The Next Generation of IIoT: Micro & Macro Connectivity
From a consumer standpoint, the impact of IoT connectivity is clear. People can purchase smart home systems and automobiles with increasingly autonomous features. Looking at the potential changes to our daily lives in the coming years, all things point to connectivity. We are eyeing a future where we can monitor and control our homes, vehicles and business around the clock. The news stories are exciting and tangible because new products are frequently unveiled and we see them being used in our everyday lives. This impact has spread beyond the scope of the consumer market, which ultimately led to the Industrial Internet of Things (IIoT). Traditional businesses, like those in utilities, oil/gas and agriculture, face a future that has the potential to transform entire industries due to the power of digital disruption. Despite the growing pains and challenges of “going digital,” industrial businesses face almost limitless potential to streamline operations and control large distributed networks with a level of precision that was previously impossible. As these industries pick up on the value of data and connectivity, next generation applications have emerged that will drive competition and increase productivity. Data and analytics will be available via the cloud and accessible from any device. And even better, the quality of data will be controlled through automation and the incorporation of third party applications. What this means for businesses is they will be able to monitor their networks on a micro level. This allows problems to be stopped in their tracks and for precise process adjustments that streamline operations. With third party applications, there is not only substantial business opportunity for developers, but there are endless possibilities for process control, security and operational apps that will drive down costs and support increased production. Most business decision makers are aware that there is no stopping digital transformation because research shows that it’s already happening. Many businesses are in the process of digital transformation and have already thought about these next generation systems and the research proves this: 75 percent of IoT providers say that big data and analytics are among the top skills they look for when adding talent to their teams. 50 percent of companies look to hire specialists in mobile development. A recent TechBullion article states: “they already have noticed the close relationship of mobile and IoT and plan to launch IoT projects for their businesses within the nearest 5 years.” Gartner says that by the end of 2017 demand mobile application development will grow five times faster than the number of IT companies able to meet this demand. A new report from Frost & Sullivan anticipates a trend in the transition from connected devices to the use of cognitive or predictive computing and sentient tools in the next 12-18 months. So what does this mean for industrial business? It means they need to invest now in the communication technologies that will deliver the data that is absolutely critical for future networking needs. It means they need to think about how they can enable programmability at all network endpoints – even at the edge. And lastly, it means they need to start working through the challenges of a digital shift now so they are prepared for an automated, connected future.
Autonomous Tech and Self-Driving Cars Dominate the Headlines
The autonomous tech industry is poised to explode, driving job growth and technological innovation. Everything from self-driving vehicles to automated infrastructure is sitting on a precipice of advancement that can be a truly momentous step into the era of the connected world. This week, we are focusing on some of the industry news surrounding autonomous vehicles, including the manufacturing aspect, their space in a smart city, and how major metropolitan areas initially resistant to the technology are starting to come around. In Japan the Race is On for Self-Driving Cars IMAGE by Takashi Aoyama According to a recent study by the Boston Consulting Group, fully autonomous vehicles are expected to account for a quarter of all new cars by 2035 — a slice of the auto industry totaling around $77 billion. While automakers across the globe are racing to become a leader in this new tech, no where is the competition more intense than in the auto-manufacturer rich island nation of Japan. This recent article from the San Francisco Chronicle notes that Toyota, Nissan and Honda have all made significant investments in developing autonomous tech. The autonomous vehicle race is particularly impactful because of the major implications to not only car OEMs who have to fundamentally change the way they approach their product, but to the hardware and software companies building the technology that will support these highly sophisticated (and regulated) vehicles. Could Owning an Autonomous Car Make You “Traffic Elite”? IMAGE courtesy ZDNet If you end up being an early adopter of new autonomous tech, you may find your commute becomes shorter. ZDNet explains that a recent proposal from UC Berkeley grad students suggested the creation of a “Hyperland” — a special traffic lane reserved just for self-driving vehicles. If you want to be in the Hyperlane, you better not mind a brisk ride as the special lanes would allow for speeds over 100mph. The traffic on the Hyperlane would be controlled by a central computer that monitors traffic congestion, speed, and other variables through advanced sensor arrays and keeps traffic flowing freely. The project will cost a cool $11.4 per mile of road, so travel will likely come with a toll to ease the financial burden. Self-Driving Cars Job Market Booming IMAGE by Gene J. Puskar, AP With so much emphasis on autonomous driving, cities are rushing to cash in on the movement. According to the Detroit Free Press, the advanced driver assistance systems and autonomous vehicle market was around $5 billion in 2015. It’s projected to grow to $96 billion by 2025 and a staggering $290 billion by 2035. This massive market growth has led to a number of cities across the country pitching their location as the “place to be” for autonomous tech. From Austin to Pittsburgh, automakers, OEMs and even government officials are pushing for their city as the best spot for innovation in the autonomous vehicle space. So will it be Detroit or Silicon Valley? Or one of a host of other cities vying for a slice of this massive cash cow? Time will tell. Better Late than Never: New York Easing Up on Laws for Driverless Vehicles Back in 1971, New York passed a state law insisting all motor vehicles have a driver with at least one hand on the wheel at all times. Back then, this seemed that a pretty standard rule — but with the advent of self-driving cars, the rules of the game have changed. A recent article from the Democrat and Chronicle noted that until recently, New York was the only state the explicitly banned driverless cars from its roadways. However, the state has now approved a pilot program to allow the testing of driverless vehicles under certain conditions. State Senator Joe Robach was a vocal advocate for the new change. “While the technology for fully driverless cars is in the future, consumers certainly appreciate the automated technology that is currently in cars, including lane assist, self-braking, hands-free park assist and collision avoidance,” he said. “The legislation that was passed earlier this year ensures that driverless cars can be tested on the roads that future consumers in our state will use them on and are tested responsibly.” Audi of America is the first automaker to get approved for the new program, with other manufactures expected to jump on board in the coming months.
Ships that Sail Themselves
Is it time for ships to sail off on a journey by themselves? As the Internet of Things (IoT) connects the world, while the robotics industry continues to innovate, man and machine are merging together like never before. Unmanned aerial vehicles (UAVs) have impacted a number of industries from agriculture to security. If recent news is correct, it won’t be long before autonomous cars are traveling roads alongside us. Now, organizations and government agencies around the world are actively working to bring autonomous vessels to our oceans. What can we expect from unmanned ships operating in our largest bodies of water? IoT and robotics are being considered for a variety of commercial and military purposes at sea. For most of the world, it seems autonomous ships are in the testing phase, but there are big plans in the works around the globe: The British engine maker Rolls Royce Holdings, PLC is leading the Advanced Autonomous Waterborne Applications initiative with several other organizations and universities. The company is eyeing a timeline of remotely controlled ships setting sail by 2030 with completely autonomous ships in service by 2035. The timeline will be heavily dependent upon automation technologies’ ability to carry large amount of data from ship to shore to ensure safe operations. Recently, the UK’s Automated Ships Ltd and Norway’s Kongsberg Maritime, unveiled plans for a light-duty ship for surveying, delivering cargo to offshore installations and launching and recovering smaller remote-controlled and autonomous vehicles. “This ship is considered the world’s first unmanned ship for offshore operations and is being eyed for many uses including offshore energy, fish farming and scientific industries.” In the U.S., the Navy has begun to consider autonomous ships for a number of applications, but is cautiously approaching these new technology advancements. According to National Defense Magazine, “The Navy for now appears to be in no hurry to pour big money into drone ships and submarines. And there is little tolerance these days for risky gambles on technologies.” However, the article acknowledges that robots at sea could help do the jobs that are dangerous or costly for human operators, such as hunting enemy submarines, detonating sea mines, medical evacuations and ship repairs. The European Union (EU) appears to have a vested interest in sea robotics. As infrastructure costs rise for improving rails and roads, they have begun to seek alternative ways to move large quantities of cargo. According to Maritime Executive they have, “had a long-term goal of making short sea shipping more competitive with road and rail transport, which is under stress from the transportation bottlenecks caused by increasing volumes of internal trade.” As the EU faces massive infrastructure costs to upgrade road and rail, there is increased attention and effort directed at the “motorways of the sea.” The Defense Advanced Research Projects Agency (DARPA) has been testing a robotic ship called the “Continuous Trail Unmanned Vessel,” and has been running sea trials on its radar system. The radar is fastened to a parasail that enables heights of 500-1,500 feet. These are just a few of the autonomous vessel projects in the works. In order for unmanned vessels to operate, it is clear the ability to transport data in massive amounts will play a critical role in the success and safety of those sharing the sea with autonomous ships. As technologies evolve to meet these big data needs, we can eventually expect to see more unmanned vessels in the sea, improving offshore applications, making human jobs safer, and creating new efficiencies for organizations looking to optimize international trade.
Big Data: Election Analytics and More
During the 2016 election season, we’ve seen considerable media coverage on big data and predictive analytics. The access to massive quantities of data has played an increasingly important role not only for predicting the election winner, but also for driving candidates’ campaigns. During the 2012 election we saw political data science and big data leveraged by campaign managers to tap into the public opinions of the candidates. The information garnered from those data points led to decisions that shaped campaign strategies. Since 2012, we’ve seen substantial advancements in political data analytics. A recent Forbes article explains this well, “In recent years, political data analytics has advanced from simple micro targeting to true predictive data science, and the track record is good. Some of the brightest minds in the field are using massive amounts of data, complex models and advanced algorithms to determine the best way to appeal to big swathes of the electorate without alienating possible converts.” A GOP strategist recently claimed that analysts have about 400 data points stored for the average American voter and noted that they are constantly querying the database for insight. Predictive Analytics is an increasingly useful and complex practice — and it is not limited to presidential elections. It can be used in almost every industry to drive intelligent and informed business decisions. First, let’s define predictive analytics in relation to this post. This definition from TechTarget highlights the role of statistical analysis and machine learning to arrive at an actionable model: “Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast future activity, behavior and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value, or score, on the likelihood of particular events happening.” Beyond the Election With the rise of the Internet of Things (IoT) we are currently seeing predictive analytics leveraged for applications across industries to help organizations make better operating decisions. Here are a few application examples recently highlighted in Forbes: Models designed to predict where crimes will be committed Predicting the price of oil Insight into how upcoming events might influence a business Predicting the probability of success for a startup Identifying trends in the academic literature Predictive Analytics and S2S Communications Today, there are technology solutions designed for intelligence-enabled decision making. Sensor-2-Server (S2S) communication solutions in particular, help meet the increasing demand for data. S2S by definition is an intelligent communication that begins at the sensor level and targets servers for specific reasons. With an intelligent communication system to enable predictive analytics, operators can leverage new technology to improve the profitability of their businesses. As an example, let’s look at the one of the predictive analytics use cases listed above– a model for predicting the price of oil. If an oil and gas company has an intelligent system in place, it can respond in real-time to its oil production levels. The data can help operators determine if production should be increased or decreased in certain areas to maximize profitability. Predictive Analytics Recap Predictive analytics engines allow organizations to analyze more data, faster. Key decision makers gain insight into trends and patterns that may be otherwise overlooked. They can make intelligent predictions that shape business operations and strategy. With the right techniques in place, an organization will make better decisions, cut costs and increase profitability. And for those who are running for public office? They now have more insight into the opinions and trends for voters than ever before. This has changed the game in a lot of ways because campaigns can be tailored to an audience based on specific data.
An Industrial IoT Anniversary
Wow, what a year! This post marks the one year anniversary of publishing Industrial IoT top news, trends and highlights, and we wanted to dedicate a recap post to our favorite articles throughout the past year. In particular, a lot of attention has been paid to the happenings in precision agriculture, oil and gas, unmanned systems, the smart grid, public utilities, manufacturing, machines and machine learning, fog computing, big data, sensor technology, wireless technology and cybersecurity, to name a few. Read on for the top 10 articles we’ve posted since last August and make sure to see the special bonus at the end! Precision Ag: Big data is precision agriculture’s best tool to feed the world By @LuxResearch | Published on @AgProfessionalhttp://www.agprofessional.com/news/big-data-precision-agriculture%E2%80%99s-best-tool-feed-world“Big data can be the most flexible tool for increasing the efficiency of food production through precision agriculture – a quantified approach to cultivation that uses sensing, input modulation, and data analytics to enhance the efficiency of agriculture.” Oil and Gas: In the digital oil field, “no wires” is a no-brainerBy Zach Wertenberger @WPXEnergy | Published on @WorldOilhttp://www.worldoil.com/magazine/2015/september-2015/features/in-the-digital-oil-field-no-wires-is-a-no-brainer“Wireless technology plays an integral part in the day-to-day operations of virtually every industry on the planet. However, if you spent your time visiting most of the world’s oil fields, you wouldn’t believe that.Despite being a rather obvious fit with the inherent nature of the oilfield services sector (OFS), wireless I/O has been adopted by producers at a slow pace, with most continuing to rely upon miles and miles of fault-prone wire to connect onsite control centers with wellsite instrumentation.” Smart Grid: Wireless Lifts Focus on Grid Resiliency By Brad Gilbert @freewavetech | Published on @POWERGRIDmaghttp://www.elp.com/articles/powergrid_international/print/volume-21/issue-6/features/wireless-lifts-focus-on-grid-resiliency.html“Industrial Internet of Things (IIoT) networking technology and wireless Machine-to-Machine (M2M) communications solutions are critical to the daily operations of an increasingly connected and industrial world. With a greater dependence on providing reliable and secure high-speed connectivity to personnel, smart devices, machinery and many other geographically dispersed assets, electric utility operators require powerful, yet flexible, communications solutions for their business demands.” Utilities: Wastewater Treatment: Out of Sight, Out of Mind (Thanks to IIoT)By Scott Allen @S_Allen_IIoT | Published on @Ulitzerhttp://scottallen.ulitzer.com/node/3527211“Water is a crucial piece of any city’s – or country’s – infrastructure. The United States is fortunate to have some of safest drinking water in the world, for a number of reasons, one of which is its many water and wastewater treatment facilities.” Manufacturing: Bringing Smart Technology to Old Factories Can Be Industrial-Size ChallengeBy @mcoc | Published on @wsjhttp://www.wsj.com/articles/bringing-smart-technology-to-old-factories-can-be-industrial-size-challenge-1465351322“It’s a tantalizing vision: Bright and shiny factories where robotic arms and conveyors never break down and production goals are never missed—all thanks to internet-connected sensors that monitor machine health and respond to the slightest supply or logistics hiccup.” Machine Learning: 10 Ways Machine Learning is Revolutionizing ManufacturingBy @LouisColumbus | Published on @Forbeshttp://www.forbes.com/sites/louiscolumbus/2016/06/26/10-ways-machine-learning-is-revolutionizing-manufacturing/#3f10cd992d7f“Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production.” Fog Computing: Why IoT Needs Fog ComputingBy @BanafaAhmed | Published on @bbvaOpenMindhttps://www.bbvaopenmind.com/en/why-iot-needs-fog-computing/“The Internet of Things (IoT) is one of the hottest mega-trends in technology – and for good reason , IoT deals with all the components of what we consider web 3.0 including Big Data Analytics, Cloud Computing and Mobile Computing.” Sensors: The Army Wants to Implant Body Sensors into Combat SoldiersBy @tjenningsbrown | Published on @vocativehttp://www.vocativ.com/342014/army-body-sensors/“In the near future, American soldiers might all be implanted with a sensor before going to battle.The United States Department of Defense is interested in monitoring the health of soldiers in real-time. But wearable health trackers have faults and limitations. That’s why the Army Research Office and Defense Advanced Research Projects Agency have awarded $7.5 million to San Francisco-based Profusa to develop tissue-integrated health-monitoring sensors for service members.” Wireless Tech: Industrial Wireless RevolutionBy Soliman A. Al-Walaie @Saudi_Aramco | Published on @ISA_Interchangehttps://www.isa.org/intech/20151001/“Wireless technology is an essential business enabler for the automation world. It has gained rapid acceptance in many industrial sectors because of its cost effectiveness, reliability, fast deployment, and flexibility. Over the past four decades, ultrahigh frequency (UHF) radios have been widely used for long-range supervisory control and data acquisition (SCADA) connectivity in the oil and gas and power and utility sectors.” Cybersecurity: Navigating Industrial IoT risk and complexityBy @EStarkloff | Published on @AMDMaghttp://www.aerospacemanufacturinganddesign.com/article/amd1015-industrial-iot-complex-systems/“As massive networks of systems come online, they will need to communicate with each other and with the enterprise, often over vast distances. Both the systems and the communications need to be secure or millions of dollars in assets will be put at risk. One example of the need for security is on the smart utility grid, which is on the leading edge of the IIoT.” Bonus! Eliminate the cost of your next IIoT deployment Now is the time to brave the digital transformation in your industry while you continue to future-proof your systems. All you need to do is submit a use case for your radio network for a chance to win a next generation industrial wireless IoT solution. All entries must be received by August 19th. FreeWave will announce the winner on August 31st chosen based on submission (US and Canada only). The winning network must be deployed by October 31st. In return for the free radio network, the winning candidate will be able to gain additional promotion of their installation and network implementation! Submit here for your chance to win: http://bit.ly/2awdmkC. Learn more about ZumLink.
Machine Hackathon: DARPA Plays Cyber Capture the Flag
A machine hackathon is about to take on a whole new meaning as Defense Advanced Research Projects Agency (DRAPA) prepares to hold it’s first ever machine-only hackathon. With a specific focus on cybersecurity, this cyber version of Capture the Flag (CTF), is DARPA’s way of combating the onset of cyber attacks in real-time. DARPA’s normal approval process is lengthy; once a potential threat is recognized and a software solution has been created, it has to be tested and approved before it can be implemented, and by the time the software fix is ready to be used across the board, another threat looms on their horizon. Some of you might be asking, “What is DARPA and who are their finalists in this cyber challenge?” Not to worry, the short video below provides some background and context. The contest is truly a battle of the minds, as hacker teams try their hand at reverse-engineering software to seek out and find weakness in the system and fix those holes while attacking other machines at the same time. Those teams that are successful in both attacking and fixing holes capture the digital flag and win points in the ongoing process. This competition will take place in conjunction with the annual DEFCON, the longest running annual hacker competition. Before we start thinking that we’re living a modernized version of “Hackers,” there are a few more things to know. First, this is really a battle of software. The final teams were given a DARPA computer to code and must create a software platform to interact with the DARPA database. Once the competition begins, the teams will not be able to intervene if their software fails to see a weakness or is attacked by another team. The goal is to create an artificial intelligence (AI) software that is capable of responding in real-time to potential threats and weakness within its databases. Wired has added this contest to their radar, saying, “DARPA has gone full Tron. It might feel more like a video game, than a hacking contest, as DARPA has arranged for a visual diagram to be displayed on the big screen, that will show each attack and from what machine the attack came from.” Whether you believe Wired or the other tech experts, this type of machine AI is hoping to turn the tables on the war on cyber safety. Instead of waiting for an attack to strike, DARPA’s intuitive software will attempt to seek out weakness autonomously giving the Defense Department the added edge it needs to prevent leaks in the system. This is another intriguing example of how machine learning is becoming integrated into so many facets of the world at-large. Whether you make your way to Las Vegas to witness the DARPA’s version of CTF or not, that fact is we continue to add more M2M and IoT solutions to our daily lives. It’s only natural we find new ways to have machines assist us.
IIoT Top News: Machine Learning
Machine-to-machine (M2M) learning is an integral apart of the expanding world of Industrial IoT. Over the past few months we have given attention to manufacturing and its current digital disruption, but have failed to show the direct impact smart M2M and IoT technology is having on the industry. So, this week we are diving deeper into the term machine learning and how it connects to manufacturing both today and in the future. Before we get to our news round up let’s start by re-defining M2M, to ensure we are all on the same page with its purpose and meaning. Gartner has defined machine-2-machine communications as “something used for automated data transmission and measurement between mechanical or electronic devices.” Now, that we have defined M2M, its time to check out our top news round up for the week on how M2M applies to both manufacturing and IoT. 10 ways machine learning is revolutionizing manufacturing Machine learning is poised to improve manufacturing by streamlining the process of OT and IT, thus increasing efficiency and lowering overall operation costs. Louis Columbus at Forbes believes that “Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.” IoT will recharge Machine Manufacturers Manufacturing can look to software companies as an example of how IoT can implement creating a smarter M2M network. Timothy Chou with CFO.com writes, “Today, manufacturers of machines — whether seed drills, chillers, or CT scanners — can leverage the path paved by the software product companies through three new business models: service and support; assisted services and machine-as-a-service.” Climbing the IoT Mountain–by adding M2M to manufacturing Manufacturing is only at the beginning of its ascent into IoT and M2M, so there are many more bumps and obstacles a long the way for the industry to fully integrate. Ronnie Garrett with Supply & Demand Chain Executive describes IoT and M2M manufacturing implementation as, “Standing at the foot of Mount Everest, ready to climb the world’s tallest mountain. You know you want to get to the top but you aren’t really sure how you will get there or what obstacles you’ll encounter along the way.” Cybersecurity is manufacturing’s biggest risk factor Manufacturing needs to continue to add M2M automation and big data analytics to the shop floor, but a threat to the overall industry is manifesting itself in the cybersecurity world. Ian Wright with Engineering.com informs writes, “A new report from BDO indicates that 92 percent of manufacturers cited cybersecurity concerns in their SEC disclosures this year. According to BDO, this represents a 44 percent increase compared to the first Manufacturing Risk Factor report in 2013.” As we wrap up our top news for the week, we realize the need to fully implement advanced machine learning across the manufacturing world will take more than a simple flick of the wrist. With that said, we leave you with a cautionary tale of when automation goes wrong. It was recently discovered an airport in India had an sign translated with automation software which read, “eating carpet strictly prohibited” — of course this was not the translation they had meant to display. Regardless, as we move towards a fully integrated M2M world, we will have to adjust our equations depending our our intended outcome, much like the world is finding with the love/hate of language automation. Hope you have enjoyed this week’s top news, as always tell us your thoughts on M2M and how it might impact your world!
Fog Computing: Answering the IoT Challenge
Fog Computing is being touted as the data communication solution our Internet of Things (IoT) devices are asking for by bringing the power of cloud computing closer to the end user. The fact is, the number of connected devices is going to continue to grow exponentionally. In fact, Gartner predicts that by 2020 IoT will include 26 billion connected things. Consider the impact that amount of data collected and processed will have. The Challenge Naturally, with billions of devices all connected to the cloud for manufacturing, oil and gas, utilities, municipalities and enterprise, to name a few, the data transmission and processing rate is bound to slow down – especially if the current cloud architecture is upheld. Some IoT devices use the cloud to store data long term, where other connected things send data to the cloud to be analyzed and sent back to the devicewith operational instructions. Ahmed Banafa with SemiWiki explains, “As dependence on our newly connected devices increases along with the benefits and uses of a maturing technology, the reliability of the gateways that make the IoT a functional reality must increase and make up-time a near guarantee.” What is Fog Computing? Fog Computing is a term coined by Cisco, that offers a way to analyze the data closer to the IoT device, thus saving valuable milliseconds. It may be hard to believe, but a millisecond has the power to prevent a M2M line shut-down, increase the speed at which power is restored to utilities and prevent an oil rig from leaking, just to name a few. An easy way to visually understand where Fog Computing fits in our IoT world, is by looking at the diagram above. It clearly shows that Fog Computing hangs between the cloud and the device, much like the fog on an early San Francisco morning. Fog Computing operates at the network edge, extending the cloud capabilities closer to the source (IoT device). Each IoT connection works with what’s called Fog Nodes to digest the intelligent data and then coordinate operational next steps, whether that be acting directly and or transmitting results to the cloud. The diagram below covers the types of response times IoT devices face from both Fog Nodes and main cloud locations. Fog Computing Brings Efficiency to Enterprise A recent report by Machina Research highlights the companies that pioneered Fog Computing and those poised to capitalize on the benefits in their near future. These companies are able to collect, protect, transport and control the data via IoT devices at the edge of the network, saving time and creating a more stream-line approach to sending and receiving data efficiently and more securely. Overall, as our need to connect explodes, we will not only need to think about IoT, but also the way in which intelligent data is processed from the critical infrastructure and back to the cloud. Fog Computing will continue to open more efficient channels across our IoT, as long as we allow it.
IoT Top News: M2M Propels Machines
Time and again, those keeping a pulse on the Internet of Things (IoT) space frequently hear about the “rise of the machines.” Humanity is not only discovering fascinating ways to integrate machines into our daily lives, but also finding new uses for machines as well. How? Machines are now “internet-connected” just like the smartphones we carry around in our pockets. And this isn’t just on the commercial side with the likes of smart thermostats or connected vehicles – even tractors and oil and gas machinery are industrial examples of where new “things” are now on the digital network. In fact, there are more M2M or “machine-to-machine” communication devices on this planet than humans. As GSMA Intelligence reported in 2014, there are 7.2bn M2M devices versus 7.19bn humans. Stuart Taylor from Cisco also wrote a prediction that “The Internet of Things (IoT) is a world where up to 50 billion things (or devices) will be connected to the Internet by 2020; or, the equivalent of 6 devices for every person on the planet.” Realizing the major role M2M devices continue to have in our connected world, specifically as it relates to the advent of machine learning, it’s only natural to highlight the impact of machines and M2M in the past, present and future. The Machines are Coming: How M2M Spawned the Internet of Things In the digital world, M2M wireless solutions will work for us quietly, in the background solving all our day-to-day needs. John Kennedy with Silicon Republic reports that, “M2M is at the heart of the industrial internet of things (IIoT), powering smart factories that can be run remotely from a tablet computer, and smart buildings that monitor their environment and feed data back to the cloud.” Is Machine Learning Over Hyped? In the now 24-hour news cycle, often the top news lingers around lighter topics. So how much hype should be given to machine learning (ML)? The Huffington Post respondent Scott Aaronson, theoretical computer scientist at MIT, seems to think that “There’s no doubt in my mind that people 30 years from now will agree with us about the central importance of ML, but which aspects of ML will they rage at us for ignoring, or laugh at us for obsessing about when we shouldn’t have? Machine Learning: Demystifying Linear Regression and Feature Selection Machine learning needs to integrate domain knowledge in order to improve the quality of data collected from analysts. Josh Lewis with Computerworld thinks that, “Business people need to demand more from machine learning so they can connect data scientists’ work to relevant action.” Machine Learning Examples Crop up for Data Center Management Data centers appear to be the perfect place for enterprises to implement machine learning to its fullest. Christopher Yetman, COO at Vantage Data said, “There are also sensors that generate data about air pressure, humidity, temperature and supply voltage and typically feed into a programmable logic controller.” M2M Technology Driving Agriculture’s Industrialization On a global front, M2M is driving agriculture’s industrialization in South Africa. IT News Africa informs us that, “Given the ability to automate many monitoring and control functions through intelligent devices, agriculture is a prime target for leveraging M2M capabilities.” We hope you have enjoyed this week’s roundup, and as M2M connections continue to pile-up, we urge you to consider the plethora of commercial and industrial use cases that can benefit from these innovations.
Robots Will Steal Your Future Paychecks
We’ve spent many words on this blog talking through new Industrial IoT technologies, hardware and software, and the way that the status quo has shifted to demand better connectivity, smarter infrastructure, and better access to real-time data across the spectrum. Where we haven’t spent much time is considering the economic impact these technologies will have on the average person. Without looking too far into the future, we can already see the impact of a more automated workforce. With that in mind, and on top of all our other daily worries, do we need to be worried about robots stealing our paychecks in the future? Eric Brynjolfsson, recently presented a TED Talk about this very topic, but unlike the sci-fi fear mongers, Eric had a different approach. Brynjolfsson suggests we stop trying to compete with machines and focus in on how they can complement our work-life. It’s true today it takes less people to get the job done. This shift to automation is forcing companies to rethink infrastructure and think more about speed, efficiency and overall time. This isn’t the time to reinvent the wheel, it’s time to think about how that wheel can be tweaked to operate more smoothly and consistently over time. Now, before you let your imagination run wild of a robot powered world, that will be lucky to be apart of, take a moment and watch Brynjolfsson’s TED Talk. Not to worry there is still hope, you may not have to hand over your paychecks to tomorrow’s robots, just yet!
