TechDay New York 2018: 500 Local Startups’ Displays, Demos and Delights for the Crowd

All photos on this page by Alan Rothman.

Sometimes in traditional advertising for creative works like movies, TV shows, books and plays, the quoted reviews and taglines include the exclamation “This one’s got it all!” Yet this is only rarely, if ever, true.

Well, wait a minute. Let’s check that.

Last Thursday, May 10th, I had the great pleasure of attending TechDay New York 2018, held at Pier 94, on the West Side of midtown Manhattan. This is a monumental annual exhibition of 500 startups located throughout NYC almost did have it all. In addition to all of these new companies, there were separate areas set up for brief products and services demos and talks by industry experts. Even the TV show Shark Tank was on site there.

First and foremost, massive amounts of thanks to everyone at Techday for putting on such a terrifically enjoyable, informative and memorable event. Their efforts clearly showed that they worked long and hard to get everything about it right.

On to the show …

One of New York City’s greatest economic and cultural strengths has always been its incredible global diversity of it population. So, too, is that dynamic comparably evident in the breadth of it startup ecosystem. From one end of Pier 94 to the other, there was artificial intelligence this, blockchain that, and data analytics everything infused everywhere.

Just a sampling of who and what were on display, among many others, were startups in legal services, architecture, editorial software, video search, incubators and accelerators, social media support services, event planning platforms, programmer aptitude testing, intellectual property protection, pharmacy order and delivery services, office design consultants, branding and digital experience designers, augmented and virtual reality hardware and software, venture capitalist, crowdfunding services, multi-platform public relations strategists, fashion designers, food services (some displaying much chocolate!), consumer data tracking analyst, competitive intelligence trackers and analysts, restaurant reservations, media consultants, phone apps, online security planning and systems, fully integrated electronic health records and billing systems, and dedicated tech recruiters as well as exhibitors themselves looking for new talent. Whew!

Notwithstanding the vastness of the exhibition space, hundreds of startups and thousands of attendees, the organization and presentations of the startups’ display areas was well planned and easy to navigate. The startups were grouped in helpful sectors for social media, e-commerce, fintech and others into more general categories.

The three among the twelve TechDay Talks I attended were quite compelling and evinced great enthusiasm by both the speakers and their audiences. These included:

Above all other considerations, I found every entrepreneur I stopped and spoke with, asking them to tell me about their company, to be highly enthusiastic, engaging and sincere. They were knowledgeable about their markets and competitors, sounded willing to adapt to changing market conditions and, most importantly, convinced that they would become successful. At no point did any of them move on to their next visitors until they sensed that I understood what they were saying and encouraging me to follow their progress online. They were not so much giving visitors hard sales pitches, but rather, much more of the who, what, where, how and when of their businesses. My gratitude to all of them for their patience with me and many of the other attendees I saw them talking to with the same level of professionalism.

Below are some of the photos I took while I was there. I was trying to capture some sense of infectious energy and engagement being generated across entire day’s events.

My very best wishes to all 500 startups to succeed and prosper.

 


 *   For some very worthwhile deep and wide analysis of the effects of AI upon current and future employment, I highly recommend the recently published book entitled Human + Machine: Reimagining Work in the Age of AI, by Paul Daugherty (Harvard Business Review Press, 2018).


 

 

 

 

 

Artificial Swarm Intelligence: There Will be An Answer, Let it Bee

Honey Bee on Willow Catkin", Image by Bob Peterson

“Honey Bee on Willow Catkin”, Image by Bob Peterson

In almost any field involving new trends and developments, anything attracting rapidly increasing media attention is often referred to in terms of “generating a lot of buzz”. Well, here’s a quite different sort of story that adds a whole new meaning to this notion.

A truly fascinating post appeared on TechRepublic.com this week on January 22, 2016 entitled How ‘Artificial Swarm Intelligence’ Uses People to Make Smarter Predictions Than Experts by Hope Reese. It is about a development where technology and humanity intersect in a highly specialized manner to produce a new means to improve predictions by groups of people. I highly recommend reading it in its entirety. I will summarize and annotate it, and then pose a few of my own bug-free questions.

A New Prediction Platform

In a recent switching of roles, while artificial intelligence (AI) concerns itself with machines executing human tasks¹, a newly developed and highly accurate algorithm “harnesses the power” of crowds to generate predictions of “real world events”. This approach is called “artificial swarm intelligence“.

A new software platform called UNU has being developed by a startup called Unanimous AI. The firm’s CEO is Dr. Louis Rosenberg. UNU facilitates the gathering of people online in order to “make collective decisions”. This is being done, according to Dr. Rosenberg “to amplify human intelligence”. Thus far, the platform has been “remarkably accurate” in its predictions of the Academy Awards, the Super Bowl² and elections.

UNU is predicated upon the concept of the wisdom of the crowds which states that larger groups of people make better decisions collectively than even the single smartest person within that group.³  Dr. Roman Yampolskiy, the Director of the Cybersecurity Lab at the University of Louisville, has also created a comparable algorithm known as “Wisdom of Artificial Crowds“. (The first time this phenomenon was covered on The Subway Fold, in the context of entertainment, was in the December 10, 2014 post entitled Is Big Data Calling and Calculating the Tune in Today’s Global Music Market?)

The Birds and the Bees

Swarm intelligence learns from events and systems occurring in nature such as the formation of swarms by bees and flocks by birds. These groups collectively make better choices than their single members. Dr. Rosenberg believes that, in his view there is “a vast amount of intelligence in groups” that, in turn generates “intelligence that amplifies their natural abilities”. He has transposed the rules of these natural systems onto the predictive abilities of humans in groups.

He cites honeybees as being “remarkable” decision-makers in their environment. On a yearly basis, the divide their colonies and “send out scout bees” by the hundreds for many miles around to check out locations for a new home. When these scouts return to the main hive they perform a “waggle dance” to “convey information to the group” and next decide about the intended location. For the entire colony, this is a “complex decision” composed of “conflicting variables”. On average, bee colonies choose the optimal location by more than 80%.

Facilitating Human Bee-hive-ior

However, humans display a much lesser accuracy rate when making their own predictions. Most commonly, polling and voting is used. Dr. Rosenberg finds such methods “primitive” and often incorrect as they tend to be “polarizing”. In effect, they make it difficult to assess the “best answer for the group”.

UNU is his firm’s attempt to facilitate humans with making the best decisions for an entire group. Users log onto it and respond to questions with a series of possible choices displayed. It was modeled upon such behavior occurring in nature among “bees, fish and birds”. This is distinguished from individuals just casting a single vote. Here are two videos of the system in action involving choosing the most competitive Republican presidential candidate and selecting the most beloved sidekick from Star Wars4. As groups of users make their selections on UNU and are influenced by the visible onscreen behavior of others, this movement is the online manifestation of the group’s swarming activity.

Another instance of UNU’s effectiveness and accuracy involved 50 users trying to predict the winners of the Academy Awards. On an individual basis, they each averaged six out of 15 correct. This test swarm was able to get a significantly better nine out of the 15.  Beyond movies, the implications may be further significant if applied in areas such as strategic business decision-making.

My Questions

  • Does UNU lend itself to being turned into a scalable mobile app for much larger groups of users on a multitude of predictions? If so, should users be able to develop their own questions and choices for the swarm to decide? Should all predictions posed be open to all users?
  • Might UNU find some sort of application in guiding the decision process of juries while they are resolving a series of factual issues?
  • Could UNU be used to supplement reviews for books, movies, music and other forms of entertainment? Perhaps some form of “UNU Score” or “UNU Rating”?

 


1.  One of the leading proponents and developers of AI for many decades was MIT Professor Marvin Minsky who passed away on Sunday, January 24, 2016. Here is his obituary from the January 25, 2015 edition of The New York Times entitled Marvin Minsky, Pioneer in Artificial Intelligence, Dies at 88, by Glenn Rifkin.

2.  For an alternative report on whether the wisdom of the crowds appears to have little or no effect on the Super Bowl, one not involving UNU in any way, see an article in the January 28, 2016 edition of The New York Times entitled Super Bowl Challenges Wisdom of Crowds and Oddsmakers, by Victor Mather.

3.  An outstanding and comprehensive treatment of this phenomenon I highly recommend reading The Wisdom of the Crowds, by James Surowiecki (Doubleday, 2004).

4.  I would really enjoy seeing a mash-up of these two demos to see how the group would swarm among the Star Wars sidekicks to select which one of these science fiction characters might have the best chance to win the 2016 election.

New IBM Watson and Medtronic App Anticipates Low Blood Glucose Levels for People with Diabetes

"Glucose: Ball-and-Stick Model", Image by Siyavula Education

“Glucose: Ball-and-Stick Model”, Image by Siyavula Education

Can a new app jointly developed by IBM with its Watson AI technology in partnership with the medical device maker Medtronic provide a new form of  support for people with diabetes by safely avoiding low blood glucose (BG) levels (called hypoglycemia), in advance? If so, and assuming regulatory approval, this technology could potentially be a very significant boon to the care of this disease.

Basics of Managing Blood Glucose Levels

The daily management of diabetes involves a diverse mix of factors including, but not limited to, regulating insulin dosages, checking BG readings, measuring carbohydrate intakes at meals, gauging activity and exercise levels, and controlling stress levels. There is no perfect algorithm to do this as everyone with this medical condition is different from one another and their bodies react in individual ways in trying to balance all of this while striving to maintain healthy short and long-term control of BG levels.

Diabetes care today operates in a very data-driven environment. BG levels, expressed numerically, can be checked on hand-held meter and test strips using a single drop of blood or a continuous glucose monitoring system (CGM). The latter consists of a thumb drive-size sensor attached with temporary adhesive to the skin and a needle attached to this unit inserted just below the skin. This system provides patients with frequent real-time readings of their BG levels, and whether they are trending up or down, so they can adjust their medication accordingly. That is, for A grams of carbs and B amounts of physical activity and other contributing factors, C amount of insulin can be calculated and dispensed.

Insulin itself can be administered either manually by injection or by an insulin pump (also with a subcutaneously inserted needle). The later of these consists of two devices: The pump itself, a small enclosed device (about the size of a pager), with an infusion needle placed under the patient’s skin and a Bluetooth-enabled handheld device (that looks just like a smartphone), used to adjust the pump’s dosage and timing of insulin released. Some pump manufacturers are also bringing to market their latest generation of CGMs that integrate their data and command functions with their users’ smartphones.

(The links in the previous two paragraphs are to Wikipedia pages with detailed pages and photos on CGMs and insulin pumps. See also, this June 27, 2015 Subway Fold post entitled Medical Researchers are Developing a “Smart Insulin Patch” for another glucose sensing and insulin dispensing system under development.)

The trickiest part of all of these systems is maintaining levels of BG throughout each day that are within an acceptable range of values. High levels can result in a host of difficult symptoms. Hypoglycemic low levels, can quickly become serious, manifesting as dizziness, confusion and other symptoms, and can ultimately lead to unconsciousness in extreme cases if not treated immediately.

New App for Predicting and Preventing Low Blood Glucose Levels

Taking this challenge to an entirely new level, at last week’s annual Consumer Electronics Show (CES) held in Las Vegas, IBM and Medtronic jointly announced their new app to predict hypoglycemic events in advance. The app is built upon Watson’s significant strengths in artificial intelligence (AI) and machine learning to sift through and intuit patterns in large volumes of data, in this case generated from Medtronic’s user base for their CGMs and insulin pumps. This story was covered in a most interesting article posted in The Washington Post on January 6, 2016 entitled IBM Extends Health Care Bet With Under Armour, Medtronic by Jing Cao and Michelle Fay Cortez. I will summarize and annotate this report and then pose some of my own questions.

The announcement and demo of this new app on January 6, 2016 at CES showed the process by which a patient’s data can be collected from their Medtronic devices and then combined with additional information from their wearable activity trackers and food intake. Next, all of this information is processed through Watson in order to “provide feedback” for the patient to “manage their diabetes”.

Present and Future Plans for The App and This Approach

Making the announcement were Virginia Rometty, Chairman, President and CEO of IBM, and Omar Ishrak, Chairman and CEO of Medtronic. The introduction of this technology is expected in the summer of 2016. It still needs to be submitted to the US government’s regulatory review process.

Ms. Rometty said that the capability to predict low BG events, in some cases up to three hours before they occur, is a “breakthrough”. She described Watson as “cognitive computing”, using algorithms to generate “prescriptive and predictive analysis”. The company is currently making a major strategic move into finding and facilitating applications and partners for Watson in the health care industry. (The eight Subway Fold posts cover other various systems and developments using Watson.)

Hooman Hakami, Executive VP and President, of the Diabetes Group at Medtronic, described how his company is working to “anticipate” how the behavior of each person with Diabetes affects their blood glucose levels. With this information they can then “make choices to improve their health”. Here is the page from the company’s website about their partnership with IBM to work together on treating diabetes.

In the future, both companies are aiming to “give patients real-time information” on how their individual data is influencing their BG levels and “provide coaching” to assist them in making adjustments to keep their readings in a “healthy range”. In one scenario, patients might receive a text message that “they have an 85% chance of developing low blood sugar within an hour”. This will also include a recommendation to watch their readings and eat something to raise their BG back up to a safer level.

My Questions

  • Will this make patients more or less diligent in their daily care? Is there potential for patients to possibly assume less responsibility for their care if they sense that the management of their diabetes is running on a form of remote control? Alternatively, might this result in too much information for patients to manage?
  • What would be the possible results if this app is ever engineered to work in conjunction with the artificial pancreas project being led in by Ed Damiano and his group of developers in Boston?
  • If this app receives regulatory approval and gains wide acceptance among people with diabetes, what does this medical ecosystem look like in the future for patients, doctors, medical insurance providers, regulatory agencies, and medical system entrepreneurs? How might it positively or negatively affect the market for insulin pumps and CGMs?
  • Should IBM and Medtronic consider making their app available on and open-source basis to enable other individuals and groups of developers to improve it as well as develop additional new apps?
  • Whether and how will insurance policies for both patients and manufacturers, deal with any potential liability that may arise if the app causes some unforeseen adverse effects? Will medical insurance even cover, encourage or discourage the use of such an app?
  • Will the data generated by the app ever be used in any unforeseen ways that could affect patients’ privacy? Would patients using the new app have to relinquish all rights and interests to their own BG data?
  • What other medical conditions might benefit from a similar type of real-time data, feedback and recommendation system?

NASA is Providing Support for Musical and Humanitarian Projects

"NASA - Endeavor 2", Image by NASA

“NASA – Endeavor 2”, Image by NASA

In two recent news stories, NASA has generated a world of good will and positive publicity about itself and its space exploration program. It would be an understatement to say their results have been both well-grounded and out of this world.

First, NASA astronaut Chris Hadfield created a vast following for himself online when he uploaded a video onto YouTube of him singing David Bowie’s classic Space Oddity while on a mission on the International Space Station (ISS).¹ As reported on the October 7, 2015 CBS Evening News broadcast, Hadfield will be releasing an album of 12 songs he wrote and performed in space, today on October 9. 2015. He also previously wrote a best-selling book entitled An Astronaut’s Guide to Life on Earth: What Going to Space Taught Me About Ingenuity, Determination, and Being Prepared for Anything (Little, Brown and Company, 2013). I highly recommend checking out his video, book and Twitter account @Cmdr_Hadfield.

What a remarkably accomplished career in addition to his becoming an unofficial good will ambassador for NASA.

The second story, further enhancing the agency’s reputation, concerns a very positive program affecting many lives that was reported in a most interesting article on Wired.com on September 28, 2015 entitled How NASA Data Can Save Lives From Space by Issie Lapowsky. I will summarize and annotate it, and then pose some my own terrestrial questions.

Agencies’ Partnership

According to a NASA administrator Charles Bolden, astronauts frequently look down at the Earth from space and realize that borders across the world are subjectively imposed by warfare or wealth. These dividing lines between nations seem to become less meaningful to them while they are in flight. Instead, the astronauts tend to look at the Earth and have a greater awareness everyone’s responsibilities to each other. Moreover, they wonder what they can possibly do when they return to make some sort of meaningful difference on the ground.

Bolden recently shared this experience with an audience at the United States Agency for International Development (USAID) in Washington, DC, to explain the reasoning behind a decade-long partnership between NASA and USAID. (This latter is the US government agency responsible for the administration of US foreign aid.) At first, this would seem to be an unlikely joint operation between two government agencies that do not seem to have that much in common.

In fact, this combination provides “a unique perspective on the grave need that exists in so many places around the world”, and a special case where one agency sees it from space and the other one sees it on the ground.

They are joined together into a partnership known as SERVIR where NASA supplies “imagery, data, and analysis” to assist developing nations.  They help these countries with forecasting and dealing “with natural disasters and the effects of climate change”.

Partnership’s Results

Among others, SERVIR’s tools have produced the following representative results:

  • Predicting floods in Bangladesh that gives citizens a total of eight days notice in order to make preparations that will save lives. This reduced the number to 17 during the last year’s monsoon season whereas previously it had been in the thousands.
  • Predicting forest fires in the Himalayas.
  • For central America, NASA created  a map of ocean chlorophyll concentration that assisted public officials in identifying and improving shellfish testing in order to deal with “micro-algae outbreaks” responsible for causing significant health issues.

SERVIR currently operates in 30 countries. As a part of their network, there are regional hubs working with “local partners to implement the tools”. Last week it opened such a hub in Asia’s Mekong region. Both NASA and USAID are hopeful that the number of such hubs will continue to grow.

Google is also assisting with “life saving information from satellite imagery”. They are doing this by applying artificial intelligence (AI)² capabilities to Google Earth. This project is still in its preliminary stages.

My Questions

  • Should SERVIR reach out to the space agencies and humanitarian organizations of other countries to explore similar types of humanitarian joint ventures?
  • Do the space agencies of other countries have similar partnerships with their own aid agencies?
  • Would SERVIR benefit from partnerships with other US government agencies? Similarly, would it benefit from partnering with other humanitarian non-governmental organizations (NGO)?
  • Would SERVIR be the correct organization to provide assistance in global environmental issues? Take for example the report on the October 8, 2015 CBS Evening News network broadcast of the story about the bleaching of coral reefs around the world.

 


1.  While Hatfield’s cover and Bowie’s original version of Space Oddity are most often associated in pop culture with space exploration, I would like to suggest another song that also captures this spirit and then truly electrifies it: Space Truckin’ by Deep Purple. This appeared on their Machine Head album which will be remembered for all eternity because it included the iconic Smoke on the Water. Nonetheless, Space Truckin‘ is, in my humble opinion, a far more propulsive tune than Space Oddity. Its infectious opening riff will instantly grab your attention while the rest of the song races away like a Saturn Rocket reaching for escape velocity. Furthermore, the musicianship on this recording is extraordinary. Pay close attention to Richie Blackmore’s scorching lead guitar and Ian Paice’s thundering drums. Come on, let’s go space truckin’!

2. These eight Subway Fold posts cover AI from a number of different perspectives involving a series of different applications and markets.

New Startup’s Legal Research App is Driven by Watson’s AI Technology

"Supreme Court, 60 Centre Street, Lower Manhattan", Image by Jeffrey Zeldman

[New York] “Supreme Court, 60 Centre Street, Lower Manhattan”, Image by Jeffrey Zeldman

May 9, 2016: An update on this post appears below.


Casey Stengel had a very long, productive and colorful career in professional baseball as a player for five teams and later as a manager for four teams. He was also consistently quotable (although not to the extraordinary extent of his Yankee teammate Yogi Berra). Among the many things Casey said was his frequent use of the imperative “You could look it up”¹.

Transposing this gem of wisdom from baseball to law practice², looking something up has recently taken on an entirely new meaning. According to a fascinating article posted on Wired.com on August 8, 2015 entitled Your Lawyer May Soon Ask for This AI-Powered App for Legal Help by Davey Alba, a startup called ROSS Intelligence has created a unique new system for legal research. I will summarize, annotate and pose a few questions of my own.

One of the founders of ROSS, Jimoh Ovbiagele (@findingjimoh), was influenced by his childhood and adolescent experiences to pursue studying either law or computer science. He chose the latter and eventually ended up working on an artificial intelligence (AI) project at the University of Toronto. It occurred to him then that machine learning (a branch of AI), would be a helpful means to assist lawyers with their daily research requirements.

Mr. Ovbiagele joined with a group of co-founders from diverse fields including “law to computers to neuroscience” in order to launch ROSS Intelligence. The legal research app they have created is built upon the AI capabilities of IBM’s Watson as well as voice recognition. Since June, it has been tested in “small-scale pilot programs inside law firms”.

AI, machine learning, and IBM’s Watson technology have been variously taken up in these nine Subway Fold posts. Among them, the September 1, 2014 post entitled Possible Futures for Artificial Intelligence in Law Practice covered the possible legal applications of IBM’s Watson (prior to the advent of ROSS), and the technology of a startup called Viv Labs.

Essentially, the new ROSS app enables users to ask legal research questions in natural language. (See also the July 31, 2015 Subway Fold post entitled Watson, is That You? Yes, and I’ve Just Demo-ed My Analytics Skills at IBM’s New York Office.) Similar in operation to Apple’s Siri, when a question is verbally posed to ROSS, it searches through its data base of legal documents to provide an answer along with the source documents used to derive it. The reply is also assessed and assigned a “confidence rating”. The app further prompts the user to evaluate the response’s accuracy with an onscreen “thumbs up” or “thumbs down”. The latter will prompt ROSS to produce another result.

Andrew Arruda (@AndrewArruda), another co-founder of ROSS, described the development process as beginning with a “blank slate” version of Watson into which they uploaded “thousands of pages of legal documents”, and trained their system to make use of Watson’s “question-and-answer APIs³. Next, they added machine learning capabilities they called “LegalRank” (a reference to Google’s PageRank algorithm), which, among others things, designates preferential results depending upon the supporting documents’ numbers of citations and the deciding courts’ jurisdiction.

ROSS is currently concentrating on bankruptcy and insolvency issues. Mr. Ovbiagele and Mr. Arruda are sanguine about the possibilities of adding other practice areas to its capabilities. Furthermore, they believe that this would meaningfully reduce the $9.6 billion annually spent on legal research, some of which is presently being outsourced to other countries.

In another recent and unprecedented development, the global law firm Dentons has formed its own incubator for legal technology startups called NextLaw Labs. According to this August 7, 2015 news release on Denton’s website, the first company they have signed up for their portfolio is ROSS Intelligence.

Although it might be too early to exclaim “You could look it up” at this point, my own questions are as follows:

  • What pricing model(s) will ROSS use to determine the cost structure of their service?
  • Will ROSS consider making its app available to public interest attorneys and public defenders who might otherwise not have the resources to pay for access fees?
  • Will ROSS consider making their service available to the local, state and federal courts?
  • Should ROSS make their service available to law schools or might this somehow impair their traditional teaching of the fundamentals of legal research?
  • Will ROSS consider making their service available to non-lawyers in order to assist them in represent themselves on a pro se basis?
  • In addition to ROSS, what other entrepreneurial opportunities exist for other legal startups to deploy Watson technology?

Finally, for an excellent roundup of five recent articles and blog posts about the prospects of Watson for law practice, I highly recommend a click-through to read Five Solid Links to Get Smart on What Watson Means for Legal, by Frank Strong, posted on The Business of Law Blog on August 11, 2015.


May 9, 2016 Update:  The global law firm of Baker & Hostetler, headquartered in Cleveland, Ohio, has become the first US AmLaw 100 firm to announce that it has licensed the ROSS Intelligence’s AI product for its bankruptcy practice. The full details on this were covered in an article posted on May 6, 2016 entitled AI Pioneer ROSS Intelligence Lands Its First Big Law Clients by Susan Beck, on Law.com.

Some follow up questions:

  • Will other large law firms, as well as medium and smaller firms, and in-house corporate departments soon be following this lead?
  • Will they instead wait and see whether this produces tangible results for attorneys and their clients?
  • If so, what would these results look like in terms of the quality of legal services rendered, legal business development, client satisfaction, and/or the incentives for other legal startups to move into the legal AI space?

1.  This was also the title of one of his many biographies,  written by Maury Allen, published Times Books in 1979.

2.  For the best of both worlds, see the legendary law review article entitled The Common Law Origins of the Infield Fly Rule, by William S. Stevens, 123 U. Penn. L. Rev. 1474 (1975).

3For more details about APIs see the July 2, 2015 Subway Fold post entitled The Need for Specialized Application Programming Interfaces for Human Genomics R&D Initiatives

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

IMAG0082

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.

 

How Robots and Computer Algorithms are Challenging Jobs and the Economy

"p8nderInG exIstence", Image by JD Hancock

“p8nderInG exIstence”, Image by JD Hancock

A Silicon Valley entrepreneur named Martin Ford (@MFordFuture) has written a very timely new book entitled Rise of the Robots: Technology and the Threat of a Jobless Future (Basic Books, 2015), which is currently receiving much attention in the media. The depth and significance of the critical issues it raises is responsible for this wide-beam spotlight.*

On May 27, 2015 the author was interviewed on The Brian Lehrer Show on radio station WNYC in New York. The result is available as a truly captivating 30-minute podcast entitled When Will Robots Take Your Job?  I highly recommend listening to this in its entirety. I will sum up. annotate and add some questions of my own to this.

The show’s host, Brian Lehrer, expertly guided Mr. Ford through the key complexities and subtleties of the thesis of his provocative new book. First, for now and increasingly in the future, robots and AI algorithms are taking on increasingly difficult task that are displacing human workers. Especially for those jobs that involve more repetitive and routine tasks, the more likely it will be that machines will replace human workers. This will not occur in just one sector, but rather, “across the board” in all areas of the marketplace.  For example, IBM’s Watson technology can be accessed using natural language which, in the future, might result in humans no longer being able to recognize its responses as coming from a machine.

Mr. Ford believes we are moving towards an economic model where productivity is increasing but jobs and income are decreasing. He asserts that solving this dilemma will be critical. Consequently, his second key point was the challenge of detaching work from income. He is proposing the establishment of some form of system where income is guaranteed. He believes this would still support Capitalism and would “produce plenty of income that could be taxed”. No nation is yet moving in this direction, but he thinks that Europe might be more amenable to it in the future.

He further believes that the US will be most vulnerable to displacement of workers because it leads the world in the use of technology but “has no safety net” for those who will be put out by this phenomenon. (For a more positive perspective on this, see the December 27, 2014 Subway Fold post entitled Three New Perspectives on Whether Artificial Intelligence Threatens or Benefits the World.)

Brian Lehrer asked his listeners to go to a specific page on the site of the regular podcast called Planet Money on National Public Radio. (“NPR” is the network of publicly supported radio stations that includes WNYC). This page entitled Will Your Job be Done by a Machine? displays a searchable database of job titles and the corresponding chance that each will be replaced by automation. Some examples that were discussed included:

  • Real estate agents with a 86.4% chance
  • Financial services workers with a 23% chance
  • Software developers with a 12.8% chance

Then the following six listeners called in to speak with Mr. Ford:

  • Caller 1 asked about finding a better way to get income to the population beyond the job market. This was squarely on point with Mr. Ford’s first main point about decoupling income and jobs. He was not advocating for somehow trying to stop technological progress. However, he reiterated how machines are “becoming autonomous workers, no longer just tools”.
  • Caller 2 asked whether Mr. Ford had seen a YouTube video entitled Humans Need Not Apply. Mr. Ford had seen it and recommended it. The caller said that the most common reply to this video (which tracks very closely with many of Mr. Ford’s themes), he has heard was, wrongly in his opinion, that “People will do something else”. Mr. Ford replied that people must find other things that they can get paid to do. The caller also said that machine had made it much easier and more economical for his to compose and record his own music.
  • Caller 3 raised the topic of automation in the medical profession. Specifically, whether IBM’s Watson could one day soon replace doctors. Mr. Ford believes that Watson will have an increasing effect here, particularly in fields such as radiology. However, it will have a lesser impact in those specialties where doctors and patients need to interact more with each other. (See also these three recent Subway Fold posts on the applications of Watson to TED Talks, business apps and the legal profession.)
  • Caller 4 posited that only humans can conceive ideas and be original. He asked about how can computers identify patterns for which they have not been programmed. He cited the example of the accidental discovery of penicillin. Mr. Ford replied that machines will not replace scientists but they can replace service workers. Therefore, he is “more worried about the average person”. Brian Lehrer then asked him about driverless cars and, perhaps, even driverless Uber cabs one day. Mr. answered that although expectations were high that this will eventually happen. He is concerned that taxi drivers will lose jobs. (See this September 15, 2014 Subway Fold post on Uber and the “sharing economy”.)  Which led to …
  • Caller 5 who is currently a taxi driver in New York. They discussed how, in particular, many types of drivers who drive for commerce are facing this possibility. Brian Lehrer followed-up by asking whether this may somehow lead to the end of Capitalism. Mr. Ford that Capitalism “can continue to work” but it must somehow “adapt to new laws and circumstances”.
  • Caller 6 inquired whether one of the proposals raised in VR pioneer Jaron Lanier’s book entitled Who Owns the Future (Simon & Schuster, 2013), whereby people could perhaps be paid for the information they provide online. This might be a possible means to financially assist people in the future. Mr. Ford’s response was that while it was “an interesting idea” it would be “difficult to implement”. As well, he believes that Google would resist this. He made a further distinction between his concept of guaranteed income and Lanier’s proposal insofar he believes that “Capitalism can adapt” more readily to his concept. (I also highly recommend Lanier’s book for its originality and deep insights.)

Brian Lehrer concluded by raising the prospect of self-aware machines. He noted that Bill Gates and Stephen Hawking had recently warned about this possibility. Mr. Ford responded that “we are too far from this now”. For him, today’s concern is on automation’s threat to jobs, many of which are becoming easier to reduce to a program.

To say the very least, to my own organic and non-programmatic way of thinking, this was an absolutely mind-boggling discussion. I greatly look forward to this topic will continue to gather momentum and expanded media coverage.

My own questions include:

  • How should people at the beginning, middle and end of their careers be advised and educated to adapt to these rapid changes so that they can not only survive, but rather, thrive within them?
  • What role should employers, employees, educators and the government take, in any and all fields, to keep the workforce up-to-date in the competencies they will need to continue to be valuable contributors?
  • Are the challenges of automation most efficiently met on the global, national and/or local levels by all interested contingencies working together? What forms should their cooperation take?

*  For two additional book reviews I recommend reading ‘Rise of the Robots’ and ‘Shadow Work’ by Barbara Ehrenreich in the May 11, 2015 edition of The New York Times, and Soon They’ll Be Driving It, Too by Sumit Paul-Choudhury in the May 15, 2015 edition of The Wall Street Journal (subscription required).