Breathing clean air depends on filters that do more than trap particles—they must hold them. In many conventional systems, captured dust can loosen under airflow and mechanical disturbances, which lowers filtration efficiency, shortens service life, and forces more frequent replacement. The bottleneck is not capture alone, but durable retention of what the filter catches.

A research team led by Professor Sanghyuk Wooh and Professor Chae Bin Kim has introduced a dynamic imine bond adhesive (DIBA) designed to keep airborne particles embedded. The concept is to marry solid-like stability with liquid-like adhesion, so the filter surface can “refresh” itself while working.

The coating is built by crosslinking polydimethylsiloxane (PDMS) via dynamic Schiff base chemistry. This chemistry enables bond exchange at the interface, allowing adhesive sites to reorganize over time rather than degrade into inert residue. The result is a coating that is both mechanically robust and chemically active.

When sprayed onto commercially available filter media, the DIBA layer behaves like an internal reservoir of stickiness. As particles are collected, they are drawn into the adhesive film rather than remaining only on the exterior fibers. Capillary-driven wetting then forms adhesive bridges around individual particulates, increasing the real contact area that governs holding strength.

Performance tests combined chemical, mechanical, and interfacial characterization—rheology, FTIR, atomic force microscopy, and microscopy—with filtration trials. The coating boosted particle adhesion dramatically, approaching a nearly seventy-fold improvement compared with typical behavior. Importantly, the adhesive renewal process helps counteract the gradual loss of attachment that plagues standard filters.

Filtration efficiency improved by roughly 10–30% across multiple commercial materials, without raising pressure drop. The upgrade is significant in practical terms: one reported shift is from MERV 6 toward MERV 11, indicating better capture at the same airflow penalty.

The coated filters also sustained operation under high-speed airflow (up to 20 m/s) and delayed pore clogging. By maintaining effective particulate capture for longer intervals, the DIBA approach extended filter lifespan by nearly a factor of two relative to conventional designs.

Because DIBA is applied through simple spray-coating, it is compatible with existing manufacturing and retrofit workflows. The researchers highlight applications ranging from HVAC and air purifiers to cleanrooms, industrial dust collectors, automotive cabin filters, and personal protective equipment.

More broadly, the work suggests a platform for next-generation filtration where energy use and replacement schedules improve simultaneously. The dynamic adhesive strategy could be adapted to other separations, including water purification and environmental remediation, wherever particles need to be captured and kept immobilized.

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Subject of Research: Not applicable
Article Title: A Super-Adhesive Air Filter With Capillarity-Mediated Spontaneous Particle Absorption via Dynamic Bond Exchange
News Publication Date: 22-Apr-2026
Web References: https://doi.org/10.1002/adma.202600006
References: DOI: 10.1002/adma.202600006
Image Credits: Professor Sanghyuk Wooh from Chung-Ang University, Republic of Korea and Professor Chae Bin Kim from Pusan National University, Republic of Korea

Keywords

Air filtration, dynamic adhesive chemistry, Schiff base, PDMS, particle retention, capillarity, MERV, particulate matter, surface engineering, polymers

Physicists (and engineers) are great at estimating. We’ve obviously never counted the number of electrons in the visible universe, but we reckon there are between 10⁸⁰ and 10⁸² of them. As for the “Planck length” at which quantum effects start to dominate gravity, that’s estimated to be 10^–35 metres. The transition between laminar and turbulent flow in fluids, meanwhile, comes in at Reynolds’ numbers of between 2500 and 4000.

These estimation skills, which are a familiar part of any physicist’s toolkit, can also come in handy for anyone working in the business world. In particular, the ability to estimate is useful when examining the business cases drawn up by start-up companies seeking to drum up financial support from investors and banks.

Most business cases completely overestimate a company’s predicted profits and make wild – and mostly nonsensical – statements

Such documents inevitably paint a rosy picture of a firm’s potential because, without money, nothing can be done. However, in my experience, most business cases completely overestimate a company’s predicted profits. They also make wild – and mostly nonsensical – statements about the transformational impact it will have on a particular industry.

Now I appreciate that business cases need to contain a bit of hyperbole if they are to attract investor funding; there’s no point being unnecessarily gloomy and down-beat. But given that up to 90% of start-up companies fail, it’s essential to have the ability and skills to evaluate a start-up company’s business case as realistically as possible.

Here’s the rub

Let me give you a specific example from tribology – the science of friction, lubrication and wear. Tribology might seem mundane, but it is a big deal, especially when you realize that 25% of the world’s energy is used simply to overcome friction and wear. (The Institute of Physics has a special interest group in tribology if you want to find out more about the field.)

One topic of huge interest, on which Google Scholar reckons almost 15,000 papers were published in 2025 alone, is that of nanoparticles as potential lubricant additives. Despite that large amount of research, I am not aware of any mainstream commercial lubricants that use any of these additives. So why is there such a large disconnect between academia and actual practice?

This is where our estimation skills come in. We know that lubricants are mostly hydrocarbon oils plus chemical additives such as antioxidants, friction modifiers, dispersants, detergents and anti-wear and anti-foaming agents. According to data from Kline and Company, about 40 million tonnes of lubricants are used annually around the world, roughly evenly split between industry and the automotive sector.

Automotive lubricants typically contain 15% additives, whilst industrial lubricants have about 1%. The “average” additive content, over all lubricants, is therefore about 8%, which equates to 3.2 million tonnes. With Polaris Market Research estimating that the lubricant-additives business being worth about $20 billion, we can see that the “average” additive price is roughly $6250 per tonne.

Particularly important to lubricants are anti-wear additives, which stop metal surfaces from breaking down when the oil that normally keeps them apart has disappeared. Lubricants usually have about 1% of such additives. Given the numbers above, we can estimate that the overall market for them should be about 400,000 tonnes.

That’s not far off the real market size, which business analysts Markets and Markets reckon to be about 300,000 tonnes. (The figure is slightly less than my estimate because some lubricants, such as transformer and circulating oils, don’t contain such additives.) With that market being worth almost $1 billion a year, the average price of anti-wear additives is about $3300 per tonne.

There’s also another key component of modern engine oils, namely friction modifiers – “slippery” substances that form films on moving surfaces to reduce friction and stop them from shearing. Some of the best friction modifiers, which are based on molybdenum, cost about $10,000 per tonne. Given that base oils typically cost between $1000 and $2500 per tonne, molybdenum-based friction modifiers are much pricer than anti-wear additives.

Graphene isn’t gold

Armed with this information, we can now see why graphene – the “poster child” of lubricant nanoparticles – has flopped so badly from a commercial point of view as a lubricant additive.  This 2D carbon material costs a staggering $50,000–$200,000 per tonne, which is more than five times pricier than the most expensive additives in lubricant formulations.

The message is clear. For lubricant nanoparticles to become commercially competitive additives, their price per tonne needs to be come down by at least an order of magnitude. Now most Physics World readers are unlikely to be in the business of selling lubricants. But for anyone developing a new technology, the point is you cannot assume customers will come running.

Businesses need to be aware of what existing technologies are out there – and how much those rival technologies cost

Businesses need to be aware of what existing technologies are out there – and how much those rival technologies cost. If a new technology is too expensive for that application, different markets may need to be considered. Graphene, for example, has proved more useful in composites and materials reinforcement, coatings, energy storage and electronics, rather than lubricants.

Looking at new technologies even more broadly, the most successful by far are those that have fallen in price year-on-year for a decade or more. In computing, for example the cost of CPU time per dollar has typically dropped by a third every single year for the last 70 years, while computer memory has plummeted from $604trillion per terabyte in 1959 to just over $1000 per terabyte in 2023. As for electric cars, they are selling like hot cakes now that batteries have dropped from $10,000 per kWh in 1991 to barely $100 per kWh today.

My message is simple. When it comes to any technology, a sure-fire way to judge if it’ll succeed is to see if its price is likely to drop. No-one can know what the future will hold, but as physicists and engineers, we have a great ability to make estimations. By applying those skills when we sift through business cases, we’re more likely than most to identify the real gems from the rest.

The post Want to spot a winning technology? Here’s the key skill you need appeared first on Physics World.

“There are two types of people when it comes to quantum,” joked Howard Dawber, deputy mayor of London for business and growth at a meeting at the Institute of Physics last night to celebrate the first anniversary of the London Quantum Cluster.

“There are those who understand quantum mechanics. There are those who don’t. And there are those who are in superposition of understanding and not understanding until they are observed.”

It was a light-hearted remark that matched the mood of what was essentially an evening of boosterism for quantum technology in London ahead of London Tech Week next week.

As chair of London and Partners – the growth agency for London – Dawber told the gathering of more than 100 “quantum influencers” that the organization was “100% behind the London Quantum Cluster”.

Founded in 2025 by University College London, King’s College London and Imperial College London, with support from the Mayor of London and the UK government, the cluster seeks to establish the capital as a powerhouse of quantum tech.

Georgia Siora from Warwick Economics and Development presented data to show that London is already doing well in the sector, being home to more than 160 quantum companies, with seven of the top 10 UK quantum firms based in the city. Small- and medium-sized quantum firms in the capital, she added, contribute an estimated £153m annually to the economy.

“Quantum and deep tech are at the heart of the capital’s 10-year growth plan,” added Janet Coyle, managing director of London and Partners.

Geraint Rees, vice-provost for research, innovation and global engagement at UCL, said his aim was “to make London the best place on the planet for serious quantum companies”. He pointed to UCL spin-out Quantum Motion, which has just won $160m of venture-capital funding, as an example of the kind of firm making a name in the city.

The evening ended with a panel debate chaired by Jess Wade from Imperial College London, featuring Maria Maragkou from Nu Quantum as well as Richard Murray, founder of ORCA Computing, who drew a distinction between universities being all about expanding the frontiers of knowledge, whereas for start-ups “the aim is to win”.

Also on the panel was physicist Alejandro Montblanch – head of quantum communication and networking at banking group HSBC – who made it clear that what he wanted to know was: “How can your quantum company help HSBC make more money?”

A welcome note of caution came from London-based venture-capital investor Eloisa Angeles, who pointed out that while the UK has a good track record of government-funded research, the UK  is weak at “follow through”, with the government not focused on procurement and not having the end customer of the research in sight.

• For more about the commercial impact of quantum economy, check out this feature by Philip Ball.

The post Quantum influencers gather to celebrate London’s role in quantum tech appeared first on Physics World.

From heatwaves to extreme rainfall, the impact of climate change is rapidly becoming a reality in our daily lives and a danger to our planet. But physicists are in a great position to help, with physics-based research bringing about practical, real-world solutions, whether it’s more efficient solar cells, better climate models, or novel materials for capturing carbon dioxide from the atmosphere.

There are huge economic and commercial benefits from such work too. A 2023 report from the Institute of Physics (IOP), entitled Physics Powering the Green Economy, estimates there are almost 1800 companies in the UK and Ireland taking green technologies to market with a combined turnover of £750bn.

Last year a follow-up IOP Impact report entitled Unleashing Physics to Power the UK Energy Sector identified the most promising physics technologies for transforming the UK’s energy system. These fall into three main areas: energy generation (nuclear power, photovoltaics), storage (batteries) and transmission (high-temperature superconductors).

The clean-energy revolution will not be easy, however. As the IOP report points out, the UK has a strong research base, good international collaborations, and a growing pipeline of spin-out and early-stage companies. But the country doesn’t invest enough in technology scale-up facilities, faces critical skill shortages, and isn’t great at recycling either.

To discuss how physicists are supporting the green economy – and what more they can do – a panel debate was recently held at the IOP in central London. Attended by Prince Edward, the Duke of Edinburgh, as well as about 100 business leaders, policy chiefs, senior physicists, and IOP and IOP Publishing staff, it was chaired by Tara Shears, the IOP’s vice-president for science and innovation.

The panel featured ex-BP boss John Browne, who now works in green energy, Emily Nurse from the UK’s Climate Change Committee, former Sizewell C energy-strategy director David Cole, solar-cell physicist Jenny Nelson from Imperial College, and Nellie Technologies founder Stephen Milburn. The following is an edited extract of the discussion.

What role are physicists currently playing in our quest for a greener economy?

John Browne: I made a wonderful decision 60 years ago, when I was 18, which was to read physics. After graduating, I became an engineer, but over the last 30 years physics has come back in to my life as I’ve found myself doing something very important – trying to get to net zero. Physics, you see, touches absolutely everything.

All that I’ve ever done – whether it’s renewable energy or “old energy” [fossil fuels] in my old life – starts with physics. Whether you’re involved in chemistry, biology, electronics or engineering, it could not exist without a much deeper understanding of physics. We have to make sure everybody knows that – but I don’t think people currently do. They tend to think engineering is the only enabler for commercialization, but physics is there.

Emily Nurse: I started out as a particle physicist working at CERN on the Large Hadron Collider but for the last four years, I’ve been involved in climate policy and now work with the UK’s Climate Change Committee. We are the UK government’s official advisers on its climate targets – and assessing progress towards meeting those targets. As we celebrate global decarbonization to date, we need to remember it’s all underpinned by physics.

Take the rise of solar power for example, which has been the fastest growing source of global electricity generation for the last 20 years in a row. Solar installations in 2024 were double those in 2022. Along with wind, solar has led to a reduction in electricity from fossil fuels. We’re seeing the costs of solar plummeting and they just keep falling further.

In the UK, solar power has been growing more slowly, but it’s starting to pick up and is going to be a really important part of the electricity mix. We’ve also got a lot of wind here in the UK – it’s a very windy island after all. I would also like to give a shout out to heat pumps: as a physicist, how can you not love their efficiency?

David Cole: I am an engineer, not a physicist, but I’ve spent my career in lots of different sectors and been fascinated with the role that energy plays in creating a better society. What’s really interesting at Sizewell C is the ownership structure, which involves both state and private investment. It’s the first time private investment has been used for a new nuclear build in the UK.

I hope it leads to a virtuous circle, in which the more plants we build, the more we can reap from that investment

David Cole

Getting this hybrid financial structure over the line was not trivial – it took a lot of effort – but I think it will drive great performance. We’re also trying to use as much UK content in the plants as possible, whether that’s materials, skills or technology. I hope it leads to a virtuous circle, in which the more plants we build, the more we can reap from that investment. Sizewell C will, in other words, bring down energy costs, which is fundamental to economic growth.

Jenny Nelson: I have been active in research into solar photovoltaic (PV) materials and devices for over 30 years and we should celebrate how much has happened in the field during that time. In the last 10 years, we have seen capacity increase globally by more than a factor of 10, we’ve seen the efficiency of solar cells increase, and we’ve seen the cost come down almost by a factor of eight, all of which is remarkable.

Those innovations are firmly rooted in physics – whether it’s changes in device structure… or of the optical properties of materials

Jenny Nelson

The cheapest form of electricity globally, in other words, is now from solar PV, which was not the case 30 years ago. These developments have come partly from economies of scale and partly from technological innovations that have now fed through into production. Those innovations are firmly rooted in physics – whether it’s changes in device structure due to our understanding of semiconductor physics or new developments in the optical properties of materials.

The next generation of PV cells, which are likely to be silicon-based tandem devices, will also depend on scientific breakthroughs and innovations.

Stephen Milburn: I’m chief executive of Nellie Technologies, which is based in South Wales on the site of a former chemical-weapons storage facility. We’re using biomass waste for removing atmospheric carbon dioxide, and if you visit us, you’ll see all kinds of activity: in one corner there’s chemistry, in another engineering and in the next there’s biology and biochemistry. But physics is at the heart of the technology. Physicists are a bit arrogant when we say we think we can do everything, but the fact is we probably can.

But we should also celebrate the work that has gone on to create a market in which carbon-emission credits can be bought and sold. Trading carbon credits has been a bit of a dark activity over the last 10 years, with double counting and bad things happening purely by firms wishing to make a profit. However, the market does have the power to regulate itself – in fact the alignment we’re starting to see between the UK and the EU will help greatly.

What are the biggest growth opportunities for the green-economy sector?

John Browne: First, we can do much more with what we’ve already got – for example we could increase our offshore wind or rethink whether we should go back into onshore wind. Second, we can improve what we’re doing – for instance, by increasing the efficiency of solar panels to their theoretical maximum, which would make rooftop solar economically attractive. Third, there are new opportunities, such as metallic organic frameworks and nuclear fusion.

What we do here in the UK needs to move the needle globally, which means thinking about how to scale and finance it properly

John Browne

However, the UK needs to avoid doing things that others are doing much better. The race for the best battery in the world is, for example, probably going to be won elsewhere. What we do here in the UK needs to move the needle globally, which means thinking about how to scale and finance it properly. The UK shouldn’t end up as a secondary player.

Emily Nurse: The UK has made a lot of progress in our quest to reach net zero by 2050. Since 1990, for example, we’ve halved our carbon emissions, mainly by decarbonizing electricity – phasing out coal, reducing gas generation, while significantly increasing wind, solar and other renewables. Electricity generation now accounts for only around 7% of UK emissions, which are dominated by transport (cars and vans) and heating (oil and gas boilers).

Reducing emissions still further will predominantly come from moving to electric technologies, including electric vehicles and heat pumps, and by further decarbonizing the electricity supply. There will be a backbone of wind and solar, but to ensure a secure supply, we’ll need nuclear, carbon capture and storage, hydrogen and batteries. We’ll have to reduce emissions from agriculture and land use too.

A report from the Confederation of British Industry (CBI) last year estimated that the net-zero economy grew by 10% in 2024, which is three times faster than the rest of the UK. But we’ll need more innovations to continue to bring costs down – and we’ll also need to provide incentives to boost the take-up of electric technologies. If we do that, there’ll be an overall saving to the UK economy in about 15 years’ time, our analysis suggests. There are huge opportunities for green growth to come from this investment.

David Cole: I agree that for the UK to be competitive, the cost of energy has to come down – not just for domestic customers but businesses too. In fact, there are two main opportunities First, we have to adopt a “whole-systems” approach. If we’re building a power station, for example, can we use every bit to its maximum potential?

Let’s say I’m running a direct air-capture plant operating at 25–30 ºC – can I use the waste flow from my coolant system to encourage new industries? Can it support nearby hydrogen generation plants or companies making, say, synthetic aviation fuel? Those questions involve thinking about physics and engineering as well as materials science, which is also super important.

Whichever way you look at it, we’re talking about building a lot of hardware, which involves materials. How much energy per unit mass are they using? Can we recycle those materials? What can we do with the waste products? Ultimately, what is really important is energy security: where does your energy come from, who made it and what impact does it have on the environment?

Jenny Nelson: The net-zero economy is growing significantly faster than the rest of the economy and I think that will continue. But decarbonizing the power sector only addresses part of the problem and we’re going to see a big transition across the rest of industry, agriculture and elsewhere that will generate a wide range of opportunities and stimulate the economy too. I’m not just talking about rolling out more renewables, but about integration – bringing together the generation and storage of energy, ensuring that we are managing demands and have the right infrastructure.

As for my area of photovoltaics, we’ve seen great ideas and technologies come out of the UK that are very likely going to be developed outside the UK because the manufacturing capacity isn’t here. Nevertheless, those ideas and innovations can still benefit the country through licensing, partial manufacturing and new technology.

One thing to remember about solar power is it’s distributed. You can have solar generation without being connected to the grid. That not only opens some markets for certain applications where you want to generate electricity locally, but it also provides a route to energy security through back-up generation, towards which solar power will be an important part.

Stephen Milburn: Having a strong green-technology manufacturing base is a huge opportunity for the UK. My company is based in South Wales, where we have lots of highly skilled people who used to work in traditional industries but now don’t have many places to go. Yes, there’s a fantastic semiconductor industry here, but when it comes to deploying green technology we cannot outsource that responsibility to other parts of the world.

Green tech needs to be deployed in the UK’s industrial heartlands… if we don’t nurture jobs and skills here there’s a real risk they will be gone forever

Stephen Milburn

Green tech needs to be deployed in the UK’s industrial heartlands to take advantage of the skills we already have, but which we are at risk of losing. In fact, if we don’t nurture those jobs and skills here there’s a real risk they will be gone forever. Having a strong green-technology manufacturing base is a huge opportunity for the UK.

What needs to happen so that these opportunities can be put into practice?

Stephen Milburn: Many science graduates leave university equipped with solid academic rigour and a great scientific understanding, but they often lack practical green-technology skills. This summer my company is therefore hoping to launch a climate apprenticeship programme, which will allow graduates to pick up those skills. We need to build green-tech skills in the real economy, in particular those that will deal with climate change.

Jenny Nelson: The UK must do more to support its own innovations. We need better regulations to avoid unnecessary bottlenecks. We need to invest in infrastructure like the grid. We should completely avoid subsidizing fossil fuels and instead divert any subsidies into alternative economies. Finally, we need to train and educate people, showing the public the potential of green technology so that they become part of the transition, for example by generating their own electricity.

David Cole: We need to integrate our policies on industry, energy, land use and AI so that we can invest in them all as growth areas. In particular, I’d like to see a long-term nuclear programme in which we build a fleet of new reactors all of the same design, which will drive down costs by letting us replicate a particular technology. It’s also vital that we get a high proportion of UK content and technology into these reactors, which will lead to a virtuous circle, with money coming back into the economy that we can re-invest in industrial and academic partnerships.

Emily Nurse: What’s vital is consistency in policies; we need certainty. In the UK, we are fortunate to have world-leading climate legislation in the form of the 2008 Climate Change Act, which does not just make it a legal requirement to reach net zero by 2050 but also gives us targets along the way. It means we know what we need to do in both the medium- and long-term, which gives certainty to investors, businesses, innovators and consumers.

What’s really important is communication – supporting communities through the transition and making sure they realize the benefits

Emily Nurse

So the first thing we need to do is keep the Climate Change Act. Then, of course, we’ve got to address barriers to delivery, including having the right incentives to electrify the economy. And what’s really important is communication – supporting communities through the transition and making sure they realize the benefits, not just in terms of reducing carbon production but of having cleaner, better and more efficient technologies too.

John Browne: First, we must never stop investing in people who can discover things and translate them into real commercial products. Second, we need to understand how to scale things, which means focusing on the winners and getting rid of things that are “nice to have” but aren’t going anywhere. That’s not easy because you have to push people to say, “You’ve done great work, but you’ll have to stop”.

What’s more, to scale new technology, people have to learn what it takes. When I’m in the US, I often speak to chief executives who can explain their technology to the financier who’s supporting it, whereas here in the UK that often doesn’t happen.

Third, we need to maintain confidence in what we’re doing. I often talk to people who think that it’ll be really expensive to get to net zero, but in fact estimates suggest that each household would only have to spend an average of about £150 a year to get there. So it’ll be less than the cost of a TV licence to get to net zero.

Of course the investment needed will be “lumpy” – it’s not as simple as just levying a fee – but the point about governments is that they can smooth things out. That is what they have done in the past and it’s what they should continue to do.

• For more information about the IOP’s work on the green economy, click here. You can also keep up to date with the all latest research in the field through IOP Publishing’s series of open-access Environmental Research Journals at this link.

The post What physicists can do to support the green economy appeared first on Physics World.

US firm Rescale, a digital engineering platform that combines AI, simulation, and high-performance computing to help R&D and engineering teams design, test, and bring new products to market faster, has received funding under the Genesis Mission, a national initiative led by the US Department of Energy to build an advanced integrated science discovery platform.

As part of the program, Rescale will join Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, and Oak Ridge National Laboratory as a funded partner in the Agentic HPC Pipeline Initiative (AHPI). 

The initiative aims to use agentic AI on Rescale’s platform to help accelerate the adoption of national laboratory-developed computing tools and simulations by US industry.

Doubling scientific research output with AI and advanced computing

The Genesis Mission is an ambitious initiative aimed at accelerating scientific and technological breakthroughs by combining artificial intelligence, supercomputing, quantum systems, and advanced scientific instruments into a single research platform. 

Established through an executive order in November 2025, the program brings together the department’s 17 national laboratories and around 40,000 scientists and engineers to advance research in areas including energy, scientific discovery, and national security. 

Department officials have compared the scale and ambition of the initiative to landmark US efforts such as the Manhattan Project and the Apollo program, with the goal of doubling the productivity of the country’s scientific research within the next decade.

The US Department of Energy launched the first Genesis Mission funding round earlier this year, awarding $293 million to projects focused on areas including advanced manufacturing, biotechnology, nuclear energy, and quantum science. The program received more than 8,000 applications, making it the department’s largest-ever funding call.

Rescale’s AHPI was selected for Phase I funding to address a key challenge: helping industry access advanced simulation tools developed by US national laboratories that have often required specialized expertise and infrastructure.

Helping manufacturers access advanced AI-powered simulations

The company will work with three US national laboratories to bring agentic AI capabilities to advanced simulation tools. The project will integrate AI agents into WarpX, LiDO, and Adamantine, codes developed by Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, and Oak Ridge National Laboratory.

Using the Rescale platform, AI agents will help engineers choose simulation tools, configure inputs, monitor runs, and analyze results. The initiative aims to reduce the time and specialized expertise needed to use these simulations by more than five times, making advanced research capabilities more accessible to US manufacturers.

According to Rescale CEO Joris Poort, the Genesis Mission highlights the importance of making industrial AI technologies available to leading research and development teams that can apply them in real-world applications.

Building on Rescale’s existing partnership with ORNL’s Manufacturing Demonstration Facility, AHPI addresses a common gap: DOE-industry collaborations run for a fixed term, and once they end, partners keep the insight but lose the tools to act on it. AHPI keeps the resulting agentic workflows, surrogate models, and code environments live on Rescale, letting labs and industry keep collaborating long after a project’s funding ends.

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Siri Wolfram Alpha integration helped create one of the defining moments in the early history of consumer artificial intelligence. When Apple introduced Siri with iPhone 4S in October 2011, users were not merely given another form of voice control. They could ask a spoken question and receive a calculated answer rather than a page of search results.

Questions involving mathematics, measurements, dates, nutrition, astronomy and factual comparisons could be routed to Wolfram Alpha, a computational knowledge engine launched in 2009. Siri handled the conversation, while Wolfram Alpha supplied structured information and calculations behind many of the responses.

The experience could feel remarkably advanced for its time. Asking how many days remained before a holiday, comparing the populations of two cities or converting miles into kilometers no longer required opening Safari, entering a query and selecting a website.

That direct path from natural language to a useful answer established an expectation that continues shaping AI products today.

Siri Began as an Action-Oriented Assistant

Siri did not begin inside Apple.

Its technological roots extend to research conducted at SRI International, including work associated with the DARPA-funded CALO project. The objective was to develop systems capable of learning, organizing information and helping people complete everyday tasks.

SRI later spun the technology into Siri Inc., founded by a team including Dag Kittlaus, Adam Cheyer and Tom Gruber. The company released a standalone Siri app for iPhone in early 2010.

The original product was designed around actions rather than ordinary web search. A user could ask for a restaurant reservation, request a taxi, check entertainment listings or search for local services. Siri attempted to understand the intent and connect the request with an appropriate online provider.

This approach was ambitious because the assistant needed to coordinate several different systems. Speech had to be converted into text, the request had to be interpreted, context had to be considered and an external service had to return something useful.

Apple acquired Siri in April 2010. Steve Jobs had personally recognized the potential of moving beyond a collection of apps toward a conversational layer capable of reaching them on behalf of the user.

When the assistant returned as an integrated feature of iPhone 4S, Apple had narrowed some aspects of the original startup vision while making the technology accessible to a much larger audience.

Siri could send messages, create reminders, place calls, manage calendar events, check weather conditions and answer questions. Apple initially labeled the feature as a beta, an unusual decision for a prominent part of a new iPhone launch.

The beta label acknowledged that natural-language interaction remained unpredictable. Accents, ambiguous phrasing, network conditions and external data providers could all influence whether a request succeeded.

Siri Wolfram Alpha Made Answers Feel Immediate

Siri Wolfram Alpha became especially memorable because it showed the difference between retrieving information and computing an answer.

A conventional search engine could locate pages containing a formula. Wolfram Alpha could apply that formula to the values supplied by the user. It could interpret units, compare quantities, calculate probabilities and organize factual data into a structured response.

That distinction allowed Siri to appear more knowledgeable than a voice interface simply reading search results aloud.

A request such as “What is 15% of $86?” could return the calculation directly. A question about the distance between planets could produce a value without requiring the user to inspect several websites. Wolfram Alpha could also answer unusually specific requests, including information about aircraft flying overhead when the necessary location and aviation data were available.

The system was not a large language model. It did not generate open-ended prose in the way current AI assistants do. Wolfram Alpha relied on curated data, symbolic computation, algorithms and formal representations of knowledge.

That structure provided an advantage that remains relevant: many answers could be calculated and traced through defined information rather than improvised statistically.

Early Siri combined several specialized capabilities instead of depending on one system to do everything. One provider might supply restaurant information, another could handle maps and Wolfram Alpha could process computational questions.

The assistant acted as the conversational coordinator.

This architecture made Siri seem like one intelligence even though the response could come from a network of separate services. Modern AI platforms have returned to a similar idea through tools, agents and external data connections. A language model may manage the conversation while calling a calculator, calendar, search engine or business system when a task requires reliable external action.

Siri demonstrated that model on a consumer smartphone more than a decade earlier.

A Small Integration Created a Large Expectation

The Wolfram Alpha partnership influenced how people began judging digital assistants.

Before Siri, voice features on phones were commonly associated with rigid commands such as calling a contact or selecting music. Siri invited users to speak more naturally and attempt questions that would previously have been typed into a browser.

The results were inconsistent, but successful interactions changed perceptions immediately. The iPhone appeared capable of understanding intent, reaching into a knowledge system and returning a concise answer through one conversational interface.

That apparent simplicity concealed substantial infrastructure. Speech recognition, language interpretation, location, personal context and external databases all had to work together quickly enough to preserve the feeling of a conversation.

Wolfram Alpha also gave Apple access to expertise that would have taken years to reproduce internally. Stephen Wolfram had spent decades building Mathematica and computational systems before launching the public knowledge engine.

Following the Siri announcement, Wolfram described the integration as a characteristically direct Steve Jobs decision: users wanted access to knowledge and actions without unnecessary intermediate steps.

That idea became more influential than any individual answer Siri provided.

The assistant did not need to show how many services had participated. It needed to understand the request and return the result.

Early Siri Also Revealed the Limits of the Model

The first version of Siri could feel futuristic one moment and frustrating the next.

Some questions reached Wolfram Alpha correctly, while similar wording could trigger a web search or an unrelated response. Siri occasionally struggled to determine whether the user wanted a calculation, a local result or an action inside the phone.

Wolfram Alpha responses were also better suited to some subjects than others. Mathematical and scientific questions matched its computational structure, while casual conversation, current events and subjective requests required different sources.

Network dependence created another weakness. Early Siri processing relied heavily on remote servers, so delays or outages could make a basic request fail even when the iPhone itself was functioning normally.

Those limitations foreshadowed the central challenge surrounding modern assistants. A convincing interface encourages people to ask almost anything, but no single technical system performs equally well across every type of request.

Current generative AI can produce more natural language and handle a wider range of topics. It can also create confident errors when factual grounding is weak. The computational approach represented by Wolfram Alpha remains valuable precisely because some questions require verified data and exact calculation rather than fluent speculation.

The story behind the first Siri steps is therefore not only about an old iPhone feature. It shows that a powerful assistant needs more than a conversational personality. It requires access to dependable tools, structured knowledge and the ability to recognize when another system should provide the answer.

Siri Wolfram Alpha offered that lesson in 2011, long before AI assistants became capable of writing essays, interpreting screens and acting across apps.

The post Siri Wolfram Alpha Defined the First AI Assistant appeared first on AppleMagazine.

Shiso, a ring-necked parakeet, can't speak English, but she knows how to make her preferences known. She has screaming fights with the dogs and cats in her household, snorts for attention and food, and perches on her human to interact with her.

A new study from the University of Toronto finds that people perceive their bodies as larger in virtual reality than in the physical world—and that effect doesn't end when the headset comes off.

On Tuesday, the White House Office of Science and Technology Policy (OSTP) released a report entitled "Science: A New Golden Age," in which it lays out how it has viewed science, found it lacking, and believes the Trump administration is in the perfect position to fix things. It's a bit unexpected coming from an administration that has been proposing crippling funding cuts to research and trying to enable political appointees to terminate grants awarded based on scientific merit.

The report presents itself as the spiritual successor of "Science, the Endless Frontier," a policy document that laid out the case for government-funded science in the wake of World War II. The New Golden Age (SNGA) says that, while the concepts promoted by the original remain vital, the circumstances have changed such that we need major revisions to how the government is implementing things.

The result is an odd mix. It completely ignores or glosses over many things that the administration is doing to harm scientific progress. In some cases, it identifies issues that have already been discussed as problems within the scientific community, but misrepresents them. Elsewhere, it's a mixture of political grievances, ideas without a solid intellectual foundation, and an injection of Silicon Valley's perspective on innovation (Michael Kratsios, the director of the OSTP, formerly worked with Peter Thiel). As a result, it's unlikely to have anything like the impact of "Science, the Endless Frontier."

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EU tech regulation, Digital Markets Act, Apple Europe sales, Apple regulation, European Union, Siri AI, iPhone features, device interoperability, App Store changes, alternative marketplaces, iPhone Mirroring, AirPods translation, Apple privacy, iPhone security, app sideloading, platform competition, DMA compliance, Apple Intelligence, European technology, government control

EU tech regulation is no longer limited to App Store payment rules or competition disputes hidden from ordinary consumers. It is beginning to determine which features reach European devices, how quickly they arrive and how deeply third-party companies can interact with the iPhone, iPad and Mac.

The Digital Markets Act was designed to reduce the control of large technology platforms over software distribution, payments, browsers, digital assistants and connected hardware. For Apple, compliance has already produced alternative app marketplaces, web distribution, third-party payment options, additional default-app controls and expanded access to iPhone technologies.

The next phase reaches much deeper into the operating system. European regulators want competing accessories, cloud services and AI assistants to receive access to capabilities traditionally reserved for Apple’s own products.

Apple argues that some of those demands could weaken privacy and security. The European Commission says the company is responsible for creating compliant systems that preserve both competition and user protection.

Between those positions sits a large and commercially significant group of customers. Europe generated $28.06 billion in Apple sales during the quarter ending March 28, 2026, up from $24.45 billion one year earlier. Six-month European sales reached $66.20 billion, compared with $58.32 billion during the same period of fiscal 2025.

Europe therefore represented more than one-quarter of Apple’s latest quarterly revenue. It is too valuable to leave behind, but increasingly difficult to serve with the same software experience available elsewhere.

EU Tech Regulation Is Splitting Feature Availability

Regional differences once centered mainly on language support, licensing agreements or local services. EU tech regulation is creating a more structural divide.

Apple has blamed Digital Markets Act requirements for delaying Siri AI across the European Union. The company says regulators expect competing AI assistants to receive extensive access to personal information, apps and device actions before Apple can reserve those capabilities for its own assistant.

The disagreement concerns more than answering questions. Siri AI is designed to read personal context, understand onscreen information and complete actions across apps. That may include finding messages, accessing files, making purchases or sending information on the user’s behalf.

Opening those capabilities to outside AI systems requires new permission structures, technical interfaces and safeguards. Apple says it cannot release its assistant first and add competing access later without risking regulatory action. The Commission says Apple had sufficient time to design an interoperable solution.

Other delayed European features have included iPhone Mirroring on Mac, Live Translation through AirPods and certain location-based Maps capabilities. Each feature touches a different part of the ecosystem, but all depend on privileged communication between Apple devices, software and services.

The consequence is becoming visible at purchase. Two customers can buy the same iPhone model at similar prices and receive different capabilities because one lives inside the European Union.

Feature Adoption Becomes Slower and Less Predictable

Delayed availability can weaken the adoption of new hardware features even when the devices themselves continue selling well.

Siri AI is intended to become a central part of newer iPhones, iPads and Macs. Buyers who cannot access it lose part of the justification for newer chips, additional memory and on-device AI processing. Developers also face a smaller addressable audience for apps built around the new assistant.

AirPods Live Translation presents a similar problem. A feature designed to demonstrate the combination of iPhone processing, microphones and wearable audio becomes unavailable in a region built around dozens of languages and frequent cross-border travel.

iPhone Mirroring affects the relationship between iPhone and Mac. Users outside the EU can interact with the phone from macOS, while European users may receive notifications without gaining the complete experience.

These differences do not necessarily stop hardware sales. Apple’s newest results show strong European demand despite years of regulatory conflict. They can, however, reduce the speed at which customers use the ecosystem’s newest capabilities.

The effect may grow as software becomes a larger part of hardware differentiation. A faster processor has limited emotional value when the feature designed to demonstrate it is unavailable.

Apple Sales Show Why Europe Cannot Be Ignored

Apple does not report European Union revenue separately. Its Europe segment also includes countries outside the bloc, along with India, the Middle East and Africa. The official segment remains the best regularly published measure of the region’s commercial weight.

European sales rose approximately 15% year over year in Apple’s latest reported quarter. During the first six months of fiscal 2026, the segment expanded by roughly 14%.

Those figures weaken the argument that regulation is already driving customers away from Apple hardware. Demand remains strong, supported by the iPhone 17 family, Mac sales, services and the company’s installed base.

The financial risk is more gradual. Regional software fragmentation can raise engineering costs, delay launches and make European devices less competitive against products able to offer complete feature sets.

Apple must maintain separate distribution rules, payment systems, browser options and eligibility checks. Developers must decide whether alternative stores, external payments or EU-specific business terms justify additional work. Support teams must explain why features differ between countries using the same operating-system version.

A market responsible for more than $28 billion in one quarter can absorb considerable compliance expense. It can also influence Apple’s global product decisions when regulators demand interfaces that cannot be restricted to Europe easily.

A Slow Transfer of Ecosystem Control

The escalating regulatory environment also creates a more fundamental concern: control over complex technology ecosystems can gradually move away from the companies that designed them and toward government committees, regulatory bodies and administrative processes.

That transfer rarely happens through one dramatic order. It develops through repeated obligations covering app distribution, payment systems, browser engines, digital assistants, hardware access, data portability and interoperability. Each requirement may appear narrow when considered alone. Together, they can redefine who determines how the ecosystem operates.

Apple built its platform around tight integration between hardware, software, services and security. Regulators increasingly treat parts of that integration as market power that must be opened to competitors. The danger is that an ecosystem designed as one coordinated environment becomes subject to decisions made by committees that do not carry direct responsibility for product quality, reliability or long-term maintenance.

This can become a slow trap. Once a platform is required to expose one layer, regulators may use that access as the basis for demanding another. A payment exception can be followed by alternative app stores. Alternative stores can lead to broader installation rights. Device interoperability can expand into access for AI assistants, personal data and system actions.

The boundaries of the platform are then set less by engineering judgment and more by regulatory negotiation.

Government agencies have legitimate authority to address competition and consumer harm. The concern appears when regulation shifts from preventing abusive conduct to directing product architecture. At that point, public authorities begin influencing feature design, release timing, software permissions and the relationship between first-party and third-party services.

That creates consequences beyond Apple. Any company considering a tightly integrated product may decide that deep integration invites intervention. Businesses could avoid building ambitious ecosystems if successful coordination later becomes grounds for forced access.

Innovation then becomes shaped by what regulators are likely to permit rather than by what engineers believe will produce the safest or most coherent experience.

Competition Gains Come With Real Consumer Choices

EU policy has produced benefits that Apple rarely emphasizes. Users can install apps through alternative marketplaces or directly from authorized developers. Developers can offer external payment methods, promote different prices and avoid relying entirely on Apple’s distribution system.

Alternative browser engines can compete with WebKit, while users have more control over default apps. Contactless payment access has expanded beyond Apple Pay, allowing competing wallet providers to use the iPhone’s NFC technology under approved conditions.

Interoperability requirements could also improve third-party accessories. Smartwatches, headphones and other devices may gain better notification access, background connectivity and proximity pairing with the iPhone.

These changes can increase competition and reduce the advantage Apple gives its own services and accessories. A third-party smartwatch should not become artificially less useful simply because it was not made in Cupertino.

The difficulty is separating legitimate platform access from unlimited access. A notification interface is one thing. An autonomous AI assistant capable of reading messages, accessing files and acting across apps introduces a different level of sensitivity.

Privacy and Security Risks Are Not Theoretical

Alternative app distribution increases user choice, but it also moves some responsibility away from the App Store. Apple continues to notarize apps distributed through approved alternative channels, checking for known malware and severe fraud. That process is narrower than a complete App Store review.

Purchases made outside Apple’s payment system also fall outside its normal refund and billing support. Users may need to resolve disputes directly with the developer or payment provider.

A malicious marketplace, deceptive payment page or compromised developer account can create opportunities for fraud. Enterprise users face additional concerns when employees install software from sources their organization has not approved.

The AI question reaches further. A third-party assistant may need permission to access messages, photos, calendars, documents and app controls. Even a reputable provider can collect more information than the user expects or introduce vulnerabilities through poorly designed integrations.

Apple can build permission prompts, app isolation and revocation controls, but technical access always creates some exposure. Security depends on how narrowly permissions are defined, how visibly they are used and whether the operating system can stop unauthorized actions.

The Commission maintains that interoperability and privacy can coexist. Apple maintains that regulators are demanding access before adequate protections are ready. Both positions contain a legitimate concern: dominant platforms should not block competition, and competition should not require users to surrender control of personal data.

Government-Controlled Architecture Creates Accountability Gaps

When regulators require a company to alter its architecture, responsibility can become blurred.

Apple remains the company customers blame when an iPhone feature fails, a third-party marketplace distributes a harmful app or an outside assistant misuses personal data. Regulators may have required the access, but they do not operate the device, provide technical support or repair the damage to the platform’s reputation.

This creates an accountability gap. Government committees can mandate openness without assuming the commercial and security consequences of that openness. Apple must comply, maintain the system and explain the outcome to users.

The same problem applies to delayed features. Regulators may argue that Apple chose not to release a service, while Apple may argue that the required conditions made the launch unsafe or technically impractical. Customers are left with less functionality and no simple way to determine who caused the delay.

Over time, the ecosystem can become a negotiated compromise rather than a product with one coherent direction. That may satisfy formal compliance while producing a weaker experience for everyone involved.

The risk is not that governments will literally operate Apple’s software. It is that committees increasingly define the conditions under which Apple can design, connect and release its own features. Control shifts through constraints.

The Industry Will Follow Europe’s Experiment

The consequences extend beyond one company. Google, Meta, Microsoft, Amazon and other platform operators face related European obligations. Hardware makers must consider whether ecosystem advantages can survive when core services are required to support rivals.

Smaller developers may gain new distribution and payment choices, but they may also inherit more compliance, customer-service and security responsibilities. Accessory companies could compete more effectively while becoming accountable for handling sensitive device data.

Other governments are watching. Rules introduced in Europe can influence legislation elsewhere, particularly when companies decide that maintaining one global technical framework is easier than operating several regional versions.

Apple is unlikely to abandon Europe or stop adapting its products. Its sales provide too much evidence of the region’s importance. The company will continue negotiating, appealing decisions and building compliance systems while withholding features it considers unsafe under current demands.

European customers may receive more app stores, payment choices and accessory compatibility. They may also wait longer for headline features included with the same hardware elsewhere.

The deeper consequence may be a technology industry that becomes more cautious about building tightly integrated products. Companies may design around anticipated regulatory demands from the beginning, limiting exclusive features, reducing private-system integration or avoiding innovations that depend on controlled access.

EU tech regulation began as an effort to constrain dominant platforms. Its long-term effect may be to transfer part of the power to shape those platforms from companies and users to regulatory institutions.

That shift deserves scrutiny equal to the market power it is intended to correct. Competition rules can restrain abuse. They can also become a slow mechanism for placing product design, security choices and ecosystem evolution under government supervision.

Shady Control and Bureaucracy

The danger grows when regulatory discretion becomes buried beneath permits, approvals, negotiations and technical committees that operate beyond meaningful public scrutiny.

The more power officials hold over which features, contracts or technologies may proceed—and when—the greater the opportunity for lobbying, favoritism and selective enforcement to influence outcomes. What begins as oversight can gradually become a system in which delay itself is leverage, while responsibility disappears inside layers of administration.

For the technology industry, that risks replacing transparent product decisions with political access, negotiated exceptions and regulatory influence exercised far from the consumers expected to live with the consequences.

The post EU Tech Regulation Creates a Costly Split for Apple appeared first on AppleMagazine.

Georgia Tech’s Learn to Teach framework trains AI models concurrently, helping a humanoid robot navigate sand, gravel, slopes, and slippery surfaces.

Chinese AI models are gaining business interest as companies weigh lower costs, open-weight flexibility, performance trade-offs, and enterprise risks.

Google’s new Gemma model brings offline AI processing to the Pixel 10, enabling local text, image, and audio tasks while reducing reliance on the cloud.

China’s new AI companion rules restrict emotional dependency, require crisis protections, and impose safeguards for children and user data.

Hyundai workers in South Korea strike over pay and job protections as the automaker plans to deploy Atlas humanoid robots across Hyundai and Kia factories.

ARK augmented reality describes a form of spatial interaction in which artificial intelligence does more than place digital objects in the physical world. It uses knowledge from foundation models to understand unfamiliar scenes, generate useful information and adapt its response to the person, task and environment.

The concept comes from a 2023 research paper titled “ArK: Augmented Reality with Knowledge Interactive Emergent Ability.” Its authors proposed a system that transfers knowledge from large foundation models into new physical and virtual environments. Instead of training an AI agent separately for every room, object or activity, ArK uses existing knowledge memory to interpret what it sees and generate appropriate 2D or 3D content.

Traditional augmented reality places information over reality. ARK augmented reality tries to understand reality before deciding what should appear.

That distinction creates a compelling direction for Apple. Vision Pro already blends digital content with physical space. Apple Intelligence adds language, image understanding and personal context. Visual Intelligence lets users look at objects, text and scenes before asking for information. Siri AI adds a conversational layer. ARKit, RealityKit and visionOS provide the spatial foundation.

Apple now has many of the components required to build a knowledge layer over the physical world.

ARK Augmented Reality Goes Beyond Digital Overlays

Most familiar augmented reality experiences are relatively fixed. A furniture app places a digital sofa in a room. A navigation app displays arrows over a street. A museum app identifies an artwork. A repair app shows where a component belongs.

These experiences can be useful, but they usually depend on predefined content and rules. The system recognizes a known object or location, then presents prepared information.

ARK augmented reality proposes something more adaptive. A knowledge-enabled system could enter an unfamiliar environment, interpret what is present and generate assistance based on a broader understanding of objects, relationships and possible actions.

A user looking at a complicated electrical panel would not only see labels. The system could identify the panel type, explain the purpose of each section, compare the arrangement with standard configurations and warn the user not to touch dangerous components.

A student examining a machine could ask how it works and receive a spatial explanation attached to its real parts. A surgeon could review anatomical information positioned around a medical model. A technician could receive repair guidance even when the exact equipment had not been included in a previous training dataset.

The display becomes less like a floating screen and more like an informed interpretation of the environment.

Why Apple Is Positioned to Build It

Apple’s advantage would not come from inventing every part of ARK augmented reality. It would come from combining hardware, software, artificial intelligence and personal context into one controlled ecosystem.

Vision Pro already understands rooms, surfaces, hands, eyes and objects well enough to place digital content convincingly in physical space. Its cameras and sensors continuously map the environment while visionOS manages windows, volumes and immersive experiences.

Apple Intelligence provides another layer. Foundation models can interpret language, summarize information, generate content and connect requests with app actions. Visual Intelligence can identify text, images and real-world objects. Siri AI can act as the conversational interface.

The Apple ecosystem also provides personal context that a generic AR platform may not have. Calendar knows the user’s schedule. Maps understands location. Photos contains visual history. Notes and Mail hold project information. Health contains personal trends. Home understands rooms, accessories and automations.

An Apple-built knowledge layer could combine the environment in front of the user with the information already available across trusted devices and apps.

A person preparing for a meeting could look at a conference room and see the agenda, participant names and relevant documents arranged around the table. A traveler could look across a train station and receive directions connected to the correct platform, departure time and digital ticket. A homeowner could examine a room and see live information from temperature, air-quality and energy sensors.

The physical environment becomes the interface.

Vision Pro Is the Laboratory

Vision Pro is the obvious development platform for this idea because it already offers the cameras, displays, sensors and processing required for advanced spatial understanding. Its size and price limit broad adoption, but those same characteristics allow Apple and developers to test experiences that would be harder to build first on lightweight glasses.

ARK augmented reality on Vision Pro could become especially useful in enterprise environments. Apple already promotes the headset for employee training, product design, field service, collaboration and specialized visualization.

A worker could enter an unfamiliar facility and receive contextual instructions attached to real equipment. An engineer could inspect a physical prototype while design changes, performance data and maintenance history appear around it. A new employee could learn a workflow through spatial guidance generated from company manuals and operational knowledge.

The system would not need to display every available fact. It would need to understand which information fits the current task.

That is where AI knowledge becomes more valuable than a traditional AR overlay. Too much information can be as disruptive as too little. A strong system should filter, prioritize and explain rather than cover the room with floating labels.

The iPhone Could Bring ARK to More People

Vision Pro may be the development environment, but iPhone could provide the scale. The iPhone already combines cameras, LiDAR on Pro models, location, motion sensing, Visual Intelligence and Apple Intelligence.

An iPhone-based ARK experience would be less continuous because users would need to hold up the device. It could still deliver useful knowledge interaction without requiring a headset.

A user could point the camera at a plant and receive care guidance based on its species, visible condition, local weather and placement in the room. Someone assembling furniture could receive generated guidance positioned over the actual parts. A shopper could compare products by looking at them rather than manually searching for each model.

The difference from ordinary visual search would be interaction. The user could ask follow-up questions, change the goal and let the system update the visual guidance.

Future glasses could eventually make the experience persistent. The iPhone would help Apple develop the intelligence, privacy controls and app ecosystem before lightweight eyewear becomes ready.

A World That Explains Itself

The most ambitious version of ARK augmented reality is a world in which information is attached to objects, places and situations only when requested or needed.

A city could become understandable without searching through maps, review sites and separate transit apps. Buildings could reveal history. Streets could explain temporary closures. Restaurants could surface reservations, dietary options and accessibility details. Public transport could show the correct entrance and platform.

Inside a home, Apple’s knowledge layer could explain energy use, identify maintenance problems and guide repairs. In a classroom, physical models could become interactive lessons. In healthcare, patients could receive visual instructions for approved home procedures or medication devices.

The experience would depend on context rather than menus. The user looks, asks and receives information positioned where it belongs.

This is the transition from augmented reality as decoration to augmented reality as understanding.

Privacy Will Decide Whether It Works

A knowledge layer capable of understanding the physical world also creates serious privacy questions. The system may see homes, workplaces, documents, faces, screens and private conversations. The more context it receives, the more useful it becomes. The same access can also make it invasive.

Apple’s privacy architecture could become one of its strongest advantages. On-device processing would allow some scene understanding without sending continuous camera feeds to external servers. Private Cloud Compute could handle more complex requests while preserving stricter controls than an ordinary cloud AI service.

Apple would also need visible controls over what is analyzed, remembered and shared. A user should know when the system is interpreting the environment and whether information is temporary or retained. Workplaces and public spaces would need rules for recording, identification and sensitive data.

ARK augmented reality cannot become socially acceptable if people feel that nearby devices are constantly cataloging them.

The successful version would behave less like surveillance and more like intentional assistance. The user asks for understanding. The system processes only what is needed. The knowledge disappears when the task ends unless the user chooses to save it.

Developers Could Build Knowledge Worlds

Apple would not need to create every knowledge experience itself. visionOS, ARKit, RealityKit and App Intents could let developers connect specialized knowledge with physical environments.

A medical company could build guidance for approved equipment. A manufacturer could create spatial maintenance systems. A university could attach interactive explanations to laboratories and museums. A retailer could create product comparisons based on inventory and customer preferences.

Apple could provide the shared intelligence layer: scene understanding, object recognition, language interaction, privacy permissions and spatial presentation. Developers would provide the trusted domain knowledge.

This division would be essential. General AI can explain many things, but professional environments require verified information. A generated answer about a historical building has different consequences from an answer about industrial machinery or medical treatment.

Apple’s platform could distinguish between general knowledge and certified domain sources, allowing users to see where information comes from before acting.

From Spatial Computing to Contextual Computing

Apple describes Vision Pro as a spatial computer because digital content can occupy physical space. ARK augmented reality suggests the next progression: contextual computing.

A spatial computer knows where digital content is placed. A contextual computer understands why the content is useful in that location, what the user is trying to accomplish and which knowledge should appear.

That would connect several Apple strategies that currently look separate. Vision Pro supplies spatial awareness. Apple Intelligence supplies reasoning and generation. Siri AI supplies conversation. Visual Intelligence supplies real-world understanding. The ecosystem supplies personal context.

Together, they could create a system that does not wait for users to open the correct app and search through the correct menu. The environment itself becomes the starting point.

Apple’s opportunity is to make that interaction restrained rather than overwhelming. The company does not need to cover reality with digital noise. It can use design, privacy and contextual intelligence to make information appear only when it improves the moment.

ARK augmented reality offers a useful description of that future. Reality remains visible. Knowledge becomes interactive. The computer begins to understand the world in front of the user instead of asking the user to translate everything into a search box.

The post ARK Augmented Reality Could Build Apple’s Knowledge Layer appeared first on AppleMagazine.

Siri Wolfram helped define the original promise of Apple’s voice assistant. When Siri debuted on the iPhone 4S in 2011, it could answer factual and computational questions without sending users through a list of search results. Ask about a mathematical calculation, scientific quantity, historical statistic or unit conversion, and Wolfram Alpha could provide a structured answer.

That partnership gave early Siri something many modern AI assistants still struggle to guarantee: a source designed to compute answers rather than produce plausible sentences. Wolfram Alpha was not a chatbot. It was a computational knowledge engine built around curated data, symbolic mathematics and algorithms.

Siri AI enters a far more ambitious era. Apple now wants its assistant to hold natural conversations, understand personal context, interpret images, find information across apps and complete actions. Yet the old Siri Wolfram relationship remains useful for understanding what Apple gained, what was lost as assistants became more conversational and why verified computation may become even more valuable in the generative AI age.

Siri Wolfram Was Built Around Answers

The original Siri Wolfram integration reflected a straightforward idea: users wanted direct answers without manually searching, opening websites and comparing sources.

Wolfram Alpha was particularly effective for questions with a computable result. It could solve equations, compare financial data, convert measurements, retrieve astronomical information, calculate dates and present structured scientific facts. Siri provided the voice interface, while Wolfram supplied the knowledge engine behind selected responses.

A user could ask how many days remained until a date, calculate a percentage, compare populations or request information about a mathematical concept. Siri translated the spoken request and delivered the result in a format suitable for the iPhone.

The experience was narrow compared with modern generative AI, but it had a valuable discipline. Wolfram Alpha attempted to determine an answer from structured information. It did not need to improvise an explanation every time.

That made Siri Wolfram especially useful for mathematics, science, education and quick factual calculations. The assistant could sound intelligent because it was connected to a system built to compute.

What Siri Wolfram Alpha Could Do Well

Siri Wolfram Alpha worked best when the request had a definable structure. Arithmetic, percentages, conversions, equations and statistics were natural categories. The system could also retrieve information about weather history, geography, nutrition, astronomy, transportation and other data-rich subjects.

The integration helped make Siri feel different from ordinary web search. Instead of showing several links, the assistant could present one answer with supporting figures or charts.

That approach had limitations. Wolfram Alpha could only respond effectively when it understood the request and had the relevant data or computational method. It was not designed for open-ended conversation, personal writing, emotional nuance or the countless informal questions users ask assistants.

Siri also relied on several external services, with Wolfram Alpha handling only certain knowledge categories. The experience could therefore feel inconsistent. One request produced a polished computational response, while another returned a web search or a limited canned answer.

Still, the partnership demonstrated a principle Apple has never fully abandoned: the best assistant does not need to create every answer itself. It needs to route each request to the system most qualified to handle it.

Generative AI Changed the Definition of an Assistant

Modern AI assistants are expected to do far more than retrieve facts. Users want them to explain ideas, write messages, summarize documents, analyze images, brainstorm projects and remember the direction of a conversation.

Siri AI is Apple’s response to that expanded expectation. Powered by Apple Intelligence and Apple Foundation Models, the new assistant can handle open-ended requests and continue natural conversations. It can use personal context from supported apps, understand onscreen content and connect requests with app actions.

This is a dramatic expansion from the Siri Wolfram era. The old model was largely transactional: ask a question, receive an answer. Siri AI is intended to interpret intent, understand relationships between information and help complete a larger task.

A user may ask Siri AI to find a restaurant recommendation buried in Messages, locate a hotel confirmation in Mail and add the details to a travel note. That request involves personal context and several system services rather than one public factual answer.

The assistant becomes less like a talking search box and more like an operating-system layer.

Conversation Creates a Reliability Problem

Generative AI makes assistants more flexible, but it also introduces uncertainty. A language model can produce an elegant response even when the underlying information is incomplete or wrong. The same fluency that makes the assistant enjoyable can make an inaccurate answer sound convincing.

The old Siri Wolfram model avoided much of that risk within its strongest categories. A mathematical calculation came from a computational engine. A unit conversion followed a defined relationship. A data query could be tied to structured information.

Siri AI needs both capabilities. It needs conversational intelligence for requests that cannot be reduced to a formula, while retaining access to reliable tools for tasks that demand precision.

Apple can use specialized systems for calculations, calendars, maps, weather, app actions and personal information rather than asking one generative model to invent every result. Foundation models can understand the request, then direct the work to an appropriate source.

That structure would preserve the strongest lesson from Siri Wolfram: intelligence also means knowing when not to guess.

Personal Context Is the New Knowledge Engine

Wolfram Alpha gave Siri access to public computational knowledge. Siri AI adds a second knowledge source: the user’s own information.

Apple says Siri AI can understand personal context across messages, emails, photos, notes and other supported content. It can help retrieve a detail without requiring the user to remember which app contains it.

This changes the assistant’s value. Wolfram Alpha could explain the distance between two cities. Siri AI could find the hotel reservation, identify the arrival time and retrieve the message where a friend recommended a nearby restaurant.

That personal layer also requires stricter privacy. Apple uses on-device processing for many requests and Private Cloud Compute when more processing power is required. The company has designed the system so personal data does not become part of an ordinary cloud AI profile.

Siri Wolfram answered questions about the world. Siri AI is being designed to answer questions about the user’s place within it.

Apps Complete the Transition

Siri AI also moves beyond knowledge by connecting directly with apps through App Intents. Developers can expose content and actions so the assistant can help users complete tasks instead of only describing how to do them.

That could include editing a photo, retrieving a document, starting a workout, updating a task or sending information through a supported app. Onscreen awareness adds another route, allowing users to ask about content already visible without explaining every detail again.

Wolfram Alpha remains valuable because some questions require specialized computation. App Intents extend the same routing philosophy into actions. Siri does not need to contain every capability internally. It needs to understand the request and connect it with the correct tool.

This may become the defining structure of Siri AI: one conversational interface backed by models, apps, services, personal context and specialized knowledge engines.

The Old Siri Still Has a Lesson for the New One

Siri Wolfram belonged to a more limited assistant era, but its underlying approach was disciplined. The system used a specialized source for questions that demanded structured knowledge and computation.

Siri AI has a wider assignment. It must converse naturally, interpret images, understand personal information and complete actions across Apple devices. That breadth can make Siri far more useful, but only if Apple preserves dependable pathways for calculations and factual tasks.

The future assistant should not choose between Wolfram-style precision and generative flexibility. It should combine them. A conversational model can understand what the user wants. A computational engine can provide the numerical answer. Personal context can supply relevant details. App Intents can complete the task.

The old Siri Wolfram integration showed that a voice assistant could make expert knowledge accessible through one spoken request. Siri AI now aims to place conversation, personal context and action around that foundation.

The assistant has become larger, more personal and more capable. Its credibility will still depend on something the 2011 version understood well: when a question has a correct answer, intelligence should calculate before it speaks

The post Siri Wolfram Built a Powerful Foundation for Siri AI appeared first on AppleMagazine.

A team of ten domino artists has set a new Guinness World Record for the most dominoes toppled in a three-dimensional pyramid, bringing down 29,193 tiles in a single chain reaction at a gym in Garden City, Michigan, on June 20.

Guinness World Records has officially certified the achievement, which was set by the FALLDOWN Domino Team led by Steven Price of Sprice Machines as part of a larger installation that incorporated no fewer than 123,456 dominoes in total.

The feat, documented in a YouTube video titled “123,456 Dominoes SUPERSIZED — FALLDOWN 2026,” has attracted widespread attention online for the scale and precision of its construction

Price has held four domino-related Guinness World Records previously and has served as a judge on Fox’s competition show Domino Masters. He holds a mechanical engineering degree, a background that is evident in the structural ambition of this project.

Building a 35×35 pyramid without triggering it

The three-dimensional pyramid that forms the centerpiece of the record sits on a base of 35 by 35 dominoes and rises in layers to a pointed apex. Constructing a 3D pyramid of this scale requires a level of precision that flat domino runs do not — every tile must be positioned accurately while the structure beneath it remains stable, and a single mistimed touch anywhere in the grid can bring the entire build crashing down prematurely.

Watch the video here.

The FALLDOWN team, which includes Michael Fantauzzo, Matt VanVleck, Gabe Dean, Alex Koops, Derek Koops, Stephan Burton, Brady Dolan, Evan Voeltner, and Aaron Bimrah alongside Price, spent multiple days installing the complete setup.

A world built from supersized everyday objects

The pyramid was not the only engineering feat in the room. The team built a themed installation around 123,456 dominoes arranged into giant versions of ordinary objects — enormous paperclips, oversized friendship bracelets, massive bonsai trees, school supplies and a rubber duck that appears mid-sequence.

These elements fed into a series of Rube Goldberg-style chain reactions that snaked through the entire installation before converging on the pyramid, making the final topple a product of everything that came before it.

Guinness required a traditional domino-to-domino start

The team had originally planned to use a special mechanical trigger to bring the pyramid down. Guinness World Records rejected the approach: to qualify for an official record, the entire chain must begin with a conventional domino touching the next in sequence, with no external mechanism initiating the fall. The team revised their plans accordingly, and the record-setting run began exactly as the rules required.

Price’s fifth Guinness record in domino art

The FALLDOWN record is Steven Price’s fifth Guinness certification in domino-related categories. His prior titles include the largest domino circle field, the longest domino wall, and the largest two-dimensional pyramid. Price first gained wider recognition as a 22-year-old when his Incredible Science Machine project, which involved 250,000 domino tiles, set records and went viral.

His YouTube channel, Sprice Machines, includes videos with over 30 million views, with the Lemonade Machine — a chain reaction that travels through an entire house to serve lemonade — among the most-watched.

Researchers at the University of Toronto Engineering have developed six new metal alloys using an artificial intelligence-driven discovery platform that could improve the durability of parts used in jet engines, nuclear power plants, and other extreme environments. The findings show that the AI-assisted system identified promising alloys within just a few weeks, significantly speeding up the search for high-performance materials.

These new alloys also work well with 3D metal printing, enabling the production be complex parts that are hard or impossible to produce with traditional methods.

AI speeds up alloy discovery

Led by Yu Zou, Canada Research Chair in Materials and Manufacturing for Extreme Environments, the team worked with Jason Hattrick-Simpers to build a system that uses computer modeling, machine learning, and robots to help with manufacturing.

Their method, called active learning, acts like a self-driving laboratory. Rather than testing thousands of metal combinations by hand, the system picks the best options, makes them, tests how they perform, and uses those results to guide the next experiments.

“There’s enormous demand for materials that can stand up to huge swings of temperature and pressure, such as what you would find inside a jet engine or in the steam generators inside nuclear power plants, anywhere conventional steel just can’t survive,” said Yu Zou, who led the project.

“We also need materials that can be printed layer by layer, enabling us to make components that can’t be created by traditional manufacturing processes. For example, to make a material that is both lightweight and strong, you can vary the composition: a hard, tough alloy on the outside to something softer and lighter on the inside.”

The project was partly supported by the University of Toronto’s Acceleration Consortium, a group that uses AI and automation to accelerate the discovery of new materials.

Active learning cuts the need for massive datasets

Most AI systems need a large amount of experimental data to make accurate predictions. This becomes a problem when researchers look at materials that have not been tested yet.

“One problem you often run into when trying to use AI to design materials is that most machine learning models require lots of data about material properties to learn from,” revealed Ajay Talbot, a Ph.D. student in Zou’s lab and lead author of the study.

“But if you’re working in part of the design space that hasn’t been explored yet, that data doesn’t exist, so you’re kind of flying blind.”

“The way we get around that challenge is to use data-lean models that essentially feel their own way along. Our active learning model strategically selects a few samples to manufacture and test, and the data from those experiments is fed back into the model to inform where we go next. It really speeds things up.”

<a href=”https://www.youtube.com/watch?v=RFYJV7WvtOM“>https://www.youtube.com/watch?v=RFYJV7WvtOM</a>

New alloys outperform industry benchmark

To demonstrate the system, the researchers focused on compositionally complex alloys made from nickel, cobalt, and chromium. Within weeks, the automated platform identified six new alloy formulations with strong performance.

“One of the properties we were targeting was puncture resistance at temperatures of up to 1,112°F (600°C), which is what you’d find in the front section of a jet engine,” Talbot said.

“The industry standard in this space is nickel-based alloys such as Inconel 625. But we found one made of 12 percent nickel, 62 percent cobalt, and 26 percent chrome that was great for retaining hardness at extremely high temperatures. Even with just three components, our alloy outperformed Inconel 625, an alloy of more than 10 different elements, by 4.5 percent in our lab tests.”

The team also developed another alloy designed for hotter sections of jet engines, where temperatures can reach 1,832°F (1,000°C).

“One of the things that happens in an environment like that is the formation of oxide scale, which essentially means that your material is just getting burned away,” Talbot explained.

“We found a material made of 36 percent nickel, 14 percent cobalt, and 50 percent chrome that was extremely good for oxidation resistance at these high temperatures: It even outperforms Inconel 625 by 85 percent. We’re eventually aiming to ramp up to even higher temperatures, up to 2,192°F (1,200°C).”

Researchers plan to expand to more complex materials

The researchers say the current alloys represent an early demonstration of what the AI-driven discovery platform can achieve.

“This nickel-cobalt-chrome system has just three elements in it. In the grand scheme of things, it’s a relatively simple system,” he added.

“But it’s great for showing that this whole closed-loop discovery platform really works. What we want to do next is ramp up the complexity a bit more to make even crazier stuff, with maybe up to 10 or 12 different elements.”

“As you add more components, you can get different strengthening mechanisms, different kinds of useful properties. There’s a lot more out there just waiting to be discovered.”

The study was published in the journal npj Advanced Manufacturing.

A foldable, self-propelled pontoon barge has played a key role in rescuing more than 6,000 stranded students and staff from a flood-hit region in southern China.

The rapid-deployment vessel was used during evacuation efforts in the Guangxi Zhuang Autonomous Region after flooding due to Typhoon Maysak inundated communities, according to Chinese media reports. The modular barge can be assembled in just over 10 minutes, carry more than 500 people per trip, and transport loads exceeding 66.1 US tons. 

According to its manufacturer, the platform is engineered to operate reliably in harsh environments, including high-altitude regions up to 10,800 feet (3,300 meters) above sea level and areas with extreme cold.

Pontoon powers rescue

The self-propelled pontoon barge helped to evacuate more students and staff trapped after floodwaters nearly five meters deep cut off the campus.

The modular vessel, described by Chinese Foreign Ministry spokeswoman Mao Ning as “China’s real-life transformer,” played a central role in a large-scale evacuation at Guangxi Logistics Vocational and Technical College in Guigang, Guangxi Zhuang Autonomous Region, reported the South China Morning Post (SCMP). 

The rescue operation took place after torrential rains left the college campus submerged under nearly five meters (16.4 feet) of floodwater, cutting off thousands of people from surrounding areas. According to China’s state news agency Xinhua, conventional rescue methods initially struggled to cope with the scale of the emergency.

Emergency teams first relied on inflatable rescue boats and assault boats to reach those stranded. While effective for small-scale operations, these vessels could transport only a limited number of passengers at a time, making the evacuation process slow and difficult.

To accelerate the rescue effort, the state-owned China Anneng Construction Group, China’s emergency rescue force, deployed three heavy-duty self-propelled pontoon barges to the flooded area between Wednesday night and Thursday. The arrival of the modular barges dramatically increased evacuation capacity, allowing rescuers to move hundreds of people during each trip. By midday Thursday, every student and staff member had been safely evacuated, according to Guangxi Television.

Heavy-duty lifeline

The pontoon barge used in the operation measures approximately 60 meters in length and eight meters in width. It is capable of carrying more than 500 passengers in a single journey while supporting a total payload exceeding 66.1 US tons. Unlike conventional floating bridges or barges that require external towing, the vessel is equipped with its own propulsion system, enabling it to travel at speeds of up to 6.7 mph (10.8 km/h) even when fully loaded.

One of its biggest advantages is its rapid deployment capability. China Anneng said the modular system can be transported to a disaster site, assembled, and placed into operation in just over 10 minutes. Its foldable design allows emergency responders to quickly establish a large-capacity transport platform without relying on permanent infrastructure.

The rescue barge is regarded as one of China’s most advanced modular heavy-duty water rescue systems. It was specifically developed to address one of the biggest challenges during major floods—transporting large groups of people, emergency responders, vehicles, and heavy equipment across inundated areas or rivers made impassable by rising water, according to the SCMP.

The vessel is manufactured by China Harzone Industry Corporation, a subsidiary of the state-owned China State Shipbuilding Corporation (CSSC). The company says the platform is designed to operate reliably in demanding environments, including high-altitude regions up to 10,800 feet (3,300 meters) above sea level and areas experiencing extremely cold temperatures.

CSSC noted that the high-tech rescue platform has been widely deployed in domestic emergency response operations since 2020. Its successful use during the Guangxi floods demonstrates how rapidly deployable modular engineering systems are becoming an increasingly important tool for large-scale disaster response, enabling emergency teams to evacuate thousands of people far more quickly than traditional rescue boats alone. thousands of people far more quickly than traditional rescue boats alone.

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