Samsung Biologics made an all-cash public tender offer of approximately $1.8 billion to acquire Switzerland-based PolyPeptide Group, a CDMO specializing in peptide-based active pharmaceutical ingredients (APIs).
Samsung views the deal as expanding its capabilities beyond antibodies and ADCs to include peptide therapeutics, particularly in obesity and diabetes, including GLP-1 therapies, while advancing innovation across high-growth areas such as oncology and other emerging indications. The transaction brings together Samsung Biologics’ global manufacturing scale with PolyPeptide’s specialized peptide expertise to create a differentiated, end-to-end multi-modality CDMO platform, notes a Samsung spokesperson.
PolyPeptide operates an integrated development-to-commercial model with growth focused on a modular, automation approach which, the company points out, gives it the flexibility to adapt quickly to changing market demand.
The planned acquisition extends beyond adding capacity in that it also lays the foundation for Samsung Biologics’ next phase of growth, supported by a strong pipeline of active peptide projects that includes a deep late-stage portfolio, notes a Samsung official. PolyPeptide operates global sites across Sweden, Belgium, France, the U.S., India, together with a corporate office in Switzerland and a separate Innovation Center in Strasbourg, France, with capabilities in R&D, development, and commercial manufacturing.
Upon completion of the transaction, Samsung will bring together PolyPeptide’s experienced team and specialized peptide expertise with Samsung’s scientific and manufacturing strengths and global operations, says John Rim, chairman of the board of directors and CEO of Samsung Biologics.
“This acquisition reinforces our long-term growth strategy by not only broadening our service portfolio with modality expansion into peptides including GLP-1, but by also boosting our geographic reach and proximity further within the U.S., Europe, and India,” continues Rim.
“After a comprehensive review of strategic options, the Board is convinced that Samsung Biologics’ offer is compelling for our shareholders, delivering an attractive cash price and immediate, certain value today,” adds Peter Wilden, chairman of the board of directors of PolyPeptide. “At the same time, it represents a transformational opportunity to accelerate our strategic ambitions at a scale we could not reach alone.”
Scientists in Switzerland have developed a portable breath detector that can accurately measure acetone released into the breath when the body burns fat. The smartphone-assisted device allows patients to monitor their metabolism at home and could help doctors personalize treatment for metabolic diseases such as obesity and diabetes.
Acetone levels in the breath have long been recognized as an indicator of metabolic activity, since its concentration rises when the body shifts from using carbohydrates to fats as the primary energy source. However, accurate measurements have traditionally required bulky and expensive laboratory equipment, while consumer devices have lacked the sensitivity needed to reliably measure acetone, especially at lower concentrations.
“If we want to make that information available to patients, we need to shrink those technologies into compact, user-friendly devices,” said Andreas Güntner, PhD, assistant professor at ETH Zurich and senior author of the study published today in the Device journal.
To make a compact breath analyzer, Güntner’s team used a chemoresistive sensor that changes its electrical properties in the presence of acetone. These types of sensors are known for their high sensitivity, rapid response, low power consumption, and small size. During each measurement, the accompanying smartphone app coaches users to exhale with the right force and duration, while quality controls can reject improper breaths or contaminated air. This design makes the sensor easy to use while ensuring accurate measurements.
The breath detector was used to analyze 312 breath samples from 12 healthy adults, with measurements closely matching those obtained using gold-standard mass spectrometry. “These findings show that we have the high performance needed for applications such as clinical studies, where you really want to distinguish these slight differences in fat metabolism,” said Simone Hersberger, graduate student at ETH Zurich and first author of the study.
The researchers then used the sensor to monitor breath acetone under four metabolic scenarios: light exercise followed by a high-carbohydrate meal, intense exercise followed by a high-carbohydrate meal, a high-fat ketogenic meal, and fasting. Breath acetone levels remained low during light exercise but increased with intense exercise, dropping after a high-carbohydrate meal. Acetone levels rose after a ketogenic meal and increased further during fasting.
Through Alivion, a spin-off from ETH Zurich, the technology is already available to individuals interested in tracking breath acetone to monitor weight loss and athletic performance. The device is currently being used to monitor individual progress in clinical studies of epilepsy, where a ketogenic diet is a standard medical treatment.
“Now it’s really time to spread it out into clinical trials and answer questions such as the effectiveness of different fasting therapies by providing personalized guidance,” said Güntner. “We’re really moving toward healthcare solutions that, in the future, you won’t need to go to the hospital for anymore. You’ll be able to do them at home.”
Good morning, and welcome to a new week from STAT’s London outpost, with Andrew Joseph here filling in for Mr. Pharmalot. This country is celebrating its World Cup win last night (or rather, very early this morning local time — lots of bleary-eyed people out in the neighborhood today), and I can only hope that the U.S. will be in the same place tomorrow. Yet the World Cup (and Wimbledon) can’t completely distract from the news of the day, so onto the headlines we go. …
Novartis is pushing deeper into antibody-drug conjugate development, paying $1.1 billion upfront to buy Myricx Bio for a pipeline based on a novel payload, Fierce Biotech writes. The Swiss pharma company has been slower to buy into ADCs than some of its peers, but it’s made its move with London-based Myricx, which is designing cancer treatments that deliver N-myristoyltransferase inhibitor (NMTi) payloads. It’s a bit of a different approach from other ADCs, which Novartis predicts could tackle resistance and other limitations of existing payloads, broadening the use of ADCs across multiple tumor types.
While clinical trials got caught up in the Trump administration’s attack on policies related to diversity, equity, and inclusion, congressional Republicans would like to change that, STAT says. Last month, the Republican-controlled House passed a bill, mostly along party lines, to fund the Food and Drug Administration that was accompanied by a report that, while not legally binding, tells agency officials what congressional appropriators expected of them. The report states that it wants the FDA to continue implementing a law that requires companies to give the FDA their plans for diversifying clinical trials.
Apple. Anthropic. Disney Research. Google. Meta. Microsoft. NVIDIA. OpenAI. Few places outside Silicon Valley can claim R&D hubs from all of these companies. Fewer still are concentrated in a city of just over 400,000 people—roughly half the size of San Francisco.
Over the past two decades, however, many of the world’s most influential technology companies have established R&D operations in and around Zurich, Switzerland. What began with Google’s decision to build its largest R&D hub outside the United States has evolved into one of the world’s most concentrated centers for AI research, talent, and commercialization, in certain areas at a higher density than Silicon Valley.
The question is why so many technology leaders keep choosing the same place to build and scale.
Located at the center of Europe, Greater Zurich Area, a region spanning the cantons of Glarus, Graubünden, Schaffhausen, Schwyz, Solothurn, Tessin, Uri, Zug, and Zürich, the region of Winterthur, and the city of Zurich, combines access to major markets with political stability, regulatory predictability, and strong intellectual property protection. And Zurich Airport connects the region directly with key business hubs across Europe, North America, and Asia, making it an efficient base for international operations.
The country’s innovation performance reinforces this position. Switzerland has ranked first in the Global Innovation Index for more than a decade, leads the world in patents per capita, and invests over 3.3% of GDP in research and development. Earlier this year, google.org pledged a $1 million grant to the Swiss National AI Institute, a joint effort to advance AI research for the public good.
Switzerland’s venture ecosystem reflects a similar focus. Over 60% of Swiss venture capital is invested in deep tech—the highest share globally by a large margin and nearly twice the share of major economies like Germany, France, and the UK. And, according to the Swiss Deep Tech Report 2026, at $1,470 invested per capita, Switzerland commits more to deep tech per capita than any other country in Europe.
The economics of specialization
While Switzerland is one of Europe’s most expensive locations for talent and operations, salaries remain at a fraction of those in Silicon Valley. The talent pool is small by global standards. Scaling a team quickly is harder in Zurich than in London, Paris, or Amsterdam. For early-stage companies that need to hire fast and burn lean, that trade-off is real. For companies building specialized AI capabilities, however, the equation works: The objective is to assemble the right team, not the largest one.
Switzerland’s economy is built around high-value, knowledge-intensive work. Productivity is among the highest in the world, and companies concentrate on functions that depend on specialized expertise rather than large workforces. For companies developing advanced AI capabilities, cost is often weighed against factors that are harder to replicate elsewhere: direct access to leading universities and research institutions, regulatory stability, and a quality of life that helps attract and retain skilled international talent.
A high-density AI ecosystem
Within Switzerland, the Greater Zurich Area concentrates many of the ingredients required to build and deploy AI systems.
The defining characteristic of this region is density. Many of the world’s leading AI companies, research institutions, investors, and startups operate in close proximity, creating connections between talent, capital, and ideas.
For example, Google engineers teach at ETH Zurich. ETH graduates join companies such as Anthropic. Researchers launch startups, while former employees of global technology firms go on to found new ventures of their own. Investors, founders, academics, and corporate teams encounter each other repeatedly through shared networks, industry events, and professional circles. In a region of this size, collaboration often happens less through formal introductions than through proximity. While talent flows freely, it rarely leaves the ecosystem.
One indicator of the region’s maturity is its ability to convene. Events such as the Zurich AI Festival will bring together more than 6,500 guests this September 28 to October 3. With more than 35 confirmed events across AI and the arts, AI literacy, health, technology, and policy, it is designed as a platform for cross-sector exchange. Its flagship events, the AI + X Summit, AI + Environment, and the AI + Policy Summit, will bring together internationally recognized leaders alongside researchers, policymakers, venture capitalists, and entrepreneurs, convening international voices and fostering dialogue across sectors.
Research, talent, and company creation
At the center of the country’s AI capabilities are institutions such as ETH Zurich, the University of Zurich, École Polytechnique Fédérale de Lausanne (EPFL), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), and Zürcher Hochschule für Angewandte Wissenschaften (ZHAW).
ETH Zurich ranks among Europe’s leading universities for deep tech commercialization, generating more than 40 spin-offs and startups in 2025 alone, helping create some of the continent’s most valuable technology companies.
The Stanford AI Index 2026 reinforces that picture: Switzerland ranks first globally for AI researchers and inventors per capita, with 110.5 per 100,000 inhabitants—ahead of Singapore (109.5), Sweden (80.6), and the United States (64.8). And the IMD World Talent Ranking ranked Switzerland as number 1 for the 10th consecutive year, leading globally in investment, development, and talent appeal.
Engineers, researchers, and founders move frequently between universities, startups, and established technology firms, creating strong knowledge flows across organizations. That density is increasingly attracting companies from outside the region too. Even before formally announcing their Zurich office, Exa.ai received a strong pipeline of candidate applications. ‘To assemble the greatest search team in the world, you’ve got to meet people where they are,’ says Will Bryk, the company’s CEO and co-founder. ‘And many are in Greater Zurich.’
Former Google Switzerland employees alone have founded approximately 210 companies and created around 2,600 jobs over the past two decades. For a country of around nine million inhabitants, the multiplier effect is significant. Large technology firms contribute not only through direct employment, but also through the creation of new companies and the transfer of expertise.
Why the Greater Zurich Area complements Silicon Valley
For many technology companies, Switzerland is not a substitute for Silicon Valley. The two serve different functions within the AI value chain.
Silicon Valley remains unmatched in scale, venture capital, and frontier model development, but for global technology companies, an R&D presence in Switzerland has increasingly become a strategic complement: a way to access specialized talent, stay close to leading research, and build capabilities that will shape the next generation of products and services.
This is particularly relevant for companies working at the intersection of AI and the physical world. Switzerland offers direct access to leading universities, industrial partners, and sectors such as healthcare, finance, manufacturing, and robotics, where reliability, compliance, and precision are often as important as raw model performance.
Geography is strategy
Global AI leaders came to the Greater Zurich Area because the region concentrates capabilities that are often distributed across multiple locations: world-class research, specialized talent, industrial partners, capital, and pathways to deployment. Those advantages were built over decades, not years.
For companies evaluating where to build the next generation of AI products, the answer may not be another larger ecosystem. It may be one where the distance between research, talent, capital, and deployment is measured in minutes rather than hours.
A study led by the Mayo Clinic Arizona shows women with Parkinson’s disease have greater amyloid plaque burden than men with the condition, even after controlling for factors like carriage of the APOE4 Alzheimer’s disease susceptibility gene variant.
As reported at the European Academy of Neurology Congress in Geneva this week, 57% of women included in the study had a high amyloid plaque burden versus 40% of the men.
Amyloid-beta is a protein fragment that normally gets cleared from the brain. In Alzheimer’s disease, it misfolds and aggregates into oligomers and plaques between neurons. This disrupts synaptic signaling, activates neuroinflammation, and promotes tau protein hyperphosphorylation into neurofibrillary tangles as the disease progresses.
In contrast, Parkinson’s disease is caused by the misfolding and clumping of a protein called alpha-synuclein into toxic deposits known as Lewy bodies, which build up in and destroy the neurons that produce dopamine in a brain region called the substantia nigra. While Parkinson’s is known for its characteristic motor symptoms, at least 25% also have dementia-like symptoms similar to those seen in Alzheimer’s disease. Amyloid beta plaques are thought to worsen Parkinson’s disease and increase the risk of dementia symptoms.
There are known differences in the prevalence and symptoms shown by men and women with Parkinson’s disease. To investigate this further, 230 people enrolled in the Arizona Study of Aging and Neurodegenerative Disorders and Brain and Body Donation Program were included in this study after death. Amyloid burden in the brain was assessed during autopsy. Other clinical factors such as cognition and symptoms were recorded prior to death.
The study found that amyloid plaque burden in women was higher than in men with Alzheimer’s. For example, mean cortical total plaque score in women was 6.5/15 vs 4.9/15 in men. Neuritic plaque density was also higher in women at 1.7/3 compared with 1.3/3 in men.
After correcting for age at death and APOE4 status, women in the study were more than twice as likely to have a high plaque burden than men.
This did not seem to translate to cognitive differences between men and women in the study though. “Men and women with Parkinson’s disease had similar rates of Alzheimer’s dementia and similar results on cognitive testing. However, women showed a higher amyloid plaque burden compared with men,” explained presenting author Erika Driver-Dunckley, MD, Mayo Clinic Arizona, in a press statement.
Notably, in standard Parkinson’s disease, men are at higher risk of developing dementia than women, so it is possible women have some protection from alpha-synuclein-driven decline but not from damage linked to amyloid accumulation. Women with Parkinson’s also live longer than men with the condition, as well as being more prone to amyloid buildup and Alzheimer’s disease, which complicates understanding the meaning of these results.
“Our findings highlight the need for further research into sex differences in Parkinson’s disease and Alzheimer’s-related pathology,” concluded Driver-Dunckley. “An important next step will be to confirm these findings in additional large clinicopathological studies and better understand the biological mechanisms that may underlie these differences.
A new rehabilitation platform combining virtual reality (VR) and nerve stimulation significantly improved the recovery of arm and hand function after a stroke compared to conventional rehabilitation approaches. Published today in Nature Medicine, results from a small-scale clinical study show early promise for a more effective and accessible rehabilitation approach that can be personalized to each patient’s needs.
Approximately 60% of stroke survivors develop long-term disability affecting their mobility. Even after extensive physiotherapy and occupational therapy, many continue to live with reduced arm and hand function, which severely impacts their ability to perform day to day tasks and live independently.
“Our aim was to go beyond mere movement training,” said Stanisa Raspopovic, PhD, professor of biomedical engineering at the Medical University of Vienna and senior author of the study. “After a stroke, patients often have difficulty not only moving the affected limb, but also feeling it and perceiving it correctly. MultiSensy was developed to reconnect movement, sensation and body awareness during rehabilitation.”
The MultiSensy rehabilitation platform combines immersive VR with electrical nerve stimulation. The VR goggles present users with interactive virtual tasks designed to train arm and hand functions such as reaching, grasping, pinching, and forearm rotation. Meanwhile, electrodes on the skin stimulate sensory nerves in real time to make patients feel virtual objects as if they were physically touching them.
The system was tested on a cohort of 34 patients who had suffered a stroke over three months before. Participants were divided into two groups who were treated either with MultiSensy or conventional rehabilitation including physiotherapy and occupational therapy. Both groups completed a total of 12 training sessions over the course of three weeks.
Patients who used the VR system saw a greater recovery of arm and hand movement compared to those in the control group, achieving nearly twice the improvement according to a standard assessment of motor impairment after stroke. In addition, MultiSensy was able to address body awareness and sensory deficits caused by stroke, which are often left aside by conventional rehabilitation strategies.
“After a stroke, some patients struggle to feel touch in their affected hand and may even perceive the arm as distorted in size, shape, or position,” said Valerio Aurucci, PhD, lead author of the study and former graduate student at ETH Zurich. “Participants treated with the new system showed improvements in their sense of touch and in perception of their affected arm.”
Another advantage of the MultiSensy platform is that each task can be adapted to the impairment level of the user, tailoring treatment to their unique needs. The VR system collects movement data during training, providing objective measurements of progression that clinicians can rely on to monitor a patient’s performance and recovery over time.
“The results provide early clinical evidence that immersive virtual reality combined with sensory nerve stimulation can support recovery after stroke, even after months from the event”, said Raspopovic. “The technology is still at the research stage, and larger clinical trials are needed to confirm its benefits. However, the study opens a promising perspective for future personalized and potentially home-based stroke rehabilitation.”
Background: Digital assessment technologies, such as optical motion capture and inertial measurement units, enable detailed kinematic analysis and continuous monitoring of upper limb activity in persons with neurological conditions. While such are increasingly recognized in research, their uptake in clinical neurorehabilitation is limited. It remains unclear which clinicians perceive as most meaningful and how these are integrated into patient-centered care. Understanding clinicians’ information needs and reasoning processes is a prerequisite for implementing digital assessment technology. Objective: This study aims to characterize how rehabilitation professionals perceive, prioritize, and integrate into clinical reasoning and to identify features that would support their routine use. Methods: Three 90-minute focus groups were conducted in 3 Swiss neurorehabilitation centers, involving 11 clinicians with diverse professional backgrounds (5 physiotherapists, 4 occupational therapists, 1 movement scientist, and 1 medical practitioner). Participants discussed essential parameter domains and individually rated the relevance and meaningfulness of 17 kinematic metrics for the well-studied drinking task and 10 established arm use performance metrics. Verbatim transcripts were analyzed using reflexive thematic analysis, and rating data were summarized descriptively. Results: Five main themes were identified. (1) (active/passive range of motion, strength, selective muscle control, and grasp) form the basis for interpreting movement. (2) (smoothness, efficiency, and compensatory movement) are valued when aligned with observable task execution. (3) (hourly activity profiles, arm-use symmetry, and functional workspace) represents the reference for patient-centered reasoning. (4) , including diagnosis-specific preferences, shapes assessment selection. (5) reflects clinicians’ reliance on visual judgment complemented by normative values. Intuitive metrics such as task duration, number of movement units, and range of motion were favored, whereas confidence was lower in more complex metrics (eg, jerk and interjoint coordination). Conclusions: Clinicians value intuitive when they are clearly linked to patient-centered outcomes and supported by normative references. The findings highlight the need for targeted educational strategies and digital competency training that help clinicians interpret digital metrics and integrate them with contextual information and clinical reasoning.
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Here’s a problem you probably didn’t solve in school: You’re an ambitious young plumber from Brooklyn in a world inhabited by violent human-size mushrooms called Goombas. The love of your life has been kidnapped, so you embark on a quest to rescue her, venturing through stretches of pipe-filled and monster-ridden terrain where your only means of protection are your powers of jumping and stomping.
It’s a journey so arduous that no computer—real or hypothetical—is powerful enough to figure out if you can reach her. And according to research published by the MIT Hardness Group, determining whether your quest is possible at all is at least as complicated as decoding the encryption behind financial transactions. But if this problem could talk, the first thing it would say is “Hello, it’s a-me, Mario!”
For the love of the game
Though it does have a YouTube channel, the MIT Hardness Group isn’t an official research group. Instead, it’s a placeholder name for theoretical computer science projects—including several related to Super Mario—from Erik Demaine’s class Algorithmic Lower Bounds: Fun with Hardness Proofs.
Demaine, a professor of computer science, received a MacArthur fellowship (also known as a “genius” grant) for his work in computational geometry on protein folding and origami. But he also researches complexity theory, which focuses on organizing problems into categories based on how much time and memory space it takes for computers to solve them.
He happens to be an avid Super Mario fan as well. “I grew up playing NES [Nintendo Entertainment System] games,” Demaine says. “I poured many hours into playing as a kid, so it’s fun to come back to it these many years later and tie it into my research.”
Erik Demaine researches complexity theory, which examines the amount of time and memory that computers need to solve problems. He’s also an avid Super Mario fan.
DONNA COVENEY/MIT
Super Mario takes place on a horizontally scrolling universe of platforms, pipes, and other obstacles. The object of the game is to rescue Princess Peach, the monarch of the Mushroom Kingdom, by racing through this terrain while sidestepping or dueling monsters like Goombas and deadly porcupines called Spinies. The game takes place over several levels; in the original version, each level ends with a flagpole that sends Mario on to the next part of his mission.
Over the last 14 years, Demaine and his collaborators have proved many things about Super Mario, such as that it’s even harder than the infamous traveling-salesman problem (which seeks the most efficient route between many different locations) or the problem of factoring large numbers. But the result that surprised Demaine the most came from four of his students: Hayashi Ani ’21, MEng ’23; Holden Hall ’26; Ricardo Ruiz ’24, MEng ’25; and Naveen Venkat ’23, MEng ’24. For their final project in that 2023 class, the team used a combination of fan-made Super Mario level editors and a platform called Super Mario Maker to create levels so hard that they are undecidable. In other words, it’s impossible to write a computer program that always correctly predicts whether, in those levels, Mario can reach the castle.
Previously, Demaine had believed that Super Mario belonged in the PSPACE complexity class, which contains problems that are solvable but whose solutions become impractically complex as the problem gets bigger. At the time, he had even said that PSPACE was Mario’s “permanent home.” But the new findings pushed Super Mariointo RE-Complete, the class of undecidable problems. “It’s the hardest complexity class we could imagine for these sorts of games,” Demaine says.
What computers can’t solve
In 1936, Alan Turing, the father of modern computer science,created a puzzle now known as the Halting Problem to prove it’s not possible to construct a computer that can solve everything.
At the core of the Halting Problem lies a paradox, and it goes like this: Suppose you have a fancy computer, called the Oracle, that looks at any program and correctly determines whether a computer following it will ever come to a stop. For example, if it sees the program “Take 1 and add 3,” the Oracle will say the program halts, but if the program says “Take 1 and add 1 to it until it becomes 0,” the Oracle will say it runs forever.
Now suppose you have another computer, the Contrarian, and you put the Oracle inside it. When you give the Contrarian a program, it passes it to the Oracle and then does the opposite of whatever the Oracle says the program will do. So if the Oracle assesses the Contrarian’s program and thinks it will halt, the Contrarian will run forever. If the Oracle thinks the program will run forever, the Contrarian will halt. Either way, the Oracle’s assessment is wrong, so the classification problem is undecidable.
The proofs that Super Mario is undecidable rely on a more complex version of this idea. The team’s argument breaks down the video game using a technique called a reduction, in which mathematicians convert a problem they’re trying to solve into a problem they already know something about. “The classic example I remember in a math class is: How do you make a pot of boiling water?” Demaine recalls. “Well, I fill up the pot with water from the sink, and then I put it on the stove, and then it eventually boils. Okay, now I’ll give you a pot of water that’s already filled. How do you make a pot of boiling water? Well, I empty out the pot first and reduce to the previous problem.”
In their particular world of platforms and porcupines, the team broke down their Super Mariolevel into localized parts of Mario’s path called gadgets, which they could use to prove that the level was undecidable.
“A gadget in our sense is anything in your environment that decides whether or not you can go through one pattern [within a level],” explains Jayson Lynch ’12, MEng ’15, PhD ’20, a CSAIL research scientist and head of algorithms at MIT FutureTech. For example, in one gadget Mario might need to jump on a platform to avoid a monster as he makes his way across the screen. As a PhD student mentored by Demaine, Lynch spearheaded the formalization of gadget theory and worked on some of the earlier Super Mario papers but did not study the game’s undecidability.
One of Lynch’s favorite Super Mario gadgets is the door gadget, which works like a door that Mario can open, traverse, and close. The door in question is always either open (when the Spiny is on the right) or closed (when the Spiny is on the left). So if a Spiny is pacing back and forth on the left of the door, Mario has to navigate beneath the moving Spiny and jump up to hit a brick block just as the Spiny reaches it. This bumps the Spiny to the right side, which opens the door and allows Mario to travel across the traverse path and get to the spot where he can close the door. Once there, he must time another jump beneath the pacing Spiny to send it back to the left side of the gadget, closing the door behind him.
Mario opens the door by bumping the Spiny from the left to the right.With the Spiny out of the way, Mario can go through the open door and follow the traverse path to the other side. Once there, he’ll be able to bump the Spiny back to the left and close the door.
Since a door is always open or closed, its state can be used to simulate a true or false statement, with open being true and closed being false. Earlier Super Mario papers had strung together multiple door gadgets to simulate a true-or-false problem that complexity researchers already knew to be hard. But to show undecidability, the team used Super Mario level editors to put together another device, called a counter gadget, that tallies the game’s monsters and obstacles.
If you can build a machine with even just a few of those counters, Demaine says, you can simulate an arbitrary computer—one that could essentially do anything a non-quantum computer could do, given enough time and memory. And with no limit on the number of monsters, such a machine could have infinitely expandable memory, even though the level size stays the same, which he calls “pretty wild.” In other words, any theoretical computer can be built in a Super Mario level. “You could use it to solve anything you can use a computer to do,” says Demaine. “You could have it do your taxes, or compile your code, or run an LLM, or optimize your class schedule.” You might even build Super Mario levels that could excel at sudoku, construct optimal chess strategies, or prove any provable mathematical theorem.
The MIT mathematician Marvin Minsky invented counter machines in 1961 to figure out how simple a computer could be while still being “universal” (as powerful as any other computer, given enough time). These theoretical computers each store two numbers and can change them by adding 1, subtracting 1, or doing something special if a number hits a set value.
In the counter gadgets the students designed for Super Mario, the numbers reflect how many Goombas the levels contain. A number increases when a pipe spits out a Goomba and decreases when Mario stomps on one. Mario dies if he collides with a Goomba without stomping on it, so he can continue along the path only when the counter is at 0.
The MIT Hardness group designed this counter gadget in Super Mario Maker 1 to prove undecidability.
Minsky had already proved that counter machines are undecidable because they can run undecidable problems. Since the researchers proved that counter gadgets simulate counter machines, then any level of Super Mario containing a counter gadget will also be unsolvable. “In the future, if someone wants to show a game is undecidable,” explains Holden Hall, one of the students behind the project, “they just have to make one of these gadgets.”
The existence of undecidable problems like the Halting Problem implies that it’s possible to construct an undecidable Super Mario level. Just as the singular undecidable program for the Halting Problem meant thatit’s impossible to figure out if a computer program will run forever, the team’s undecidable level means that it is impossible to determine whether an arbitrary Mario level can be beaten.
Putting the “super” in Super Mario
More than two years after Demaine’s class on hardness proofs, some of his students continue to meet weekly to discuss their Super Marioresearch.
“From the point of view of complexity theory, studying video games is interesting mostly for didactical reasons,” Fabrizio Grandoni, a research professor at the University of Applied Sciences and Arts of Southern Switzerland, told MIT News in 2016. “It’s a simple, natural way to attract students to study this specific topic.”
Hall, who had very little exposure to the ideas of complexity theory before taking Demaine’s class, is a case in point, noting: “I took the class because a bunch of people I knew were taking it. But since I took it, I really enjoyed the class, and so I’ve taken a lot more classes in that realm.”
The applications of the MIT Hardness Group’s work go way beyond stomping on mushrooms and collecting coins. For example, researchers at the University of Texas Rio Grande Valley (including Timothy Gomez, now a PhD student at MIT) have used the gadget theory developed for analyzing games like Super Marioto study the complexity of problems relating to planning robotic motion and modeling chemical reaction networks.
“[Gadget theory] can be used in the negative way to say ‘Oh, well, we should stop searching for algorithms because we know this problem is too hard’—or it can be used in this positive way, because usually, to prove something hard, you’re showing that you can build a computer of a certain type,” Demaine says.
Though there’s no way of knowing what mark Super Mariowill leave on the future of math and computer science, one thing’s for sure: No matter how many princesses he does or doesn’t save, the legacy of this little plumber is set to extend far beyond video screens.
Researchers in Zurich say they have developed a method to use microrobots delivering stem cell therapies to treat spinal cord injuries. According to ETH Zurich and the University of Zurich (UZH), modern therapies for spinal cord injuries attempt to influence implanted stem cells using electrical stimulation, promoting the growth of new nerve cells. However, researchers…
