#multimodal imaging systems

[neturbizenterprises]

Revolutionize Tech with Multimodal AI!

Multimodal AI is revolutionizing technology by seamlessly combining text, images, and audio to create comprehensive and accurate systems.

This cutting-edge innovation enables AI models to process multiple forms of data simultaneously, paving the way for advanced applications like image recognition through natural language prompts. Imagine an app that can identify the contents of an uploaded image by analyzing both visual data and its accompanying text description.

This integration means more precise and versatile AI capabilities, transforming how we interact with digital content in our daily lives.

Does Leonardo AI, Synthesia AI, or Krater AI, leverage any of these mentioned Multimodal AI's?

Leonardo AI - Multimodal AI:

Leonardo AI is a generative AI tool primarily focused on creating high-quality images, often used in the gaming and creative industries. While it is highly advanced in image generation, it doesn't explicitly leverage a full multimodal AI approach (combining text, images, audio, and video) as seen in platforms like GPT-4 or DALL-E 3. However, it might utilize some text-to-image capabilities, aligning with aspects of multimodal AI.

Synthesia AI - Multimodal AI:

Synthesia AI is a prominent example of a platform that leverages multimodal AI. It allows users to create synthetic videos by combining text and audio with AI-generated avatars. The platform generates videos where the avatar speaks the provided script, demonstrating its multimodal nature by integrating text, speech, and video.

Krater AI - Multimodal AI:

Krater AI focuses on generating art and images, similar to Leonardo AI. While it excels in image generation, it doesn't fully incorporate multimodal AI across different types of media like text, audio, and video. It is more aligned with specialized image generation rather than a broad multimodal approach.

In summary, Synthesia AI is the most prominent of the three in leveraging multimodal AI, as it integrates text, audio, and video. Leonardo AI and Krater AI focus primarily on visual content creation, without the broader multimodal integration.

Visit us at our website: INNOVA7IONS

Video Automatically Created by: Faceless.Video

[notapersob]

I wrote an essay on this topic, which is what the comic is based off of. If you care to read it it's beneath the cut , as well as my works Cited, and alt text.

This was a college English assignment, first the essay then the multimodal project. I wanted to share it with the internet people on my phone because this is something that is important to me. (i added it up and i spent roughly 40+ hours on this comic in two weeks, guys, the carpal tunnel is coming for me...)

i would also like to give a huge thanks to some of my best friends for helping me, @ellalily my wonderful talented friend who i love so much and adore their work. (i love her art so much). I know you'll see this, love you king <22223333.

and my partner, @totallynotagremlin . amazing artist and the person i admire every day. thankyou for helping me with this and listening to me rant about this project. i love you so much *kisses you on the forehead.

If anyone reads this, please go check out their art.

THE ESSAY

If you're not paying attention you could mistake AI art for art made by real artists. Many people use AI without much knowledge about it, thinking it's something harmless and fun. However, AI art has a real impact on the art community. AI art is largely harmful to the art community because it negatively impacts artists by stealing and plagiarizing their work.

Knowing how AI generators create art provides important context in understanding the negative impacts of AI-generated art and why it is bad. In an article by The Guardian, Clark L. explains, “The AI has been trained on billions of images, some of which are copyrighted works by living artists, it can generally create a pretty faithful approximation”. On its own, this doesn't sound that bad, and many fail to see the issue with this. However, the corporations training these AI art generators use artists' work without their knowledge or consent. Stable diffusion, an online AI art generator, has provided artists the option to opt out of future iterations of the technology training. However, the damage has already been done. AI is ‘trained’ by being fed images. It analyzes them. It works by being given large amounts of data and input codes. In an article by  The Guardian, written by Clark L, there is a quote from Karla Ortiz, an illustrator, and board member of CAA, concerning this issue. She says, “It’s like someone who already robbed you saying, ‘Do you want to opt out of me robbing you?”.

Another article by Yale Daily News has several categories, the first being, “How does AI generate art”.  As the heading explains, the first section of the article explains how AI text-to-image generators like DALL-E2 and Midjourney create images by “analyzing data sets containing thousands to millions of images” (Yup K.). In the same article, they cite an artist, Ron Cheng, a Yale Visual Arts Collective board member who is against AI because AI fails to obtain consent from artists before stealing their art. Cheng says “There are enough artists out there where there shouldn't really be a need to make AI to do that.” (Yup K.). The article says Cheng views AI as a tool but not at the cost of the people who spent their lives developing artistic skills.

Many artists feel that they should be compensated or that this should fall under copyright laws but because proving this machine-made art has taken elements of their style is so difficult, the AI companies get off with no consequences. For an artist to take action against an AI image generator, they would have to prove that one of their art pieces had been copied into the system which can be difficult. They would have to prove specific elements of their personal art style have been directly copied and prove that their art has been used and imitated without their consent. Many artists feel that this technology will take their jobs and opportunities in the creative field of work. Kim Leutwyler, a six-time Archibald Prize finalist artist, expressed her issues with AI companies stealing her art in an ABC news article.  Leutwyler said that they had found almost every portrait they created, included in a database used to train AI without their knowledge or consent. They said it “feels like a violation” (Williams T.). 

  With AI art relying on, often, stolen artwork, and creating an interpretation of what it sees, it blurs the line between what is copyright infringement and what is not. In a BBC article by Chris Vallance,  Professor Lionel Bently, director of the Centre for Intellectual Property and Information Law at Cambridge University said that in the UK, “it's not an infringement of copyright in general to use the style of somebody else” (Vallance). Another point to keep in mind is that not many artists have the means to fight these legal battles for their art even if they wanted to. This same BBC article speaks about the Design and Artists Copyright Society (DACS), an organization that collects payments on behalf of artists for the use of their images. One quote helps illustrate their point, “I asked DACS’ head of policy Reema Aelhi if artists’ livelihoods are at stake. “Absolutely yes,” she says” (Vallance).

Another concern about AI mentioned in this article is deep fakes, porn, and bias. “Google warned that the data set of scraped images used to train AI systems often includes pornography, reflected social stereotypes, and contained “derogatory, or otherwise harmful associations to marginalize identity groups.” (Vallance). These are all important things to consider when using AI because an AI system can harmfully replicate biases and negative stereotypes because of what it learned. For example, if you input the prompt criminal, it is more likely for the image to be of a person of color. On the other hand, if you input the prompt, CEO, it is strongly probable that an image of an old white man in a suit will show up, not a woman, or a person of color. These stereotypes go much further and much deeper than just these two examples, but the AI recreates what it was taught and can follow patterns that are harmful to minorities.

Another concern many artists have is about their jobs and livelihoods. With how AI art has progressed in the past few years, it is starting to take opportunities from real artists. “It’s been just a month. What about in a year? I probably won’t be able to find my work out there because [the internet] will be flooded with AI art,” Rutkowski told MIT Technology Review (Clarke L.). Many of the articles I researched mentioned the Colorado State Art Fair, where an AI-generated image won first place. The BBC article written by Vallance talks about how a man (Allan) entered an AI-generated image mid-journey and won. Many artists were outraged by this and suddenly aware of how AI could take opportunities like these from them. The artists who entered this competition spent hours and hours on their pieces. As you can imagine they were angry, rightfully so, that an AI-generated piece that took no more than a few seconds won. There is a level of unfairness to this and many artists feel that AI should not be allowed in art competitions like this. It feels like they got cheated out of something they worked hard for. Nobody would let a robot compete in the Olympics or a cooking competition, so why should a machine be allowed to enter an art fair? AI could start taking jobs from artists working on animated projects, or taking commissions.

With AI’s ability to imitate a certain artist's style, some people may feel that they no longer have to pay an artist for work when they could just input a few words into a machine and get something done in seconds. There were artist and writer strikes in Hollywood, in part because of this. These creative people wanted to be paid fairly and have better working conditions, as well as a promise that not all of them would be replaced with AI. When SORA AI came out, I saw many artists online who aspired to have jobs in the animation industry, losing hope and motivation. A soulless and emotionless machine can rip away a lifelong hobby and passion.

Many artists were upset but Allen, the winner of the Colorado State Art Fair, stood by his point and said, “It's over. AI won. Humans lost” (Clark L.). The article quoted a game and concept artist, RJ Palmer's tweet, “This thing wants our job, it's actively anti-artist”.  The article speaks of how artists often take inspiration from other artists, “great artists steal”, but Mr. Palmer said, “This (AI) is directly stealing their essence in a way”. In an article by The Guardian, Clarke L. writes about how AI art has raised debates on just how much AI can be credited with creativity.  Human art has thoughts, memories, and feelings put behind it and takes a lot of skill, whereas, on the opposite end, AI art can't handle concepts like that. AI does not experience life like real people do. It does not have feelings or emotions and it can only think with the knowledge we give it. Since it cannot have these emotions, the art it creates will never have the emotions that art made by real artists has.

Cansu Canca, a research associate professor at Northeastern University and founder and director of the AI Ethics Lab said, “It is important to be mindful about the implications of automation and what it means for humans who might be ‘replaced’” (Mello-Klein, C.). She went on to say that we shouldn't be fearful but instead ask what we want from machines and how we can best use them to benefit people. The article says “With the push of a button, he was able to create a piece of art that would have taken hours to create by hand” (Mello-Klein, C.). Some artists said, “We’re watching the death of artistry unfold right before our eyes” (Mello-Klein, C.). In an article by the New Yorker, Chayka, K., started by giving three reasons why artists feel wronged by AI image generators that are trained using their artwork. The “three C’s”, they didn't consent, they were not compensated and their influence was not credited. The article states how it is hard for copyright claims based on style to get picked up because in visual art “courts have sometimes ruled in favor of the copier rather than the copied” (Chayka, K.). This applies to music as well, where some songs can sound similar but nothing will be done about it because they are different enough, or the source material was changed enough not to be seen as a complete copy. The article said, “In some sense. You could say that artists are losing their monopoly on being artists” (Chayka, K.). Some people are even hiring AI to make book covers instead of hiring artists.

While I am personally against the use of AI art as well as many of my artist friends, all people have their own opinions about the technology. The article by the New Yorker, written by Chayka, K, quotes Kelly Mckernan, who said they watch Reddit and Discord chats about AI. This provides opinions on some everyday people who aren't in the art field. on the situation and said, “They have this belief that career artists, people who have dedicated their whole lives to their work, are gatekeeping, keeping them from making the art they want to make. They think we’re elitist and keeping our secrets.” (Chayka, K.). I remember an acquaintance of mine said that he used AI art because he could not afford to commission an artist. Not everyone can afford to commission an artist and pay them fairly for their time. However, this does not mean artists should settle for less than their work is worth. Art takes time and that is time the artist could be doing something else. 

Northeastern Global contacted Derek Curry, an associate professor of art and design at Northeastern, who gave his thoughts on the subject and he does not believe AI art will ever replace humans because technology has limits. “The cycle of fear and acceptance has occurred with every new technology since the dawn of the industrial age, and there are always casualties that come with change” (Mello-Klein, C.). The article goes on to say how auto-tune was once controversial but it has become a music industry standard. It's used as a tool, and AI art could be similar. It is true that with new technology, people always fear it before it is accepted. For example, the car. People feared it would take jobs and replace people, and this did happen, but it offered more convenience and opened up more jobs for people than it took. Now cars are used by everyone and it is almost impossible to get around in America without one because it wasn't made for walking, it was built around roads. There are many more examples of people fearing a new technology before accepting it, so this could be the case with AI, but for AI to be used as a tool and aid to artists, greedy corporations have to change the way they think about the technology. They have to see it as, not a replacement, but a tool. Big animation companies want to replace a lot of their human artists, who need their jobs to support themselves and their families, with AI. This prospect is something that is discouraging to artists who want to enter the animation field, which is already competitive.

The Yale Daily News (Yup, K.), cites Brennan Buck, a senior critic at the Yale School of Architecture. He uses AI as a tool to colorize and upscale images. He does not think AI is a real threat to artists. This is a very different take from most artists I’ve heard about and talked to. I can see how this technology can be used as a tool and I think that is one of the only right ways to use AI art. It should be used as a tool, not a replacement. Another way AI art can be used as a tool is to learn how to draw. New artists can study how art is made by looking at colors and anatomy for inspiration, though it should be taken with a grain of salt because AI tends to leave out details, and things merge and some details make no sense. These are all things real artists would notice and not do in their pieces. Young artists could also study the process of real artists they admire. Getting good at art takes years and practice. Seeing all kinds of different art can help with the learning process. On the topic of some people feeling like AI is not a real threat to artists, some people feel that eventually the technology will fade in popularity and will become more of a tool. Only time can tell if AI art will take the jobs of artist.

  With everything being said, AI art is actively harming artists and the art community. Even if some artists like Brennan Buck feel that AI isn't a real threat to artists, presently, it is taking opportunities and jobs from artists and it will only get worse as the technology progresses. We need to prioritize real artists instead of a machine, a machine that will never be able to replicate the authenticity of living people's art that reflects their experiences and lives. Some artists use art to express and spread awareness of real-life issues. I have neurodivergent, transgender and queer friends who create art to show what it feels like to experience the world when it seems everyone is against you. I make art to reflect the beautiful things I see and read. I too am queer and fall under the trans umbrella term and I'm autistic, and I use art as a way to express myself through these things that make up my identity.

AI could never put the emotion that real people put into the things they create. Art is a labor of love and pain. Art like Félix González-Torres free candy contemporary art piece cannot ever be replicated by AI and have the same meaning. He “created nineteen candy pieces that were featured in many museums around the world. Many of his works target HIV”(Public Delivery, n.d.). The opinions and views on this, relatively, new technology differ from person to person. Some artists view generative AI art as a tool to utilize in their art while others see it as a threat and something that is taking away from artists. AI art can be used for bad, as it has and will be used to make deep fakes unless limitations are put on it. The AI systems are trained on thousands of images of real people and of art made by artists, all without their consent and most of the time, without their knowledge. On the other hand, some artists use it to aid their process and don't see the issue. Based on what I have learned, I do not think AI art is good, nor should it have a place in the creative job fields. Companies should not copy and steal work from artists. Artists work their whole lives to learn to create, and that should not be replaced by a machine.

ALT TEXT (I didn't know where to put the alt text, sorry, also, this is the first time I've ever done alt text so I'm sorry if its not the greatest, i tried. if you have feedback though, that would be greatly appreciated)

Page 1

“AI art is NOT real art” under  a picture of the letters AI, crossed out in red.

“AI text-to-image generators like DALL-E2 and Midjourney create images by, “analyzing data sets containing thousands to millions of images” (Yup K.)” 

Beneath the test is a set of polaroid photos strung up, with a black crow sitting on the wire. There is a computer with a few tabs open and two ladybugs near it.

“AI art generators are trained off of artwork used without the artist's consent.”

To the side of the text is a small person holding up something they drew. There are lines leading from their drawing to an ai recreated version of it.

Page 2

There is a picture of Kim Leutwyler 

“Feels like a violation”

“I found almost every portrait I've ever created on there as well as artworks by many Archibald finalists and winners”

Kim Leutwyler

(Williams T.)

There is a picture of Tom Christopherson

“I didn't think I would care as much as I did. It was a bit of a rough feeling to know that stuff had been used against my will without even notifying me.”

“It just feels unethical when it's done sneakily behind artists' backs… people are really angry, and fair enough”. 

Tom Christopherson

(Williams T.)

There is a drawing of Ellalily drawn by them,  with their cat sitting on top of the bubble they're in.

“AI sucks the life out of art… there’s no love, no creativity, no humanity to the finished product. And that's not even scratching the surface of the blatant violations put upon artists whose work has been stolen to fuel this lifeless craft” 

EllaLily

(@ellalily on tumbrl)

There is a drawing of Gremlin/Cthulhu 14 with small mushrooms growing off of their bubble

“AI art isn't real art because it just copies from real artists. Art is something that is so very human and it has human emotions in it. A robot can't replicate that emotion and cant give meaning to an art piece”

gremlin/cthulhu14

(@totallynotagremlin on tumbrl)

There is a drawing of myself gesturing towards the text.

“AI art is actively harming the art community by:

Taking jobs

Opportunities

Hope and motivation

From artists.”

Page 3

“Most artists can't do anything against the people feeding their art into these AI systems.”

There are two drawings of myself, sitting down, crisscross, underneath the text with speech bubbles showing that I'm theI'm person talking.

“Many artists don't have the means to fight these legal battles for their art, even if they wanted to.”

“Some dont have the:

Money” 

drawing of a dollar and some coins

“Time” 

drawing of a clock with the numbers jumbled

“Capability” 

drawing of a green frog in a purple witch hat and dress holding up a magic wand with its tongue.

“And even if they did…

Most AI art escapes copyright laws”

Beneath this is an image of Professor Lionel Bently and a small drawing of the university of cambridge.

“Professor Lionel Bently, faculty of law at university of cambridge said (In the UK) “its not an infringement of copyright in general to use the style of someone else””

There is a drawing of the same wizard frog from before. It is laying down.

“so … AI gets away with stealing from artists with no consequences.”

The text is surrounded by a yellow and orange comic emphasis speech bubble

Image of van gogh, starry night, and fake ai recreation.

Image of Zeng Fanzhi art, image of john chamberlain art, “art by artists inspired by Van Gogh

“Artists take inspiration from each other. AI only companies what it sees.”

Page 4

There is a drawing of a green beetle with yellow wings in the top right corner. On the other side of the page, there is an image of Reema Aelhi.

Design and Artist Copyright Society (DACS) is an organization that collects payments on behalf of artists for the use of their images. “I asked DACS’ head of policy Reema Aelhi if artists' livelihoods are at stake, “absolutely yes,” she says”. (Vallance).

There is a brown bat hanging upside down from red swirls on the page.

“Deep fakes and biases

Another problem with generative AI is that often, the data sets used to train it contains, “pornograhy, reflected social stereotypes and contains “derogatory… or harmful associations to marginalized identity groups””. (vallance)”

There is a cartoonish small white and brown cat underneath the text.

“Example, Prompt CEO”, image of a white old man.

“Prompt, criminal”, image of person of color

“These are examples of HARMFUL BIASES”

There is a moth emerging from a green cocoon through three images. The first is an untouched cocoon, the second has a yellow, red, and green moth halfway emerged from the cocoon. The third has the moth fully emerged, resting on the cocoon. There is one last moth flying across the page underneath the text.

“AI art also threatens the jobs and livelihoods of artists.”

There is a drawing of a brown suitcase with stickers on it, and college certificates around it.

“The artist and writer strike in 2023 that lasted 148 days happened in part, due to the fear of being replaced by AI.”

There is a broken yellow, red, and green moth wing at the bottom of the page.

Page 5

“AI also takes opportunities” two green shoes are hanging from a red dot.

“Animated jobs”

Two cartoon birds are on a television screen with a red/pink background.

“commission work”

There are two people, one is a person in a purple shirt who is handing over a drawing to a girl in a blue shirt with ginger hair.

“Book cover art jobs”

There is a fake book with a person on the cover, who has a big orange bird on her arm. There are clouds and three stars in front of her.

“The Colorado State Art Fair was won by an AI image, entered by Jason M. Allen”

Arrow from Jason M. Allens name to quote, “it's over. AI won. Humans lost” - quote from Allen (Clark L.)

“Artists were outraged. You don't let robots compete in sports competitions, why was it allowed in an art competition?”

Tweet from RJ Palmer, @arvalis - august 13, 2022

“This thing wants our jobs. It's actively anti-artist”

“Great artists steal…[but] this (AI) is directly stealing their essence in a way.”

How much can AI be credited with creativity? Human art has emotions /feelings, thoughts/memories, and takes skill and time.

AI art has none of that”

Beneath the text, there is an image of a desert with two clouds, one partially covering the sun. The sky is blue and there are cacti in the background. There is a singular tumbleweed bouncing through the scene.

Page 6

“With a push of a button, he (Allan) was able to create a piece of art that would have taken hours to create by hand… we’re watching the death of artistry unfold right before our eyes.” (Mello- Klein C.)

There is a person in a coffin. There is water in the coffin covering most of them. There are stars over their chest. There are leaves surrounding the coffin.

Page 7

“It is important to be mindful about the implications of automation and what it means for humans who might be replaced”

-Cansu Canca, research associate professional at Northeastern University, founder and director of AI ethics lab. (Mello-Kline, C.)”

There is an image of Cansu Canca. There is also an orange owl in flight.

“Most artists taken advantage of by AI feel wronged in 3 main areas

They didn't consent”

There is tea in a  white and blue cup. Steam is coming up from the brown tea.

“They weren’t compensated”

There is a bronze coin. Next to it is a stamp with the words “the three C’s (Chayka, K)”

“Their influence wasn't credited”

There is a blue credit card with waves on it and a silver chip. On the credit card, there are the words “credit card numbers :D”

“Courts have sometimes ruled in favor of the copier rather than the copied”

There is a red fox with a blue butterfly on its nose and a turquoise background.

Page 8

“If AI art should be used at all, it should be used as a tool and not a replacement”

There is a hammer with a red handle and two wrenches, one on either side of it, followed by two files and yellow pencil. 

“Brennan Buck, senior critic and Yale School of Architecture uses AI also a tool to colorize and upscale images.”

Next to the text is an image of Brennan Buck.

“New artists can look at art made by artists and AI to learn new techniques. However, learning from real artists is more ethical and effective.”

Beneath and between the text is a drawing of a woman with long flowing ginger hair. Her body is obscured by waves like clouds or mist. Six white wings are coming out of her back. She has several hands surrounding a woman with shorter brown hair.

Page 9

“AI is actively harming artists and the art community. It's presently taking jobs and opportunities. Art is a labor of love and pain. Artists cannot and should not be replaced by machines.”

There is a drawing of myself in a birch wood forest. There are bits of sunlight streaming through the gaps in the leaves. I am painting a picture of the scene I see before me. I am in a green dress with a white off-the-shoulder top and there is a brown easel.

Works Cited

Chayka, K. (2023, February 10). Is A.I. Art Stealing from Artists? The New Yorker. https://www.newyorker.com/culture/infinite-scroll/is-ai-art-stealing-from-artists?irclickid=xyOXQL259xyPRBuWV7XlJViKUkH17cVGIzN7Xs0&irgwc=1&source=affiliate_impactpmx_12f6tote_desktop_FlexOffers.com%2C%20LLC&utm_source=impact-affiliate&utm_medium=29332&utm_campaign=impact&utm_content=Online%20Tracking%20Link&utm_brand=tny. February 28, 2024.

Clarke, L. (2022, November 18). When AI can make art – what does it mean for creativity? The Guardian. https://www.theguardian.com/technology/2022/nov/12/when-ai-can-make-art-what-does-it-mean-for-creativity-dall-e-midjourney. February 28, 2024.

Mello-Klein, C. (2022, October 12). Artificial intelligence is here in our entertainment. What does that mean for the future of the arts? Northeastern Global News. https://news.northeastern.edu/2022/09/09/art-and-ai/. February 28, 2024.

Public Delivery. (n.d.). Why did Félix González-Torres put free candy in a museum? https://publicdelivery.org/felix-gonzalez-torres-untitled-portrait-of-ross-in-l-a-1991/

Vallance, B.B.C. (2022, September 13). “Art is dead Dude” - the rise of the AI artists stirs debate. BBC News. https://www.bbc.com/news/technology-62788725. February 28, 2024.

Williams, T. (2023, January 9). Artists angry after discovering artworks used to train AI image generators without their consent. ABC News. https://www.abc.net.au/news/2023-01-10/artists-protesting-artificial-intelligence-image-generators/101786174. February 28, 2024.

Yup, K. (2023, January 25). What AI art means for society, according to Yale experts - Yale Daily News. Yale Daily News. https://yaledailynews.com/blog/2023/01/23/what-ai-art-means-for-society-according-to-yale-experts/. February 28, 2024.

[not-terezi-pyrope]

Folks are going to have to decide whether they want to rethink AI tools and the reasons for their attitudes towards them pretty quick, because both Adobe and NVIDIA just released massive suites of tools for individual creatives and enterprise respectively, and the image generating components for both are apparently sourced from the massive proprietary image databases these companies can arrange access to. So the objection that these models are unethical because they "steal" from public data (they really didn't, but that's sort of besides the point with these now) is null and void. (N.b.: It does put the power to control these tools almost exclusively in the hands of large companies who can license image datasets though, so. Decide how you want to feel about this becoming the standard.)

NVIDIA's offering sounds particularly impressive. They say incorporate multimodal capacity, including text, images, videos and 3D models. Adobe on the other hand has a demo of their tech integrated into Photoshop and Illustrator.

These aren't emerging technologies anymore, these are becoming universal tools that are being deployed at scale, and people are going to need to decide where they stand real quick on their use. The vast majority of people and all of companies that are presented with access to these systems are going to be using them to streamline their pipelines, for better or ill, and if you conscientiously object to their use that will potentially come with consequences in terms of keeping up in your field.

Personally I still think that these can be made into useful developments for society. I think objecting outright to these tools existing now that they do exist is asking for an impossible reversal and throwing out the possibility of working to leverage the tech to benefit the public rather than the corporations who will be using it anyway. No, we should be leaning in and working together in order to shape how these tools are integrated, instead of abjuring and clinging to our current dystopia until machine learning overtakes us.

No, we should be preparing to use these tools constructively, and, Jesus fucking Christ, people need to be organizing to lobby and elect their governments such that we can institute and automation tax and/or UBI now. The expectation that there will be paid work for every human to support themselves is already unrealistic, and clinging to it is the only thing preventing automation via cognitive tools from becoming a massive labour saver instead of the looming scary spectre many people treat it as today. Make no mistake, a policy like that will almost certainly be forced through by sheer necessity if increasing automation makes the current model unsustainable, but we need to get out ahead of it if we want to avoid a transition crisis and unhelpful widespread backlash.

[usafphantom2]

U.S. Navy puts StormBreaker smart weapon into operation on the F/A-18E/F Super Hornet

Fernando Valduga By Fernando Valduga 11/07/2023 - 14:00 In Armaments, Military

Raytheon, an RTX company, announced today that the U.S. Navy fielded the company's StormBreaker smart weapon in the F/A-18E/F Super Hornet fighter.

The StormBreaker smart weapon is a network-enabled air-surface ammunition that can hit moving targets in all weather conditions using its multi-effect warhead and triple-mode seeker.

The F/A-18 is the first aircraft approved by the U.S. Navy to carry the StormBreaker. Leveraging the field knowledge of the F-15E, Raytheon was able to reduce the number of flight tests required, saving time and resources to provide this capability to the U.S. Navy.

“The gun's unprecedented capabilities give aviators the ability to attack targets in difficult and dynamic scenarios,” said Paul Ferraro, president of Raytheon's Air Power. "StormBreaker is an excellent example of how we are using digital technologies to provide advanced aerial domain weapons, ensuring the continued relevance of fourth-generation aircraft."

StormBreaker features an innovative multimode search engine that guides the weapon using an infrared imaging camera, millimeter wave radar and semi-active laser, as well as, or with, GPS and inertial navigation system orientation. The small size of the StormBreaker allows fewer aircraft to reach the same number of targets compared to larger weapons that require multiple jets. It can also fly more than 40 miles to hit moving land and sea targets, reducing the amount of time crews spend in danger.

The U.S. Air Force declared initial operational capability for StormBreaker in the F-15E Strike Eagle in 2022, and all three variants of the F-35 Joint Strike Fighter are currently in integration tests with StormBreaker.

Tags: weaponsMilitary AviationF/A-18E/F Super HornetRaytheonStormBreakerUSN - United States Navy/U.S. Navy

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Fernando Valduga

Fernando Valduga

Aviation photographer and pilot since 1992, has participated in several events and air operations, such as Cruzex, AirVenture, Dayton Airshow and FIDAE. He has work published in specialized aviation magazines in Brazil and abroad. Uses Canon equipment during his photographic work in the world of aviation.

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[jcmarchi]

Machine learning and the microscope

New Post has been published on https://thedigitalinsider.com/machine-learning-and-the-microscope/

Machine learning and the microscope

With recent advances in imaging, genomics and other technologies, the life sciences are awash in data. If a biologist is studying cells taken from the brain tissue of Alzheimer’s patients, for example, there could be any number of characteristics they want to investigate — a cell’s type, the genes it’s expressing, its location within the tissue, or more. However, while cells can now be probed experimentally using different kinds of measurements simultaneously, when it comes to analyzing the data, scientists usually can only work with one type of measurement at a time.

Working with “multimodal” data, as it’s called, requires new computational tools, which is where Xinyi Zhang comes in.

The fourth-year MIT PhD student is bridging machine learning and biology to understand fundamental biological principles, especially in areas where conventional methods have hit limitations. Working in the lab of MIT Professor Caroline Uhler in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, and collaborating with researchers at the Eric and Wendy Schmidt Center at the Broad Institute and elsewhere, Zhang has led multiple efforts to build computational frameworks and principles for understanding the regulatory mechanisms of cells.

“All of these are small steps toward the end goal of trying to answer how cells work, how tissues and organs work, why they have disease, and why they can sometimes be cured and sometimes not,” Zhang says.

The activities Zhang pursues in her down time are no less ambitious. The list of hobbies she has taken up at the Institute include sailing, skiing, ice skating, rock climbing, performing with MIT’s Concert Choir, and flying single-engine planes. (She earned her pilot’s license in November 2022.)

“I guess I like to go to places I’ve never been and do things I haven’t done before,” she says with signature understatement.

Uhler, her advisor, says that Zhang’s quiet humility leads to a surprise “in every conversation.”

“Every time, you learn something like, ‘Okay, so now she’s learning to fly,’” Uhler says. “It’s just amazing. Anything she does, she does for the right reasons. She wants to be good at the things she cares about, which I think is really exciting.”

Zhang first became interested in biology as a high school student in Hangzhou, China. She liked that her teachers couldn’t answer her questions in biology class, which led her to see it as the “most interesting” topic to study.

Her interest in biology eventually turned into an interest in bioengineering. After her parents, who were middle school teachers, suggested studying in the United States, she majored in the latter alongside electrical engineering and computer science as an undergraduate at the University of California at Berkeley.

Zhang was ready to dive straight into MIT’s EECS PhD program after graduating in 2020, but the Covid-19 pandemic delayed her first year. Despite that, in December 2022, she, Uhler, and two other co-authors published a paper in Nature Communications.

The groundwork for the paper was laid by Xiao Wang, one of the co-authors. She had previously done work with the Broad Institute in developing a form of spatial cell analysis that combined multiple forms of cell imaging and gene expression for the same cell while also mapping out the cell’s place in the tissue sample it came from — something that had never been done before.

This innovation had many potential applications, including enabling new ways of tracking the progression of various diseases, but there was no way to analyze all the multimodal data the method produced. In came Zhang, who became interested in designing a computational method that could.

The team focused on chromatin staining as their imaging method of choice, which is relatively cheap but still reveals a great deal of information about cells. The next step was integrating the spatial analysis techniques developed by Wang, and to do that, Zhang began designing an autoencoder.

Autoencoders are a type of neural network that typically encodes and shrinks large amounts of high-dimensional data, then expand the transformed data back to its original size. In this case, Zhang’s autoencoder did the reverse, taking the input data and making it higher-dimensional. This allowed them to combine data from different animals and remove technical variations that were not due to meaningful biological differences.

In the paper, they used this technology, abbreviated as STACI, to identify how cells and tissues reveal the progression of Alzheimer’s disease when observed under a number of spatial and imaging techniques. The model can also be used to analyze any number of diseases, Zhang says.

Given unlimited time and resources, her dream would be to build a fully complete model of human life. Unfortunately, both time and resources are limited. Her ambition isn’t, however, and she says she wants to keep applying her skills to solve the “most challenging questions that we don’t have the tools to answer.”

She’s currently working on wrapping up a couple of projects, one focused on studying neurodegeneration by analyzing frontal cortex imaging and another on predicting protein images from protein sequences and chromatin imaging.

“There are still many unanswered questions,” she says. “I want to pick questions that are biologically meaningful, that help us understand things we didn’t know before.”

[ladyserenity04]

"Papel": A Gabay Guro Short Film

Analysis

The film is more like a tribute to our unsung heroes which are the teachers.

At the beginning of the short film it was seen there the teacher who just recently woke up from a buzzing and loud noise of the alarm clock and looks tired from work. In that certain part it is was also shown the mess on the top of the table with all the things like coffee mug, phone, pens, and papers. This signifies that she is about to get to work. While in rush the teacher have noticed bills left unpaid. She was caught on a heavy traffic which shows the disorder and messed up transportation system of the country. Before coming in to the classroom she keeps herself composed and professional despite the problems that she faced in the earlier part of the film. She then gave back the checked papers to her students and gave them assurance and compliments about their work.

Through the lens of multimodal and semiotic approach, identifying elements such as language, image, sound, emotions, gestures, and circumstances are essential. As you have seen in the film, on the first part of it is evident that there are cultural signifiers accompanied in the story line. These are the actual sound of car horns amidst the traffic, the loud buzzing sound of alarm clock, and the lively soft music that signifies a change of mood to positive one. On succeeding part Filipino language was being used. The entirety of this short film focuses on the image of being teacher in the Philippines. This signifies the hard work, perseverance, determination, and most especially the passion that teachers are giving to the children inside and outside the portals of academic institution.

#Semiotics

#MultimodalTexts

#ReadingVisualArts

[blogbyahad]

Advancements in Voice and Image Recognition Technologies

Natural Language Processing (NLP): NLP algorithms enhance voice recognition by understanding and interpreting human language, allowing for more accurate transcription and response generation.

Deep Learning Techniques: The use of deep learning models has significantly improved the accuracy of both voice and image recognition, enabling systems to learn from vast amounts of data and identify patterns effectively.

Real-Time Processing: Advances in computing power allow for real-time voice and image processing, enabling instant feedback and interaction, such as in virtual assistants and security systems.

Multimodal Integration: Combining voice and image recognition allows for richer user experiences, where systems can understand and respond to both spoken commands and visual inputs simultaneously.

Contextual Understanding: Modern recognition technologies leverage context to improve accuracy. For instance, voice recognition can interpret commands more effectively based on previous interactions or situational context.

Improved Accuracy in Noisy Environments: Innovations in filtering background noise and enhancing signal clarity have led to better performance of voice recognition systems in challenging environments.

Facial Recognition Advances: Image recognition technologies have become more sophisticated, allowing for accurate facial recognition that can be used in security, marketing, and social media applications.

Emotion Detection: Some advanced image and voice recognition systems can analyze facial expressions and vocal tones to assess emotional states, adding a layer of understanding in user interactions.

Accessibility Features: Voice and image recognition technologies are enhancing accessibility for individuals with disabilities, allowing for voice commands and image descriptions to improve user experiences.

Ethical Considerations: As these technologies evolve, there is a growing emphasis on ethical considerations, including privacy, consent, and the mitigation of biases in recognition systems.

These advancements in voice and image recognition technologies are transforming how we interact with devices and access information, making systems more intuitive and user-friendly.

[jclreditors1]

In the era of "Internet +," government new media plays an indispensable role in guiding positive public opinion. Despite the implementation of China’s Double Reduction policy, some local governments' responses to public opinion still need improvement. This study aimed to investigate the characteristics and patterns of the verbal and visual modes in public opinion response discourse on government new media and provide suggestions for these platforms to handle public events more effectively.

Methodology: This study was conducted using multimodal analysis of 68 microblogs responding to the Double Reduction policy from the official Weibo of People’s Daily, Xinhua News Agency and CCTV News. Based on the systemic functional grammar and visual grammar, this research explored the verbal mode in terms of ideational, interpersonal and textual metafunctions and the visual mode in terms of the representational, interactive and compositional meanings of government new media discourse.

Results: The findings indicated that verbal response from government new media mainly consisted of heading, hashtags and texts, and the visual includes images and videos. The visual mode was in line with the verbal mode to respond to public concerns at issue. The information conveyed was strengthened by the application of images or complemented by the images.

Conclusion: This study contributes to establishing the image of government authority. By capturing the potential meaning of verbal and visual modes, this study also provides some suggestions for improving the discourse quality of government new media when responding to public events.

#language #education #language_learning #translation #applied #linguistics #teaching #research

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[healthcare-skyquest]

Multimodal Imaging Market: Advancing Diagnostic Precision

The Multimodal Imaging market is revolutionizing medical diagnostics by combining multiple imaging techniques to provide comprehensive insights into patient conditions. This integrated approach enhances diagnostic accuracy and treatment planning, driving significant growth in the imaging industry. This article delves into the latest trends, market segmentation, key growth drivers, and leading companies in the multimodal imaging sector.

Market Overview

According to SkyQuest’s Multimodal Imaging Market report, the market is valued at USD 2.26 billion in 2023 and is expected to grow at a CAGR of 4.3% during the forecast period. The rise in chronic diseases, advancements in imaging technology, and increasing demand for precise diagnostics are propelling market expansion.

Request Your Free Sample: - https://www.skyquestt.com/sample-request/multimodal-imaging-market

Market Segmentation

By Modality:

PET/MRI: Combines Positron Emission Tomography (PET) with Magnetic Resonance Imaging (MRI) for detailed anatomical and functional information.

PET/CT: Integrates PET with Computed Tomography (CT) to offer comprehensive imaging for oncology and cardiology.

SPECT/CT: Merges Single Photon Emission Computed Tomography (SPECT) with CT for enhanced diagnostic capabilities in nuclear medicine.

Others: Includes hybrid modalities like PET/MR and PET/CT in various combinations for specific diagnostic needs.

By Application:

Oncology: Utilizes multimodal imaging for accurate tumor detection, staging, and treatment planning.

Cardiology: Enhances the assessment of cardiac conditions and evaluation of heart diseases.

Neurology: Provides detailed brain imaging for diagnosing neurological disorders and monitoring disease progression.

Orthopedics: Assists in the diagnosis and treatment of musculoskeletal conditions.

Others: Includes applications in trauma care, vascular imaging, and preoperative planning.

By End-User:

Hospitals: Major users of multimodal imaging systems for comprehensive diagnostic and treatment services.

Diagnostic Imaging Centers: Specialized facilities offering advanced imaging services to patients.

Research Institutions: Engage in the development and validation of new imaging technologies and applications.

Others: Includes outpatient clinics and specialized medical centers.

Read More at: - https://www.skyquestt.com/report/multimodal-imaging-market

Key Growth Drivers

Technological Advancements: Innovations in imaging technology, such as hybrid imaging systems and software, are driving market growth.

Rising Prevalence of Chronic Diseases: Increased incidence of cancer, cardiovascular diseases, and neurological disorders fuels the demand for advanced diagnostic solutions.

Growing Focus on Precision Medicine: The shift towards personalized healthcare requires detailed imaging for accurate diagnosis and tailored treatment plans.

Increase in Healthcare Spending: Enhanced investment in medical infrastructure and advanced diagnostic tools supports market expansion.

Leading Companies in the Market

SkyQuest’s report highlights key players in the Multimodal Imaging market, including:

Siemens Healthineers

GE Healthcare

Philips Healthcare

Canon Medical Systems

Hitachi Medical Systems

Toshiba Medical Systems Corporation

Hologic, Inc.

Fujifilm Holdings Corporation

Medtronic Plc

Esaote S.p.A.

Take Action Now: Secure Your Report Today - https://www.skyquestt.com/buy-now/multimodal-imaging-market

Challenges and Opportunities

The multimodal imaging market faces challenges such as high costs associated with advanced imaging systems and the need for specialized training for operators. However, opportunities lie in developing cost-effective solutions, expanding applications across various medical fields, and integrating AI to enhance imaging accuracy and efficiency.

Future Outlook

The Multimodal Imaging market is poised for robust growth, driven by continuous technological advancements and an increasing emphasis on precision medicine. Companies that innovate with new imaging modalities and focus on expanding their service offerings will lead the market. For a comprehensive analysis and strategic insights, consult SkyQuest’s Multimodal Imaging Market report.

The Multimodal Imaging market is crucial for advancing diagnostic capabilities and improving patient outcomes. As technology evolves and healthcare needs grow, multimodal imaging will play an increasingly significant role in medical diagnostics. Decision-makers in the healthcare industry should leverage these advancements to stay ahead in this dynamic market. For more detailed information, refer to SkyQuest’s in-depth Multimodal Imaging Market report.

[jc-msp-infotech]

Understanding Google Gemini: Google’s Next-Gen AI Revolution

In the rapidly evolving world of artificial intelligence, Google has consistently been at the forefront of innovation. With the recent introduction of Google Gemini, the tech giant is once again redefining the boundaries of what AI can achieve. This blog aims to provide an in-depth exploration of Google Gemini, its capabilities, and its potential impact on various industries.

What is Google Gemini?

Google Gemini is Google's latest AI model, representing a significant leap forward from its predecessors. Announced as part of Google’s broader AI strategy, Gemini is designed to be a versatile and highly advanced system that can handle a wide array of tasks with unprecedented efficiency. It integrates the latest advancements in machine learning and natural language processing to offer a more sophisticated and intuitive AI experience.

Key Features of Google Gemini

Enhanced Natural Language Understanding: Google Gemini boasts improved capabilities in understanding and generating human-like text. This means it can engage in more nuanced conversations, comprehend context better, and generate more accurate and relevant responses.

Multimodal Capabilities: Unlike traditional AI models that specialize in either text or image processing, Gemini integrates multimodal capabilities, allowing it to process and understand both text and visual information simultaneously. This opens up new possibilities for applications in areas like content creation, digital media, and interactive AI experiences.

Contextual Awareness: Gemini's advanced contextual awareness enables it to maintain coherent and contextually relevant interactions over extended conversations. This is particularly useful for applications in customer service, virtual assistants, and educational tools.

Scalability and Adaptability: Designed with scalability in mind, Gemini can be adapted to various industries and use cases. Its architecture allows for customization and fine-tuning to meet specific needs, making it a versatile tool for businesses and developers.

Ethical AI Framework: Google Gemini incorporates an ethical AI framework to ensure responsible usage. This includes measures to prevent bias, protect user privacy, and promote transparency in AI decision-making processes.

To continue reading follow below link.

[techytoolzataclick]

Latest AI Trends in 2024

There is no slowing down AI and by 2024, it has come a long way. In 2020, the AI world is currently moving under several key trends that provide fantastic opportunities for businesses and tech enthusiasts. Let us discuss the top AI trends in 2024.

1. Generative AI Advancements

Generative AI seems to be on a roll, and it has this been hot topic for some years now. Five years later in 2024 we have even more advanced generative models that can generate realistic images, videos and text. In turn, they also are in the race as powerhouse gateway to ensure customer attention and content availability by businesses thus empowering creative industries.

2. Multimodal AI Models

Blended AI is an example of how you can use multimodal techniques for building more advanced and complex ML models as illustrated below. This trend, i.e., combining text with images and audio is rising and is mainly spurred by the requirement for AI systems to play a greater role, as an assistive technology with many of the key attributes that enable complex human input and be able to interpret it more holistically than ever before. Use cases span from more advanced virtual assistants to richer, more interactive experiences.

3. Democratization of AI

Democratizing AI is to make them more accessible empowering access to a wider audience beyond experts. For 2024 — Growth of user-friendly AI platforms for individuals and small businesses to capitalize on the benefits of using AI without much technical knowledge. As more people are riding this wave, they have started leveraging AI in their day to do activities.

4. AI in the Workplace

Of course, AI has been restructuring the workplaces by eradicating automaton into work. tasks, making better decisions and increasing productivity. The tools powered by AI will only get better at the same thing, making suggestions for what you want them to do and maybe even some things that are predicted in your future during 2024. It is a change that has enabled a fast-paced business to operate more efficiently and maintain pace with an ever-changing industry, yet also moved it away from some basic tenets.

5. New Use Cases for AI

Grows new use cases as AI tech matures AI is used in 2020 for things like health companies using it for accurate diagnosis way before they appear, finance businesses with fraud protection and retail shops that will offer a next level personalized shopping experiences by 2024. These applications not only add efficiency but also improve the quality of services to customers.

6. Personalization at Scale

AI has always been the goal of personalization for businesses. Next year, B2B personalization can finally reach the level of precision and efficacy that allows businesses to target specific offers at a single consumer. Nowhere is this more apparent than in marketing, where AI has shown its greatest strengths by creating pinpointed ad campaigns that work into the hearts and minds of certain demographics.

7. Digital Humans and Digital Twinning

More 2024 Tech Predictions—Digital humans and digital twinning are trends making headway Digital humans are defined as ai avatars capable of naturalistic and engaging interaction with users, while digital twinning consists in making a virtual copy for any physical object or system. Companies are leveraging these technologies across industries, from customer service to manufacturing in order to improve user experience and operational efficiency.

8. Ethical and Regulatory Issues

Increasingly, questions of ethics and regulation are raised as AI enters further into our lives. 2024: Transparency, fairness and accountability of AI systems are getting broad attention These frameworks and guidelines, in collaboration with governments as well organizations are being developed to solve challenges pertaining bias protection of individuals privacy & security.

Conclusion

Dynamic AI Landscape 2024 From generative AI innovations to the democratization of AI tools, these movements are defining a changing philosophical landscape around technology and business. Keeping up-to-date and changing according to alterations like these could empower industries like businesses as well as make it possible for development companies use AI at their disposal.

[businesscommunicationposts]

AI Image Generation Today

Today, AI image generation capabilities have advanced significantly. Leading models like DALL-E 3, Midjourney, and Stable Diffusion continue to produce increasingly realistic and creative images from text prompts. These systems can generate a wide array of styles, from photorealistic scenes to abstract art, and have become valuable tools for artists, designers, and content creators. Recent improvements focus on enhancing image quality, increasing resolution, and providing better control over specific elements within generated images. Some models now offer features like outpainting (extending images beyond their original borders) and inpainting (selectively modifying parts of an image). The integration of AI image generation into popular software and platforms has accelerated its adoption. Many graphic design tools, social media apps, and content creation platforms now incorporate some form of AI image generation, making the technology accessible to a broader audience. Ethical and legal concerns surrounding AI-generated images persist, particularly regarding copyright issues, potential misuse for creating deepfakes, and the impact on human artists and photographers. Efforts to address these concerns include developing better detection methods for AI-generated images and establishing clearer guidelines for their use and attribution. Research continues in areas such as multimodal AI, which combines text, image, and sometimes audio understanding to create more context-aware and sophisticated image generation systems. There's also ongoing work to reduce computational requirements, making high-quality AI image generation more efficient and accessible on consumer-grade hardware. While AI image generation has made remarkable strides, it still has limitations. Complex scenes, consistent text rendering, and accurate human anatomy remain challenging in some cases. However, the rapid pace of development suggests further improvements will occur in the near future. See the brochure for Excellence in Business Communication, 14th Edition: https://lnkd.in/eCSg9rv6. Video: https://lnkd.in/eJE9K28f. How Does Your Text Compare? https://lnkd.in/et2Mvp9v. To request examination copies of Bovee and Thill's award-winning business communication textbooks (instructors only), visit https://lnkd.in/bvxGGmT.

[drmikewatts]

IEEE Transactions on Artificial Intelligence, Volume 5, Issue 8, August 2024

1) Memory Prompt for Spatiotemporal Transformer Visual Object Tracking

Author(s): Tianyang Xu;Xiao-Jun Wu;Xuefeng Zhu;Josef Kittler

Pages: 3759 - 3764

2) A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence

Author(s): Justus Renkhoff;Ke Feng;Marc Meier-Doernberg;Alvaro Velasquez;Houbing Herbert Song

Pages: 3765 - 3779

3) A Comprehensive Survey on Graph Summarization With Graph Neural Networks

Author(s): Nasrin Shabani;Jia Wu;Amin Beheshti;Quan Z. Sheng;Jin Foo;Venus Haghighi;Ambreen Hanif;Maryam Shahabikargar

Pages: 3780 - 3800

4) A Survey on Neural Network Hardware Accelerators

Author(s): Tamador Mohaidat;Kasem Khalil

Pages: 3801 - 3822

5) Efficient Structure Slimming for Spiking Neural Networks

Author(s): Yaxin Li;Xuanye Fang;Yuyuan Gao;Dongdong Zhou;Jiangrong Shen;Jian K. Liu;Gang Pan;Qi Xu

Pages: 3823 - 3831

6) A Perceptual Computing Approach for Learning Interpretable Unsupervised Fuzzy Scoring Systems

Author(s): Prashant K. Gupta;Deepak Sharma;Javier Andreu-Perez

Pages: 3832 - 3844

7) Octant Spherical Harmonics Features for Source Localization Using Artificial Intelligence Based on Unified Learning Framework

Author(s): Priyadarshini Dwivedi;Gyanajyoti Routray;Rajesh M. Hegde

Pages: 3845 - 3857

8) Additive Noise Model Structure Learning Based on Spatial Coordinates

Author(s): Jing Yang;Ting Lu;Youjie Zhu

Pages: 3858 - 3871

9) Securing User Privacy in Cloud-Based Whiteboard Services Against Health Attribute Inference Attacks

Author(s): Abdur R. Shahid;Ahmed Imteaj

Pages: 3872 - 3885

10) Complexity-Driven Model Compression for Resource-Constrained Deep Learning on Edge

Author(s): Muhammad Zawish;Steven Davy;Lizy Abraham

Pages: 3886 - 3901

11) A Deep Learning-Based Cyber Intrusion Detection and Mitigation System for Smart Grids

Author(s): Abdulaziz Aljohani;Mohammad AlMuhaini;H. Vincent Poor;Hamed M. Binqadhi

Pages: 3902 - 3914

12) Proximal Policy Optimization With Advantage Reuse Competition

Author(s): Yuhu Cheng;Qingbang Guo;Xuesong Wang

Pages: 3915 - 3925

13) Self-Supervised Forecasting in Electronic Health Records With Attention-Free Models

Author(s): Yogesh Kumar;Alexander Ilin;Henri Salo;Sangita Kulathinal;Maarit K. Leinonen;Pekka Marttinen

Pages: 3926 - 3938

14) quantile-Long Short Term Memory: A Robust, Time Series Anomaly Detection Method

Author(s): Snehanshu Saha;Jyotirmoy Sarkar;Soma S. Dhavala;Preyank Mota;Santonu Sarkar

Pages: 3939 - 3950

15) An Attention Augmented Convolution-Based Tiny-Residual UNet for Road Extraction

Author(s): Parmeshwar S. Patil;Raghunath S. Holambe;Laxman M. Waghmare

Pages: 3951 - 3964

16) Encoder–Decoder Calibration for Multimodal Machine Translation

Author(s): Turghun Tayir;Lin Li;Bei Li;Jianquan Liu;Kong Aik Lee

Pages: 3965 - 3973

17) Improving Source Tracking Accuracy Through Learning-Based Estimation Methods in SH Domain: A Comparative Study

Author(s): Priyadarshini Dwivedi;Gyanajyoti Routray;Devansh Kumar Jha;Rajesh M. Hegde

Pages: 3974 - 3984

18) Optimal Inference of Hidden Markov Models Through Expert-Acquired Data

Author(s): Amirhossein Ravari;Seyede Fatemeh Ghoreishi;Mahdi Imani

Pages: 3985 - 4000

19) X-Fuzz: An Evolving and Interpretable Neuro-Fuzzy Learner for Data Streams

Author(s): Md Meftahul Ferdaus;Tanmoy Dam;Sameer Alam;Duc-Thinh Pham

Pages: 4001 - 4012

20) Focal Transfer Graph Network and Its Application in Cross-Scene Hyperspectral Image Classification

Author(s): Haoyu Wang;Xiaomin Liu

Pages: 4013 - 4025

21) An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection

Author(s): Kun Zhu;Nana Zhang;Weiping Ding;Changjun Jiang

Pages: 4026 - 4041

22) A New Causal Inference Framework for SAR Target Recognition

Author(s): Jiaxiang Liu;Zhunga Liu;Zuowei Zhang;Longfei Wang;Meiqin Liu

Pages: 4042 - 4057

23) Distributed Optimal Formation Control of Multiple Unmanned Surface Vehicles With Stackelberg Differential Graphical Game

Author(s): Kunting Yu;Yongming Li;Maolong Lv;Shaocheng Tong

Pages: 4058 - 4073

24) Boundary-Aware Uncertainty Suppression for Semi-Supervised Medical Image Segmentation

Author(s): Congcong Li;Jinshuo Zhang;Dongmei Niu;Xiuyang Zhao;Bo Yang;Caiming Zhang

Pages: 4074 - 4086

25) Deep Transfer Learning for Detecting Electric Vehicles Highly Correlated Energy Consumption Parameters

Author(s): Zeinab Teimoori;Abdulsalam Yassine;Chaoru Lu

Pages: 4087 - 4100

26) Self-Bidirectional Decoupled Distillation for Time Series Classification

Author(s): Zhiwen Xiao;Huanlai Xing;Rong Qu;Hui Li;Li Feng;Bowen Zhao;Jiayi Yang

Pages: 4101 - 4110

27) Context-Aware Self-Supervised Learning of Whole Slide Images

Author(s): Milan Aryal;Nasim Yahya Soltani

Pages: 4111 - 4120

28) CTRL: Clustering Training Losses for Label Error Detection

Author(s): Chang Yue;Niraj K. Jha

Pages: 4121 - 4135

29) Remote Sensing Image Semantic Segmentation Based on Cascaded Transformer

Author(s): Falin Wang;Jian Ji;Yuan Wang

Pages: 4136 - 4148

30) Text-Guided Portrait Image Matting

Author(s): Yong Xu;Xin Yao;Baoling Liu;Yuhui Quan;Hui Ji

Pages: 4149 - 4162

31) An Iterative Optimizing Framework for Radiology Report Summarization With ChatGPT

Author(s): Chong Ma;Zihao Wu;Jiaqi Wang;Shaochen Xu;Yaonai Wei;Zhengliang Liu;Fang Zeng;Xi Jiang;Lei Guo;Xiaoyan Cai;Shu Zhang;Tuo Zhang;Dajiang Zhu;Dinggang Shen;Tianming Liu;Xiang Li

Pages: 4163 - 4175

32) Alternating Direction Method of Multipliers-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control

Author(s): Zilong Cheng;Jun Ma;Wenxin Wang;Zicheng Zhu;Clarence W. de Silva;Tong Heng Lee

Pages: 4176 - 4191

33) Adaptive Iterative Learning Control for Nonlinear Multiagent Systems With Initial Error Compensation

Author(s): Zhiqiang Li;Qi Zhou;Yang Liu;Hongru Ren;Hongyi Li

Pages: 4192 - 4201

34) Enhance Adversarial Robustness via Geodesic Distance

Author(s): Jun Yan;Huilin Yin;Ziming Zhao;Wancheng Ge;Jingfeng Zhang

Pages: 4202 - 4216

35) Shapley Value-Based Approaches to Explain the Quality of Predictions by Classifiers

Author(s): Guilherme Dean Pelegrina;Sajid Siraj

Pages: 4217 - 4231

36) Multistream Gaze Estimation With Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning

Author(s): Zunayed Mahmud;Paul Hungler;Ali Etemad

Pages: 4232 - 4246

37) Model-Based Online Adaptive Inverse Noncooperative Linear-Quadratic Differential Games via Finite-Time Concurrent Learning

Author(s): Jie Lin;Huai-Ning Wu

Pages: 4247 - 4257

38) Dynamic Long-Term Time-Series Forecasting via Meta Transformer Networks

Author(s): Muhammad Anwar Ma'sum;MD Rasel Sarkar;Mahardhika Pratama;Savitha Ramasamy;Sreenatha Anavatti;Lin Liu;Habibullah Habibullah;Ryszard Kowalczyk

Pages: 4258 - 4268

39) Distilled Gradual Pruning With Pruned Fine-Tuning

Author(s): Federico Fontana;Romeo Lanzino;Marco Raoul Marini;Danilo Avola;Luigi Cinque;Francesco Scarcello;Gian Luca Foresti

Pages: 4269 - 4279

40) Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial Buildings

Author(s): Zhiqiang Chen;Liang Yu;Shuang Zhang;Shushan Hu;Chao Shen

Pages: 4280 - 4292

41) Feedback Generative Adversarial Network With Channel-Space Attention for Image-Based Optimal Path Search Planning

Author(s): Tao Sun;Jian-Sheng Li;Yi-Fan Zhang;Xin-Feng Ru;Ke Wang

Pages: 4293 - 4307

[usafphantom2]

SeaGuardian remotely piloted aircraft starts operations for Japan's Coast Guard

Fernando Valduga By Fernando Valduga 10/20/22 - 15:00 in Military, UAV - UAV

The Japan Coast Guard (JCG) began flight operations using a remotely piloted MQ-9B SeaGuardian (RPA) aircraft from General Atomics Aeronautical Systems, Inc. (GA-ASI) on October 19.

JCG is operating the SeaGuardian of the Hachinohe Air Station of the Japan Maritime Self-Defense Force (JMSDF). The RPA will mainly carry out the Maritime Area Research (MWAS) on the Sea of Japan and the Pacific Ocean. Other missions will include search and rescue, disaster response and maritime law enforcement.

SeaGuardian has a multimode marine surface search radar with an Inverse Synthetic Aperture Radar (ISAR) image mode, an Automatic Identification System (AIS) receiver and a high-definition video - Full-Motion sensor equipped with optical and infrared cameras. This set of sensors allows real-time detection and identification of surface vessels in thousands of square nautical miles and provides automatic tracking of marine targets and correlation of AIS transmitters with radar bands.

The GA-ASI MQ-9B SkyGuardian and SeaGuardian are revolutionizing the long-term RPAS market, providing all-climate capacity and full compliance with STANAG-4671 (NATO UAS airworthiness standard). This feature, along with the operationally proven collision prevention radar, allows flexible operations in civil airspace.

Tags: Military AviationGeneral AtomicsJapan Coast GuardMQ-9B Sea GuardianUAS

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AI-Based Digital Pathology: Can Artificial Intelligence Transform The Future Of Pathology?

Pathology is a medical specialty that plays a pivotal role in disease diagnosis and treatment planning. However, the field is facing some key challenges due to factors like workload increasing pressures, lack of pathologists and limitations of manual microscopy. This is where artificial intelligence can help address existing gaps and enhance pathology practices. With the volume of tumor biopsy and tissue samples rising sharply, AI-powered digital pathology promises to ease diagnostics workflow and help pathologists manage workload more efficiently.

The Advent Of Whole Slide Imaging

The transition from traditional glass slides to digitalWhole Slide Imaging (WSI) technology has allowed pathology samples to be digitized, stored and examined on computer screens. WSI involves scanning glass microscope slides at high magnifications to generate large, high-resolution digital images that retain all information contained in traditional glass slides. This digitalization of pathology has laid the foundation for AI-Based Digital Pathology applications as deep learning algorithms can be trained on huge anonymized image datasets. Several studies have validated the diagnostic accuracy of digital pathology compared to conventional light microscopy.

AI Algorithms To AI-Based Digital Pathology

Using deep convolutional neural networks, AI systems are being developed that can detect various diseases by analyzing visual features in whole slide images. For example, algorithms have been created that can accurately detect cancerous regions in lung, breast or prostate tissue samples. In lymph node pathology, AI aids in detecting structures like tumor cells and diagnosing conditions like lymphoma or metastasis. Such AI tools do not aim to replace pathologists but serve as a “second opinion” to enhance diagnostic consistency and speed. They can also prioritize areas for manual review, reducing diagnosis time. As AI gains more exposure to rare disease patterns, it promises more accurate histopathological assessment.

Automating Tedious Tasks Using Computer Vision

Beyond diagnosis, AI is being applied to automate other routine tasks involved in pathology workflow. Digital image analysis tools use computer vision for functions like automated scanning of whole slides, section detection, cellular segmentation, mitosis counting in breast cancer, etc. This allows pathologists to spend more time on complex diagnostic decisions instead of time-consuming manual counting and measurements. AI systems can also standardize quantitative features extraction from digital slides for prognostic and predictive analytics. Such automated quantification holds potential to drive more consistent and data-driven clinical decision making.

Prognostic And Predictive Analytics Using Large Image Databases

With huge image repositories now available due to digital pathology adoption, AI shows promise in predictive analytics. Deep learning models can extract quantitative image features correlated to cancer prognosis when trained on large annotated datasets. For example, AI may help predict survival rates or likelihood of metastasis based on cell morphology, lesion characteristics in whole slide images. Furthermore, integration of omics data with pathology images opens up possibilities of precision oncology using multimodal AI approaches. This could support treatment stratification and facilitate clinical trials in future. However, more validation research is still needed before such AI applications enter clinical settings.

Addressing Challenges Like Data Annotations And Model Interpretability

While digital pathology and AI present immense opportunities, some challenges currently limit their widespread adoption. One key issue is the extensive effort and expertise required to annotate high-resolution whole slide images - a crucial process for training deep learning algorithms. Strategies to efficiently collect large labeled datasets continue to be explored. Interpretability of complex AI decision making is another area needing attention to gain pathologist acceptance. Development of interpretable models that can provide visualize reasoning is important. Additionally, standardization of digital pathology image formats and development of annotation/AI application platforms remain ongoing processes. With concerted research efforts, these hurdles can be overcome to make AI a integral part of pathology workflow in the near future.

There is enormous potential for artificial intelligence in digital pathology to enhance workflow efficiency, diagnostic performance as well as enable predictive and prognostic analytics. Integration of AI-based decision support tools promises to aid pathology practices facing increasing workload pressures and workforce shortages. While technical and data challenges persist, ongoing research and innovation are delivering new AI applications that align well with pathology's goal of improved healthcare. Widespread adoption of digital pathology imaging is also facilitating data-driven AI progress in this area. Continued validation studies will be important to establish generalizability before full clinical integration of AI-powered digital pathology solutions.

Get more insights on this topic:  https://www.trendingwebwire.com/ai-based-digital-pathology-how-ai-is-revolutionizing-the-field-of-digital-pathology/

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Vaagisha brings over three years of expertise as a content editor in the market research domain. Originally a creative writer, she discovered her passion for editing, combining her flair for writing with a meticulous eye for detail. Her ability to craft and refine compelling content makes her an invaluable asset in delivering polished and engaging write-ups. (LinkedIn: https://www.linkedin.com/in/vaagisha-singh-8080b91)

*Note: 1. Source: Coherent Market Insights, Public sources, Desk research 2. We have leveraged AI tools to mine information and compile it