#light bulbs clipart

[micondesigns]

Watercolor Hanging Light Bulb Clipart PNG Transparent Sublimation Designs, Colourful String Lights, Vintage Light Bulbs, Christmas Lights

[cluelessbees]

OKAY SO I FOUND SOME STUFF ABOUT RINK O'MANIA

Hi, Hello, Welcome as I take you down the journey I fucking went through

So I was watching byler clips, as one does, and I was wondering whether or not they got lucky with the yellow and blue lighting in Rink O'Mania or whether they like – change the light bulbs or whatever. So I decided to stalk the Skating Rink they filmed at.

And holy shit did I find some interesting things.

I have no idea whether or not this was discovered before but I JUST NEED TO SHARE. SO HERE WE GO.

So, first of all, the place was originally called Roller King but they changed it to Skate O'Mania. Which, pretty similar to Rink O'Mania right? THAT'S BECAUSE THEY REMODELLED THE ENTIRE FUCKING ROLLER RINK FOR THE SHOW

LIKE I'm not even kidding.

They posted this message

Followed by a video (which is just a photo) with a little clipart of "under construction"

THEN, on August 10th they posted that they wrapped filming.

"Remodel courtesy of Hollywood" They deadass redecorated the entire roller rink for the however many minutes spent in ONE episode of the season. (The probably spent half the month redecorating and the other half filming which I'm losing my mind over?? no way this is a one time setting).

LIKE– Let me show you the difference in before and after the remodelling

The Roller Rink Before and After.

They added the blue and yellow light strips and the disco ball. And I can’t say 100 but it looks like they removed the pink and green coloured light(?)

And they added the whole ‘Skate/Rink O’mania’ sign (basically everything honestly was added by them–)

The Snack Station Before and After.

Again, we see the blue and yellow lighting.

The Table/Seating Area Before and After.

And okay I’m sorry about the quality of the second one I couldn’t find a better photo from their Google maps gallery or Facebook bUT- you can see that originally the lighting scheme was pink and blue but now it’s yellow and blue.

(here's another photo)

Like idk I’m losing my mind over this- they really like remodelled the entire fucking roller rink and added so much yellow and blue like?? I just think it's insane and I needed to share this discovery.

[sunaleisocial]

Understanding the visual knowledge of language models

New Post has been published on https://sunalei.org/news/understanding-the-visual-knowledge-of-language-models/

Understanding the visual knowledge of language models

You’ve likely heard that a picture is worth a thousand words, but can a large language model (LLM) get the picture if it’s never seen images before?

As it turns out, language models that are trained purely on text have a solid understanding of the visual world. They can write image-rendering code to generate complex scenes with intriguing objects and compositions — and even when that knowledge is not used properly, LLMs can refine their images. Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) observed this when prompting language models to self-correct their code for different images, where the systems improved on their simple clipart drawings with each query.

The visual knowledge of these language models is gained from how concepts like shapes and colors are described across the internet, whether in language or code. When given a direction like “draw a parrot in the jungle,” users jog the LLM to consider what it’s read in descriptions before. To assess how much visual knowledge LLMs have, the CSAIL team constructed a “vision checkup” for LLMs: using their “Visual Aptitude Dataset,” they tested the models’ abilities to draw, recognize, and self-correct these concepts. Collecting each final draft of these illustrations, the researchers trained a computer vision system that identifies the content of real photos.

“We essentially train a vision system without directly using any visual data,” says Tamar Rott Shaham, co-lead author of the study and an MIT electrical engineering and computer science (EECS) postdoc at CSAIL. “Our team queried language models to write image-rendering codes to generate data for us and then trained the vision system to evaluate natural images. We were inspired by the question of how visual concepts are represented through other mediums, like text. To express their visual knowledge, LLMs can use code as a common ground between text and vision.”

To build this dataset, the researchers first queried the models to generate code for different shapes, objects, and scenes. Then, they compiled that code to render simple digital illustrations, like a row of bicycles, showing that LLMs understand spatial relations well enough to draw the two-wheelers in a horizontal row. As another example, the model generated a car-shaped cake, combining two random concepts. The language model also produced a glowing light bulb, indicating its ability to create visual effects. 

“Our work shows that when you query an LLM (without multimodal pre-training) to create an image, it knows much more than it seems,” says co-lead author, EECS PhD student, and CSAIL member Pratyusha Sharma. “Let’s say you asked it to draw a chair. The model knows other things about this piece of furniture that it may not have immediately rendered, so users can query the model to improve the visual it produces with each iteration. Surprisingly, the model can iteratively enrich the drawing by improving the rendering code to a significant extent.”

The researchers gathered these illustrations, which were then used to train a computer vision system that can recognize objects within real photos (despite never having seen one before). With this synthetic, text-generated data as its only reference point, the system outperforms other procedurally generated image datasets that were trained with authentic photos.

The CSAIL team believes that combining the hidden visual knowledge of LLMs with the artistic capabilities of other AI tools like diffusion models could also be beneficial. Systems like Midjourney sometimes lack the know-how to consistently tweak the finer details in an image, making it difficult for them to handle requests like reducing how many cars are pictured, or placing an object behind another. If an LLM sketched out the requested change for the diffusion model beforehand, the resulting edit could be more satisfactory.

The irony, as Rott Shaham and Sharma acknowledge, is that LLMs sometimes fail to recognize the same concepts that they can draw. This became clear when the models incorrectly identified human re-creations of images within the dataset. Such diverse representations of the visual world likely triggered the language models’ misconceptions.

While the models struggled to perceive these abstract depictions, they demonstrated the creativity to draw the same concepts differently each time. When the researchers queried LLMs to draw concepts like strawberries and arcades multiple times, they produced pictures from diverse angles with varying shapes and colors, hinting that the models might have actual mental imagery of visual concepts (rather than reciting examples they saw before).

The CSAIL team believes this procedure could be a baseline for evaluating how well a generative AI model can train a computer vision system. Additionally, the researchers look to expand the tasks they challenge language models on. As for their recent study, the MIT group notes that they don’t have access to the training set of the LLMs they used, making it challenging to further investigate the origin of their visual knowledge. In the future, they intend to explore training an even better vision model by letting the LLM work directly with it.

Sharma and Rott Shaham are joined on the paper by former CSAIL affiliate Stephanie Fu ’22, MNG ’23 and EECS PhD students Manel Baradad, Adrián Rodríguez-Muñoz ’22, and Shivam Duggal, who are all CSAIL affiliates; as well as MIT Associate Professor Phillip Isola and Professor Antonio Torralba. Their work was supported, in part, by a grant from the MIT-IBM Watson AI Lab, a LaCaixa Fellowship, the Zuckerman STEM Leadership Program, and the Viterbi Fellowship. They present their paper this week at the IEEE/CVF Computer Vision and Pattern Recognition Conference.

[jcmarchi]

Understanding the visual knowledge of language models

New Post has been published on https://thedigitalinsider.com/understanding-the-visual-knowledge-of-language-models/

Understanding the visual knowledge of language models

You’ve likely heard that a picture is worth a thousand words, but can a large language model (LLM) get the picture if it’s never seen images before?

As it turns out, language models that are trained purely on text have a solid understanding of the visual world. They can write image-rendering code to generate complex scenes with intriguing objects and compositions — and even when that knowledge is not used properly, LLMs can refine their images. Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) observed this when prompting language models to self-correct their code for different images, where the systems improved on their simple clipart drawings with each query.

The visual knowledge of these language models is gained from how concepts like shapes and colors are described across the internet, whether in language or code. When given a direction like “draw a parrot in the jungle,” users jog the LLM to consider what it’s read in descriptions before. To assess how much visual knowledge LLMs have, the CSAIL team constructed a “vision checkup” for LLMs: using their “Visual Aptitude Dataset,” they tested the models’ abilities to draw, recognize, and self-correct these concepts. Collecting each final draft of these illustrations, the researchers trained a computer vision system that identifies the content of real photos.

“We essentially train a vision system without directly using any visual data,” says Tamar Rott Shaham, co-lead author of the study and an MIT electrical engineering and computer science (EECS) postdoc at CSAIL. “Our team queried language models to write image-rendering codes to generate data for us and then trained the vision system to evaluate natural images. We were inspired by the question of how visual concepts are represented through other mediums, like text. To express their visual knowledge, LLMs can use code as a common ground between text and vision.”

To build this dataset, the researchers first queried the models to generate code for different shapes, objects, and scenes. Then, they compiled that code to render simple digital illustrations, like a row of bicycles, showing that LLMs understand spatial relations well enough to draw the two-wheelers in a horizontal row. As another example, the model generated a car-shaped cake, combining two random concepts. The language model also produced a glowing light bulb, indicating its ability to create visual effects. 

“Our work shows that when you query an LLM (without multimodal pre-training) to create an image, it knows much more than it seems,” says co-lead author, EECS PhD student, and CSAIL member Pratyusha Sharma. “Let’s say you asked it to draw a chair. The model knows other things about this piece of furniture that it may not have immediately rendered, so users can query the model to improve the visual it produces with each iteration. Surprisingly, the model can iteratively enrich the drawing by improving the rendering code to a significant extent.”

The researchers gathered these illustrations, which were then used to train a computer vision system that can recognize objects within real photos (despite never having seen one before). With this synthetic, text-generated data as its only reference point, the system outperforms other procedurally generated image datasets that were trained with authentic photos.

The CSAIL team believes that combining the hidden visual knowledge of LLMs with the artistic capabilities of other AI tools like diffusion models could also be beneficial. Systems like Midjourney sometimes lack the know-how to consistently tweak the finer details in an image, making it difficult for them to handle requests like reducing how many cars are pictured, or placing an object behind another. If an LLM sketched out the requested change for the diffusion model beforehand, the resulting edit could be more satisfactory.

The irony, as Rott Shaham and Sharma acknowledge, is that LLMs sometimes fail to recognize the same concepts that they can draw. This became clear when the models incorrectly identified human re-creations of images within the dataset. Such diverse representations of the visual world likely triggered the language models’ misconceptions.

While the models struggled to perceive these abstract depictions, they demonstrated the creativity to draw the same concepts differently each time. When the researchers queried LLMs to draw concepts like strawberries and arcades multiple times, they produced pictures from diverse angles with varying shapes and colors, hinting that the models might have actual mental imagery of visual concepts (rather than reciting examples they saw before).

The CSAIL team believes this procedure could be a baseline for evaluating how well a generative AI model can train a computer vision system. Additionally, the researchers look to expand the tasks they challenge language models on. As for their recent study, the MIT group notes that they don’t have access to the training set of the LLMs they used, making it challenging to further investigate the origin of their visual knowledge. In the future, they intend to explore training an even better vision model by letting the LLM work directly with it.

Sharma and Rott Shaham are joined on the paper by former CSAIL affiliate Stephanie Fu ’22, MNG ’23 and EECS PhD students Manel Baradad, Adrián Rodríguez-Muñoz ’22, and Shivam Duggal, who are all CSAIL affiliates; as well as MIT Associate Professor Phillip Isola and Professor Antonio Torralba. Their work was supported, in part, by a grant from the MIT-IBM Watson AI Lab, a LaCaixa Fellowship, the Zuckerman STEM Leadership Program, and the Viterbi Fellowship. They present their paper this week at the IEEE/CVF Computer Vision and Pattern Recognition Conference.

[ferrimex]

Illuminate Your Space with LURDES SUSPENSION Black and White Light Bulbs!

Introducing our LURDES SUSPENSION light bulb clipart in classic black and white! Elevate your home's ambiance with this elegant and versatile lighting solution. Crafted for style and functionality, it's the perfect addition to any space. Don't miss our exclusive light bulb sale – brighten your life today with LURDES!

[donsvgfile]

This little Light of Mine SVG Autism Awareness png clipart T-Shirt Design Light Bulb Outline Cricut files

This little Light of Mine SVG Autism Awareness png clipart T-Shirt Design Light Bulb Outline Cricut files

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[sadoeuphemist]

Kids are just amazing. Think about it, they come into the world knowing absolutely nothing, and everyday they’re looking wide-eyed at the world, so curious about even the slightest detail of it. Why, just the other day, my little Lilah came up to me and said: “Daddy, why is the sky blue?”

Well of course I knew the answer right off the bat. “You remember how rainbows work?” I asked her, lifting her up on my knee. She nodded. I’d taught her about rainbows and prisms, with a hands-on demonstration, just a couple of months before. I gave her the whole spiel about light breaking up in the atmosphere, about how red light traveled further but blue scattered wider, and so that was why the sky was painted blue in the middle of the day but red at sunset, when the sun was far away enough that all the blues got scattered behind and only the reds traveled far enough to reach us. “You got all that?” I said. “Don’t be afraid to ask questions if you don’t understand.”

She got that little pouty frown on her face that told me she was processing it, and then she said “uh-huh” again dubiously, and then asked, “Why isn’t the sun blue?”

That caught me off guard. “What?”

“When the sun -” She waved her arm in a clumsy arc that didn’t really demonstrate everything. “When the sun’s—there, it’s all red, cuz the sun’s far away, and the - the red light goes really really far, so when the blue’s all spilled everywhere, why isn’t the sun blue, too?”

I set her down off my lap. This was an unexpected wrinkle. It’s funny how you can just know something nearly all your life, and never have questions like that occur to you. Why wasn’t the sun blue? Still, I believe it doesn’t hurt kids to let them know that sometimes their parents have to look things up, so I took out my phone. “Let me just find out, and then daddy will have an answer for you,” I said.

As a first exploratory impulse, I googled ‘why isn’t the sun blue’. The first few hits were asking why the sun, if it was so incredibly hot, didn’t burn with a blue flame instead of a yellow one. I didn’t offhand know the answer to that one, either, but it wasn’t relevant to Lilah’s question so I quickly skipped over them. The Stanford website, which I skimmed over, didn’t tell me anything that I hadn’t already said, aside from the added factoid that the sun’s visible light peaked in the green part of the spectrum, so technically it was slightly greener than anything else.

That didn’t help me either.

Finally, it was the NASA website that gave the answer. “It’s because the light is straight from the sun,” I told her. “When you’re looking up at the sun, it’s still pure white, but all around it at the sides, that’s where the blue’s scattering off in all directions.” The NASA website had an awfully condescending graphic, I thought, with a cartoon sun shining white light straight down on a cartoon dog, and the blue light going off at an angle. The dog wasn’t even looking up. You’d think NASA, of all things, would be able to come up with something slicker. 

“But ... doesn’t all light come straight from the sun?” Lilah said, interrupting my train of thought.

“Well, no—” I surreptitiously checked the diagram with the cartoon dog again. “’Cause if you’re looking at the sun, that’s the light, straight from the sun, and everything else—”

“But...” Lilah screwed up her forehead in thought. “Cuz when it’s daytime, that’s when the—half the Earth is facing the sun ...”

“That’s right, and on the other half it’s night, because they’re not facing the sun,” I provided, reciting an old lesson.

“So isn’t all daytime light from the sun?”

I tried, in my head, to picture a lamp shining on half a globe. Yes, every illuminated part of the globe was lit directly by the light bulb, so if a tiny person on the surface of the globe was looking up, wouldn’t the entirety of the bright daytime sky be the sun’s light? But then again, the sun was definitely only in one specific spot in the sky, so that couldn’t possibly make sense either ... I was beginning to suspect that I didn’t actually know how light worked.

“No,” I said definitively, trusting in the cartoon sun and dog, who were clipart images staring blankly at random angles. The sun’s eyes moved between images, I  noticed, as I scrolled up and down. The dog’s didn’t. “That’s not how light works, sweetie. When you turn on a light in the room—” I pointed up. “Everything’s lit up, right? But you can only see the light—the light-light—when you’re looking straight at it.”

She considered that while I held my breath, and I only let myself breath again once I saw her nod, a series of ponderous calculations still going on inside that cute little head of hers. Kids, I’m telling you, they’re really something. She wandered off to consider it on her own for a while, and I was trying to think up a sequence of words to google that explained the difference between light directly from the sun, and light that presumably was also directly from the sun, but you weren’t looking straight at it (my first stab of ‘ambient vs direct light’ of course only brought up a bunch of hits about home decor and 3D modeling) when I felt Lilah tug on my shirt sleeve once again. “Daddy?” she said.

I looked up, forcing a grin. “Yes, sweetheart?”

“If it’s all a rainbow, and red’s the one that goes the farthest, then why’s blue the one that scatters around the most? Shouldn’t it be...” She paused to mouth the words “Roy G. Biv” to herself. “Violet?”

I opened my mouth, and then looked down at my phone and quickly typed in ‘why isn’t the sky violet’. 

“Hrmm....”

“Daddy?”

“Okay,” I said slowly, still keeping one eye on the article. “It turns out that the sky actually is violet, but because of the way our eyes work ...” I quickly glanced up at Lilah. She was still with me, listening placidly as if I hadn’t just contradicted my previous explanations, completely willing to listen to her dear old dad explain the world to her.

“We have—we have cells in our eyes,” I began, “that are how we see color—they’re called cones—and there are three kinds. One is sensitive to—One sees red things, one sees green things, and one sees blue things. And so the—” I squinted at my screen. “Blue light makes the blue cones light up, right? But, it also, uh, tickles the green and red cones, just a little bit. So if when we looked up into the sky it was really all blue light, then to us it would look ... uh, slightly .... slightly greenish. Blue-greenish. Because the green cones are also getting tickled, so it would look kind of green!”

I ran one hand over my head, reading out the penultimate paragraph. “But because the sky is actually violet, that means our blue cones can’t see it all that well. But our red cones can kinda see violet, so if it ... if it was violet we’d actually see it as reddish ...” I trailed off.  “But since the sky is actually part blue and part violet,” I finished up, all in a rush, “the red and green cancel each other out, and all we end up seeing is blue!”

I made a sort of ‘ta-da!’ pose, a smile frozen on my face, and miraculously Lilah was nodding along as if my botched rendition of the science had made perfect sense. Maybe ... it did? Maybe according to the free-floating haphazard logic of a child who has no idea how the world works and has no choice other than to accept all explanations given, it was completely sensible that red and green should cancel each other out just like lights on a traffic signal, and that blue-plus-violet should equal blue, just as a fundamental property of math. I was almost congratulating myself on having provided a successful answer, when Lilah once again spoke up.

“So if the blue sky is really violet,” she said thoughtfully, fixing her big brown eyes on me, “at the end of the rainbow, what’s violet, then?”

I’ve done a lot of research since then, squinting at my tiny screen, and some of the information I’ve pieced together includes: that some of the colors displayed on my phone don’t actually exist, since different combinations of wavelengths of light stimulate different combinations of cones and make you hallucinate colors in your head; that violet is actually just dark blue and not purplish at all; and that yellow is just red and green light without any blue—but as to what they actually mean when they say “yellow” and “red” and “green” and “blue”—that part, I’m still trying to figure out.

It took me a while to even get this far. Lilah’s long since forgotten about the question and is now absorbed with watching Minecraft let’s plays on YouTube. They’re just incredible, kids. They’re born into this world, not understanding a damn thing about it, just having to trust what people tell them, and somehow they grow up into fully-formed individuals. I’m her dad, and I’m hardly responsible for a fraction of that. What else is it but a miracle?

And if anyone ever figures out what violet is, let me know.

[espm1011umn-blog]

Why Energy is so Essential

When we talk about energy usage, the first thing that comes to your mind is probably something such as what allows light bulbs to function, the fuel that powers a car, or what you use to charge your phone. While all of these actions do require energy, they don't begin to capture how people rely on energy for their day to day lives. Of all the energy that is consumed by people in the United States, only 20% of it is used for the residential sector. That includes all electricity, water heating, appliances, and general heating and cooling (1). So, where is that other 80% of energy being used? Well, 32% of energy powers the industrial sector for activities such as mining, agriculture, construction, and manufacturing. Another 29% is used for transportation to fuel cars, ships, and planes. The consumer sector is the 4th largest energy consumer accounting for 18% of energy usage in the United States for stores, places of worship, schools, and any other public locations (1).

Image: (2)

Of course, this breakdown only accounts for energy being used in the United States, meaning that any products produced abroad are using energy not accounted for in these statistics. Whether it's buying clothing, toys, or a car from another country, any energy used to produce that product is also part of how we depend on energy. 

(1) What are the major sources and users of energy in the United States? https://www.americangeosciences.org/critical-issues/faq/what-are-major-sources-and-users-energy-united-states (accessed Jan 26, 2020).

(2) Turn F Light Switch Clip Art: Art Related Clipart. https://www.clipart.email/download/1746300.html (accessed Jan 26, 2020).

[i365art]

50% OFF Light bulb clipart, light bulb clip art, lightbulbs, bulbs, Rainbow icons, planner stickers, lightbulb PNG, Commercial, digital

[sinfulcutters]

Christmas Lights are one of the prettiest parts of Christmas! I love looking at them. The Favorite Color Plaque cutter is 3.5" W x 3.00" T and the Christmas Bulbs are 3.5" W x 3.00" T. There is also an optional stencil available to fit the Plaque Cutter. Matching PNG file clipart listed for this cutter set is in my shop. Sinfulcutters.com #sinfulcutters #royalbluecutters #sugarcookies #cookies #cookiecutter #cookiecutters #cookieart #cookiedecorating #decoratedcookies #food #instacookie #3Dcookiecutters #sugarcookiesofinstagram #sugarcookies #royalicingcookies #satisfying #royalicingart #edibleart #smallbusiness #christmas #christmascookies https://www.instagram.com/p/Ckgr0d3OL56/?igshid=NGJjMDIxMWI=

[bellabharathi]

Hand Drawing Template

This is a three nodded template. There is large clipart of a hand-drawn diagram of a light bulb. The nodes are arranged vertically near the clipart diagram.

[stephsustainabilityjournal]

Week 5: Entry 1

Topic: Sustainable Electricity

Case Study: Energy efficient lights

Lighting contributes to around 10% of electricity usage in households, and even more so in commercial properties. In 2018, new regulations suggested a push towards phasing out standard halogen lamps which are the typical batten holder lights we’re all used to, and moving towards a minimum standard of LED lamps throughout Australia. They use less energy as they are designed to create light, rather than light and heat, therefore making them more efficient.

LED lights use much less electricity, while still being quite bright. LED lights are slightly more expensive than original light options such as halogen, compact fluorescent and incandescent lights, however they are still affordable and readily available, being able to be purchased from all lighting stores, hardware stores and supermarkets. They also come in different colours and temperatures, making them quite versatile and a good option for not only owner built houses, but also social housing and commercial and retail spaces. Not only this, they last 5-10 times longer. However given the extended lifespan and the overall benefits of energy efficiency, the payback time is worth the initial extra money used for the purchasing of the light itself.

For these reasons, it is clear that LED lights are more energy efficient. Lasting longer results in less wastage, they contain no toxic elements, and are better at distrusting light. This is obviously increased and amplified with the more LED’s used throughout a house or commercial space.

https://www.kindpng.com/imgv/ioJomb_transparent-light-energy-clipart-bulb-on-off-gif/

[svghubs]

Professional over thinker svg, Light bulb clipart svg, Anxiety Svg, Mental Health Svg

Professional over thinker svg, Light bulb clipart svg, Anxiety Svg, Mental Health Svg

Professional over thinker svg, Light bulb clipart svg, Anxiety Svg, Mental Health Svg, Self Care Svg, Self Love Svg, Mom Life Svg, Stressed Svg, Funny Svg, Motivational SVG, Mental Health Awareness svg,  SVG files for Cricut, SVG bundle, design bundles, Cricut files svg, PNG files. This design is delivered instantly as a digital download. You will receive a zipped file containing files in 8…

[hecallsmehischild]

So, I did my fiance as a pony also. Did I mention he’s brony now? He’s a brony now. We’re into season 6 and thoroughly enjoying. His favorite song is We’ll Make Our Mark.

He has a light-bulb for now, unless we think of a better cutie mark, because he has a lot of bright ideas (for business, for coding, etc). Needed a more general umbrella cutie mark.

Heads up, I did not draw either of our cutie marks, I imported them. His is lightbulb clipart I found, and mine is a mark symbolizing the nine Muses. But I did draw the rest.

[oyaxle]

Download Free Lightbulbs Svg Light Bulb Monogram Svg Files Light Bulb Cliparts Crafter File Here >>https://ift.tt/3CN1QuV

[xyzzlol]

Download Free Lightbulbs Svg Light Bulb Monogram Svg Files Light Bulb Cliparts Crafter File Here >>https://ift.tt/3ucYhLl