and threats for years. Recently though, the team of Yuval Nirkin, Yosi Keller (both of Bar-Ilan University),... Read more
The History and Future of Machine Learning at Reddit
Let’s take a walk through the history of machine learning at Reddit from its original days in 2006 to where we are today, including the pitfalls and mistakes made as well as their current ML projects and future efforts in the space. Based on a talk given by Anand... Read more
How to Prepare for an Automated Future: 7 Steps to Machine Learning
The increasingly digital economy requires boards and executives to have a solid understanding of the rapidly changing digital landscape. Naturally, artificial intelligence (AI) is an important stakeholder. Those organisations that want to prepare for an automated future should have a thorough understanding of AI. However, AI is an umbrella term that... Read more
What Do Managers and Decision Makers Need to Know About AutoML?
You want to be working in machine learning and artificial intelligence, but you don’t have the talent yet. You’re telling your board members that you’re using AI when you’re really just doing some basic data analysis. You feel like everyone is working in ML and you’re here just trying... Read more
Making Fairness an Intrinsic Part of Machine Learning
Editor’s Note: At ODSC Europe 2019, Sray Agarwal will conduct a workshop on fairness and accountability demonstrating how to detect bias and remove bias from ML models.   The suitability of Machine Learning models is traditionally measured on its accuracy. A highly accurate model based on metrics like RMSE,... Read more
Optuna: An Automatic Hyperparameter Optimization Framework
Note: Please go here to see a high-resolution version of the title image) Preferred Networks has released a beta version of an open-source, automatic hyperparameter optimization framework called Optuna. In this blog, we will introduce the motivation behind the development of Optuna as well as its features. [Related Article:... Read more
IBM Research Launches Explainable AI Toolkit
Explainability or interpretability of AI is a huge deal these days, especially due to the rise in the number of enterprises depending on the decisions made by machine learning and deep learning. Naturally, stakeholders want a level of transparency for how the algorithms came up with their recommendations. The... Read more
What is Augmented Programming?
Ed Note: Gideon is speaking at ODSC Europe 2019, see his talk “Augmented Programming” there. Over the past decade, deep learning research has led to significant advances in perceptual tasks, such as object detection, face recognition, and speech recognition. In each of these use cases, raw real-world inputs have... Read more