2025-12-09 03:00:03
If you’ve shopped on Amazon in the past few months, you might have noticed it has gotten easier to find what you’re looking for. Listings now have more images, detailed product names, and better descriptions. The website’s predictive search feature uses the listing updates to anticipate needs and suggests a list of items in real time as you type in the search bar.
The improved shopping experience is thanks to Abhishek Agrawal and his Catalog AI system. Launched in July, the tool collects information from across the Internet about products being sold on Amazon and, based on the data, updates listings to make them more detailed and organized.
Employer
Amazon Web Services in Seattle
Job title
Engineering leader
Member grade
Senior member
Alma maters
University of Allahabad in India and the Indian Statistical Institute in Kolkata
Agrawal is an engineering leader at Amazon Web Services in Seattle. An expert in AI and machine learning, the IEEE senior member worked on Microsoft’s Bing search engine before moving to Amazon. He also developed several features for Microsoft Teams, the company’s direct messaging platform.
“I’ve been working in AI for more than 20 years now,” he says. ”Seeing how much we can do with technology still amazes me.”
He shares his expertise and passion for the technology as an active member and volunteer at the IEEE Seattle Section. He organizes and hosts career development workshops that teach people to create an AI agent, which can perform tasks autonomously with minimal human oversight.
Agrawal was born and raised in Chirgaon, a remote village in Uttar Pradesh, India. When he was growing up, no one in Chirgaon had a computer. His family owned a pharmacy, which Agrawal was expected to join after he graduated from high school. Instead, his uncle and older brother encouraged him to attend college and find his own passion.
He enjoyed mathematics and physics, and he decided to pursue a bachelor’s degree in statistics at the University of Allahabad. After graduating in 1996, he pursued a master’s degree in statistics, statistical quality control, and operations research at the Indian Statistical Institute in Kolkata.
While at the ISI, he saw a computer for the first time in the laboratory of Nikhil R. Pal, an electronics and communication sciences professor. Pal worked on identifying abnormal clumps of cells in mammogram images using the fuzzy c-means model, a data-clustering technique employing a machine learning algorithm.
Agrawal earned his master’s degree in 1998. He was so inspired by Pal’s work, he says, that he stayed on at the university to earn a second master’s degree, in computer science.
After graduating in 2001, he joined Novell as a senior software engineer working out of its Bengaluru office in India. He helped develop iFolder, a storage platform that allows users across different computers to back up, access, and manage their files.
After four years, Agrawal left Novell to join Microsoft as a software design engineer, working at the company’s Hyderabad campus in India. He was part of a team developing a system to upgrade Microsoft’s software from XP to Vista.
Two years later, he was transferred to the group developing Bing, a replacement for Microsoft’s Live Search, which had been launched in 2006.
Live Search had a traffic rate of less than 2 percent and struggled to keep up with Google’s faster-paced, more user-friendly system, Agrawal says. He was tasked with improving search results but, Agrawal says, he and his team didn’t have enough user search data to train their machine learning model.
Data for location-specific queries, such as nearby coffee shops or restaurants, was especially important, he says.
To overcome those challenges, the team used deterministic algorithms to create a more structured search. Such algorithms give the same answers for any query that uses the same specific terms. The process gets results by taking keywords—such as locations, dates, and prices—and finding them on webpages. To help the search engine understand what users need, Agrawal developed a query clarifier that asked them to refine their search. The machine learning tool then ranked the results from most to least relevant.
To test new features before they were launched, Agrawal and his team built an online A/B experimentation platform. Controlled tests were completed on different versions of the products, and the platform ran performance and user engagement metrics, then it produced a scorecard to show changes for updated features.
Bing launched in 2009 and is now the world’s second-largest search engine, according to Black Raven.
Throughout his 10 years of working on the system, Agrawal upgraded it. He also worked with the advertising department to improve Microsoft’s services on Bing. Ads relevant to a person’s search are listed among the search results.
“The work seems easy,” Agrawal says, “but behind every search engine are hundreds of engineers powering ads, query formulations, rankings, relevance, and location detection.”
Agrawal was promoted to software development manager in 2010. Five years later he was transferred to Microsoft’s Seattle offices. At the time, the company was deploying new features for existing platforms without first testing them to ensure effectiveness. Instead, they measured their performance after release, Agrawal says, and that was wreaking havoc.
He proposed using his online A/B experimentation platform on all Microsoft products, not just Bing. His supervisor approved the idea. In six months Agrawal and his team modified the tool for company-wide use. Thanks to the platform, he says, Microsoft was able to smoothly deploy up-to-date products to users.
After another two years, he was promoted to principal engineering manager of Microsoft Teams, which was facing issues with user experience, he says.
“Many employees received between 50 and 100 messages a day—which became overwhelming for them,” Agrawal says. To lessen the stress, he led a team that developed the system’s first machine learning feature: Trending. It prioritized the five most important messages users should focus on. Agrawal also led the launch of incorporating emoji reactions, screen sharing, and video calls for Teams.
In 2020 he was ready for new experiences, he says, and he left Microsoft to join Amazon as an engineering leader.
Agrawal led an Amazon team that manually collected information about products from the company’s retail catalog to create a glossary. The data, which included product dimensions, color, and manufacturer, was used to standardize the language found in product descriptions to keep listings more consistent.
That is especially important when it comes to third-party sellers, he notes. Sellers listing a product had been entering as much or as little information as they wanted. Agrawal built a system that automatically suggests language from the glossary as the seller types.
He also developed an AI algorithm that utilizes the glossary’s terminology to refine search results based on what a user types into the search bar. When a shopper types “red mixer,” for example, the algorithm lists products under the search bar that match the description. The shopper can then click on a product from the list.
In 2023 the retailer’s catalog became too large for Agrawal and his team to collect information manually, so they built an AI tool to do it for them. It became the foundation for Amazon’s Catalog AI system.
After gathering information about products from around the Web, Catalog AI uses large language models to update Amazon listings with missing information, correct errors, and rewrite titles and product specifications to make them clearer for the customer, Agrawal says.
The company expects the AI tool to increase sales this year by US $7.5 billion, according to a Fox News report in July.
Since Agrawal joined IEEE last December, he has been elevated to senior member and has become an active volunteer.
“Being part of IEEE has opened doors for collaboration, mentorship, and professional growth,” he says. “IEEE has strengthened both my technical knowledge and my leadership skills, helping me progress in my career.”
Agrawal is the social media chair of the IEEE Seattle Section. He is also vice chair of the IEEE Computational Intelligence Society.
He was a workshop cochair for the IEEE New Era AI World Leaders Summit, which was held from 5 to 7 December in Seattle. The event brought together government and industry leaders, as well as researchers and innovators working on AI, intelligent devices, unmanned aerial vehicles, and similar technologies. They explored how new tools could be used in cybersecurity, the medical field, and national disaster rescue missions.
Agrawal says he stays up to date on cutting-edge technologies by peer-reviewing 15 IEEE journals.
“The organization plays a very important role in bringing authenticity to anything that it does,” he says. “If a journal article has the IEEE logo, you can believe that it was thoroughly and diligently reviewed.”
2025-12-08 22:00:02
I was interviewing a 72-year-old retired accountant who had unplugged his smart glucose monitor. He explained that he “didn’t know who was looking” at his blood sugar data.
This wasn’t a man unfamiliar with technology—he had successfully used computers for decades in his career. He was of sound mind. But when it came to his health device, he couldn’t find clear answers about where his data went, who could access it, or how to control it. The instructions were dense, and the privacy settings were buried in multiple menus. So, he made what seemed like the safest choice: he unplugged it. That decision meant giving up real-time glucose monitoring that his doctor had recommended.
The healthcare IoT (Internet of Things) market is projected to exceed $289 billion by 2028, with older adults representing a major share of users. These devices are fall detectors, medication reminders, glucose monitors, heart rate trackers, and others that enable independent living. Yet there’s a widening gap between deployment and adoption. According to an AARP survey, 34% of adults over 50 list privacy as a primary barrier to adopting health technology. That represents millions of people who could benefit from monitoring tools but avoid them because they don’t feel safe.
In my study at the University of Denver’s Ritchie School of Engineering and Computer Science, I surveyed 22 older adults and conducted in-depth interviews with nine participants who use health-monitoring devices. The findings revealed a critical engineering failure: 82% understood security concepts like two-factor authentication and encryption, yet only 14% felt confident managing their privacy when using these devices. In my research, I also evaluated 28 healthcare apps designed for older adults and found that 79% lacked basic breach-notification protocols.
One participant told me, “I know there’s encryption, but I don’t know if it’s really enough to protect my data.” Another said, “The thought of my health data getting into the wrong hands is very concerning. I’m particularly worried about identity theft or my information being used for scams.”
This is not a user knowledge problem; it’s an engineering problem. We’ve built systems that demand technical expertise to operate safely, then handed them to people managing complex health needs while navigating age-related changes in vision, cognition, and dexterity.
To quantify the issues with privacy setting transparency, I developed the Privacy Risk Assessment Framework (PRAF), a tool that scores healthcare apps across five critical domains.
First, the regulatory compliance domain evaluates whether apps explicitly state adherence to the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), or other data protection standards. Just claiming to be compliant is not enough—they must provide verifiable evidence.
Second, the security mechanisms domain assesses the implementation of encryption, access controls, and, most critically, breach-notification protocols that alert users when their data may have been compromised. Third, in the usability and accessibility domain, the tool examines whether privacy interfaces are readable and navigable for people with age-related visual or cognitive changes. Fourth, data-minimization practices evaluate whether apps collect only necessary information and clearly specify retention periods. Finally, third-party sharing transparency measures whether users can easily understand who has access to their data and why.
When I applied PRAF to 28 healthcare apps commonly used by older adults, the results revealed systemic gaps. Only 25% explicitly stated HIPAA compliance, and just 18% mentioned GDPR compliance. Most alarmingly, 79% lacked breach notification protocols, which means that the users may never find out if their data was compromised. The average privacy policy readability scored at a 12th-grade level, even though research shows that the average reading level of older adults is at an 8th grade level. Not a single app included accessibility accommodations in their privacy interfaces.
Consider what happens when an older adult opens a typical health app. They face a multi-page privacy policy full of legal terminology about “data controllers” and “processing purposes,” followed by settings scattered across multiple menus. One participant told me, “The instructions are hard to understand, the print is too small, and it’s overwhelming.” Another explained, “I don’t feel adequately informed about how my data is collected, stored, and shared. It seems like most of these companies are after profit, and they don’t make it easy for users to understand what’s happening with their data.”
When protection requires a manual people can’t read, two outcomes follow: they either skip security altogether leaving themselves vulnerable, or abandon the technology entirely, forfeiting its health benefits.
We need to treat trust as an engineering specification, not a marketing promise. Based on my research findings and the specific barriers older adults face, three approaches address the root causes of distrust.
The first approach is adaptive security defaults. Rather than requiring users to navigate complex configuration menus, devices should ship with pre-configured best practices that automatically adjust to data sensitivity and device type. A fall detection system doesn’t need the same settings as a continuous glucose monitor. This approach draws from the principle of “security by default” in systems engineering.
Biometric or voice authentication can replace passwords that are easily forgotten or written down. The key is removing the burden of expertise while maintaining strong protection. As one participant put it: “Simplified security settings, better educational resources, and more intuitive user interfaces will be beneficial.”
The second approach is real-time transparency. Users shouldn’t have to dig through settings to see where their data goes. Instead, notification systems should show each data access or sharing event in plain language. For example: “Your doctor accessed your heart-rate data at 2 p.m. to review for your upcoming appointment.” A single dashboard should summarize who has access and why.
This addresses a concern that came up repeatedly in my interviews: users want to know who is seeing their data and why. The engineering challenge here isn’t technical complexity, it’s designing interfaces that convey technical realities in language anyone can understand. Such systems already exist in other domains; banking apps, for instance, send immediate notifications for every transaction. The same principle applies to health data, where the stakes are arguably higher.
The third approach is invisible security updates. Manual patching creates vulnerability windows. Automatic, seamless updates should be standard for any device handling health data, paired with a simple status indicator so users can confirm protection at a glance. As one participant said, “The biggest issue that we as seniors have is the fact that we don’t remember our passwords... The new technology is surpassing the ability of seniors to keep up with it.” Automating updates removes a significant source of anxiety and risk.
We can keep building healthcare IoT the way we have: fast, feature-rich, and fundamentally untrustworthy. Or, we can engineer systems that are transparent, secure, and usable by design. Trust isn’t something you market through slogans or legal disclaimers. It’s something you engineer, line by line, into the code itself. For older adults relying on technology to maintain independence, that kind of engineering matters more than any new feature we could add. Every unplugged glucose monitor, every abandoned fall detector, every health app deleted out of confusion or fear represents not just a lost sale but a missed opportunity to support someone’s health and autonomy.
The challenge of privacy in healthcare IoT goes beyond fixing existing systems, it requires reimagining how we communicate privacy itself. My ongoing research builds on these findings through an AI-driven Data Helper, a system that uses large language models to translate dense legal privacy policies into short, accurate, and accessible summaries for older adults. By making data practices transparent and comprehension measurable, this approach aims to turn compliance into understanding and trust, thus advancing the next generation of trustworthy digital health systems.
2025-12-06 03:00:02
Technology evolves rapidly, and innovation is key to business survival, so mentoring young professionals, promoting entrepreneurship, and connecting tech startups to a global network of experts and resources are essential.
Some IEEE volunteers do all of the above and more as part of the IEEE Entrepreneurship Ambassador Program.
The program was launched in 2018 in IEEE Region 8 (Europe, Middle East, and Africa) thanks to a grant from the IEEE Foundation. The ambassadors organize networking events with industry representatives to help IEEE young professionals and student members achieve their entrepreneurial endeavors and strengthen their technical, interpersonal, and business skills. The ambassadors also organize pitch competitions in their geographic area.
The ambassador program launched this year in Region 10 (Asia Pacific).
Last year the program was introduced in Region 9 (Latin America) with funding from the Taenzer Memorial Fund. The results of the program’s inaugural year were impressive: 13 ambassadors organized events in Bolivia, Brazil, Colombia, Ecuador, Mexico, Panama, Peru, and Uruguay.
“The program is beneficial because it connects entrepreneurs with industry professionals, fosters mentorship, helps young professionals build leadership skills, and creates opportunities for startup sponsorships,” says Susana Lau, vice chair of IEEE Entrepreneurship in Latin America. “The program has also proven successful in attracting IEEE volunteers to serve as ambassadors and helping to support entrepreneurship and startup ventures.”
Lau, an IEEE senior member, is a past president of the IEEE Panama Section and an active IEEE Women in Engineering volunteer.
People who participated in the Region 9 program say the experience was life-changing, both personally and professionally.
Pedro José Pineda, whose work was recognized with one of the region’s two Top Ambassador Awards, says he’s been able to “expand international collaborations and strengthen the innovation ecosystem in Latin America.
“It’s more than an award,” the IEEE member says. “It’s an opportunity to create global impact from local action.”
“This remarkable experience has opened new doors for my future career within IEEE, both nationally and globally.”—Vitor Paiva
The region’s other Top Ambassador recipient was Vitor Paiva of Natal, Brazil. He had the opportunity to attend this year’s IEEE Rising Stars in Las Vegas—his first international experience outside Brazil.
After participating in the program, the IEEE student member volunteered with its regional marketing committee.
“I was proud to showcase Brazil’s IEEE community while connecting with some of IEEE’s most influential leaders,” Paiva, a student at the Universidade Federal do Rio Grande do Norte, says. “This remarkable experience has opened new doors for my future career within IEEE, both nationally and globally.”
The IEEE Foundation says it will invest in the regional programs by funding the grants presented to the winners of the regional pitch competitions, similar to the funding for Region 9. The goal is to hold a worldwide competition, Lau says.
The ongoing expansion is a testament to the program’s efforts, says Christopher G. Wright, senior manager of programs and governance at the IEEE Foundation.
“I’ve had the pleasure of working on the grants for the IEEE Entrepreneurship Ambassador Program team over the years,” Wright says, “and I am continually impressed by the team’s dedication and the program’s evolution.”
To learn more about the program in your region or to apply to become an ambassador, visit the IEEE Entrepreneurship website and search for your region.2025-12-06 01:30:02
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.
Enjoy today’s videos!
EPFL scientists have integrated discarded crustacean shells into robotic devices, leveraging the strength and flexibility of natural materials for robotic applications.
[ EPFL ]
Finally, a good humanoid robot demo!
Although having said that, I never trust videos demos where it works really well once, and then just pretty well every other time.
[ LimX Dynamics ]
Thanks, Jinyan!
I understand how these structures work, I really do. But watching something rigid extrude itself from a flexible reel will always seem a little magical.
[ AAAS ]
Thanks, Kyujin!
I’m not sure what “industrial grade” actually means, but I want robots to be “automotive grade,” where they’ll easily operate for six months or a year without any maintenance at all.
[ Pudu Robotics ]
Thanks, Mandy!
When you start to suspect that your robotic EV charging solution costs more than your car.
[ Flexiv ]
Yeah uh if the application for this humanoid is actually making robot parts with a hammer and anvil, then I’d be impressed.
[ EngineAI ]
Researchers at Columbia Engineering have designed a robot that can learn a human-like sense of neatness. The researchers taught the system by showing it millions of examples, not teaching it specific instructions. The result is a model that can look at a cluttered tabletop and rearrange scattered objects in an orderly fashion.
[ Paper ]
Why haven’t we seen this sort of thing in humanoid robotics videos yet?
[ HUCEBOT ]
While I definitely appreciate in-the-field testing, it’s also worth asking to what extent your robot is actually being challenged by the in-the-field field that you’ve chosen.
[ DEEP Robotics ]
Introducing HMND 01 Alpha Bipedal — autonomous, adaptive, designed for real-world impact. Built in 5 months, walking stably after 48 hours of training.
[ Humanoid ]
Unitree says that “this is to validate the overall reliability of the robot” but I really have to wonder how useful this kind of reliability validation actually is.
[ Unitree ]
This University of Pennsylvania GRASP on Robotics Seminar is by Jie Tan from Google DeepMind, on “Gemini Robotics: Bringing AI into the Physical World.”
Recent advancements in large multimodal models have led to the emergence of remarkable generalist capabilities in digital domains, yet their translation to physical agents such as robots remains a significant challenge. In this talk, I will present Gemini Robotics, an advanced Vision-Language-Action (VLA) generalist model capable of directly controlling robots. Furthermore, I will discuss the challenges, learnings and future research directions on robot foundation models.
2025-12-05 07:30:02
When people want a clear-eyed take on the state of artificial intelligence and what it all means, they tend to turn to Melanie Mitchell, a computer scientist and a professor at the Santa Fe Institute. Her 2019 book, Artificial Intelligence: A Guide for Thinking Humans, helped define the modern conversation about what today’s AI systems can and can’t do.
Melanie Mitchell
Today at NeurIPS, the year’s biggest gathering of AI professionals, she gave a keynote titled “On the Science of ‘Alien Intelligences’: Evaluating Cognitive Capabilities in Babies, Animals, and AI.” Ahead of the talk, she spoke with IEEE Spectrum about its themes: Why today’s AI systems should be studied more like nonverbal minds, what developmental and comparative psychology can teach AI researchers, and how better experimental methods could reshape the way we measure machine cognition.
You use the phrase “alien intelligences” for both AI and biological minds like babies and animals. What do you mean by that?
Melanie Mitchell: Hopefully you noticed the quotation marks around “alien intelligences.” I’m quoting from a paper by [the neural network pioneer] Terrence Sejnowski where he talks about ChatGPT as being like a space alien that can communicate with us and seems intelligent. And then there’s another paper by the developmental psychologist Michael Frank who plays on that theme and says, we in developmental psychology study alien intelligences, namely babies. And we have some methods that we think may be helpful in analyzing AI intelligence. So that’s what I’m playing on.
When people talk about evaluating intelligence in AI, what kind of intelligence are they trying to measure? Reasoning or abstraction or world modeling or something else?
Mitchell: All of the above. People mean different things when they use the word intelligence, and intelligence itself has all these different dimensions, as you say. So, I used the term cognitive capabilities, which is a little bit more specific. I’m looking at how different cognitive capabilities are evaluated in developmental and comparative psychology and trying to apply some principles from those fields to AI.
You say that the field of AI lacks good experimental protocols for evaluating cognition. What does AI evaluation look like today?
Mitchell: The typical way to evaluate an AI system is to have some set of benchmarks, and to run your system on those benchmark tasks and report the accuracy. But often it turns out that even though these AI systems we have now are just killing it on benchmarks, they’re surpassing humans, that performance doesn’t often translate to performance in the real world. If an AI system aces the bar exam, that doesn’t mean it’s going to be a good lawyer in the real world. Often the machines are doing well on those particular questions but can’t generalize very well. Also, tests that are designed to assess humans make assumptions that aren’t necessarily relevant or correct for AI systems, about things like how well a system is able to memorize.
As a computer scientist, I didn’t get any training in experimental methodology. Doing experiments on AI systems has become a core part of evaluating systems, and most people who came up through computer science haven’t had that training.
What do developmental and comparative psychologists know about probing cognition that AI researchers should know too?
Mitchell: There’s all kinds of experimental methodology that you learn as a student of psychology, especially in fields like developmental and comparative psychology because those are nonverbal agents. You have to really think creatively to figure out ways to probe them. So they have all kinds of methodologies that involve very careful control experiments, and making lots of variations on stimuli to check for robustness. They look carefully at failure modes, why the system [being tested] might fail, since those failures can give more insight into what’s going on than success.
Can you give me a concrete example of what these experimental methods look like in developmental or comparative psychology?
Mitchell: One classic example is Clever Hans. There was this horse, Clever Hans, who seemed to be able to do all kinds of arithmetic and counting and other numerical tasks. And the horse would tap out its answer with its hoof. For years, people studied it and said, “I think it’s real. It’s not a hoax.” But then a psychologist came around and said, “I’m going to think really hard about what’s going on and do some control experiments.” And his control experiments were: first, put a blindfold on the horse, and second, put a screen between the horse and the question asker. Turns out if the horse couldn’t see the question asker, it couldn’t do the task. What he found was that the horse was actually perceiving very subtle facial expression cues in the asker to know when to stop tapping. So it’s important to come up with alternative explanations for what’s going on. To be skeptical not only of other people’s research, but maybe even of your own research, your own favorite hypothesis. I don’t think that happens enough in AI.
Do you have any case studies from research on babies?
Mitchell: I have one case study where babies were claimed to have an innate moral sense. The experiment showed them videos where there was a cartoon character trying to climb up a hill. In one case there was another character that helped them go up the hill, and in the other case there was a character that pushed them down the hill. So there was the helper and the hinderer. And the babies were assessed as to which character they liked better—and they had a couple of ways of doing that—and overwhelmingly they liked the helper character better. [Editor's note: The babies were 6 to 10 months old, and assessment techniques included seeing whether the babies reached for the helper or the hinderer.]
But another research group looked very carefully at these videos and found that in all of the helper videos, the climber who was being helped was excited to get to the top of the hill and bounced up and down. And so they said, “Well, what if in the hinderer case we have the climber bounce up and down at the bottom of the hill?” And that completely turned around the results. The babies always chose the one that bounced.
Again, coming up with alternatives, even if you have your favorite hypothesis, is the way that we do science. One thing that I’m always a little shocked by in AI is that people use the word skeptic as a negative: “You’re an LLM skeptic.” But our job is to be skeptics, and that should be a compliment.
Both those examples illustrate the theme of looking for counter explanations. Are there other big lessons that you think AI researchers should draw from psychology?
Mitchell: Well, in science in general the idea of replicating experiments is really important, and also building on other people’s work. But that’s sadly a little bit frowned on in the AI world. If you submit a paper to NeurIPS, for example, where you replicated someone’s work and then you do some incremental thing to understand it, the reviewers will say, “This lacks novelty and it’s incremental.” That’s the kiss of death for your paper. I feel like that should be appreciated more because that’s the way that good science gets done.
Going back to measuring cognitive capabilities of AI, there’s lots of talk about how we can measure progress towards AGI. Is that a whole other batch of questions?
Mitchell: Well, the term AGI is a little bit nebulous. People define it in different ways. I think it’s hard to measure progress for something that’s not that well defined. And our conception of it keeps changing, partially in response to things that happen in AI. In the old days of AI, people would talk about human-level intelligence and robots being able to do all the physical things that humans do. But people have looked at robotics and said, “Well, okay, it’s not going to get there soon. Let’s just talk about what people call the cognitive side of intelligence,” which I don’t think is really so separable. So I am a bit of an AGI skeptic, if you will, in the best way.
2025-12-05 04:45:47
The world’s first mass-produced ethanol car, the Fiat 147, motored onto Brazilian roads in 1979. The vehicle crowned decades of experimentation in the country with sugar-cane (and later, corn-based and second-generation sugar-cane waste) ethanol as a homegrown fuel. When Chinese automaker BYD introduced a plug-in hybrid designed for Brazil in October, equipped with a flex-fuel engine that lets drivers choose to run on any ratio of gasoline and ethanol or access plug-in electric power, the move felt like the latest chapter in a long national story.
The new engine, designed for the company’s best-selling compact SUV, the Song Pro, is the first plug-in hybrid engine dedicated to biofuel, according to Wang Chuanfu, BYD’s founder and CEO.
Margaret Wooldridge, a professor of mechanical engineering at the University of Michigan, in Ann Arbor, says the engine’s promise is not in inventing entirely new technology, but in making it accessible.
“The technology existed before,” says Wooldridge, who specializes in hybrid systems, “but fuel switching is expensive, and I’d expect the combinations in this engine to come at a fairly high price tag. BYD’s real innovation is pulling it into a price range where everyday drivers in Brazil can actually choose ratios of ethanol and gasoline, as well as electric.”
BYD Song Pro vehicles with this new engine were initially priced in a promotion at around US $25,048, with a list price around $35,000. For comparison, another plug-in hybrid vehicle, Toyota’s 2026 Prius Prime, starts at $33,775. The engine is the product of an $18.5 million investment by BYD and a collaboration between Brazilian and Chinese scientists. It adds to Brazil’s history of ethanol use that began in the 1930s and progressed from ethanol-only to flex-fuel vehicles, providing consumers a tool kit to respond to changing fuel prices, ongoing drought like Brazil experienced in the 1980s, or emissions goals.
An engine switching between gasoline and ethanol needs a sensor that can reconcile two distinct fuel-air mixtures. “Integrating that control system, especially in a hybrid architecture, is not trivial,” says Wooldridge. “But BYD appears to have engineered it in a way that’s cost-effective.”
By leveraging a smaller, downsized hybrid engine, the company is likely able to design the engine to be optimal over a smaller speed map—a narrower, specific range of speeds and power output—avoiding some efficiency compromises that have long plagued flex-fuel power-train engines, says Wooldridge.
In general, standard flex-fuel vehicles (FFVs) have an internal combustion engine and can operate on gasoline and any blend of gasoline and ethanol up to 83 percent, according to the U.S. Department of Energy. FFV engines have only one fuel system, and mostly use components that are the same as those found in gasoline-only cars. To compensate for ethanol’s different chemical properties and power output compared to gasoline, special components modify the fuel pump and fuel-injection system. In addition, FFV engines have engine control modules calibrated to accommodate ethanol’s higher oxygen content.
“Flex-fuel gives consumers flexibility,” Wooldridge says. “If you’re using ethanol, you can run at a higher compression ratio, allowing molecules to be squeezed into a smaller space to allow for faster, more powerful and more efficient combustion. Increasing that ratio boosts efficiency and lowers knock—but if you’re also tying in electric drive, the system can stay optimally efficient across different modes,” she adds.
Jennifer Eaglin, a historian of Brazilian energy at Ohio State University, in Columbus, says that BYD is tapping into something deeply rooted in the culture of Brazil, the world’s seventh-most populous country (with a population of around 220 million).
“Brazil has built an ethanol-fuel system that’s durable and widespread,” Eaglin says. “It’s no surprise that a company like BYD, recognizing that infrastructure, would innovate to give consumers more options. This isn’t futuristic—it’s a continuation of a long national experiment.”