All Activity

This is all so yesterday evening, the technology has moved on. You are using a bicycle to find a partner in the next village, the rest of us are using an app to find several all over the country. Technology Could a new approach to AI finally deliver artificial general intelligence? AI chatbots can describe reality in words, but don’t truly understand cause and effect in the real world. Now, a fresh kind of machine intelligence that does just that is emerging Daniel Cossins3 August 2026, updated 6 August 2026 http://localhost:56805/wp-content/external_image/aHR0cHM6Ly93d3cubmV3c2NpZW50aXN0LmNvbS93cC1jb250ZW50L3VwbG9hZHMvMjAyNi8wNy9TRUlfMzA2Njg5MTI5LmpwZw__.jpg Andrea De Santis What happens when you knock a glass off a table? Ask ChatGPT and you get a step-by-step explanation: how gravity accelerates the receptacle along a “parabolic trajectory”, why it could shatter “if the impact generates stresses greater than the material can withstand”. Ask my 3-year-old daughter and she’s less bothered with fine details: “It go smash and water everywhere!” When it comes to the future of artificial intelligence, the difference is instructive. The large language models (LLMs) powering today’s chatbots are trained on vast swathes of text to predict the next word in a sequence. Unlike little Adeline, however, no LLM has ever actually knocked a glass off a table – or thrown spaghetti at a wall, or driven a scooter into a pond – and that may impose a fundamental limitation on their capabilities. This, essentially, is why some researchers argue that what we need to take AI to the next level isn’t ever-larger LLMs, but “world models”: systems that learn through observation so they can simulate the consequences of actions in the real world. Read more A golden age of maths is dawning and mathematicians are freaking out World models have rapidly become AI’s next frontier, attracting huge buzz from industry and business thanks to their promise in autonomous robotics. What makes the world-model approach especially intriguing, though, is that it also ostensibly offers a route to artificial general intelligence (AGI), or machines with human-level reasoning that can be applied across a range of tasks. The problem is that it is tricky to parse substance from bluster. The term “world model” is confusingly elastic, for starters – for some, problematically so. It has become “a kind of shorthand for all the things that current AI systems can’t do well”, says Melanie Mitchell at the Santa Fe Institute in New Mexico. It is unclear exactly what these systems should model, never mind whether internal representations of physical reality will be sufficient for the kind of generalisable intelligence that means my 3-year-old intuitively understands what happens when she swipes a glass, even if she can’t say why. To make sense of world models as they apply in AI, it helps to understand the concept’s roots in cognitive science. AI researchers themselves typically trace it back to 1943, when psychologist Kenneth Craik wrote that the human mind “carries a small-scale model of external reality and of its own possible actions”, allowing us “to try out various alternatives” and “react to future situations before they arise”. http://localhost:56805/wp-content/external_image/aHR0cHM6Ly93d3cubmV3c2NpZW50aXN0LmNvbS93cC1jb250ZW50L3VwbG9hZHMvMjAyNi8wNy9TRUlfMzA1OTUxMzA0LmpwZw__.jpg Human minds have world models that enable us to predict what will happen next timsimages/Alamy That idea has evolved in the decades since, most notably with the theory of predictive processing. This posits that perception – possibly even consciousness itself – relies on the brain constantly generating predictions about the external world and updating them in response to incoming sensory data. But the point remains. “The key idea is that intelligence involves building models of the world in our heads so we can simulate outcomes and avoid costly mistakes,” says Josh Tenenbaum, a cognitive scientist and AI researcher at the Massachusetts Institute of Technology. “So ‘our ideas die in our stead’, as Craik put it.” It doesn’t take a genius to see why that has piqued the interest of AI researchers. LLMs have proved astonishingly capable, and they can certainly give the impression they understand things as we do. But language is a description of reality, rather than reality itself, and their lack of direct experience means that even the most powerful language models struggle when it comes to “understanding” physical phenomena in the real world. Read more Why I have changed my mind about AI and you should too LLMs often perform poorly when they are asked to reason about spatial concepts and things that might happen in the physical world, says Mitchell. “If you’ve ever uploaded a map to one of these models and asked it questions about it, you’ll know that they will often have a lot of problems with reasoning.” Indeed, in a 2024 study, when researchers trained language models on a database of turn-by-turn directions for taxi trips around New York City, they found that the system could provide reasonable routes from one point to another – but failed miserably when asked to take the odd detour. The reason why is that the LLMs have only descriptions of journeys, and therefore lack the sort of mental map that allows us to imagine what would happen in different scenarios. And navigation is just one example. LLMs demonstrate similar shortcomings in any scenario where you need to simulate the physical world – safely loading a dishwasher, say, or folding laundry. “They’re not designed for it, which is why people are interested in alternatives like world models,” says Tenenbaum. http://localhost:56805/wp-content/external_image/aHR0cHM6Ly93d3cubmV3c2NpZW50aXN0LmNvbS93cC1jb250ZW50L3VwbG9hZHMvMjAyNi8wNy9TRUlfMzA2Njg5MTgzLmpwZw__.jpg Andrea De Santis Probably the most influential proponent is Yann LeCun, a computer scientist at New York University and, until recently, chief scientist of foundational AI research at Meta. His argument, first made in a 2022 paper, is essentially that if we want to build truly intelligent systems that can reason, plan and act effectively in the real world, we need world models. “I cannot imagine we can build agentic systems without those systems having an ability to predict, in advance, what the consequences of their actions are going to be,” LeCun told artificial intelligence forum AI House Davos in January. And the key to that, he reckons, is systems that learn the rules of the world from observation. The idea has caught on. In December 2025, LeCun left Meta to found AMI Labs, raising just north of $1 billion to build world-model systems. A year earlier, Fei-Fei Li at Stanford University in California started World Labs, with $230 million of investment, to develop AI with “spatial intelligence”. Many of the established AI firms, most notably Google DeepMind, are now also actively pursuing world models in one form or another. The reason for the influx of money is primarily the promise that world models hold for advancing robotics (see “What are AI world models good for?”, below). However, it is early days and the existing prototypes are modest in their applications. With Marble, for instance, World Labs has built a system that generates coherent 3D scenes from text prompts. Similarly, DeepMind’s Genie 3 creates convincing interactive virtual environments – “snowy mountain at dusk”, say – in which you can move around for several minutes and even prompt events like rain. http://localhost:56805/wp-content/external_image/aHR0cHM6Ly93d3cubmV3c2NpZW50aXN0LmNvbS93cC1jb250ZW50L3VwbG9hZHMvMjAyNi8wNy9TRUlfMzA1OTUxMjc1LmpwZw__.jpg Google DeepMind’s Genie 3 creates worlds that users can explore from text-based prompts Google DeepMind In both cases, the companies describe their systems as “world models”. But Marble is really a 3D video generator and Genie 3 a video-game simulator, albeit one producing simulations in which agents could plausibly act, observe consequences and learn. Arguably, a proper world model would be something that exists inside an agent such that it can predict the consequences of its decisions, imagine the future and plan ahead before taking actions – and that is what DeepMind is pushing towards with its Dreamer 4 system, released in September 2025. It learns an internal predictive model of its environment, training mostly on data from the video game Minecraft, and uses that to train an agent that repeatedly “dreams” the consequences of possible actions to improve its behaviour. “The biggest difference [from Genie 3] is that Dreamer 4 is an agent, not just a world model,” says Danijar Hafner at DeepMind in San Francisco, who leads the Dreamer 4 team. “It predicts actions and improves them through iterative self-improvement, using planning and [imagined] trial and error.” The power of this approach is apparent in Hafner and his colleagues’ demonstration that Dreamer 4 can figure out how to collect diamonds in Minecraft, a complex task involving thousands of different actions – gathering resources, crafting tools, navigating the landscape – without being shown how to play. “The system has to understand its environment and generalise, because each new episode starts in a randomly generated world,” says Hafner. That is an important step, not least because this is precisely the kind of world model that could facilitate autonomous robots capable of folding laundry, say, or loading the dishwasher. “I think it’s going to solve robotics,” says Hafner. “What’s missing now is execution: data, compute, scaling. But we have the recipe, so, like with language models, it’s about scaling and details.” Read more The AI expert who says artificial general intelligence is nonsense Who would bet against DeepMind, given its impressive track record? After all, its researchers have won a Nobel prize for work on protein folding. But while Dreamer 4 demonstrates that agents with internal world models can reason and plan, it leaves a deeper question unresolved: what exactly should these systems learn about the world, or, more specifically, at what level of detail? And this has become a key fault line in the field, with two distinct approaches emerging. Many of the existing strategies are attempting to generate predictions on condition of action, as the researchers put it, by reconstructing future observations as faithfully to the training data as possible. But LeCun reckons that is the wrong approach, or at least not the best. His argument is based on the fact that humans don’t mentally simulate the world in any great detail, and certainly not pixel by pixel. When we imagine a glass falling from a table and smashing to pieces, for example, we don’t predict the position of every shard of glass, every water droplet. Instead, LeCun argues, we run highly compressed models that capture only the aspects that matter. This why LeCun advocates for a different tack, which he calls joint-embedding predictive architecture (JEPA). In this framework, world models infer abstract representations of what is relevant for reasoning, planning and action. “Generative models try to reconstruct pixels, whereas JEPA learns in a latent space and only predicts what is useful,” says Randall Balestriero at Brown University in Rhode Island, who has worked with LeCun on JEPA-based world models. “It ignores irrelevant details and focuses only on what matters for the agent.” LeCun and his investors appear to be betting that the JEPA approach will offer a swifter route to real-world applications, largely because it requires less training data. The most concrete publicly available demonstration of this technique so far is a system called V-JEPA 2, released by Meta in June 2025. Trained on video inputs, V-JEPA learns by masking certain regions of footage – obscuring a moving ball, say, over multiple consecutive frames – and repeatedly predicting not pixels, but abstract representations of what was hidden to learn a compact internal model of how the ball moves. What LeCun and his colleagues have shown, then, is that their system can model the causal structure of the physical world without having to reconstruct it in detail. Which isn’t to say that JEPA is necessarily any better than generative world models. Its advocates argue that it will be, of course. “Without abstraction, AI systems will stay limited to narrow tasks,” says Balestriero. But V-JEPA 2 is yet to clearly demonstrate that its abstract representations are sufficiently meaningful to enable an agent operating in an open-ended environment to reason and plan. “The biggest missing piece is the question of, how do we know that the abstraction is actually useful,” says Balestriero. “This is where there is a huge amount of active research right now, to understand: what do you capture, or how do you encode something very rich about the world that is useful for planning downstream.” Hafner, for his part, isn’t convinced that more compressed representations of reality are better. “Yann is right about many things, even if maybe he expresses them more controversially than necessary, and the JEPA approach is very promising,” he says. “But I don’t think that representations should be compressed and tiny. Ultimately, you want to learn strong representations, and what we did with Dreamer 4 [which trains with pixels but predicts in abstract space] is incredibly robust.” http://localhost:56805/wp-content/external_image/aHR0cHM6Ly93d3cubmV3c2NpZW50aXN0LmNvbS93cC1jb250ZW50L3VwbG9hZHMvMjAyNi8wNy9TRUlfMzA1OTUxNDI1LmpwZw__.jpg Some tech investors say that world models will be an effective way to power humanoid robots Wang Jiang/VCG via Getty Images More broadly, it is also far from clear at this stage if any of the world models in development – whether they learn and predict by reconstructing the world in high-fidelity or by inferring abstract representations – will ultimately be enough to get us to AGI. Now, it’s fair to say that AGI is another elastic term, and that claims about world models as a route towards it exist on a spectrum, with some of the most ambitious suggesting that learned simulations of physical reality could become the foundation of generally capable reasoning agents. When it released Genie 3, for instance, DeepMind insisted world models are “a key stepping stone on the path to AGI, since they make it possible to train AI agents in an unlimited curriculum of rich simulation environments”. Indeed, Hafner argues that “if you have systems that are able to represent the rules governing the world, then you’re approaching something like AGI because that’s understanding, that’s a key part of intelligence”. For his part, LeCun talks about them as a vital component of broader systems, rather than the whole story. And yet it is worth exploring the extent to which the kinds of world models in development would approximate human intelligence, because it can reveal what else might be required. Hafner says the most immediate requirement is temporal abstraction – that is, being able to reason not just about what will happen in the immediate future, but as things continue to play out. “You cannot just simulate everything at a sub-second, frame-by-frame level, because humans do not reason like that,” he says. “This is one of the open frontiers.” Tenenbaum goes further. Human world models aren’t just engines for predictions, he says, “they are much richer than that”. Human reasoning depends on various forms of causal abstraction and hypothesis-driven inference – not to mention models of other minds – which together allow us to flexibly recombine knowledge across different situations. “A key open question in all this is whether scaling up these world models will recover that richness, and my view is that it likely won’t,” says Tenenbaum. Mitchell makes a similar, if slightly broader, point. “I think the ability to have a kind of compressed, simulatable representation of aspects of the world is very important, and this notion of world models is probably going to result in useful improvements,” she says. “But I think there are probably lots of other aspects of intelligence that matter.” http://localhost:56805/wp-content/external_image/aHR0cHM6Ly93d3cubmV3c2NpZW50aXN0LmNvbS93cC1jb250ZW50L3VwbG9hZHMvMjAyNi8wMy8wNDExNTc0MS9TRUlfMjg3ODAxODkyLmpwZz93PTkwMCZoPTYwMCZjcm9wPTE_.jpg A very serious guide to buying your own humanoid robot butler You can now buy a humanoid robot housekeeper for less than the price of a second-hand car. But before splashing out, there’s something you need to know Another limitation of AI systems today, says Mitchell, is a lack of metacognition – an awareness of their own cognitive state, of what they know and don’t know, and how uncertain they are. “Is that fixable with a world model? Well, it depends what kind of world model, obviously,” she says. “But I’m a little worried that the notion of ‘world model’ is going to be used as the term for the difference between what we have now and ‘AGI’.” All of which suggests that world models as currently conceived may well be necessary for human-level machine intelligence, but not necessarily sufficient. Indeed, while it looks increasingly likely that they will be powerful and genuinely useful in robotics, and possibly scientific simulation too, it is far from a sure bet that they will replicate the way world models work in human cognition, never mind human-level intelligence more broadly. “One of the big misconceptions is that intelligence is a single thing, that there is a single world model in the brain,” says Tenenbaum. “What we actually have is the ability to run many different models depending on the context, task and goal.” Ultimately, then, the task of replicating what my 3-year-old daughter’s brain is capable of when it comes to modelling and predicting the physical world – even if it doesn’t stop her knocking glasses of water off the table – is not one to be underestimated. What are AI world models good for?

Hi all, I’ve noticed damp odours coming from sockets on an exterior wall in my new home. It’s only one wall and it’s about 4 metres. The rest of the house is good. I’m afraid the smell is coming from behind the air tightness memebrane as It must havre some punctures in that area. So I’m thinking is there mould on the studs or insulation? If it were your house what would you do? Where would you start? thanks

It’s visable and will be stained to match the rest of the timber, steel wasn’t even an option. I didn’t think it was expensive really.

I have a double check at inlet to property, same to outside tap and the combined valve at cylinder. That's it.

Hi Just popping by to say hello, I'm a plumbing and heating engineer and just bought my first property to let.

B&L ltd probably didn't add a check valve which there should be on an outside tap. You could use a double check valve as the coupler for extending the pipe vertically. Not sure it check valves cope well with freezing though

@Nickfromwales It feels completely pointless to me to have check valves at mixer taps, showers etc if there isn't proper separation between the hot and cold at the Caleffi 533201 combined PRV and monobloc manifold. Outside taps, and appliances make sense as they contain other contaminants. What are your thoughts?

Thanks Gus, nice examples of Glulams On another thread I has detailed changing to using Staddle Stones and my rough calculations there suggest a lateral force capacity at the bottom of the post of at least 80kg (without pin and resin into concrete below stone) and I'd expect much more (with pin and resin into concrete). The main structural question I am struggling with is whether I need ceiling level timber ties back to the house structure. I'd consider the coupling between the post and the staddle stone to be free to rotate, so just providing lateral and vertical constraint but not rotary. More discussion here:

My waste came with blue rubber blanks that have been useful for this. Or a washer cap from toolstation gives change from £2.

Hiya. The base brackets you show act as pins, they are just intended to carry vertical load only. They do have a minimal shear load capacity, but it is minimal. That (shear) is the load horizontal to the plane of the ground surface. The brackets you show are of minimal strength. To stop it moving sideways there are a number of different options. We can do a flat portal type of frame, often with knee braces. But this is going to introduce shear load into the brackets you show, they are not big enough and likely neither are the pads they rest on by the looks of things. The pads might be able to carry vertical load but not horizontal loading. Or you can fit a stiff floor and roof. These then act as a diaphragm and transfer the sideways loads back to the main house. But you have to check the main house can carry this extra load. To give you a better answer, with more explanation on how it works, need to see cross section drawings and a bit more of the house you are going to add load to if you use the diaphragm method. Ah like your idea. Quick story. When I was at uni one of my structures lecturers was into wood as a hobby. He used to buy tree trunks, cut them up into sizes in his garden, season the wood and make stuff. He did an extension with a curved vaulted ceiling in oak. To make it work he wanted to laminate the planks, turn them into a Glulam type beam We had remained in touch and once I started practising he asked me if I though I could prove this calculation wise. The answer is yes. I won't explain here but it's not too hard, if you know how to do it. This can be done DIY on a modest scale. In fact I'm deploying the same idea over a set of doors with a triangular glazed window above. I'm turning the individual 95 x 45 into a "Glulam" to carry the weight from the triangular glass above. But also I turn it into a solid timber so it better resists the wind load on the doors and glass. The cranked steels above carry the roof load. Here are the notes that go with the transom. For you you are going to needs lots of clamps. You can used tie bolts also, messy unless you want to leave them in place. The secret is to condition your timber and make sure all glue surfaces are clean, free from oils etc, roughen them a bit and don't rush the job. In other words cleanliness is next to godliness as is diligence. Here is a bridge near where I live. The photo, near where I live is not that good of a laminated bridge. There are bolts going down from the top to the bottom of the beam. These transfer the shear loads between the laminations. Glue is more efficient. Hope this help inspire you to give it a go!

I think it's a bit more nuanced and people give advice based on their own set up (I'm just as guilty of that which is why I try to ref my current set up when responding) If we look at the process and what we are trying to do when batch heating water there is some sound reasons for controlling flow rate via a gate valve and or pump speed Slower the pump speed the gate needs to be more open - speed the pump up and you'll need to close the gate valve more We want the HW supplied by the boiler to spend as long as possible in contact with the cooler water in the tank to allow as much heat transfer as possible We want the water to flow in at the top of the coil and out at the bottom so at every point in the coil the HW from the boiler is able to transfer heat because the water outside the coil and in the tank is cooler as you go down the tank Pump speed can have an effect - if you have a boiler that doesn't control the speed of the pump then you need to establish the lowest pump speed possible to maximise the time in the coil However - there is absolutely no point screwing the flow down to a point where the flow rate in the circuit is insufficient for the boiler resulting in repeated short cycling as this a massive waste of gas. So the target is to heat the water in the tank in preferably one burn so initial start up losses are minimised.

Not really. Steel is old tech and mass produced. Kerto is hitech and a smaller market...and heavy. You might even find second hand steel.

Installers are looking for a bigger job because of the grants. More about the roof please. What construction do you intend? Closer purlins gives lots of strength.

Almost as quick to DIY than actually speak to installers. Our first array was me fitting all the trays, the electrician wired it up the roofer did the slates around the trays etc. and all the flashing details. Panels to buy are around £100 each tray £70 plus flashing. So 10 panels (4.65kW) around £2k plus inverter/battery. Yes get a battery then run your whole house in cheap rate.

No, the 30 Deg figure is adjustable you can also run fan in manual so it runs 24/7 if you want. Our flue temp in heating is about 28 at most. So mine comes on about 24 for heating and about 18 for cooling

Next door neighbour - knows you are away - pops round for a quick top-up 🫤

Evening all. In the garden of my SB I have built a home office. It's single skin block, which I will wrap in breathable membrane before cladding. I have built a feature brickwork projection around the doors. There is a gap at the top between the brick and the block, as you can see. To protect this I planned to get a bit of arris rail, wrap it in either lead flashing or roofing rubber, and fix it over the top of the bricks. I would then dress the membrane down over that. Thing is, that wouldn't give me a drip bead. I'm not sure if that matters. I might be overthinking it! I have thought about using a pvc windowsill over the top, but I'm not sure how I'd address the junction between the sill and the block wall. In any event, I'd want a grey or black one and for the size I need I'd be paying more than seems sensible. How would you treat this area?! Thanks

Hi I have a pack with the construction drawings as a layman it’s quite detailed? But I’m not the builder im putting a screenshot below of the detail that’s been provided but builder saying not enough detail… lintels/steels etc all listed on Seperate drawings

Unfortunately the Reverso manual (installer) has no reference to this temperature and was only able to get the info from the manufacturer. Re the Myson you have the installer manual says the fans won't turn on in heating mode unless the flow is 30degrees C. Is this true? *Edit your manual looks like it cracks on if it doesn't detect a flow temp probe.

Did you price it up vs steel? Not sure if just went to a dear supplier but had a price years ago and steel was cheaper. Crazy.

I have a couple of large beams under a carport i used a kerto beam instead of gluelam, loads cheaper.

If you have half a building up I would roof the lot completely not temporary. get all the posi joists sit them on the walls you have built, sail the sheets over the bit you haven’t built and hold the wall plates up on 4x4 posts, then build the walls up under cover, will help you when the winter comes.

@ProDave Do you find that you use the balcony much?

As requested. Here are some pictures. I’m seriously gutted at how it’s cracked. But it is one of those things and although I’ve done a garage slab before when it was 10C using traditional concrete and a poker and that had no shrinkage cracks. the SCC was £178/m3

Probably just noise then. If the charger still works and there's no smell of electronics in the garage then don't worry about it.