Solar power receives more media attention and seems to capture our imagination around the ‘future of energy’, but fuel cell energy appliances represent a more compelling distributed power strategy for utilities looking to develop solutions for commercial customers who demand more resiliency, always-on power and a reduced carbon footprint.
Today we sense high expectations for the future of solar, and either complete confusion, indifference or disdain to the future of fuel cells. Fuel cells are often over-sold by evangelists or dismissed by skeptics but within a decade, energy pundits may speak of fuel cells as being a more radical (and practical) energy innovation platform than solar energy. With fuel cells you can imagine putting a power plant in every hand, home or community.
First the web was a platform to help manage networks of documents and webpage files. Then the web evolved into a platform to help us manage our social networks. Today we see the web maturing into a serious platform for lifelong learning to help us better understand the world around us.
Among the more radical (and plausible) scenarios for the future of lifelong learning is that our daily life experiences become reshaped by a learning graph that serves as visualized data record of what we know, how well we know it, where and who we learned it from and what we are trying to learn more about.
News organization could adapt their content with our learning graph to make us more informed. Celebrity chefs might access our learning graph and deliver new experiences to teach us more about food. Libraries might make book and course recommendations based on subjects that motivate us to learn more. Work training programs might tap into our learning graphs to make sure our skills stay relevant and can be tied to real world business challenges.
The most idealized, techno-optimistic vision is a world where people become less focused on consuming and more demanding for experiences that support a lifetime of learning. On the flip side, it does not take too much effort to imagine – or focus only on a dystopian future of learning graph abuse.
Today our social graphs shape our daily interactions with people, organizations and information flows. The next phase of our web-influenced culture will be teaching people the risks, rewards and responsibilities of managing a personal learning graph.
Bringing this vision of a learning graph influenced world will require us to go through the same transition as we saw with social norms confronting the era of social graphs. At first people will likely be confused, indifferent or threatened by the very idea of knowing more about what they know. Then we’ll enter a phase where people can say ‘how did we ever live without it’.
When Social Seemed Confusing and Creepy Think way back to 2004 when the idea of a ‘social graph’ impacting our daily lives seemed like a confusing and creepy concept. The early social graphs attempted to understand who I know and how I know them?
The information was structured in graph databases that are visualized by circles and lines. Circle nodes (e.g. people, concepts, places, resources) and lines that reveal relationships or types (e.g. is a friend, is a co-worker, ‘likes’). Unlike traditional ‘row and column’ databases, graph databases are perfect for data+information that is rich in connections and relationships.
Ten years ago, nobody was begging for a social graph. No organization thought about packaging its content to be compatible with social graph databases. There were no algorithmic ‘bots’ designed to scrape social network data for sentiment and behavior.
Today, hundreds of millions of people across the world cannot get through a day without tapping their social graphs in Facebook, Twitter, LinkedIn, Match.com or some other equal.
Social graphs, for better and worse, influence the thoughts and stories we share and the content we receive. Social graph databases are behind everything from crowd funding Kickstarter projects, Twitter ‘hash tag’ activism, to helping us find a new job or life partner.
All of this is possible because the social data is structured in graph databases made up of circles (nodes) and lines (edges) that make it easier to find connections, understand relationships and identify the right pathways to reaching a goal.
How about the learning graph?
Welcome to the (very) early days of the Learning Graph.
The learning graph is a visualized data record of what we know (which concepts and domains have we explored), how well we know it (where are we along the path of mastery), and where and who we learned it from (connection to people and resource collections). It might also include hints of what we are trying to learn more about.
Today the learning graph is only an idea. It is an idea with several names (e.g. Learning Record Store; Adaptive Learning Platforms) and potential ally technical specifications (e.g. ExperienceAPI, Mozilla Open Badges)
Yet it is a logical and plausible evolution based on today’s current direction of change in this networked society.
Here is a short list of assumptions to explore and learn more about.
#1 Graph Databases Play a Big Role
Learning is about making connections between things. It is about understanding relationships and abstractions. How we structure data-information will matter.
The category of ‘graph databases’ (e.g. Neo4j, Titan) offers the most appealing foundation to integrating web content into a world of personal learning graphs. The more content is structured around relationships, the more likely they will fit into the ecosystem of learning graphs.
Graph data-what?!
Think circles and lines! Circle nodes (e.g. people, concepts, places, resources) and lines that reveal relationships or types.
The circle (node) of Pablo Picasso would have a line connected (type of art movement) to another circle node of Cubism Art Movement. From the Cubism Art Movement circle you would find many other lines to other artists from the movement.
The future of learning has more to do with our we structure our data than any devices that we use to learn.
Instead of ‘writing books’ or developing learning material content in simple text or video form, we might see all content providers structuring information in graph format so that they have more seamless integration with other connected database frameworks.
#2 Learning Graphs will Stumble if We Limit Implementation to ‘Schools’
Technology-led solutions for school-based learning always seem to run into challenges and constraints that limit the effectiveness of the tool.
A more desirable path for the learning graph might be found in lifelong learning that occurs via self-direct experiences, social learning communities or with civic institutions like libraries, museums and arts organizations.
Instead imagine focusing on lifelong learning. Empowering likely early adopters like young people with learning graph experiences that simply capture their passion for learning – not taking a high stakes test.
Imagine a teenager inspired to learn more about street art. Their learning graph might contain location of where they encountered a particular artist. The graph might include the names of well-known artists and the connections between them in terms or style or cities. The graph might include links to widely read books, blogs or Twitter feeds. The graph might include connections of street artists to other art genres and artists that proceeded this movement. The graph might include connections to social, cultural or political themes explored by artists.
In this visualized experience the teenager can jump from concept to concept – and at surface level realize the relationships between concepts, people and resources. They can see at a glance how much there is to learn vs the concepts that they have attempted to learn about.
#3 Privacy & Data Ownership will be an Issue
Digital Data. Privacy.
We know this story well. Our digital lives have turned individuals into personal data factories. Organizations (public-private sector) see enormous value in knowing more about our lives through access to personal data. The notion of ‘privacy’ is being challenged at all levels.
Social norms and legal frameworks have not caught up. This has not stopped the mainstream embrace of social graph experiences that go far beyond Facebook.
The way forward is to anticipate challenges and work to develop solutions.
Individuals working in the world of learning data and analytics are hoping to avoid a repeat of this missed opportunity to bring people and institutions up to speed on the risks, rewards and responsibilites of managing personal data that drives our lifelong learning.
There will likely be a push for ‘Own Your Own Data‘ policies around lifelong learning. There will also likely be a push for companies to ‘own your data’. There will be learning graph data leaks and break-ins.
It is impossible to predict how it will unfold. We cannot resolve this emerging issue. People will have to stand up for their right to own their learning data. Keep calm, Carry on.
Learn More….
Folks talking about learning graph – learning map related visions:
Danny Hillis – best known for a visual learning map (a concept likely built on top of a learning graph)
OSCON 2012
Danny Hillis, Applied Minds – 2012 talk
Jon Bischke (Twitter; LinkedIn; CEO of Entelo) –
The Learning Graph & Reputation Graph (He references an earlier post by Kirstin Winkler)
The Web’s Evolution from the Social Graph to Learning Graph May 24th, 2014garrygolden@gmail.com
The Institute for the Future (IFTF) is leading an important foresight research series called Aging Forward with a focus on care-giving and technology. In this 90 minute webinar IFTF looks at community assets and equality issues with two guests who are working to overturn assumptions about the challenges and opportunities ahead for an aging population:
Declara is one of the most unique startups in the world of enterprise-scale learning platforms. The company has built an intelligent social learning system that is often referred to in the media as a combination of Google’s Knowledge Graph and Facebook’s Social Graph.
Declara’s vision is to create and leverage a Cognitive Graph that delivers neuroscience-inspired personalized learning based on the context of real-world experiences, intent, outcomes and social relationships.
The system aims to deliver content recommendations and facilitate the most appropriate social connections between learners across large organizations and social communities. The platform integrates the latest capabilities of artificial intelligence subdomains – machine-learning and deep-learning to scale-up predictive analytics and prescriptive learning experiences based on an individual’s intentions, capabilities and needs.
The company sees a very rich and untapped landscape of learning analytics that will benefit from neuroscience-based insights on learning experiences. The ‘adaptive’ and ‘intelligent’ labels simply mean that Declara’s infrastructure learns over time based on real-world interactions and outcomes.
Declara’s CEO Ramona Pierson (Twitter) has an amazing comeback life story and a brilliant mind that sees the convergence of neuro-cognitive science, intelligent social systems, semantic search, graph databases, et al. Co-Founder Nelson Gonzalez (LinkedIn; Twitter) brings a pragmatic and optimistic lens to learning analytics and the intersection of local cultural elements and semantic search.
Declara has a very clear scale-out oriented business model that targets large customers such as national government associations (e.g. Mexico’s SNTE, Australia’s CSE) and enterprises like Genetech. They picked a wonderful problem to solve. Declara is a startup to watch…!!
Fuel cells are solid-state power plants. They convert chemical energy (fuels such as hydrogen, natural gas, propane, methanol) into electrical energy in a single electrochemical reaction.
Fuel cells can be scaled in design and manufacturing from the size of a thumbnail to tractor trailer-sized units. In 2014, the most viable market for fuel cells remains stationary distributed power. Beginning in 2015, major automakers such as Toyota, Honda and Hyundai will sell fuel cell electric vehicles. Both stationary and vehicle applications are in early stages of growth.
Micro Fuel Cells: Vision of Personal Power The more interesting fuel cell application is even more nascent in its market maturity. Micro fuel cells are probably the most overlooked or easily dismissed energy technology on the longer term horizon of energy market transitions beyond 2025. Yet there is nothing more disruptive then the use of portable fuels and micro fuel cells to address issues of energy access (energy poverty). The lofty vision is to reinvent the energy ladderwith market solutions that put clean hydrogen-rich fuels and micro fuel cells into the hands of every person on the planet.
Let’s look at the potential paths ahead for this small scale approach to portable fuels and energy conversion.
Micro fuel cell applications by size and application:
Thumb-sized – a fuel-fed mini ‘power plant’ embedded within an electronic device (e.g. phones, tablets, industrial-grade devices)
Hand held – a fuel-fed recharger for batteries inside portable devices; portable auxillary for industrial equipment
Small bread box sized – a fuel-fed auxillary power unit (APU) for vehicles, military, recreational camping, distributed or remote infrastructure
Near Term Market: Premium Portable Power (2014-2020) The market-ready applications for micro fuel cells are for premium forms of energy acess that frees us from connections to electrical sockets and the grid. People who need reliable energy while at music festivals, camping trips, military excursions. The market demand remains soft but likely to follow the same cost-curve and adoption model of previous disruptive technology platforms.
Companies such as Brunton, Intelligent Energy, MyFC and Neah Power systems sell hydrogen cartridges (usually solid state H2) and micro fuel cell units that can give you power on the go. The cost of ‘refueling’ a portable device ranges across product lines but it a premium to no or low cost of plugging into a wall socket. If you take a longer view on solid state hydrogen storage (e.g. MOFs) and increased output from fuel cell membranes this refueling model starts to look disruptive. In the short term it is a premium portable power solution for recharging batteries within our electronic gadgets.
Mid-term Market: Embedding Fuel Cells – Refueling beats Recharging (2020-2025) What if we could embed fuel cells within electronic devices? ‘Better batteries’ can only take us so far with portable devices that continue to consume more power. Integrating micro fuel cells within portable electronics would provide clear market signals to manufacturers and portable fuel providers. In this scenario fuel cells replace batteries as the primary source of electricity within portable electronics. We would ‘refuel’ our devices rather than ‘recharge’ them. You could purchase portable fuel packets in any retail setting. Never have to worry about finding a socket to plug into – simply refuel with a new cartridge.
Long-term Disruptive Vision: Personal Power for the Entire Planet – (2025-50) The long term disruptive vision of micro fuel cells is to reinvent the Energy Ladder by placing clean hydrogen-rich fuels at the bottom of the rung. In this scenario individuals around the world could purchase ‘packets’ of fuels (solid H2; liquid H2 rich fuels) and micro-fuel cells (the power plant) in typical retail setting. In this future we could produce small micro power systems that are sold across retail channels and provide a ‘leapfrog’ option for billions of people seeking an alternative to the ‘grid’.
To arrive at this scenario we would need to see both incremental and breakthrough performance gains. Solid state hydrogen storage via adsorption (not hydrides) would have to deliver on energy and power density. Fuel cell membranes would need to leverage nano-structured materials design for non-precious metal catalysts and improved reduction side reactions that increase electricity output. Even if these capabilities did not arrive until 2030 or 2040 it is still in time to meet the changing needs of a planet that will have doubled its energy demands from 2014.
So that is most simple roadmap for micro fuel cells. Who are the companies that could take us there?
Micro Fuel cell Companies – without commentary and in no particular order!
TMI – contract manufactur of industrial-military grade
*I have not include bio micro fuel cells.
Haters, will hate 😉 If you follow the energy ‘cleantech’ blogger world conversations there is a lot of skepticism over fuel cells and hydrogen. I will not go into the long list of reasons why skepticism is warranted yet short-sighted and lacking in understanding of long-term dynamics of energy market transitions. Nothing can change the energy industry overnight!
The micro fuel cell market is in a nascent stage so shake-outs, failures, and breakouts are all part of this phase of development.
There is no need to write-off or be dismissive about hydrogen or fuel cells.
The energy story is a marathon not a sprint. In addition to talking about the ‘future of solar in 2020’ we need to be able to invest in alternative visions that go beyond 2025 and can carry us through the century. Portable fuels and power systems is critical to the foundation of a 21st century energy marketplace.
But if you are a doubter and keeping score here is a list of companies who have failed, folded or been absorbed:
Jadoo
Voller Energy
Medis Technologies
Why continue to dream of fuel cells when solar costs are dropping?
Solar rooftop panels can only take the world so far. Solar requires Solar’s business model is challenged in a world where people need portable fuels that can satisfy high energy consumption. Solar rooftop is a structural solution to energy demands and requires significant investment in installation and maintenance. Selling portable fuels and micro fuel cells can use existing retail store channels.
Expect Lab‘s Marsal Gavalda walks us through 26 minute video of techno-optimistic geek goodness by looking at present day enthusasiam for personal assistant technologies and some of the historical milestones that brought us here.
Why the enthusiasm in 2014? The Spike Jonze’s movie Her gets credit for popularizing the idea of a likeable and lovable personal assistant but the real source of optimism is just old fashion innovation from our learning curve. Artificial intelligence sub-domains of machine-learning and deep-learning (used for real-time understanding of natural language) are making steady progress. The past few years have given the world very positive advances around knowledge graphs for natural language, sentiment analysis of unstructured data, and anticipation oriented recommendation systems.
The next five years will bring hype and real hope for functional contextual search and conversation-based experiences that make personal assistant beyond 2020 likely and doable. I have waxed poetic about Expected Labs MindmeldAPI and have the same respect for companies like NextIt and Artificial Solutions (Indigo) who are creating the early market demand.
My highlights from his talk: min 2:20 Github workflow and productivity visualization]
https://www.youtube.com/watch?v=fKen7IkdAm0
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Marsal mentioned the Apple 1987 video of the Knowledge Navigator
Summary: Siri and IBM Watson for everyone? Organizations might soon be able to rent brain power by the hour from the cloud – creating a new marketplace for contextually-aware and adaptive applications powered by software programs that learn from real-world human user interactions. Our educational and workflow experiences might soon access cogntive computing applications to learn more effectively and make better decisions on complex issues.
Companies are starting to experiment with business models to deliver cloud based cognition in the form of ‘contextual and cognitive computing’ via API Engines and stand alone software solutions.
Contextual & Cognitive Computing Cognition as a Service is just a buzzword today. It is far from defined! The most likely path towards bringing cognition level abilities to organizations will likely pass through stages. The first will be an evolution towards contextual experiences followed by more sophisticated ‘IBM Watson’-like applications.
Contextual web experiences move us from information being delivered by ‘keyword’ connections to a more personalized sense of the right ‘context’ for our lives based on: location, activity, life experiences and preferences, et al. Contextual experiences are more personalized and certainly ‘learn’ from interactions with users – but there is a new paradigm of ‘cognitive’ systems that elevate the experience.
Cognitive Computing is an era where software systems learn on their own and can teach themselves how to improve their performance. IBM Watson is today’s most sophisticated cognitive application. Watson is currently providing decision support for cancer treatments at Sloan Kettering, financial service support for companies such as ANZ and CitiGroup, and working to evolve the retail customer experience for Northface.
Cognition as a Service?
In practical terms this means an organization might be able to buy ‘as a service’ (e.g. not an application they paid to develop or maintain) – natural language processing for human-like Question & Answer interactions.
Companies pushing both of these capabilities are simultaneously trying to improve performance, integration and business model design. Early industry adopters will range from health, finance, energy, education, et al. Industries with connected data and a need for augmenting human knowledge building. Early adopters industries will likely have lots of data and compliance heavy regulatory frameworks.
The cloud-based business models of ‘software-as-service’ and ‘platform/infrastructure-as-service’ are the most likely first path for contextual and cognitive platforms. Startups such as Expected Labs are bringing their API engine MindmeldAPI into the marketplace. IBM Watson is hedging its bets by delivering stand alone solutions and also opening up its API to developers.
Developers are just now getting their hands dirty with advanced contextual computing applications. Truly transformational applications will likely emerge 2015-2025.
Sean Gourley – Founder of Quid has been wrapping his head around the future of mankind working ‘with’ machines for several years. I’ve seen Sean’s talk evolve over two years– and it continues to be among the most solid framings of this massive transition towards ‘augmented intelligence.
This is a great talk by Sean Gourley from the March 2014 GigaOm Data Structure Conference.
Summary: Public Libraries are the best positioned civic institution to help improve early childhood literacy (reading and writing) with a focus on reducing the Word Gap – which refers to the estimated 30 million word differential experience of words heard from birth to 3 years old across a spectrum of affluent to poverty-stricken famlies. This smaller vocabulary coupled with other stress inducers of poverty can impede the healthy brain development in young children that is critical for a lifetime of learning. The challenge for libraries will be in confronting the range of possible creepy lines associated with scaling a technology-led, behavior change-focused effort needed to close the word gap and enable the positive development of young brains.
30 Million Words? The Word Gapterm grew out of research by University of Kansas child psychologists Betty Hart and Todd Risley and a 1995 study which measured the differences in words heard by young children. (Details of Research here). The research was revisited in 2008 by LENA (short for Language ENvironmental Analysis) and essentially confirmed the findings that a typical gap that exists between lower and higher income families – from birth to 3 years old was estimated at 30 million words.
[Similiar word sensing assessments have been used in Autism screening.]
Why is the Word Gap a lever for the future?
Studies suggest that early literacy (reading-writing) is critical in brain development and social skills empowered by a greater ability to listen and communicate. The healthy development of young brains is critical to a lifetime of learning and active engagement.
Despite its significant conclusions, the research has failed to gain mainstream traction as a lens for bringing positive social change. It is a scientifically defensible human policy lever that remains far off the radar of most people. The relative low cost and return on investment (in financial or social capital) in the healthy development of young brains appeals to even the most bottom-line focused business leaders. Libraries could help to elevate the importance of early brain development through reading and face-to-face engagement.
As a trusted institution with tremendous staff knowledge and experience in early childhood experiences, public libraries are well positioned to make a case for helping families recognize the importance of word-based experiences for their children. Expectations need to be managed. Libraries cannot solve all the complex problems that underlie poverty, but they can help create the conditions for parents and communities to understand the connection between words heard and early brain development.
Making a case for new funds to public libraries would certainly invite controversy and push libraries to test the creepy line where technology creates amazing new capabilities that makes us feel uncomfortable around potential trade offs that challenge personal boundaries and social norms.
There are two creepy lines to consider:
The Creepy Lines of Hardware the Listens:
In 2013, Providence Rhode Island won Bloomberg Foundation’s Mayor Challenge to develop an early literacy engagement strategy that would help reduce the community word gap. The project included a plan to provide selected families with a portable device able to listen to the number of words heard by a child. Feedback data from the device would allow parents to know how well their child was progressing in hearing a targeted number of words each day, week, month and year.
These types of sensing and listening devices raise concerns of privacy. Namely, Are you going to record the conversations my child hears?Things that I say to my spouse?
The device, developed by Boulder, Colorado based LENA (Language ENvironmental Analysis) does not record words or conversations – rather it only listens to the number of words spoken to, or around the child. It can distinguish between words spoken by a human vs words coming from a television, computer or radio.
Yet the potential for abuse or misunderstanding will never disappear. We are in the early days of our physical technology being rooted in ‘sense and listen’ capabilities. Right now, the idea of having our phones listen to us – crosses the creepy line.
At the same time, it is important to recognize that we have already stepped into this strange future where the recording of web-based experiences and ebook-based behavior data is now readily available to publishers and software-device makers. (Read: The eBook is Reading You WSJ, 2012).
The question for parents focused on shrinking the word gap will be – Does crossing a creepy line to raise awareness of my child’s progress in hearing words – present more benefits than the potential risks and trade offs?
There is a creepy line associated with library collections that include devices and/or software programs that can sense the world around its patrons. Libraries might hold enough trust with patrons and communities to encourage this leap in embracing devices that listen (but do not record!)
The Creepy Lines of Changing Parenting Culture:
The second creepy line relates to how much libraries should shape culture and aim to change social norms of parenting and early childhood experiences. Some believe libraries should stay away from influencing social conditions – they believe it should be ‘just books’. Others see the role of libraries in creating an environment where anyone in a community can find the resources to thrive.
In reality, it has never been ‘just books’. In the United States, public libraries have played a role in shaping community culture for much of their history. Speak to older Americans from small towns and rural communities about their library experiences as a child and you are likely to hear about enriching moments that went far beyond checking out books. Before the post WWII era of larger government social service agencies it was public libraries who played a critical role in areas such as health, wellness and parenting.
There are many cultural assimilation challenges baked into the idea of helping parents become more self-aware of the vocabulary environment within their homes — and places where their children live. Research findings can be taken out of context or lead improper framing in the media where poor families are framed as bad parents when they are simply struggling to feed their families rather than focus on increasing the word count.
Libraries might possess the trust and open-door quality to parents — where they see an institution with programs and staff able to provide guidance and support as needed without the pressures that might come from a formal government agency or test-heavy school setting.
The intervention in parenthood also brings with it a sense of pedestal paternalism. Author Annie Murphy Paulcaptures the creepy line here in writing about the LENA device and Providene project:
“I find this completely fascinating, and also somewhat troubling. Recording parents’ speech to their children in order to show them that they are not talking to their children “enough” seems potentially rather intrusive and paternalistic.”
This is the essence of the creepy line– the fascinating capabilities of sensing technologies in changing outcomes are directly coupled with the need for more transparency and accountability. We can easily see this dynamic playing out beyond listening for word-count into the quantification. Companies and health insurance companies are testing the creepy line with quantified self or self-tracking programs for health and wellness.
Closing the word gap will require radical solutions. It is simply not enough to expect an education and awareness campaign to solve the problems. The use of listening devices and parent engagement will create new creepy lines that require use to talk about the trade-offs and all the risks, rewards and new responsibilities of this new era. The question for libraries will be how far do we step into the creepy zone
In 2013 Bloomberg Philanthropies Mayors Challenge awarded Providence, RI for its Providence Reads project. The Project is using a recorder to measure the words (Developed by LENA)
Summary: The ‘driverless car’ headlines are misleading! Humans ‘drivers’ will evolve into ‘Captains’ and still play a critical role in the age of connected cars and autonomous vehicles. Similar to airline pilots who Captain largely automated planes, humans will soon contribute less to decisions on acceleration, braking, and steering and more thinking and control over higher order operations of smarter, connected vehicles. The testing ground Captain-like experiences will be the near term transition of connected cars with ‘active assist’ vehicle systems like adaptive cruise control and platooning. >> One scenario for this Captain-style command and control culture is that it becomes regulated and led by insurance companies to insure humans learn how to use advanced systems safely. The Drivers License might evolve into a Captain’s license 😉
Background
Self-driving cars suitable for real-world operation are closer to reality than most people might believe. Bold claims of bringing autonomous vehicles to markets by 2020 have been made by Nissan and Daimler. Across the world, transportation agencies are outlining the roadmaps and regulatory frameworks needed to support testing and commercialization. Insurance companies are figuring out risk guidelines to deal with liabilities and inevitable incidents within this new autonomous age.
>>> Who Flew the Plane? The Captain or Computer Think about the last time you were on an airplane. You boarded, buckled your belt, paid very little attention to the safety instructions then put your faith in the human Captain to get you to the destination safely and on time! Even though the plane was largely controlled by automated systems, we felt it was the human Captain in charge.
Who will ‘Drive’ the Connected Cars and Autonomous Cars of the Future? We are not entering the age of ‘driverless cars’, it is the transition to the era of ‘Captain’ culture where human thinking and vehicle operation moves up the value chain of new forms of command and control.
In the years ahead we will gradually share and cede ‘control’ of acceleration and braking, and gain a sense of responsibility over higher level thinking around active assist systems.
The age of Active Assist is defined by vehicle systems that can sense problems and alert the driver — or sense-and-control the vehicle to avoid an incident. Active Assist includes features such as adaptive cruise control, lane-depature warning systems, collision warning and collision avoidance systems. Many of these features are already in the marketplace of luxury vehicles but not yet mainstream or part of our popular culture.
I love the idea of autonomous vehicles but don’t expect people to just fall in love with self-driving cars overnight. It is the era of active assist where our relationship with vehicles will evolve.
Captains are looking at dashboards of information and recommendation systems. They are tuned into geospatial (map-based) information on infrastructure and other connected cars. Captains will be looking at mission critical software from LIDAR to smart tire sensors. Captain will be watching for notifications of changing conditions (e.g. accidents; road debris) coming from connected and sensing vehicles across the road networks. Being Captain of a smarter vehicle might seem more human than being a ‘driver’ of cars that we know of today.
These are possible higher order thinking activities associated with Captain culture but the scenario is not inevitable.
Captain Era: Risks, Rewards & Responsibilities There are considerable risks including an over reliance on computing systems, loss of skills, software system failures, malware attacks, et al. The assumption of a Captain role is to maintain awareness and attention to respond to system failures and resume control.
The rewards are improving safety and flow. We can imagine a dramatic reduction of deaths and injuries as human error is dampened by active assist and autonomous vehicles. We can imagine commuting within major metropolitan regions moving more smoothly with ‘flow’ being the most desirable condition. We might not go as fast as we want – but flow means we will not see stop and go traffic patterns.
The responsibilities require us to be more transparent and accountable. Folks who fear big brother will not be happy in this future.
Critique: This Captain Culture is Nonsense There is another scenario (or vision) of the age of autonomous vehicles where humans do not ‘have’ to pay attention – or they will not ‘want’ to pay attention. The assumption might be humans are lazy or not-interested. Why would someone want to oversee advanced active assist systems?
This critique is perfectly reasonable, but I think the answer to the question is: ‘because they will have to pay attention’.
I would expect over the next twenty years for transportation regulators to force humans to stay engaged and attentive. In-cabin sensing systems will know if people within autonomous or active assist vehicles are not paying attention.
If I had to place a bet – it would be that the Captain culture era is regulated into social norms. There is simply too much risk to confront given the stage of maturity of both active assist and autonomous vehicles.
Driverless Cars (=We don’t trust humans!) vs Active Assist (=We love people!)
Self-driving cars suitable for real-world operation are closer to reality than most people might believe. Bold claims of bringing autonomous vehicles to markets by 2020 have been made by Nissan and Daimler. Across the world, transportation agencies are outlining the roadmaps and regulatory frameworks needed to support testing and commercialization. Insurance companies are figuring out risk guidelines to deal with liabilities and inevitable incidents within this new autonomous age.
The most empowering headlines would be those that frame the transition in a ‘pro’-human factors and talk more about empowering humans than making them seem irrelevant. We can find the human factors within vehicles based on active assist.
The age of Active Assist is defined by vehicle systems that can sense problems and alert the driver — or sense-and-control the vehicle to avoid an incident. Active Assist includes features such as adaptive cruise control, lane-depature warning systems, collision warning and collision avoidance systems. Many of these features are already in the marketplace of luxury vehicles but not yet mainstream or part of our popular culture.
I love the idea of autonomous vehicles but don’t expect people to just fall in love with self-driving cars overnight. It is the era of active assist where our relationship with vehicles will evolve.
How will the role of people evolve in this near term transition towards active assist? What is the ideal image of human operators in this new era of software assisted driving? What would be the worst case outcome?
It is not a ‘Driverless Car’, It is the Beginning of Captain Culture January 4th, 2014garrygolden@gmail.com
Garry delivers keynotes, workshops and consultation for organizations around the world!
Lets talk about how he can help yours.
Solar power receives more media attention and seems to capture our imagination around the ‘future of energy’, but fuel cell energy appliances represent a more compelling distributed power strategy for utilities looking to develop solutions for commercial customers who demand more resiliency, always-on power and a reduced carbon footprint.
