The Edge is where commerce happens
HP has a simple definition for masses: The Edge is where commerce happens
Recent innovation within the industry has revealed a trifecta of distinct elements—(1) edge computing, (2) edge networking, and (3) edge data management—that make up the building blocks of a successful edge computing strategy.
The digitalization creates an ever growing torrential flow of data in corporations and organizations. The customers applications require HPC loads to interpret what it means. They need to use AZURE, AWS and Google clouds. Some multinationals have up to hundred thousands employees in up to seventy countries. UberCloud created customizations with mind-blowing HPC-on demand at the edge This is more impressive, as classic HPC was always plagued by queuing. According to HBR
We define an “edge” as the outer rim that frames what you do and separates it, quite conveniently, from what you don’t. Edges are frontiers beyond which something changes. When you proceed beyond this border in business, the main thing that changes is risk.
The goal of “Edge Strategy” is to discover new and lucrative ways to monetize your company’s foundational assets. The approach is to look beyond the core business to near-field offerings, where the most leverage (and, importantly, the least risk) resides. As with many worthwhile endeavors, the challenge is in knowing where to start.
The biggest cloud companies do not know what a customer needs. Specific companies e,g. in the consumer and retail business, do not know how to access a giant cloud. But UberCloud does know both the client and top cloud companies' needs.
The Datanami article, based on a more extensive UberCloud case study, presents the application of an NLP – machine learning model to predict sentiments based on consumers’ product review evaluations retrieved from social media and e-commerce websites.
Rapidly growing e-commerce firms have yielded actual big data as a result of these developments. The enormous popularity of big data on social media allows buyers to express their opinions and views on a wide range of topics, such as the state of the economy, or to express their unhappiness with specific products or services, or to express their joy with their purchases.
Sentiment analysis and text analysis are applications of big data analysis, which aim to aggregate and extract emotions and feelings from many types of reviews.
These sentiment calculation, like many AI an ML compute data require an investment in developing human consciousness. From Quanta magazine
A major aspect of the problem is that humans often don’t know what goals to give our AI systems, because we don’t know what we really want. Researchers two major challenges. “One is the fact that our behavior is so far from being rational that it could be very hard to reconstruct our true underlying preferences,” AI systems will need to reason about the hierarchy of long-term, medium-term and short-term goals — the myriad preferences and commitments we’re each locked into. If robots are going to help us (and avoid making grave errors), they will need to know their way around the nebulous webs of our subconscious beliefs and unarticulated desires. I find this fascinating. In other words, in teaching robots to be good, we might find a way to teach ourselves. I think we are in the process to teach ourselves what an Edge is
UberCloud listens and UberCloud responds. Reports are porous, have respiration, so others can breathe-in to continuously refine the ideas and build consciousness, Yuval Noah Harari writes
The danger is that if we invest too much in developing AI and too little in developing human consciousness, the very sophisticated artificial intelligence of computers might only serve to empower the natural stupidity of humans.
For every dollar and every minute we invest in improving artificial intelligence, it would be wise to invest a dollar and a minute in advancing human consciousness. Unfortunately, at present we are not doing much in the way of research into human consciousness and ways to develop it.
Ericsson network slicing
Eriksson creates impeccable products and services ready to be used.
From Ericsson official blog
Network slicing has been a topic of conversation since the launch of 5G in 2013, but now with the advent of 5G core it is hotter than ever. In our previous post Network slicing: A USD 200 billion opportunity for CSPs (communications service providers) we discussed how network slicing, needed by 30 percent of use cases, is the key to monetizing 5G investments. This blog digs deeper to analyze the key question: “What are the top 10 industries, and which use cases will benefit from network slicing?”
In our recently (Ericsson) released report, "Network slicing: Top 10 use cases to target" Ericsson with partner Arthur D. Little uncovered the answers to this question. To do so they analyzed more than 70 external market reports about the global digitalization of industries. As part of that work, they also reviewed more than 400 digital use cases from 70 industries and took a deeper look into one or two use cases in each industry.
Here are the results
This analysis revealed the clear business potential for network slicing – a valuation of USD 200 billion by 2030 with a strong CAGR (Compound Annual Growth Rate).
The prime benefit of 5G for CSPs, lies in the opportunity to capture new revenues in services for industries and enterprises. This is opposed to focusing on the consumer market - an area where CSPs typically derive 80 percent of their revenue but shows limited future potential, with growth projected at only 0.1 percent. Here is the big upside and CAGR for the coming years, and network slicing is the key enabler for that. Network slicing will also be highly relevant for many consumer use cases, such as mobile gaming, augmented reality (AR) and virtual reality (VR).
These additional revenues are not possible today. It seems 200 billions USD are created "ex nihilo"
I end with a quote from Peter Thiele
Unless they invest in the difficult task of creating new things, American companies will fail in the future no matter how big their profits remain today. What happens when we’ve gained everything to be had from fine-tuning the old lines of business that we’ve inherited? Unlikely as it sounds , the answer threatens to be far worse than the crisis of 2008. Today’s “best practices” lead to dead ends; the best paths are new and untried. In a world of gigantic administrative bureaucracies both public and private, searching for a new path might seem like hoping for a miracle. Actually, if American business is going to succeed, we are going to need hundreds, or even thousands, of miracles. This would be depressing but for one crucial fact: humans are distinguished from other species by our ability to work miracles. We call these miracles technology.
This is what I humbly think the edge, where commerce happens, is.