[Blog Draft] GSI Tech Partner Highlight

_community_members/nateboot.md

@@ -20,4 +20,6 @@
   - author
 permalink: '/community/members/nate-boot.html'
 ---
-At work, I'm a Developer Advocate for OpenSearch. At home, I'm a devoted husband and father. No matter where I am, I am at your service. 
+I'm hardcore, but approachable. 
+I love tech, video games, bad jokes and my padawan youngling. 
+I'm at your service. 

_posts/2024-04-15-gsi-tech-blog.md

@@ -0,0 +1,75 @@
+---
+# List of one or more author names that map to the value of short_name in site.community_members. See the content in the _community_members collection for reference.
+authors: 
+    - nateboot
+
+# Content categorization. One or more categories can be specified. 
+category:
+  - community
+
+# Publish date in the format of four digit year, two digit month, two digit day, hour, minute, second, and timezone offset; e.g., 2021-04-12 01:01:01 -0700
+date: 2024-04-15 01:01:01 -0700
+
+# An excerpt of the blog post. It is used in the blog post list view, and in the home page what's new list of N most recent blog posts. It is also used as a fallback value for the twittercard:description field if not explictly defined in the front matter.
+excerpt: 
+
+# Used as a fallback for the Twitter Card image, otherwise not currently used. Is only present in content up to June 3, 2021.
+#feature_image: /assets/path/to/image/asset.ext
+
+# Setting to true will result in the inclusion of the MathJax JavaScript library for rendering math equations. For reference see: _includes/include-mathjax.html.
+#has_math: true
+
+# Setting to true will result in the inclusion of CSS styles specific to using borders for the table, for table header cells, and table data cells. scientific data tables. For reference see: _includes/science-table-styles.html.
+#has_science_table: true
+
+# The layout template to use for rendering the content.
+# Options are default, fullwidth, homepage, and post.
+layout: post
+
+# Value used for the meta description tag. Also used as a final fallback value for the Twitter Card description field after the excerpt property.
+meta_description: "" 
+
+# Value used for the meta keywords tag.
+meta_keywords: ""
+
+# Set to true to indicate content that was imported from the Open Distro For Elasticsearch blog.
+#odfeimport: true
+
+# URL for use by the jekyll-redirect-from plugin.
+# When importing your posts and pages from, say, Tumblr, it's annoying and impractical to create new pages in the proper subdirectories so they, e.g. /post/123456789/my-slug-that-is-often-incompl, redirect to the new post URL.
+# For reference see: https://github.com/jekyll/jekyll-redirect-from
+#redirect_from: /blog/odfe-updates/2019/09/Check-out-earlier-blogposts-on-Open-Distro-for-Elasticsearch/
+
+# The title of the post.
+title: "What if satellites spoke in vector?"
+
+# Meta data for the twitter card. The twitter card is used when a link to the blog post is shared on twitter. The twitter card is also used by other social media sites when a link to the blog post is shared on those sites. The twitter card is also used by search engines when a link to the blog post is shared on those sites.
+
+---
+Every once in a while I run across a use case that gets me really excited, not just because it falls inside of my personal interests, but also because I never would have thought that open source tools would find their way into certain use cases. Recently I was lucky enough to learn about some very complicated real world problems that could be improved by applying OpenSearch, Artificial Intelligence and Machine Learning. That field just happens to be in the realm of observability. In particular, things that observe our own Earth.
+
+## What is the actual problem to solve?
+
+There’s a handful. Here on earth we’re starting to see more and more cars that can drive themselves, illustrating the need for collision avoidance. It’s pretty easy to simplify the problem statement here. It’s just making sure that stuff doesn’t smash into each other. Compared to our roads, space really is big, but the space where satellites orbit around the earth isn’t quite as big. So, we have a common problem of avoiding collisions, whether it’s a satellite or a car. It is very easy for a small piece of debris, even as small as a fleck of paint, to cause significant damage to orbiting vehicles. For things that can’t move themselves, there are only passive things that can be done. Orbital altitudes, graveyard orbits, etc, are options, but these are passive solutions that help segregate orbits for things that can’t move themselves. Once one of these is used for a launched, there’s not much you can do to move it.
+
+Communications satellites are great at talking to each other, but earth observation satellites don’t always have such a luxury. They have large arrays of sensors, and collect large quantities of data. However they still have to send their data to the ground for processing. That is to say, these observational satellites don’t really compute on their own sensor data. To further complicate this issue, sending sensor data to earth has to happen in small windows of time, due to satellites only being able to transmit to ground stations in small windows in their orbit. This means that not all of their data is sure to make it. Even after all of this happens, a human still has to review the results.
+
+The number of satellites is increasing. We have five times more satellites in space than we did ten years ago. The proportional amount of humans we have to do this processing is decreasing. Humans have become a bottleneck.
+
+![](/assets/media/blog-images/2024-04-15-gsi-tech-blog/objects-launched-annual.jpg)
+
+## What if...
+
+Suppose these types of satellites could be outfitted with a card that both stored and computed, equivalent to having both RAM and CPU, with the capability to run their sensor data through an ML model. The goal being to vectorize and pre-process sensor data. Sending a vector or change detection earthside is a much quicker operation than sending a complete set of sensor data. One of our partners, [GSI Technology](https://gsitechnology.com), might just change some of the landscape in this regard. They’ve introduced a new kind of chip into the market. The project, called “Gemini-II”, is an APU. It is an extremely power efficient processing unit that can both store and compute in its own distributed memory. You may have seen their booth at [OpenSearchCon 2023](http://www.opensearch.org/events/opensearchcon/2023/north-america/index.html).
+
+The APU technology family is a Compute-in-memory (CiM) architecture seeking to eliminate computation time wasted on data movement by closing the traditional gap between processors (millions in GSI's case) and storage elements, which is highly beneficial for data-intensive workloads. Computation occurs where the data lives - in the storage elements, avoiding traditional Processor/Memory bottlenecks!
+
+## Several applications
+
+Many institutions often use things like SAR (synthetic aperture radar) to monitor landscapes to check for things like the progress of deforestation, forest fires, as well as the erosion of coastal areas. Even corporations use this tech to count the number of cars in their stores’ parking lots to help forecast business decisions. We can even use this tech to predict and detect ships adrift at sea for the purposes of rescue. The same advantage is available here. Instead of sending entire collections of satellite data, we now have the potential to send results that represent those changes instead.
+
+If you’re curious about what GSI Technology works on you should definitely head over to their [website](https://gsitechnology.com). They’re applying AI and ML to a variety of modern problems. There are many potential applications that are up to their imagination - aerospace, genomics, computer vision, cyber security for space, vector search in space, data centers in space for the purposes of computing large volumes of data and more.
+
+## Parting food for thought
+
+Satellites and space sure sound cool, but there may be many other places in which the observability of our own Earth can be improved. Not only on a cosmic scale, but any place in which we collect large amounts of data. Imagine a microscope with a bit of computational memory, equivalent to RAM and a CPU that could supplement the years and years of experience doctors and scientists have accumulated. Not replace, but supplement. Imagine an MRI machine equipped with a model to recognize brain scan anomalies. As a long time sufferer of epilepsy, a condition for which only three out of ten cases ever have a root cause determined, I sure wouldn’t mind donating the vector of my brain scans to science. With an open source license, of course.