The Race to Build the Rail of the Future Heats Up: Hyperloop Startup Signs Deal Overseas

The race to create the rail of the future is getting tighter.

Hyperloop Transportation Technologies, one of the startups trying to build a high-speed tubular rail, announced yesterday that it has signed an agreement with the Indian state of Andhra Pradesh. According to Wired, the company and the state government will spend six months studying potential routes between Vijaywada and Amaravati, two cities with a combined 1.7 million people that sit 27 miles apart.

HTT hopes to eventually build the high-speed passageway first proposed by Elon Musk. The hyperloop system would send magnetic pods through a low-pressure tube at speeds of around 700 mph. In the case of the Indian route, it would turn what’s normally an hour-long drive into a six-minute trip.

HTT is yet to demonstrate its technology to the public. Its main rival, fellow Los Angeles startup Hyperloop One, showed off a small scale prototype in the Nevada desert in May 2016, sending a pod on a five-second test run at about 300 mph while a crowd looked on. This past July, Hyperloop One completed a full-size test run, reaching speeds of 192 mph.

Completing a hyperloop project will require getting through significant bureaucratic red tape. It will also be extremely expensive: Internal Hyperloop One documents leaked last year showed estimates at more than $100 million per mile. That company currently has $160 million in funding, while HTT has pulled in about $32 million.

Musk, who said when he outlined the technology four years ago that he wouldn’t pursue it himself, has recently changed his tune. The entrepreneur announced that he was launching The Boring Company, whose aim is to dig underground tunnels to alleviate traffic, earlier this year. In July, he revealed that he was pursuing building a hyperloop system connecting New York and Washington, D.C., and last month, his team sent a full-scale pod through a hyperloop tube at a record 220 mph.

While Musk’s focus has been in the U.S.–he’s currently digging a tunnel under Los Angeles and has proposed routes in Chicago and on both the East and West Coasts–other companies have been setting their sights around the globe. Weeks before announcing the India deal, HTT revealed it has an agreement for a feasibility study with the South Korean government. Hyperloop One has similar deals in the U.K., Russia, Finland and Sweden, Switzerland, the Netherlands, and the United Arab Emirates, in addition to having received a number of proposals from organizations in the U.S.

In June, Richard Branson was quoted in an interview with British GQ saying he’d have an announcement regarding the hyperloop coming soon. Soon after, a representative for Branson told Inc. that while Branson “is active in the future of transportation,” there was “nothing to report.” Neither Hyperloop One nor HTT would confirm nor deny Branson’s potential involvement with their respective companies.

Regardless, it seems the various projects are picking up momentum, though no company is yet to provide a full-scale, full-speed demonstration. If the time it takes to build other underground rail lines is any indication, a finished product is still at least a decade away, but support from any government is an important step toward making the hyperloop a reality–wherever it might happen.


MTA doubles fines for littering in the NYC subway

In a week from now, fines will increase from $50 to $100

In its latest push to fix the city’s ailing subway system, the MTA has decided to double the fine for littering, the New York Post reports. In a week from today, the state will direct the Department of Environmental Conservation to increase the fine for littering from $50 to $100.

“Littering is not only illegal but dangerous and directly causes hundreds of thousands of delays, inconveniencing millions of New Yorkers,” Governor Andrew Cuomo said in a statement. In addition to the fines, the state is planning to step up enforcement as well.

Littering on the tracks contributes to track fires, which in turn leads to delays, and has become of the MTA’s biggest headaches. The Post analyzed track fires over the past few years and found that when there were a greater number of tickets handed out for littering, there were fewer track fires.

For instance in 2012, The Post points out that 669 tickets were handed out for littering, and 261 track fires occurred over the entirety of the year. This year however, less than 100 tickets have been handed out for littering, and already 470 track fires have disrupted the subway.

Littering also tends to block the drains in the system, which in turn leads the tracks to flood. If the water comes up to the third rail, then the power needs to be shut off. Water-related delays are the second largest reason for delays on the system.

This latest push is part of Governor Cuomo’s “Keep It Clean” initiative, and will be accompanied by a public awareness campaign about littering. The initiative is looking to support the MTA’s water management and debris removal work.

The Autonomous and Self-Learning Supply Chain: How AI is Heralding the Next Frontier of Supply

Global supply chains have become even more complex, involving a lot of businesses that must interact to bring us the goods we use and consume.

In the past, businesses had created Supply Chain Control Towers to patch together the different functions and disparate parties in their supply chains. Capgemini Consulting defines a control tower as “a central hub with the required technology, organization and processes to capture and use supply chain data to provide enhanced visibility for short and long-term decision making that is aligned with strategic objectives.” However, new architectures for control towers are going one step further by leveraging innovations in artificial intelligence (AI) technologies to achieve unprecedented levels of performance.
The Evolution of Supply Chain Control Towers

The earliest control towers served as consoles, which provided visibility for managing the supply chain for a given business. In recent years, these early “1.0 Control Towers“ have evolved to include predictive alerts and prescriptive decision-support capability.

Control Tower 2.0 systems took an “inside-out” view of the world. This view placed one’s own business at the center of the control tower, looking out to immediate suppliers and immediate customers, while everyone else was treated as an afterthought. This created several limitations in terms of reduced operational reach, an explosion in point-to-point integrations, decision-support, and the inability to coordinate planning and execution across trading partners.

Control Tower 3.0 took an outside-in perspective, which placed the business and its trading partners as part of a multi-enterprise network, working together to serve the consumer. These true consumer-centric networks provide a digital network platform that brings people, process and technology from multiple independent businesses. More importantly, it allows all to execute together in lock-step around the consumer’s needs and deliver superior performance.

According to Nucleus Research, customers who adopted a customer-centric network realized a 56 percent increase in inventory turns and a 38 percent decrease in safety stock holdings on average. The research also found that customers benefit from greater visibility, better coordination, and superior optimization within their extended supply chain across multiple tiers of trading partners and suppliers.
Control Tower 4.0

Now, AI technology is disrupting the status quo once again by enabling a performance leap that is virtually impossible to achieve with traditional software. These AI-enabled Control Tower 4.0 solutions move beyond just decision-support to include decision-making and autonomous control. The network is predictive, resilient and capable of running itself.

For example, at one retailer, intelligent agents are monitoring consumer purchases at stores to continuously anticipate, sense and respond to real-time changes in buying behavior. Agents automatically adjust replenishments based on supply demand balance, and the company has already seen a 40 percent improvement in forecast accuracy.

Another example involves an e-commerce retailer who was struggling with the supply chain due to its rapid growth. In one quarter alone, the organization saw a five-fold increase in number of products, six-fold increase in number of customers and an eight-fold increase in number of suppliers. Despite this, they were able to maintain 99.5 percent on-time delivery performance without adding headcount to their supply chain team by on-boarding to an intelligent consumer-driven network. By deploying a touch-less fulfillment process, their staff achieved these results by focusing on only the most critical issues and engaging proactively with impacted customers.
A New Functional Architecture

Today, the 3.0 networks act as a System of Engagement (SOE) that orchestrates execution over the many Systems of Record (SOR), people and things to provide joint transaction and execution services in real-time. Unlike traditional enterprise-centric systems, the network becomes a digital platform that AI technologies can leverage to provide scalable and efficient decision-making.

New layers of agent-based systems, called Systems of Intelligence (SOI), have emerged that sit on top of the SOE and add even greater functionality. These systems use trained decision models to make rapid optimized decisions during execution with up-to-the minute information across the network and on-the-ground local context about people and things.

Above the SOI is yet another layer called the Systems of Cognition (SOC), whose function is to learn and adapt to the decision models in use by SOI, so the network can get smarter over time. Each of these technology elements serve a valuable function.
System of Engagement

SOEs require a multi-party network as it bridges the physical world with the virtual. Digital twins represent every actor, such as people and things, while physical events and multi-party transactions trigger real-time changes in state in the digital world. Digital models, known as state machines, control the possible activity and process lifecycles.

Multi-party master data management services provide a shared vocabulary across the network. Business social apps and conversational user interfaces provide means for both structured and unstructured data to be gleaned from the physical world, put in context, interpreted and acted upon.

Finally, blockchains, or other permissioned ledger technologies, maintain a secure audit trail of the business transactions across all parties. The SOE provides real-time governance and orchestration of multi-party workflows across the network.
Systems of Intelligence

At the heart of the SOI layer are intelligent agents and a transaction grid. Agents don’t just plan; they execute, and they act independently within a sub-network to perform micro-tasks such as “micro-optimizations” with specialized purpose and goals.

Agents have predictive intelligence and they act very fast and communicate with each other as needed, working directly on transactions. At any given point in time there could be millions of agents “in flight.” It is the job of the transactional grid to manage this.

Supervisory agents operating on the transactional grid orchestrate agent activities. They can rollback and retry agent reactions. The grid can scale horizontally and grow as more companies, items, facilities, and/or orders are added. The cloud network only has to add more servers to scale. This is in stark contrast to traditional planning engines and in-memory computing technologies, which use vertical scaling and max out quickly.

The agent-based grid enables smart decisions, fast, in a highly scalable architecture.
System of Cognition

Finally, a third layer called SOC has emerged, which consists of agents that can learn from experience and modify the behavior of the SOI agents.

These systems use a variety of AI technologies, such as deep neural networks, to perceive patterns, diagnose root causes of anomalies and learn from Big Data to train digital memories.

Learning agents essentially observe the decisions and results from the SOI agents and modify their underlying assumptions and models. Machine learning algorithms improve with data, which is what the SOE network readily provides. The more the network runs, the smarter it gets.

Getting Value from AI

AI is starting to make its presence felt in supply chains, as the benefits realized by early adopters are significant and include the following:

1. Automation—The lowest hanging fruit for AI applications has always been to automate the most routine and mechanical tasks that humans do, so they can be free to focus on higher value tasks. The answer is not necessarily to replace people with robots, but rather to automate some of the tasks in order to get the most effective outcome.

2. Expertise for All—The second and natural by-product of building decision models is that they can identify insights that can help more people to replicate the insights and trade-offs that are uncovered. Known as “institutionalizing knowledge,” this is like having an expert looking


Dr. Rufus Henry Gilbert’s Plan for an NYC Transit System Powered By Air

In the 1870s, long before Elon Musk, Dr. Rufus Henry Gilbert originated a plan for a New York City public transportation based on an elevated pneumatic tube system.

In August 2013, Elon Musk announced an idea that has the potential to radically change how people move over long distances.

He challenged tech companies to develop the technology for a super high-speed, train-like conveyance called the Hyperloop that would rely largely on the power of air; pressurized pods would transport people and goods through depressurized tunnels to achieve speeds not possible in conventional trains.

As this vision slowly moves towards reality, the Hyperloop has tantalized frequent travelers with dreams of the next best thing to teleportation—traveling from New York City to D.C. in under 30 minutes. While this technology would be a game changer, Musk is by no means the first to propose it.

In fact, he was beat over a century and a half ago by a former Civil War surgeon named Dr. Rufus Henry Gilbert who came up with the idea for a public transportation system for New York City that would have established an elevated pneumatic tube system in place of the underground subway that New Yorkers love to hate today.

Gilbert may have seemed like an unlikely candidate to invent such an innovative solution for New York City’s transportation woes, but his idea was rooted in his original profession.

It all started before the Civil War when the doctor went on a tour of Europe following the death of his wife. There, a grieving Gilbert was gripped by the terrible conditions in the slums, and he became convinced that the overcrowded and dirty environment was to blame for the high rates of disease and death among the poor. If only they could escape the cramped conditions of the inner city and live out in the fresh air, he thought, all their health problems would be solved.

“He reasoned that fast and cheap public conveyances would allow the poor to flee their teeming, disease-infested neighborhoods, and live in the hinterlands, where they could enjoy clean air and water, and plentiful sunshine,” Sam Lubell and Greg Goldin, co-authors of Never Built New York, wrote in a piece for The Gotham Center. “The pathways to good health were the tracks to suburbia.”

The Civil War interrupted Gilbert’s big plans, but after the North prevailed and his services as the medical director and superintendent of U.S. Army Hospitals were no longer needed, he set about making his vision come to life.

In the meantime, while the country was embroiled in violence, New York City was starting to think about creating a public transportation system to service the city. When the war finally ended, they decided it was time to get serious and come up with a plan.

Most of the proposals being fielded at the time were for more conventional-style public transportation systems, something of a choose your own combo of height (elevated, underground, or depressed) and mode of transportation (retro horse-drawn train, conventional steam-engine, or new-age electric model).

But Gilbert had a more radical idea. He wanted to take the pneumatic technology being used in other projects in the area (he was beat to the pneumatic “first” award by a slew of projects in Europe and a mini underground version in Lower Manhattan created by Alfred Ely Beach, which is an incredible story of its own) and combine it with the burgeoning idea of placing the train system in the skies of Manhattan.

His technological ideas were impressive and cutting-edge for his day—and even for our day—but he also conceived of a look for the system that was downright beautiful. Elaborate, Gothic metal arches would top the streets of New York, extending out of sleek columns secured to the sidewalk at regular intervals. Plenty of scrolls, flourishes, and metal detailing decorated each arch, and they were all capped by two large tubes that would serve as the conduit for passengers to get around the city.

The Daily Beast