Monitoring a Complex System Over time for Patterns

Brought to my close attention because this is in the astronomy space.  A long time academic interest.  But then it came to mind that any system be observed in this way over time.  Then examine the data for anomalies, patterns, trends.   Probably with different observable parameters, and goals and needs. Thinking that further.Will be attending for inspiration.

Via DSC:

The Vera C. Rubin Observatory, currently under construction in Chile, will conduct a vast astronomical survey of our dynamic Universe starting in 2022. They plan to collect 500 petabytes of image data by observing the skies continuously for 10 years and produce nearly instant alerts for objects that change in position or brightness every night. In addition to astronomical data, their dataset will include DevOps, IoT, and real-time monitoring data.

In this latest Data Science Central webinar, Dr. Angelo Fausti will demonstrate:

How a time-series database has the versatility to address their needs
How they created a solution to enhance visibility across their organization and improve actionable insights
How they pull software development and sensor data from their telescope, camera and observatory IoT devices

Speaker:
Dr. Angelo Fausti, Software Engineer -- Vera C. Rubin Observatory

Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central 

Title: 500 Petabytes of Data to Understand the Universe Better
Date: Wednesday, March 18th, 2020
Time: 9:00 AM - 10:00 AM PDT 

Space is limited so please register early:
Reserve your Webinar seat now 
After registering you will receive a confirmation email containing information about joining the Webinar.    .... '

Celestial Observation by Smartphone.

Have had a long time background in observational astronomy, have been impressed how smartphones can support observation, but always thought there would be much room for stronger integration between  smartphones and telescopes.   I see that the well known scope supplier Celestron has done this.  Ultimately c0uld be an AI application for distributing,  gathering and analyzing observations.  Want me to test and report further on this, let me know.

Celestron’s StarSense Explorer brings telescope optics to smartphones    in VentureBeat By Jeremy Horowitz

If you’ve ever tried to map distant stars or planets using a smartphone or tablet app, you know the apps have been pretty amazing for years but are constrained by the devices’ tiny, light-limited cameras. Now Celestron is leveraging its six decades of telescope-making expertise to let novices enjoy the “sophisticated sky recognition technology used by professional observatories,” combining a modern telescope with an Android phone and iPhone dock.

The StarSense Explorer series includes four models, ranging from $180 LT 80mm refractor and 114mm reflector telescopes to larger $400 DX 102mm refractor and 130mm reflector telescopes with better light-gathering purposes and bigger tripods. In each case, users simply position the phone in a dock on the telescope’s right side, launch the StarSense Explorer app — compatible with Android 7.1.2 or later, and iPhone 6 or newer phones — and use on-screen arrows to move the telescope on its tripod, quickly and automatically locating objects in the sky.    ...."

Building Behavioral Models of People

Intriguing behavioral modeling approach.

Computers Learn to Understand Humans Better by Modeling Them
Aalto University

Researchers at Aalto University in Finland, the University of Birmingham in the U.K., and the University of Oslo in Norway have developed a method that could help computers learn psychologically plausible models of individual people by observing them. The researchers showed that by observing how long a user takes to click on menu items, a program can infer a model that reproduces similar behavior and accurately estimates some characteristics of that user's visual system, such as fixation durations. 

The method is based on Approximate Bayesian Computation, which was developed to deduce very complex models from observations. The researchers say the method clears a path for automatic inference of complex models of human behavior from naturalistic observations. "The benefit of our approach is that much smaller amount of data is needed than for 'black box' methods," says Aalto's Antti Kangasraasio. The method could be useful in human-robotic interaction, or in automatically assessing individuals' capabilities.  ... " 

Mysteries, Experiences and Value of Unsupervised Learning

I am reminded of  a conversation with a manager  early in my data analytics career when I mentioned using "unsupervised learning" ... .  He asked in essence:   "How can you find a result if you don't supervise the process?   It sounds so messy and casual"  I answered:   " We will first simply observe and learn, so we can later  supervise ... ".    You can start with just observation.  Just like you should start every project with visualizing your data.

In O'Reilly. a David Beyer interview with " .... Ilya Sutskever ... a research scientist at Google and the author of numerous publications on neural networks and related topics. Sutskever is a co-founder of DNNresearch and was named Canada’s first Google Fellow .... "  

Very good piece,  some very technical thoughts, but further Sutskever remarks:

" .... Unsupervised learning is always a means for some other end.   In supervised learning, the learning itself is what you care about. You’ve got your cost function, which you want to minimize. In unsupervised learning, the goal is always to help some other task, like classification or categorization. For example, I might ask a computer system to passively watch a lot of YouTube videos (so unsupervised learning happens here), then ask it to recognize objects with great accuracy (that’s the final supervised learning task).. .... " 

Follows with thoughts on the evolution and direction of deep learning.