Category: UAP Intelligence

Most of us have heard the adage “an ounce of prevention is worth a pound of cure.” But when it comes to personal health, many people overlook preventative measures such as diet and exercise. Instead, they tend to rely on medical professionals to save the day after they’ve gotten sick.

Ximena Montserrat Ramirez Aguilar is working to change that by educating her fellow Mexicans about how to manage their health so they can avoid undergoing treatment for preventable conditions such as Type 2 diabetes and its associated conditions affecting the eyes, cardiovascular system, brain, heart, kidneys, and other organs.

Ximena Montserrat Ramirez Aguilar

MEMBER GRADE:

Student member

UNIVERSITY:

Universidad Autónoma de Nuevo León, in Monterrey, Mexico

MAJOR:

Biomedical engineering

Ramirez envisions her career as advancing health through disease prevention, but she acknowledges that, as an undergraduate, she is still discovering how to turn her vision into reality. A senior studying biomedical engineering at the Universidad Autónoma de Nuevo León (UANL), in Monterrey, Mexico, she is the founding chair of her school’s IEEE Engineering in Medicine and Biology Society (EMBS) student branch. The student member’s research interests in neuroengineering and artificial intelligence are shaping her vision for the future of health care.

“I’ve always been passionate about technology and health,” she says. “Biomedical engineering is giving me a way to combine these two worlds and work on solutions that make a real difference in people’s lives.”

Her growing influence in IEEE coupled with her academic achievements signal a promising, influential career.

From chemistry to caring

Ramirez was born in Zacatecas, Mexico, known for its silver mines, agriculture, and strong cultural pride. From a young age, she loved science—particularly chemistry—and thrived in schools designated for advanced learners.

Her first exposure to the health care field came during high school, when she trained as a nursing technician. Her high school curriculum was organized as a co-op program, which included traditional classes alternating with internships in nursing. Ramirez interned at the Hospital Universitario Dr. Jose Eleuterio Gonzalez in Monterrey, Mexico.

Alternating between the academic and vocational tracks allowed her to graduate with a diploma and a technical degree at the same time. Speaking of her early experiences, she says, “I saw how many patients struggled, not just with their conditions but also with the logistics of seeking and coordinating treatment,” she says. “That made me want to work at the intersection of medicine and innovation.”

With her father working as a materials engineer and her mother as an accountant, she grew up in a household where technical problem-solving and analytical thinking were part of daily life.

That blend of influences reinforced her decision to pursue engineering as a career rather than the medical field, she says.

Exploring neuroengineering and AI

Since beginning her studies at UANL in 2021, Ramirez has focused on neuroengineering, one of three specializations the school offers. She has explored the role artificial intelligence plays in diagnosing and treating conditions including Alzheimer’s disease, depression, epilepsy, and schizophrenia.

Through the IEEE mentoring program, she received guidance from global experts including a doctor from India who helped refine her early AI projects.

Her work quickly evolved from class assignments to projects with real-world potential.

“The project I’m most excited about has not been published, but it mainly consisted of using convolutional neural networks in medical image processing (MRI) and machine learning in the diagnosis of neurodegenerative diseases,” she says.

This year she broadened her scope by attending the IEEE International Conference on Robotics and Automation in Atlanta, where she gained exposure to both industrial and academic applications of robotics.

“In Mexico, people usually don’t think about their health until they’re already sick. I want to focus on using technology and education to keep people healthy.”

Currently she is an intern at Auna, a health care network in Latin America. She contributes to improving the patient experience in hospitals across Mexico, Colombia, and Peru.

“I design projects aimed at improving the quality of care and making the hospital intervention more effective for patients across different stages: prevention/wellness, diagnosis, hospitalization, rehabilitation, and post-discharge follow-up,” She declined to provide specific examples, citing medical confidentiality agreements.

“My internship is about finding ways to make health care not just effective but also more humane,” she says. “It’s about improving processes so patients feel cared for—from the moment they enter the hospital until they leave.”

Finding leadership and purpose in IEEE

Ramirez founded the IEEE EMBS student branch in 2023. As chair, she represents the branch at IEEE Region 9 meetings, where she advocates for mentorship opportunities and collaboration with other IEEE groups.

Through her involvement, she says, she has gained not only technical knowledge but also critical soft skills in leadership, time management, and teamwork.

“IEEE taught me how to lead with empathy and how to work with people from different backgrounds,” she says. “It has expanded my vision beyond Mexico, showing me challenges and innovations happening all over the world.”

She says she plans to pursue a master’s degree abroad—potentially in public health or AI for medical devices—and ultimately a Ph.D. Her long-term goal is to launch a business focused on developing health care innovations, specifically in disease prevention.

A future built on innovation

For Ramirez, improving health care means more than developing cutting-edge technology. It also involves rethinking how people understand and manage their own health.

“In Mexico, people usually don’t think about their health until they’re already sick,” she says. “I want to focus on using technology and education to keep people healthy.”

Her vision is as ambitious as it is personal, rooted in her own journey from Zacatecas to Monterrey and beyond.

As her career advances, she says, she intends to keep IEEE at the center of her professional life.

“In IEEE I’ve found a community that challenges me to grow, supports me when I fail, and celebrates when I succeed,” she says. “It’s not just about engineering; it’s about building a better future, together.”

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Taiwan failed to pass an August referendum on whether or not a nuclear plant should be restarted, if it were deemed safe to operate. While the more than 4 million votes for “yes” outnumbered the more than 1.5 million “no” votes, the number of affirmative votes failed to surpass the 25 percent threshold of eligible voters also required for the referendum to pass. As a result, Taiwan remains on the nuclear-free path it has followed since the shutdown of the nuclear plant in question, Maanshan Nuclear Power Plant, in southern Taiwan on 17 May, fulfilling a 2016 government pledge made as a result of Japan’s 2011 Fukushima disaster.

However, high-tech industries, including semiconductor manufacturing, AI data centers, and AI infrastructure operators, will continue fueling electricity demand. The question remains as to whether or not Taiwan can deliver reliable clean power to support the growth of these industries amid Chinese geopolitical pressure—and without nuclear energy.

Taiwan’s Nuclear Energy Debate

Taiwan’s energy landscape remains complex. Nuclear power, developed since the 1970s, has seen older reactors retired since 2018. Taiwan imports 95 percent of its energy and has a growing reliance on natural gas. But it also aims to reduce carbon emissions, improve grid reliability, and expand its energy storage options.

“Without energy, there’s no industrial growthand nuclear is an excellent option,” Nvidia CEO Jensen Huang said during a prereferendum visit to Taipei on 22 August. He met with key players in high-tech supply chains, including Taiwan Semiconductor Manufacturing Co. (TSMC), the world’s largest chip foundry producing advanced chips for smartphones, high-performance computing, and AI applications.

It was not Huang’s first time advocating for nuclear energy. During Computex Taipei in May he said, “We need energy from any single source: wind, solar, nuclear. Taiwan should absolutely invest in nuclear, and it shouldn’t be a stigma to have energy.”

Nvidia has been expanding in Taiwan, partnering with Foxconn and the government to build a 10,000-Blackwell GPU AI training and supercomputing facility in the south, opening a larger Taipei office, and collaborating with Taiwanese companies such as TSMC to build an AI infrastructure ecosystem.

Taiwan president Lai Ching-te promised to honor the referendum result while focusing on diverse energy sources. He said Taiwan might consider advanced nuclear options if technology improves, waste decreases, and public support grows.

In late August, the government approved a draft piece of legislation, the AI Basic Act, designed to create a supportive environment for AI development and use. The draft emphasizes the government’s role in promoting AI research, applications, and infrastructure. Meanwhile, the newly reshuffled Cabinet is under pressure by industry and the broader public to maintain energy security.

In mid-September, newly appointed Minister of Economic Affairs Ming-hsin Kung emphasized that Taiwan is a global hub for chips and technology, shaping strategies for the next 10 to 20 years.

Taiwan’s Renewable Energy Goals

Kung stressed that businesses require both stable power supply and green energy to meet commitments to 100 percent renewable energy from global corporate initiative RE110. He said the new Cabinet will continue focusing on renewable energy while adjusting rollout speed. The goal is to lift renewables to 20 percent of Taiwan’s power supply by the end of 2026—a challenging target critical in keeping Taiwan competitive in global supply chains. He estimated renewable energy will account for around 15 percent of power generation by the end of 2025, up from 11.9 percent in 2024.

A wind turbine and its solar power system are part of the Taipower Exhibit Center in Pingtung, in southern Taiwan on 29 April 2025.I-Hwa Cheng/AFP/Getty Images

For solar, Kung pledged to strengthen existing projects, resolve land-use conflicts with fish farms in solar-fishery initiatives, and replace older solar panels with newer ones that produce twice as much energy. Offshore wind construction will be accelerated, and a trial program for floating wind turbines will resume. Taiwan will also actively develop other green energy sources, such as geothermal and hydrogen.

On nuclear, Kung reaffirmed Taiwan’s nuclear-free path but left open the possibility of adopting advanced technologies like small modular reactors. Guidelines for evaluating potential restarts of existing plants will be released by the end of October. The first step will see the Taiwan Power Co. (Taipower) conducting assessments of all three halted nuclear plants, with initial results due next year. Maanshan, which began commercial operations in 1984, is regarded as the most likely to pass the safety self-assessments, which will focus on the ability to maintain aging equipment and upgrade earthquake resilience.

In a report released on 26 September, Taiwan’s Energy Administration projects electricity demand to grow 1.7 percent annually from 2025 to 2034. The forecast factors in expansions to Taiwan’s semiconductor industry, investments in AI development, and expected energy savings.

To meet rising power demand, the government currently plans to boost natural-gas generation while phasing out large nuclear, coal, and oil plants. Net additions of 12.2 gigawatts in gas-fired capacity are expected by 2034.

Semiconductor Industry Concerns

But high-tech industries express concern. In early September, at Semicon Taiwan, Charles Lee, the managing director of Topco Group, a major semiconductor supplier, told IEEE Spectrum that manufacturers worry about grid stability as AI and semiconductor growth accelerates. “Highly polluting coal-fired plants are no longer an option, so we will rely more on liquefied natural gas and less-stable renewables. If nuclear plants could be restarted, I would personally welcome it,” Lee says.

Meanwhile, a memory manufacturing director, who spoke on condition of anonymity because he isn’t authorized by his company to speak to the media, told Spectrum that Taiwan’s economy is still manufacturing-driven. “We’re concerned about the low efficiency of green energy. We’ve also noticed a trend abroad, with countries resuming nuclear plant construction,” he says.

In a televised debate ahead of the August referendum, Tzu-Hsien Tung, chairman of Pegatron Corp., voiced support for restarting nuclear power plants. He warned that if Taiwan continues to rely on carbon-heavy electricity, local firms could face steep carbon taxes overseas, undermining their global competitiveness.

Visitors view AI server samples at the Zhen Ding Tech Group booth during the Semicon Taiwan exhibition in Taipei on 10 September 2025.I-Hwa Cheng/AFP/Getty Images

As Taiwanese society debated whether to restart nuclear power plants, some Taiwanese energy experts, including Tze-Luen Lin, deputy executive director of the Taiwanese government’s Office of Energy and Carbon Reduction and a political science professor at National Taiwan University, have called for fresh approaches to Taiwan’s energy resilience amid ongoing Chinese threats, echoing to notions brought by nongovernmental organizations and think thanks, such as the U.S.-based Center for Climate and Security, that a clean-energy transition can strengthen national security.

At the Society for Environmental Economics and Policy Studies conference in Japan on 21 September, Lin highlighted that renewable energy is central to both energy and national security. He emphasized, “Energy resilience can only be strengthened through decentralized, locally sourced renewables, combined with microgrids and energy storage,” and warned that large, centralized power plants are easier targets for attack.

Commenting on Taiwan’s possible nuclear options, Jusen Asuka, a professor at Tohoku University and chair of the session in the conference, cautioned that small modular reactors remain immature and costly, and investing heavily in them could slow renewable-energy development.

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This article is part of The Scale Issue.

A gibbous moon hangs over a lonely mountain trail in the Italian Alps, above the village of Malles Venosta, whose lights dot the valley below. Benjamin Wiesmair stands next to a moth trap as tall as he is, his face, bushy beard, and hair bun lit by its purple glow. He’s wearing a headlamp, a dusty and battered smartwatch, cargo shorts, and a blue zip sweater with the sleeves pulled up. Countless moths beat frenetically around the trap’s white, diaphanous panels, which are swaying with ghostly ripples in a gentle breeze. Wiesmair squints at his smartphone, which is logged on to a database of European moth species.

“Chersotis multangula,” he says.

“Yes, we need that,” comes the crisp reply from Clara Spilker, consulting a laptop.

Wiesmair, an entomologist at the Tyrolean State Museums, in Innsbruck, Austria, and Spilker, a technical assistant at the Senckenberg German Entomological Institute, in Müncheberg, are taking part in one of the most far-reaching biological initiatives ever: obtaining a genome sequence for nearly every named species of eukaryotic organism on the planet. All 1.8 million of them. The researchers are part of an expedition for Project Psyche, which is sampling European butterflies and moths and will feed its data into the global initiative, called the Earth BioGenome Project (EBP).

Eukaryotes are organisms whose cells contain a nucleus. From protozoa to human beings, all have the same basic biological mechanism for building, maintaining, and propagating their form of life: a genome. It’s the sum total of the genes carried by the creature.

Twenty-two years ago, researchers announced that for the first time they had mapped, or “sequenced,” nearly all of the genes in a human genome. The project cost more than US $3 billion and took 13 years, but it eventually transformed medical practice. In the new era of genomic medicine, doctors can take a patient’s specific genetic makeup into consideration during diagnosis and treatment.

The EBP aims to reach its monumental goal by 2035. As of July 2024, its tally of genomes sequenced stood at about 4,200. Success will undoubtedly depend on researchers’ ability to scale several biotech technologies.

“We need to scale, from where we’re at, more than a hundredfold in terms of the number of genomes per year that we’re producing worldwide,” says Harris Lewin, who leads the EBP and is a professor and genetics researcher at Arizona State University.

One of the most crucial technologies that must be scaled is a technique called long-read genome sequencing. Specialists on the front lines of the genomic revolution in biology are confident that such scaling will be possible, their conviction coming in part from past experience. “Compared to 2001,” when the Human Genome Project was nearing completion, “it is now approximately 500,000 times cheaper to sequence DNA,” says Steven Salzberg, a Bloomberg Distinguished Professor at Johns Hopkins University and director of the school’s Center for Computational Biology. “And it is also about 500,000 times faster to sequence,” he adds. “That is the scale, over the past 25 years, a scale of acceleration that has vastly outstripped any improvements in computational technology, either in memory or speed of processors.”

There are many reasons to cheer on the EBP and the technological advances that will underpin it. Having established a genome for every eukaryotic creature, researchers will gain deep new insights into the connections among the threads in Earth’s web of life, and into how evolution proceeded for its myriad life forms. That knowledge will become increasingly important as climate change alters the ecosystems on which all of those creatures, including us, depend.

And although the project is a scientific collaboration, it could spin off sizable financial windfalls. Many drugs, enzymes, catalysts, and other chemicals of incalculable value were first identified in natural samples. Researchers expect many more to be discovered in the process of identifying, in effect, each of the billions of eukaryotic genes on Earth, many of which encode a protein of some kind.

“One idea is that by looking at plants, which have all sorts of chemicals, often which they make in order to fight off insects or pests, we might find new molecules that are going to be important drugs,” says Richard Durbin, professor of genetics at the University of Cambridge and a veteran of several genome sequencing initiatives. The immunosuppressant and cancer drug rapamycin, to cite just one of countless examples, came from a microbe genome.

Your Genes Are a Big Reason Why You’re You

The EBP is an umbrella organization for some 60 projects (and counting) that are sequencing species in either a region or in a particular taxonomic group. The overachiever is the Darwin Tree of Life Project, which is sequencing all species in Britain and Ireland, and has contributed about half of all of the genomes recorded by the EBP so far. Project Psyche was spun out of the Darwin Tree of Life initiative, and both have received generous support from the Wellcome Trust.

To get an idea of the magnitude of the overall EBP, consider what it takes to sequence a species. First, an organism must be found or captured and sampled, of course. That’s what brought Wiesmair, Spilker, and 41 other lepidopterists to the Italian Alps for the Project Psyche expedition this past July. Over five days, they collected more than 200 new species for sequencing, which will augment the 1,000 finished Lepidoptera genome sequences already completed and the roughly 2,000 samples awaiting sequencing. There’s still plenty of work to be done; there are around 11,000 species of moths and butterflies across Europe and Britain.

After sampling, genetic material—the creature’s DNA—is collected from cells and then broken up into fragments that are short enough to be read by the sequencing machines. After sequencing, the genome data is analyzed to determine where the genes are and, if possible, what they do.

Over the past 25 years, the acceleration of gene-sequencing tech has vastly outstripped any improvements in computational technology, either in memory or speed of processors.

DNA is a molecule whose structure is the famous double helix. It resides in the nucleus of every cell in the body of every living thing. If you think of the molecule as a twisted ladder, the rungs of the ladder are formed by pairs of chemical units called bases. There are four different bases: adenine (A), guanine (G), cytosine (C), and thymine (T). Adenine always pairs with thymine, and guanine always pairs with cytosine. So a “rung” can be any of four things: A–T, T–A, C–G, or G–C.

Those four base-pair permutations are the symbols that comprise the code of life. Strings of them make up the genome as segments of various lengths called genes. Your genes at least partially control most of your physical and many of your mental traits—not only what color your eyes are and how tall you are but also what diseases you are susceptible to, how difficult it is for you to build muscle or lose weight, and even whether you’re prone to motion sickness.

How Long-Read Genome Sequencing Works

Long-read sequencing starts by breaking up a sample of genetic material into pieces that are often about 20,000 base pairs long. Then the sequencing technology reads the sequence of base pairs on those DNA strands to produce random segments, called “reads,” of DNA that are at least 10,000 pairs in length. Once those long reads are obtained, powerful bioinformatics software is used to build longer stretches of contiguous sequence by overlapping reads that share the same sequence of bases.

To understand the process, think of a genome as a novel, and each of its separate chromosomes as a chapter in the novel. Imagine shredding the novel into pieces of paper, each about 5 square centimeters. Your job is to reassemble them into the original novel (unfortunately for you, the pages aren’t numbered). What makes this task possible is overlap—you shredded multiple copies of the novel, and the pieces overlap, making it easier to see where one leaves off and another begins.

Making it much harder, however, are the many sections of the book filled with repetitive nonsense: the same word repeated hundreds or even thousands of times. At least half of a typical mammalian genome consists of these repetitive sequences, some of which have regulatory functions and others regarded as “junk” DNA that’s descended from ancient genes or viral infections and no longer functional. Long-read technology is adept at handling these repetitive sequences. Going back to the novel-shredding analogy, imagine trying to reassemble the book after it was shredded into pieces only 1 centimeter square rather than 5. That’s analogous to the challenge that researchers formerly faced trying to assemble million-base-pair DNA sequences using older, “short-read” sequencing technology.

The Two Approaches to Long-Read Sequencing

The long-read sequencing market has two leading companies—Oxford Nanopore Technologies (ONT) and Pacific Biosciences of California (PacBio)—which compete intensely. The two companies have developed utterly different systems.

The heart of ONT’s system is a flow cell that contains 2,000 or more extremely tiny apertures called, appropriately enough, nanopores. The nanopores are anchored in an electrically resistant membrane, which is integrated onto a sensor chip. In operation, each end of a segment of DNA is attached to a molecule called an adapter that contains a helicase enzyme. A voltage is applied across the nanopore to create an electric field, and the field captures the DNA with the attached adapter. The helicase begins to unzip the double-stranded DNA, with one of the DNA strands passing through the nanopore, base by base, and the other released into the medium.

OPTICAL SEQUENCING (Pacific Biosciences)

A polymerase enzyme replicates the DNA strand, matching and connecting each base to a specially engineered, complementary nucleotide. That nucleotide flashes light in a characteristic color that identifies which base is being connected.

Each DNA strand is immobilized at the bottom of a well.

As the DNA strand is replicated, each base while being incorporated emits a tiny flash of light in a color that is characteristic of the base. The sequence of light flashes indicates the sequence of bases.

What propels the strand through the nanopore is that voltage—it’s only about 0.2 volts, but the nanopore is only 5 nanometers wide, so the electric field is several hundred thousand volts per meter. “It’s like a flash of lightning going through the pore,” says David Deamer, one of the inventors of the technology. “At first, we were afraid we would fry the DNA, but it turned out that the surrounding water absorbed the heat.”

That kind of field strength would ordinarily propel the DNA-based molecule through the pore at speeds far too fast for analysis. But the helicase acts like a brake, causing the molecule to go through with a ratcheting motion, one base at a time, at a still-lively rate of about 400 bases per second. Meanwhile, the electric field also propels a flow of ions across the nanopore. This current flow is decreased by the presence of a base in the nanopore—and, crucially, the amount of the decrease depends on which of the four bases, A, T, G, or C, is entering the pore. The result is an electrical signal that can be rapidly translated into a sequence of bases.

NANOPORE
SEQUENCING
(Oxford Nanopore)

The helicase enzyme unzips and unravels the double-stranded DNA, and one strand enters the nanopore. The enzyme feeds the strand through the nanopore with a ratcheting motion, base by base.

The ionic current is reduced by a characteristic amount, depending on the base. The current signal indicates the sequence of bases.

PacBio’s machines rely on an optical rather than an electronic means of identifying the bases. PacBio’s latest process, which it calls HiFi, begins by capping both ends of the DNA segment and untwisting it to create a single-stranded loop. Each loop is then placed in an infinitesimally tiny well in a microchip, which can have 25 million of those wells. Attached to each loop is a polymerase enzyme, which serves a critical function every time a cell divides. It attaches to single-stranded DNA and adds the complementary bases, making each rung of the ladder whole again. PacBio uses special versions of the four bases that have been engineered to fluoresce in a characteristic color when exposed to ultraviolet light.

A UV laser shines through the bottom of the tiny well, and a photosensor at the top detects the faint flashes of light as the polymerase goes around the DNA sample loop, base by base. The upshot is that there is a sequence of light flashes, at a rate of about three per second, that reveals the sequence of base pairs in the DNA sample.

Because the DNA sample has been converted into a loop, the whole process can be repeated, to achieve higher accuracy, by simply going around the loop another time. PacBio’s flagship Revio machine typically makes five to 10 passes, achieving median accuracy rates as high as 99.9 percent, according to Aaron Wenger, senior director of product marketing at the company.

How Researchers Will Scale Up Long-Read Sequencing

That kind of accuracy doesn’t come cheap. A Revio system, which has four chips, each with 25 million wells, costs around $600,000, according to Wenger. It weighs 465 kilograms and is about the size of a large household refrigerator. PacBio says a single Revio can sequence about four entire human genomes in a 24-hour period for less than $1,000 per genome.

ONT claims accuracy above 99 percent for its flagship machine, called PromethION 24. It costs around $300,000, according to Rosemary Sinclair Dokos, chief product and marketing officer at ONT. Another advantage of the ONT PromethION system is its ability to process fragments of DNA with as many as a million base pairs. ONT also offers an entry-level system, called MinION Mk1D, for just $3,000. It’s about the size of two smartphones stacked on top of each other, and it plugs into a laptop, offering researchers a setup that can easily be toted into the field.

Although researchers often have strong preferences, it’s not uncommon for a state-of-the-art genetics laboratory to be equipped with machines from both companies. At Barcelona’s Centro Nacional de Análisis Genómico, for example, researchers have access to both PacBio Revio machines as well as PromethION 24 and GridION machines from ONT.

Durbin, at Cambridge University, sees lots of upside in the current situation. “It’s very good to have two companies,” he declares. “They’re in competition with each other for the market.” And that competition will undoubtedly fuel the tech advances that the EBP’s backers are counting on to get the project across the finish line.

PacBio’s Wenger notes that the 25-million-well chips that underpin its Revio system are still being fabricated on 200-millimeter semiconductor wafers. A move to 300-mm wafers and more advanced lithographic techniques, he says, would enable them to get many more chips per wafer and put hundreds of millions of wells on each of those chips—if the market demands it.

At ONT, Dokos describes similar math. A single flow cell now consists of more than 2,000 nanopores, and a state-of-the-art PromethION 24 system can have 24 flow cells (or upward of 48,000 nanopores) running in parallel. But a future system could have hundreds of thousands of nanopores, she says—again, if the market demands it.

The EBP will need all of those advances, and more. EBP director Lewin notes that after seven years, the three-phase initiative is wrapping up phase one and preparing for phase two. The goal for phase two is to sequence 150,000 genomes between 2026 and 2030. For phase two, “We’ve got to get to 37,500 genomes per year,” Lewin says. “Right now, we’re getting close to 3,000 per year.” In phase two, the cost per genome sequenced will also have to decline from roughly $26,000 per genome in phase one to $6,100, according to the EBP’s official road map. That $6,100 figure includes all costs—not just sequencing but also sampling and the other stages needed to produce a finished genome, with all of the genes identified and assigned to chromosomes.

Phase three will up the ante even higher. The road map calls for more than 1.65 million genome sequences between 2030 and 2035 at a cost of $1,900 per genome. If they can pull it off, the entire project will have cost roughly $4.7 billion—considerably less in real terms than what it cost to do just the human genome 22 years ago. All of the data collected—the genome sequences for all named species on Earth—will occupy a little over 1 exabyte (1 billion gigabytes) of digital storage.

It will arguably be the most valuable exabyte in all of science. “With this genomic data, we can get to one of the questions that Darwin asked a long time ago, which is, How does a species arise? What is the origin of species? That’s his famous book where he never actually answered the question,” says Mark Blaxter, who leads the Darwin Tree of Life Project at the Wellcome Sanger Institute near Cambridge and who also conceived and started Project Psyche. “We’ll get a much, much better idea about what it is that makes a species and how species are distinct from each other.”

A portion of that knowledge will come from the many moths collected on those summer nights in the Italian Alps. Lepidoptera “go back around 300 million years,” says Charlotte Wright, a co-leader, along with Blaxter, of Project Psyche. Analyzing the genomes of huge numbers of species will help explain why some branches of the Lepidoptera order have evolved far more species than others, she says.

And that kind of knowledge should eventually accumulate into answers to some of biology’s most profound questions about evolution and the mechanisms by which it acts. “The amazing thing is that by doing this for all of the lepidoptera of Europe, we aren’t just learning about individual cases,” says Wright. “We’ve learned across all of it.”

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The annals of UFO lore and the field of UFOs in general are littered with cases of supposed crashes of these crafts. For whatever reason, despite their incredibly mind-boggling technology, these things just sometimes go down, and they always make for a spectacular case that leaves many mysteries in its wake. Many UFO crashes are world famous, such as obviously the one that allegedly happened at Roswell, New Mexico, but some others are not nearly as well known, yet are just as mysterious and perplexing. 

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Five years ago, Eric Aguilar was fed up.

He had worked on lidar and other sensors for years at Tesla and Google X, but the technology always seemed too expensive and, more importantly, unreliable. He replaced the lidar sensors when they broke—which was all too often, and seemingly at random—and developed complex calibration methods and maintenance routines just to keep them functioning and the cars drivable.

So, when he reached the end of his rope, he invented a more robust technology—what he calls the “most powerful micromachine ever made.”

Aguilar and his team at startup Omnitron Sensors developed new microelectromechanical systems (MEMS) technology that he claims can produce more force per unit area than any other. By supplying new levels of power to micromirrors, the technology is capable of precisely steering lidar’s laser beams, even while weathering hazardous elements and the bumps and bangs of the open road. With chips under test by auto-industry customers, Omnitron is now modifying the technology to reduce the power consumed by AI data centers.

Lidar, a scanning and detection system that uses lasers to determine how far away objects are, is often adopted by self-driving cars to find obstacles and navigate. Even as the market for lidar is expected to grow by 13.6 percent annually, lidar use in the automotive industry has remained relatively stagnant in recent years, Aguilar says, in part because the technology’s lifespan is so short.

Vibration from bumpy roads and severe environmental conditions are the biggest reliability killers for automotive lidar, says Mo Li, who studies photonic systems at the University of Washington. The optical alignment within the lidar package atop self-driving cars is delicate—tremors from a poor paving job could physically alter where the mirrors sit in the housing, potentially misaligning the beam and causing the system to fail. Or temperature fluctuations could cause parts to expand or contract with the same unfortunate outcome, he explains.

Aguilar wondered which part broke most often and found the culprit to be scanners, the parts responsible for angling small mirrors that direct the laser beam out of the system’s housing. He wanted to make scanners that could withstand the tough conditions lidar faces, and silicon flexures stood out as a solution. These structures act like springs and allow for meticulous control of the mirrors within lidar systems without wearing out, as the standard metal springs do, Aguilar claims.

Designing a better chip

Aguilar hoped the new material would be the answer to the problem that plagued him, but even silicon springs didn’t make lidar systems as robust as they needed to be to withstand the elements they faced.

To make lidar even stronger, the team at Omnitron aimed to design a more powerful MEMS chip by increasing the amount of force the device can apply to control the mirrors in the lidar array. And they claim to have achieved it—their chip can exert 10 times as much force per unit area on an actuator that positions a micromirror or other sensor component as the current industry standard, they say. That extra force allows for extremely valuable control in fine adjustment.

To reach this achievement, they had to dig deep—literally.

Omnitron’s micromirrors steer lidar beams and could find use in data centers.Omnitron

In this MEMS device, the mirror and its actuator are etched into a single silicon wafer. On its nonmirror end, the actuator is covered with tiny, closely spaced plates that fit between trenches in the wafer, like the interlocking teeth of two combs. To move the mirror, voltage is applied, and electrostatic forces angle the mirror into a specific position by moving the plates up and down within the trenches as the electric field pulls across the trench sidewalls.

The force that can be used to move the mirror is limited by the ratio of depth to width of the trenches, called aspect ratio. Put simply, the deeper the trenches are, the more electrostatic force can be applied to an actuator, which leads to a higher range of motion for the sensor. But fabricating deep, narrow trenches is a difficult endeavor. Overcoming this limiting factor was a must for Aguilar.

Aguilar says Omnitron was able to improve on the roughly 20:1 aspect ratio he notes is typical for MEMS (other experts say 30:1 or 40:1 is closer to average these days), reaching up to 100:1 through experimentation and prototyping in small university foundries across the United States “That’s really our core breakthrough,” Aguilar says. “It was through blood, sweat, tears, and frustration that we started this company.”

The startup has secured over US $800 million in letters of intent from automotive partners, Aguilar says, and is two months into an 18-month plan to prove that it can produce its chips at full demand rate.

Even after verifying production capabilities, the technology will have to face “very tough” safety testing for thousands of consecutive hours in realistic conditions, like vibrations, thermal cycles, and rain, before it can come to market, Li says.

Saving power

In the meantime, Omnitron is applying its technology to solve a different problem faced by a different industry. By 2030, AI data centers are expected to require around 945 terawatt-hours to function—more than the country of Japan consumes today. The problem is “the way data moves,” Aguilar says. When data is sent from one part of the data center to another, optical signals are converted into electrical signals, rerouted, and then turned back to optical signals to be sent on their way. This process, which takes place in systems called network switches, burns huge amounts of power.

Google’s solution, called Apollo, is to keep the data packets in the form of optical signals for the duration of their travels, which yields a 40 percent power savings,