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‘It’s already way beyond what humans can do’: will AI wipe out architects?
- Thread starter bnew
- Start date
The fucc would I wanna do that for? I went into engr so I WON'T have to talk to people.
oh yea that I put that evil on you....you prolly never been on budget on time.....getting played heavy and don't even know it...or u do n don't give a fukk
I'd stay in the office you might get shaken up in the field
BaldingSoHard
Veteran
oh yea that I put that evil on you....you prolly never been on budget on time.....getting played heavy and don't even know it...or u do n don't give a fukk
I'd stay in the office you might get shaken up in the field
Budget is the accounting dept's problem. Idgaf.
Budget is the accounting dept's problem. Idgaf.
@BaldingSoHard "so can you explain on your resume why you've changed employers so often"
BaldingSoHard
Veteran
lol.@BaldingSoHard "so can you explain on your resume why you've changed employers so often"
But nah I been with the same company like 18 years. I'm actually looking for a change. I'm tryin' to get one of those sales engineer gigs. Easy money.
still need someone to plug and switch it all on..
↓R↑LYB
I trained Sheng Long and Shonuff
It's too late, breh. Passport bros already got a hold of the technologyA.I. as a tool must be Stopped
I was done When I saw thisIt's too late, breh. Passport bros already got a hold of the technology
These nikkas need to stop fr
AI is coming for architecture
Programs such as Dall-E and Midjourney are revolutionising the designs of buildings, but threatening the industry too
www.ft.com
AI is coming for architecture
Programs such as Dall-E and Midjourney are revolutionising the designs of buildings, but threatening the industry too
A sketch and the AI image generated from it © FT montage/LookxAI/Shenzhen XKool Technology
Edwin Heathcote
JANUARY 20 2024
11
Scroll through Instagram and, if the algorithm has deduced you're interested in architecture, you will find threads of bizarre, often surreal buildings that seem possible but not probable. There are futuristic swirls of space-age stuff coagulated in buildings that evoke Zaha Hadid. There are Afrofuturist cityscapes with mud towers and spaceship docking stations which might be scenes from Wakanda, home of Marvel’s Black Panther. And there are exquisite modern interiors, complete with lens flare and dust motes so real you could touch them. All of these have been generated in seconds by AI on the back of a few words of prompting.
Dall-E, Stable Diffusion and Midjourney have made what might have taken an extremely skilled illustrator or animator a week to do into something any of us can commission in a few moments. There is no doubt those jobs are at terminal risk. Architects are already using AI to handle mundane tasks from distributing parking spaces and bathrooms to arranging blocks on an urban plan.
In the more accessible and more ubiquitous visual world of social media, one designer who has made waves through the application of AI to architectural imagery is Hassan Ragab. His striking works veer from dreamlike futuristic architectures in wild natural settings to surreal mash-ups of his native Egyptian cities with steampunk organicism, embracing everything from informal settlements and shabby 1970s towers to elaborate mosques and Antoni Gaudí. “It's nonstop,” he tells me. “Every day there's something new and nobody really understands what's going on. Everybody is rushing in without really thinking about what they're doing.
“In that way, it's so different to architecture, which is so slow. I left my practice in 2019 and they're still working on the same building.” The platform for Ragab's designs is not the construction site but social media. He became viral through the seductive powers of his pictures. “It is very empowering,” he says. “It allows us a freedom.” Ragab might not be having any effect on real architecture yet, but architecture is now being used extensively by non-architects as a visual medium in itself. That is interesting and will feed back into real architecture as people become more sophisticated with seeing and understanding and manipulating AI visions.
Does he think AI will put architects out of business? “There is this idea,” he replies after a pause, “that humans are the only species, the only beings that can create ideas. That is not true any more. AI can do all these incredible things. Everything is possible and we should not be afraid, we should welcome it.”
While Ragab and others are provoking AI to hallucinate new, hybrid architectures, always strange and often alien, Wanyu He is determinedly designing them to be built. A former employee of Rem Koolhaas's OMA, she founded XKool in Shenzhen in 2016 to utilise AI for design and construction. The problem when He shows me the buildings which have resulted from the AI collaborations is that, at least so far, they don't look any different from (in fact they look clunkier than) most other mass development in China.
“If it looks like that,” she says, “that is because of human decisions. Because of economies.” She explains that the way developers are using AI now is to make buildings cheaper. “In the future, architects will be empowered to show the client thousands of options and refine the best one so that even on a low budget you will be able to get the best building.”
Unusually for an architect, she is also a writer of science fiction. “We worry about AI escaping human control and causing a disaster for mankind, and in my novels most of the future AI scenarios are not” — she thinks for a moment — “optimistic,” she says, with a slightly nervous giggle. “But it is this writing which gave me an awareness to prevent these things happening. AI should be a co-pilot and a friend, not a replacement for architects.”
Among the hyperinflating barrage of images on social media for the most extravagant and futuristic visions of AI-generated structures, the version of the future that crops up most frequently might well bear a resemblance to the work of Zaha Hadid Architects. Hadid seemed to predict a future suggested by sci-fi, rather than (the possibly more realistic) one that just resembles decline and the world as a huge informal settlement. Since she died in 2016, her practice has been headed by techno-optimist and libertarian Patrik Schumacher, who made waves when he revealed that the practice had been using AI models to regurgitate its own work, feeding in past projects to generate new ones.
At ZHA’s slick London offices, Shajay Bhooshan, head of the computation and design research team, clarifies what Schumacher meant. “Using AI as a sketching tool is low-hanging fruit. Images it has ingested come from our own buildings, so it is a pre-trained Stable Diffusion model fed with our own designs. What comes out depends on what images we choose to train the model with. So it is not just ZHA buildings but enough other architecture to give it a wider cultural understanding.”
He shows me on a screen a complex plan of a city settled into a valley. “Frankly, it is easier to just feed in our own work, though, because of copyright issues, but otherwise we put in everything, right back to Roman masonry.”
How do they find AI most useful? “It allows us to front-load,” he says. “It augments the process so we can get to what the client wants quicker with faster iterations and changes. It can then make trade-offs, say between budget and environmental impact, between pedestrians and traffic.” He then flips to another urban plan. “In many ways it makes good design more rapid and more affordable.”
And the downsides? “This is a rapidly changing technology,” he says. “There's unexplainability, it is highly complex and we don't always know how the input is converted to output. Midjourney and ChatGPT have been so successful because anyone can use them and millions are, whereas this field is still very small. We need to direct AI towards valuable architectural tasks, not just images for Instagram, otherwise it will not evolve.”
Less of a techno-optimist is Adam Greenfield. A writer, urbanist and former psyops specialist in the US Army, Greenfield suggests architects have yet to wake up to the potential destruction of their profession. “AI will strip away virtually everything that an architect does,” he says. But won’t architects be able to survive as brands, in the way fashion labels are now, with the prestige of a real Foster or Hadid building? “Do we really think that a client in the Emirates or an emerging economy is going to pay a premium for the presence of the ego when they could probably have their nephew feeding some prompts into an AI generator and probably get something even more imaginative?
“This is existential for architects . . . The people who are now most enthusiastic about AI have no idea what's being done to them. What we need to ask at this stage is what are we here for? If we're not here to bring our life experiences to bear on complex problems through our creativity, then what's left? Eat and shyt? The things AI is being called to do are the things which give us a stake in existence.”
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DatNkkaCutty
Veteran
I keep thinking about all the spooky shyt, AI could unleash in the future, or weaponize. You look thru Midjourney in the "horror" section for instance (or sci-fi), and some of the images AI conjures up. Unsettling, creepy, uncanny, alien, ETC.
Ppl inputting all these creepy ass ideas, and suggestions into these machines (AI). Whose to say, one day AI doesn't bring that shyt to life, on some horror movie shyt, or like a genie? At some point....humans will certainly be able to upload to a computer, and hopefully it isn't by force/imprisonment. Just off the potential of that alone...I'd be wary of the shyt you're suggesting to that AI shyt. Fuk around and get trapped in a "digital-hell" someday.
Ppl inputting all these creepy ass ideas, and suggestions into these machines (AI). Whose to say, one day AI doesn't bring that shyt to life, on some horror movie shyt, or like a genie? At some point....humans will certainly be able to upload to a computer, and hopefully it isn't by force/imprisonment. Just off the potential of that alone...I'd be wary of the shyt you're suggesting to that AI shyt. Fuk around and get trapped in a "digital-hell" someday.
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we will discover new materials.
Millions of new materials discovered with deep learning
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool...deepmind.google
Millions of new materials discovered with deep learning
Published29 NOVEMBER 2023Authors
Amil Merchant and Ekin Dogus Cubuk
AI tool GNoME finds 2.2 million new crystals, including 380,000 stable materials that could power future technologies
Modern technologies from computer chips and batteries to solar panels rely on inorganic crystals. To enable new technologies, crystals must be stable otherwise they can decompose, and behind each new, stable crystal can be months of painstaking experimentation.
Today, in a paper published in Nature, we share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
With GNoME, we’ve multiplied the number of technologically viable materials known to humanity. Of its 2.2 million predictions, 380,000 are the most stable, making them promising candidates for experimental synthesis. Among these candidates are materials that have the potential to develop future transformative technologies ranging from superconductors, powering supercomputers, and next-generation batteries to boost the efficiency of electric vehicles.
GNoME shows the potential of using AI to discover and develop new materials at scale. External researchers in labs around the world have independently created 736 of these new structures experimentally in concurrent work. In partnership with Google DeepMind, a team of researchers at the Lawrence Berkeley National Laboratory has also published a second paper in Nature that shows how our AI predictions can be leveraged for autonomous material synthesis.
We’ve made GNoME’s predictions available to the research community. We will be contributing 380,000 materials that we predict to be stable to the Materials Project, which is now processing the compounds and adding them into its online database. We hope these resources will drive forward research into inorganic crystals, and unlock the promise of machine learning tools as guides for experimentation
Accelerating materials discovery with AI
About 20,000 of the crystals experimentally identified in the ICSD database are computationally stable. Computational approaches drawing from the Materials Project, Open Quantum Materials Database and WBM database boosted this number to 48,000 stable crystals. GNoME expands the number of stable materials known to humanity to 421,000.
In the past, scientists searched for novel crystal structures by tweaking known crystals or experimenting with new combinations of elements - an expensive, trial-and-error process that could take months to deliver even limited results. Over the last decade, computational approaches led by the Materials Project and other groups have helped discover 28,000 new materials. But up until now, new AI-guided approaches hit a fundamental limit in their ability to accurately predict materials that could be experimentally viable. GNoME’s discovery of 2.2 million materials would be equivalent to about 800 years’ worth of knowledge and demonstrates an unprecedented scale and level of accuracy in predictions.
For example, 52,000 new layered compounds similar to graphene that have the potential to revolutionize electronics with the development of superconductors. Previously, about 1,000 such materials had been identified. We also found 528 potential lithium ion conductors, 25 times more than a previous study, which could be used to improve the performance of rechargeable batteries.
We are releasing the predicted structures for 380,000 materials that have the highest chance of successfully being made in the lab and being used in viable applications. For a material to be considered stable, it must not decompose into similar compositions with lower energy. For example, carbon in a graphene-like structure is stable compared to carbon in diamonds. Mathematically, these materials lie on the convex hull. This project discovered 2.2 million new crystals that are stable by current scientific standards and lie below the convex hull of previous discoveries. Of these, 380,000 are considered the most stable, and lie on the “final” convex hull – the new standard we have set for materials stability.
GNoME: Harnessing graph networks for materials exploration
GNoME uses two pipelines to discover low-energy (stable) materials. The structural pipeline creates candidates with structures similar to known crystals, while the compositional pipeline follows a more randomized approach based on chemical formulas. The outputs of both pipelines are evaluated using established Density Functional Theory calculations and those results are added to the GNoME database, informing the next round of active learning.
GNoME is a state-of-the-art graph neural network (GNN) model. The input data for GNNs take the form of a graph that can be likened to connections between atoms, which makes GNNs particularly suited to discovering new crystalline materials.
GNoME was originally trained with data on crystal structures and their stability, openly available through the Materials Project. We used GNoME to generate novel candidate crystals, and also to predict their stability. To assess our model’s predictive power during progressive training cycles, we repeatedly checked its performance using established computational techniques known as Density Functional Theory (DFT), used in physics, chemistry and materials science to understand structures of atoms, which is important to assess the stability of crystals.
We used a training process called ‘active learning’ that dramatically boosted GNoME’s performance. GNoME would generate predictions for the structures of novel, stable crystals, which were then tested using DFT. The resulting high-quality training data was then fed back into our model training.
Our research boosted the discovery rate of materials stability prediction from around 50%, to 80% - based on an external benchmark set by previous state-of-the-art models. We also managed to scale up the efficiency of our model by improving the discovery rate from under 10% to over 80% - such efficiency increases could have significant impact on how much compute is required per discovery.
AI ‘recipes’ for new materials
The GNoME project aims to drive down the cost of discovering new materials. External researchers have independently created 736 of GNoME’s new materials in the lab, demonstrating that our model’s predictions of stable crystals accurately reflect reality. We’ve released our database of newly discovered crystals to the research community. By giving scientists the full catalog of the promising ‘recipes’ for new candidate materials, we hope this helps them to test and potentially make the best ones.
Upon completion of our latest discovery efforts, we searched the scientific literature and found 736 of our computational discoveries were independently realized by external teams across the globe. Above are six examples ranging from a first-of-its-kind Alkaline-Earth Diamond-Like optical material (Li4MgGe2S7) to a potential superconductor (Mo5GeB2).
Rapidly developing new technologies based on these crystals will depend on the ability to manufacture them. In a paper led by our collaborators at Berkeley Lab, researchers showed a robotic lab could rapidly make new materials with automated synthesis techniques. Using materials from the Materials Project and insights on stability from GNoME, the autonomous lab created new recipes for crystal structures and successfully synthesized more than 41 new materials, opening up new possibilities for AI-driven materials synthesis.
A-Lab, a facility at Berkeley Lab where artificial intelligence guides robots in making new materials. Photo credit: Marilyn Sargent/Berkeley Lab
New materials for new technologies
To build a more sustainable future, we need new materials. GNoME has discovered 380,000 stable crystals that hold the potential to develop greener technologies – from better batteries for electric cars, to superconductors for more efficient computing.
Our research – and that of collaborators at the Berkeley Lab, Google Research, and teams around the world — shows the potential to use AI to guide materials discovery, experimentation, and synthesis. We hope that GNoME together with other AI tools can help revolutionize materials discovery today and shape the future of the field.
