Digital Smarts Everywhere: The Emergence of Ambient Intelligence

Image from Pixabay

Image from Pixabay

The Troggs were a legendary rock and roll band who were part of the British Invasion in the late 1960’s. They have always been best known for their iconic rocker Wild Thing. This was also the only Top 10 hit that ever had an ocarina solo. How cool is that! The band went on to have two other major hits, With a Girl Like You and Love is All Around.¹

The third of the band’s classic singles can be stretched a bit to be used as a helpful metaphor to describe an emerging form pervasive “all around”-edness, this time in a more technological context. Upon reading a fascinating recent article on TechCrunch.com entitled The Next Stop on the Road to Revolution is Ambient Intelligence, by Gary Grossman, on May 7, 2016, you will find a compelling (but not too rocking) analysis about how the rapidly expanding universe of digital intelligent systems wired into our daily routines is becoming more ubiquitous, unavoidable and ambient each day.

All around indeed. Just as romance can dramatically affect our actions and perspectives, studies now likewise indicate that the relentless global spread of smarter – – and soon thereafter still smarter – – technologies is comparably affecting people’s lives at many different levels.² 

We have followed just a sampling of developments and trends in the related technologies of artificial intelligence, machine learning, expert systems and swarm intelligence in these 15 Subway Fold posts. I believe this new article, adding “ambient intelligence” to the mix, provides a timely opportunity to bring these related domains closer together in terms of their common goals, implementations and benefits. I highly recommend reading Mr. Grossman’s piece it in its entirety.

I will summarize and annotate it, add some additional context, and then pose some of my own Trogg-inspired questions.

Internet of Experiences

Digital this, that and everything is everywhere in today’s world. There is a surging confluence of connected personal and business devices, the Internet, and the Internet of Things (I0T) ³. Woven closely together on a global scale, we have essentially built “a digital intelligence network that transcends all that has gone before”. In some cases, this quantum of advanced technologies gains the “ability to sense, predict and respond to our needs”, and is becoming part of everyone’s “natural behaviors”.

A forth industrial revolution might even manifest itself in the form of machine intelligence whereby we will interact with the “always-on, interconnected world of things”. As a result, the Internet may become characterized more by experiences where users will converse with ambient intelligent systems everywhere. The supporting planks of this new paradigm include:

A prediction of what more fully realized ambient intelligence might look like using travel as an example appeared in an article entitled Gearing Up for Ambient Intelligence, by Lisa Morgan, on InformationWeek.com on March 14, 2016. Upon leaving his or her plane, the traveler will receive a welcoming message and a request to proceed to the curb to retrieve their luggage. Upon reaching curbside, a self-driving car6 will be waiting with information about the hotel booked for the stay.

Listening

Another article about ambient intelligence entitled Towards a World of Ambient Computing, by Simon Bisson, posted on ZDNet.com on February 14, 2014, is briefly quoted for the line “We will talk, and the world will answer”, to illustrate the point that current technology will be morphing into something in the future that would be nearly unrecognizable today. Grossman’s article proceeds to survey a series of commercial technologies recently brought to market as components of a fuller ambient intelligence that will “understand what we are asking” and provide responsive information.

Starting with Amazon’s Echo, this new device can, among other things:

  • Answer certain types of questions
  • Track shopping lists
  • Place orders on Amazon.com
  • Schedule a ride with Uber
  • Operate a thermostat
  • Provide transit schedules
  • Commence short workouts
  • Review recipes
  • Perform math
  • Request a plumber
  • Provide medical advice

Will it be long before we begin to see similar smart devices everywhere in homes and businesses?

Kevin Kelly, the founding Executive Editor of WIRED and a renowned futurist7, believes that in the near future, digital intelligence will become available in the form of a utility8 and, as he puts it “IQ as a service”. This is already being done by Google, Amazon, IBM and Microsoft who are providing open access to sections of their AI coding.9 He believes that success for the next round of startups will go to those who enhance and transforms something already in existence with the addition of AI. The best example of this is once again self-driving cars.

As well, in a chapter on Ambient Computing from a report by Deloitte UK entitled Tech Trends 2015, it was noted that some products were engineering ambient intelligence into their products as a means to remain competitive.

Recommending

A great deal of AI is founded upon the collection of big data from online searching, the use of apps and the IoT. This universe of information supports neural networks learn from repeated behaviors including people’s responses and interests. In turn, it provides a basis for “deep learning-derived personalized information and services” that can, in turn, derive “increasingly educated guesses with any given content”.

An alternative perspective, that “AI is simply the outsourcing of cognition by machines”, has been expressed by Jason Silva, a technologist, philosopher and video blogger on Shots of Awe. He believes that this process is the “most powerful force in the universe”, that is, of intelligence. Nonetheless, he sees this as an evolutionary process which should not be feared. (See also the December 27, 2014 Subway Fold post entitled  Three New Perspectives on Whether Artificial Intelligence Threatens or Benefits the World.)

Bots are another contemporary manifestation of ambient intelligence. These are a form of software agent, driven by algorithms, that can independently perform a range of sophisticated tasks. Two examples include:

Speaking

Optimally, bots should also be able to listen and “speak” back in return much like a 2-way phone conversation. This would also add much-needed context, more natural interactions and “help to refine understanding” to these human/machine exchanges. Such conversations would “become an intelligent and ambient part” of daily life.

An example of this development path is evident in Google Now. This service combines voice search with predictive analytics to present users with information prior to searching. It is an attempt to create an “omniscient assistant” that can reply to any request for information “including those you haven’t thought of yet”.

Recently, the company created a Bluetooth-enable prototype of lapel pin based on this technology that operates just by tapping it much like the communicators on Star Trek. (For more details, see Google Made a Secret Prototype That Works Like the Star Trek Communicator, by Victor Luckerson, on Time.com, posted on November 22, 2015.)

The configurations and specs of AI-powered devices, be it lapel pins, some form of augmented reality10 headsets or something else altogether, supporting such pervasive and ambient intelligence are not exactly clear yet. Their development and introduction will take time but remain inevitable.

Will ambient intelligence make our lives any better? It remains to be seen, but it is probably a viable means to handle some of more our ordinary daily tasks. It will likely “fade into the fabric of daily life” and be readily accessible everywhere.

Quite possibly then, the world will truly become a better place to live upon the arrival of ambient intelligence-enabled ocarina solos.

My Questions

  • Does the emergence of ambient intelligence, in fact, signal the arrival of a genuine fourth industrial revolution or is this all just a semantic tool to characterize a broader spectrum of smarter technologies?
  • How might this trend affect overall employment in terms of increasing or decreasing jobs on an industry by industry basis and/or the entire workforce? (See also this June 4, 2015 Subway Fold post entitled How Robots and Computer Algorithms Are Challenging Jobs and the Economy.)
  • How might this trend also effect non-commercial spheres such as public interest causes and political movements?
  • As ambient intelligence insinuates itself deeper into our online worlds, will this become a principal driver of new entrepreneurial opportunities for startups? Will ambient intelligence itself provide new tools for startups to launch and thrive?

 


1.   Thanks to Little Steven (@StevieVanZandt) for keeping the band’s music in occasional rotation on The Underground Garage  (#UndergroundGarage.) Also, for an appreciation of this radio show see this August 14, 2014 Subway Fold post entitled The Spirit of Rock and Roll Lives on Little Steven’s Underground Garage.

2.  For a remarkably comprehensive report on the pervasiveness of this phenomenon, see the Pew Research Center report entitled U.S. Smartphone Use in 2015, by Aaron Smith, posted on April 1, 2015.

3These 10 Subway Fold posts touch upon the IoT.

4.  The Subway Fold category Big Data and Analytics contains 50 posts cover this topic in whole or in part.

5.  The Subway Fold category Telecommunications contains 12 posts cover this topic in whole or in part.

6These 5 Subway Fold posts contain references to self-driving cars.

7.   Mr. Kelly is also the author of a forthcoming book entitled The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, to be published on June 7, 2016 by Viking.

8.  This September 1, 2014 Subway Fold post entitled Possible Futures for Artificial Intelligence in Law Practice, in part summarized an article by Steven Levy in the September 2014 issue of WIRED entitled Siri’s Inventors Are Building a Radical New AI That Does Anything You Ask. This covered a startup called Viv Labs whose objective was to transform AI into a form of utility. Fast forward to the Disrupt NY 2016 conference going on in New York last week. On May 9, 2016, the founder of Viv, Dag Kittlaus, gave his presentation about the Viv platform. This was reported in an article posted on TechCrunch.com entitled Siri-creator Shows Off First Public Demo of Viv, ‘the Intelligent Interface for Everything’, by Romain Dillet, on May 9, 2016. The video of this 28-minute presentation is embedded in this story.

9.  For the full details on this story see a recent article entitled The Race Is On to Control Artificial Intelligence, and Tech’s Future by John Markoff and Steve Lohr, published in the March 25, 2016 edition of The New York Times.

10These 10 Subway Fold posts cover some recent trends and development in augmented reality.

Facebook is Now Restricting Access to Certain Data About Its User Base to Third Parties

Image by Gerd Altmann

Image by Gerd Altmann

It is a simple and straight-forward basic business concept in any area of commerce: Do not become too overly reliant upon a single customer or supplier. Rather, try to build a diversified portfolio of business relationships to diligently avoid this possibility and, at the same time, assist in developing potential new business.

Starting in May 2015, Facebook instituted certain limits upon access to the valuable data about its 1.5 billion user base¹ to commercial and non-commercial third parties. This has caused serious disruption and even the end of operations for some of them who had so heavily depended on the social media giant’s data flow. Let’s see what happened.

This story was reported in a very informative and instructive article in the September 22, 2015 edition of The Wall Street Journal entitled Facebook’s Restrictions on User Data Cast a Long Shadow by Deepa Seetharaman and Elizabeth Dwoskin. (Subscription required.) If you have access to the WSJ.com, I highly recommend reading in its entirety. I will summarize and annotate it, and then pose some of my own third-party questions.

This change in Facebook’s policy has resulted in “dozen of startups” closing, changing their approach or being bought out. This has also affected political data consultants and independent researchers.

This is a significant shift in Facebook’s approach to sharing “one of the world’s richest sources of information on human relationships”. Dating back to 2007, CEO Mark Zuckerberg opened to access to Facebook’s “social graph” to outsiders. This included data points, among many others, about users’ friends, interests and “likes“.

However, the company recently changed this strategy due to users’ concerns about their data being shared with third parties without any notice. A spokeswoman from the company stated this is now being done in manner that is “more privacy protective”. This change has been implemented to thus give greater control to their user base.

Other social media leaders including LinkedIn and Twitter have likewise limited access, but Facebook’s move in this direction has been more controversial. (These 10 recent Subway Fold posts cover a variety of ways that data from Twitter is being mined, analyzed and applied.)

Examples of the applications that developers have built upon this data include requests to have friends join games, vote, and highlight a mutual friend of two people on a date. The reduction or loss of this data flow from Facebook will affect these and numerous other services previously dependent on it. As well, privacy experts have expressed their concern that this change might result in “more objectionable” data-mining practices.

Others view these new limits are a result of the company’s expansion and “emergence as the world’s largest social network”.

Facebook will provide data to outsiders about certain data types like birthdays. However, information about users’ friends is mostly not available. Some developers have expressed complaints about the process for requesting user data as well as results of “unexpected outcomes”.

These new restrictions have specifically affected the following Facebook-dependent websites in various ways:

  • The dating site Tinder asked Facebook about the new data policy shortly after it was announced because they were concerned that limiting data about relationships would impact their business. A compromise was eventually obtained but limited this site only to access to “photos and names of mutual friends”.
  • College Connect, an app that provided forms of social information and assistance to first-generation students, could not longer continue its operations when it lost access to Facebook’s data. (The site still remains online.)
  • An app called Jobs With Friends that connected job searchers with similar interests met a similar fate.
  • Social psychologist Benjamin Crosier was in the process of creating an app searching for connections “between social media activity and ills like drug addiction”. He is currently trying to save this project by requesting eight data types from Facebook.
  • An app used by President Obama’s 2012 re-election campaign was “also stymied” as a result. It was used to identify potential supporters and trying to get them to vote and encourage their friends on Facebook to vote or register to vote.²

Other companies are trying an alternative strategy to build their own social networks. For example, Yesgraph Inc. employs predictive analytics³ methodology to assist clients who run social apps in finding new users by data-mining, with the user base’s permission, through lists of email addresses and phone contacts.

My questions are as follows:

  • What are the best practices and policies for social networks to use to optimally balance the interests of data-dependent third parties and users’ privacy concerns? Do they vary from network to network or are they more likely applicable to all or most of them?
  • Are most social network users fully or even partially concerned about the privacy and safety of their personal data? If so, what practical steps can they take to protect themselves from unwanted access and usage of it?
  • For any given data-driven business, what is the threshold for over-reliance on a particular data supplier? How and when should their roster of data suppliers be further diversified in order to protect themselves from disruptions to their operations if one or more of them change their access policies?

 


1.   Speaking of interesting data, on Monday, August 24, 2015, for the first time ever in the history of the web, one billion users logged onto the same site, Facebook. For the details, see One Out of Every 7 People on Earth Used Facebook on Monday, by Alexei Oreskovic, posted on BusinessInsider.com on August 27, 2015.

2See the comprehensive report entitled A More Perfect Union by Sasha Issenberg in the December 2012 issue of MIT’s Technology Review about how this campaign made highly effective use of its data and social networks apps and data analytics in their winning 2012 re-election campaign.

3.  These seven Subway Fold posts cover predictive analytics applications in range of different fields.

Minting New Big Data Types and Analytics for Investors

Along with the exponential growth of big data in terms of its quantity, myriad of collection points, nearly limitless storage capabilities, and complex analytics, investors are keenly interested in discovering unique advantages from this phenomenon to be applied in the securities markets.* While financial institutions of all types have used sophisticated metrics and predictions to gain tactical advantages in their trading operations for many decades, burgeoning big data methodologies have recently created new opportunities for entrepreneurs to provide the financial services industry with ever more original and arcane forms of predictive analytics.

Investors now have data services available to them offering insights never previously feasible or even imaginable. Yesterday’s (November 21, 2014) edition of The Wall Street Journal carried a fascinating report highlighting three of these operations entitled Startups Tip Investors to Hidden Data Pearls by Bradley Hope. (A subscription to the WSJ Online is required for full access to this report on WSJ.com, but this piece was available here in slightly different version on CBS’s Marketwatch.com.) This additional extract page from the article is also available online and contains explanatory graphics of their formats and analyses.

How are these new data points being mined, examined and spun into forecasts? To briefly sum up the work of these startups covered in this article:

  • Orbital Insight analyzes satellite photos of building sites in 30 cities in China, cornfields, and parking lots in order to assess how their capacities might influence the markets in various ways. They are seeking to intuit “early indicators” of trends and influences. Their clients include hedge funds.
  • Dataminr sifts through a half a billion daily tweets in order to spot potential market moving trends ahead of the news services.** The link above to the graphics from the WSJ article contains a very effective infographic on this process.*** The company’s proprietary systems categorize and analyze all tweets in real time, discerns potentially useful patterns, and then distributes the results to their clients.
  • Premise Inc. uses a global system, now in 18 countries, that provides cell phone credits as payments to individuals who monitor the prices of various goods. From this input, the company tracks early inflation rates and other economic data. They believe that their data can differ from official government sources.

I recommend reading this story in full for all of its compelling details.

My follow up questions include:

  • Who watches these watchmen? Will market forces determine which of them are producing valid and actionable collection and analytics or should they somehow be subject to regulatory oversight?
  • Because these data types and analytics are so new, how are these companies and others like them addressing the distinctions between correlation and causation in their reports to their clients? Would it be beneficial for them to form a trade association to address this and other issues that might arise in the future for this nascent industry?
  • Are there entrepreneurial opportunities here for another type of new startups to vet the practices and products of such companies? That is, analysts who produce no new data types themselves, but rather, apply existing and, perhaps develop new, analytical tools for such assessments?
  • What other fields, markets and professions might benefit from this trend to discover and assess new data types in addition to finance?

_____________________________

*    Please see this April 9, 2014 Subway Fold post entitled Roundup of Some Recent Books on Big Data, Analytics and Intelligent Systems.

**   Please see this July 31, 2014 Subway Fold post entitled New Analytical Twitter Traffic Report on US TV Shows During the 2013 – 2014 Season.

***  Please see this January 30, 2015 Subway Fold post entitled Timely Resources for Studying and Producing Infographics.

Roundup of Some Recent Books on Big Data, Analytics and Intelligent Systems

I have recently read four books concerning big data, analytics and intelligent systems that I highly recommend to anyone interested in learning more about these rapidly growing fields.

1.  The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson and Andrew McAfee. An engaging and in depth analysis of the current and future implications of the convergence of everything becoming digital, all of this digital content increasing at an exponential rate, and how future job skills and business opportunities will be combinatorial in nature.

During the past three months, NYTimes columnists Thomas Friedman, David Brooks and Joe Nocera have all very insightful analyses about different points in this book here, here and here , respectively. As well, The Times ran a very interesting Op-Ed piece Monday’s  (4/7/14) cautioning about a series of concerns with big data and analytics in a piece entitled Eight (No, Nine!) Problems with Big Data by Gary Marcus and Ernest David. All are highly recommended.

2.  Big Data: A Revolution That Will Transform How We Live, Work, and Think by by Viktor Mayer-Schönberger and Kenneth Cukier. An clear and concise primer on the concepts, applications, limits and implications of big data. This book has received a great deal of attention in the press. I was particularly impressed with their expert distinctions between causation and correlation in big data analytics.

3.  Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel. The author presents a balanced approach to examining a series of industry specific cases using data analytics to predict everything from consumer behavior or political trends. I suggest reading this book and the Big Data book above together if possible because of their contrasting perspectives on this phenomenon. Also, both this book and Big Data above provide adequate treatments of data privacy and security issues.

4.  Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia by Anthony M. Townsend. A deep analytical exploration of how big data and analytics are being devised and deployed in large urban areas by local governments and independent citizens. I found this to be a fascinating look at the nearly limitless possibilities described and forecast by the author.