Search

Its cousin, lossy compression, is easier to comprehend. Lossy algorithms are used to get music into the popular MP3 format and turn a digital image into a standard JPEG file. They do this by selectively removing bits, taking what scientists know about the way we see and hear to determine which bits we'd least miss. But no one can make the case that the resulting file is a perfect replica of the original.

It was 1995. Advances in chip technology continued apace with Moore's Law, the observation that the number of transistors on a chip doubles roughly every two years, generally because of the shrinking size of those transistors.

IT WAS 1975, and Kurt Petersen was a smart young researcher, fresh out of the Ph.D. program in electrical engineering at MIT and working in the optics group at IBM's Almaden, Calif., research center. And he was bored. Roaming the massive complex one day, he came across a huge black stain on the linoleum tiles of an otherwise nondescript hallway. That stain would change his life and the course of an entire industry.

If you're in North America, or anywhere else that uses 110-volt AC power, you'll need a step-up transformer to feed your terminal with 220-V AC power (unless you've managed to obtain a native U.S. version, of course!). If all goes well, you'll be able to turn it on, and be able to type on the keyboard and see characters echoed to the screen.

AS I DRIVE THROUGH THE VINEYARD-COVERED HILLS OF SAN LUIS OBISPO, CALIF. , THE TINY GLOBAL POSITIONING SYSTEM receiver in my phone works with Google Maps to alert me to upcoming turns. The app reassures me that I’ll arrive at my destination on time, in spite of a short delay for construction. How different this trip would have been in the pre-GPS era, when the obscured road sign at one intersection would likely have sent me off track. I have a weak sense of direction, and getting lost-or worrying about getting lost-was a stressful part of my life for a long time.

Remember vinyl records? More specifically, do you remember the way vinyl records skip when they're dusty or scratched? Let me assume you're old enough to recall that annoyance, or perhaps you've experienced that vintage technology more recently. Now think back to when you got your first CD. Small and shiny, packing 74 minutes of music, it seemed magical, even more magical when you noticed that you could treat a disc pretty badly before physical damage affected the way it played. A lot of different kinds of engineering, of course, went into figuring out how to put music on a CD and play it back so reliably. There's hardware, including a laser, optics to focus it, and mechanical systems to move the laser and turn the disc. And there’s software-including pulse-code modulation, which turns regular samples of an analog signal into bits, and error-correcting codes, which make sure those bits don't get corrupted.

In mid-2014, Magic Leap began teasing us with visions of realistic baby elephants playing in the palms of our hands, promising to soon unveil a mind-blowing augmented reality technology that would dramatically change the worlds of both entertainment and computing. Investors have ponied up an astounding US $1.39 billion so far to own a piece of this AR future, according to Crunchbase.

Remember March, when stay-at-home orders and advisories first went into effect in many U.S. states and other places around the world? That month is generally not a big time for consumer electronics sales; gadgets may fly off shelves during the December holiday season, but then there's a bit of a lull.

Accurate, instant, cheap, and portable tests for the Coronavirus and its antibodies would allow frequent testing on a mass scale. But the road to such tools is long. Or is it?

Vehicle-to-everything (V2X) technology-“everything” meaning other vehicles and road infrastructure-has long promised that a digital seatbelt would make cars safer. This year Panasonic expects to keep that promise by taking data to the cloud.

Earlier this year, as the tech workforce moved into the virtual world, Facebook announced that at least some employees could work from home permanently—and choose to relocate. There is a catch to that: Salaries, the company said, would be adjusted to reflect local costs of living. Over the summer, job search firm Hired released a study that reported that only 32 percent of tech professionals around the world would be willing to take such a cut. • Now, in the final months of 2020, as remote workers settle into their commute-less work life, the numbers of those willing to accept a pay cut seems to have grown. Blind, a company that operates private social networks for tech employees, surveyed 5,591 tech professionals and found that 44 percent would be willing to take a pay cut linked to a lower cost of living and 8 percent were indifferent; 48 percent would be opposed to such a cut. • The numbers vary by employer, with tech professionals working at Square, Lyft, Facebook, VMware, and Dropbox the most willing to sacrifice salary for the ability to relocate, and Expedia, PayPal, Microsoft, and Oracle the least. • Comments from those willing to take a cut generally fell into the what's-the-big-deal category. One Facebook employee wrote: “I don't understand all the fuss, are people demanding everyone around the world be paid the same? Let's be real, your pay range is based on your location. People who feel they moved to a place that had 30–50 percent lower range [and] shouldn't be paid like the local employees crack me up.”

ROCKET, A STARTUP THAT BUILDS AI-based tools for recruiters and offers recruitment services of its own, had been on the verge of profitability when the coronavirus pandemic hit. It quickly saw corporate recruitment dry up as the pandemic took hold. • With the halt in hiring affecting its main business, and mounting layoffs at tech companies, Rocket turned its efforts toward matching laid-off engineers with new jobs pro bono. It gathered data on layoffs, had its AI software and recruiters clean up the data and make it easier to navigate, and launched a new job portal, dubbed Parachute. • Here's what Rocket cofounder and CEO Abhinav Agrawal had to say when IEEE Spectrum talked to him about the pandemic's effect on tech jobs and creating Parachute.

As the COVID-19 pandemic began its explosive spread, tech workers were among the first to switch to working at home in mass numbers. By early March, before regional stay-at-home orders came into play in the United States, most tech professionals at Microsoft and Amazon had already switched to working at home, and others soon followed.

THE GAP BETWEEN THE AVERAGE SALARY offered to black tech professionals and what's offered to white tech professionals is closing at a snail's pace. According to an analysis by the job search firm Hired, in 2019 black tech professionals were offered an average of US $10,000 a year less than white tech workers. That's slightly better than the 2018 gap of $11,000, but not much better. • Meanwhile, Hispanic tech professionals lag $3,000 behind their white counterparts, down from $7,000 in 2019. Asian tech professionals, having pulled ahead in recent years, continue to command a slight edge in average salaries over their white colleagues. • Within each racial group, tech professionals who identified themselves as female received lower average salary offers than their male counterparts, according to Hired's 2020 State of Wage Inequality in the Workplace Report, released earlier this year. • One promising takeaway from Hired's 2020 State of Salaries Report was that tech salaries grew in the United States, Canada, and the United Kingdom in 2019, with the U.S. average up to $146,000 (an 8 percent increase over 2018) and the average of all three regions up to $130,000 (a less than 1 percent increase).

We're hearing about the problem daily: There aren't enough test kits available to accurately track the new Coronavirus. It's a global problem, although some countries-including the United States-are doing a worse job of testing than others. ¶ More testing, epidemiologists say, would give us a better understanding of how the virus is moving through the population and whether it's waxing or waning. And more testing is essential to allow us to safely return to any semblance of normal life. ¶ Part of the solution to the testing shortage, however, may come not from making more kits but from algorithms. Computer scientists and engineers who study ways to compress data, decode video, and process medical images say that biological samples from groups of people could be pooled for testing, and the results decoded by cleverly designed algorithms. This sample pooling could go a long way toward addressing the test-kit shortage, with one test kit being able to determine the health status of 5, 10, or even more people.¶ I spoke with Dror Baron, associate professor of electrical and computer engineering at North Carolina State University and an IEEE Senior Member, about his work developing algorithms for this kind of coronavirus test processing.

After falling slightly in 2018, the number of women in tech being offered less than men for the same job at the same company jumped back up in 2019—to 63 percent. Meanwhile, the average salary differential between U.S. tech workers identifying as male and those identifying as female held steady at 3 percent, according to a 2020 study by the job search firm Hired that was released at the end of March.

Larry Tesler, who died this past February at age 74, is the most famous computer scientist most people have never heard of—and one of the nicest guys I've worked with in my years as a tech journalist. I first met him when writing about the amazing things happening at Xerox Parc, when he told me that his groundbreaking work in user-interface design started with his determination to prove that the computer mouse was a bad idea. ¶ "I really didn't believe in it," he said. "I thought cursor keys were much better. We literally took people off the streets who had never seen a computer. In 3 or 4 minutes they were happily editing away using cursor keys. At that point I was going to show them the mouse and prove that they could select text faster than with the cursor keys. Then I was going to show that they didn't like it. ¶ "It backfired. I would have them spend an hour working with the cursor keys. Then I would teach them about the mouse. They would say, ‘That's interesting, but I don't think I need it.’ Then they would play with it a bit, and after 2 minutes they never touched the cursor keys again." ¶ A researcher to the core, Tesler accepted the results of the experiment—but then set out to make the mouse, then a three-button device accompanied by a five-button keypad, better. He simplified the user interface—bringing us the click-and-drag movement to select text and graphics, along with cut, copy, and paste—and paved the way for the one-button mouse that so many of us use today.

It's a good time to be an engineer specializing in augmented reality or virtual reality. That's the conclusion of the latest report by the job site Hired, which released its annual state of software engineers report in February. To compile its data, Hired reviewed 420,000 interview requests from 10,000 companies made to 98,000 job seekers throughout 2019.

I recently drove from Silicon Valley to San Francisco. It started raining on the way and I hadn't thought to take an umbrella. No matter-I had the locations of two parking garages, just a block or so from my destination, preloaded into my navigation app. But both were full, and I found myself circling in stop-and-go traffic around crowded, wet, hilly, construction-heavy San Francisco, hunting for street parking or an open garage for nearly an hour. It was driving hell. ¶ So when I finally arrived at a launch event hosted by Cruise, I couldn't have been more receptive to the company's pitch for the Cruise Origin, a new vehicle that, Cruise executives say, intends to make it so I won't need to drive or park in a city ever again.¶ The Cruise Origin is a six-passenger, autonomous, electric, SUV-size vehicle intended to disrupt not so much the car industry as urban transportation overall. Cruise (mostly owned by GM) does not plan to offer the Origin on the retail market. Instead, it will operate fleets of the vehicles as a ride-sharing service; screens inside are intended to give information about upcoming pickups and drop-offs. ¶ Uber, which launched the last big transportation disruption and has been preparing for the next by investing in its own autonomous vehicle research, might have some scrambling to do. ¶ Since the Origin won't be sold, the company isn't talking about pricing.¶ However, Cruise CEO Dan Ammann did talk a lot about what the designers did to make this autonomous vehicle as inexpensive as possible to manufacture-production costs will be about half of those required to make today's all-electric SUVs, he said. The designers started with a new, all-electric platform, made all the sensor and computer systems modular for easy replacement and upgrading, and took out everything driver-related, including rearview mirrors, windshield wipers, and, of course, the steering wheel.

SILICON VALLEY CONTINUES TO DOMINATE. In every study I've seen, it has the lion's share of tech jobs. Its engineers command the highest salaries. This is despite the fact that over the years, various regions in the United States and worldwide have pitched themselves as the “next Silicon Valley” (or “Silicon Glen” or “Silicon Fen.”) Some have indeed increased their pool of tech jobs. But none have really become a serious technology hub, outside of Boston, Seattle, San Diego, and North Carolina's Research Triangle.

What's the world's hardest machine-learning challenge? Autonomous vehicles? Robots that can fall over and get back up? Cancer detection? ¶ Julian Sanchez believes it's agriculture. ¶ He may be a little biased. Sanchez is the director of precision agriculture for John Deere, and he's in charge of adding intelligence to traditional farm vehicles. I met with Sanchez and Alexey Rostapshov, head of digital innovation at John Deere Labs, in San Francisco, where John Deere launched the spin-off in 2017 to take advantage of Silicon Valley's tech expertise. ¶ Sanchez believes agriculture is the biggest challenge for artificial intelligence because it's not just about driving tractors around, although autonomous driving is certainly part of the mix. According to Sanchez, the more complex problems revolve around issues such as crop classification. John Deere would like to create an AI system that allows farmers to know, for example, whether a grain being harvested is high quality or low quality. The many differences between grain types, and between grains grown under different conditions, make this a tough task for machine learning. ¶ Sanchez uses corn as an example. To build a deep-learning algorithm to analyze corn kernel quality, you'd feed it millions of pictures of kernels. Kernels harvested in central Illinois might have one color. But kernels of the same hybrid from a different farm 5 miles away might look slightly different. Now imagine solving that challenge for dozens of grain varieties-some of which, like canola, are nearly microscopic.

IT'S THE SALARY. AND THE location. And the mission. And the reputation. It's a combination of these things—as well as, let's face it, the coolness factor—that make a tech company a dream employer for a software engineer, product manager, data scientist, or other tech professional.

I turn the key to start the little Ford SUV I’ve rented for my visit to the University of Illinois at Urbana- Champaign, and a message flashes briefly on the dash: “Tire pressure low.” I ignore it. My own car is 12 years old; I’m not accustomed to a car that monitors its own health. Turns out, though, that the little Ford wasn’t kidding. The next morning I find the car has a flat tire.

Say hello to Ira. His head is visi ble on a screen, as though he?s in a videoconference. He seems to be in his early 30s, with a shaved head, a pronounced nose, and thin eyebrows. Ira seems a little goofy and maybe just a wee bit strange. But unless you knew his full name-it?s "Digital Ira"-you probably wouldn?t guess that he?s nothing but bits.

Happy engineers are all alike; each unhappy engineer is unhappy in his own way, Tolstoy might have said. Actually, the opposite is closer to the truth. Those who are dissatisfied all tend to spend their days pining for positions that are more challenging or fulfilling. Those who enjoy their work derive satisfaction in many different ways. . The profiles we present in this year's Dream Jobs report aptly demonstrate that proposition. Contentment can come from working with professional athletes, investigating scientific mysteries, improving access to space, solving the problems of renewable energy, or just letting your imagination run. Reading about these exemplars may help you to zero in on your own uniquely rewarding career.

Stress—we all know it can be bad for us. It affects blood pressure, metabolism, immune response, and memory. Over time, it can contribute to the development of chronic diseases. So scientists and health professionals are putting a lot of effort into finding ways to measure it.

None of the people walking around San Diego's East Village neighborhood one recent afternoon were looking up at the streetlights (except me). And if they had, they likely wouldn't have noticed that some of these lights were a little thicker around the middle than others, or that some lanterns topping old-style lampposts had a clear glass panel here and there. But unbeknownst to the people below, those streetlights were looking- and listening-all around them, while also monitoring temperature, humidity, and other characteristics of the air.

Implementing machine learning in the real world isn't easy. The tools are available and the road is well marked—but the speed bumps are many. ¶ That was the conclusion of panelists at the IEEE AI Symposium 2019, held at Cisco's San Jose, Calif., campus in September. _ The toughest problem, says Ben Irving, senior manager of Cisco's strategy innovations group, is people. ¶ It's tough to find applicants with expertise in data science, he indicated, so companies are looking into nontraditional sources of personnel, like political science. "There are some untapped areas with a lot of untapped data-science expertise," Irving says. ¶ Lazard managing director Trevor Mottl agreed that would-be data scientists don't need formal training or experience to break in. "This field is changing really rapidly," he says. "There are new language models coming out every month, and new tools, so [anyone should] expect to not know everything. Experiment, try out new tools and techniques, read; there aren't any true experts at this point because the foundational elements are shifting so rapidly."¶"It is a wonderful time to get into a field," he said, noting that it doesn't take long to catch up "because there aren't 20 years of history."¶ Confusion about what different kinds of machine-learning specialists do doesn't help the personnel situation. An audience member asked panelists to explain the difference between a data scientist, a data analyst, and a data engineer. Darrin Johnson, Nvidia global director of technical marketing for enterprise, admitted it's hard to sort out, and any two companies could define the positions differently. ¶ The competition to hire data scientists, analysts, engineers, or whatever companies call them requires that managers make sure that any work being done is structured and comprehensible at all times, the panelists cautioned.¶ "We need to remember that our data scientists go home every day and sometimes they don't come back, because they go home and then go to a different company," says Lazard's Mottl. "If you give people a choice on [how they do their development] and have a successful person who gets poached by a competitor, you have to either hire a team to unwrap what that person built or jettison their work and rebuild it."

Having sought-after skills is critical to any engineer's career. And if such skills are relatively rare in the talent pool, that can make all the difference when it comes to negotiating salary or perks. While some general rules apply—no one needs any new punch-card operators—location can be an important factor in what's most in demand.

Heap Connectivity and Storage Technologies have encouraged companies to gather as much data as possible, whether tracking user behavior on a website, how a plane's engines are performing, or how electricity flows across a grid. But all that data has no value in itself. Someone has to coax useful knowledge from it. Consequently, data scientists are in demand. • But while a lot of companies want to hire data scientists, there is little agreement about what a data scientist actually is. In a Hacker News discussion about my recent View From the Valley blog post on the demand for data scientists, one commenter wrote: “‘Data scientist’ is a title recently thrown around a lot for positions that used to be called ‘data analyst,’ with no strong [machine learning] or [software engineering] ability required.” • So can we say anything more specific? Job-search firm Indeed has sketched out a picture of a data scientist as a technologist with a degree “in computer science, statistics, or a quantitative social science, along with some training in statistical modeling, machine learning, and programming.” Another Hacker News commenter stated: “I would have thought [data science involves] a serious, sustained study of statistics—starting with a strong base knowledge of the mathematics of probability and building from there. But based on the resumes I've seen, that doesn't seem to be the common opinion.” • A fuzzy definition doesn't make filling data science jobs—or helping a data scientist figure out if a job offer is right for them—any easier.

In the 1980s, John L. Hennessy, then a professor of electrical engineering at Stanford University, shook up the computer industry by taking the concepts of reduced instruction set computing (RISC) to the masses. Hennessy wrote papers, gave talks, designed chips, started companies, and even, literally, wrote the book (a textbook that's still used today). The RISC architecture, which focused on simpler, lower-cost microprocessors, was then thought to be an academic exercise with little practical use; today it plays a major role in the industry. Hennessy, now president of Stanford, is once again designing, testing, and advocating a new architecture, this time in the field of university education. He first began rethinking research at universities and recently began reimagining university education itself.

The ranks of bona fide international celebrities in technology is rather small, if by "bona fide" you mean people whose fame has something to do with actual technological acumen and achievement. But now let's draw up a list of all the female bona fide international tech celebrities: 1. Marissa Mayer. End of list. Mayer was the first female engineer and among the first 20 people hired at Google. She was a major force behind its user interface. For 10 years, she ran its core search business while the company demolished such competitors as AltaVista, Lycos, and Excite. Now, at age 37, she's in charge of one of Google's hottest bunch of technologies: location and local services. She oversees more than 1000 engineers and product managers who are refining Google Maps, Google Places, and Google Earth and also developing the technology behind applications that may revolutionize the mobile Web as much as the search engine transformed the original one.

Engineers design things and solve problems. No surprise there—that's the textbook definition of an engineer. But there's nothing textbook about the careers these 10 engineers have forged for themselves. They're educators, explorers, and entertainers, as well as builders and problem solvers. Nicole Richard, for example, is using Lego kits to fire up young minds all over the world with the possibilities of technology. Chieko Asakawa is making the Web accessible to the blind. Kevin Hardy is part of a team attempting to send a man to the bottom of the Mariana Trench, the deepest spot in the world's oceans. And Brent Bushnell is creating complicated machines with a simple purpose: to delight and enthrall. Trying to visualize an engrossing career in technology? Problem solved.

The future of cybersecurity is in the hands of hardware engineers, says Scott Borg, director of the U.S. Cyber Consequences Unit, a nonprofit think tank based in Vermont. He spoke in May at the MEMS & Sensors Technical Congress, held at Stanford University, in California, to an audience of 130 chief technical officers, engineering directors, and key researchers from microelectromechanical systems and sensors companies and labs. Borg warned that “the people in this room are now moving into the crosshairs of cyberhackers in a way that has never happened before.”

The early hype that every home would shortly have a 3D printer fizzled fast, but there are still companies interested in catering to the home (and classroom) market. Two-XYZ printing and New Matter-were brave enough to allow me to borrow their "kid friendly" models for several months to check them out. For XYZ, that's the da Vinci miniMaker; for New Matter, it's the Mod-t. These gadgets (both under US $300) are aimed at tweens and up, though the primary colors of the miniMaker seem designed to appeal to far younger children. ("Up" includes non-tech-savvy teachers and other adults interested in 3D printing who aren't hard-core makers.)

Engineers, are you ready to shake things up? We know that the economy lately has been, well, unforgiving. But we still believe that you can–and should–find jobs you adore. This year, we met 10 engineers whose abrupt turns and occasional missteps led them to question their deepest desires, right the course, and find the jobs that are best for them. We quizzed hiring managers at top companies to learn what they seek. We also caught up with engineers we've featured in the past to see how dreams may change. Aim high, dear readers, and above all, have fun.

As a 10-Year-Old, he scrounged around in junkyards, looking for materials he needed to build crystal radio sets. As a young professor at the University of California at Berkeley, he foraged for semiconductor processing equipment being discarded by industrial laboratories, and used it to build the first semiconductor fabrication facility at a university.

The most powerful technologies take a while to mature. But when they do, they can rapidly retire mainstays that are decades old. Given in this paper are the top 11 technologies of the decade which are smartphones, social networking, voice over IP, LED lighting, multicore CPU, cloud computing, drone aircraft, planetary rovers, flexible AC transmission, digital photography and class-D audio.

Look inside an e-reader, game console, Blu-ray player, TV, or smartphone and odds are you-ll find a cluster of chips designed by Marvell, a 15-year-old Silicon Valley firm. Marvell crafts the CPU as well as the wireless transmitters and receivers, the digital signal processors, the video processors, and even the power management system. Its chips will likely appear in the wave of Android tablets expected to hit the market soon. And the One Laptop per Child organization just announced that its next generation of low-cost computers- the first to finally get below US $100 - will rely on Marvell electronics as well.

We've all been there: You're out with friends for dinner and everyone has finished their entrees and placed orders for coffee and dessert. The conversation seems to fade along with the food and, almost simultaneously, everyone suddenly realizes they have to give their phones a quick peek- any text messages? And as long as it's in their hands, maybe a glance at email or Facebook. The scrolling goes on until the coffee arrives.

Back in the earliest days of Facebook, before you could attach a second name to your Facebook account and before businesses could set up pages, I created two Facebook accounts. I use my original name professionally and my married name for my personal life, so it made sense to have a professional account and a personal account. But before I really got going with Facebook, I started using Twitter for professional postings, so the Facebook account associated with my work email just gathered digital dust. For years and years.

BM is betting on quantum computing, but it can't win without the help of startups. And how long it will take for the bet to pay off is anybody's guess. That was the message of the Q Summit, a one-day meeting of quantum-computing researchers, investors, and entrepreneurs hosted by IBM in Menlo Park, Calif., in April. Quantum computing would open the door to a new universe of problem solving, and it could disrupt some conventional digital technologies. The potential is big, but the risks are huge.

Earlier this spring, Nvidia hosted its 10th annual GPU Technology Conference (GTC) in San Jose, Calif. Attendees lined up early, some of the 8,300 people reportedly camping overnight to get into CEO Jensen Huang's overflowing keynote address. With many of the breakout sessions also standing room only, it's just a matter of time before this conference bursts out of the San Jose Convention Center.

This paper presents the story of an unusually bright and hard working professor who wanted to explain a difficult concept in clear and simple terms in order to better teach his students. Viterbi working from Jet Propulsion Laboratory helped developed phase-lock loop. Viterbi also started consulting on applications of his algorithms and called their firm Linkabit. The company worked on various defense and commercial satellite modems and terminals. It also built a satellite signal scrambler called Videocipher for the cable channel HBO. Years later OmniNet was founded, envisioned mobile satellite network and hired Qualcomm to build it. Qualcomm's solution was to use spread-spectrum communications and used the Viterbi algorithm to help extract the original signal from the noise. With the launch of OmniTracs, which is a trucking systems, scrappy start-up took on the mobile phone industry. Viterbi and his colleagues also figured out a way to have each handset and tower analyze the quality of its own signals as well as the other conversations being transmitted around it; the tower would then adjust power usage until the handset was transmitting just enough signal to work. One of Viterbi's earliest investments was in VoiceSignal Technologies, which used the Viterbi algorithm for voice recognition systems in cellphones, including the iPhone.