Natural light was an obsession — and he worked hard to let it in.

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Frank Lloyd Wright’s Usonian homes included many distinctive features, from the brick and cedar materials to the open floor plan. But one of the most distinctive features might be the windows — which reflected his broader philosophy of natural light.

As the above video shows, Wright considered natural light an important part of the house that deserved highlighting, both in the windows used and in the way the rest of the house showcased that light. The Pope-Leighey house in Alexandria, Virginia, is a particularly good showcase of the way these windows made natural light an integral part of the home.

Further Reading:
Steven M. Reiss’s book about the Pope-Leighey House is an invaluable resource for learning about the house, but it also gives a peek into the development process of a Frank Lloyd Wright home.
https://www.upress.virginia.edu/title/4293

Frank Lloyd Wright’s The Natural House details the philosophy behind his Usonian homes, as well as more about his view of organic architecture.
https://archive.org/details/naturalhouse0000wrig

John Luttropp’s model of the Pope-Leighey house is astonishingly accurate, and you can play around with it and download it for free.
https://3dwarehouse.sketchup.c....om/model/uce79d8fa-b

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The hidden history of an ancient language.

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Centuries before we had American Sign Language, Native sign languages, broadly known as “Hand Talk,” were thriving across North America. Hand Talk would be influential in the formation of American Sign Language. But it has largely been written out of history.

One of these Hand Talk variations, Plains Indian Sign Language, was used so widely across the Great Plains that it became a lingua franca — a universal language used by both deaf and hearing people to communicate among tribes that didn’t share a common spoken language. At one point, tens of thousands of indigenous people used Plains Indian Sign Language, or PISL, for everything from trade to hunting, conflict, storytelling, and rituals.

But by the late 1800s, the federal government had implemented a policy that would change the course of indigenous history forever: a violent boarding school program designed to forcibly assimilate indigenous children into white American culture — a dark history that we’re still learning more about to this day.

Because of a forced “English-only” policy, the boarding school era is one of the main reasons we lost so many Native signers — along with the eventual dominance of ASL in schools for the deaf.

Today, there are just a handful of fluent PISL signers left in the US. In the piece above we hear from two of these signers who have dedicated their lives to studying and revitalizing the language. They show us PISL in action, and help us explore how this ancient language holds centuries of indigenous history.

Note: The headline on this piece has been updated.
Previous headline: Before American Sign Language, we had "Hand Talk"

Read more from Melanie McKay-Cody on the history of Plains Indian Sign Language: https://shareok.org/handle/11244/319767

Check out Lanny Real Bird’s videos: https://www.youtube.com/channe....l/UCSFVFPZKp14gfMWhA

Much of the footage of the 1930 Indian Sign Language Council isn’t online, but check out some of it here: https://commons.wikimedia.org/....wiki/File:Indian_Sig

Here are some original books we reference on sign talk: https://archive.org/details/in....diansigntalkbe00hadl
https://archive.org/details/in....diansignlangua00tomk

The Smithsonian holds lots of photos and archives on Plains Indian Sign Language like this: https://www.si.edu/object/arch....ives/components/sova

Sarah Klotz on how Native American boarding schools like Carlisle contributed to the loss of PISL: http://constell8cr.com/issue-2..../the-historical-work She references archives that shows how students continued to use sign language like this one from the Carlisle Indian School Digital Resource Center: https://carlisleindian.dickins....on.edu/publications/

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What jobs do 15-year-olds expect to do at 30?

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Every few years, thousands of teenagers are asked a very simple question: What job do you expect to have when you're 30?

It's an important question because having an answer helps teenagers plan for the future, whether that's taking a specific class or deciding whether to attend college. That's why the OECD's PISA survey has asked this question since 2000.

But in the last 20 years, we've seen a concerning trend: More and more teenagers name the same basic jobs, like doctor or lawyer, almost as if they're picking jobs out of a children's book. And even more worrisome is that more and more teenagers don't even name a job.

All of this hints that today's teenagers aren't thinking enough about their future plans – and, fair or not, this lack of career preparation will likely have lifelong consequences.

Note: The headline on this piece has been updated.
Previous headline: Why 25% of teens can't answer this question

Sources and further reading:
This OECD report argues that the narrowing answers to this question hint that teens are confused about their future careers: https://www.oecd.org/education..../dream-jobs-teenager

Here's the data from the PISA survey: https://www.oecd.org/pisa/data/
It's difficult to work with, but the codebook provides broad summaries of the data: https://webfs.oecd.org/pisa201....8/PISA2018_CODEBOOK.

This study shows that students with science-related ambitions are far more likely to get science or engineering degrees, even if they aren’t as good at math: https://www.science.org/doi/10.....1126/science.112869

This paper looks at the long term ramifications of being “misaligned” as a teenager: https://www.jstor.org/stable/42956558

If you’re a solutions-oriented person, this OECD report is about what we can do to help teenagers better think about their professional futures: https://www.oecd.org/education..../indicators-of-teena

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Disney World really is a kingdom.

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Have you ever wondered why Disney World is located where it is? Or how it came to be such a massive, self-contained universe? It’s a story that involves secret land deals, special districts, and, now, a corporation and a government locked in a feud.

The story of why Disney World is in Florida marries business and governmental concerns about the creation of the massive property. The engineering ranges from unique physical construction (like the creation of a lake) to unique legal construction (like the creation of a self-running government that could even build its own sewer system).

Further Reading:
Buying Disney’s World (https://www.amazon.com/Buying-....Disneys-World-Florid by Aaron H. Goldberg is an authoritative work about the purchase of Disney. Drawing from corporate records, internal histories, and more, it’s a comprehensive narrative about Disney World’s creation and early years.

There are a lot of great Disney fan websites out there that are obsessive in their Disney coverage. I found Jim Hill’s history of Reedy Creek to be a good intro to the topic. (https://jimhillmedia.com/histo....ry-reedy-creek-impro

Disney news is also vibrant on YouTube. Mickey Views provides good perspective on the current battles.
https://www.youtube.com/@MickeyViews/videos

For the latest news and updates about the DeSantis-Disney conflict: bit.ly/3oOHz75

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How Los Alamos built Oppenheimer’s bomb.

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Los Alamos quickly became the centerpiece of the Manhattan Project — the United States’ successful attempt to develop a nuclear bomb. But why was Los Alamos selected, and how did that relate to the selection of the subsequent testing site? The above video tells the story of Oppenheimer’s selection of the site and the unique culture that was built there.

Los Alamos was more than a lab — it was a secret city, built for the express purpose of atomic development. Housing top scientists, their families, and support staff, this island of research led to a century-changing technological development.

Further Reading
Manhattan: The Army and the Atomic Bomb (https://history.army.mil/html/books/011/11-10/)
This logistical history of the Manhattan project provides great non-scientific background on the Manhattan Project, including the selection, distribution, and construction of key sites around the country, as well as a guide to the bureaucracy that surrounded it.

The Manhattan Project: Making the Bomb (https://www.osti.gov/biblio/1330716)
This Department of Energy history covers similar ground with additional information on the Army Corps of Engineer’s role.

Manhattan District History: Nonscientific Aspects of Los Alamos Project Y (https://www.osti.gov/biblio/4555247)
This document provides a fine-grained look at the nitty gritty of Los Alamos, from prices at the commissary to accommodations for the residents.

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One radio engineer had a plan. And it worked.

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Today, one of the hottest parts of the global economy is in Silicon Valley. And it’s thanks to, in large part, a radio engineer who had a plan.

As the above video shows, Fred Terman was key in building a technological hub in an area best known for its prunes. Thanks to his work developing Stanford Research Park, a new cycle of business innovation began and has continued to this day.

Watch the above video to learn more.

Further reading:
The Stanford Daily has extensive archives of early issues. It’s fun to peruse, and you can find an article about HP here: https://archives.stanforddaily.com/1956/02/02?page=1&section=MODSMD_ARTICLE1

Here’s a full 1969 interview with Terman:
https://www.youtube.com/watch?v=Jwk2Y4mi87w

Read Hoefler’s original article about Silicon Valley in which he coined the term:
https://www.netvalley.com/sili....con_valley/Don_Hoefl

Here’s Palo Alto’s survey of Stanford Research Park:
https://www.cityofpaloalto.org..../files/assets/public

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Phenylephrine doesn’t work better than a placebo. So why is it still on shelves?

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If you’ve taken an over-the-counter decongestant in the past 20 years, you’ve probably taken something with phenylephrine as the main active ingredient. A Food and Drug Administration panel recently recommended that it be taken off shelves because it doesn’t work any better than a placebo. What happened?

In the above video, Vox’s Phil Edwards chronicles the history of the FDA’s regulation of over-the-counter medicines. That process is the reason that, despite decades of evidence that phenylephrine doesn’t work (especially in typical OTC doses), it’s remained an option for consumers.

Watch the above video to learn more.

Further reading:

Here’s the Congressional Research report on FDA regulation of over-the-counter drugs, if you want a rich primer: https://crsreports.congress.go....v/product/pdf/R/R469

This is the PowerPoint given before the FDA panel presentation and discussion on Phenylephrine, and it’s probably the most digestible way to get the full debate: https://www.fda.gov/media/171971/download

Here’s Part II of the FDA’s Zoom meeting on Phenylephrine (including the vote and discussion): https://www.youtube.com/watch?v=aUyuzjjnGfA

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A lot of today’s contests are edited and rigged. But it wasn’t always that way.

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Today’s game shows, whether they’re Mr. Beast on YouTube or Storage Wars on cable, often feature sensationalistic editing, recreations, and straight-up fixing. But it wasn’t always that way. As the above video shows, game shows in America have gone from unregulated, to a Federally regulated activity, and back to unregulated once again.

The Quiz Show scandals of the 1950s set in motion a new law that enforced rules for games of skill. But, as the above video shows, changing media consumption habits and expectations have made game shows a wild west once again.

Further Reading:

https://content.time.com/time/....subscriber/article/0
You can check out the original Time Magazine profile of Charles van Doren and his family — central figures in the quiz show scandals.

On a few different platforms, you can watch the episode of Twenty One where Herb Stempel lost.
https://archive.org/details/TwentyOne_630

Read George Brietigam’s paper about the FCC enforcement today.
https://www.chapman.edu/law/_f....iles/publications/cl

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More than five years after the Jeddah Tower was suddenly put to a complete stop, its construction finally resumes. But why now? What has changed? And why is neighboring Dubai already planning to build an even taller structure?

For more skyscraper & megaproject content make sure to subscribe to Top Luxury!

0:00 The Jeddah Tower
1:50 Initial Plan & Construction
4:01 Jeddah Tower Resumes
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Why economists and futurists disagree about the future of the labor market.


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Sources:
https://economics.mit.edu/files/11563
https://www.aeaweb.org/full_is....sue.php?doi=10.1257/
http://voxeu.org/article/how-c....omputer-automation-a
https://www.opensocietyfoundat....ions.org/sites/defau
https://obamawhitehouse.archiv....es.gov/sites/whiteho
https://www.vox.com/2015/7/27/....9038829/automation-m
https://www.amazon.com/dp/B00P....WX7RPG/ref=dp-kindle
https://www.amazon.com/Second-....Machine-Age-Prosperi
https://www.amazon.com/New-Div....ision-Labor-Computer
https://www.oxfordmartin.ox.ac.....uk/downloads/academ

Clips:
https://www.youtube.com/watch?v=VTlV0Y5yAww
https://www.youtube.com/watch?v=_luhn7TLfWU
https://www.youtube.com/watch?v=rVlhMGQgDkY
https://www.youtube.com/watch?v=rCoFKUJ_8Yo
https://www.youtube.com/watch?v=yeyn9zzrC84
https://www.youtube.com/watch?v=7Pq-S557XQU
https://www.youtube.com/watch?v=WSKi8HfcxEk

///

Recent advancements in artificial intelligence and robotics have commentators worrying about the coming obsolescence of the human worker. Some in Silicon Valley are even calling for a basic minimum income provided by the government for everyone, under the assumption that work will become scarce. But many economists are skeptical of these claims, because the notion that the the economy offers a fixed amount of work has been debunked time and time again over the centuries and current economic data show no signs of a productivity boom. Fortunately, we don't need to divine the future of the labor market in order to prepare for it.

///

Vox.com is a news website that helps you cut through the noise and understand what's really driving the events in the headlines. Check out http://www.vox.com to get up to speed on everything from Kurdistan to the Kim Kardashian app.

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Current AI is impressive, but it's not intelligent.


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Sources:
https://hbr.org/2016/11/what-a....rtificial-intelligen
https://www.mckinsey.com/globa....l-themes/digital-dis
https://www.amazon.com/Master-....Algorithm-Ultimate-L
https://ai100.stanford.edu/sit....es/default/files/ai_
https://www.bloomberg.com/prof....essional/blog/busine
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.368.2254&rep=rep1&type=pdf
http://yann.lecun.com/exdb/pub....lis/pdf/jackel-95.pd
https://www.recode.net/2016/5/....4/11634228/learning-
https://www.theverge.com/2016/....7/12/12158238/first-
https://www.theverge.com/2017/....3/30/15124466/ai-pho
https://www.techspot.com/news/....71935-convolutional-
https://openreview.net/pdf?id=BkjLkSqxg
https://med.stanford.edu/news/....all-news/2016/08/com
https://arxiv.org/pdf/1609.06647.pdf
https://www.theverge.com/2017/....4/12/15271874/ai-adv
https://arstechnica.com/gaming..../2016/06/an-ai-wrote
https://www.ibm.com/blogs/rese....arch/2017/12/ai-vide
https://hbr.org/cover-story/20....17/07/the-business-o
https://rodneybrooks.com/the-s....even-deadly-sins-of-

Something incredible has taken place in the past 5 years: a revolution in artificial intelligence. After decades of little progress, the combination of big data and advances in computer hardware have brought AI applications to life: from self-driving cars to home assistants to augmented reality and instant language translation. If some of these applications feel like science fiction it's because deep learning algorithms are powering a true breakthrough in machine intelligence. But with these truly impressive advances comes a great deal of hype: fears of terminator-type bots turning on humans and stealing all our jobs. In this video we sort out the fact from fiction in this very exciting field.


///

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How machines can mimic our language.

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Something big happened in the past year: Researchers created computer programs that can write long passages of coherent, original text.

Language models like GPT-2, Grover, and CTRL create text passages that seem written by someone fluent in the language, but not in the truth. That AI field, Natural Language Processing (NLP), didn’t exactly set out to create a fake news machine. Rather, it’s the byproduct of a line of research into massive pretrained language models: Machine learning programs that store vast statistical maps of how we use our language. So far, the technology’s creative uses seem to outnumber its malicious ones. But it’s not difficult to imagine how these text-fakes could cause harm, especially as these models become widely shared and deployable by anyone with basic know-how. Read more here: https://www.vox.com/recode/202....0/3/4/21163743/ai-la

Open Sourced is a year-long reporting project from Recode by Vox that goes deep into the closed ecosystems of data, privacy, algorithms, and artificial intelligence. Learn more at http://www.vox.com/opensourced

This project is made possible by the Omidyar Network. All Open Sourced content is editorially independent and produced by our journalists.

Watch all episodes of Open Sourced right here on YouTube: http://bit.ly/2tIHftD

Try out natural language generation and detection with these tools:
https://demo.allennlp.org/next-token-lm
https://talktotransformer.com/
https://transformer.huggingface.co/
https://grover.allenai.org/
https://www.ai21.com/haim
http://gltr.io/
https://play.aidungeon.io/
https://huggingface.co/openai-detector/

Sources:
https://ruder.io/nlp-imagenet/
https://medium.com/@ageitgey/d....eepfaking-the-news-w
https://openai.com/blog/better-language-models/
https://blog.einstein.ai/intro....ducing-a-conditional
https://veredshwartz.blogspot.....com/2019/08/text-gen
http://www.mattkenney.me/gpt-2-345/
http://www.mattkenney.me/gpt-2/
https://jalammar.github.io/illustrated-gpt2/
https://mc.ai/introduction-to-....language-modelling-a
https://fortune.com/2020/01/20..../natural-language-pr
https://www.vox.com/future-per....fect/2019/2/14/18222
https://www.newyorker.com/maga....zine/2019/10/14/can-
https://www.youtube.com/watch?v=GEtbD6pqTTE
https://arxiv.org/pdf/1905.12616.pdf
https://arxiv.org/abs/1911.03343
https://arxiv.org/abs/1904.09751
https://techscience.org/a/2019121801/
https://www.middlebury.edu/ins....titute/sites/www.mid
http://newsyoucantuse.com/
https://aiweirdness.com/post/1....68051907512/the-firs
https://aiweirdness.com/post/1....59302925452/the-neur
https://www.nytimes.com/intera....ctive/2018/10/26/opi
https://aiweirdness.com/post/1....60985569682/paint-co
https://www.reddit.com/r/SubSimulatorGPT2/
https://twitter.com/dril_gpt2
https://cloud.google.com/text-to-speech/

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A new hyper-accurate technology, and referees' eternal quest for objectivity.

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The offside rule, which requires attacking players to be behind either the ball or the last defender, is a rule that sounds objective, but has led to a lot of questionable calls, partly because it can only be judged from an individual perspective. Until now. Meet the new “semi-automated AI offsides technology” at the 2022 World Cup.

This technology relies on a sensor in the ball that relays its position on the field 500 times a second, and 12 motion tracking cameras mounted underneath the roof of the stadium that use machine learning to track 29 points in players’ bodies. In other words, FIFA is mo-capping players, just without the funny gray suits. And the whole system will alert referees when a player is offside. If you’ve been watching the World Cup, you may have also seen the motion tracking information being used to create an immediate 3D replay.

This system seems like it could be capable of eliminating “bad” offside calls, or maybe bad calls altogether - but its new precision will inevitably impact gameplay no matter what. And the first World Cup to feature it will show us exactly how.

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Can a color really beat the AI revolution? For now, it looks like it can.

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The greenscreen is a staple of visual effects — and it may stick around even in the age of AI “magic.” The video above explains why.

It turns out that greenscreens, while imperfect, provide certain background separation benefits that are tough for AI to replicate due to the way it’s been trained and the limitations of available data. Preparation can help improve results, but this video shows why, ultimately, AI tools will remain one in a suite of options rather than a greenscreen killer.

Further reading
https://www.cs.unc.edu/~ronisen/
You can find more of Soumyadip (Roni) Sengupta’s papers here, including links to his various greenscreen work.

https://segment-anything.com/
If you want to try the latest breakthrough in image segmentation, Meta’s demo lets you upload your own images.

https://runwayml.com/
https://research.runwayml.com/....publications/towards
Runway ML has a variety of tools that sit on top of AI infrastructure, allowing you to play with all sorts of applications. They’ve documented a bit of their process at the link above.

https://openaccess.thecvf.com/....content_cvpr_2017/pa
If you want to really learn more, you can read this paper that explains image matting on an important dataset.

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And remember the Mannequin Challenge? Yep, they used that too.

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The quest for computer vision requires lots of data — including real world images. But that can be hard to find, which has led researchers to look in some pretty creative places.

The above video shows how researchers used Tik Tok dances and the Mannequin Challenge to train AI. The quest is for “ground truth” — real world examples that can be used to train or grade an AI on its guesses. Tik Tok datasets provide this by showing lots of movement, clothing types, backgrounds, and people. That diversity is key to train a model that can handle the randomness of the real world.

The same thing happens with the Mannequin Challenge — all those people pretending to stand still gave researchers — and their models — more real world data to train with than they ever could have hoped for.

Watch the above video to learn more.

Further Reading:

Here’s the original project pages for each researcher in the video:

Tik Tok aided depth: https://www.yasamin.page/hdnet_tiktok
Mannequin Challenge: https://google.github.io/manne....quinchallenge/www/in
Geofill and Reference-Based Inpainting: https://paperswithcode.com/pap....er/geofill-reference
Virtual Correspondence: https://virtual-correspondence.github.io/
Densepose: http://densepose.org/

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It could learn them all. But will it?

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Large language models are astonishingly good at understanding and producing language. But there’s an often overlooked bias toward languages that are already well-represented on the internet. That means some languages might lose out in AI’s big technical advances.

Some researchers are looking into how that works — and how to possibly shift the balance from these “high resource” languages to ones that haven’t yet had a huge online footprint. These approaches range from original dataset creation, to studying the outputs of large language models, to training open source alternatives.

Watch the video above to learn more.

Further reading:
https://ruth-ann.notion.site/r....uth-ann/JamPatoisNLI

This is the hub for Ruth-Ann Armstrong’s JamPatois NLI. You can see the dataset and read the paper.

https://arxiv.org/search/cs?searchtype=author&query=Melero%2C+M
You can read Maite Melero’s work on Catalan here.

https://huggingface.co/bigscience/bloom
This is the Hugging Face home for BLOOM, the open source large language model.

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How it works — and why it takes a surprisingly long time to make something good.

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How does an AI artist maintain consistency with a recurring character? While AI art may appear to involve just a few clicks, it can be quite time-consuming.

The video above demonstrates the abridged workflow of the anonymous creator behind "Stelfie" — a time-traveling selfie-taker. The artist's process involves custom 3D-generated heads, initial sketches, and extensive toggling between Photoshop and the AI program Stable Diffusion to achieve the ideal appearance.

Throughout this process, the artist employs typical AI art tools such as inpainting (modifying specific image areas), outpainting (extending beyond the frame), and denoising (controlling image alterations). AI art can be as labor-intensive as traditional art, but its distinct outcomes make the final product unique.

Check out more of Stelfie here:
https://www.stelfiett.com/
https://www.instagram.com/stelfiett/
https://twitter.com/StelfieTT
https://www.tiktok.com/@stelfiett?lang=en

Want to learn more? Watch our playlist on the ins and outs of AI here: https://www.youtube.com/playli....st?list=PLJ8cMiYb3G5

Or read more! There's plenty of text coverage on our website. Read the rise of artificial intelligence, explained: https://bit.ly/41S1Tmg

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We interviewed students and teachers on how schools should handle the rise of the chatbots.

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For a year now, students have had access to AI chatbots, otherwise known as Large Language Models, that can write at a high-school level and answer specific and diverse questions related to many school subjects. OpenAI's ChatGPT kicked off a race among tech companies to release their own chatbots and integrate them into existing consumer products.

The most advanced language models, like GPT-4 and Claude2 are kept behind paywalls. They offer more nuanced answers and make fewer mistakes but because reliability is not guaranteed, many businesses cannot yet deploy these systems. That means a significant portion of chatbot use cases are for low-stakes applications, like school work.

This presents a major challenge to educators, who now need to rethink their curriculum to either incorporate chatbot use or to attempt to deter it. In this video, we hear from students and teachers about how they're thinking through the problem, and review research in the science of learning to understand how the "fluency" of a chatbot experience could disrupt the learning process that we go to school for.

00:00 Intro
02:28 Path 1: Banning AI
06:03 Path 2: Allowing AI
09:52 The problem with the calculator analogy
11:18 The science of learning
15:44 Conclusion

Sources:
https://www.similarweb.com/blo....g/insights/ai-news/c
https://help.openai.com/en/art....icles/8313351-how-ca
https://hai.stanford.edu/news/....ai-detectors-biased-
https://www.ibo.org/news/news-....about-the-ib/stateme
"Rethinking GPS navigation: creating cognitive maps through auditory clues" https://www.nature.com/articles/s41598-021-87148-4
"Habitual use of GPS negatively impacts spatial memory during self-guided navigation" https://www.nature.com/articles/s41598-020-62877-0
"Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom" https://www.pnas.org/doi/10.1073/pnas.1821936116
https://wac.colostate.edu/repo....sitory/collections/a

AI-text-detection error rates:
Turnitin https://www.turnitin.com/produ....cts/features/ai-writ
Originality.ai https://originality.ai/blog/ai....-content-detection-a
GPTZero https://gptzero.me/faq
Sapling.ai https://sapling.ai/ai-content-detector
Compilatio https://support.compilatio.net..../hc/en-us/articles/1

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How programmers turned the internet into a paintbrush. DALL-E 2, Midjourney, Imagen, explained.

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Beginning in January 2021, advances in AI research have produced a plethora of deep-learning models capable of generating original images from simple text prompts, effectively extending the human imagination. Researchers at OpenAI, Google, Facebook, and others have developed text-to-image tools that they have not yet released to the public, and similar models have proliferated online in the open-source arena and at smaller companies like Midjourney.

These tools represent a massive cultural shift because they remove the requirement for technical labor from the process of image-making. Instead, they select for creative ideation, skillful use of language, and curatorial taste. The ultimate consequences are difficult to predict, but — like the invention of the camera, and the digital camera thereafter — these algorithms herald a new, democratized form of expression that will commence another explosion in the volume of imagery produced by humans. But, like other automated systems trained on historical data and internet images, they also come with risks that have not been resolved.

The video above is a primer on how we got here, how this technology works, and some of the implications. And for an extended discussion about what this means for human artists, designers, and illustrators, check out this bonus video: https://youtu.be/sFBfrZ-N3G4

Midjourney: www.midjourney.com

List of free AI Art tools: https://pharmapsychotic.com/tools.html

Sources:
https://arxiv.org/abs/1511.02793
https://arnicas.substack.com/p..../titaa-28-visual-poe
https://va2rosa.medium.com/cop....yright-storm-authors
https://tedunderwood.com/2021/....10/21/latent-spaces-
https://medium.com/artists-and....-machine-intelligenc
https://jxmo.notion.site/The-W....eird-and-Wonderful-W
https://ml.berkeley.edu/blog/posts/clip-art/
https://multimodal.art/
https://openai.com/blog/dall-e/
https://openai.com/blog/clip/
https://openai.com/dall-e-2/
https://laion.ai/laion-5b-a-ne....w-era-of-open-large-
https://arxiv.org/abs/2110.01963

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Matte paintings have transformed movies for over a hundred years. AI could be the next step in making them.

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This video is sponsored by Microsoft Copilot for Microsoft 365. Microsoft has no editorial influence on our videos, but their support makes videos like these possible.

When I look at movies from the ’20s to the ’90s, I’m blown away by the worlds that filmmakers were able to create with their visuals. From Mary Poppins to Ben-Hur to Star Wars, they truly made things that people had never seen before — all with little to no help from computers.

How did they pull off such striking and novel visuals? Well, often, it was just with a paintbrush and some glass.

With a technique called matte painting, skilled artists would paint a scene and black out a portion of the frame for live-action photography. The actors would be filmed on footage that blacked out the painted backdrop, and then filmmakers would combine the two exposures to make one seamless scene.

This, of course, all changed once computers entered the industry. By the late 1990s, matte paintings were almost entirely digital. Just a few decades later and now they’re almost all made in 3D.

With the development of AI, a new evolution might be on the horizon. Tune in to Vox’s latest to find out how AI might soon change the matte painting industry — again.

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