On first thought, agtech seems something of a contradiction. Agriculture involves growing crops. There’s no real world shortcut or fast track in that plants require planting in appropriate seasons with access to sun, water and soil nutrients and time to grow. By comparison, tech is about speed, rapid prototyping and timely deployment.

But agtech is actually an evolving nexus between the two practices of agriculture and next-generation connected tech.

See also: Platagon partners with Dubai university to create urban agriculture center

Agriculture is an industry that, while rooted firmly in traditions passed down through generations of farmers, is responding to technical advances in machines, robotics, analytics and software. One of the companies at the forefront of this revolution is Arable Labs. Headed by CEO Dr Adam Wolf, their team includes product engineers, biologists, and mathematicians and they work to solve the critical issue of accurate forecasting in the food supply chain.

I spoke to Dr. Wolf to learn more about their progress over the last year. The company had the evening before, come runner-up at a competition by water innovation accelerator, Imagine H20. Arable have previously worked with the Met service for Zambia, to facilitate weather monitoring as well as the New York City Parks Service for assistance with weather tracking for storm management so they are well placed to apply their technology to water technology.

But their biggest success of late is a $4.25 million Series A round of funding led by Middleland Capital’s agriculture technology fund and S2G Ventures. With the new funds, the company will support the expansion of data science and analytics for the food and ag supply chain, fuelled by mass production of the Arable Mark IoT device (formerly known as PulsePod) later this year.

“We have really outstanding investors who understand the space. They are well connected, they know people who are either out ahead in growing, processing or retailing. When you’re in food and agriculture it’s a real secret world that is far enough from most people’s lived experience that it’s hard to really discern what the pain points are who gets what margin,” explained Wolf.

“It’s important for us to be really focused on supply chain risk, which is the relationship between the producer and the processor and from the processor to the retailer. To understand how those contracts between those parties are structured and what’s the risk of failure. For example, what happens when a restaurant chain asks for so much cilantro and but then the farmer delivers half as much as they promised. This happens all the time. And so for someone to be able to get the people in the room and go, ok let’s get some conversation going. Well, that is very powerful. That’s where we’re fortunate to have these investors.”

The Arable Mark delivers a level of plant health data that is unmatched in the industry. The Mark measures more dimensions of physical meteorology, at greater spatial density than any weather model or station network, and more plant attributes, at a greater frequency than any satellite or aircraft. By measuring over 40 individual environmental data streams, the Mark is the most data-rich device available in IoT.

The Mark’s launch coincides with the all-new cloud-based Arable Insights software platform for crop consultants, farmers, large-scale producers, and food processors in the agricultural supply chain. Insights enable each stakeholder to communicate with trusted business partners based on real-time field-level data for the first time ever. It is now possible for managers to benchmark crop performance and seasonal progress across hundreds of fields, while also being able to drill down and understand the details of growth or weather events.  The data synthesis of weather and crop growth enables Arable to predict timing, quality perishability, and yield.

“When we think of our business model as hardware-enabled software as a service where the data from the hardware enables an array of different services. What we’ve learned is that data from the field is like a well from which many people can drink” notes Wolf.

How big is automation in farming?

The general public tends to have a highly romantic image of farmers as salt of the earth folk who eschew the conveniences of modern technology for the simple life. But the reality is that farmers are hacking their tractors, drones are being deployed for crop surveying, and a farmer is more likely to consult a data platform than an almanac before sowing crops. Wolf explained that one of the values their company promoted was customer service:

“The initial vision that we had was to treat the farmers with more respect, with good design and price, and it will reward us later.”

A 2012 Agriculture Census revealed that during the past 30 years the average age of U.S. farmers has grown by nearly eight years, from 50.5 years to 58.3 years. Does this mean that people will be replaced by robots? Wolf counters this with the comment:

“Someone who is growing 35,000 acres of leafy greens to deliver it to restaurants, you want that person to be really good and consistent at it. They really have figured out how to grow it at scale and deliver safe affordable food on time.

But young people don’t want to go into low-paid jobs. In places with year-round production, there’s a lot of families, but the children of the Latino workers are going to college, they’re studying software engineering, they don’t want to work in the fields. So the farms have real concerns about maintaining the workforce that they already have and capturing the institutional knowledge of older generations. There is a tendency towards consolidation. So then that team is stuck with managing an ever more sprawling field.

We’re definitely not displacing people in what we do, we’re empowering people with productivity. By being able to automate data collection, people get better jobs. People can prioritize and plan where to put their focus.”

The company will begin shipping the Mark this spring and make it more widely available through distributors later this year.

The market for wireless connected devices is exploding. We know this. From garage doors to refrigerators to healthcare devices to alarm systems to pet products, it’s reasonable to expect that nearly everything we purchase in the future will have a chip and a platform.  How functional, viable and useful many of these devices will actually become in our day-to-day lives, however, is still up for debate.

Debated less are the revenue opportunities associated with the connected device we use with the greatest frequency – our cars. The latest research suggests the connected car market will grow to just shy of $60 billion by 2021. GM alone already has 12 million connected cars on the road today.

See also: Are connected cars only as good as your network?

From infotainment to in-car Wi-Fi, the revenue possibilities are tantalizing and manufacturers are already seeking to capitalize. And while these services are certainly marketable to the consumer and potentially allow brands to distinguish themselves in a hotly competitive market, they are not necessarily transformational to the bottom line.

The greatest revenue opportunities for manufacturers will come from the ability to conveniently service their fleets of vehicles from afar and own the relationship with the car owner long after the new car has been driven off the lot.

100 million lines of code

Today’s personal vehicle typically has over 100 million lines of software code. By comparison, the F-22 fighter jet has just 1.7 million. As is the case with your laptop computer, updates are required quite frequently to enhance performance and security.

Today, each time an update is required, it means a trip to the dealer for the owner where hot coffee, free Wi-Fi and a usually longer than anticipated wait time awaits. If the manufacturer could instead update a connected vehicle’s internal software remotely over-the-air, the cost savings associated with warranty support and recalls would be dramatic. Certainly, the owner would appreciate a reduction in the number of annoying trips to the service station.

This all sounds great in theory, but making it practical and realistic depends on access to a reliable and cost-efficient network. For early days connected services, manufacturers are relying primarily on terrestrial networks. But for the more advanced type capabilities such as OTA updates, satellite communications needs to be in the mix.

Joel Schroeder, Vice President, Strategy & Business Development, Inmarsat

It’s true; satellite communications has never been considered mainstream. It’s often thought of as a last resort due to costs, speed challenges and size of equipment. But things have changed, and satellite will be a strong player within the 5G “network of networks” as costs have come down and hardware has been reduced to the point where they can be embedded quite easily into any number of devices, including the automobile.

Network reach is what rules

Why does satellite have such an upside in the connected car arena? Let us count the ways.

First, satellite’s broadcast capability makes it ideal for shooting out a single update to an entire fleet of vehicles at once via a single transmission. Does GM want to send one message to its 12 million connected cars or 12 million to each individual car? The efficiencies of satellite broadcast would save OEM’s millions in network usage costs.

Second, while AT&T and Verizon may claim to be everywhere in the US, they are certainly not everywhere in the world. Satellite networks, on the other hand, are. They are global in nature, have much larger footprints and can reach a vehicle anywhere. Using a satellite network will eliminate the need for OEMs to negotiate and manage tricky international roaming agreements.

Third, the advent of all these connected devices has also resulted in a much larger playground for hackers with nefarious intentions. While no network can claim to be completely impervious, the topology of a satellite network is inherently more secure as it operates as a private network that does not traverse the public Internet where there is a greater likelihood of intrusion.  With encryption technologies layered on top, satellites can offer even stronger protections for users.

Finally, once the vehicle is connected to the satellite network – and the latest componentry now allows for easy embedding into shark fin antennas and internal control units – the potential then emerges for OEM’s to introduce, upsell and cross-sell a host of services that not only deliver new revenue streams but also allow for an enhanced relationship with the vehicle owner, something they have always craved.

As the connected car market matures and education of satellite’s inherent advantages continues, expect to see satellite emerge as the preferred method of connectivity and the key component that will not only drive down costs for OEMs but truly transform the relationship between the automaker and car owner.

The author is Vice President, Strategy & Business Development for Inmarsat’s Connected Car Program of Inmarsat’s Connected Car division, which is working with automotive suppliers and manufacturers to bring satellite connective to the next generation of connected vehicles.

Companies in the manufacturing sector for years have been striving for lean production or processes to create more efficient operations. One of the latest trends in technology, the emergence of the Internet of Things (IoT), could give lean efforts a major boost.

Lean manufacturing, a systematic method for eliminating waste within a manufacturing system, is based on the concept of making obvious what adds value by reducing everything else. It’s a management philosophy that stems mainly from the Japanese manufacturing sector, and specifically Toyota Production System, which focuses on the reduction of waste to improve overall customer value.

Lean encompasses a set of tools that help in the identification and steady reduction of waste. And as waste is eliminated, quality improves and at the same time production time and cost are reduced. The ultimate goal of lean is to get the right things to the right place at the right time and in the right quantity, in order to achieve perfect workflow while minimizing waste and being flexible.

The Internet of Things involves the linking of physical objects such as devices, consumer products, vehicles, corporate assets, buildings and other “things” via the Internet. These “smart” objects are embedded with electronics, sensors, actuators, software and network connectivity that allow them to gather and share a variety of data and respond to control messages.

The IoT enables connected objects to be sensed and controlled remotely via an existing network infrastructure. This connectivity creates opportunities for a more direct integration of physical objects with digital systems. The potential benefits include increased efficiency, improved product development and enhanced customer service—to name a few.

The potential scope of IoT is enormous. Research firm Gartner Inc. has estimated that 6.4 billion connected things were in use worldwide in 2016, up 30% from 2015, and 5.5 million new things were being connected every day. The firm forecasts that the total number of connected things is forecast to reach 20.8 billion by 2020.

In the enterprise, Gartner considers two classes of connected things. One consists of generic or cross-industry devices used in multiple industries, and vertical-specific devices found in particular industries. Cross-industry devices include such items as connected light bulbs and building management systems.

The other class includes vertical-specific devices such as specialized equipment used in hospitals and tracking devices in container ships. Connected things for specialized use are the largest category, but this is quickly changing with the increased use of generic devices, Gartner says.

Taking lean to the next level

Within the context of building IoT-based manufacturing solutions, IoT opens up all kinds of possibilities, such as the ability to monitor the performance of products after they have been purchased to ensure adequate maintenance and customer satisfaction, optimizing supply chain logistics and streamlining the distribution chain. Information about product usage can be fed back to companies so that they can analyze the data to make improvements in design and production.

With this constant exchange of data, combined with the new automation technologies that are emerging and advancement in data analytics, manufacturers can achieve the dream of the truly “smart factory”.

IoT intersects with lean methodology and has the potential to take lean to the next level. The information gleaned from connected devices, including users’ experiences with a variety of products, can be fed back to instrumented factories to provide unprecedented opportunities to enhance manufacturing processes and reduce waste.

As consulting firm Deloitte has stated, “in operating the existing business, IoT and analytics are helping companies to connect a diverse set of assets. This results in efficiency gains throughout the manufacturing process.”

The firm describes a number of areas in which efficiencies can be added. One is through the acceleration of planning and pre-manufacturing. The processes of choosing suppliers, considering risk and managing material costs can be fine-tuned through the interconnectivity IoT and analytics bring, Deloitte says.

“Analytics can deliver insight to help companies gain a better understanding of customer preferences and desires, potentially resulting in improved predictability and performance in the marketplace,” Deloitte says. “Understanding the products, and the specific features, that are being purchased allows companies to plan production to meet market needs.”

Another potential benefit of IoT is streamlining the manufacturing process, which is changing dramatically as more companies incorporate IoT and analytics capabilities. “Predictive tools and machine learning allow potential problems to be identified and corrected before they occur,” the firm says. “The value of lean manufacturing and just-in-time processes like Kaizen and Kanban improves exponentially” when intelligence obtained via IoT and analytics can be applied.

And a third area where IoT can add value is in improving post-manufacturing support and service. In the past, Deloitte says, manufacturers often lost track of their products once they were sold. Now, because of new levels of connectedness and the greater insights provided by IoT and analytics, manufacturers can gather information from their customers effectively while improving service and support in the aftermarket.

The benefits of IoT for lean manufacturing extend well beyond processes within a single organization. IoT can help optimize the interaction of manufacturers and their business partners, enhancing the flow of materials along the pipeline based on more accurate data on product demand and usage. An IoT service creation and enrichment platform such as Accelerite Concert can go a long way in making such collaborations happen.

Manufacturers will be able to fully realize production efficiencies that were extremely difficult and in some cases impossible to achieve through traditional, manual processes.

Dean Hamilton, Senior Vice President and General Manager of the Service Creation Business Unit, Accelerite

The vital need for analytics

Organizations that successfully leverage the Internet, mobile technology, business analytics, digital performance dashboards, and integrate other enabling technology with strategic improvement end up with a much more advanced version of lean and continuous improvement in general, according to Terence Burton, president and CEO of The Center for Excellence in Operations Inc., a management consulting firm.

Enterprises “need a higher order paradigm of lean to benefit from these complex emerging technology-enabled innovations in business models, rather than suffer the inevitable waste creep and margin erosion,” Burton says. “The Internet of Things will undoubtedly play a large role in evolving lean to a higher order, enterprise-wide and technology-enabled paradigm of improvement.”

The potential benefits IoT can deliver for manufacturers stem from improved availability of timely and precise data. The ability to instrument, at low cost, almost every aspect of the manufacturing process and to deliver that data quickly to business stakeholders via the Internet is already transforming business operations and business models. But the promise of an evolved “higher order paradigm of lean” is entirely dependent on manfacturers’ ability to derive meaningful insight from data.

As valuable as IoT data can be for manufacturers’ lean efforts, it’s important for them to keep in mind that having enormous volumes of information will not necessarily be of help if they don’t have a timely and effective way of analyzing the meaning and context of the data.

Only advanced analytics and artificial intelligence (AI) technologies (such as machine learning and predictive maintenance), combined with the flexibility, processing and storage capabilities of cloud computing, will give manufacturers the ability to optimize IoT data and leverage it as part of their lean methodologies.

The smart factories of tomorrow will need to deploy a next-generation, cloud-based, big-data analytics platform that enables them to use newly acquired information to the fullest. The platform should be capable of analyzing structured as well as unstructured data, both at-rest (in databases) and in-flight (from streaming data sources) and include a single tool for data acquisition, storage, transformation, AI and visualization.

Manufacturers need to be able to drill down into IoT data via easy to understand dashboards, so they can find patterns and detect anomalies that can directly contribute to creating more lean operations. They need to be able to quickly identify useful correlations and make inferences that can lead to enhanced processes.

While business intelligence (BI) and data visualization tools are nothing new, current technologies often require the use of data analysts, BI developers and ETL developers before insight can be exposed to business users. The next generation of analytics tools, such as Accelerite ShareInsights will place more power in the hands of business owners and subject matter experts who fully understand the factory processes instead of data scientists and programmers. They also will be made accessible to factory operations teams and development teams, who can help provide an integrated flow of data to make products and processes more efficient.

Ultimately, the most significant transformation in how lean methodologies will be applied to smart factories will come from the use of AI to perform sophisticated forms of big data analysis that are impossible for human analysts. AI algorithms now drive semi-autonomous vehicles; recommend what we should watch on TV, read or listen to; recognize our speech patterns and faces; diagnose our illnesses and so much more.

These algorithms are not just capable of learning; they are also capable of detecting patterns, correlations and anomalies in large data sets that would go undetected by humans. They’re able to predict the behavior of complex, inter-connected systems and recommend the optimal course of action to accomplish a particular goal.

This type of capability will be especially important as manufacturers move toward product personalization, where products can be catered to specific users and predictive insight will be needed to configure production lines and supply chains in the most efficient manner.

The next generation of IoT analytics will place the power of AI directly in the hands of business stakeholder to drive continuous optimization. And AI-powered lean methodology will not simply be better at eliminating waste that inevitably creeps into complex systems; it will predict that waste before it occurs and take steps to ensure that it never does.

Manufacturing in the future will be about building the product the customer wants at just the right time, and together lean processes, IoT, big data analytics and AI will allow the smart factories of tomorrow to operate with unprecedented efficiency.

This article was produced in partnership with Accelerite. The author is Senior Vice President and General Manager of the Service Creation Business Unit at Accelerite.

Internet of Things (IoT) is changing the world around us. This may look like a bold statement to many, but that’s the truth. From elevators to planes, there will be 30 billion connected devices in the next three years. Clearly, IoT is moving fast, and tech giants are taking bold measures to constantly push the boundaries of what can be achieved with the IoT.

The recent Genius of Things Summit that was organized in Munich, Germany was a demonstration of how IBM’s unrivaled IoT ecosystem is changing the way we live, work, and play. Here are a few notable developments that caught my interest.

Digital twin

The traditional way of conceptualizing, designing, and developing a product is time- and resource-intensive. Digital twin is an initiative to improve the efficiency of the process by using cloud-based virtual image of an asset maintained throughout the lifecycle. The asset, in turn, remains accessible to every individual involved in the process, thereby allowing people to work collaboratively, reduce errors, and improve efficiency.

Airbus and Schaeffler are the two companies that are currently using digital twin engines and bearings. Airbus is using this technology to create a digital thread that allows different engineering divisions to collaborate. This way, in case a problem is discovered, the company can explore whether the product is due to inadequate maintenance, poor manufacturing, or a fault in the design.

Cognitive commerce

The ultimate objective of cognitive commerce is to offer a truly personalized service to customers based on their precise preferences. To achieve this, a wide spectrum of technologies is used, from speech recognition to machine learning.

Visa is working in collaboration with IBM to offer its customers the flexibility to make payments from any IoT connected device. This, in turn, will eliminate the need to carry sensitive financial information embossed on payment cards, thereby making the customer journey simpler, easier, and more secure.

Predictive maintenance

As the name implies, predictive technology analyzes the data collected using sensors to predict the maintenance needs of an asset. This, in turn, improves asset availability, reduces maintenance costs, and improved customer satisfaction.

SNCF, which is a leading freight and passenger transport service, has collaborated with IBM to connect its entire rail system to the IoT ecosystem. Using the data collected from sensors, SNCF will be able to predict repair and maintenance needs of its trains and tracks, improve the security and availability of its assets, and reduce the downtimes associated with unexpected downtimes.

While the Internet of Things (IoT) has the power to change our world, we are still at the beginning of the transformational journey that will revolutionize the way we live and work for the better. In the next few years, we can expect to see incredible advancements being made by tech giants, such as IBM and other companies.

We live in a cyber-vulnerable world – a world governed by data. Data encapsulates almost every aspect of our personal and public life. It is heavily shared, distributed, stored and accessed, and it is constantly at risk. Recent mega-hacks, such as the ones on Target, Yahoo, and Ashley Madison, among others, demonstrate that leaking of personal information and misuse of our data are inevitable in a world that is becoming increasingly more connected and data-centric.

As concerning as these hacks are, we should be aware that at the end of the day, the risk is merely the misuse of data. Without question, attacks such as these can have devastating effects on people’s lives, but their outcome is limited to the virtual world and cannot touch the physical world, at least not directly.

See also: The U.S. VA protects your hacking cough from hacking

This is all changing now as our world is gradually developing into a cyber-physical one. Wikipedia defines a cyber-physical system as one where physical and software components are deeply intertwined. That is, physical systems control, and are controlled, by electronic components.

A good example of a cyber-physical system is a modern car, with approximately 60% of its costs associated with electronic engineering. There are often more than 100 electronic units in a modern vehicle that affect critical mechanical and physical components, such as brakes, the steering system and the door lock mechanism.

Another example is a manufacturing plant that is not only monitored by software, but to a large degree, is also controlled by it, often via remote wireless interfaces.

These are only two examples, but many others exist in countless industry verticals, such as intelligent transportation systems, medical devices and home automation — aka the smart home.

Not the same as IoT

A cyber-physical system is closely coupled with, but is not synonymous with, the Internet of Things (IoT). IoT devices are typically the controllers of the cyber-physical domain. They use one or multiple connective technologies (e.g. cellular or Bluetooth) and are governed by service providers or user applications on a mobile device. For instance, the iPhone application provided by your vehicle manufacturer enables you to unlock your car or start the engine remotely. The Amazon Echo smart speaker app that controls your home lighting is another good example.

What is common to these examples is that they allow us, as end users, to wirelessly manipulate physical functions.

Our control over these systems is terminated at the IoT controller (the vehicle telematics system or the smart speaker, respectively). The IoT controllers communicate with physical objects using two key elements – sensors and actuators. Sensors measure properties of the physical world (e.g. the temperature of a centrifuge) and report them to the controller. Actuators manipulate the physical world (e.g. keep the car in its lane) at the command of the controller.

The glue that connects all of this is software in the form of code and data, and it is software, not love, that makes our 21st Century world go ’round. Therein lies the problem – software code inevitably has bugs and design smells, which can potentially lead to serious security issues – what we would call exploitable.

The number of actual exploitable bugs per 1,000 lines of code depends on numerous factors, including what research you care to read. But even by the most optimistic research, the average vehicle, with some 100,000,000 lines of code, is likely to have a few thousand exploitable bugs – that’s what we call a hacker’s paradise.

Before we panic, get rid of our cars and revert to horse-drawn carriages, it is worth noting that cars are safe and that this number does not necessarily represent a few thousand ways in which your car could be manipulated to kill you as you are driving. To truly assess how vulnerable a system is to attack it needs to be thoroughly analyzed. There are many factors to take into account, such as attacker motivation and access to the target. Still, the overall concern is valid, and no less of a valid question is “how did we get here?”

My answer might catch you by surprise. I think that it was almost inevitable that we arrive at this point. History has shown, time and again, that markets are driven by features, with security lagging behind. When the first computers were connected to the Internet, only a handful of experts thought of the issues of its inherent insecure architecture. Of course, they were largely ignored because they didn’t contribute to the bottom line.

The result was that we gradually became acquainted with a world of mystical cyber-creatures, such as viruses, worms and malware. It took time for the industry to react, because it’s always harder to add security to a production environment. Gradually, antivirus programs and firewalls were invented and we managed to move on. PCs are not without their security issues, but the economy didn’t collapse and the sky didn’t fall.

The next wave of connectivity

A similar path could be traced as we moved on to the next wave of connectivity – smart mobile devices and the evolution of IT systems. The security guys are always blowing their whistles at the gate, shouting that we must build in security at the initial design stages, but market drivers are pushing for features, more of them and faster.

Is the same happening in the IoT world? Recent data suggests that this is indeed the case. So, you might be comforted by the premise that we let the dynamic of technology run its course, adding in security as an afterthought, the same way we did in the past. However, there is one very important observation, one which I would like to be the takeaway of this article – the tolerance for error falls dramatically every time we become more dependent on software.

We have managed to overcome loss of data on our personal computers and hacked phones. We struggle with colossal data breaches to credit cards databases and our most personal information. Will we be able to cope with a real mega-hack that compromises, or worse yet, irrevocably harms, our physical world?

A better idea might be to invest in security by design. Those experts might be right this time around.

VB Profiles Connected Cars Landscape

This article is part of our connected cars series. You can download a high-resolution version of the landscape featuring 250 companies here.

Is industrial IoT new or old?

It’s a more complex question than you think. If you’ve experienced the advertising barrage of the past decade, you might have concluded that large industrial companies are just discovering the power of data. Ports are being rigged with sensors and software to optimize boat traffic. Cities are saving millions by switching to smart LEDs that cut energy and reduce maintenance demands. People are scurrying up and down escalators in an artsy blur.

The underlying message is that the 19^th century has suddenly discovered the 21^st.

But when you dig into it, you’ll soon discover that data—and systems animated by data—have been an essential player on the factory floor for decades. The first industrial robot, Unimate, went live in a General Motors facility in 1959. SCADA systems for controlling industrial equipment have been around so long many have forgotten, or never learned, what the acronym stands for. Oil and gas producers played a big part in bringing technologies like GPS and data visualization to the mainstream.

Here’s another fun fact: the average age of a transformer in the U.S. is now around 43 years and some are 70, or well beyond the 35-year length of their warranties. (The picture you’re looking at is a collection of pipes and pumping equipment from a wastewater treatment center in San Francisco. The equipment dates back to the 1950s. It’s no longer in use but it’s still in place.)

So what’s the right answer? Both.

See also: Industrial IoT set to turbocharge lean manufacturing

What we are seeing in industrial IoT is a marriage of young and old. Manufacturers aren’t racing out to replace aging equipment with intelligent new models. Instead, they are grafting on IoT gateways and wireless radios to effectively get the same (or better) results with far fewer headaches. Old becomes young again.

Take, for example, J.D. Irving. The 135-year old Canadian conglomerate is the fourth largest supplier of frozen French fries, produces paper for magazines like Vogue and plants approximately 20 million trees a year in Canada’s largest reforestation project.

It also makes toilet tissue. At its factories, a large roll of luxuriously soft paper goes in one end of a piece of equipment called a log saw and out comes a cascade of identical six-inch rolls. Even though they are located in factories, however, logs saws are “remote,” i.e. the information inside the control system is effectively landlocked. Replacing the existing system to would have cost close to $31,500, mostly due to labor and cabling.

Adding wireless sensors ran $9,600, says Keith Flynn of RtTech Software, which collaborated with J.D. Irving on the project. As a result, the company can now do things like vibration analysis, preventative maintenance or peak power management. Wireless also opens up options for things like managing traffic for the automated forklifts.

Likewise, rail operators are looking at ways of rigging rugged wireless sensors onto freight cars to prevent mishaps and conduct forensics better.

Not everything is a retrofit

Not every situation is a retrofit. Dell monitors power consumption and equipment health of its micro-modular data centers with IoT gateways. Think of the micro modular as your neighborhood Netflix outlet. Carriers and content providers will install these to locate the most popular videos (or business documents) closer to users to cut down lag time and telecommunications costs.

Are retrofits the answer to everything? No. Many of these assets were put into place decades before computer viruses were weaponized. Installing gateways means reviewing and tightening security policies. Companies will also have to go through the process of determining whether they want to keep and analyze most of these data locally and in-house, or whether to outsource it.

Still, the declining cost and increasing sophistication of sensors and algorithms, along with the growing portfolio of analysis platforms, mean that wireless upgrade will increasingly become the de facto choice. It’s simply an easier way to experiment.

And that means that a lot of equipment nearing retirement age will get a new lease on life.

The author is a technical analyst at OSIsoft.

General Motors announced on Thursday a major expansion of its self-driving operations in Silicon Valley.

The automaker intends to hire 1,100 people and double the research and development space available for its self-driving subsidiary, Cruise Automation.

See Also: Automotive 2.0: The new road ahead to autonomous vehicles

GM has, in collaboration with Cruise, been testing 50 Chevrolet Bolt cars in San Francisco, Arizona, and Detroit for over a year. Rumors suggest GM will heavily expand the amount of self-driving vehicles on the road next year, to potentially number in the thousands.

“As autonomous car technology matures, our company’s talent needs will continue to increase,” said Cruise Automation’s CEO, Kyle Vogt. “Accessing the world-class talent pool that the San Francisco Bay Area offers is one of the many reasons we plan to grow our presence in the state.”

GM following Ford?

The expansion comes a few weeks after Ford, its main competitor in the U.S., announced a $1 billion investment in Argo AI, to be sent over the next five years. The investment gives Ford access to a talent-filled startup working on all sorts of autonomous technology.

General Motors has not set a clear roadmap for its self-driving program, unlike Ford, which wants to have a fully driverless vehicle on the road by 2021. Both automakers are likely to launch ride-sharing services for the self-driving cars, GM may use Lyft as the default service.

Other competitors, including Waymo and Tesla, want to see self-driving cars on the road earlier, but lack the manufacturing (Waymo) and distribution (Tesla) prowess of GM and Ford.

Artificial Intelligence. I can’t think of a reference to the intellect that would feel more unauthentic and fake. It’s no wonder people turn to The Terminator or The Matrix to fathom what it’s all about. Fortunately, after 60 years of AI rumors fueled by academia and movies, we’re finally starting to see signs that it means more than just robots taking over.

Working in the tech industry, it’s ironic that the AI lightbulb clicked not through an understanding of machine learning or engineering — but through the human challenges we face in the technology world.

See also: Look at all the amazing things AI can do for lawyers

While at Yahoo! and Apple, it was amazing to be part of technologies that not only helped enable the modern cloud today but continue to support its more than doubling in performance every year. But on the people side, opening a new data center meant hiring a team with over 100 man-years of various expertise and experience. As these skills became harder and harder to find, we realized that since we couldn’t find more people that had them, we needed to figure out how to get more out of people that didn’t.

We’re living in a world where the evolution of technology is exponentially outpacing that of people. AI is now empowering machines with the intelligence to form its own insights and thoughts. What were sensors are now evolving into senses, where machines can acutely comprehend things like sight, sound, and touch.

The Internet of Things (IoT) is enabling “self-driving” actions to be performed, based on those thoughts and senses. Right in front of our eyes, technology is making the leap from being the inanimate tools of yesterday to serving as our collaborative and conscious AI-powered co-workers of tomorrow. As people, our responsibility is to serve as the manager and mentor of this new form of co-worker.

Using comprehensive human-to-machine learning interfaces, all people will have the ability to effectively teach and mentor our new co-workers using one’s own natural human language. Yes, human knowledge will be the foundation of what makes artificial intelligence real. This is why AI will prove to be more useful as an extension of human intelligence, than a substitution for it. It is then that it will no longer be viewed as Artificial Intelligence, but understood as Augmented Intelligence.

Enter augmented intelligence

AI is going to augment natural human intelligence and enable people to gain the world’s collective expertise while requiring less time and study than what has been required to become an expert in any one thing today. Traditionally in humans, an expert’s mind possesses fewer possibilities for slower growth, while a beginners mind offers many possibilities for rapid growth.

Augmented Intelligence will empower us with the best of both. In doing so, there is no reason to think that our own personal capabilities for intellectual advancement cannot equal or surpass the doubling per year that we see in computers. Yes, we will see the day when the augmented human expert will be able to get 2x smarter, every year.

Being a leader of people, I learned long ago that my own success is more defined by the augmented intelligence and capabilities that my team provides than anything I could solely do as an individual. The same will apply to the new performance enhancing co-workers that will augment our intelligence and capabilities. Through today’s eye, this picture of our future may be seen as heresy or a superhuman threat. That said, the same fear occurred in the past when it was suggested that all people could be given the power of reading.

Evolution is life. Embrace the future.

Apple’s secretive self-driving project has received a permit to test driverless vehicles in California, which could be our first preview of the iPhone maker’s autonomous technology.

The self-driving tech has been in development, under the codename Project Titan, for a few years. During that time, the project has supposedly moved from a hardware, software, and services package to a more software oriented project.

See Also: Detroit passes Silicon Valley as center for self-driving research

Apple has registered three Lexus RX vehicles and six drivers, which does give some credence to the reports of it focusing primarily on software. The permit does not disclose what sensors the company plans to use.

All companies registered with the California’s DMV have to file disengagement and crash reports every month, this could be our first insight into the sophistication of Apple’s tech.

Apple technically isn’t even working on a car?

Apple has still not publicly acknowledged it is working on a self-driving vehicle and declined to comment on the permit.

It said in a letter to the National Highway Traffic Safety Administration (NHTSA) that it is “investing heavily in the study of machine learning and automation, and is excited about the potential of automated systems in many areas, including transportation,” which looks like the closest we’re going to get to a confirmation until near launch.

The company will join a growing list of tech firms testing autonomous vehicles in California, including Waymo (Google’s self-driving division), Tesla, General Motors (with Cruise Automation) and Uber.

All machines eventually break down. Self-driving vehicles are no exception.

Autonomous vehicles pose two problems for the future of vehicles. The removal of the driver means there is no person providing feedback on how the vehicle performs over time. You are removing the point-person who says “something feels wrong, this needs to be checked out.” An autonomous truck could easily arrive at its destination with one fewer wheels than it left with at its origin without recognizing there is a problem.

An autonomous truck could easily arrive at its destination with one fewer wheels than it left with at its origin without recognizing there is a problem.

See also: Automotive 2.0 – the new road ahead for autonomous vehicles

Autonomous vehicles also create a second maintenance problem – the sensor systems they use can fail. An autonomous vehicle operating with a faulty sensor is an on-the-road hazard. Imagine the wheel-speed sensor that reports the vehicle has stopped when it is actually traveling at highway speeds. A human would know to disregard that faulty input because it “feels” wrong. An autonomous vehicle could respond by continuously accelerating.

With the advent of autonomous vehicles, now, more than ever, systems need to be created for advanced diagnostics. AI should not just be used to make vehicles drive autonomously, but also to allow vehicles self-diagnose future and upcoming issues.

The new AI for maintenance

The concept of predicting breakdowns is nothing new. People have been using statistics for decades to calculate mean-time-to-failure – it is how the automotive industry came up with replacing parts based on number of miles driven. However, the ‘mean-time’ means that some parts repaired will have significant useful life yet and others will break before you get around to fixing them. AI allows something to be done that was unfeasible in the past – actively monitor every vehicle while it is in use.

Established companies have been working in this place for a while now. They operate on the notion that if you have been collecting data then they can put enough experts on the problem to create a solution. SAP, IBM, and Pivotal Labs are all making plays into the space. The problem is that they require that a company has been collecting data, knew what data to take, and knew to keep it.

As the industry matures and companies collect more data and gain large historical datasets, their solutions will be powerful. But nimble startups can use speed to their advantage in this situation by rapidly deploying a solution that will give them a permanent head start on collecting the sensor data needed to train the AI systems.

One such company is Uptake. They have had phenomenal growth, breaking a $1 billion dollar valuation within a year of incorporation and being named Forbes’ 2015 Hottest Startup. They did this by collecting a dataset from scratch, first with locomotives and then with other vehicles, through this they ended up with a partnership with Caterpillar. Their future looks to be diverging away from vehicles and towards bringing predictive maintenance to other industries.

Preteckt follows Uptakes footsteps in collecting its own dataset, but it targets the vehicles that are more commonly seen on the roads. Preteckt started with 18-wheelers and has already diversified into buses, and the hardware and software architecture that has been developed is portable to smaller vehicles. Preteckt’s technology has already been deployed in an autonomous truck and could be migrated to autonomous cars in the future.

The future is not “Star Trek”

Science fiction has people asking machines to run diagnostics to see if something is wrong, or what is wrong. The concept is flawed. The machines will know before you ask and will tell you what will go wrong with them next. They will tell you how to best take care of them to ensure that they do not fail on you. This is what you can look forward to with the autonomous vehicles of the future – a peace of mind in that your vehicle will not have any on-the-road surprises for you.

But why stop at eliminating surprises. Once vehicles can know their upcoming maintenance needs and drive themselves – why won’t they just take themselves to the mechanic when your schedule says you don’t need it. Maintenance will become an “out-of-sight, out-of-mind” concept making the ownership of a vehicle that much more enjoyable.

VB Profiles Connected Cars Landscape

This article is part of our connected cars series. You can download a high-resolution version of the landscape featuring 250 companies here.

Microsoft announced on Thursday a new “software-as-a-service” (SaaS) offering, called IoT Central, aimed at reducing the complexity of building Internet of Things solutions.

IoT Central allows developers to create software and hardware without cloud expertise, a necessity for large-scale IoT solutions in the past. Microsoft does not say exactly how the service reduces the need for cloud experience but said it will continue to update over the coming months.

See Also: The bot invasion is on, powered by $24B in funding

The new service is powered by Azure IoT Suite, Microsoft’s current central platform for IoT development. We assume for developers not accustomed to cloud computing, it will automate parts of the process.

“[IoT Central] has the potential to dramatically increase the speed at which manufacturers can innovate and bring new products to market, as well as lower the barriers to creating IoT solutions that generate new revenue opportunities and better experiences for customers,” said Microsoft.

Also updating Azure

Microsoft will also be updating Azure IoT Suite with a new “pre-configured solution”, called Connected Factory. The solution “makes it easy to connect on-premises open platform communications (OPC) UA and OPC Classic devices to the Microsoft cloud and get insights to help drive operational efficiencies.”

Connected Factory has built-in cloud security to make configuring devices in the cloud a safe experience. Microsoft has partnered with Unified Automation, Softing, and Hewlett-Packard Enterprise to build “turnkey gateway solutions” for the Connected Factory, using Azure cloud services.

Microsoft has made big investments in IoT and cloud over the past year and they appear to be paying off. Even though Amazon still controls most of the market-share for cloud computing, Azure is catching up.

We’ve all been inundated with the hype surrounding Internet of Things “smart stuff” and the impending arrival of our robot overlords, so we tend to minimize the mind-blowing wonder of the responsive and intelligent computing metamorphosis that is upon us.

For years, the IoT community has been saying that if we really want “things” to be of value, they cannot be dumb. The first wave was getting everything connected, and we have made headway there. The next step is to actually make “things” smarter. 

There are a variety of commercial solutions that do not really deliver on the promise of automating our way to more productive lives. And the concerns over properly securing our connected things still weigh heavily. But there really have been transformative leaps in computing capability and achievable functionality. The killer use case for IoT is on the horizon, but before defining what that is and describing how it is going to manifest, I think it’s important to broadly identify how we got here.

The “Trinity”

The impact of the open source movement in driving exponential leaps in technological advancement cannot be minimized. The algorithms and computing infrastructure that drive “smart” things — IoT, Artificial Intelligence, and machine learning capabilities — have been around for decades. Anyone at the NSA can tell you as much.

The difference now is in accessibility to the masses. These technologies were once jealously guarded, closed off from the wider world, and only available within formidable institutions possessing vast resources in both personnel and compute power. Open source changed all that. New things no longer have to be constructed from ground zero, thus supercharging the innovation cycle. The widespread access to knowledge bases and software allows anyone so inclined to build upon the shoulders of giants and leverage the wisdom of crowds.

The creative explosion fueled by open source helped give rise to the cloud, which is the second movement responsible for ushering in our new era of computing. Freed from the physical limitations and expense of individual server stacks and on-premise storage, the “app for everything” age dawned and the capacity for on-demand collection and consumption of big data was unleashed. Once we could scale compute power unconstrained by geography, our technology became mobile and the dream of smaller and increasingly powerful devices trafficking in colossal quantities of information became a reality.

Big data gives lifeblood to modern computing. But data does not do anything and, in itself, has no value. This brings us to the third movement in the “smart” revolution: analytics. The types of augmented computing that people encounter in everyday life now — voice recognition, image recognition, self-driving and driver-assisting cars — are founded in concepts that rose out of analytics and the pursuit of predictive analytics models, which was all the rage just a few short years ago.

The disheartening realization with predictive analytics was that, to train effective models, you need both massive amounts of data and scores of data scientists to continually build and maintain and improve data models. We were once again running up against the roadblocks of access and resource constraint.

And so we arrive at the present, where things are shifting in a new direction. The difference now is that we do not need to recruit an army of data scientists to build models; we have taught our programs to remove some of those roadblocks for themselves.

Inherent intelligence

Our AI-driven systems, especially Deep Learning systems, can now be fed millions upon millions of training sets, train in days/hours, and continuously re-train as more data becomes available. Open source tools and cloud computing are still important and evolving, and we still traffic in loads of data to perform lightning-fast analysis, but our programs now incorporate AI as the engine to make themselves “smarter.”

Expertise from vastly different computing realms has congealed to imbue programs with previously unimagined capabilities. The paradox is that as the cloud becomes ever more powerful and less expensive, the smart IoT strategy is to move much of the first line of entry processing away from the cloud and to the edge. This serves two purposes: to enable on-device decisions without needing cloud intervention and to deliver edge patterns and analytics to the cloud for fast second-stage analytics. Tiny AI engines can now perform analysis in near real time on edge devices and “things” no larger than a matchbook. And as these points of computational power grow increasingly commonplace in ordinary objects — intelligent routers and gateways, autonomous vehicles, real-time medical monitoring devices — their potential functionality expands exponentially.

John Crupi, Vice President and Engineering System Architect, Greenwave Systems

Artificial intelligence at the edge

In the early days of IoT (aka M2M), the focus was on getting data up to the cloud when possible. FTPing log files every night was the rage. When General Electric came on the scene with the “industrial internet,” everyone began talking about real-time live data connectivity. That was a big jump from FTP, but people treated edge devices as simply “things” that transferred data to the cloud for analytics. We are now in the midst of an exponential reverse fan out of that thinking. Real-time requirements are redefining the paradigm. The cloud is now shifting into the role of IoT support and second-tier analytics, and the processing is getting pushed out to the edge.

For example, we have been working with a company developing a next-generation medical monitoring device. Initially, we assumed with such a small device, we would send raw data from the device to the cloud for analysis. But that is not what was desired, nor is it what transpired. The company wanted the analytics on the monitor. They wanted the analytics and pattern detection to occur directly on the device, to take actions on the device, and for only “intelligent” (as opposed to raw) data be sent up to the cloud. The model differed dramatically from standard industrial M2M operations — where everything would be connected, and batches of data coming in from all sources would be collected and processed on some set timeline at some central repository.

The whole purpose of connecting now is to obtain instantaneous precision results at the point of entry for immediate answers. Even the low latency involved in “traditional” cloud-processing with hundreds of thousands if not millions and billions of devices is not as efficient for real-time edge analytics as using this new architecture. In some cases, you can achieve a data reduction of 1,000x by just sending the analytics and patterns vs. raw data to the cloud. 

We no longer deal in dumb collection devices; we need them to do more than just curate. They must be artificially (and naturally) intelligent — capable of doing pattern recognition and analytics in their tiny engines. They push those results up to the cloud for other uses. As this ideal proliferates, so, too, do the possible applications.

As is perfectly embodied in the example of an autonomous car, this dual edge/cloud analytics model produces precision, real-time results that can be continually and automatically refined against ever-growing troves of more data, thus producing valuable, useable information and powering productive action. Even a year ago, I would have called B.S. on this notion for widespread IoT and AI integration — but edge computing and AI have really broken out of the lab and into our world. It will yield outcomes we have never seen before.

The killer use cases for IoT are manifesting through truly intelligent edge devices — in solutions that are purpose-built for specific problems or tasks, then interconnected and subjected to patterns that move beyond their initial application. As more and more smarter, AI-enabled “things” are incorporated into our everyday lives and operate at the edges of our inter-communicating networks, we will see things moving beyond merely being connected and into actively embodying intelligence. Smart stuff indeed.

This article is produced in partnership with Greenwave Systems.

The author is Vice President and Engineering System Architect at Greenwave Systems, where he guides development on the edge-based visual analytics and real-time pattern discovery environment AXON Predict. He has over 25 years of experience executing enterprise systems and advanced visual analytics solutions.

Parking garages take up an enormous amount of space in retail zones and bring less value than a retail store, business block, or housing complex in the same area.

Sadly, in a world where almost everyone drives, they’re a necessity if a city or supermarket wants to avoid congestion and road accidents.

See Also: Taking a look at the future of next-generation transportation

It is no surprise then that AvalonBay Communities, a real-estate investment trust, is eager to see the introduction of self-driving cars.

The Virginia-based developer has already shown future plans for the two-floor underground parking garage, part of a residential complex under development in the Los Angeles Arts District, to the LA Times.

When it is finished in four years, the parking garage will still serve approximately 1,000 cars, but as people switch to ride-sharing vehicles, it will make way for shops, a gym, and a theater.

Not just AvalonBay

AvalonBay is not the only property developer eager rid the world of parking garages. Upscale shopping centre Grove has talked to Google about how to prepare for the future.

There is no definitive time when parking garages will become obsolete, but some analysts have projected 2020 to be the peak point for car ownership. After that, ride-sharing and shuttle solutions will start to rise.

Even though most automakers have not said it publicly, it sounds like self-driving cars will be for rental or ride-sharing services only, at least for the first few years. Ford’s head of research, Ken Washington, recently said customers will not be able to purchase self-driving cars until at least 2026.

A lot has been said about the Internet of Things (IoT) — a broad development in various technologies across industries that is fundamentally changing the innovation cycle everywhere —  but how much is real?

“One of the things that we should grasp about the IoT is that we are currently in that stage when technology gets incredibly hyped,” says Jason Collins, Vice President of IoT Marketing at Nokia.

Continuing further, he describes the hype by comparing the early days of the internet when static pages and hyperlinks did not ignite the full potential of the internet and the crazy boom and predictable bust over web-based businesses came and went.  Still, the world was left with the valuable piece of network and services infrastructure we now call the Internet.

“So the internet turned out to be kind of a big deal,” says Collins half-jokingly, before getting serious about how we can size the Internet of Things.

Keeping that outcome in mind, what is the potential size of the Internet of Things and how do we value it?

Valuing the Internet is tough.  Today we are going online with our computers and smartphones and connecting billions of nodes; however, the value will extend well beyond that. 

“Members of our Bell Labs team analyzed this and determined that it will be 36x the value of today’s Internet,“ he says. “That potential value of the IoT is dependent upon the number of devices connected and users’ perceived and experienced value of IoT devices and applications.”

If you think about that potential, we quickly recognize that we’re in the very early stages of how this connected technology can change the very fundamentals of digital transformation and business growth in the next decades.

How can enterprises leverage this growth opportunity?

Prior to the dawn of this new machine-type (M2M) connectivity, there were two main drivers of business — developing products and services and the sales of those products and services.

But this approach is now getting a major upgrade thanks to IoT technology. Key to this pivotal transformation is the data being produced in torrents by the connected devices that are expanding rapidly across businesses.

“But while this new connected world seems to be allowing enterprises and their customers alike to benefit from a huge pool of data, it’s not as simple as that,” says Marc Jadoul, Market Development Director in IoT at Nokia.

Perhaps it’s best to think of this in terms of “analog to digital.” Machines and networks that learn about their effective behavior through gathering data, and analyzing how to use them.   He explained further that we should think of the IoT beyond an environment of communicating things and instead as a “connective tissue” or a “global nervous system” that provides context, and – why not? – meaning.  This is the first step towards getting value out of the IoT.

Building upon that, the IoT then provides a “platform to solve problems” like the Internet once did via search and discovery. “Platforms like Google not only gave us access to the information but provided context,” says Jadoul. “In that same sense, Uber has provided a disruptive model for public transport and Airbnb a new platform for guest housing.  They use connectivity and data to transform business models today and, eventually, you will see the IoT becoming an innovation platform in many other areas, like connected cars, digital healthcare, or smart homes.”  The possibilities are endless because big data and new services will be driving the growth. 

Wireless sensor networks are evolving into analytics-enabled applications, making IoT into a “bigger and richer experience than the current M2M,” says Jadoul.

However, digital transformation must go beyond the platform, the data, and the (still too often) siloed applications. It requires a shift in the culture and mindset of organizations in order to generate significant benefit from this technology.

Who’s leading the growth within an enterprise?

New innovation found in M2M often came from internally focused and driven cost savings and process optimization efforts, a.k.a. command and control.  This is what we often call the Industrial IoT, or Industry 4.0.

While the early days of the IIoT were focused on these drivers, a new emerging Enterprise IoT approach will enable greater growth through product and service innovation, and yet-unseen business models.  With that in mind, it isn’t surprising that the early enthusiasts of this new technology are not only on the traditional IT side of the corporate “houses,” but also in their product management ranks, the people who face the customers and are looking for portfolio innovation, an enhanced customer experience, and of course new revenue opportunities.

“The sooner that companies start seeing IoT as a catalyst for growth rather than a way for the IT guys to trim costs, the faster IoT will get off the ground in enterprises,” says Jadoul.

Where is IoT headed?

As connected technology matures and a shift in mindset occurs, IoT will create new value for its stakeholders.

“Companies have to start looking at solving business problems and extend their thinking beyond vertical, point applications,” says Lee L’Esperance, Business Modeling Principal at Nokia.”If they remain strictly verticalized, its siloed and the value is limited.” But seeing the benefit across traditional business groups, products and services will unlock true value, he adds.

IoT can be very impactful to business but it needs to be architected for creating a connective tissue rather establishing than point-to-point links.  Motivating the ability to architect an IoT solution within a business context is about getting the business models right – and finding the sweet spots for creating value, growth, and RoI.  We will explore developing business models in the next article and how you can create new value opportunities for your stakeholders.

This article was produced in partnership wth Nokia.

Uruguay — best known for its art deco buildings and many steakhouses — will now add leading tech hub to its many great titles.  

Sinergia Tech raised $500,000 to launch the very first hardware accelerator in Latin America. The firm, known for its technological development, digital fabrication, and prototyping laboratory, can offer a unique environment for aspiring hardware startups.

See also: Readwrite Labs launching their new incubation program

The accelerator, based in Montevideo, Uruguay, plans to fund close to 500 tech startups with a focus on IoT, smart cities, smart agriculture and smart hemp.  The chosen startups will receive investment sizes of between $25,000 to $100,000. 

Montevideo is looking to position itself as a melting pot for tech leadership due to its highly sought-after tech talent, free exchange market and a stable social, legal and political environment.

First cohort will have 20 startups

Sinergia Tech will be selecting 20 startup finalists into the first batch, with a goal of choosing 5 winners to continue the program and assist with international growth.  A network of 15 local mentors, as well as more than 80 international profiles, will form a support network to assist the startups.  The mentorship of these startups will include personalized feedback, and a tailored approach based on the needs of each team.

The startups chosen to participate in the accelerator will have access to state of the art equipment — such as a laser cutter, 3D printers, CNC router, circuit printer, cut plotters, and traditional tools.  Moreover, startup teams will also have access to robotics and electronics gear. 

The goal of Sinergia Tech, aside from creating a hardware accelerator to assist entrepreneurs and startup teams within the hardware space; is to empower young men and women by helping them achieve their dreams.  By doing so, Sinergia Tech hopes to motivate and inspire others to take up the entrepreneurial journey.

Entrepreneurs and startup teams requiring more information should visit Sinergia’s website.

As a technology executive, I’ve overseen the buildout of multiple data centers in my career. Without exception, all were designed with an uninterrupted power supply (UPS) and backup power.

In fact, I can’t imagine a data center of any consequence being built without these safeguards in place. No IT engineer worth his or her salt would consider it. Simply, the grid is not a sufficiently reliable foundation on which to rely for the ongoing operation of critical equipment.

Recognizing that, I ask you to think about the megatrend around smart city development. The current efforts around smart cities completely fail to address and incorporate resilience as a core strategy.

That is not smart.

We’re deploying “smart” cities such that they will fail our citizens and service providers the minute the grid goes down, when that information and capability is of critical importance and maximum need. Earthquake, bombing, super storm, tsunami, attack, hurricane, or rioting… That’s when we need guarantees that our city infrastructure will shine and support emergency response, empower “boots on the ground” and, of course, help our citizens.

When these scenarios arise, we’re in critical need of many services. Backup power. Communication network availability. Active information resources like public kiosks and intelligent lighting to direct people to safety. Data assets such as cameras and sensors to provide intelligence about local weather, the wind, water levels, flow rates, tilt, vibration, foot traffic and vehicle traffic. Systems to identify citizens in need. Security mechanisms to deter crime and damage to property.

Brian Lakamp, Founder & CEO, Totem

This sort of resilience has, so far, been an afterthought in the smart city dialogue. We’ve been focused on incremental additions of new capability, without considering performance in adverse scenarios. That’s nuts.

The good news is that we’re in the process of rebuilding the grid around advanced energy like solar, wind and batteries. Some, like me, refer to that new network as the “Enernet.” As we build out the Enernet with energy storage to optimize the network and integrate renewables, we also have an opportunity to address resilience of critical services.

It would be a complete failure in our energy strategy if we were to overlook distributed batteries as part of the solution, and if we were to fail to deploy those assets as a resilient underpinning to smart city nodes and functionality.

Governments and utilities need to take a more active role on this front. Municipalities, states and public utility commissions (PUCs) need to demand resilience for strategic services. Utilities need to enable it. Smart city certification programs like that recently announced by Bloomberg need to evolve to incorporate resilience measurement.

Utilities need to embrace the future

Worth noting, the utilities shouldn’t do it only because their PUC requires it of them. Utilities who embrace the future have the opportunity to act as the backbone of the smart city, on which all other services reside. Paula-Gold Williams, CEO at CPS Energy understands the smart city opportunity.

It starts with imagining a future different from the historic, centralized “power plant” architecture to one where the network acts much more dynamically, like the communications network today, supporting new capabilities and services. The utilities that figure it out first and fastest stand to be the Verizon or Comcast of the Enernet.

Back to the matter at hand. To build a resilient smart city, the Enernet needs to be woven by utilities directly into the infrastructure, with deliberation. Fealty to foreseeable futures requires the Enernet be built as a resilient underpinning to modern municipal services. That is not yet happening today. And, that is not smart.

The author is the Founder/CEO of Totem Power, a startup transforming the future of distributed energy and smart cities. Previously, Brian worked for media giants including Sony and most recently iHeartMedia.

Uruguay — best known for its art deco buildings and many steakhouses — will now add leading tech hub to its many great titles.  

Sinergia Tech raised $500,000 to launch the very first hardware accelerator in Latin America. The firm, known for its technological development, digital fabrication, and prototyping laboratory, can offer a unique environment for aspiring hardware startups.

See also: Readwrite Labs launching their new incubation program

The accelerator, based in Montevideo, Uruguay, plans to fund close to 500 tech startups with a focus on IoT, smart cities, smart agriculture and smart hemp.  The chosen startups will receive investment sizes of between $25,000 to $100,000. 

Montevideo is looking to position itself as a melting pot for tech leadership due to its highly sought-after tech talent, free exchange market and a stable social, legal and political environment.

First cohort will have 20 startups

Sinergia Tech will be selecting 20 startup finalists into the first batch, with a goal of choosing 5 winners to continue the program and assist with international growth.  A network of 15 local mentors, as well as more than 80 international profiles, will form a support network to assist the startups.  The mentorship of these startups will include personalized feedback, and a tailored approach based on the needs of each team.

The startups chosen to participate in the accelerator will have access to state of the art equipment — such as a laser cutter, 3D printers, CNC router, circuit printer, cut plotters, and traditional tools.  Moreover, startup teams will also have access to robotics and electronics gear. 

The goal of Sinergia Tech, aside from creating a hardware accelerator to assist entrepreneurs and startup teams within the hardware space; is to empower young men and women by helping them achieve their dreams.  By doing so, Sinergia Tech hopes to motivate and inspire others to take up the entrepreneurial journey.

Entrepreneurs and startup teams requiring more information should visit Sinergia’s website.

The recent Mobile World Congress in Barcelona revealed that while few brand new and innovative smart city deployments are center stage, it’s an area of technology that is intensely interesting to many and worthy of much consideration.

Here are some of my thoughts based on what was showcased:

Idea #1: Traditional industries should continue to form alliances with start-ups to create innovative solutions to local problems

In 2017, Deutsche Telekom will roll out NB-IoT commercially in eight countries: Germany, the Netherlands, Greece, Poland, Hungary, Austria, Slovakia, and Croatia.  They used their NB-IoT prototyping hub, to showcase their work with selected partners and start-ups to develop cutting-edge solutions for Smart Cities.  Some of the notable examples showcased included a collaboration with Ayyeka,  a California start-up that makes smart water management a reality. It develops end-to-end remote monitoring solutions that streamline and secure the process of bringing field data to decision makers and SCADA systems, enabling smart infrastructure and environmental networks.

Also showcased was a beehive monitoring solution by  BeeAndMe which provides technical assistance to beekeepers: A microprocessor unit measures all significant beekeeping parameters. The data collected by the “baby monitor” for bees is also processed via data mining techniques, helping find answers to important scientific questions. Such partnerships and alliances mean that start-ups can bring creative innovation to smart city deployments, a cohort that could easily be otherwise outnumbered in decision making by the big players.

Idea #2: Smart city solutions should be interoperable and multilayered. 

We need smart cities and not just a disparate collection of smart city projects. In terms of practicality and streamlining, there’s a need for smart city solutions that solve multiple problems rather than stand-alone devices with single use capacities.  This needs to be managed by a primary vendor so that issues such as planning, implementation, and repairs are straightforward to resolve. For example, a light pole was showcased by AT&T equipped with sensors that can communicate to public safety and traffic officials about road, parking and pedestrian conditions. Sensors can also be installed to monitor air pollution, weather or the sound of gunshots. The light pole alone has a range of different abilities (with more to come) as well as data that is distributed to multiple service providers. It is important that stewardship for the device is clear and straightforward.

See also: Smart city development opportunities target $1.3 trillion market

For example, a light pole was showcased by AT&T equipped with sensors that can communicate to public safety and traffic officials about road, parking and pedestrian conditions. Sensors can also be installed to monitor air pollution, weather or the sound of gunshots. The light pole alone has a range of different abilities (with more to come) as well as data that is distributed to multiple service providers. It is important that stewardship for the device is clear and straightforward.

Idea #3: Big telcos are propping up the funding of smart cities 

Current, GE’s digital industrial startup business, used MWC to announce a deal with the City of San Diego to upgrade thousands of the city’s outdoor light fixtures to sensor-enabled LED technology, making it the world’s largest smart city IoT platform. AT&T will act as the data carrier and provide highly secure connectivity for the San Diego deployment, which is expected to save the city approximately $2.4 million in annual energy costs.

Such deals involve a reasonable amount of costly, upfront infrastructure that, even if it reaps financial benefits in the future, may be out of the financial reach of many local councils and municipalities. As a result, there are many big companies offering time and research at a reduced cost or for free.

A 2016 report into smart city development in the UK that revealed that the task of achieving smarter, more connected cities in the UK lies with local councils a cohort that was in many instances, struggling to deploy funds into smart city research and deployment. This can also mean that smart city efforts that are funded by research grants and university think tanks may never be funded outside of their trial, however effective. Public-private partnerships need to, however, be entered into with an egalitarian approach, to ensure that those without the most funds are not the primary decision makers.

Idea #4: Smart city solutions need to be citizen-centric

In reviewing a range of smart city events over the last year, I found that many were at a cost only accessible to well-funded business people and academics, invitation only or at times suitable for those working in smart city development, making them hardly accessible to the average citizen working 9-to-5. It is important that smart city projects are relevant to the local citizens and local issues.

Different cities have different approaches to citizen engagement. An interesting example is Amsterdam’s utility of citizen science to create interesting opportunities for local engagement. It’s not without its critics in that the city has in the past been criticized for projects where volunteers mostly ended up being nothing more than “citizen sensors” (i.e. tech-enabled corporal data collectors for academic and governmental research. Yet Amsterdam has generated social capital and fostered relationships between scientists, designers and everyday people that would otherwise not occur.

Idea #5: Some smart city solutions are not without controversy

Moscow’s smart city efforts were boosted last year with the implementation of 160,000 outdoor cameras focused on traffic and areas of possible crime. This is part of the Moscow Traffic Control Center, the headquarters of an elaborate monitoring and control system that also includes 40,000 traffic lights and a vast data storage facility that contain all the video data transmitted from the streets. The data has been used to fine citizens for violating road signs and signals with cameras recording license plates on cars.

I spoke to one engineer from a Moscow-based start-up who commented that locals were against the cameras but resigned to paying the fines. As part of a smart city panel discussion at MWC,  Andrey Belorezov, deputy CIO for Moscow, elected not to disclose how much revenue has been generated by fines so far. It’s tempting to compare this situation to the UK where there are over 6 million CCTV cameras in public places, ostensibly used to detect and prevent crime. I can’t help wondering how things would change if air quality sensors were as widespread with real-time data to detect and prosecute big business polluters?

But all is not lost, at the other end of the spectrum, with their commitments to oper data and open government, the city of Barcelona has implemented a city portal-a digital connection between citizens and local government where citizens can report government corruption, according to Francesca Bria, Chief Technology & Digital Innovation Officer at the City of Barcelona during MWC. It’s an example where smart cities with an underlying citizen first approach can really get things right.

Idea #6: Smart city deployment needs to be financially viable

The inconvenient question is how smart city deployments can be made financially viable to ensure that there is a reasonable parity between neighboring counties and cities, and that smart city development is not limited to a range of pilot tests of projects that never reach their full potential. It’ll be interesting to see what happens in the smart city space over the next decade, and what lessons are learned from the early adopters as the rest struggle to keep up.