Mycroft Brings Open-Source Revolution to Home Assistants

Brilliant!  Crowd-funded (even better!) Mycroft brings the rich potential of open-source to the growing field of digital home assistants.   I suspect it won’t be long until it claims a major part of the field, because the Mycroft platform can evolve and grow exponentially by capitalizing on the contributions of many, many people, not unlike the way IFTTT has with its crowd-sourced smart home “recipes.”

According to a fascinating ZD Net interview with its developer, Joshua Montgomery, his motivation was not profit per se, but to create a general AI intelligence system that would transform a start-up space he was re-developing:

“He wanted to create the type of artificial intelligence platform that ‘if you spoke to it when you walked in the room, it could control the music, control the lights, the doors’ and more.”

                         Mycroft

Montgomery wanted to do this through an open-source voice control system but for there wasn’t an open source equivalent to Siri or Alexa.  After building the natural language, open-source AI system to fill that need (tag line, “An Artificial Intelligence for Everyone”) he decided to build a “reference device” as the reporter terms it (gotta love that techno speak. In other words, a hardware device that could demonstrate the system). That in turn led to a crowdsourced campaign on Kickstarter and Backerkit to fund the home hub, which is based on the old chestnut of the IoT, Raspberry Pi. The result is a squat, cute (looks like a smiley face) unit, with a high-quality speaker.  

Most important, when the development team is done with the AI platform, Mycroft will release all of the Mycroft AI code under GPL V3, inviting the open-source community to capitalize and improve on it.  That will place Mycroft squarely in the open-source heritage of Linux and Mozilla.

Among other benefits, Mycroft will use natural language processing to activate a wide range of online services, from Netflix to Pandora, as well as control your smart home devices.

Mycroft illustrates one of my favorite IoT Essential Truths: we need to share data, not hoard it. I don’t care how brilliant your engineers are: they are only a tiny percentage of the world population, with only a limited amount of personal experience (especially if they’re callow millennials) and interests. When you go open source and throw your data open to the world, the progress will be greater as will be the benefits — to you and humanity.

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Liveblogging from Internet of Things Global Summit

Critical Infrastructure and IoT

Robert Metzger, Shareholder, Rogers Joseph O’Donnell 

  • a variety of constraints to direct government involvement in IoT
  • regulators: doesn’t trust private sector to do enough, but regulation tends to be prescriptive.
  • NIST can play critical role: standards and best practices, esp. on privacy and security.
  • Comparatively, any company knows more about potential and liabilities of IoT than any government body. Can lead to bewildering array of IoT regulations that can hamper the problem.
  • Business model problem: security expensive, may require more power, add less functionality, all of which run against incentive to get the service out at lowest price. Need selective regulation and minimum standards. Government should require minimum standards as part of its procurement. Government rarely willing to pay for this.
  • Pending US regulation shows constant tension between regulation and innovation.

             2017 IoT Summit

Gary Butler, CEO, Camgian 

  • Utah cities network embedding sensors.
  • Scalability and flexibility needed. Must be able to interface with constantly improving sensors.
  • Expensive to retrofit sensors on infrastructure.
  • From physical security perspective: cameras, etc. to provide real-time situational awareness. Beyond human surveillance. Add AI to augment human surveillance.
  • “Dealing with ‘data deluge.'”  Example of proliferation of drones. NIST might help with developing standards for this.
  • Battery systems: reducing power consumption & creating energy-dense batteries. Government could help. Government could also be a leader in adoption.

 

Cyber-Criminality, Security and Risk in an IoT World

John Carlin, Chair, Cybersecurity & Technology Program, Aspen Institute

  • Social media involved in most cyberwar attacks & most perps under 21.  They become linked solely by social media.
  • offensive threats far outstrip defenses when it comes to data
  • now we’re connecting billions of things, very vulnerable. Add in driverless cars & threat even greater. Examples: non-encrypted data from pacemakers, and the WIRED Jeep demo.

Belisario Contreras, Cyber Security Program Manager, Organization of American States

  • must think globally.
  • criminals have all the time to prepare, we must respond within minutes.
  • comprehensive approach: broad policy framework in 6 Latin American countries.

Samia Melhem, Global Lead, Digital Development, World Bank

  • projects: she works on telecommunications and transportation investing in government infrastructure in these areas. Most of these governments have been handicapped by lack of funding. Need expert data integrators. Integrating cybersecurity.

Stephen Pattison, VP Public Affairs, ARM

  • (yikes, never thought about this!) cyberterrorist hacks self-driving car & drives it into a crowds.
  • many cyber-engineers who might go to dark side — why hasn’t this been studied?
  • could we get to point where IoT-devices are certified secure (but threats continually evolve. Upgradeability is critical.
  • do we need a whistleblower protection?
  • “big data starts with little data”

Session 4: Key Policy Considerations for Building the Cars of Tomorrow – What do Industry Stakeholders Want from Policymakers?

Ken DiPrima, AVP New Product Development, IoT Solutions, AT&T

  • 4-level security approach: emphasis on end-point, locked-down connectivity through SIM, application level …
  • deep in 5-G: how do you leverage it, esp. for cars?
  • connecting 25+ of auto OEMs. Lot of trials.

Rob Yates, Co-President, Lemay Yates Associates

  • massive increase in connectivity. What do you do with all the data? Will require massive infrastructure increase.

Michelle Avary, Executive Board, FASTR, VP Automotive, Aeris

  • about 1 Gig of data per car with present cars. Up to 30 with a lot of streaming.
  • don’t need connectivity for self-driving car: but why not have connectivity? Also important f0r the vehicle to know and communicate its physical state. Machine learning needs data to progress.
  • people won’t buy vehicles when they are really autonomous — economics won’t support it, will move to mobility as a service.

Paul Scullion, Senior Manager, Vehicle Safety and Connected Automation, Global Automakers

  • emphasis on connected cars, how it might affect ownership patterns.
  • regulatory process slow, but a lot of action on state level. “fear and uncertainty” on state level. Balance of safety and innovation.

Steven Bayless, Regulatory Affairs & Public Policy, Intelligent Transportation Society of America

  • issues: for example, can you get traffic signals to change based on data from cars?
  • car industry doesn’t have lot of experience with collaborative issues.

How Are Smart Cities Being Developed and Leveraged for the Citizen?

Sokwoo Rhee, Associate Director of Cyber-Physical Systems Program, National Institute of Standards and Technology (NIST)

  • NIST GCTC Approach: Smart and Secure Cities. Partnered with Homeland Security to bring in cybersecurity & privacy at the basis of smart city efforts “Smart and Secure Cities and Communities Challenge”

Bob Bennett, Chief Innovation Officer, City of Kansas, MO

  • fusing “silos of awesomeness.”
  • 85% of data you need for smart cities already available.
  • “don’t blow up silos, just put windows on them.”
  • downtown is 53 smartest blocks in US
  • can now do predictive maintenance on roads
  • Prospect Ave.: neighborhood with worst problems. Major priority.
  • great program involving multiple data sources, to predict and take care of potholes — not only predictive maintenance but also use a new pothole mix that can last 12 years 
  • 122 common factors all cities doing smart cities look at!
  • cities have money for all sorts of previously allocated issues — need to get the city manager, not mayor, to deal with it
  • privacy and security: their private-sector partner has great resoures, complemented by the city’s own staff.

Mike Zeto, AVP General Manager, IoT Solutions, AT&T

  • THE AT&T Smart Cities guy. 
  • creating services to facilitate smart cities.
  • energy and utilities are major focus in scaling smart cities, including capital funding. AT&T Digital Infrastructure (done with GE) “iPhone for cities.”
  • work in Miami-Dade that improved public safety, especially in public housing. Similar project in Atlanta.
  • privacy and security: their resources in both have been one of their strengths from the beginning.

Greg Toth, Founder, Internet of Things DC

  • security issues as big as ever
  • smart city collaboration booming
  • smart home stagnating because early adopter boom over, value not sure
  • Quantified-Self devices not really taking hold (yours truly was one of very few attendees who said they were still using their devices — you’d have to tear my Apple Watch off).
  • community involvement greater than ever
  • looming problem of maintaining network of sensors as they age
  • privacy & security: privacy and security aren’t top priorities for most startups.

DAY TWO:

IoT TECH TALKS

  • Dominik Schiener, Co-Founder , IOTA speaking on blockchain
    • working with IoT version of blockchain for IoT — big feature is it is scaleable
    • why do we need it?  Data sets shared among all parties. Each can verify the datasets of other participants. Datasets that have been tampered are excluded.
    • Creates immutable single source of truth.
    • It also facilitates payments, esp. micropayments (even machine to machine)
    • Allows smart contracts. Fully transparent. Smart and trustless escrow.
    • Facilitates “machine economy”
    • Toward “smart decentralization”
    • Use cases:
      • secure car data — VW. Can’t be faked.
      • Pan-European charging stations for EVs. “Give machines wallets”
      • Supply chain tracking — probably 1st area to really adopt blockchain
      • Data marketplace — buy and sell data securely (consumers can become pro-sumers, selling their personal data).
      • audit trail. https://audit-trail.tangle.works
  • DJ Saul, CMO & Managing Director, iStrategyLabs IoT, AI and Augmented Reality
    • focusing on marketing uses.

Raising the bar for federal IoT Security – ‘The Internet of Things Cybersecurity Improvement Act’

  • Jim Langevin, Congressman, US House of Representatives
    • very real threat with IoT
    • technology outpacing the law
    • far too many manufacturers don’t make security a priority. Are customers aware?
    • consumers have right to know about protections (or lack thereof)
    • “failure is not an option”
    • need rigorous testing
  • Beau Woods, Deputy Director, Cyber Statecraft Initiative, Atlantic Council
    • intersection of cybersecurity & human condition
    • dependence on connected devices growing faster than our ability to regulate it
    • UL developing certification for medical devices
    • traceability for car parts
  • John Marinho, Vice President Cybersecurity and Technology, CTIA
    • industry constantly evolving global standards — US can’t be isolated.
    • cybersecurity with IoT must be 24/7. CTIA created an IoT working group, meets every two weeks online.
    • believe in public/private partnerships, rather than just regulatory.

Session 9: Meeting the Short and Long-Term Connectivity Requirements of IoT – Approaches and Technologies

  •  Andreas Geiss, Head of Unit ‘Spectrum Policy’, DG CONNECT, European Commission
    • freeing up a lot of spectrum, service neutral
    • unlicensed spectrum, esp. for short-range devices. New frequency bands. New medical device bands. 
    • trying to work with regulators globally to allow for globally-usable devices.
  • Geoff Mulligan, Chairman, LoRa Alliance; Former Presidential Innovation Fellow, The White House
    • wireless tradeoffs: choose two — low power/long distance/high speed.
    • not licensed vs. unlicensed spectrum. Mix of many options, based on open standards, all based on TCP/IP
    • LPWANs:
      • low power wide area networks
      • battery operated
      • long range
      • low cost
      • couple well with satellite networks
    • LoRaWAN
      • LPWAN based on LoRa Radio
      • unlicensed band
      • open standards base
      • openly available
      • open business model
      • low capex and opex could covered entire country for $120M in South Korea
      • IoT is evolutionary, not revolutionary — don’t want to separate it from other aspects of Internet
  • Jeffrey Yan, Director, Technology Policy, Microsoft
    • at Microsoft they see it as critical for a wide range of global issues, including agriculture.
  • Charity Weeden, Senior Director of Policy, Satellite Industry Association
    • IoT critical during disasters
    • total architecture needs to be seamless, everywhere.
  • Andrew Hudson, Head of Technology Policy, GSMA
    • must have secure, scalable networks

Session 10: IoT Data-Ownership and Licencing – Who Owns the Data?

  • Stacey Gray, Policy Lead IoT, Future Privacy Forum 
    • consumer privacy right place to begin.
    • need “rights based” approach to IoT data
    • at this point, have to show y0u have been actually harmed by release of data before you can sue.
  • Patrick Parodi, Founder, The Wireless Registry
    • focus on identity
    • who owns SSID identities? How do you create an identity for things?
  • Mark Eichorn, Assistant Director, Division of Privacy and Identity Protection, Federal Trade Commission 
    • cases involving lead generators for payday loan. Reselling personal financial info.
  • Susan Allen, Attorney-Advisor, Office of Policy and International Affairs, United States Patent & Trademark Office 
    • focusing on copyright.
    • stakeholders have different rights based on roles
  • Vince Jesaitis, Director, US Public Affairs, ARM
    • who owns data depends on what it is. Health data very tough standards. Financial data much more loose.
    • data shouldn’t be treated differently if it comes from a phone or a browser.
    • industrial side: autonomous vehicle data pretty well regulated.  Pending legislation dealing with smart cities emphasis open data.
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Human Side of IoT: Local Startup Empowers Forgotten Shop Floor Workers!

Let’s not forget: human workers can and must still pay a role in the IoT!

Sure, the vast majority of IoT focus is on large-scale precision and automated manufacturing (Industrie 4.0 as it is known in Germany, or the Industrial Internet here). However, an ingenious local startup, Tulip, is bringing IoT tools to the workbench and shop floor, empowering individual industrial engineers to create no-code, low-code apps that can really revolutionize things in the factory.  Yes, many jobs will be replaced by IoT tech, but with Tulip, others will be “enabled” — workers will still be there to make decisions, and they’ll be empowered as never before.

Um, I’m thinking superhuman factory Transformers, LOL!

The Tulip IoT gateway allows anyone to add sensors, tools, cameras and even “pick to light bins” (never heard that bit of shop lingo, but they looked cool in video) to the work station, without writing a line of code, because of the company’s diverse drivers support factory floor devices. It claims to “fill the gap between rigid back-end manufacturing IT systems and the dynamic operations taking place on the shop floor.”

Rony Kubat, the young MIT grad who’s the company’s co-founder is on a mission “to revolutionize manufacturing software,” as he says, because people who actually have to play a hands-on roll in product design and production on  shop floor have been ignored in the IoT, and many processes such as training are still paper-based:

“Manufacturing software needs to evolve. Legacy applications neglect the human side of manufacturing and therefore suffer from low adoption. The use of custom, expensive-to-maintain, in-house solutions is rampant. The inability of existing solutions to address the needs of people on the shop floor is driving the proliferation of paper-based workflows and the use of word processing, spreadsheet and presentation applications as the mainstay of manufacturing operations. Tulip aims to change all this through our intuitive, people-centric platform. Our system makes it easy for manufacturers to connect hands-on work processes, machines and backend IT systems through flexible self-serve manufacturing apps”.

While automation in factory floors continues to grow, manufacturers often find their hands-on workforce left behind, using paper and legacy technology. Manufacturers are seeing an enormous need to empower their workforce with intuitive digital tools. Tulip is a solution to this problem. Front-line engineers create flexible shop-floor apps that connect workers, machines and existing IT systems. These apps guide shop-floor operations enabling real-time data collection and making that data useful to workers on factory floors. Tulip’s IoT gateway integrates the devices, sensors and machines on the shop floor, making it easy to monitor and interact with previously siloed data streams (you got me there: I HATE siloed data). The platform’s self-serve analytics engine lets manufacturers turn this data into actionable insights, supporting continuous process improvement.

The company has grown quickly, and has dozens of customers in fields as varied as medical devices, pharma, and aerospace. The results are dramatic and quite varied:

  • Quality: A Deloitte analysis of Tulip’s use at Jabil, a global contract manufacturer, documented 10+% production increases. It reduced quality issues in manual assembly by more than 10%. found production yield increased by more than 10 percent, and manual assembly quality issues were reduced by 60 percent in the initial four weeks of operation.
  • Training: Other customers reduced the amount of time to train new operators by  90 percent, in a highly complicated, customized and regulated biopharmaceutical training situation: “Previously, the only way to train new operators was to walk them repeatedly through all the steps with an experienced operator and a process engineer. Tulip quickly deployed its software along with IoT gateways for the machines and devices on the process, and managed to cut training time almost by half.”
  • Time to Market: They reduced a major athletic apparel maker’s time to market by 50% for hundreds of new product variations. That required constantly evaluating the impact of dozens of different quality drivers to isolate defects’ root causes — including both manual and automated platforms. Before Tulip, it could take weeks of analysis until a process was ready for production. According to the quality engineer on the project, “I used Tulip’s apps to communicate quality issues to upstream operators in real-time. This feedback loop enabled the operators to take immediate corrective action and prevent additional defects from occurring.”

Similar to my friends at Mendix, the no-code/low-code aspect of Tulip’s Manufacturing App Platform lets process engineers without programming backgrounds create shop floor apps through interactive step-by-step work instructions. “The apps give you access through our cloud to an abundance of information and real-time analytics that can help you measure and fine-tune your manufacturing operations,” Tulip Co-Founder Natan Linder says (the whiz-kid is also chairman of 3-D printer startup Formlabs). 

Linder looked at analytics apps that let users create apps through simple tools and thought why not provide the same kind of tools for training technicians on standard operating procedures or for building product or tracking quality defects? “This is a self-service tool that a process or quality engineer can use to build apps. They can create sophisticated workflows without writing code…. Our cloud authoring environment basically allows you to just drag and drop and connect all the different faucets and links to create a sophisticated app in minutes, and deploy it to the floor, without writing code,” he says. Tulip enables sharing appropriate real-time analytics with each team member no matter where they are and to set up personal alerts for the data that’s relevant to each.

IMHO, this is a perfect example of my IoT “Essential Truth” of “empowering every worker with real-time data.”  Rather than senior management parceling out (as they saw fit) the little amount of historical data that was available in the past, now workers can share (critical verb) that data instantly and combine it with the horse sense that can only be gained by those actually doing the work for years. Miracles will follow!

Writ large, the benefits of empowering shop floor workers are potentially huge.  According to the UK Telegraph, output can increase 8-9 %, while cutting costs 7-8%, cutting costs approximately 7-8 percent. The same research estimates that industrial companies “could see as much as a 300 basis point boost to their bottom line.”

Examples of the relevant shop-floor analytics include:

  • “Show real-time metrics from the shop floor
  • Report trends in your operations
  • Send customized alerts based on user defined triggers
  • Inform key stakeholders with relevant data”

IDC Analyst John Santagate neatly sums up the argument for empowering workers through the IoT thusly:

“With all of the talk and concern around the risk of losing the human element in manufacturing, due to the increasing use of robotics, it is refreshing to see a company focus on improving the work that is still done by human hands.  We typically hear the value proposition of deploying robots and automation of improvements to efficiency, quality, and consistency.  But what if you could achieve these improvements to your manufacturing process by simply applying analytics and technology to the human effort?  This is exactly what they are working on at Tulip.  

“Data analytics is typically thought about at the machine level. Manufacturers measure things such as throughput, efficiency, and quality by applying sensors to their manufacturing equipment, capturing the data signals, and conducting analytics.  The analytics provide an understanding of the health of the manufacturing process and enable them to make any necessary changes to improve the process.  Often, such efforts are top down driven.  Management drives these projects in order to improve the performance of the business.  An alternative approach is to enable the production floor to proactively identify improvement opportunities and take action, a bottom-up approach. For this self-service approach to succeed shop-floor engineers need a flexible platform such as Tulip’s, that allows them to replace paper-based processes with technology and build the applications that enable them to manage hands-on processes.  The real time analytics and visibility of hands-on manufacturing processes from Tulip’s platform puts the opportunity to identify improvement opportunities directly in the hands of people engaged in the work cells.

“Digital transformation in manufacturing is about leveraging advanced digital technology to improve how a company operates.  But, as the manufacturing industry focuses on digital transformation it must not forget the value of the human element.  Indeed, we don’t often think about digital transformation in relation to human effort, but this is exactly the sort of thinking that can deliver some of the early wins in digital transformation. “ 

Well said — and thanks to Tulip for filling a critical and often overlooked aspect of the IoT!

I’m reminded of my old friend Steve Clay-Young, who managed the BAC’s shop in Boston, and first alerted me to the “National Home- workshop Guild” which Popular Science started in the Depression and then played a critical part in the war effort. Craftsmen who belonged all got plans and turned out quality products on their home lathes.  I can definitely see a rebirth of the concept as the cost of 3-D printers from Kubat’s other startup, Formlabs drops, and we can have the kind of home (or at least locally-based production that Eric Drexler dreamed of in his great Engines of Creation (which threw in another transformational production technology, nanotech). 

I’m clearing space in my own workshop so I can begin production on IoT/nanotech/3-D printed products. Move over, GE.

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OtoSense: the next level in sound-based IoT

It sounds (pardon the pun) as if the IoT may really be taking off as an important diagnostic repair tool.

I wrote a while ago about the Auguscope, which represents a great way to begin an incremental approach to the IoT because it’s a hand-held device to monitor equipment’s sounds and diagnose possible problems based on abnormalities.

Now NPR reports on a local (Cambridge) firm, OtoSense, that is expanding on this concept on the software end. Its tagline is “First software platform turning real-time machine sounds and vibrations into actionable meaning at the edge.”

Love the platform’s origins: it grows out of founder Sebastien Christian’s research on deafness (as I wrote in my earlier post, I view suddenly being able to interpret things’ sounds as a variation on how the IoT eliminates the “Collective Blindness”  that I’ve used to describe our past inability to monitor things before the IoT’s advent):

“[Christian} … is a quantum physicist and neuroscientist who spent much of his career studying deaf children. He modeled how human hearing works. And then he realized, hey, I could use this model to help other deaf things, like, say, almost all machines.”

(aside: I see this as another important application of my favorite IoT question: learning to automatically ask “who else can use this data?” How does that apply to YOUR work? But I digress).

According to Technology Review, the company is concentrating primarily on analyzing car sounds from IoT detectors on the vehicle at this point (working with a number of car manufacturers) although they believe the concept can be applied to a wide range of sound-emitting machinery:

“… OtoSense is working with major automakers on software that could give cars their own sense of hearing to diagnose themselves before any problem gets too expensive. The technology could also help human-driven and automated vehicles stay safe, for example by listening for emergency sirens or sounds indicating road surface quality.

OtoSense has developed machine-learning software that can be trained to identify specific noises, including subtle changes in an engine or a vehicle’s brakes. French automaker PSA Group, owner of brands including Citroen and Peugeot, is testing a version of the software trained using thousands of sounds from its different vehicle models.

Under a project dubbed AudioHound, OtoSense has developed a prototype tablet app that a technician or even car owner could use to record audio for automated diagnosis, says Guillaume Catusseau, who works on vehicle noise in PSA’s R&D department.”

According to NPR, the company is working to apply the same approach to a wide range of other types of machines, from assembly lines to DIY drills. As always with IoT data, handling massive amounts of data will be a challenge, so they will emphasize edge processing.

OtoSense has a “design factory” on the site, where potential customers answer a variety of questions about the sounds they must monitor (such as whether the software will be used indoors or out, whether it is to detect anomalies, etc. that will allow the company to choose the appropriate version of the program.

TechCrunch did a great article on the concept, which underscores really making sound detection precise will take a lot of time and refinement, in part because of the fact that — guess what — sounds from a variety of sources are often mingled, so the relevant ones must be determined and isolated:

“We have loads of audio data, but lack critical labels. In the case of deep learning models, ‘black box’ problems make it hard to determine why an acoustical anomaly was flagged in the first place. We are still working the kinks out of real-time machine learning at the edge. And sounds often come packaged with more noise than signal, limiting the features that can be extracted from audio data.”

In part, as with other forms of pattern recognition such as voice, this is because it will require accumulating huge data files:

“Behind many of the greatest breakthroughs in machine learning lies a painstakingly assembled dataset.ImageNet for object recognition and things like the Linguistic Data Consortium and GOOG-411 in the case of speech recognition. But finding an adequate dataset to juxtapose the sound of a car-door shutting and a bedroom-door shutting is quite challenging.

“’Deep learning can do a lot if you build the model correctly, you just need a lot of machine data,’ says Scott Stephenson, CEO of Deepgram, a startup helping companies search through their audio data. ‘Speech recognition 15 years ago wasn’t that great without datasets.’

“Crowdsourced labeling of dogs and cats on Amazon Mechanical Turk is one thing. Collecting 100,000 sounds of ball bearings and labeling the loose ones is something entirely different.

“And while these problems plague even single-purpose acoustical classifiers, the holy grail of the space is a generalizable tool for identifying all sounds, not simply building a model to differentiate the sounds of those doors.

…”A lack of source separation can further complicate matters. This is one that even humans struggle with. If you’ve ever tried to pick out a single table conversation at a loud restaurant, you have an appreciation for how difficult it can be to make sense of overlapping sounds.

Bottom line: there’s still a lot of theoretical and product-specific testing that must be done before IoT-based sound detection will be an infallible diagnostic tool for predictive maintenance, but clearly there’s precedent for the concept, and the potential payoff are great!

 


LOL: as the NPR story pointed out, this science may owe its origins to two MIT grads of an earlier era, “Click” and “Clack” of Car Talk, who frequently got listeners to contribute their own hilarious descriptions of the sounds they heard from their malfunctioning cars.   BRTTTTphssssBRTTTT…..

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A Vision for Dynamic and Lower-Cost Aging in Cities Through “SmartAging”

I’ve been giving a lot of thought recently about how my vision of I0T-based “SmartAging” through a combination of:

  • Quantified Self health apps and devices to improve seniors’ health and turn their health care into more of a partnership with their doctors
  • and smart home devices that would make it easier to manage their homes and “age in place” rather than being institutionalized

could meld with the exciting developments in smart city devices and strategy.  I believe the results could make seniors happier and healthier, reduce the burdens on city budgets of growing aging populations, and spur unprecedented creativity and innovation on these issues. Here’s my vision of how the two might come together. I’d welcome your thoughts on the concept!

 

A Vision for Dynamic and Lower-Cost Aging in Cities Through “SmartAging”

It’s clear business as usual in dealing with aging in America won’t work anymore.  10,000 baby boomers a day retire and draw Social Security. Between now and 2050, seniors will be the fastest growing segment of the population.  How can we stretch government programs and private resources so seniors won’t be sickly and live in abject poverty, yet millennials won’t be bankrupted either?

As someone in that category, this is of more than passing interest to me! 

I propose a new approach to aging in cities, marrying advanced but affordable personal technology, new ways of thinking about aging, and hybrid formal and ad hoc public-private partnerships, which can deal with at least part of the aging issue. Carving out some seniors from needing services through self-reliance and enhancing their well-being would allow focusing scarce resources on the most vulnerable remaining seniors. 

The approach is made possible not only by the plummeting cost and increasing power of personal technology but also the exciting new forms of collaboration it has made possible.

The proposal’s basis is the Internet of Things (IoT).  There is already a growing range of IoT wearable devices to track health indicators such as heart rates and promoting fitness activities, and IoT “smart home” devices controlling lighting, heat, and other systems. The framework visualized here would easily integrate these devices, but they can be expensive, so it is designed so seniors could benefit from the project without having to buy the dedicated devices.

This proposal does not attempt to be an all-encompassing solution to every issue of aging, but instead will create a robust, open platform that government agencies, companies, civic groups, and individuals can build upon to reduce burdens on individual seniors, improve their health and quality of life, and cut the cost of and need for some government services. Even better, the same platform and technologies can be used to enhance the lives of others throughout the life spectrum as well, increasing its value and versatility.

The proposal is for two complementary projects to create a basis for later, more ambitious one.

Each would be valuable in its own right and perhaps reach differing portions of the senior population. Combined, they would provide seniors and their families with a wealth of real-time information to improve health, mobility, and quality of life, while cutting their living costs and reducing social isolation.  The result would be a mutually-beneficial public-private partnerships and, one hopes, improve not only seniors’ lives, but also their feeling of connectedness to the broader community. Rather than treat seniors as passive recipients of services, it would empower them to be as self-reliant as possible given their varying circumstances. They would both be based on the Lifeline program in Massachusetts (and similar ones elsewhere) that give low-income residents basic Internet service at low cost.

Locally, Boston already has a record of achievement in internet-based services to connect seniors with others, starting with the simple and tremendously effective SnowCrew program that Joe Porcelli launched in the Jamaica Plain neighborhood. This later expanded nationwide into the NextDoor site and app, which could easily be used by participants in the program.

The first project would capitalize on the widespread popularity of the new digital “home assistants,” such as the Amazon Echo and Google Home.  One version of the Echo can be bought for as little as $49, with bulk buying also possible.  A critical advantage of these devices, rather than home monitoring devices specifically for seniors, is that they are mainstream, benefit from the “network effects” phenomenon that means each becomes more valuable as more are in use, and don’t stigmatize the users or shout I’M ELDERLY. A person who is in their 50s could buy one now, use it for routine household needs, and then add additional age-related functions (see below) as they age, amortizing the cost.

The most important thing to remember about these devices regarding aging is the fact that they are voice-activated, so they would be especially attractive to seniors who are tech-averse or simply unable to navigate complex devices. The user simply speaks a command to activate the device.

The Echo (one presumes a variation on the same theme will soon be the case with the “Home,” Apple’s forthcoming “Home Pod” and other devices that might enter the space in the future) gets its power from “skills,” or apps, that are developed by third-party developers. They give it the power, via voice, to deliver a wide range of content on every topic under the sun.  Several already released “skills” give an idea of how this might work:

  • Ask My Buddy helps users in an emergency. In an emergency, it can send phone calls or text messages to up to five contacts. A user would say, “Alexa, ask my buddy Bob to send help” and Bob would get an alert to check in on his friend.
  • Linked thermostats can raise or lower the temperature a precise amount, and lights can also be turned on or off or adjusted for specific needs.
  • Marvee can keep seniors in touch w/ their families and lessen social isolation.
  • The Fitbit skill allows the user who also has a Fitbit to trace their physical activity, encouraging fitness.

Again looking to Boston for precedent, related apps include the Children’s Hospital and Kids’ MD ones from Children’s Hospital. Imagine how helpful it could be if the gerontology departments of hospitals provided similar “skills” for seniors!

Most important to making this service work would be to capitalize on the growing number of city-based open-data programs that release a variety of important real-time data bases which independent developers mash up to create “skills”  such as real-time transit apps.  The author was a consultant to the District of Columbia in 2008 when it began this data-based “smart city” approach with the Apps for Democracy contest, which has spawned similar projects worldwide since then.  When real-time city data is released, the result is almost magic: individuals and groups see different value in the same data, and develop new services that use it in a variety of ways at no expense to taxpayers.

The key to this half of the pilot programs would be creating a working relationship with local Meetups such as those already created in various cities for Alexa programmers, which would facilitate the relationship) to stage one or more high-visibility hackathons. Programmers from major public and social service institutions serving seniors, colleges and universities, and others with an interest in the subject could come together to create “skills” based on the local public data feeds, to serve seniors’ needs, such as:

  • health
  • nutrition
  • mobility
  • city services
  • overcoming social isolation (one might ask how a technological program could help with this need. The City of Barcelona, generally acknowledged as the world’s “smartest” city, is circulating an RFP right now with that goal and already has a “smart” program for seniors who need immediate help to call for it) .

“Skills” are proliferating at a dizzying rate, and ones developed for one city can be easily adapted for localized use elsewhere.

Such a project would have no direct costs, but the city and/or a non-profit might negotiate lower bulk-buying rates for the devices, especially the l0wer price ($59 list) Amazon Dot, similar to the contract between the Japan Post Group, IBM, and Apple to buy 5 million iPads and equip them with senior-friendly apps from IBM which the Post Group would then furnish to Japanese seniors. Conceivably, the Dots bought this way might come preloaded with the localized and senior-friendly “skills.” 

The second component of a prototype SmartAging city program would make the wide range of local real-time location-based data available by various cities usable by cities joininh the 100+ cities worldwide who have joined the “Things Network” that create free citywide data networks specifically for Internet of Things use.

The concept uses technology called LoRaWAN: low-cost (the 10 units used in Amsterdam, each with a signal range of about 6 miles, only cost $12,000 total — much cheaper ones will be released soon), and were deployed and operative in less than a month!  The cost and difficulty of linking an entire city has plummeted as more cities join, and the global project is inherently collaborative.

With Things Network, entire cities would be converted into Internet of Things laboratories, empowering anyone (city agencies, companies, educational institutions, non-profits, individuals) to experiment with offering new services that would use the no-cost data sharing network.  In cities that already host Things Networks,  availability of the networks has spawned a wide range of novel local services.  For example, in Dunblane, Scotland, the team is developing a ThingsNetwork- based alarming system for people with dementia.  Even better, as the rapid spread of citywide open data programs and resulting open source apps to capitalize on them has illustrated, a neat app or service created in one city could easily be copied and enhanced elsewhere — virtuous imitation!

The critical component of the prototype programs would be to hold one or more hackathons once the network was in place.  The same range of participants would be invited, and since the Things Network could also serve a wide range of other public/private uses for all age groups and demographics, more developers and subject matter experts might participate in the hackathon, increasing the chances of more robust and multi-purpose applications resulting.

These citywide networks could eventually become the heart of ambitious two-way services for seniors based on real-time data, similar to those in Bolsano, Italy

The Internet of Things and smart cities will become widespread soon simply because of lowering costs and greater versatility, whether this prototype project for seniors happens or not. The suggestions above would make sure that the IoT serves the public interest by harnessing IoT data to improve seniors’ health, reduce their social isolation, and make them more self-sufficient. It will reduce the burden on traditional government services to seniors while unlocking creative new services we can’t even visualize today to enhance the aging process.

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#IoT Sensor Breakthroughs When Lives Are On the Line!

One of my unchanging principles is always to look to situations where there’s a lot at stake — especially human lives — for breakthroughs in difficult issues.

Exhibit A of this principle for the IoT is sensor design, where needing to frequently service or recharge critical sensors that detect battlefield conditions can put soldiers’ lives at stake (yes, as long-time readers know, this is particularly of interest to me because my Army officer son was wounded in Iraq).

FedTech reports encouraging research at DARPA on how to create sensors that have ultra-low power requirements, can lie dormant for long periods of time and yet are exquisitely sensitive to critical changes in conditions (such as vehicle or troop movements) that might put soldiers at risk in battlefield conditions.

The  N-ZERO (Near Zero RF and Power Operations)  program is a three-year initiative to create new, low-energy battlefield sensors, particularly for use at forward operating bases where conditions can change quickly and soldiers are constantly at risk — especially if they have to service the sensors:

“State-of-the-art military sensors rely on “active electronics” to detect vibration, light, sound or other signals for situational awareness and to inform tactical planning and action. That means the sensors constantly consume power, with much of that power spent processing what often turns out to be irrelevant data. This power consumption limits sensors’ useful lifetimes to a few weeks or months with even the best batteries and has slowed the development of new sensor technologies and capabilities. The chronic need to service or redeploy power-depleted sensors is not only costly and time-consuming but also increases warfighter exposure to danger.”

…. (the project has) the goal of developing the technological foundation for persistent, event-driven sensing capabilities in which the sensor can remain dormant, with near-zero power consumption, until awakened by an external trigger or stimulus. Examples of relevant stimuli are acoustic signatures of particular vehicle types or radio signatures of specific communications protocols. If successful, the program could extend the lifetime of remotely deployed communications and environmental sensors—also known as unattended ground sensors (UGS)—from weeks or months to years.”

A key goal is a 20-fold battery size reduction while still having the sensor last longer.

What cost-conscious pipeline operators, large ag business or “smart city” transportation director wouldn’t be interested in that kind of product as well?

According to Signal, the three-phase project is ahead of its targets. In the first part, which ended in December, the DARPA team created “zero-power receivers that can detect very weak signals — less than 70 decibel-milliwatt radio-frequency (RF) transmissions, a measure that is better than originally expected.” This is critical to the military (and would have huge benefits to business as well, since monitoring frequently must be 24/7 but reporting of background data  (vs. significant changes) would both deplete batteries while requiring processing of huge volumes of meaningless data). Accordingly, a key goal would be to create “… radio receivers that are continuously alert for friendly radio transmissions, but with near zero power consumption when transmissions are not present.” A target is  “exploitation of the energy in the signal signature itself to detect and discriminate the events of interest while rejecting noise and interference. This requires the development of passive or event-powered sensors and signal-processing circuitry. The successful development of these techniques and components could enable deployments of sensors that can remain “off” (that is, in a state that does not consume battery power), yet alert for detecting signatures of interest, resulting in greatly extended durations of operation.”

The “exploitation of .. energy in the signal signature itself sounds reminiscent of the University of Washington research I’ve reported in the past that would harness ambient back-scatter to allow battery-less wireless transmission, another key potential advance in IoT sensor networks.

The following phrases of N-ZERO will each take a year.

Let’s hope that the project is an overall success, and that the end products will also be commercialized. I’ve always felt sensor cost and power needs were potential IoT Achilles’ heels, so that would be a major boost!

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Hippo: IoT-based paradigm shift from passive to active insurance companies

I’m a big advocate of incremental IoT strategies (check out my recent webinar with Mendix on this approach), for existing companies that want to test the waters first. However, I’m enough of a rabble-rouser to also applaud those who jump right in with paradigm-busting IoT (and big data) startups.

Enter, stage left, a nimble (LOL) new home insurance company: Hippo!

IMHO, Hippo’s important both in its own right and also as a harbinger of other startups that will exploit the IoT and big data to break with years of tradition in the insurance industry as a whole, no longer sitting passively to pay out claims when something bad happens, but seizing the initiative to reduce risk, which is what insurance started out to do.

After all, when a Mr. B. Franklin (I’ll tell you: plunk that guy down in 2017 and he’d create a start-up addressing an unmet need within a week!) and his fellow firefighters launched the Philadelphia Contributionship in 1752, one of the first things they did was to send out appraisers to determine the risk of a house burning and suggest ways to make it safer.

Left to right: Eyal Navon, CTO and cofounder; Assaf Wand, CEO cofounder of Hippo

In fact, there’s actually a term for this kind of web-based insurance, coined by McKinsey: insuretec” (practicing what he preached, one of Hippo’s founders had been at McKinsey, and what intrigued the founders about insurance as a target was that it’s a huge industry, hasn’t really innovate for years, and didn’t focus on the customer experience.).

I talked recently to two key staffers, Head of Product Aviad Pinkovezky and Head of Marketing, Growth and Product Innovation Jason White.  They outlined a radically new strategy “with focused attention on loss reduction”:

  • sell directly to consumers instead of using agents
  • cut out legacy coverage leftovers, such as fur coats, silverware & stock certificates in a home safe) and instead cover laptops, water leaks, etc.
  • Leverage data to inform customers about appliances they own that might be more likely to cause problems, and communicate with them on a continuous basis about steps such as cleaning gutters that could reduce problems.

According to Pinkovezky, the current companies “are reactive, responding to something that takes place. Consumer-to-company interaction is non-continuous, with almost nothing between paying premiums and filing a claim.  Hippo wants to build must more of a continuous relationship, providing value added,” such as an IoT-based water-leak detection device that new customers receive.

At the same time, White said that the company is still somewhat limited in what if can do to reduce risk because so much of it isn’t really from factors such as theft (data speaks: he said thefts actually constitute little of claims) but from one, measured by frequency and amount of damage (according to their analysis) that’s beyond their control: weather. As I pointed out, that’s probably going to constitute more of a risk in the foreseeable future due to global warming.

Hippo also plans a high-tech, high-touch strategy, that would couple technnology with a human aspect that’s needed in a stressful situation such as a house fire or flood. According to Forbes:

The company acknowledges that its customers rely on Hippo to protect their largest assets, and that insurance claims often derive from stressful experiences. In light of this, Hippo offers comprehensive, compassionate concierge services to help home owners find hotels when a home becomes unlivable, and to supervise repair contractors when damage occurs.”

While offering new services, the company has firm roots in the non-insuretech world, because its policies are owned and covered by Topa, which was founded more than 30 years ago.

Bottom line: if you’re casting about for an IoT-based startup opportunity, you’d do well to use the lens McKinsey applied to insurance: look for an industry that’s tradition-bound, and tends to react to change rather than initiate it (REMEMBER: a key element of the IoT paradigm shift is that, for the first time, we can piece “universal blindness” and really see inside things to gauge how they are working [or not] — the challenge is to capitalize on that new-found data). 

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IoT: LiveBlogging PTC’s LiveWorx

Got here a little late for CEO Jim Heppelman’s keynote, so here goes!

  • Vuforia: digital twin gives you everything needed for merging digital “decorations” on the physical object
  • Unique perspective: AR takes digital back to the physical. Can understand & make better decisions.
  • Virtual reality would allow much of the same. Add in 3-D printing, etc.
  • “IoT is PLM.” Says PTC might be only company prepared to do both.
  • Says their logo captures the merger of digital and physical.
  • Case studies: they partnered with Bosch’s Rexroth division. Cytropac built-in IoT connectivity–  used Creo. Full life-cycle management. Can identify patterns of usage, etc. Using PTC’s analytics capacity, machine learning analysis. Want to improve cooling efficiency (it was high at first). Model-based digital twin to monitor product in field, then design an upgrade. How can they increase cooling efficiency 30%??  Came up with new design to optimize water channel that they will build in using 3-D printing. Cool (literally!). 43% increase in cooling efficiency. The design change results in new recommendation engine that helps in sales. Replaced operating manual with 3-D that anyone can understand. (BTW: very cool stagecraft: Heppelmann walks around stage interviewing the Rexroth design team at their workstations).
  • Ooh: getting citizen developers involved!!!  Speeds process, flexibility. App shows how products are actually operating in the field. Lets sales be much more proactive in field. Reinventing CRM.  May no longer need a physical showroom — just put on the AR headset.
  • Connectivity between all assets. The digital twin is identical, not fraternal. Brings AR into factory. They can merge new manufacturing equipment with legacy ones that didn’t have connectivity.  ABB has cloud-based retrofit sensors. Thingworx can connect almost anything, makes Industry 4.0 possible. Amazing demo of a simulated 3-D disassembly and replacement.
  • Hmmm — closing graphic of his preso is a constantly rotating circular one. Anticipating my “circular company” talk on Wednesday????

Closing the Loop With Enterprise Change Management. Lewis Lawrence of Weatherford, services to petroleum industry:

  • former engineer. In charge of Weatherford’s Windchill installation (they also use Creo).
  • hard hit by the drop in gas prices
  • constant state of flux
  • 15 years of constant evolution
  • their mantra: design anywhere, build anywhere.
  • enterprise change — not just engineering.
  • hmmm: according to his graphics, their whole change process is linear. IMHO, that’s obsolete in era of constant change: must evolve to cyclical. Ponderous process…
  • collect data: anything can be added, if it’s latest

The IoT Can Even Help You Breathe Better: GCE Group’s Zen-O portable oxygen concentrator for people with respiratory problems (not actually launched yet):

  • InVMA has built IoT application using ThingWorx to let patients, docs and service providers carefully monitor data
  • GCE made radical change from their traditional business in gas control devices. Zen-O is in the consumer markets. They were very interested in connected products — especially since their key competitor launched one!
  • Goals: predictive maintenance, improved patient care, asset management, development insight.
  • Design process very collaborative, with many partners.

The Digital Value Chain: GE’s Manufacturing Journey. Robert Ibe, global IT Engineering Leader at GE Industrial Solutions:

  • supports Brilliant Factory program.
  • they design and manufacture electrical distribution equipment, 30 factories worldwide.
  • “wing-to-wing” integrated process
  • had a highly complex, obsolete legacy
  • started in 2014: they were still running really old CAD technology. 14 CAD repositories that didn’t talk to each other. 15 year old PLM software. No confidence in any of data they had.
  • They began change with PLM — that’s where the digital thread begins.  PLM is foundation for their transformation.
  • PLM misunderstood: use it to map out cohesive, cross-functional, model-based strategy. Highlight relevance of “design anywhere — manufacture anywhere.” Make PLM master of your domain. Make it critical to commercial & manufacturing. Advertise benefits & value.
  • Whole strategy based on CAD. Windchill heart of the process.
  • Rate of implementation faster than business can keep up with!
  • Process: implementation approach:
    • design systems integration
    • model-based design
    • digital thread
    • manufacturing productivity.
  • common enterprise PLM framework
  • within Windchill, can see entire “digital bill of documents.”
  • focused on becoming critical for supply chain.
  • total shift from their paper-based legacy.
  • integrated regulatory compliance with every step of design.

It’s Not Your Grandmother’s IoT: Blockchain and IoT Morph Into An Emerging Technology Powerhouse:

  • Example of claims for fair-traded coffee that I’ve used in past

Finding Business Value in IoT panel:

  • Bayer — been in IoT (injection devices for medicine) for 7 years.  Reduced a lot of parts inventory.
  • Remote control of vending machines replaces paper & pencil
  • Your team needs to evangelize for biz benefits of IoT
  • New Opportunities:
    • vision and language
    • interacting with physical world
    • problem solving.
  • Didn’t know!  Skype can do real-time translation.
  • Google Deep Mind team worked internally, cut energy costs at its server farms. 15% energy reduction.
  • Digital progress makes economic pie bigger, BUT  most people aren’t benefitting economicallly. Some may be worse off. “Great decoupling” — mushrooming economic gap. One reason is that tech affects different groups differently.
  • “Entirely possible to create inclusive prosperity” through tech!

 

WEDNESDAY

Delivering Smart City Solutions and an Open Citywide Platform to Accelerate Economic Growth and Promote New Solution Innovation, Scott McCarley, PTC:

  • $40 trillion potential benefits from smart cities
  • 1st example & starting point for many cities, is smart lightpoles. Major savings plus value added. Real benefit is building on that, with systems of systems (water, traffic, energy, etc.) — the systems don’t operate in isolation.
  • Future buildings may have built-in batteries to add to power supply. Water reclamation, etc.
  • Cities are focused on KPIs across all target markets.
  • Cornerstone systems for a city: power & grid, water/wastewater, building management, city services & infrastructure.
  • Leveraging ThingWorx to address these needs:
    • deploy out-of-box IoT solutions from a ThingWorx Solution Provider: All examples, include Aquamatix, DEPsys (grid), Sensus, All Traffic, Smoove (bike sharing).
    • leverage ThingWorx to rapidly develop new IoT solutions.
      connect to any device, rapidly develop applications, visually model systems, quickly develop new apps. Augmented reality will play a role!
    • create role-based dashboards:
      one for your own operations, another for city.
    • bring the platform to create a citywide platform.
      Sum of connected physical assets, communication networks, and smart city solutions.

Digital Supply Networks: The Smart Factory. Steven Shepley, Deloitte:

  • 3 types of systems: 1) foundational visualization solutions:  KPIs, etc. 2) advanced analytical solutions 3) cyber-physical solutions.
  • Priority smart factory solutions:
    • advanced planning (risk-adjusted MRP), dynamic sequencing, cross network.
    • value chain integration: signal-based customer/supplies integration, dynamic distribution routing/tracking, digital twin.
    • asset efficiency: predictive maintenance, real-time asset tracking intelligence, energy management
    • labor productivity: robotic and cognitive automation, augmented reality-driven efficiency, real-time safety monitoring
    • exponential tech: 3-D printing, drones, flexible robots.
  • How to be successful: think big, start small, scale fast
  • Act differently: multi-disciplinary teams,
  • sensors getting simpler, easier to connect & retrofit. National Connectors particularly good.

Global Smart Home, Smart Enterprise, and Smart Cities IoT Use Cases. Ken Herron, Unified InBox, Pte.

  • new focus on customer
  • H2M: human to machine communication is THE key to IoT success. Respect their interests.
  • Austin TX: “robot whisperer” — industrial robot company. Their robots aging out, getting out of tune, etc. Predictive analytics anticipates problems.
  • Stuttgart: connected cow — if one cow is getting sick, may spread to entire herd. Intervene.
  • Kuala Lumpur: building bot — things such as paper towel dispensers communicating with management.
  • London: Concierge chatbot — shopper browsing can chat with assistant on combining outfits.
  • Dubai: smart camera. Help find your car in mega-shopping center: read license plates, message the camera, it gives you map to the car.
  • Singapore: Shout — for natural disasters. Walks the person making the alert through process, confirms choices.
  • Stuttgart: Feinstaubalarm — occasional very bad airborne dust at certain times. Tells people with lung problems options, such as taking mass transit.
  • Singapore: Smart appliances — I always thought smart fridge was stupid, but in-fridge camera that lets you shoot a “shelfie” does make sense
  • Fulda Germany: smart clothing for military & police: full record of personal health at the moment. Neat!
  • Noida India — smart sneakers can automatically post your run results (see connection to my SmartAging concept)

Business Impact of IoT, Eric Schaeffer, Accenture:

  • Michelin delivery trucks totally reinvented, major fuel savings, other benefits.
  • manufacturing being deconstructed
  • smart, connected products are causing it
  • industrial companies must begin transformation today

Thingworx: Platform for Management Revolution. W. David Stephenson, Stephenson Strategies:

Here are key points from my presentation about how the IoT can allow radical transformation from linear & hierarchical companies to IoT-centric “circular companies” (my entire presentation can be found here):

  • The IoT can be the platform for dramatic management change that was impossible in the past.
  • Making this change requires an extraordinary shift in management thinking: from hierarchy to collaboration.
  • The results will be worth the effort: not only more efficiency & precision, but also new creativity, revenue streams, & customer loyalty. 
  • In short, it will allow total transformation!

Kickstarting America’s Digital Transformation. Aneesh Chopra & Nicholas Thompson!

  • on day one, Our President (not the buffoon) told Chopra he wanted default to be switch from closed to open government & data.
  • National Wireless Initiative: became law 1 yr. after it was introduced.  Nationwide interoperable, secure wireless system.
  • Obama wanted to harness power of Internet to grow the economy. Talked to CIO of P & G, who was focused on opening up the company to get ideas from outside.
  • Thompson big on open data, but he thinks a lot more now is closed, we’re going wrong way.
  • Interesting example of getting down cost of solar to $1 per installed watt!!
  • Thompson: growing feeling that technology isn’t serving us economically. Chopra: need to democratize the benefits.
  • Chopra talking about opening up Labor Dept. data to lead to creative job opportunities for underserved.

 

 

 

 

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Amazon Echo Silver: bringing a little laughter (& the IoT) to aging

Some of you may remember that I’ve blogged several times about my enthusiasm for Amazon’s Alexa as a cornerstore of what I call SmartAging, the combination of IoT health-monitoring devices to keep you healthier and smart home devices to make it easier to manage your home and avoid institutionalization.

However, I’m in awe of how the crackerjack gerontology researchers at SNL (don’t forget, kiddies, we were your age when the show began in 1975. Sobering, eh?) in “partnership” with AARP, LOL, have custom crafted a special edition Echo for the “Greatest Generation”: Amazon Echo Silver!

My particular favorite feature is the random “uh huh” to punctuation seniors’ rambling stories, but Kate McKinnon’s bit about turning up the thermostat when it’s already 100 is also priceless, and all the other vignettes are pretty over-the top as well (thank goodness I can ping my iPhone with my Apple Watch — I find increasingly creative places to put the phone down). I’ve never been that great on names, so the range of acceptable variations on “Alexa” would be welcomed (BTW: I could swear that one day recently when I was talking to Alexa Siri responded. Why can’t those gals get along?).

I could point out that the “uh huh” might really be a first step toward a really interactive device that could help seniors overcome social isolation, but why weigh down with social significance something that’s an absolute riot?

Aging: if you can’t laugh about it, you’re in serious, serious trouble.

 

 

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ThingWorx Analytics Video: microcosm of why IoT is so transformative!

I’ll speak at PTC’s LiveWorx lollapalooza later this month (ooh: act quickly and I can get you a $300 registration discount: use code EDUCATE300) on my IoT-based Circular Company meme, so I’ve been devouring everything I can about ThingWorx to prepare.

Came across a nifty 6:09 vid about one component of ThingWorx, its Analytics feature. It seems to me this video sez it all about both how you can both launch an incremental IoT strategy (a recent focus of mine, given my webinar with Mendix) that will begin to pay immediate benefits and can serve as the basis for more ambitious transformation later, especially because you’ll already have the analytical tools such as ThingWorx Analytics already installed.

What caught my eye was that Flowserve, the pump giant involved in this case, could retrofit existing pumps with retrofit sensors from National Instruments — crucial for two reasons:

  • you may have major investments in existing, durable machinery: hard to justify scrapping it just to take advantage of the IoT
  • relatively few high-end, high-cost machinery and devices have been redesigned from the ground up to incorporate IoT monitoring and operations.

Note the screen grab: each of these sensors takes 30,000 readings per second. How’s that for real-time data?  PTC refers to this as part of the “volume, velocity and variety challenge of data” with the IoT.

As a microcosm of the IoT’s benefits, this example shows how easy it is to use those massive amounts of data and how they can be used to improve understanding and performance.

There are three major components:

  • ThingWatcher:
    This is the most critical component, because it sifts through the incredible amount of data from the edge, learns what constitutes normal performance for that sensor (creating “pop-up learning flags”), and then monitors it future performance for anomalies and, as the sample video shows, delivers real-time alerts to users (without requiring human monitoring) so they can make adjustments and/or order repairs.  Finds anomalies from edge devices in real-time. Automatically observes and learns the normal state pattern for every device or sensor. It then monitors each for anomalies and delivers re- al-time alerts to end users.
  • ThingPredictor:
    For the all-important new function of predictive maintenance, two different types of ThingPredictor indicators pop up when if anomalies are detected, predicting how long it may be until failure, allowing plenty of time for less-costly, anticipatory repairs. Because the specific deviation is identified in advance, repair crews will have the needed part with them when needed, rather than having to make an additional trip back to pick up parts.

    If you ask for a standard predictive scoring you don’t specify which performance features to include and get a simple predictive score. However, you can specify several key features to evaluate and get a more detailed (and probably more helpful) answer. For example,  “if you indicate an important feature count of three, the causal scoring output will include the three most influential features for each record and the percentage weights of each feature’s influence on the score.”

  • ThingOptimizer:
    Finally, you can use “ThingOptimizer” to do some what-if calculations to decide which possible “levers,” as ThingWorx calls the key variables, could change the projections to either maximize a positive factor or minimize the negatives. “Prescriptive scoring results include both an original score (the score before any lever attributes are changed) and an optimized score (the score after optimal values are applied to the lever attributes). In addition, for each attribute identified in your data as a lever, original and optimal values are included in the prescriptive scoring results.” It sort of reminds me how the introduction of VisiCalc allowed users, for the first time, to play around with variables to see which would have the best results.
Best of all, as the video illustrates, ThingWorx Analytics would facilitate the kind of “Circular Company” I’ll address in my speech, because the exact same real-time data could simultaneously be used by operating personnel to fine tune operations and catch a problem in time for predictive maintenance, and by senior management to get an instant overview of how operations are going at all the installations. Same data, many uses.
Bottom line: a robust IoT platform could be the key to an incremental strategy to begin by improving daily operations and reducing maintenance problems, and also be the underpinning for more radical transformation as your IoT strategy becomes more advanced!  See you at LiveWorx!
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