Local Airport Optimizes Takeoff Operations with Machine Learning

 I see that our local airport is using this scheduling application.  A presentation on the method is linked to in the article.  Deep earning and scheduling using NVIDIA.  Useful example of operations application. Uses cameras for gathering data about operations.  " ...By installing cameras at several gates, airports can optimize the cleaning, restocking and servicing of planes — saving time for customers and costs for the airlines. ... "

Inception Spotlight: Assaia AI Ready for Takeoff at Kentucky Airport   By Isha Salian

Tags: Business Intelligence & Analytics, Computer Vision & Machine Vision, featured, Machine Learning & Artificial Intelligence, News, NVIDIA Inception

Discuss

Switzerland-based Assaia International AG, an NVIDIA Metropolis partner and member of the NVIDIA Inception acceleration platform for AI startups, is deploying a deep learning solution at Cincinnati/Northern Kentucky International Airport (CVG) to help airport employees monitor the turnaround time between flights. 

The Turnaround Control tool will help the airport work with its airline partners to improve turnaround transparency, identify situations that most often cause delayed flights, and notify employees of deviations from the schedule. 

“Assaia’s technology adds critical data points to CVG’s early-stage neural network for operational advancements,” said Brian Cobb, the airport’s chief innovation officer. “Structured data generated by artificial intelligence will provide information to make decisions, optimize airside processes, and improve efficiency and safety.”

The company uses NVIDIA Jetson AGX Xavier modules and the NVIDIA Metropolis intelligent video analytics platform to run image recognition and predictive analysis algorithms on video streams from multiple cameras around an airport. 

By installing cameras at several gates, airports can optimize the cleaning, restocking and servicing of planes — saving time for customers and costs for the airlines.

Assaia is also deploying AI solutions at London Gatwick Airport and Seattle-Tacoma International Airport. Watch a replay from the recent GPU Technology Conference for more:  ... " 

Democratizing AI in Health Care

Perhaps surprising because of the particularly technical aspect of healthcare data.   Interpretation of results are also most important.

Democratizing artificial intelligence in health care  in MIT News

Leo Celi, a researcher at MIT and a physician at Beth Israel Deaconess Medical Center, leads a global series of health care hackathons, or datathons, to bring doctors and data scientists together and encourage hospitals to make use of their electronic medical records.

Hackathons promote doctor-data scientist collaboration and expanded access to electronic medical-records to improve patient care.

By Kim Martineau | MIT Quest for Intelligence 

An artificial intelligence program that’s better than human doctors at recommending treatment for sepsis may soon enter clinical trials in London. The machine learning model is part of a new way of practicing medicine that mines electronic medical-record data for more effective ways of diagnosing and treating difficult medical problems, including sepsis, a blood infection that kills an estimated 6 million people worldwide each year. 

The discovery of a promising treatment strategy for sepsis didn’t come about the regular way, through lengthy, carefully-controlled experiments. Instead, it emerged during a free-wheeling hackathon in London in 2015. ... " 

Towards Democratizing Data Science

Nice thought, though I wonder how the use/presentation of the results can spin the outcome.  Note the use of Bayesian methods, which we also used extensively to provide more explainable results.   Use of the Jupyter notebook is good for understanding.    Also related to some of our own work modeling data valuation.   Looking deeper

Democratizing Data Science 
MIT News   by Rob Matheson

Massachusetts Institute of Technology (MIT) researchers have developed a tool for nonstatisticians that automatically generates models for analyzing raw data. The tool takes in datasets and generates sophisticated statistical models normally used by experts to analyze, interpret, and predict underlying patterns in data. The tool currently resides on Jupyter Notebook, an open source Web framework that allows users to run programs interactively in browsers; users can write just a few lines of code to uncover insights into a range of topics. The system uses Bayesian modeling, a statistical method that continuously updates the probability of a variable as more information about the variable becomes available. The tool uses a modified version of "program synthesis," a technique that automatically creates computer programs given data and a language to work within. Said MIT’s Feras Saad, “People have a lot of datasets that are sitting around, and our goal is to build systems that let people automatically get models they can use to ask questions about that data.”

NVidia Democratizing AI

Specialized hardware is becoming important for edge resident AI and analytics.  Also for more broadly available analytic and cognitive skills.

Nvidia Corp. is moving to lower the entry barrier to developing artificial intelligence models.      By Maria Deutscher in SiliconAngle

The chip maker, which sells graphics processing units commonly used for running AI applications, today updated its Nvidia GPU Cloud to support everyday desktops. Machines running the company’s consumer-oriented Titan accelerators can now take advantage of deep learning capabilities that were previously available only on enterprise platforms. .... " 

Need for Democratization of Machine Learning

 Good overview piece.    Statistics and pushing more need for open source capabilities and Transparency.

Why do we need the Democratization of Machine Learning?

We are living in an era of hype. In this article, I am trying to discover the hype around Artificial Intelligence. The First thing I want to clear is that ML/DL are algorithms, neither conscious nor intelligent or smart machines.

There’s more to Artificial General Intelligence than just Machine Learning or Deep Learning. I agree that Deep Learning has penetrated industries and it holds the potential to disrupt industries, but it is nowhere near to being conscious or an intelligent machines.  ... " 

Democratization of Machine Learning

This topic came up in the talk I gave on advances in AI and analytics yesterday.   Via Data science Central a white paper by IBM:

The Democratization of Machine Learning
Machine learning automates the development of analytic models that can learn and make predictions on data. It has been one of the fastest growing disciplines within the world of statistics and data science. However, the barrier to entry has been high, not only in cost, but also in the need for specialized talent.

In the past, data science professionals were intimidated by the words "algorithms" and "machine learning statistics". For many years, these capabilities were delegated to people with PhDs, but Apache Spark is changing the perception of these key functions and democratizing computer learning at the same time.

The demand for machine learning keeps growing. Download this complimentary white paper and learn how machine learning will both support and drive today’s data scientists and advanced analytics leaders to the future.

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