1. Advanced Modeling

With very little explicit supervision and feedback, humans are able to learn a wide range of motor skills by simply interacting with and observing the world through their senses. While there has been significant progress towards building machines that can learn complex skills and learn based on raw sensory information such as image pixels, acquiring large and diverse repertoires of general skills remains an open challenge. Our goal is to build a generalist: a robot that can perform many different tasks, like arranging objects, picking up toys, and folding towels, and can do so with many different objects in the real world without re-learning for each object or task. While these basic motor skills are much simpler and less impressive than mastering Chess or even using a spatula, we think that being able to achieve such generality with a single model is a fundamental aspect of intelligence.

Generalized Mortality Estimator (GenEst)Command-line and ‘shiny’ GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Da …

Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python

In this post we are going to develop a java application for tracking cars in a video using deeplearning4j. Considering the achieved accuracy in the new era of deep learning tasks such image recognition or even object detection are considered as solved problems.  Because of that a lot of attention and effort is directed towards more difficult problems like the fascinating problem of Object Tracking. Please feel free to check out the  code at github as part of Java Machine Learning for Computer Vision additionally please find find a video sample of the running application.

Machine Learning for Middle Schoolers

At: University of Tennessee Knoxville Location: Knoxville, TNWeb: utk.eduPosition: Assistant or Associate Professor in Data Science

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. An increasing number of online companies are utilizing recommendation systems to increase user interaction and enrich shopping potential. Use cases of recommendation systems have been expanding rapidly. They across many aspects of eCommerce and online media, and we expect this trend to continue.

The Two (Conflicting) Definitions of AI

Data scientists and developers can now quickly and easily organize, track, and evaluate their machine learning (ML) model training experiments on Amazon SageMaker. We are introducing a new Amazon SageMaker Search capability that lets you find and evaluate the most relevant model training runs from the hundreds and thousands of your Amazon SageMaker model training jobs. This accelerates the model development and experimentation phase, improves the productivity of data scientists and developers, and reduces overall time to market of machine-learning-based solutions. The new search capability is available in beta through both the AWS Management Console and the AWS SDK APIs for Amazon SageMaker. It’s available in 13 AWS Regions where Amazon SageMaker is currently available, at no additional charge to you.