Wired Android Auto is about as plug-and-play as it gets. The moment you plug the USB cable into your phone, everything connects in less time than it takes your engine to warm up a little before you roll off. However, annoyingly, it always seems to turn on Bluetooth too, but why does that keep happening when you’re using wired Android Auto?
A mother in New York State who claims to oppose vaccines because of her Catholic faith lost an emergency appeal to the Supreme Court of the United States (SCOTUS) this week—though Justices Samuel Alito and Neil Gorsuch said they would have granted her application. For now, the woman’s kindergarten-aged son must be vaccinated to attend school.
Steven Strogatz coauthored a book about how math is moving beyond human understanding. He spoke with WIRED about the seismic impact artificial intelligence has had on his life’s work.
Games may be life with all the hard bits removed, but they provide a way to study why people make the choices they make. Traditional games are usually played against a static background: the rewards per outcome are constant. That limits their relevance to behavior because, in real life, the rewards and consequences of strategic choices are ever changing. Now, researchers have used a mathematical model to study a series of games that include evolving strategies and randomly varying returns.
A bit of history
Perhaps the most famous game-theory contest is the prisoner’s dilemma. In the prisoner’s dilemma, a pair of thieves have been captured and are being separately interrogated by the police. If both clam up, they will be punished for a lesser crime. If one prisoner makes a deal (defects) then that prisoner gets to go free and the other gets a heavier sentence. If both make a deal, they both get an in-between punishment.
The person running the game can start it with different rewards for cooperating and defecting to explore how the optimum strategy varies with reward and risk, which the players can figure out by varying the strategies across multiple rounds. Depending on the balance between the reward for staying silent (cooperating) and betrayal, the game stabilizes with everyone betraying everyone. In this simple situation, everyone loses.
For a child bursting with energy, few things are more fun than hurtling through a bouncy castle, launching into the air, and ping-ponging between every surface. But that childhood buoyancy will quickly deflate when it turns out those surfaces are smeared with a hypervirulent, multidrug-resistant pathogen.
That was the horrifying reality for a community in Ireland in fall 2025. Neighbors had gathered for an afternoon of merriment, complete with a barbecue, a sweets station, and three bouncy castles. Officials estimate that about 120 people joined the festivities, and around half of them were children and teens. Within a day, some children began developing signs of an infection. In all, 48 children in the community developed aggressive skin and soft-tissue infections.
Of the 48 cases, 33 were treated by their regular doctor, and 15 sought emergency care. Four children ended up being hospitalized. Luckily, all of the children recovered. The results of the outbreak investigation were reported this week in the journal Eurosurveillance.
Argonne researchers study the movement of dust particles from concrete surfaces during a simulated radiological contamination scenario, measuring how pedestrians and moving cars affect the spread of the particles.
Every year, MIT Technology Review puts together a list of some of the brightest and best young minds working across science and technology. Our 35 Innovators Under 35 are the ones to watch—people whose research and technical work stands to shape the future of their fields.
This year, the list includes nine people who are transforming biotech. And this week, I’m going to give you a taste of some of the very cool stuff five of them are working on, which includes lifesaving innovations and groundbreaking “age reversal” tech.
1. Preventing maternal deaths
Let’s start with Paschal Kija, a 28-year-old who has developed a device to treat postpartum hemorrhage—a dangerous birth complication that contributes to around 29% of maternal deaths in his home country, Tanzania. The Mkanda Salama (“Safe Wrap” in Swahili) is easy to use and costs just $70. A study found that it stopped postpartum bleeding in 73% of women within 20 minutes.
2. Making brain electrodes inspired by Japanese art
For decades, scientists have been developing, testing, and implanting brain electrodes. These devices are literally inserted into people’s brains, so while they can help us understand brain activity and treat various neurological disorders, it’s not totally surprising that they can also cause a bit of damage. Xiao Yang, 34, is working on ultra-small electrodes, which she hopes will have less of an impact on surrounding brain tissue. Her electrodes are flexible, too—in fact, they look a lot like actual neurons.
Yang is also creating sheets of electrodes to study brain cells in the lab. Inspired by kirigami—the traditional Japanese art of cutting paper to form three-dimensional shapes—she’s created a sheet of electrodes with a honeycombed structure shaped like a spiral basket. And she’s already using it to study brain cells.
3. Developing an all-new treatment for baby KJ
In 2024, Kyle “KJ” Muldoon Jr. was born with a rare and potentially fatal genetic disorder. Sarah Grandinette was a member of a team that developed an entirely new, personalized treatment for him—a gene-editing therapy essentially designed to correct a genetic misspelling.
Grandinette, who is now 26, created cells with KJ’s genetic variant and used them to screen gene-editing approaches; then she tested potential medicines in mice and monkeys. KJ ultimately got his first dose of the resulting treatment when he was about seven months old. He responded well and was eventually discharged from hospital. He’s “doing pretty great,” she says.
4. Reversing the aging process to treat eye disease
The buzziest tech in longevity right now centers on reprogramming—attempts to rewind the age of cells by resetting them to a more embryonic-like state. In a study published in 2020, Yuancheng (Ryan) Lu (now 34) and his colleagues showed that a reprogramming therapy reversed vision loss in aged, blind mice. Now an almost identical version of that therapy is being tested in people with eye disease. Life Biosciences, the company developing the drug, dosed its first volunteer in June.
5. Using AI to design new viruses
Last year, Samuel King used a generative AI model to come up with new genetic blueprints for bacteriophages—teeny viruses that can infect bacteria. Once he had those blueprints, he printed them out as strands of DNA. In experiments, he found that those AI-designed viruses could create new copies of themselves, burst out of bacterial cells, and infect other nearby bacteria. Viruses aren’t alive, but King, 27, hopes that AI-designed life forms might one day be used to make drugs or soak up pollution.
You can read more about these innovators, and the others on the biotech list, here.
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
Longtime Slashdot reader AmiMoJo shares a report from the BBC: August was the joint-hottest month ever recorded, tying with July 2023, according to the EU's climate service. The record was driven by a combination of climate change and the rising influence of El Nino, pushing heat into the atmosphere across the Pacific. Temperatures for the month were 1.65C above pre-industrial levels -- surpassing the critical 1.5C threshold agreed by countries to avoid the worst consequences of climate change. While a single month above 1.5C does not mean it has been permanently crossed, it shows a trend towards breaching that limit. The month also capped western Europe's warmest summer, with high temperatures pushing the season past the deadly heat of 2003.
An anonymous reader quotes a report from The Guardian: Rising global temperatures caused by the burning of fossil fuels are transforming the world's forests, wetlands and tundra to the point they are releasing emissions that could further worsen global heating by as much as 30%, a new study has found. As the world heats up, permafrost in the high latitudes is thawing, fiercer wildfires are burning in drying forests and wetlands and lakes are warming. All of these changes are themselves causing the release of planet-heating methane and carbon dioxide locked in trees and soils, therefore worsening the climate crisis, the paper states.
While scientists have long known of these "feedback loops" that amplify the impact of direct pollution from burning coal, oil and gas, the emissions from natural sources are largely unaccounted for in climate projections. This additional heating is however significant, researchers found, with emissions from natural sources set to amplify global heating by 20% to 30% by the end of this century. This would add an estimated 0.2C to 0.4C to the global average temperature by this time, depending on the overall action taken by countries to combat the climate crisis.
[...] This latest paper makes clear that action by countries to cut fossil fuel pollution will still lessen the amount of additional emissions that come from landscapes. Under the most optimistic climate scenario where emissions are cut quickly, natural sources will add 0.2C of warming, the paper found. Under a worse case higher emissions scenario, 0.4C will likely be added. Current policies put in place by governments are likely to see the global temperature rise to 2.6C above pre-industrial times, according to Climate Action Tracker. This will likely cause severe disruption and hardship to billions of people through sea level rise, crop losses and other consequences. "This hidden multiplier is missing from the models guiding climate policy, so those models are likely underestimating how much warming is coming -- and overestimating how much carbon we can still afford to emit," said Sam Abernethy, a scientist at Spark Climate Solutions who led the research, published in Environmental Research Letters.
The disruption of the worldâ(TM)s carbon cycles is now being seen in "real time," said Rob Jackson, a Stanford University scientist and study co-author. "The results are a wake-up call, and itâ(TM)s imperative that they be included in the next generation of climate policies," he added. "Failure to do so will make meeting the worldâ(TM)s climate goals less and less likely."
Fears that modern electronic devices may harm children are nothing new. Gadgets with screens of any size have long been allegedly melting, rotting, and/or corrupting the brains of youths for decades. However, a medical case report published this week offers a new and alarming way our digital doodads may cause physical harm.
In BMJ Case Reports, two UK doctors, Mara Znagoveanu and Edward Artley, report the case of a boy who came to an emergency department with alarming marks on his abdomen. The marks were described as being in a patch about 15 centimeters (6 inches) wide, made of flat, reddish-brown "interlacing lines forming irregular circles and a lace-like morphology." A picture of the marks is here.
Mysterious marks
The boy, whom they described only as being in "mid-childhood," was not in any pain, and the rash was not warm to the touch or tender. He and his parents said they couldn't think of any recent injuries or trauma that might explain the marks. He was otherwise healthy, hadn't recently been ill, and had no systemic symptoms, such as fever or fatigue. Everything about the boy's health, growth, and medical history looked normal.
Skullcandy Dime 3 wireless earbuds have a serious vulnerability related to unauthenticated Bluetooth pairing. This flaw allows nearby attackers to silently pair with the earbuds, disrupt legitimate audio sessions, and potentially capture microphone audio. This issue, tracked as VU#859658 by the CERT Coordination Center, affects the Skullcandy Dime 3 earbuds (model S2DCW) running firmware version […]
The entrance of major AI companies into healthcare is a meaningful and welcome development, accelerating the technical foundation available to the industry.
Their models are increasingly capable of processing long clinical records, interpreting complex terminology, comparing documentation against evidence and generating coherent summaries from large volumes of information. For clinicians, operators, and administrative teams who spend significant time searching through fragmented data, these advances are helping reduce cognitive burden and make high-value information easier to access.
But healthcare leaders should not confuse model capability with operational capability.
Healthcare’s administrative challenges are caused by fragmented information, fragmented workflows, and fragmented accountability, not a lack of information. The industry has spent decades investing in systems that capture activity: electronic health records, billing platforms, payer portals, scheduling systems, call center platforms, and analytics applications. Each system records something important. But few were designed to reason across the full chain of decisions that determines whether patients get timely access, clinicians have the right documentation and providers are reimbursed appropriately.
This is the problem that AI must now confront.
Revenue cycle is becoming one of healthcare AI’s proving grounds
The revenue cycle is the process healthcare providers use to get paid for care — from scheduling and registration through coding, billing, payer follow-up, and payment collection.
It is unusually suited to rigorous AI deployment because it combines high transaction volume, complex reasoning, structured and unstructured data, measurable outcomes, and significant operational variation. It also sits at the intersection of financial performance, patient access, and administrative workload.
A single claim can be influenced by patient insurance information, clinical documentation, coding rules, payer-specific policies, prior authorization requirements, medical necessity criteria, and many other data sources and operational processes. A breakdown in any one of those areas can create downstream consequences weeks or months later.
This is why generic automation has often fallen short.
Traditional robotic process automation works well when workflows are stable and rules are predictable, but healthcare administration is neither. Payer requirements change. Documentation expectations evolve. Exceptions are common and often material.
Large language models improve part of the equation, extracting meaning from narrative text, summarizing records and supporting reasoning over complex documentation. But when used alone, they inherit important limitations. They may produce plausible outputs without sufficient traceability. They may lack awareness of local workflow constraints. They may miss payer-specific history or context that determines whether an action is likely to change an outcome.
Why foundation models will become necessary but insufficient
The major AI firms are solving real technical problems for healthcare.
Better context windows make it easier to process longitudinal records. Stronger reasoning improves the interpretation of complex clinical scenarios. Better multimodal capabilities may eventually help connect text, imaging, structured data, and clinical signals in more useful ways. Safer model behavior and healthcare-specific tuning will continue to improve adoption.
These capabilities will make healthcare work faster, more consistent and easier to navigate. But they will not, on their own, solve deep-rooted administrative complexity.
Much of healthcare’s operational knowledge does not live in general medical literature, coding manuals, or public payer guidance. It lives in the accumulated experience of what actually happens after decisions are made. For example:
Why does one appeal strategy outperform another?
Which documentation gaps are most likely to cause reimbursement delay?
How does a specific payer respond to a particular clinical argument?
These insights are behavioral, operational, and longitudinal. They emerge from years of transactions, outcomes, exceptions, and human judgment.
As foundation models become more capable, access to baseline healthcare knowledge will become less differentiating. Most leading systems will be able to interpret ICD-10 codes, recognize medical terminology, summarize payer policies, and reason over public clinical criteria. The durable advantage will come from how organizations combine that model intelligence with proprietary operational data, structured knowledge, workflow context, and governance.
The technical shift: From automation to orchestration
Agentic orchestration turns foundation model understanding into coordinated action — intelligence that can follow work across systems, apply the right rules, adapt when something changes, and keep learning from what happens next.
A prior authorization workflow, for example, may require retrieving clinical documentation through fast healthcare interoperability resources (FHIR) APIs, mapping patient history to payer criteria, identifying missing evidence, generating a submission packet, routing exceptions to a specialist, monitoring payer response, adjusting patient care pathways, and learning from the outcome.
This type of workflow requires coordination. It also requires guardrails: regulatory requirements, privacy standards, clinical policies, coding rules, payer criteria, and organizational risk thresholds. One promising approach is hybrid architecture that combines LLMs with structured knowledge bases, symbolic logic, reinforcement learning, and deterministic validation layers.
At Ensemble, this is the design principle behind EIQ, our revenue cycle intelligence engine. EIQ brings together operational activity, clinical documentation, payer behavior, and reimbursement outcomes into a continuously learning intelligence layer that’s integrated with the hospital’s electronic health record (EHR). It supplements the system of record with a system of intelligence, designed to connect information and surface actions most likely to improve outcomes.
EIQ uses a neuro-symbolic approach that combines LLMs and custom small language models with rules-based reasoning. That architecture is built on one of the most robust datasets in healthcare, informed by more than a decade of award-winning operational performance, transaction history, payer behavior, and operator decision-making. The language models help interpret information and generate human-readable outputs. The symbolic layer represents policies, rules, payer requirements, and workflow constraints so the system can apply guardrails, make reasoning steps more traceable and recommend actions that fit the specific operational context.
What the next decade will reward
The contribution of major AI firms to healthcare will be significant. Their models will become faster, safer, more capable, and more accessible.
But the next decade of healthcare AI will be defined by integration, not model capability alone.
The organizations that create the most value will be those that connect models to governed data, operational workflows, domain expertise, human oversight, and measurable outcomes. They will understand that healthcare intelligence cannot live in a separate interface. It has to exist inside the decisions that shape access, documentation reimbursement, and patient experience.
This content was produced by Ensemble. It was not written by MIT Technology Review’s editorial staff.
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