Terahertz Spectrum Technology May Produce Major Increases in Wireless Network Speeds

Communications Hardware", Image by Tom Blackwell

“Communications Hardware”, Image by Tom Blackwell

Remember when upgrading from a 14.4 baud modem to a 33.6 baud modem felt as though you had moved from bicycle to Indy racing car online? What about when you had DSL installed and you had never experienced anything like it? How about when you next had a T1 line at the office and then a cable modem hooked up at home? All of these drastic jumps in transmission speeds helped to fuel the exponential growth in the web’s evolving architecture, rich and limitless content, and integration into nearly every aspect of modern life.

While the next disruptive jump in speed has yet to occur, researchers and developers are currently working on technology to exploit a still little used area of the electromagnetic spectrum called terahertz (“THz”) waves. Should this come to pass, wireless bandwidth rates could potentially increase by 100 fold or more over today’s WiFi and mobile networks. Beyond increasing the velocity at which videos of cats playing the piano can be distributed and viewed, this technology could have a major impact on the entire world of wireless access, services and devices.

Nonetheless, despite the alluring promise of THz wireless, some key engineering challenges remain to be solved.

The latest significant advance in this early field was reported in a most interesting article posted on Phys.org on September 14, 2015 entitled Physicists Develop Key Component for Terahertz Wireless. (No author is credited.) I will summarize, annotate and pose some of my own questions derived from the blog-wave portion of the spectrum.

A team of researchers from Brown University and Osaka University have developed the “first system for multiplexing terahertz waves”. This is, by definition, a technological means to share multiple communication streams over a single resource such as a cable simultaneously carrying multiple TV channels or phone calls. (However, it is distinctly different from the multiplex movie theaters currently showing a dozen or more of the latest movies at time along with offering way overpriced snacks at the concession stands.) Another device often needed to reverse this process is called a demultiplexer.

The development team’s work on this advancement was published in the September 14, 2015 online edition of Nature Photonics in a paper entitled Frequency-division Multiplexing in the Terahertz Range Using a Leaky-wave Antenna by Nicholas J. Karl, Robert W. McKinney, Yasuaki Monnai, Rajind Mendis & Daniel M. Mittleman. (A subscription is required for full access.)

The “leaky wave antenna” at the core of this consists of “two metal plates place in parallel to form a waveguide“. As the THz waves move across this waveguide they “leak out a[t] different angles depending on their frequency”. In turn, the various frequencies can disperse individual streams of data riding on these THz waves. Devices at the receiving end will be able to capture this data from a unique stream.

According to the researchers, their new approach has the advantage of being able “to adjust the spectrum bandwidth that can be allocated to each channel”. This could be quite helpful if and when their new multiplexer is added to a data network. In effect, bandwidth can be apportioned to the network users’ individual data needs.

The team is planning to continue their development of the THz multiplexer. This includes integrating, testing and improving it in a “prototype terahertz network” they are building. A member of the team and co-author of their paper, Daniel M. Mittleman, hopes that their work will inspire other researchers to join in developing other original THz network technologies.

Assuming that THz wireless networks will be deployed in the future, my questions are as follows:

  • Will today’s wireless service providers adapt their networks if THz technology proves to be technically and economically feasible? Will new providers emerge in the telecom marketplace?
  • What new types of services will become enabled by THz?
  • Will it bring broadband transmission rates to underserved geographic areas around the world?
  • How will providers model and test the elasticity of the pricing for their THz services? Are current pricing schemes sufficient or are new alternatives needed?
  • What entrepreneurial opportunities await for companies developing THz systems and those leveraging its capabilities for content creation and delivery?
  • As more advertising continues to migrate to wireless platforms, how will marketing and content strategists use THz to their advantage?

Watson, is That You? Yes, and I’ve Just Demo-ed My Analytics Skills at IBM’s New York Office

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My photo of the entrance to IBM’s office at 590 Madison Avenue in New York, taken on July 29, 2015.

I don’t know if my heart can take this much excitement. Yesterday morning, on July 29, 2015, I attended a very compelling presentation and demo of IBM’s Watson technology. (This AI-driven platform has been previously covered in these five Subway Fold posts.) Just the night before, I saw I saw a demo of some ultra-cool new augmented reality systems.

These experiences combined to make me think of the evocative line from Supernaut by Black Sabbath with Ozzie belting out “I’ve seen the future and I’ve left it behind”. (Incidentally, this prehistoric metal classic also has, IMHO, one of the most infectious guitar riffs with near warp speed shredding ever recorded.)

Yesterday’s demo of Watson Analytics, one key component among several on the platform, was held at IBM’s office in the heart of midtown Manhattan at 590 Madison Avenue and 57th Street. The company very graciously put this on for free. All three IBM employees who spoke were outstanding in their mastery of the technology, enthusiasm for its capabilities, and informative Q&A interactions with the audience. Massive kudos to everyone involved at the company in making this happen. Thanks, too, for all of attendees who asked such excellent questions.

Here is my summary of the event:

Part 1: What is Watson Analytics?

The first two speakers began with a fundamental truth about all organizations today: They have significant quantities of data that are driving all operations. However, a bottleneck often occurs when business users understand this but do not have the technical skills to fully leverage it while, correspondingly, IT workers do not always understand the business context of the data. As a result, business users have avenues they can explore but not the best or most timely means to do so.

This is where Watson can be introduced because it can make these business users self-sufficient with an accessible, extensible and easier to use analytics platform. It is, as one the speakers said “self-service analytics in the cloud”. Thus, Watson’s constituents can be seen as follows:

  • “What” is how to discover and define business problems.
  • “Why” is to understand the existence and nature of these problems.
  • “How” is to share this process in order to affect change.

However, Watson is specifically not intended to be a replacement for IT in any way.

Also, one of Watson’s key capabilities is enabling users to pursue their questions by using a natural language dialog. This involves querying Watson with questions posed in ordinary spoken terms.

Part 2: A Real World Demo Using Airline Customer Data

Taken directly from the world of commerce, the IBM speakers presented a demo of Watson Analytics’ capabilities by using a hypothetical situation in the airline industry. This involved a business analyst in the marketing department for an airline who was given a compilation of market data prepared by a third-party vendor. The business analyst was then assigned by his manager with researching and planning how to reduce customer churn.

Next, by enlisting Watson Analytics for this project, the two central issues became how the data could be:

  • Better understand, leveraged and applied to increase customers’ positive opinions while simultaneously decreasing the defections to the airline’s competitors.
  • Comprehensively modeled in order to understand the elements of the customer base’s satisfaction, or lack thereof, with the airline’s services.

The speakers then put Watson Analytics through its paces up on large screens for the audience to observe and ask questions. The goal of this was to demonstrate how the business analyst could query Watson Analytics and, in turn, the system would provide alternative paths to explore the data in search of viable solutions.

Included among the variables that were dexterously tested and spun into enlightening interactive visualizations were:

  • Satisfaction levels by other peer airlines and the hypothetical Watson customer airline
  • Why customers are, and are not, satisfied with their travel experience
  • Airline “status” segments such as “platinum” level flyers who pay a premium for additional select services
  • Types of travel including for business and vacation
  • Other customer demographic points

This results of this exercise as they appeared onscreen showed how Watson could, with its unique architecture and tool set:

  • Generate “guided suggestions” using natural language dialogs
  • Identify and test all manner of connections among the population of data
  • Use predictive analytics to make business forecasts¹
  • Calculate a “data quality score” to assess the quality of the data upon which business decisions are based
  • Map out a wide variety of data dashboards and reports to view and continually test the data in an effort to “tell a story”
  • Integrate an extensible set of analytical and graphics tools to sift through large data sets from relevant Twitter streams²

Part 3: The Development Roadmap

The third and final IBM speaker outlined the following paths for Watson Analytics that are currently in beta stage development:

  • User engagement developers are working on an updated visual engine, increased connectivity and capabilities for mobile devices, and social media commentary.
  • Collaboration developers are working on accommodating work groups and administrators, and dashboards that can be filtered and distributed.
  • Data connector developers are working on new data linkages, improving the quality and shape of connections, and increasing the degrees of confidence in predictions. For example, a connection to weather data is underway that would be very helpful to the airline (among other industries), in the above hypothetical.
  • New analytics developers are working on new functionality for business forecasting, time series analyses, optimization, and social media analytics.

Everyone in the audience, judging by the numerous informal conversations that quickly formed in the follow-up networking session, left with much to consider about the potential applications of this technology.


1.  Please see these six Subway Fold posts covering predictive analytics in other markets.

2.  Please see these ten Subway Fold posts for a variety of other applications of Twitter analytics.

 

Artificial Intelligence Apps for Business are Approaching a Tipping Point

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“Algorithmic Contaminations”, Image by Derek Gavey

There have been many points during the long decades of the development of business applications using artificial intelligence (AI) when it appeared that The Rubicon was about to be crossed. That is, this technology often seemed to be right on the verge of going mainstream in global commerce. Yet it has still to achieve a pervasive critical mass despite the vast resources and best intentions behind it.

Today, with the advent of big data and analytics and their many manifestations¹ spreading across a wide spectrum of industries, AI is now closer than ever to reaching such a tipping point. Consultant, researcher and writer Brad Power makes a timely and very persuasive case for this in a highly insightful and informative article entitled Artificial Intelligence Is Almost Ready for Business, posted on the Harvard Business Review site on March 19, 2015. I will summarize some of the key points, add some links and annotations, and pose a few questions.

Mr. Power sees AI being brought to this threshold by the convergence of rapidly increasing tech sophistication, “smarter analytics engines, and the surge in data”. Further adding to this mix is the incursion and growth of the Internet of Things (Iot), better means to analyze “unstructured” data, and the extensive categorization and tagging of data. Furthermore,  there is the dynamic development and application of smarter algorithms to  discern complex patterns in data and to generate increasingly accurate predictive models.

So, too, does machine learning² play a highly significant role in AI applications. It can be used to generate “thousands of models a week”. For example, a model premised upon machine learning can be used to select which ads should be placed on what websites within milliseconds in order to achieve the greatest effectiveness in reaching an intended audience. DataXu is one of the model-generating firms in this space.

Tom Davenport, a professor at Babson College and an analytics expert³, was one of the experts interviewed by Power for this article. To paraphrase part of his quote, he believes that AI and machine learning would be useful adjuncts to the human analysts (often referred to as “quants”4). Such living experts can far better understand what goes into and comes out of a model than a machine learning app alone. In turn, these people can persuade business managers to apply such “analytical insights” to actual business processes.

AI can also now produce greater competitive efficiencies by closing the time gap between analyzing vast troves of data at high speeds and decision-making on how to apply the results.

IBM, one of the leading integrators of AI, has recently invested $1B in the creation of their Watson Group, dedicated to exploring and leveraging commercial applications for Watson technology. (X-ref to the December 1, 2014 Subway Fold post entitled Possible Futures for Artificial Intelligence in Law Practice for a previous mention and links concerning Watson.) This AI technology is currently finding significant applications in:

  • Health Care: Due to Watson’s ability to process large, complex and dynamic quantities of text-based data, in turn, it can “generate and evaluate hypotheses”. With specialized training, these systems can then make recommendation about treating particular patients. A number of elite medical teaching institutions in the US are currently engaging with IBM to deploy Watson to “better understand patients’ diseases” and recommend treatments.
  • Finance: IBM is presently working with 45 companies on app including “digital virtual agents” to work with their clients in a more “personalized way”; a “wealth advisor” for financial planning5; and on “risk and compliance management”. For example, USAA provides financial services to active members of the military services and to their veterans. Watson is being used to provide a range of financial support functions to soldiers as they move to civilian status.
  • Startups: The company has designated $100 million for introducing Watson into startups. An example is WayBlazer which, according to its home page, is “an intelligence search discovery system” to assist travelers throughout all aspects of their trips. This online service is designed to be an easy-to-use series of tools to provide personalized answers and support for all sort of journeys. At the very bottom of their home page on the left-hand side are the words “Powered by IBM Watson”.

To get a sense of the trends and future of AI in business, Power spoke with the following venture capitalists who are knowledgeable about commercial AI systems:

  • Mark Gorenberg, Managing Director at Zetta Venture Partners which invests in big data and analytics startups, believes that AI is an “embedded technology”. It is akin to adding “a brain”  – – in the form of cognitive computing – – to an application through the use of machine learning.
  • Promod Haque, senior managing partner at Norwest Venture Partners, believes that when systems can draw correlations and construct models on their own, and thus labor is reduced and better speed is achieved. As a result, a system such as Watson can be used to automate analytics.
  • Manoj Saxena, a venture capitalists (formerly with IBM), sees analytics migrating to the “cognitive cloud”, a virtual place where vast amounts of data from various sources will be processed in such a manner to “deliver real-time analytics and learning”. In effect, this will promote smoother integration of data with analytics, something that still remains challenging. He is an investor in a startup called Cognitive Scale working in this space.

My own questions (not derived through machine learning), are as follows:

  • Just as Watson has begun to take root in the medical profession as described above, will it likewise begin to propagate across the legal profession? For a fascinating analysis as a starting point, I highly recommend 10 Predictions About How IBM’s Watson Will Impact the Legal Profession, by Paul Lippe and Daniel Katz, posted on the ABA Journal website on October 4, 2014. I wonder whether the installation of Watson in law offices take on other manifestations that cannot even be foreseen until the systems are fully integrated and running? Might the Law of Unintended Consequences also come into play and produce some negative results?
  • What other professions, industries and services might also be receptive to the introduction of AI apps that have not even considered it yet?
  • Does the implementation of AI always produce reductions in jobs or is this just a misconception? Are there instances where it could increase the number of jobs in a business? What might be some of the new types of jobs that could result? How about AI Facilitator, AI Change Manager, AI Instructor, AI Project Manager, AI Fun Specialist, Chief AI Officer,  or perhaps AI Intrapreneur?

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1.  There are 27 Subway Fold posts in the category of Big Data and Analytics.

2.  See the Subway Fold posts on December 12, 2014 entitled Three New Perspectives on Whether Artificial Intelligence Threatens or Benefits the World and then another on December 10, 2014 entitled  Is Big Data Calling and Calculating the Tune in Today’s Global Music Market? for specific examples of machine learning.

3.  I had the great privilege of reading one of Mr. Davenport’s very insightful and enlightening books entitled Competing on Analytics: The New Science of Winning (Harvard Business Review Press, 2007), when it was first published. I learned a great deal from it and this book was responsible for my initial interest in the applications of analytics in commerce. Although big data and analytics have grown exponentially since its publication, I still highly recommend this book for its clarity, usefulness and enthusiasm for this field.

4.  For a terrific and highly engaging study of the work and influence of these analysts, I also recommend reading The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It (Crown Business, 2011), by Scott Patterson.

5.  There was a most interesting side-by-side comparison of human versus automated financial advisors entitled Robo-Advisors Vs. Financial Advisors: Which Is Better For Your Money? by Libby Kane, posted on BusinessInsider.com on July 21, 2014.