Adding Creativity to Engineering.

By Tom Imerito

3D Printed Robotic Hand

3D-Printed Robotic Hand
Credit: Oak Ridge National Laboratory

Since that day long ago when one of our early ancestors knapped a chunk of flint into a projectile point, manufacturing has largely entailed removing unwanted material from a larger mass until the desired item emerged amidst a pile of scrap. Through the millennia, as flint-nappers evolved into stone cutters, wood workers, iron masters and manufacturing engineers, their tools became increasingly capable of producing complex shapes.

The trend to complexity culminated in the twentieth century’s CNC machines which took the handiwork out of the manufacturing equation [Continue Reading…]

Ancient and Modern Printing Technologies Combine to Solve a Stubborn Nano-Fabrication Problem.

By Tom Imerito

UCLA-CNSI -K copyIn the half-century since Nobel Laureate, Richard Feynman gave his landmark, 1959 lecture, “There’s Plenty of Room at the Bottom,” the field of nanotechnology has come a long way. As Professor Feynman prophesied, microscopes 100 times more powerful than those available then have become a reality. Today, scientists can pick up individual atoms, put them exactly where they want, and take pictures to prove they did it.

Nevertheless, the grand challenge of scaling bottom-up manufacturing to commercial production levels has remained elusive. [Continue Reading…]

by Tom Imerito
Originally Published in Pittsburgh Quarterly – Summer 2014

PQ privacy ImageI first became aware that my online privacy wasn’t nearly as confidential as I thought while shopping online with my sister who lives in Florida. Separated by 1,000 miles, phones pressed to our ears, eyes glued to computer screens, my price for a particular web cam was a bargain at $3.37; hers was $4.66, over $1 more than mine. After checking to make sure we were viewing the exact same web page with the exact same Internet address, I couldn’t help but conclude that we had been sized up by a computer algorithm somewhere in cyberspace and offered different prices for the same piece of merchandise—all within a fraction of a second. The experience made me wary. I wanted to know more. [Continue Reading…]

Pittsburgh’s Leadership in Artificial Intelligence Is Spawning the Technologies of Tomorrow

“JACK” – The Seegrid Intelligent Pallet Jack maps the ceiling as it traverses a previously traveled path and compares it to images stored in its map atlas to find its way around a warehouse.

by Tom Imerito

As I passed through the double doors to Seegrid’s manufacturing floor a driverless robotic pallet jack that was headed toward me stopped abruptly.  A horizontal bank of orange LEDs blinking above a row of steadily lit green ones at the top of its control module gave me the impression that the eight-foot mechanical giant was thinking – which it was – but not about me.   My host, Seegrid’s Director of Research and Development of Controls and Capabilities, Dr. Darrin Bentivegna, told me that the machine was going through a pre-shipment burn-in.  I had coincidentally entered the room just as “Jack,” (as I came to anthropomorphize the robotic Titan) was pausing in response to a command in his memory

I was excited, because I was about to train Jack to travel a new course.  It would be my first hands-on experience with an intelligent machine.  Training Jack was as easy as grasping his control handle and leading him around a corner, stopping to drop of a pallet, sounding his horn and returning to the starting point.

As I walked ahead of him, the four pairs of stereographic cameras mounted beneath his LEDs captured hundreds of images of the surrounding environment.  As we walked, Jack’s onboard processor converted the images into digital maps which he would retrieve from his memory and compare with the images he collected on his next trip along the newly navigated course.

When we finished the training run I sent Jack off on his own.  He retraced the course perfectly, gathering new image maps as he went and comparing them with the ones he had saved during our training run.  In response to differences in the maps he continuously corrected his course while creating new maps of his surroundings for use on his next trip.

Jack’s comparison of the maps in his memory to the maps he sees at any given moment and his consequent course-correction is emblematic of artificial intelligence (AI).  At its most basic level AI compares data from different sources and modifies activity based upon the difference between them.  Data can be created on the fly by sensing the environment, as with jack’s stereo cameras, or it can be searched and retrieved from a data base, as with jack’s memory atlas.

Although the differences between the way machines and humans think are vast, it is convenient to think about artificial intelligence as machines learning in a step-wise fashion by – 1) acquiring data by either sensing or searching; 2) comparing the data to find the most likely solution to a problem; 3) deciding on the best course of action (including providing an answer, changing the formula or algorithm being used to solve the problem, looking somewhere else or starting all over and trying something else). Although such a tedious way of thinking about everything we do would likely drive most human minds crazy, computers are so much faster than we humans they can do tedious tasks over and over again without a blink of their a digital-camera eyes.

Jack is the embodiment of the vision of Seegrid founder and world renowned roboticist, Hans Moravec who, in his 1988 book, Mind Children, envisioned a future world in which intelligent machines perform the mundane tasks of civilized life.  Today Moravec’s vision is becoming reality in Pittsburgh where, in addition to Seegrid’s’ robotic industrial trucks, a host of artificial intelligence companies are engaged in activities as varied as building instant online communities, translating languages, training health care professionals, teaching math, improving driver performance, inspecting sewers and helping people lose weight and stay fit.


CMU spinout, FlashGroup creates instantaneous topical interest communities based upon a user’s current browsing habits.  The company’s widget scours a user’s recent web history for the central topic of interest while he or she is engaged with it then searches the web for related resources.  By comparing a user’s current topic with millions of related resources such as expert web pages, social media discussions and individuals looking at similar web pages at the same moment, FlashGroup provides an instantaneous battery of resources related to a user’s interest.  Just as Jack compares maps, FlashGroup compares user behavior with related web resources.  Unlike search engines which rely on words typed by a user, FlashGroup’s widget decides what a user is interested in by assessing web activity on a moment-by-moment basis.

The company is currently working on a confidential basis with a number of well known companies and plans to open its digital gateway to smaller users soon.


Taking AI on a tour of the world, Carnegie Mellon spinoff Safaba Translation Solutions employs parallel text processing to automatically translate corporate communications from one language to another at levels of accuracy approaching human translations.   Similar to the way Jack compares one map to another to navigate, Safaba compares large bodies of human-translated digital texts in both the original and translated versions to produce a customized translation engine.  The translation engine utilizes statistical models that it learns from the side-by-side comparison of texts in two languages to translate new texts automatically.  Safaba fills a niche in corporate localization efforts by enabling the high-speed translation of institutional communications such as web pages, chat sessions and other real-time communications into a unique a corporate language which is highly precise at the same time it is friendly and comfortable to local stakeholders around the world.

The company is currently providing automated translation services to several household-name global organizations.


Employing a form of artificial intelligence called synthetic interview, CMU spinout, MedRespond has developed an inexpensive training application for health care professionals.  The MedRespond system is composed of a data base of hundreds of pre-recorded video responses to thousands of issues typically posed by patients to health care professionals such as doctors, pharmacists, nurses, and technicians.

In practice, an actor/patient presents questions, symptoms and responses in the form of pre-recorded videos, to which a health care provider responds by keyboard in real time.  The response is analyzed by another AI technique, called natural language processing which analyzes the words in a sentence by occurrence, meaning, position, frequency, and proximity to other words, to establish both the literal meaning of the response, as well as its tone.  As a result of its analysis, the program makes a decision about which video response to present next.  The machine’s response leads to a reply by the human trainee, which, in turn leads to another – just like a human conversation.  MedRespond vastly reduces the cost of training health care professionals by eliminating the redundancy of human trainers responding over and over to the same issues, face-to-face, with hundreds or thousands of health care professionals.

The company is currently conducting a pharmacist training program with drug retailer Rite Aid Corporation.


If AI can be used to train health care professionals, it would seem a simple matter to use it to teach kids in school.  Located in downtown Pittsburgh, Think Though Math (TTM) is doing just that.  The company is web-tutoring students in math in grades four through first-year algebra.  The TTM system uses online tests to determine a student’s performance level or zone of proximal development (ZPD) and gauges lessons and tests to make the subject matter ever-so-slightly beyond a student’s current grasp, but definitely reachable with some effort.  TTM’s algorithm compares an individual student’s accuracy and response time with normalized tables to determine when a he or she needs a hint, a chapter review or, when completely stumped, intervention by a human tutor.  Upon meeting the algorithm’s standard for subject mastery the student goes to the next level.  Students earn points redeemable for consumer products as a reward for excellence.

According to the company’s vice president of marketing, Peter Cipkowski, “Think Through Math is having a positive impact on tens of thousands of students every day – and growing.”


If earning points can get school students to perform better, shouldn’t it work for – let’s say truck drivers?  Pittsburgh’s Propel IT is proving that it most cases it does.  Propel IT employs the data already captured by the on-board computers legally required in every over-the-road truck to increase fuel efficiency by modifying driver behavior.  By collecting about 100 of the 4,000 variables measured by a truck’s computers, and comparing them with a self-learning data base of driving behaviors and fuel efficiencies, the Propel IT system decides who is driving efficiently and who is not.  The system’s machine-learning algorithm modifies itself based upon differences between a driver’s behavior, the nature of the load and trip as well as fleet-wide performance.   Off the road, the system suggests ways for drivers to improve their personal driving habits and allows them to earn points redeemable for cash and gifts for improved performance.

Of the 25, 000 trucks currently using or testing Propel IT, fuel consumption has dropped by an average of five percent.  When multiplied out over the 8 million trucks on U.S. highways each running 100,000 miles a year, Propel IT’s potential national energy savings comes to more than $37 billion in diesel fuel per year.


Easily the least glamorous, but possibly the most profitable of Pittsburgh’s AI ventures, RedZone Robotics provides services that take place beneath the road – robotic sewer inspection services.  RedZone’s robots detect the condition of a sewer pipe’s structural integrity and interior condition with digital cameras, laser scanners, chemical and inertial sensors, and sonar to create a digital record of a length of pipe ranging from a few feet to a several miles.  The autonomous devices are powered by lithium ion batteries, controlled by software in an on-board CPU, and propelled by neoprene tracks.

Once lowered into a sewer the machine follows a pipe for a pre-programmed distance, gathering visual, chemical and mechanical information about the pipe’s geographic location, physical condition, damage and obstructions – information which is virtually impossible to know short of digging it up after a catastrophic failure.  At the end of its pre-programmed trip, the completely wireless robot returns to the surface, where it automatically uploads its data to the cloud for integration with existing municipal infrastructure data to form a comprehensive overview of an entire sewer system.  RedZone’s resulting maps allow city managers to view an interactive street map of a municipality and click anywhere to view a video and quantified data about the condition of the sewer system beneath the street.

According to RedZone CEO, Eric Close, the company currently serves 250 municipalities around the world.   With an estimated annual sewer renovation market of $11.5 billion and 740,000 miles of sewer lines in the United States, most of which have never been visually inspected, RedZone expects to see continued enormous growth for the company.


Because large amounts of data tend to ensure greater accuracy of machine-generated results, in AI the rule is – the more data the better.  With a database of human energy intake and expenditure comprising millions of user-days of activity, BodyMedia’s Free-Living Energy Expenditure Database is believed to be the largest such aggregation of data of its type.

The company’s arm-worn multi-sensor energy monitor detects body heat, perspiration and motion at a rate of thousands of data points per minute to determine the number of calories a user is burning. Data is uploaded to a user’s Activity Manager – the company’s web-based, online tool – and compared with his or her food log to determine a net calorie surplus or deficit, from which eating habits and exercise regimes can be maintained or modified.

In 2011 the company was awarded the Innovative Applications award by the International Association for the Advancement of Artificial Intelligence.   BodyMedia Armbands are available online and at a many retail stores, including Giant Eagle, Target, and Best Buy.


If two decades ago, Hans Moravec’s vision for the future looked like pie-in-the-sky speculation, today artificial intelligence looks like a very real industry – especially in Pittsburgh.


This article first appeared in the Pittsburgh Technology Council’s TechBurgher Magazine.

Pittsburgh Husband and Wife Develop a Different Sort of Knowledge
for the Next Generation of Computing

Quantum Couple – Jeremy Levy and Chandralekha Singh explore the far reaches of the physical world. He conducts research in the arcane field of quantum computing, She explores innovative methods of teaching burgeoning physicist how to think about the world of quanta.

by Tom Imerito

He grew up in Manhattan.  She – in Patna, India.   Both were taken with the way physics accounts for the world around them.  After completing undergraduate studies at Harvard and the Indian Technology Institute, they met as first year physics doctoral students at the University of California, Santa Barbara.  Upon arrival she found herself the sole woman among thirty-six male classmates.  Searching for a female companion, she pored over the class roster.  A first and last name ending in the letter Y caught her attention because in her native India, Y is a female suffix.  She tracked down the classmate only to find that it was just another of her thirty-six male colleagues.  But despite her initial disappointment, fortune smiled on her. The classmate with the Ys turned out to be Jeremy Levy, the man Chandralekha Singh would marry eighteen months later.

Today they are professors of physics at the University of Pittsburgh and live with their teenage sons, Akesh and Ishan, in Schenley Farms, adjacent to Pitt. Jeremy explores the far reaches of quantum computing, one of the most challenging and elusive disciplines in all of academia.  Chandralekha’s  (Chaun-Dra-Lay-Kah) experiments take place in the college classroom where she devises innovative ways to teach burgeoning physicists how to think about the infinitesimally small and intractably weird world of quantum mechanics – the rules of  how things smaller than atoms work.

A handsome couple to be sure, they can’t help but chuckle at nerdy physics jokes.  Chandralekha tells one about Neils Bohr, one of the founding fathers of quantum mechanics, who is stopped for speeding by a highway patrolman.  The trooper inquires, “Do you know how fast you were going?”  Bohr replies, “No officer, but I do know exactly where I was.”  The couple responds to my blank expression with kindly patience.  The joke is funny to quantum physicists, they explain, because it puts the man who gave us the orbital model of the atom in the mundane position of trying to beat a speeding ticket by invoking one of quantum mechanics’ most sacred mysteries and fundamental truths: You can know either the speed or the position of a quantum particle, such as an electron, but not both at the same time.

In a similar vein, Jeremy relates a story about teasing his 13-year old son, Ishan, who has managed, over a period of months, to memorize the first 500 decimal places of Pi.  He tells him that because Pi is an infinite number, he, Jeremy, can easily reel off 500 digits of Pi by simply naming any random sequence of numbers, reasoning that any string of numbers must necessarily be found an infinite number of times in an infinite number such as Pi.  The only catch is he cannot say precisely where his sequence might appear.  “It’s a math joke,” Jeremy explains sheepishly, as I force a smile to conceal my befuddlement. “It’s about infinity,” he adds.  “It’s a different sort of knowledge.”

Such different sorts of knowledge comprise the bulk of Jeremy and Chandralekha’s reality which is laden with  such notions as the well established fact that observing a quantum particle, changes it – not just optically or metaphorically, but really – physically; or that an electron can be in two places at one time – not very close by or in rapid succession – but in two completely different places at the very same instant; or that electrons can “tunnel” through solid objects; or that two unconnected subatomic particles can become entangled at long distances and communicate with each other.

Despite the impenetrably obscure nature of their chosen field of interest, Jeremy and Chandralekha each exhibit gifts for bringing the world of quantum physics within the grasp of mere Earthlings.  She demonstrates the effects of angular momentum with rotating bicycle wheels and spinning figure skaters.  He uses a child’s Etch-A-Sketch as a model for his sketched oxide single electron transistors.

In 2010, Levy was awarded a $7.5 million grant from the United States Air Force to develop a quantum computer.  Then in September of 2011, Levy and Singh were awarded a $1.8 million grant from the National Science Foundation and the Nanoelectronics Research Initiative (NRI) of the Semiconductor Research Corporation (SRC) to bring a new kind of computer out of the lab and into the real world. In addition to Levy’s innovation research, the grant includes funding for Singh’s educational research. In collaboration with colleagues, she is developing a new “OnRamp” education program aimed at lowering the steep and treacherous learning curve for early stage quantum physics researchers.

Jeremy’s innate appreciation of physics is evinced by his enthusiastic assertion that everything around us is based on quantum physics, from two-by-fours to cell phones.  But beyond its ubiquity, quantum mechanics holds very serious consequences for computers – and so, for all of us.

Although they are sure to differ from today’s computers in both the materials used to create and store data as well as in the processes used to operate on it, once quantum computers make the transition from laboratory bench to the real world, the most important difference will be speed.  Today’s computers use the physical on/off states of microscopic switches – transistors – to process the most basic information possible –binary data bits – on/off – yes/no – by means of sets of logical arguments or algorithms executed on PCs, laptops and smart phones.  In contrast to today’s computers, quantum computers are expected to use physically infinitesimal and computationally much more flexible bits of information called Q-bits which will enable the compression, consolidation, multiplexing and acceleration of advanced computing operations.  The result will be a computer with heretofore unimaginable speed.

Such an advance in processing speed is likely to have powerful consequences for Internet data security.  Because the strength of Internet security is based upon the length of time it would take the most powerful computer to crack a given encryption code, a vastly faster computer could change the game forever.  Experts estimate that today’s most secure encryption codes would take longer than the age of the universe to crack with today’s computers.  Since the universe has been around for more than 13 billion years, we’re in good shape as of right now. However, a quantum-speed computer could change things quickly by cracking any imaginable encryption code in short order, including those employed for top secret government communications and – closer to home – your personal financial transactions.  Clearly, whoever builds the first quantum computer will win the information technology sweepstakes.

In an effort to get there first, Levy (short ě) is focusing his efforts on developing a transistor the size of a single electron.  He begins with a layered pair of exotic materials that act as electrical conductors when they are four or more atomic layers thick, but serve as insulators at three or fewer. In a process that resembles a child’s Etch-A-Sketch, Levy “writes” and “erases” nanoscale circuits, roughly fifteen atoms wide, by adding and removing electric charges on the top surface of this new double-layered material using the ultra-fine-tipped electronic probe of an atomic force microscope (AFM).  By drawing a pair of intersecting circuits, at right angles and zapping the intersection with the AFM tip, Levy creates a tiny, 1.5 nanometer insulated trap in the all-but-non-existent space where the layers meet.  Inside the trap a group of negatively charged electrons become a synthetic atom known as a quantum dot. The remaining intersecting lines extending from the quantum dot become four leads which act as nanoscale circuits.  With a subsequent, positive zap, single electrons can be coaxed into tunneling through the insulating walls of the trap and into the leads.  Then with an oppositely charged zap, they tunnel back into the quantum dot again.  Because these trapped electrons can control electric current flowing into the leads, much the way a conventional transistor controls current into and out of its circuits in today’s computers, the result is a transistor that is several thousand times smaller than today’s.  Scaling things down even further, Levy is working on ways of using the direction of an electron’s spin (clockwise or counterclockwise) as a data bit – a Q-bit – the holy grail of quantum computing.

Although Jeremy and Chandralekha make understanding quanta look easy, for many students, the different sort of knowledge demanded by quantum topics stubbornly resists comprehension. In response, Chandralekha focuses her efforts on devising strategies to effectively teach students how to think about quantum mechanics, before actually teaching them the details of the subject.  She begins with what students already know and builds on that.  For instance, she gives students a real-life, long-term memory of the effect of angular momentum on spinning bodies as various as orbiting electrons and collapsing neutron stars by sitting students in a swivel stool and spinning them while they hold a dumbbell in each hand.   As they pull the dumbbells into their bodies, they spin faster as the energy of motion moves nearer the center of their bodies, just like figure skaters, subatomic particles and celestial bodies. It’s a practical lesson students can apply to their theoretical studies, in addition to being one they will remember throughout their careers.

At the Summer 2012 meeting of the American Association of Physics Teachers, Singh was awarded the association’s Distinguished Service Citation for her pioneering research in the teaching and learning of quantum mechanics.

The couple’s work promises to lead to a new paradigm of computing.  But unlike today’s computers, which are based on 0’s and 1’s this one started with a couple of Y’s.


This story first appeared in the Fall 2012 issue of Pittsburgh Quarterly. You can read the original here.