2026-01-01 23:00:02
Every September as we plan our January tech forecast issue, IEEE Spectrum’s editors survey their beats and seek out promising projects that could solve seemingly intractable problems or transform entire industries.
Often these projects fly under the radar of the popular technology press, which these days seems more interested in the personalities driving Big Tech companies than in the technology itself. We go our own way here, getting out into the field to bring you news of the hidden gems that genuinely—as the IEEE motto goes—advance technology for the benefit of humanity.
A look back at the last 20 years of January issues reveals that while we’ve certainly covered our share of huge tech projects, like the James Webb Space Telescope, many of the stories touch on subjects most people would have otherwise missed.
Last January, Senior Associate Editor Emily Waltz reported on startups that are piloting ocean-based carbon capture. This issue, she’s back with another CO2-centric story, this time focused on grid-scale storage, which is poised to blow up—literally. Waltz traveled to Sardinia to check out Milan-based Energy Dome’s “bubble battery,” which can store up to 200 megawatt-hours by compressing and decompressing pure carbon dioxide inside an inflatable dome.
This kind of modular, easy-to-deploy energy storage could be especially useful for AI data centers, says Senior Editor Samuel K. Moore, who curated this issue and wrote about gravity energy storage back in January 2021.
Big bubbles could help with grid-scale storage; tiny bubbles can liquefy cancer tumors.
“When we think about energy storage, our minds usually go to grid-scale batteries,” Moore says. “Yet these bubbles, which are in many ways more capable than batteries, will be sprouting up all over the place, often in association with computing infrastructure.”
For his story in this issue, Moore dove into the competition between two startups that are developing radio-based cables to replace conventional copper cables and fiber optics in data centers. These radio systems can connect processors 10 to 20 meters apart using a third of the power of optical-fiber cables and at a third of the cost. The next step is to integrate the radio connections directly with GPUs, to ease cooling burdens and help data centers and the AI models running on them continue to scale up.
Big bubbles could help with grid-scale storage; tiny bubbles can liquify cancer tumors, as Greg Uyeno found when reporting on HistoSonics’ ultrasound treatment. Feared for its aggressive nature and extremely low survival rate, pancreatic cancer kills almost half a million people per year worldwide. HistoSonics uses noninvasive, focused ultrasound to create cavitation bubbles that destroy tumors without dangerously heating surrounding tissue. This year, the company is concluding kidney trials as well as launching pancreatic cancer trials.
Over the last two decades, Spectrum has regularly covered the rise of drones. In 2018, for instance, we reported that the startup Zipline would deploy autonomous drones to deliver blood and medical supplies in rural Rwanda. Today, Zipline has a market cap of about US $4 billion and operates in several African countries, Japan, and the United States, having completed almost 2 million drone deliveries. In this issue, journalist Robb Mandelbaum takes us inside the Wildfire XPrize competition, aimed at providing another life-saving service: dousing wildfires before they grow out of control. Zipline succeeded because it could make deliveries to remote locations much faster than land vehicles. This year’s XPrize teams plan to detect and suppress fires faster than conventional firefighting methods.
In addition to these emerging technologies, we’ve packed this issue with a dozen others, including Porsche’s wireless home charger for EVs, the world’s first electric air taxi service, neutral-atom quantum computers, interoperable mesh networks, and robotic baseball umpires. Let’s see which of this year’s picks make it to the big leagues.
2026-01-01 21:00:02
Elon Musk says his company Neuralink is aiming to restore partial sight to fully blind patients in 2026. The company plans to test its newest and most powerful implant, Blindsight, in humans early this year. The chip will be wirelessly connected to an external video camera and implanted into the brain’s visual cortex. Bypassing the eyes, it is designed to generate the perception of vision based on what the camera captures, even for people born blind. The resulting vision will be low resolution in early tests but will hopefully get better over time, Musk says, though some experts worry he is overpromising on the quality of the brain-computer interface.
It’s like the 1990s all over again! You will soon be able to punctuate your angry conversations by slamming your iPhone shut. Apple plans to bring a foldable version of its phone to market in late 2026, aiming not just to catch up with the competition but improve on some longstanding issues with existing popular foldable phones. The iPhone Fold, as it’s called, will have an inner hinge mechanism that leads to a less visible crease in the display, the company claims. The device is expected to cost at least US $2,000—that’s $800 more than some 2025 base iPhone prices. The phone will have to compete with strong rivals that have already cultivated model loyalty, like Samsung’s Galaxy Z Fold series.
In July 2026, a Chinese sample-return mission is expected to rendezvous with 469219 Kamoʻoalewa, a near-Earth asteroid. The mission, called Tianwen-2, will also use instruments that include multiple spectrometers and cameras, a magnetometer, and a dust analyzer to collect data about the asteroid. After more than six months of study, Tianwen-2 will leave the asteroid and drop the collected sample down to Earth before heading off to investigate 311P/PanSTARRS, a complicated celestial object that’s part asteroid and part comet.
In another giant leap for mankind, the first crewed mission to the moon since 1972 is scheduled to launch in April 2026. The 10-day flight will usher in NASA’s efforts to have a sustained human presence on the moon by testing hardware and systems for future lunar exploration. This will be the first time a crew assesses the SLS rocket and Orion spacecraft for human use. While the astronauts won’t actually land on the moon, they will get as close as 7,400 kilometers from its surface and spend time investigating how near-lunar space travel affects their health.
Meta is spending its way to AI excellence, experts say. The company plans to take its first “AI supercluster” online in 2026, consuming as much as 1 gigawatt of power. Prometheus, as it’s called, is on a site near Columbus, Ohio, with a footprint that approaches the size of Manhattan. But it’s just part of a wider project that will cost Meta hundreds of billions of dollars, according to CEO Mark Zuckerberg. In addition to Prometheus, Meta is developing an even larger data center, Hyperion, that will be able to scale up to 5 gigawatts and is expected to be operational in 2028.
How can missions to Mars refuel on the red planet? Blue Origin suggests that its in situ resource-utilization system, called Blue Alchemist, could be the answer. The company plans to run an autonomous demonstration of the system in a simulated lunar environment this year, demonstrating how it uses electric current to extract breathable oxygen and valuable metals from regolith without also releasing toxic chemicals or carbon emissions. Blue Origin claims that Blue Alchemist could facilitate lunar and Martian settlements, both for humans and robots. It could also help make deep-space exploration possible by using asteroids, the company says.
By the end of the year, we could be one step closer to commercial fusion energy. The first-ever project to demonstrate the deuterium-tritium fuel cycle—the most viable route to practical fusion energy—plans to be operational by late 2026. Unity-2 will run through the entire D-T fuel process, including discharge, purification, and resupply, to establish that tritium recycling is a sufficient method to produce fusion energy. It will also be a testbed for related technology—members of industry will be able to stress-test their fusion-related innovations with Unity-2 and push them up the technology readiness scale.
You may soon see privately owned completely self-driven cars on the road, as automotive startup Tensor plans to release its SAE Level 4 car to consumers in the second half of 2026. This level of autonomy means that the car comes equipped with tools for control, including a steering wheel, gas pedal, and brake, but can safely travel without a person in the driver’s seat. Although some driverless taxi services, such as Waymo, function at Level 4, Tensor’s product would likely be the first private car at this level of self-sufficiency. Tensor’s chief business officer suggests that riders can even watch Netflix or do work while on the road in the company’s cars.
2026 could be a monumental year for the future of chips. Intel announced that it plans to decide whether it will pursue its advanced 14A chipmaking process this year. The company’s chief financial officer says that Intel will pursue 14A manufacturing capacity only if enough external customers commit to using the process. Stepping away from 14A would signal that Intel is forgoing efforts to compete with TSMC and Samsung for chip-technology leadership.
Soon, an algorithm will determine not only what ads you see on social media, but also what’s in them. Meta plans to fully automate ad creation and delivery on its platforms by the end of 2026, putting every step of the process in the “hands” of AI. Though there are already some AI tools integrated into the company’s ad platform, Meta wants to do more. It’s developing a way for any brand to present only a product and budget to an AI tool, which will then create an entire ad (including text, images, and video), determine the users to target, and offer business suggestions.
Major League Baseball (MLB) will take a big swing on new tech for the 2026 season. The league will debut the Automated Ball-Strike (ABS) Challenge System to check the accuracy of umpires’ pitch calls. Each team will start the game with two challenges. ABS uses an array of 12 cameras around each stadium to track a pitch’s movement to determine whether it crossed through the strike zone, delivering its verdict in about 15 seconds. The Korea Baseball Organization debuted a similar system for its league in 2024, and MLB has previously tested the tech in the minor leagues, spring training games, and the 2025 All-Star Game.
2026-01-01 03:00:02
High school sophomore Abigail Merchant has made it her mission to use technology to reduce flood-related deaths. The 15-year-old lives in Orlando, Fla., a state where flooding is frequent in part because of its low elevation.
The changing climate is increasing the risk. Warmer air holds more water, leading to heavier-than-usual rainfall and more flooding, according to the U.S. Environmental Protection Agency.
School
Orlando Science Middle High Charter, in Florida
Grade
Sophomore
Hobbies
Basketball and playing the drums
Currently satellites, synthetic aperture radar, and GPS are used to collect data on flood damage, track the location of victims, and communicate with emergency responders. But technology failures and slow data transmission speeds lead to delays in response time, Merchant says. The increase in global flooding has intensified the need for more accurate and reliable methods.
Last year Merchant built what she says is a more effective way to track and collect data during floods: a small, inexpensive, standardized CubeSat integrated with artificial intelligence. The little satellites use a multiple of 10- by 10- by 10-centimeter units—which allows manufacturers to develop their batteries, solar panels, computers, and other parts as off-the-shelf components.
The CubeSat takes images of an area and uses pattern recognition to detect flooding, assess infrastructure damage, and track survivors.
Merchant presented her paper on the device at this year’s IEEE Region 3 annual conference, IEEE SoutheastCon.
“IEEE is a foundational part of my growth as a young researcher,” she says. “It turned engineering from my dream to reality.”
Merchant says her interest in disaster response was sparked after learning that it can take several hours for emergency workers to receive satellite data.
Determined to find a faster method, she began researching technologies and discovered what CubeSats can do.
“CubeSats are very agile, scalable, and capable of forming constellations (multiple-satellite groups) that update data in nearly real time,” she says. “The idea that these small satellites—which fit into the palm of your hand—could deliver life-saving insights faster than traditional systems really inspired me to push the concept further.”
Last year Merchant and three of her classmates were accepted into MIT’s Beaver Works Build a CubeSat Challenge, where teams of up to five U.S. high school students were given eight months to develop a satellite capable of completing a space-based research mission.
Merchant’s team—the Satellite Sentinels—built a CubeSat powered by a convolutional neural network (CNN) that can identify heavily impacted flood zones and remotely collect data for disaster relief and environmental monitoring. CNNs analyze image data for pattern recognition.
Merchant was the group’s payload programmer and led the mission’s design and simulation efforts, which included planning, configuring hardware, and developing autonomous software and algorithms to manage the payload.
The team began by creating a 3D model of the device to visualize and refine the placement of its parts. The technology used—including a Raspberry Pi, multiple sensors, and a camera—was housed in a clear plastic cube.
The middle CubeSat was developed by Merchant and her team during the MIT Beaver Works Build a CubeSat Challenge. On the left is a commercial 1U CubeSat while on the right is a prototype of Merchant’s current design. Abigail Merchant
The device, which cost US $310 to build, weighs about 495 grams and was remotely connected to a laptop via Bluetooth during ground-based testing. The computer contains a machine learning algorithm—written by Merchant using Python—that analyzes collected images to detect flooding.
The CubeSat takes a high-definition image of its surroundings every 2 minutes and transmits it to the laptop. The satellite transfers up to 1,500 images daily and stores them on a 16-gigabyte SD card.
The algorithm then analyzes patterns, including changes in the water’s color and the image’s pixel density. When the algorithm detects flooding, the device can alert emergency responders.
“While many existing systems operate on multihour cycles, the CubeSat captures high-resolution images every 2 minutes,” Merchant says. “The system can then trigger alerts that are delivered to first responders via SMS or email.”
To test their system, Merchant and her team built a city model made of Lego blocks in an empty bathtub. They positioned the CubeSat over it, and it took images of the scene. They then added water and dirt to make it look more like a real flood. The CubeSat successfully transferred the images to the laptop, and the algorithm detected the flooding.
Out of 30 teams, the Satellite Sentinels placed third.
Merchant is continuing her research on flood-prevention technologies at Accenture in Richmond, Va., where she works remotely as a payload owner and designer for the company’s CubeSat launch team.
After the MIT program ended, Merchant decided to scale her project. She reached out to her former mentor Chris Hudson, the global technical lead in space cybersecurity at Accenture. He offered her an internship.
Merchant is working to make the transition from prototype to functional product but, she says, needs to overcome obstacles she encountered with her MIT project.
The main one was that the model struggled to detect flooding in variable conditions. It’s because the CNN model needs context, she says. Without it, the model can misinterpret complex visual cues. To fix the issue, Merchant trained the algorithm to spot flooding by identifying colors in individual pixels.
Transmitting images using Bluetooth worked in her bathroom, but it isn’t quite as useful when CubeSats are orbiting 700 kilometers above the ground.
“If you’ve used a Bluetooth headset before, you know it disconnects the moment you walk away from the device it’s connected to,” she says. “That isn’t going to work when the CubeSat constellation is in orbit.”
She suggested the Accenture team switch to SubMiniature Version A (SMA) antennas. The RF antennas connect to the CubeSats using an SMA connector.
“The development process has been one of the most formative experiences of my career so far,” Merchant says. “Working through the payload design and validation and meeting with these teams has given me so much experience, especially for my age.”
Her payload is expected to be launched early next year.
Merchant is an intern at the MIT Computer Science and Artificial Intelligence Laboratory, the school’s largest interdisciplinary lab, with 60 research groups. CSAIL is led by IEEE Fellow Daniela Rus, recipient of the 2025 IEEE Edison Medal.
The internship is remote, and Merchant conducts research in a laboratory at the University of Central Florida, in Orlando.
“IEEE is a foundational part of my growth as a young researcher. It turned engineering from a dream to reality.”
In that role, Merchant is focusing on cognitive cartography, a method for structuring complex information into semantic maps that reveal how ideas and concepts relate to one another. She uses embedding models, a type of machine learning that converts information into numerical representations. The embeddings allow computers to recognize similarities and relationships between concepts, even when they are described in different ways. The approach helps an AI product understand how ideas connect, rather than treating each piece of data as isolated.
“Being one of the youngest people in the lab is daunting,” Merchant says. “However, I’m really excited to learn from engineers and researchers who are working at the cutting edge of the field.”
She says she is hoping to attend MIT or Stanford.
Merchant was introduced to IEEE by Joe Jusai, former finance chair of the IEEE Orlando Section.
Her first personal experience with the organization happened in 2023 while she was conducting research for a science fair project. She was working on a robotic arm that could pick up items using an electroencephalogram and Bluetooth. The project was inspired by her grandmother, who suffers from mobility issues and was wheelchair-bound.
“I kept seeing IEEE mentioned in every regulation and standard I found,” Merchant says. When she learned about an upcoming Orlando Section meeting, she asked her mother to take her.
At the meeting, several members presented their research. Merchant asked Masood Ejaz and Varadraj Gurupur—the chapter chair and cochair—if she could discuss her science fair project.
“After presenting my work, IEEE quickly became a community that has shaped my understanding of what engineering can accomplish,” she says.
She felt on top of the world, she says, when she presented her paper about her CubeSat project at IEEE SouthEastCon.
“It’s one of those experiences that really changes you,” she says.
She is excited to become an IEEE student member when she starts college, she says. She also has her sights set on being elected as its president someday.
“I met Kathleen Kramer at one of my local IEEE events before she was elected IEEE president, and we spoke about my work,” she says. “After she was elected, I realized that I would love to become the president of IEEE someday.
“I hope one day that I can step into the same shoes as her and continue to help IEEE the same way it helped me.”
2025-12-31 22:00:01
Artificial intelligence in 2025 was less about flashy demos and more about hard questions. What actually works? What breaks in unexpected ways? And what are the environmental and economic costs of scaling these systems further?
It was a year in which generative AI slipped from novelty into routine use. Many people got accustomed to using AI tools on the job, getting their answers from AI search, and confiding in chatbots, for better or for worse. It was a year in which the tech giants hyped up their AI agents, and the general public seemed generally uninterested in using them. AI slop also became impossible to ignore—it was even Merriam-Webster’s word of the year.
Throughout it all, IEEE Spectrum’s AI coverage focused on separating signal from noise. Here are the stories that best captured where the field stands now.
Alamy
AI coding assistants have moved from novelty to everyday infrastructure—but not all tools are equally capable or trustworthy. This practical guide by Spectrum contributing editor Matthew S. Smith evaluates today’s leading AI coding systems, examining where they meaningfully boost productivity and where they still fall short. The result is a clear-eyed look at which tools are worth adopting now, and which remain better suited to experimentation.
Amanda Andrade-Rhoades/The Washington Post/Getty Images
As AI’s energy demands raise concerns, water use has emerged as a quieter but equally pressing issue. This article explains how data centers consume water for cooling, why the impacts vary dramatically by region, and what engineers and policymakers can do to reduce the strain. Written by the AI sustainability scholar Shaolei Ren and Microsoft sustainability lead Amy Luers, the article grounds a noisy public debate in data, context, and engineering reality.
iStock
When AI systems fail, they don’t fail like people do. This essay, by legendary cybersecurity guru Bruce Schneier and his frequent collaborator Nathan E. Sanders, explores how machine errors differ in structure, scale, and predictability from human mistakes. Understanding these differences, the researchers argue, is essential for building AI systems that can be responsibly deployed in the real world.
Christie Hemm Klok
In this insider account, John Dean, the cofounder and CEO of WindBorne Systems, tells readers how his team built one of the most technically ambitious AI forecasting systems to date. The company’s approach combines autonomous, long-duration weather balloons that surf the wind with a proprietary AI model called WeatherMesh, which both sends the balloons high-level instructions on where to go next and analyzes the atmospheric data they collect.
WindBorne’s platform can produce high-resolution predictions faster, using far less compute, and with greater accuracy than conventional physics-based methods. In the article, Dean walks readers through the engineering trade-offs, design decisions, and real-world tests that shaped the system from concept to deployment.
Eddie Guy
This elegantly written article is my personal favorite from 2025. In it, Spectrum freelancer Matthew Hutson tackles one of the most consequential and contentious questions in AI today: how to define artificial general intelligence (AGI) and measure progress toward that elusive goal. Drawing on historical context, current debates about benchmarks, and insights from leading researchers, Hutson shows why traditional tests fall short and why creating meaningful benchmarks for AGI is so fraught. Along the way, he explores the deep conceptual challenges of comparing machine and human intelligence.
Bonus: Try the test that AIs take to see how smart they are!
IEEE Spectrum
Each year, I roll up my sleeves as Spectrum’s AI editor and go through the sprawling Stanford AI Index to surface the data that really matters for understanding AI’s progress and pitfalls. 2025’s visual roundup distills a 400-plus-page report into a dozen charts that illuminate key trends in AI economics, energy use, geopolitical competition, and public attitudes.
2025-12-31 21:00:01
Last September, the Defense Advanced Research Projects Agency (DARPA) unleashed teams of robots on simulated mass-casualty scenarios, including an airplane crash and a night ambush. The robots’ job was to find victims and estimate the severity of their injuries, with the goal of helping human medics get to the people who need them the most.
Kimberly Elenberg is a principal project scientist with the Auton Lab of Carnegie Mellon University’s Robotics Institute. Before joining CMU, Elenberg spent 28 years as an army and U.S. Public Health Service nurse, which included 19 deployments and serving as the principal strategist for incident response at the Pentagon.
The final event of the DARPA Triage Challenge will take place in November, and Team Chiron from Carnegie Mellon University will be competing, using a squad of quadruped robots and drones. The team is led by Kimberly Elenberg, whose 28-year career as an army and U.S. Public Health Service nurse took her from combat surgical teams to incident response strategy at the Pentagon.
Why do we need robots for triage?
Kimberly Elenberg: We simply do not have enough responders for mass-casualty incidents. The drones and ground robots that we’re developing can give us the perspective that we need to identify where people are, assess who’s most at risk, and figure out how responders can get to them most efficiently.
When could you have used robots like these?
Elenberg: On the way to one of the challenge events, there was a four-car accident on a back road. For me on my own, that was a mass casualty event. I could hear some people yelling and see others walking around, and so I was able to reason that those people could breathe and move.
In the fourth car, I had to crawl inside to reach a gentleman who was slumped over with an occluded airway. I was able to lift his head until I could hear him breathing. I could see that he was hemorrhaging and feel that he was going into shock because his skin was cold. A robot couldn’t have gotten inside of the car to make those assessments.
This challenge involves enabling robots to remotely collect this data—can they detect heart rate from changes in skin color or hear breathing from a distance? If I’d had these capabilities, it would have helped me identify the person at greatest risk and gotten to them first.
How do you design tech for triage?
Elenberg: The system has to be simple. For example, I can’t have a device that’s going to force a medic to take their hands away from their patient. What we came up with is a vest-mounted Android phone that flips down at chest height to display a map that has the GPS location of all of the casualties on it and their triage priority as colored dots, autonomously populated from the team of robots.
Are the robots living up to the hype?
Elenberg: From my time in service, I know the only way to understand true capability is to build it, test it, and break it. With this challenge, I’m learning through end-to-end systems integration—sensing, communications, autonomy, and field testing in real environments. This is art and science coming together, and while the technology still has limitations, the pace of progress is extraordinary.
What would be a win for you?
Elenberg: I already feel like we’ve won. Showing responders exactly where casualties are and estimating who needs attention most—that’s a huge step forward for disaster medicine. The next milestone is recognizing specific injury patterns and the likely life-saving interventions needed, but that will come.
This article appears in the January 2026 print issue as “Kimberly Elenberg.”
2025-12-31 03:00:02
As a college student, are you concerned that your knowledge alone won’t be enough to impress potential employers? Do you feel you lack the necessary hands-on technical skills to secure a job? Maybe you’ve thought of an engineering solution for a problem in your school or community but are unsure how to take the next step.
I struggled to bridge the gap between classroom theory and real-world application. But when you combine academic knowledge with practical projects that solve a societal problem with technology, you can ace any interview.
You don’t have to navigate the journey alone. Here are some lessons I learned as a student.
I’m a cloud support engineer at a company in Hyderabad, India. I’m also an active IEEE volunteer as one of its young professionals, an impact creator, and a brand ambassador.
In my role as impact creator, I share my insights on engineering, computing, and technology with the news media to highlight trends and consumer habits. As a brand ambassador, I educate students and professionals on how to display IEEE branding on websites, newsletters, banners, event materials, and other items.
When I was in my first semester as a computer engineering student at Guru Gobind Singh Indraprastha University, in New Delhi, I became frustrated by the long lines to check books in and out of the library of the affiliated college, the HMR Institute of Technology and Management. Even getting a new library card took a long time. I was determined to solve the problem.
For six months, I singlehandedly developed a software program to scan student ID cards and speed up the processes. I received the school’s first Technocrat Award for my efforts.
Word got out about my programming skills, and I received many requests to help solve other problems. An intriguing one was from the director of India’s largest national broadcasting company, All India Radio. I was asked to streamline its accounting process. At the time, the company used only Microsoft Excel along with a pen-and-paper system. It took me just six months to build a full-stack accounting software program to make the process significantly more efficient.
“When you combine academic knowledge with practical projects that solve a societal problem with technology, you can ace any interview.”
That opportunity was a big break for me. The technology I created redefined the broadcaster’s operations and could be used in its other offices, expanding my reach.
In my first corporate job interview after graduating from university, the interviewer was surprised to learn that I’d published 15 research papers, completed 15 projects, and even had a pending patent application. (The government has since granted the patent.)
The human resources representative and the technical-round interviewer weren’t expecting a recent graduate to have research published, and they were impressed. I was excited to see their reactions.
Students need to understand the importance of doing something exceptional beyond learning theory and concepts. Having practical skills before leaving school is a great way to set yourself apart from other new engineering graduates.
Before taking on any new projects, I ask myself five simple questions. They might seem obvious, but some of the details are often forgotten. Even as a student, when you start working with clients, you must have a process for gathering the information you need.
When it comes to getting the correct information, I focus on the five W’s: who, what, why, when, and where.
Once I get those answers, I begin using design thinking to strategize.
My clients generally are looking to improve existing solutions rather than starting from scratch. I must know what is and isn’t working with the current program.
Remember that although the process might be easy for you, it might be new for your client.
Here are what I consider to be the five stages of the process.
Understand the problem. Once you identify the client’s issue, the next step is to listen to the client in full without making judgments. You need to really understand the pain points and why the current application isn’t working. Listen fully, ask questions, and try to empathize with the client’s issues.
Research and ideation. Do your own research. It’s essential to conduct field research to better understand the client’s requirements. One of my projects was to help farmers secure loans directly from the Indian government, rather than go through loan service agencies and banks. The farmers were frustrated over how long it took to get loans. While doing my research, I was shocked by the high fees the agencies charged to process the necessary paperwork.
I wouldn’t have learned about that from just reading research papers. You have to explore your client’s pain points.
Next, start brainstorming. Consider how you can improve the current model. Maybe you should conduct research to find other products that might solve the problem. Also consider redesigning the current version. Let yourself think of as many ideas as possible, then review them with your client and request feedback.
That can give you a clear idea of what the client likes about the options, and it can help you better direct the rest of your research and ideation.
Technology research and prototyping. By this stage, you’ve created a short list of ideas to address your client’s pain points. Next, research all the technologies you need to use. If you need training, use learning platforms such as Coursera, EdX, the IEEE Learning Network, Udacity, and Udemy.
Once you identify and learn the technology needed, it’s time to create the first prototype.
Test and improve. Test the prototype, gather feedback from your client while you take meticulous notes, and then revise it according to the feedback.
That helps you understand what improvements are needed and helps identify gaps in your model. It gets you closer to the client’s requirements. Use the information to refine the design and build the product.
It is important to note that this stage might go through multiple iterations. You might have to continue to improve the results until the design works for the client. Refer back often to your original notes on the pain points to ensure you haven’t forgotten anything in the final design.
Protect your intellectual property. Many students and young professionals skip the important step of safeguarding their idea such as copyrighting it, publishing a paper, or filing a patent. I have seen many students who present their ideas at hackathons and competitions and assume that receiving cash prizes is enough to list on their résumé. They really should protect their ideas.
After speaking at more than 1,000 IEEE workshops and other events in more than 25 countries, I’m concerned that students aren’t using their technical knowledge to its fullest potential. To learn more about how to use your time and skills as a new engineer, view my YouTube channel.
Don’t wait for an opportunity to knock on your door. Create your own opportunities by participating in IEEE technical and nontechnical events and getting involved with the organization’s student service-learning program, EPICS in IEEE.
The participation, volunteering, and networking (PVN) model of IEEE—which I coined—works.