Cade Metz

This bike can roll over a bump on its own, staying perfectly upright. When the man walking just behind it says “left,” it turns left, angling back in the direction from which it came.

It also has eyes: It can follow someone jogging several yards ahead, turning each time the person turns. And if it encounters an obstacle, it can swerve to the side, keeping its balance and continuing its pursuit.

It is not the first-ever autonomous bicycle (Cornell University has a project underway) or, probably, the future of transportation, although it could find a niche in a future world swarming with package-delivery vehicles, drones and robots. (There are even weirder ideas out there.) Nonetheless, the Chinese researchers who built the bike believe it demonstrates the future of computer hardware. It navigates the world with help from what is called a neuromorphic chip, modeled after the human brain.

In a paper published Wednesday in Nature, the researchers described how such a chip could help machines respond to voice commands, recognize the surrounding world, avoid obstacles and maintain balance. The researchers also provided a video showing these skills at work on a motorized bicycle.

The short video did not show the limitations of the bicycle (which presumably tips over occasionally), and even the researchers who built the bike admitted in an email to The Times that the skills on display could be duplicated with existing computer hardware. But in handling all these skills with a neuromorphic processor, the project highlighted the wider effort to achieve new levels of artificial intelligence with novel kinds of chips.

This effort spans myriad startup companies and academic labs, as well as big-name tech companies like Google, Intel and IBM. And as the Nature paper demonstrates, the movement is gaining significant momentum in China, a country with little experience designing its own computer processors but which has invested heavily in the idea of an “AI chip.”

The hope is that such chips will eventually allow machines to navigate the world with an autonomy not possible today. Existing robots can learn to open a door or toss a Ping-Pong ball into a plastic bin, but the training takes hours to days of trial and error. Even then, the skills are viable only in very particular situations. With help from neuromorphic chips and other new processors, machines could learn more complex tasks more efficiently and be more adaptable in executing them.

“That is where we see the big promise,” said Mike Davies, who oversees Intel’s efforts to build neuromorphic chips.

Over the past decade, the development of artificial intelligence has accelerated thanks to what are called neural networks: complex mathematical systems that can learn tasks by analyzing vast amounts of data. By metabolizing thousands of cat photos, for instance, a neural network can learn to recognize a cat.

This is the technology that recognizes faces in the photos you post to Facebook, identifies the commands you bark into your smartphone and translates between languages on internet services like Microsoft Skype. It is also hastening the advance of autonomous robots, including self-driving cars. But it faces significant limitations.

A neural network doesn’t really learn on the fly. Engineers train a neural network for a particular task before sending it out into the real world, and it can’t learn without enormous numbers of examples. OpenAI, a San Francisco artificial intelligence lab, recently built a system that could beat the world’s best players at a complex video game called “Dota 2.” But the system first spent months playing the game against itself, burning through millions of dollars in computing power.

Researchers aim to build systems that can learn skills in a manner similar to the way people do. And that could require new kinds of computer hardware. Dozens of companies and academic labs are developing chips specifically for training and operating AI systems. The most ambitious projects are the neuromorphic processors, including the Tianjic chip under development at Tsinghua University in China.

Such chips are designed to imitate the network of neurons in the brain, not unlike a neural network but with even greater fidelity, at least in theory.

Neuromorphic chips typically include hundreds of thousands of faux neurons, and rather than just processing 1s and 0s, these neurons operate by trading tiny bursts of electrical signals, “firing” or “spiking” only when input signals reach critical thresholds, as biological neurons do.

“This is about trying to bridge and unify computer science and neuroscience,” said Gordon Wilson, the chief executive of Rain Neuromorphics, a startup company that is developing a neuromorphic chip.

Neuromorphic chips are by no means a re-creation of the brain. In so many respects, the workings of the brain remain a mystery. But the hope for such chips is that, by operating a bit more like the brain, they can help AI systems learn skills and execute tasks more efficiently.

Because each faux neuron fires only on demand rather than continuously, neuromorphic chips consume less energy than traditional processors. And because they are designed to process information in short bursts, some researchers believe they could lead to systems that learn on the fly, from much smaller amounts of data.

In the video, the bicycle is not learning; it is merely executing software that had been trained to handle specific tasks, including recognizing spoken words and avoiding obstacles. But it is executing the software in an efficient way, which is important to vehicles that run on battery power. Researchers believe they can eventually merge the training process and the in-the-moment execution, so that a bicycle could learn as it goes, from just a few moments of experience.

The rub is that building the right hardware may require at least several more years of research. “We are still in the trial and error stage,” said Georgios Dimou, who previously worked on Intel’s neuromorphic project.

The Chinese researchers believe that time will bring far more than just autonomous bicycles. Their paper paints the Tianjic chip as a step toward “artificial general intelligence,” a machine that can do anything you and your brain can do. But that is merely the promise du jour. Maybe start with helping it learn to ride a bike.

This article originally appeared in <a href="https://www.nytimes.com/2019/07/31/science/bikes-robot-autonomous.html" id="22f7eb0d-b719-3ccd-a07e-4e0d553536b5">The New York Times</a>.

As corporate giants like Ford, GM and Waymo struggle to get their self-driving cars on the road, a team of researchers in China is rethinking autonomous transportation using a souped-up bicycle.

And Now, a Bicycle Built for None

His wife, Phyllis (Lee) Merryman, said the cause was heart and kidney failure. He had been hospitalized since late December for complications arising from surgery to install a pacemaker.

In 1965, two years after he joined electronics maker Texas Instruments without a college degree, the company asked Merryman and two other engineers to build a calculator that could fit into a shirt pocket.

He designed the fundamental circuitry in less than three days, and when Texas Instruments unveiled the device two years later, the moment marked a transformational shift in the way Americans would handle everyday mathematics for the next four decades.

“Silly me, I thought we were just making a calculator, but we were creating an electronic revolution,” Merryman told the NPR program “All Things Considered” in 2013.

With this device, Merryman and his collaborators, Jack Kilby and James Van Tassel, also pioneered rechargeable batteries and “thermal printing,” which used heat to print numbers onto a special kind of paper. Speaking with NPR, Merryman said he was reminded of their work whenever he used a cellphone or was handed a thermally printed receipt by a grocery store cashier.

Their Texas Instruments prototype, code-named Cal Tech, is now enshrined at the Smithsonian Institution, tucked between the work of Thomas Edison and Alexander Graham Bell.

Jerry Dale Merryman was born on June 17, 1932, at his family’s home outside Hearne, Texas, an old railroad town 150 miles south of Dallas. His mother died two days later. His father, a railroad worker, was forced to retire in the 1950s after going blind from complications of diabetes.

By the time he was 11, Jerry was working for the local appliance store, repairing radios. According to family lore, Hearne police officers would hunt him down whenever their car radios broke — no matter the time of day.

He learned the ins and outs of electronics from a book he picked up at the appliance store — “Radio Engineering,” by Frederick Terman — and he held onto it for the next 75 years. “He read it all his life,” his wife said.

After a stint with the railroad — he packed ice into refrigerator cars carrying bananas — Merryman worked as an engineer at a local radio station. Then, in the late 1950s, he enrolled at Texas A&amp;M; University in nearby College Station. He left without finishing his degree.

In March 1963, he applied for an engineering job at Texas Instruments, near Dallas, and was hired without the traditional qualifications. “That was one of the smartest things the company ever did,” Ed Millis, who joined the company in the early ‘50s and worked alongside Merryman for decades, said in a telephone interview.

Merryman immediately joined a team that was developing what were called integrated circuits, the breed of microchip that would later drive personal computers. His boss was Kilby, who had helped build the first integrated circuit in 1958. (Kilby, who later shared the 2000 Nobel Prize in physics for this work, died in 2005.)

Seven years later, these microchips had yet to find their market niche, and Texas Instruments’ president at the time, Patrick E. Haggerty, decided that the company needed to prove its worth with a consumer product. He called for a pocket calculator.

Merryman was pulled into the project at the end of 1965, when he was summoned to a meeting in Kilby’s office. There, Kilby held up a small book and asked for a computer that size.

Years later, when a friend mentioned that Merryman had designed the calculator’s circuitry in only three days, Merryman leaned toward him and said, “And three nights.”

He, Kilby and Van Tassel initially built a prototype, which spanned an entire room at their Texas Instruments lab. Then, over the next two years, they packed the same circuit design into a hand-held casing using microchips.

The device had 18 keys, and it could handle addition, subtraction, multiplication and division, printing calculations on a tiny spool of paper. It reached the market in 1970 after Texas Instruments licensed the technology to Canon, carrying a $400 price tag. Soon a second partner, Bowmar, introduced a $250 version called the Bowmar Brain.

Over the next few decades, these devices got smaller and prices continued to drop, and the pocket calculator became a nearly ubiquitous household item. It lost its place in American homes only after the arrival of the Apple iPhone and other smartphones a decade ago.

In addition to his wife, Merryman is survived by his daughter, Melissa Merryman; his stepdaughter, Kim Ikovic; and two granddaughters. His two previous wives, Vernette (Posey) and Sally (Simon) Merryman, died before him.

During his brief stint at Texas A&amp;M;, Merryman entered a contest alongside 600 other students. They competed to see who was best at using a slide rule, the wood and plastic device that helped with multiplication, division, trigonometry and other mathematical calculations.

After buying a used slide rule for $6, Merryman won the contest with a nearly perfect score. “Hearne Student ‘Pulverized ′em’ in A&amp;M; Contest,” the headline in the local paper read.

Just a few years later, he helped make the slide rule obsolete.

This article originally appeared in <a href="https://www.nytimes.com/2019/03/07/obituaries/jerry-merryman-dead.html" id="1e22446c-7957-379f-add2-b910f010256e">The New York Times</a>.

Jerry Merryman, a self-taught electrical engineer who helped design the first pocket calculator, died Feb. 27 in Dallas. He was 86.

Jerry Merryman, Co-Inventor of the Pocket Calculator, Dies at 86

Now a group of researchers in the United States and China has tested a potential remedy for all-too-human frailties: artificial intelligence.

In a paper published Monday in Nature Medicine, the scientists reported that they had built a system that automatically diagnoses common childhood conditions — from influenza to meningitis — after processing the patient’s symptoms, history, lab results and other clinical data.

The system was highly accurate, the researchers said, and one day may assist doctors in diagnosing complex or rare conditions.

Drawing on the records of nearly 600,000 Chinese patients who had visited a pediatric hospital over an 18-month period, the vast collection of data used to train this new system highlights an advantage for China in the worldwide race toward artificial intelligence.

Because its population is so large — and because its privacy norms put fewer restrictions on the sharing of digital data — it may be easier for Chinese companies and researchers to build and train the “deep learning” systems that are rapidly changing the trajectory of health care.

On Monday, President Donald Trump signed an executive order meant to spur the development of AI across government, academia and industry in the United States. As part of this “American AI Initiative,” the administration will encourage federal agencies and universities to share data that can drive the development of automated systems.

The new system relies on a neural network, a breed of artificial intelligence that is accelerating the development of everything from health care to driverless cars to military applications. A neural network can learn tasks largely on its own by analyzing vast amounts of data.

Able to recognize patterns in data that humans could never identify on their own, neural networks can be enormously powerful in the right situation. But even experts have difficulty understanding why such networks make particular decisions and how they teach themselves.

As a result, extensive testing is needed to reassure both doctors and patients that these systems are reliable.

This article originally appeared in <a href="https://www.nytimes.com/2019/02/11/health/medical-diagnosis-artificial-intelligence.html" id="bd019228-7fca-304b-ac78-7ffb020d6aec">The New York Times</a>.

Each year, millions of Americans walk out of a doctor’s office with a misdiagnosis. Physicians try to be systematic when identifying illness and disease, but bias creeps in. Alternatives are overlooked.

AI Shows Promise as a Physician Assistant

Pooling health care data is a particularly difficult endeavor. Whereas researchers went to a single Chinese hospital for all the data they needed to develop their artificial-intelligence system, gathering such data from U.S. facilities is rarely so straightforward.

“You have go to multiple places. The equipment is never the same. You have to make sure the data is anonymized,” said Dr. George Shih, associate professor of clinical radiology at Weill Cornell Medical Center and co-founder of MD.ai, a company that helps researchers label data for AI services. “Even if you get permission, it is a massive amount of work.”

After reshaping internet services, consumer devices and driverless cars in the early part of the decade, deep learning is moving rapidly into myriad areas of health care. Many organizations, including Google, are developing and testing systems that analyze electronic health records in an effort to flag medical conditions such as osteoporosis, diabetes, hypertension and heart failure.

Similar technologies are being built to automatically detect signs of illness and disease in X-rays, MRIs and eye scans.

Using the technology, Dr. Kang Zhang, chief of ophthalmic genetics at the University of California, San Diego, has built systems that can analyze eye scans for hemorrhages, lesions and other signs of diabetic blindness. Ideally, such systems would serve as a first line of defense, screening patients and pinpointing those who need further attention.

Now Zhang and his colleagues have created a system that can diagnose an even wider range of conditions by recognizing patterns in text, not just in medical images. This may augment what doctors can do on their own, he said.

“In some situations, physicians cannot consider all the possibilities,” he said. “This system can spot-check and make sure the physician didn’t miss anything.”

The experimental system analyzed the electronic medical records of nearly 600,000 patients at the Guangzhou Women and Children’s Medical Center in southern China, learning to associate common medical conditions with specific patient information gathered by doctors, nurses and other technicians.

First, a group of trained physicians annotated the hospital records, adding labels that identified information related to certain medical conditions. The system then analyzed the labeled data.

Then the neural network was given new information, including a patient’s symptoms as determined during a physical examination. Soon it was able to make connections on its own between written records and observed symptoms.

When tested on unlabeled data, the software could rival the performance of experienced physicians. It was more than 90 percent accurate at diagnosing asthma; the accuracy of physicians in the study ranged from 80 to 94 percent.

In diagnosing gastrointestinal disease, the system was 87 percent accurate, compared with the physicians’ accuracy of 82 to 90 percent.

Experts said extensive clinical trials are now needed for Zhang’s system, given the difficulty of interpreting decisions made by neural networks.

“Medicine is a slow-moving field,” said Ben Shickel, a researcher at the University of Florida who specializes in the use of deep learning for health care. “No one is just going to deploy one of these techniques without rigorous testing that shows exactly what is going on.”

It could be years before deep-learning systems are deployed in emergency rooms and clinics. But some are closer to real-world use: Google is now running clinical trials of its eye-scan system at two hospitals in southern India.

Deep-learning diagnostic tools are more likely to flourish in countries outside the United States, Zhang said. Automated screening systems may be particularly useful in places where doctors are scarce, including in India and China.

The system built by Zhang and his colleagues benefited from the large scale of the data set gathered from the hospital in Guangzhou. Similar data sets from U.S. hospitals are typically smaller, both because the average hospital is smaller and because regulations make it difficult to pool data from multiple facilities.

Zhang said he and his colleagues were careful to protect patients’ privacy in the new study. But he acknowledged that researchers in China may have an advantage when it comes to collecting and analyzing this kind of data.

“The sheer size of the population — the sheer size of the data — is a big difference,” he said.

That’s more than 10 million people, and for half of them, the misdiagnosis could be harmful, a 2014 study in the British Medical Journal concluded. Doctors try to be systematic when identifying illness and disease, but they’re only human. Bias creeps in. Alternatives are overlooked.

With a paper published Monday in Nature Medicine, a group of researchers from universities in the U.S. and China tested a potential remedy: artificial intelligence.

The researchers built a system that automatically diagnoses common childhood conditions — from influenza to meningitis — after reading a patient’s symptoms, medical history, lab results and other clinical data. The system was highly accurate, and one day could assist doctors in diagnosing patients with complex or rare conditions, the researchers said.

“In some situations, physicians cannot consider all the possibilities,” said Kang Zhang, a professor of ophthalmology, genetics and nanoengineering at the University of California, San Diego, and one of the authors of the paper. “This system can spot-check and make sure the physician didn’t miss anything.”

The system relies on a neural network, a breed of artificial intelligence that can learn tasks by analyzing vast amounts of data. In this case, it analyzed electronic health records from more than 1.3 million patient visits to a pediatric hospital in China, learning to associate common medical conditions with specific patient information gathered by doctors, nurses and other technicians.

Able to recognize patterns in data that humans could never identify on their own, neural networks can be enormously powerful in the right situation. But even experts have difficulty understanding why such networks make particular decisions. As a result, extensive testing is needed to reassure both doctors and patients that these systems are reliable.

The new system analyzed the electronic medical records of nearly 600,000 patients at the Guangzhou Women and Children’s Medical Center, a hospital in southern China.

It was more than 90 percent accurate at diagnosing asthma; the accuracy of physicians in the study ranged from 80 to 94 percent. In diagnosing gastrointestinal disease, the system was 87 percent accurate, compared to the physicians’ accuracy of 82 to 90 percent.

This year, 1 in every 20 Americans will walk into a medical clinic and receive the wrong diagnosis.

The same “deep learning” technology underpins everything from Google Translate (a smartphone app that translates between languages) to Amazon Alexa (a coffee-table gadget that recognizes voice commands from across the living room). After reshaping internet services, consumer devices and driverless cars in the early part of the decade, the technique is now moving into health care.

Many organizations, including Google, are developing and testing systems that analyze electronic health records, in an effort to flag potential medical conditions such as osteoporosis, diabetes, hypertension and heart failure. Similar technologies are being built to automatically detect signs of illness and disease in X-rays, MRIs and eye scans.

“These AI tools have a real chance to make a difference,” said Eric Topol, a cardiologist and geneticist with Scripps Research in San Diego and author of a forthcoming book on the use of machine learning in health care, who was not involved in the research. “But it is going to take a while.”

Using neural-network technology, Zhang has built systems that can analyze eye scans for hemorrhages, lesions and other signs of diabetic blindness. Ideally, such systems would serve as a first line of defense, screening patients and pinpointing those who need further attention.

Zhang and his colleagues now have created a system that can diagnose an even wider range of conditions, by recognizing patterns in text, not just in medical images. The new system analyzed the electronic medical records of nearly 600,000 patients at the Guangzhou Women and Children’s Medical Center, a hospital in southern China.

First, a group of trained physicians annotated the Guangzhou records, adding labels that identified information related to certain medical conditions. The system then analyzed the labeled data. When it was done, and was presented with new data — a patient’s symptoms determined during a physical examination — it was able to make connections on its own.

When tested on unlabeled data, the system could rival the performance of experienced physicians. It was more than 90 percent accurate at diagnosing asthma; the accuracy of physicians in the study ranged from 80 to 94 percent. In diagnosing gastrointestinal disease, the system was 87 percent accurate, compared to the physicians’ accuracy of 82 to 90 percent.

Experts said extensive clinical trials are now needed, particularly given the difficulty of interpreting decisions made by neural networks.

It could be years before deep-learning systems are deployed in emergency rooms and clinics. But some are closer to real-world use. Google is now running clinical trials of its eye-scan system at two hospitals in southern India.

Deep-learning diagnostic tools are more likely to flourish in countries outside the U.S., Zhang said. Automated screening systems may be particularly useful in places where doctors are scarce.

The system built by Zhang and his colleagues benefited from the enormousness of the data set gathered from the hospital in Guangzhou. Similar data sets from U.S. hospitals are typically smaller, both because the average U.S. hospital is smaller and because regulations make it difficult to pool data from multiple facilities. Moreover, privacy norms are less stringent in China than in the U.S. and Europe.

“China does not have nearly the same obstacles,” Topol said.

Zhang said he and his colleagues were careful to protect patient privacy when gathering data for their new system. But he acknowledged that researchers in China may have an advantage when it comes to collecting and analyzing this kind of data. “The sheer size of the population — the sheer size of the data — is a big difference,” he said.

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Cade Metz

Articles written by the author

And Now, a Bicycle Built for None

Jerry Merryman, Co-Inventor of the Pocket Calculator, Dies at 86

AI Shows Promise as a Physician Assistant

AI Shows Promise as a Physician Assistant

AI Shows Promise as a Physician Assistant

AI Shows Promise as a Physician Assistant