IMRSV, Inc Provides Facial Analytics

Cara is coming to a brick-and-mortar store near you. But don't be insulted when she doesn't recognize who you are.

Recently, I met with Jason Sosa, the CEO and Founder of IMRSV, Inc… twice. What came through to me was his passion for understanding the societal and human impacts of the technologies he creates and brings to market. This passion makes their mantra of and adherence to "privacy by design" very real and central to their approach.

Cara is the core software product from IMRSV, Inc. Cara analyses your face, and determines demographic, attention and emotive statistics about you, without attempting to identify you. As IMRSV states, Cara turns any connected camera into an intelligent sensor, but does so anonymously. Move from one Cara camera to another, or move away and back again to the same Cara camera, and the temporary ID number associated with you changes.

While Cara is pre-launch, I'm excited both by the technology, and by the IMRSV, Inc business model. The business model is very simple, whether a small shop owner or a developer interested in using Cara as part of a sensor analytics ecosystem, you pay $39.95 per camera, which includes the stand-alone Cara software and the Cloud-based data-as-a-service. The possibilities presented by Cara are what really got me going, fueling both an exciting initial briefing and a follow-up four-hour "lunch" and demo.

  • small shop owners now have demographic information available to them that was previously beyond their reach
  • larger organizations can better understand why campaigns in the physical world succeed or fail, just as they can online
  • developers can build attention and emotions into their sensor platforms

What's does any of this mean? Here's a few examples.

  • While Cara doesn't recognize a person, it can say that the person who bought X at the register was a young adult male or the person who bought Y in the drive-through was a senior female, based upon time stamps.
  • Online retailers have long established ways of determining who is buying what online. Brick-and-mortar stores have tried to do the same through loyalty programs. Privacy concerns have affected adoption of loyalty cards. By anonymously providing demographic and attention data, physical stores can glean the same understanding of their customers as the online versions, without violating privacy.
  • End-cap displays at retailers or kiosks at events can provided targeted messages, increasing effectiveness.
  • A simple toy, using a smartphone to draw a face and its camera to record the smile of a child, can smile back.
  • A car can respond to a driver glancing away from the road, perhaps "kicking" the driver in the butt through a vibration motor in the seat.
an image of the Cara software player
The Cara Player

In addition to starting companies, Jason is very interested in the Singularity, and the impending impact of technology upon human employment and self-identity. This has led both to the "Privacy by Design" and "Principles of Good Use" for developers/partners. If you don't believe me, maybe you'll believe Jules Polonetsky.

"Privacy by design solutions are critical to implementing new technologies in a world were data collection has become ubiquitous. Steps that Cara takes such as not collecting any personal information, and not storing, transferring or recorded any images are key to ensuring privacy concerns are addressed as these technologies are rolled out.”
- Jules Polonetsky
- Facebook.com/FutureofPrivacy
- @JulesPolonetsky

There are various pieces of research out there that show that the Internet of Things will be a 15 trillion dollar market right now. By 2020, I strongly believe that there will be over a trillion sensors deployed and that if your "thing" isn't connected, it won't be a viable product. Companies like IMRSV, Inc are providing the ecosystem to allow sensor analytics from everyday objects at very affordable prices. This will push this market even further and faster than the pundits anticipate. So, let me put on my tinfoil hat and stand on my soap box:

  1. Every conceivable facet of everyday life will take advantage of connected data for informed decisions
  2. Current sensor, internet of things, and data management & analytic companies will be assimilated into sensor analytics ecosystems or die
  3. Privacy will be a major concern for those who care, but the majority don't know enough to care. Individuals like Jason and Jules will protect the masses and ensure adherence to privacy by design guidelines.

New Hope from Big Data

Big Data is a catchy phrase. Unfortunately, it is often misused and misunderstood. Often, Hadoop and Big Data are used interchangeably; as if the Apache Hadoop family of projects are the only solutions for Big Data, or that that only use for these projects is from Big Data. Neither is true.

As an EDW/BI practitioner, I watched the Hadoop, or really, the Map/Reduce framework, be embraced and forced into being by software developers who were frustrated by Structured Query Language (SQL) and the need to create Entity-Relationship Diagrams (ERD) as data models or schæmas. They were equally unhappy with the various work-arounds to access Relational Database Management Systems from within their programs, such as Object Relational Models (ORMs) and Data Access Objects (DAOs). At first, I felt that these developers were simply lazy.

However, as I worked more with these so-called NoSQL technologies, it helped to clarify the dissatisfaction that I felt during the years I was leading EDW and BI projects. Thirty years ago, I worked in Aerospace System Engineering, developing methods and algorithms for risk assessment using Bayesian statistics. But, by 1996, I became involved in my first EDW project. Since then, the actual structure and functions associated with the data - defined by the data, became less important than fitting the data into an artificial structure imposed by business process models.

Don't get me wrong. Relational algebra, relational calculus and the DBMS technologies that came out of this mathematics, are all very useful. And, in the right hands, SQL is a very powerful language. ERDs provide a wonderful way to map data to business processes and to both transactional and analytic systems.

But… There is so much more that can be done with the data coming from traditional human-to-machine (H2M) interactions, but increasingly from human-to-human (H2H), machine-to-machine (M2M) and machine-to-human (M2H) exchanges. The interweaving of the flows of data from such disparate sources is what drives my research today.

  • Gamification driving the adoption of smart meters for utilities
  • Self-quantification use cases in the workplace
  • Sustainability for increasing the bottom line
  • Combing social media and sensor data for profitability
  • Sensor analytics as an ecosystem

These, and over 70 other use cases that I'm cataloguing, come from the innovation surrounding hype of Big Data, and the Data Science movement. In a recent Quark, I've classified this innovation into 11 areas. A compete mindmap is linked from the initial mindmap shown below, and in the report.

A Mind Map of the 11 Big Data Innovation Trends
A Mindmap of the 11 Innovation Trends from Big Data

The Quark covers the trends coming from these innovations, and develops the four keys required to bring valuable decision making processes into your organization from these innovations. It's entitled "Big Data: It's Not the Size, It's How You Use It". For such a simple report, it took over 8 months to develop. Mostly this delay was caused by the fast-paced evolution of the innovations. The executive summary from the Quark is linked from the title.

I hope that you find that information, as well as the mindmap, useful in incorporating inference, prediction, insight and performance with intuition for making better decisions.

DataGrok

For all the silliness surrounding Big Data and Data Science, all the hype and all the controversy, there are actually very innovative and disruptive technologies coming from this area, this new approach to data management and analytics [DMA]. How do we categorize the vendors or the technologies that have never existed before?

Predictives

One new area is Predictive Analytics, also called Predictive Intelligence. Since predictions are not analytics, as the term is used in BI, and certainly not the Intelligence used in BI, I don't like either, but prefer the simpler "Predictives". Four companies with which I've had briefings, fall into the Predictives category, but each of these companies have very different approaches and technologies for performing predictives. These companies are Opera Solutions, Alpine Data Labs, INRIX and Zementis. There are other companies that I'll include in a full report after receiving briefings, such as KXEN, Soft10 and Numenta. By the way, Numenta's product is named "Grok". Given their differences, do they really all belong in the same category?

Opera Solutions: Acting on petabytes of data, Opera Solutions provides a signal hub stack starting with data management, going through pattern matching in the signal layer, and, enhanced by their own Data Science teams, resulting in predictions and inferences for better decisions for enterprise advantage, understanding the "signal" is more important than the underlying technology, to actually create front line productivity through signals manifesting and adjusting "gut feel" where machines don't direct humans but do the heavy lifting.

Alpine Data Labs: Alpine Data Labs brings mathematical, statistical and machine learning predictive methods to the data in situ, no matter how small nor how big the data sets, within a variety of RDBMS technologies and Hadoop distributions. Alpine Data Labs helps data science teams address the data where it lays, across data types and functional areas, working with all the data to bring insight to bear on better decisions.

INRIX: INRIX data science teams and technology provides unique predictives using connected cars, connected devices and connected people.

Zementis: Zementis brings predictive modeling into decision management through their data science teams, Adapa product and strong commitment to the predictive markup modeling language [PMML]. Through partners and customers Zementis works with traditional and innovative data sources to provide decision management from predictives, data mining and machine learning for marketing solutions, financial services, predictive maintenance and energy/water sustainability.

DataGrok

One of the more interesting things to come out of data science is how do you really understand the data that is being gathered and presented. Two of the companies with which I've recently have had briefings, challenge the categories of Data Discovery or Data Exploration. However, each of these companies have different technologies, and different approaches to fully, deeply understanding your data, and to being able to draw conclusions from the data before doing other, more formal analytics. Over the past month, I've had the good fortune of having very in-depth, in-person briefings by both of these companies. Both of these companies are helping those who need it most to truly, fully, deeply, easily understand their data. These approaches, while very, very different, both constitute an entirely new category. Beyond data discovery, beyond data exploration, I call this new category Data Grokking.

"Grok" as I wrote in 2007, means to

"to fully and deeply understand"; [but to you need some background on the word's origins]. It's Martian and not from any Terran language at all. It comes from the fertile mind of Robert A. Heinlein, and was brought to Earth by Valentine Michael Smith in Heinlein's wonderful 1961 novel Stranger in a Strange Land.

One of these companies is still in stealth mode, and I won't mention their name here. The other is Ayasdi, and Ayasdi takes a very, very interesting approach to grokking your data.

These two very different technologies, based upon very different science and mathematics, do indeed allow us to fully and deeply understand our data. Much like the Martian ceremony, the DataGrok allows us to mentally ingest our data, to realize creative insights from our data sets, and to recognize the fundamental interweaving among the data, that, prior to these two innovative firms, could only come about through a long, arduous struggle with the data sets.

As I mentioned, the one company is still in stealth mode, so I'll write about Ayasdi here.

Ayasdi

Ayasdi comes out of the intersection of Topology and Computer Science, as brought together by a Stanford Professor, Gunnar Carlsson, and Gurjeet Singh. The project started as a DARPA contract that has spanned more than four years, comptop. The CompTop project included Duke, Rutgers & Stanford nodes. Topological methods discover the structure of the data - this is somewhat analogous to, but not the same as the probabilistic or cumulative distribution or density functions [pdf, PDF, cdf or CDF].

Ayasdi is focused on four markets:

  1. Pharmaceuticals, Healthcare and Biotech
  2. Oil & gas
  3. Government
  4. Financial Services

From this, you can see that Ayasdi customers go after expensive data, i.e. expensive to collect, expensive to use. Iris is the front end to the Ayasdi Platform, and while available as a private cloud, their offering is primarily SaaS.

The analyst community is trying to figure out where to put Ayasdi, thus my category of DataGrok. Another area of confusion is "What is the right tool of each step of the process from DataGrok to inferences and predictions?" Some of this stems from mistrust of machines, but we need machines that do more than count and sort, we need machines that help us to find insight and improve performance.

Sensors Sensors Everywhere

A sensor is anything that can create data about its environs. A more formal definition is

a device that detects or measures a physical property and records, indicates, or otherwise responds to it -New Oxford American Dictionary

A very simple example is a thermocouple.

A picture of a k-type thermocouple showing the standard connector
This is a picture of a k-type thermocouple taken from the FAA under a CC By license

Essentially, two metals are bound together such that when the environment around this wire becomes hotter or colder, the metals produce a voltage. Through this thermoelectric effect, this strain translate into a voltage differential across the wire, producing an electrical signal. A simple voltmeter can read this signal, and one could calibrate that electrical signal to be read as degrees of temperature change.

You likely have one of these in your home thermostat. Perhaps you have a very simple thermostat that turns your home heater on and off.

A picture of an older home thermostat with cover removed
This is a picture of an older model, simple home thermostat, with the cover removed, showing the inner workings, under a CC By license

Perhaps you have a more complex, programmable thermostat that can control the temperature and humidity of your home through a furnace, air conditioner, humidifier/dehumidifier and fans, with different settings for different times of the day and days of the week.

This is a picture of an advanced Honeywell Programmable Thermostat
This is a picture of an advanced Honeywell Programmable Home Thermostat with a green backlit LCD display from the Honeywell website.

Perhaps you have something that looks very simple, but is now part of a complex system that includes not only your home HVAC system, but your computer and smartphone, and computers and analytic software at your utility company.

This is a picture of the very advanced Nest home thermostat.
This is a picture of the very advanced Nest home thermostat, which looks very simple but connects to your computers, smartphones, tablets and more, from the Nest website press downloads.

And this progression is why the Internet of Things is about to explode with Connected Data, with sensors being the new nerve endings of an increasingly intelligent world.

A Section of my Internet of Things mindmap showing the sensor branches
This is a section of my Internet of Things mindmap showing just the sensor branches.

Imagine sensors streaming Connected Data from your home entertainment system, refrigerator & most of its contents, toaster, coffee maker, alarm clock, garden, irrigation, home security, parking on the street in front of your home, traffic flowing by your home to your destination, air quality, and so much more.

We will interact with the world around us in ways that will change our decision making processes in our personal lives, in business, and in the regulatory processes of governments.

If you want to learn more, join IBM and my fellow panelists on Thursday, Sept. 13, from 4 to 5 p.m. ET to chat about cloud and the connected home using hashtag #cloudchat.

The Internet of Things and Change

Will You Be Ready For the M2M World?

The Internet of Things, the Connected World, the Smart Planet… All these terms indicate that the number of devices connected to, communicating through, and building relationships on the Internet has exceeded the number of humans using the Internet. But what does this really mean? Is it about the number of devices, and what devices? Is it about the data, so much data, so fast, so disparate, that will make current big data look like teeny-weeny data?

I think that it's about change: the way we live our lives, the way we conduct business, the way we walk down a street, drive a car, or think about relationships. All will change over the next decade:

  1. Sensors are everywhere. The camera at the traffic light and overseeing the freeway; those are sensors. That new bump in the parking space and new box on the street lamp; those are sensors. From listening for gun shots to monitoring a chicken coop, sensors are cropping up in every area of your life.
  2. Machine to Machine [M2M] relationships will generate connected data that will affect every aspect of your life. Connected Data will be used to fine-tune predictives that will prevent crimes, anticipate your next purchase and take over control of your car to avoid traffic jams. The nascent form of this is already happening: Los Angeles and Santa Cruz police are using PredPol to predict & prevent crimes, location aware ads popping up in your favorite smartphone apps, and Nevada and California are giving driver licenses to robotic cars.
  3. Sustainability isn't about saving the planet, it's about saving money. Saving the planet, reducing dependence on polluting energy sources and reducing waste in landfills are all good things, but they aren't part of the fiduciary responsibilities of most executives. However, Smart Buildings, recycling & composting, and Green IT all increase a company's bottom line and that does fall under every executive's fiduciary goals.

Making Sense of Inter-Connectedness - Introducing My Internet of Things Mind Map

As you can tell from the mindmap associated with this post, I've been thinking about the Internet of things quite a bit lately. It's a natural progression for me. I'm fascinated by all the new sensors, the Connected Data [you heard it here first] that will swamp Big Data, the advances in data management and analytics that will be needed, the impact upon policy and regulation, and the vision of the people and companies bringing about the Internet of Things. But more, as I've been reading and thinking about the SmartPlanet, SmartCities, SmartGrid and SmartPhones, and that ConnectedData, I realized that I can never look at the world around me in the same way again.

Let's look at some of the "facts" [read guesses] that have been written about the IoT.

Looking to the future, Cisco IBSG predicts there will be 25 billion devices connected to the Internet by 2015 and 50 billion by 2020. From The Internet of Things: How the Next Evolution of the Internet Is Changing Everything by Dave Evans, April 2011 [links to PDF]

Between 2011 and 2020 the number of connected devices globally will grow from 9 billion to 24 billion as the benefit of connecting more and varied devices is realised. The Connected Life: A USD4.5 trillion global impact in 2020, [links to PDF] February 2012 by Machine Research for the GSMA.

Two different estimates, one of 24 billion devices of many different types, connected by wireless broadband, and one of 50 billion mobile devices using different types of cellular networks, all by the year 2020. And neither of these estimates include the trillions of other types of things that will deployed over the next eight years. Trillions, not billions, using a variety of personal, local, and wide-area wireless networks.

My Focus Starts at The Intersection of Sensors, Analytics and Smart Cities, with Energy Management and Sustainability

One of the things that will change over time is the way that I look at the Internet of Things. All of it is interesting. But for now, I'll be focusing on the intersection of Sensors, Analytics and Smart Cities, with Energy Management and Sustainability.

Count RFID, Zigbee, MEMS, Smartdust and more traditional sensors, Robots, autonomous vehicles, Healthcare monitors, Smart Meters and more, being distributed in cities, cars, factories, trains, farms, planes, animals and people, and the number of connected devices in 2020 will be in the trillions. Data generated by less than one billion humans using the Internet a few times a day swamped traditional data management & analytics systems, spawning "Big Data". Trillions of devices updating ConnectedData every few nanoseconds will indeed change everything.

Of paramount importance moving forward is determining how to extract business, personal and social value from the intersections, interfaces and interstices of the infrastructure, connected data, objects and people building relationships through the Internet of Things.

Come join me as I look at this convergence and the business impact ahead of us.

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The TeleInterActive Press is a collection of blogs by Clarise Z. Doval Santos and Joseph A. di Paolantonio, covering the Internet of Things, Data Management and Analytics, and other topics for business and pleasure. 37.540686772871 -122.516149406889

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