Freight Rail as a Key Supply Chain Component

Brought to my attention.   Am currently working a related project.  By the Association of American Railroads (AAR)   An industry group,  not representing Rail Unions.

Freight Railroads: A 24/7 Link in the U.S. Supply Chain  By the AAR

Youtube Video Overview:  https://youtu.be/OeBtjQ_G99c 

Freight railroads are working with customers to solve the current supply chain disruptions and improve service and reliability. Actions include expanding network capacity, ensuring appropriate staffing levels and collaborating closely with trucking and other logistics partners.

SUPPLY CHAIN DISRUPTIONS FACT SHEET ... 

SERVICE IMPROVEMENT FACT SHEET ... 

Global Supply Chains are Complex.

Global freight supply chains are complex systems composed of steamship lines; truckers; railroads; ports; drayage providers; owners of truck chassis, shipping containers and warehouses; manufacturers; wholesalers and retailers of goods. All stakeholders must do their part to maintain a consistent flow of freight at every step of the process to avoid bottlenecks and ensure that freight is delivered safely, efficiently and when expected.

Freight Railroads are Managing Disruptions with a Multi-prong Approach to Improve Service.

The interdependent supply chain has been rattled by the impact of a global pandemic on consumer purchasing trends, worker preferences, and rapidly changing global and national economies. Railroads continue to move huge amounts of cargo, despite current supply chain challenges. In the first quarter of 2022, railroads moved more chemicals than in any other quarter in history; the second-most grain for a first quarter since 2011; and the fourth-most intermodal units for a first quarter in history. The rail industry continues to work 24/7 to meet the nation’s freight transportation needs and return service to a level customers deserve and expect. Here are some examples:  ... ' 

Hangouts and Chats in Google

Quite interesting.  Could a company implement a whole set of standards for interaction, recording, goal management, process detail,  After Action Reviews?

Google’s Hangouts Chat gets chatbot boost with Dialogflow
Dialogflow should make it easier for developers to create natural language bots for Google’s team collaboration platform.
         
By Matthew Finnegan  ... Senior Reporter, Computerworld

Google is looking to make it easier to build chatbots for Hangouts Chat, thanks to an integration with its Dialogflow conversational AI platform.

Google launched Hangouts Chat early last year, a chat-based collaboration tool that replaces the Hangouts app for G Suite customers, of which there are are now more than 5 million, according to Google’s latest stats.

As announced last week, developers can now build chatbots for Hangouts Chat using Dialogflow – Google’s machine learning-based development suite that enables the creation of natural language processing (NLP) and natural language understanding (NLU) apps that mimic human interactions. .... " 

Military Needs Explainable AI

Well we all need explainable methods,   and this always means explainable in a context.   Reminds me of the After Action Review ....  AAR ....  We used it to support our results  and their outcome.  And learning about the next outcome.   Its about context and context is about metadata used for specific decisions process involved. 

What Is Explainable AI and Why Does the Military Need It?
Go to the profile of Benjamin Powers    By Benjamin Powers in Medium

Last summer, the Defense Science Board’s report on autonomy found that investing in artificial intelligence (AI) warfare is a crucial part of maintaining the United States’ national security and military capability. As the report reads, “It should not be a surprise when adversaries employ autonomy against U.S. forces.” In other words, AI warfare is likely on the horizon; it’s just a matter of who gets there first.

This immediately sparks dystopian and apocalyptic reactions from most people, who may envision a Terminator-esque system that will at some point choose to overthrow its human masters. But don’t worry. We aren’t there just yet. The report concludes that “autonomy will deliver substantial operational value across an increasingly diverse array of DoD missions, but the DoD must move more rapidly to realize this value.” Meaning that while the value of autonomy is clear from a military perspective, the Department of Defense has to devote more money and time to realize its full potential — and do so quickly.

Those robots would be a result of artificial general intelligence (AGI), which is only a small area of research within AI that works on neural evolution and, perhaps in time, the creation of sentient machines. Much more prevalent, however, is machine learning (a computer’s ability to learn without being explicitly programmed) and neural nets (computer systems modeled on the human brain and nervous system) being drawn upon to augment human decision-making capabilities. Indeed, the Department of Defense is charging ahead with Project Maven, which established an Algorithmic Warfare Cross-Functional Team to have computers and neural nets lead the hunt for Islamic State militants in Iraq and Syria. The project synthesizes hundreds of hours of aerial surveillance video into actionable intelligence, which is then reviewed by analysts.

The thing is that we often don’t really know why AI makes the decisions or recommendations it does. While the computing capacity of AI expands on an almost daily basis, the study of how to make machine learning explain its decision-making process to a human has languished. So, while AI might recommend a target or offer up what it deems important intelligence footage, it can’t tell the military why. The extent of an explanation currently may be, “There is a 95 percent chance this is what you should do,” but that’s it.

This is why the Defense Advanced Research Projects Agency (DARPA) launched a call last year for proposals as part of its newly created Explainable Artificial Intelligence (XAI) program. The project’s goal is to develop a variety of explainable machine learning models while maintaining their prediction accuracy and to enable human users to understand and trust (while managing) the artificially intelligent partners being developed. After fielding hundreds of proposals, the XAI program settled on 12 that would make up the various areas of focus under the XAI umbrella. (DARPA puts out a call for nascent programs and then funds them for an amount of time under the program umbrella.)  .... " 

Data Mining of Failure

Reminds me of the AAR: After Action Review, which we did for a while.  But the important thing is to gather the data consistently, often an issue.

How the data mining of failure could teach us the secrets of success in Technology Review
These data researchers found that for startups, scientists, and terrorists alike, learning too little from experience spells doom.

by Emerging Technology from the arXiv 

Thomas Edison is often described as America’s greatest inventor. His successes include electric power generation, sound recording, and the electric lightbulb.

But Edison was no stranger to failure. He famously tested 1,000 different designs before settling on the carbon filament that became the first commercially successful lightbulb. This tenacity set him apart. “Many of life’s failures are people who did not realize how close they were to success when they gave up,” he said.

Many groups and individuals have studied the nature of success. These studies have yielded varying degrees of insight. The flip side—the nature of failure—is much less well studied but arguably more important. Little is known about the mechanisms that govern the dynamics of failure. ...."

A Truth Seal via Predictive Markets

A recent inquiry had me looking at the detection and analysis of 'disinformation'.  Much in and of the news.  That led me to work by Rick Hayes-Roth, who was the CEO of an AI company called Teknowledge that I have written about here before (See tag) .  Teknowledge was one of the AI giants in the late 80s. We used their capabilities, even bought a major equity position with them.  Did some great things, but alas, they are no more.

Around 2011 Hayes-Roth and his colleagues came up with an idea called 'Truth Seals', related to the and driven by Predictive Markets, where you could deliver some measure of the validity of information.  Now needed more than ever.    The startup existed until at 2014 and then folded.  But in my research I noted that Rick, now Prof Emeritus at the Naval Postgraduate School, had written a document that did a post mortem, ala After-Action-Review (AAR) that covered what was done.  Very informative.  Also points to some of the intellectual property developed.    Useful for anyone thinking about the topic.    Reviewing.

I also notice that the idea of 'Predictive Markets' is far less talked about recently, any pointers to work still going on there?

Learning from Failure

Think also, the After Action Review (AAR):

How Coca-Cola, Netflix, and Amazon Learn from Failure  By Bill Taylor

Why, all of a sudden, are so many successful business leaders urging their companies and colleagues to make more mistakes and embrace more failures?

In May, right after he became CEO of Coca-Cola Co., James Quincey called upon rank-and-file managers to get beyond the fear of failure that had dogged the company since the “New Coke” fiasco of so many years ago. “If we’re not making mistakes,” he insisted, “we’re not trying hard enough.”

In June, even as his company was enjoying unparalleled success with its subscribers, Netflix CEO Reed Hastings worried that his fabulously valuable streaming service had too many hit shows and was canceling too few new shows. “Our hit ratio is too high right now,” he told a technology conference. “We have to take more risk…to try more crazy things…we should have a higher cancel rate overall.”

Even Amazon CEO Jeff Bezos, arguably the most successful entrepreneur in the world, makes the case as directly as he can that his company’s growth and innovation is built on its failures. “If you’re going to take bold bets, they’re going to be experiments,” he explained shortly after Amazon bought Whole Foods. “And if they’re experiments, you don’t know ahead of time if they’re going to work. Experiments are by their very nature prone to failure. But a few big successes compensate for dozens and dozens of things that didn’t work.”

The message from these CEOs is as easy to understand as it is hard for most of us to put into practice. I can’t tell you how many business leaders I meet, how many organizations I visit, that espouse the virtues of innovation and creativity. Yet so many of these same leaders and organizations live in fear of mistakes, missteps, and disappointments — which is why they have so little innovation and creativity. If you’re not prepared to fail, you’re not prepared to learn. And unless people and organizations manage to keep learning as fast as the world is changing, they’ll never keep growing and evolving. .... " 

Celebrating Failures

From Knowledge@Wharton:    After action reviews?

The digital transformation of a company requires not a mere shuffling of the organizational chart, but rather a “chemical” change in the culture and business practices, says Ganesh Ayyar, CEO of Mphasis, a major IT services company. But it is easy to say and more difficult to do. One place to start is by encouraging experimentation through the celebration of failures, adds Wharton marketing professor Jerry (Yoram) Wind. Another is to learn to co-create with clients. As always, the CEO and other senior executives set the tone: The old command-and-control style of managing is becoming passe, replaced with a more collaborative model recognizing that good ideas can come from anywhere in the company. ... " 

On the Ethics of an Analytical Hammer

Related thoughts were brought up when we used analytics in HR.   But I do not recall an instance where in an after action review it was decided that there were any ethical issues.  So a caution rather than a strong risk.   As with any system implementation.

In MIT Sloan Review:   by Sam Ransbotham

Data Analytics Hammer
Organizations no longer ask themselves “Could we do X with data?” The answer is now often yes. Instead, a key question now is, “Should we do X with data?”

With analytics as a hammer, so many questions can start to look like nails. It is difficult for organizations to know what to do. But the “should” in “What should we do?” goes beyond just selecting what to hammer on for maximum insight — the possibilities that analytical abilities create involve responsibilities as well.

Questions involving “could” are about ability. Can we estimate which customers are unlikely to renew their contracts? Can we determine people who are about to make large financial decisions such as real estate purchase or retirement? Can we figure out which machine part is mostly likely to break so that we can order a replacement ahead of time?  .... "