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If you are a researcher and want to know more about the people driving innovation and change, then look no further than these six impressive women.

In all industries, there are the movers and shakers whose work often leads to the evolution of what is possible and the reimagining of what could be. The field of research, perhaps more than most, embraces those who are of a curious mind and have a vision for the future – think of people like Marie Curie, Rosalind Franklin and Alice Ball.

In the 20th century, these women were at the forefront of the research space and their work has gone on to impact the lives of people today as much as it did when first shared.

But who are the movers and shakers of today? Who are the women driving the research answering life’s most pressing questions?

Yvonne Buckley

Yvonne Buckley is a professor of zoology at Trinity College Dublin. She was also the founding co-chair of the All-Island Climate and Biodiversity Research Network, the senior editor of Journal of Ecology, a member of the National Carbon Budgets Working Group and a member of both the Royal Irish Academy and Academia Europaea. She leads a team of researchers, post-docs, PhD and undergraduate students aiming to better understand the fundamental drivers of animal and plant population processes. 

The discoveries she is a part of are often used to provide support for environmental decisions in the areas of biodiversity conservation, invasive species management and habitat restoration. Earlier this year, she was the recipient of the 2026 Royal Irish Academy Gold Medal in the Environmental Sciences, Geography and Geosciences in recognition of her outstanding contributions to science, research and scholarship.

Emer McGrath

Prof Emer McGrath is an associate professor in the College of Medicine, Nursing and Health Sciences at the University of Galway. She is also an adjunct associate professor at UT Health San Antonio, a consultant neurologist at Galway University Hospital, an investigator for the Framingham heart study and a research collaborator at the Boston University School of Medicine.

Her main focus at the moment is research around dementia risk factors and identifying biomarkers of the illness before the patient suffers memory loss and irreversible brain damage, which can make treatment significantly more complicated. 

Ieva Plikusienė

Ieva Plikusienė is a professor and senior researcher in the Faculty of Chemistry and Geosciences and the Faculty of Medicine at Vilnius University. Her area of research focuses primarily on the development of advanced biosensing systems, designed to detect early warning signs of illness in the human body. The aim of her work is to help source the best candidates for drug design and detect important cancer, viral or bacterial biomarkers.

In recognition of her contributions to the research and medtech spaces, Plikusienė was recently awarded the prestigious André Mischke Award from the Young Academy of Europe.

Sarah Gilbert

Prof Sarah Gilbert is a professor of vaccinology at the Pandemic Sciences Institute at the University of Oxford. Her chief research interest is the development of viral vectored vaccines that work by inducing strong and protective T- and B-cell responses. She has led work on influenza vaccine development as well as vaccines for different emerging pathogens, including MERS and Lassa virus. She is in frequent collaboration with colleagues working in the Clinical Biomanufacturing Facility and Centre for Clinical Vaccinology and Tropical Medicine at Oxford. 

In 2020, she was named the Oxford project leader for ChAdOx1 nCoV-19, a vaccine against the novel coronavirus SARS-CoV-2. If you are interested in vaccinology and related fields, such as future pandemic prevention, Gilbert’s career trajectory could offer some inspiration. 

Friederike Otto

Prof Friederike Otto is a renowned researcher and scientist operating at the intersection of climate science and environmental policy at Imperial College London.

She is the lead of World Weather Attribution (WWA), which is an international effort to analyse and communicate the impact of climate change on extreme weather events. She studies how major weather events, such as droughts, heat waves and storms, are intensified and made more frequent by changes in the climate. She is also interested in the merging of science, law and policy, and aims to better understand how scientific evidence can be used in legislation, litigation and more informal governance for resilient societies.

She has authored numerous reports, was recognised for her co-founding of WWA on the Time 100 list as one of the world’s most influential individuals, is the author of two non-fiction books – ‘Angry Weather’ and ‘Climate InJustice’ – and has had her work featured extensively in global media, including in The Economist, Financial Times, The New York Times, Der Spiegel, Times of India and the BBC, among others. 

Abeba Birhane

A cognitive scientist researching AI accountability, Prof Abeba Birhane is a principal investigator for the Artificial Intelligence Accountability Lab and assistant professor of AI in the school of Computer Science and Statistics at Trinity College Dublin. Her work ranges from the systematic examination of AI ecology and governance structures, to the execution of algorithmic audits. Also central to her research are goals of challenging and dismantling societal and historical inequalities and power asymmetries, holding responsible bodies accountable, and paving the way for a future marked by equitable AI systems that work for everyone. 

She has contributed to several peer and non-peer reviewed journals, and is a driving force in continued research into AI, ethics and the long-term, societal implications of the technology. She has also been the recipient of several awards and commendations, including a mention on the Time 100 most influential persons in AI 2023 list, and the Distinguished Paper Award 2024 at the IEEE Conference on Secure and Trustworthy Machine Learning.

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How old is the seven-segment display? Surely it is a product of the 1970s. After all, calculators started showing up, and the height of junior high humor was plugging 7734 into your calculator and showing it to someone upside down. Of course, for it to go mainstream, maybe they really originated in the 1960s, but no earlier than that, right? Actually, no. Sure, the LED seven-segment display had to wait for LEDs. But the actual idea is much older than that.

The concept of building numbers from a small set of reusable segments predates LED displays by decades. In fact, the basic idea appears in patents from the early 1900s and may have roots in even older mechanical signs and printing techniques.

The history isn’t entirely straightforward. Unlike vacuum tubes or transistors, segmented displays evolved gradually through a series of practical ideas rather than one defining invention.

Blacking out the Eight

While looking into the history of segmented displays, I was reminded of something I’d seen years ago in retail stores: reusable price tags printed with rows of eights.

Rather than printing every possible price, the clerk simply used a marker to black out portions of each figure, transforming an 8 into whatever digit was needed. Cover a few strokes, and the eight becomes a three. Remove a different set, and it becomes a zero or a five. It was, in essence, a manual segmented display.

Finding the exact origin of these price tags is akin to finding out where Romans bought sponges. They were inexpensive commercial supplies, not the sort of products that historians carefully documented. My recollection is from the middle of the twentieth century, but the underlying concept is almost certainly older.

Everything New is Old Again

George Mason’s 1898 21-segment display used 21 lamps and a complicated switch to display any digit or letter in a very stylish font. You can see a modern recreation of these ancient displays in the video below. While this is the basic idea, certainly, it is more ambitious than a simple 7-segment display.

I couldn’t determine that Mason’s displays were ever used for anything.

You could argue that an early 1903 invention by Carl Kinsley to draw characters telegraphically using six pens was an even better precursor, but using magnets to draw with pens on tapes hardly seems to qualify as a display, although figure 12 in the patent clearly shows the formation of numbers and even letters with this arrangement.

Digits of Patent

The direct parent of modern segmented digit displays appeared in the early 1900s (filed 1908; granted in 1910). Technically, this was an 8-segment display because it had a bar dedicated to forming a proper four, with the top-left part slanted. But removing that one segment is just an optimization. It may or may not have been the first, but by 1910, seven-segment displays were in use and not just curiosities on a workbench or dreams in a patent application.

Even for lit-up displays, the first implementations weren’t LEDs. Early displays used incandescent lamps or neon-filled tubes. By the 1930s and 1940s, segmented neon or incandescent indicators were appearing in industrial equipment and counters, instead of the common columns of ten neon bulbs, pointers, or rotating wheels.

Then, too, there were different approaches. Nixie tubes used individual character forms that lit up. Decatrons could count with ten different glowing points, each representing a digit.

Enter the LED

The real explosion came in the late 1960s when practical LED displays arrived. Suddenly, segmented displays could be compact, rugged, inexpensive, and operate at low voltages. Calculators, clocks, frequency counters, digital multimeters, and every imaginable piece of consumer electronics adopted them almost overnight.

Of course, it wasn’t just LEDs. Numitrons used seven tiny incandescent filaments. Vacuum fluorescent displays used segments with phosphor that glowed when excited. LCDs adopted the same pattern, blocking or passing light to produce the segments. But the key idea was something that lights up, arranged in seven segments.

Early LED displays often had all the diodes on a single die to reduce cost. That made them very tiny. It wasn’t unusual to see displays with magnifying bubbles to make it easier to read.

The important point isn’t whether they used exactly seven segments. Many didn’t. Some divided the numerals differently or used additional pieces to produce more attractive typography. The insight was the same: begin with the most complex digit and selectively remove strokes to create all the others.

Why Seven?

One of the reasons seven segments became the standard is that they strike a perfect balance (although not everyone agrees, as you can see in the video below). With fewer than seven elements, several decimal digits become difficult to distinguish. Adding more segments certainly improves appearance, but every additional segment increases wiring, decoding logic, manufacturing cost, and the number of possible failure points. Seven is the right number for — um — numbers. But what about letters?

You can make some compromises to show some letters. For example, old computers would display hex digits using seven segments, but the A would be uppercase and the B would be lowercase. You also had to light the top segment for the 6 to make it look different from a B. But you only need A-F for hex. If you need, say, the letter S, there’s no real way to make it not look like a 5 with 7 segments. But sometimes, the letters you can make are good enough.

There were also 8- and 9-segment displays that could do better with letters and special characters. You can increase the segments further to get more glyphs.

The Union Jack

The solution for full letter displays was to add more segments. Despite Mason, you didn’t really need 21. By the 1970s, fourteen-segment and sixteen-segment displays had become common in instruments, telephones, calculators, and video cassette recorders. Their pattern of diagonals and crossbars earned them the nickname “Union Jack” display because the arrangement resembles the British flag. These are also sometimes called starburst displays. You occasionally see 16-segment displays, too.

Even Today

Today, you have a plethora of options for adding alphanumeric screens to just about anything. Yet, you still see 7-segment displays hanging around.

The seven-segment display isn’t successful because it’s beautiful. It isn’t even especially flexible. It’s successful because it’s close to the minimum solution that works. It delivers readable numbers with very little hardware, whether the technology behind it is incandescent bulbs, neon, vacuum fluorescence, LEDs, LCDs, or even ink on a printed price tag.

Perhaps that’s why it has survived every technological transition for more than a hundred years. Good engineering ideas often outlive the technologies that first bring them to life.

And once you start looking for segmented displays, you’ll notice they’ve been hiding in plain sight for much longer than the digital age.

Featured image: [Arduino Enigma]’s marvelous Seven Segment Art Installation

Presented by SAP

Generating code with AI is fast, but getting that code to run reliably inside a large enterprise, integrated with live systems, governed for compliance, and maintainable over years requires foundational work that most organizations underestimate.

While 81% of all organizations have a detailed strategy, only 12–16% reach AI‑driven execution, says SAP’s Michael Ameling, CPO of SAP Business Technology Platform, and the reasons rarely come down to the quality of the generated code.

“Across industries, enterprises that have invested heavily in AI tooling are hitting a wall when generated code meets the reality of their existing environments, because generating code and operationalizing it are not the same problem,” Ameling says.

There are specific requirements for deploying AI-generated logic at enterprise scale: what data and integration readiness actually look like, how governance works when AI agents move from producing recommendations to executing workflows, and how development teams are changing their role as AI takes over more of the coding work.

Why AI code generation fails in enterprise production environments

The productivity gains from AI code generation are real and well-documented, but the ease of prototyping has given many organizations a misleading sense of how far along they actually are.

“Generating code is one thing,” Ameling says. “Enterprise customers, including multinationals and large organizations, need to ensure there are no compromises in compliance or security. Code that runs reliably for ten or twenty years, as it does at many of SAP’s largest customers, also has to be maintained, patched, and understood by whoever inherits it. Life cycle management, in other words, does not generate itself.”

The issue is rarely the generation quality. Teams build something compelling, then discover they lack access to the data it depends on, or the integrations it assumes, or the permissions required to run it in a real environment. The problem is essentially that AI amplifies an organization’s existing data and process maturity, but it can’t substitute for it.

This dynamic intensifies as AI moves from producing code to executing actions. Latency, cost, and system load all increase when logic runs continuously against live data rather than rendering a one-time output. The performance requirements of an autonomous agent operating across a multinational’s transaction systems are categorically different from those of a developer copilot.

How to connect AI-generated logic to fragmented enterprise systems

The architecture challenge that most enterprise AI projects underestimate is integration. Real enterprise environments are not clean slates: they combine cloud systems, legacy on-premise infrastructure, fragmented data stores, and dozens of business applications that were never designed to talk to each other. Getting AI-generated logic to operate reliably across all of them requires a layer that unifies data access, process context, and governance, and it has to be in place before any agent starts executing. And organizations that see AI as a reason to defer infrastructure modernization are making a mistake.

“The question is not whether to modernize or not. Of course you need to modernize,” Ameling says. “But the value you get on top of this is much higher with AI. Federated data access and harmonized process layers are not alternatives to upgrading a fragmented landscape, they’re what make the upgrade worthwhile.”

At the platform level, this translates into a set of practical requirements: structured data integration, end-to-end process visibility, and the ability to discover and connect to APIs across both modern and legacy systems. SAP’s approach with the Business AI Platform draws on tools including its Joule Studio, Integration Suite, Business Data Cloud, and SAP AI Agent Hub enterprise architecture layer to provide that context. The goal is to give AI-generated logic accurate, current knowledge of what a business is doing and how, rather than just access to raw data.

AI agents handle large challenges by dividing them into smaller, autonomous tasks, with each agent responsible for a specific domain, and all coordinated toward a shared outcome. A financial close, for example, involves dozens of discrete sub-processes. Agents handling each task in parallel, within defined constraints, can compress cycle times dramatically, but only if the underlying systems they interact with are coherent and accessible.

The governance and oversight that AI agents require in production

When AI moves from assistant to operational actor, the governance questions loom large, because agents that trigger workflows, update records, and interact with live business systems need the same accountability framework that applies to human employees, i.e., identities, defined privileges, and auditable behavior.

There are two distinct models:

Principal propagation, where an agent acts on a user’s behalf, inheriting that user’s permissions and scope.

System-triggered agents, where the agent operates under its own identity and role-defined privileges, functioning more like an automated HR role than a personal assistant.

Both models require the same underlying infrastructure: an agent hub where operators can see which agents exist, what APIs they can access, and what they are authorized to do. Observability also needs to be operationalized correctly for AI, combined with both technical and business evals.

“In production, openness is very important,” Ameling says. “We use OpenTelemetry as a framework, so we can integrate with other solutions, for end-to-end observability of the tool, third-party agents and the like.”

On top of that, standard technical evals, which test whether an agent produces consistent outputs, are necessary but not enough. Business evals assess whether an agent is actually moving the performance indicators it was deployed to improve, but it has to work end-to-end.

Where the testing happens is equally important. The traditional software development cycle across dev, test, and production environments breaks down when a model produces different outputs depending on whether it is running against test data or live data. Getting to trustworthy AI in production means accepting that validation looks fundamentally different from what engineering teams have practiced for decades, with live environment testing, even A/B/C testing to ensure outcomes are reliable.

How AI-driven code generation is changing software engineering roles

The role of the developer is not disappearing in this environment, but its center of gravity is shifting. The productivity multiplier is significant when developers can run multiple coding agents in parallel across open terminals, each working on a separate problem and each taking several minutes to complete. But it introduces a new kind of cognitive demand, because humans have to stay in the loop. That means tracking context across concurrent workstreams, evaluating outputs that range across large codebases, and making architectural judgments that no agent can be trusted to make alone.

“The more specific and complete the prompt, the less intervention is required, and developers are learning that bringing more context upfront pays dividends in reduced back-and-forth,” Ameling says. “But the output still needs to be understood, not just accepted.”

The competitive edge will remain intellectual property, not tooling. The companies that pull ahead will be those that most effectively encode their domain knowledge into the systems they build.

“A manufacturer’s process expertise, a financial institution’s risk logic, a logistics firm’s routing intelligence, these are the assets that AI can accelerate, but only if the organizations that hold them do the work to make them accessible and usable,” Ameling says. “Protect that, and apply AI to accelerate your differentiation.”

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

If you just need a reliable Windows laptop for everyday use, this Lenovo deal is a genuinely good price – it’s an easy recommendation for remote and hybrid workers, and for my money, it ticks all the boxes for general business work.

So, right now, the Lenovo IdeaPad Slim 3i has dropped to £499 (was £599) at Argos. For an all-rounder business laptop, this one of the best budget picks around for under £500.

Inside, the machine is equipped with an efficient Intel Core i5 13420H processor and 16GB RAM, so essential office tasks, photo editing, browsing, and streaming won’t be a problem – no slowdowns or sluggishness here.

This is a budget-friendly configuration built around Intel’s Core i5-13420H, an 8-core, 12-thread chip from the 13th-gen Raptor Lake-H family, with four performance cores boosting up to 4.6GHz and four efficiency cores.

The 15.3in WUXGA (1920×1200) IPS panel uses a 16:10 aspect ratio, which gives you a bit more vertical space for documents and browsing than the more common 16:9 laptops in this price range. It’s rated at 300 nits with an anti-glare coating, with decent contrast and color accuracy (provided you calibrate it). That makes it ideal for creating images for, say, your work’s social media pages, though I wouldn’t recommend it for colour-critical tasks required by creative professionals.

16GB of RAM and a 512GB SSD is a sensible balance for this price point — enough headroom for normal multitasking across browser tabs, office apps, and streaming without feeling cramped. The laptop weighs 1.59kg and measures 17.9mm thick, with Lenovo quoting up to 14.5 hours of battery life, though real-world use — especially on the Intel variant — will likely fall short of that figure.

Also consider

As a current high school student, the first thing I noticed about chemistry was that it was not only hard because of math or vocabulary. It was hard because so much of it was invisible.

In class, I could write a chemical formula. I could memorize that a molecule had a certain shape or that a reaction moved in a certain direction. But much of chemistry did not feel like something I could touch, move, test, or explore. The most important parts happened in a way I would never see.

A Two-Dimensional Perspective

Chemistry is a subject built on relationships. Atoms connect, electrons move, bonds form, molecules bend, acids and bases shift equilibrium, gases respond to pressure and temperature, and reactions balance because matter must be conserved. When those ideas stay flat on a page, students memorize answers without understanding the system behind them.

As a rising 12th-grade student at Suitland High School in Prince George’s County, Maryland, I set out to build a solution because I wanted a serious chemistry workspace students like me could use. I wanted something visual and interactive that could run on school devices without complicated setup — without the need for students to create accounts, download resources, or collect student data. I also wanted something that could open in a browser and allow students to begin learning right away.

Atomency started from a simple question: What would chemistry look like if students could build, manipulate and test ideas instead of only reading about them?

I began by building a workspace where students could create molecules and see structure-based information. From there, I added VSEPR-style analysis so students could connect formulas to molecular geometry instead of treating shapes as something to memorize from a chart. Then I kept expanding it: I incorporated reaction simulations, nuclear decay tools, kinetics tool, acid–base and pH tools, gas-law models and assignment workflows to help teachers bring the platform into the classroom.

I built all of this independently while still being a high school student. That meant I was not just designing a product in theory; I was building from inside the problem. I knew what it felt like to sit in a classroom and need a better way to see what was happening. I knew what it felt like when a concept almost made sense, but the missing piece was the ability to interact with it.

A Three-Dimensional Solution

Students usually receive educational tools instead of imagining or creating them. But students notice things adults can miss. We notice when a website is too slow on a school Chromebook. We notice when a platform requires an account before we can try it. We notice when a tool looks impressive in a presentation but does not align with the way a classroom actually works.

Access shaped how I built Atomency. A chemistry platform should not require expensive software or perfect devices. It should not require students to provide personal information. It should not assume all students have tutors, can pay to access resources, and have personal laptops powerful enough to run advanced programs. If a tool is meant to help students, it should reflect students’ reality.

For me, that reality was a public school classroom where students needed more ways to understand chemistry. Chemistry can become a gatekeeping subject. If students fall behind early, later topics become harder because everything builds on earlier ideas. Atomic structure connects to bonding. Bonding connects to molecular geometry. Geometry connects to polarity. Reactions connect to stoichiometry and equilibrium. Once one link in that chain breaks, the whole subject can start to feel impossible.

I wanted Atomency to help repair those links by making the relationships clearer. Instead of only telling students that a molecule has a certain shape, the platform can help them see how structure connects to geometry. Instead of only practicing reaction-balancing in steps, students can work with reactions as systems. Instead of treating acids, bases, gases, kinetics and nuclear decay as separate units, students can see chemistry as a connected field where patterns repeat in different forms.

Early usage showed me that students and teachers were looking for a platform like this. Atomency’s aggregate GoatCounter analytics indicated 25,162 visits from Feb. 24 to May 24, 2026, with strong engagement in the builder and simulations, the molecular workspace where students can create molecules and see structure-based information. This mattered to me not because it made the platform look popular but because it showed that people were using the parts of the platform that matched the original reason I built it: to give students a place to experiment with chemistry visually.

Atomency has been featured by Eric Curts in Control Alt Achieve and mentioned by Middle School Matters. My AP Chemistry teacher, Dr. Glenn Soltes, described the platform as having meaningful instructional potential. These moments helped me understand that the platform was not just something I made for myself. Other educators could see value in it too.

Still, the biggest lesson I learned was that students should be taken seriously as designers of learning environments. We are close to the problems because we experience them every day. When students say a tool is confusing, inaccessible, slow or disconnected from the way they learn, that feedback is not a complaint. It is design information.

Educational technology should not only be built for students but also with students and by students. That does not mean every student must become a software developer. It means schools and education companies should recognize that students have insight, creativity and lived experiences that can improve tools inside real classrooms.

I built Atomency because I needed a better way to learn chemistry. But as the platform grew, it became about something larger than my own classroom. It became a way to ask what happens when a student is trusted not only as a learner but also as a builder.

Chemistry became easier for me to understand when I could make it visible. Maybe school technology can become better when student experience becomes more visible too.

Friendly home robots have been the dream of sci-fi for a long time. Docile, helpful machines that do your laundry, take out the trash, maybe make dinner, and clean up afterward too. But if robots are going to do all that, they’re going to need some hands to make all that happen.

1X, a Norwegian-American robotics company, today revealed details about the five-finger hands attached to its soft, helpful robot companion, Neo.

The hands are built with actuators designed to replicate how tendons in the arms move human hands. 1X says this gives Neo’s hands 25 degrees of freedom of movement, which is just a little less than the 27 degrees of freedom human hands usually have. Cameras and AI smarts help sort out the broader context of what the fingers are trying to grab. It’s a dexterous mix that gives the Neo bot a very broad range of motion. 1X says the hands can grip odd shapes and detect when something is slipping out of its grip. The fingers can also move extremely quickly and hyperextend in directions human digits can’t. They also have an IP68 waterproof rating, meaning the robot can wash its own hands.

It’s a range that the engineers have aimed to dial in to align with what a human can actually do. Jonathan Terfurth, 1X’s director of actuators and hands, says the range of motion might even be better than a human’s, enabling it to open doors, lift heavy objects, and even plug itself in when its battery starts to die.

“You want to be able to operate with a human who has never worked or interfaced with a robot, and you still want it to be safe and compliant and soft,” Terfurth says. “Range of motion can be a bit extreme, but we try to be very close to what humans can do so that we can live in the world.”

Together, this is part of a growing ChatGPT-esque moment for robotics, where bots have gone from clumsy claw machines to capable handlers, able to gently carry fragile objects and become increasingly helpful for managing menial daily tasks and boring office chores.

The humanoid robot market tends to be dominated by tough, hulking Terminators meant to be paraded out for some defense industry contract or another. 1X is taking a different tack for its robot. The Neo is a soft, supple robot wrapped in a 3D lattice shell. Its design is inspired by characters like Baymax, the affable robot from the Disney movie Big Hero 6. In limited quantities, early access pricing is $20,000 or $500 per month to have it in your home, though the lump sum will prioritize delivery for 2026.