Animation has revealed the first Trailer and Poster for the new animated take on ‘The Cat in the Hat’ featuring the voice of Bill Hader as the title character. Leading the voice cast is Barry and “Saturday Night Live” alum Bill Hader, stepping into the chaotic shoes (or paws) of the Cat himself. A couple of “things” are going on with the latest adaptation of the Dr. Seuss classic, The Cat in the Hat. Number one, the release date has been pushed back more than a whole eight months, possibly to avoid competition with Disney/Pixar’s highly anticipated Hoppers. Number two, we don’t know if the new 2026 version will be any good. Netflix’s new star-studded murder mystery movie has become a global smash hit on streaming shortly after its release this past weekend.
Consider naming your dog after this popular Hawaiian coffee and tea company. This can be the perfect puppy name for your chocolate-colored dog–or if you’re a big fan of Coco Chanel. This is the feminine form of Cassius, meaning “cinnamon,” so it might be a good name for dogs with brown fur or a spicy-yet-sweet personality. The name of a luxury English automobile, Bentley is a unique dog name reserved for the poshest of pups. One of the Social Security Administration’s top 10 baby names of recent years and a unique dog name. Name her after Amelia Earhart, famous aviator of the 1930s.
Buddy is another term for “friend” or “companion,” and this makes it a suitable name for any canine pet. It’s one of the best names for dogs who always accompany their owners anywhere they go. The name Zoe has Greek origins, which means “life,” making it a great name for a female dog who is the life of the party or gives meaning to their pet owner’s life. It adds a depth of flavor to food, which makes it an ideal name for girl dogs who are bold and lively.
It’s hard enough to come up with a bunch of names on the spot, but finding a name that both sounds good and fits your dog’s personality? At Letusbark.com, we are passionate about everything related to our furry companions. From the smallest tail-wagging pup to the largest gentle giant, we celebrate the joy, love, and companionship dogs bring into our lives. Choosing the right name for your dog is a decision that carries meaning and significance.
It’s also a fitting name for dogs who love to gnaw on toys. Cannolis might be hard on the outside, but they’re soft and sweet on the inside. This could be a creative dog name for a rough-looking pup who’s actually a softy. This could be a funny dog name for a small pup who’s perfectly sweet and cuddly.
The world’s smartest dog, a Border Collie, had a vocabulary of thousands of words. Short for Benjamin, one of the Social Security Administration’s top male names in recent years. The French love their bread, and this quintessentially French loaf is a good name for any dog. It’s also an important decision since your new dog will have this name for the rest of its life.
Many pups are satisfied and happy to please their owners and spend time with them. Theo is a shorter version of Theodore and a friendlier and more fun name for male dogs. It can suit all dogs with various temperaments; they can be well-behaved and refined or playful and adventurous. Lucky is another popular dog name among pet owners because many themselves are fortunate for having a loyal addition to their families. It’s also a great name for rescue dogs who are given a second chance. Hunter is a name given to male dogs who love to explore and practice their predatory nature, like hunting for treasures and treats in their homes.
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Particularly, dogs with white paws that run up to their legs are suitable to be named Boots because they appear like they’re wearing footwear. Toto is a popular character from The Wizard of Oz, who is played by an adorable Cairn Terrier. Toto is portrayed as a brave and loyal companion who remains resilient and determined despite the challenges. It means “friend” or “companion,” making it a perfect name for any canine pet.
Naming trends often spike dramatically in response to hit movies, viral TV shows, trending music, celebrity news, and major sporting events. Owners connect with characters, artists, or public figures and choose names that reflect these interests. Jackson literally means “son of Jack,” but you could also pick this creative dog name inspired by Jackson Pollock, the abstract painter.
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AI has been creating art since the 1970s: the evolution of a paradox
The AI artist can continuously adapt to the preferences of its collectors, modifying the aesthetics of its works based on feedback from its community of over 5,000 participants. To ensure generative AI serves society without undermining creators, we need new legal and ethical frameworks that address these challenges head-on. Only by evolving beyond traditional fair use can we strike a balance between innovation and protecting the rights of those who fuel creativity. The fair use doctrine was designed for specific, limited scenarios—not for the large-scale, automated consumption of copyrighted material by generative AI.
Over the past few decades, advances in information technologies have allowed firms to move from decision-making on the basis of intuition and experience to more automated and data-driven methods. As a result, businesses have seen efficiency gains, substantial cost reductions, and improved customer service. For one project, our artists drew the main character from every single pose and angle, a handful of background characters and four buildings. Then we can go and make a whole city out of that, and it retains the artist’s style,” said Trillo. “It allows us to do this world building and iterating faster, rather than having the artists do each and every thing.” This isn’t overly shocking when you realize that most of these datasets are crafted by using AI or some related online tool.
The person devising the dataset tells the AI or tool to generate tons and tons of personas and store them in a dataset. The surprise for many is that the number of AI personas in these datasets is usually in millions or billions of instances. You don’t have to be dogmatic about using the AI personas strictly as specified in the datasets. When AI-generated content competes with human creators, courts are unlikely to view its use of copyrighted material as fair. This process turns a chaotic data ecosystem into something that can be queried with precision.
Why does AI art screw up hands and fingers?.
Posted: Wed, 15 Jan 2025 08:00:00 GMT [source]
You can invoke multiple AI personas and use just the one from the dataset as the core baseline. Another equally fine approach consists of describing the overall nature of a persona that you want to have invoked. On one side, it invites us to celebrate innovation and the expansion of creativity; on the other, it forces us to confront the limits of our definition of what creation itself means. Perhaps it’s not about determining whether all this is good or bad but about learning to live with a future where these questions will remain open.
And lastly, the biggest concern is that some fear that generative AI might replace human jobs in creative fields. A commonly referenced method of custom-model training is creating LoRAs, which refers to low-rank adaptation. Sources suggested that an IP or specific project could involve creating and applying a set of distinct LoRAs, such as one for a specific character and another for the animation style. I am going to look at one called FinePersonas and another dataset known as PersonaHub. The datasets that provide AI personas are pretty much all relatively similar. The typical format is a spreadsheet-like structure that houses the AI persona descriptions.
In the film and gaming industries, generative AI creates realistic characters, landscapes, and animations. AI-generated music is also used for background scores and soundtracks. Generative AI meaning can be defined as a type of artificial intelligence that is used to create content. It differs from traditional AI models, which are typically used to recognise patterns or make predictions.
Governments and organizations will likely establish regulations to address ethical and legal concerns. The term “generative” comes from the word “generation,” meaning the creation or production of something. Essentially, generative AI enables machines to simulate creativity and produce outputs that closely resemble human-made content. Companies face a variety of complex challenges in designing and optimizing their supply chains. Increasing their resilience, reducing costs, and improving the quality of their planning are just a few of them.
Conventional spreadsheet skills are usually all that you need to know. While fair use—a legal framework allowing limited use of copyrighted material without permission—has long been a pillar of creativity and innovation, applying it to generative AI is fraught with legal and ethical challenges. We can use retrieval + generative technology; grounded on our ontologies and known prior knowledge, to assist in this interrogation. We can begin to identify gaps in our knowledge, areas of contradiction, or create focus and reduce unnecessary duplication.
This technology can help synthesise information into insights you can use, making sense of your data, connecting dots and highlighting patterns that would be impossible for humans to identify alone. Data Engineering is the discipline that takes raw, unstructured data and transforms it into actionable, high-value insights. Without a strong data foundation, the $10M average that 1 in 3 enterprises are spending on AI projects next year alone, are setting themselves up for failure. Generative AI is a new and cutting-edge technology that is changing the way we create and consume content.
Fair use traditionally applies to specific, limited uses—not wholesale ingestion of copyrighted content on a global scale. Yet even with the positives described above, fine-tuning for content creation still holds a plausible degree of ethical and legal risk for studios. Likewise, even as a few AI studios and independent creators pursue new methods, sources told VIP+ the major traditional studios still see legal and consumer backlash risks as reasons not to use AI for consumer-facing content. These studio teams see fine-tuning as a way of executing on original IP developed in-house. Sources reflected that training custom models speeded and scaled artistic output while remaining visually consistent with the original IP or project.
Sources described this process being done and seen as creatively viable for animation. In-house artists or animators develop a “core set” of original concept art representative of the original character or project. These assets form the dataset used to train any foundation image or video model the studio prefers (e.g., Stable Diffusion). The resulting fine-tuned model can then be used to drive subsequent content creation, whether producing outputs that replicate the studio’s specific characters or an aesthetic style present in the art assets. Generative AI is powered by advanced algorithms and machine learning techniques.
For others, if you are conducting a subject-based study and want to have a swath of AI personas, or if you are unsure of what AI persona you want to invoke, these datasets can be quite valuable. Indeed, any kind of large-scale testing of AI or using AI to generate lots of outputs of synthetic data can be streamlined by leveraging an AI persona dataset. That being said, I don’t want to seemingly diminish the heroic and thankful effort of those who put together these datasets. There is admittedly more elbow grease and hard work that goes into establishing a useful and usable personas dataset.
The use cases for generative range over various topics, from writing to art and marketing to healthcare. One important thing to keep in mind is that it must be used responsibly, like any other AI tool. We can make the most of generative AI by understanding its meaning, workings, and implications. “No scraped data will be part of the pipeline once that becomes available,” said Trillo.
Everyone is enamoured with generative AI and state-of-the-art model releases, often overlooking that it’s the data foundation that will make or break your use case (& the relative investment you’ve made). In today’s column, I showcase a novel twist on the prompting of personas when using generative AI and large language models (LLMs). You conventionally enter a prompt describing the persona you want AI to pretend to be (it’s all just a computational simulation, not somehow sentience). Well, good news, you no longer need to concoct a persona depiction out of thin air.
• Automated writing tools might undercut opportunities for professional writers. • AI-generated text might reorganize or paraphrase existing content without offering unique insights or value. While these factors have worked well in traditional scenarios like criticism, parody or education, generative AI presents unique challenges that stretch these boundaries. Generative AI has been making headlines for it’s potential to revolutionise the way we think,work and solve problems, with McKinsey projecting it will contribute up to $4.4 trillion dollars to the global economy annually.
Yet the prospect of using generative AI for animation still poses bigger-picture ethical and legal challenges for the industry. No need to derive AI personas from scratch when you can leisurely and conveniently lean into an AI persona dataset. Of course, this is based simply on the numerous speeches, written materials, and other collected writings that suggest what he was like. The AI has pattern-matched computationally on those works and mimics what Lincoln’s tone and remarks might be.
In an amazing flair, the AI seemingly responds as we assume Lincoln might have responded. These cases underscore the difficulty of applying traditional fair use principles to generative AI’s large-scale, automated processes. The answer depends on whether the AI’s use of copyrighted material satisfies the fair use criteria, and in most cases, it does not. • An AI art generator might create an image resembling a copyrighted painting. Generative AI has emerged as a transformative force in technology, creating text, art, music and code that can rival human efforts.
Why AI art will always kind of suck.
Posted: Thu, 23 May 2024 07:00:00 GMT [source]
In those two examples, I used first a physics teacher and then an art teacher. I might want to run through a wider range of teachers that cover a variety of academic specialties. I then used that text in a prompt and got AI to pretend to be that persona.
AI has been creating art since the 1970s: the evolution of a paradox
The AI artist can continuously adapt to the preferences of its collectors, modifying the aesthetics of its works based on feedback from its community of over 5,000 participants. To ensure generative AI serves society without undermining creators, we need new legal and ethical frameworks that address these challenges head-on. Only by evolving beyond traditional fair use can we strike a balance between innovation and protecting the rights of those who fuel creativity. The fair use doctrine was designed for specific, limited scenarios—not for the large-scale, automated consumption of copyrighted material by generative AI.
Over the past few decades, advances in information technologies have allowed firms to move from decision-making on the basis of intuition and experience to more automated and data-driven methods. As a result, businesses have seen efficiency gains, substantial cost reductions, and improved customer service. For one project, our artists drew the main character from every single pose and angle, a handful of background characters and four buildings. Then we can go and make a whole city out of that, and it retains the artist’s style,” said Trillo. “It allows us to do this world building and iterating faster, rather than having the artists do each and every thing.” This isn’t overly shocking when you realize that most of these datasets are crafted by using AI or some related online tool.
The person devising the dataset tells the AI or tool to generate tons and tons of personas and store them in a dataset. The surprise for many is that the number of AI personas in these datasets is usually in millions or billions of instances. You don’t have to be dogmatic about using the AI personas strictly as specified in the datasets. When AI-generated content competes with human creators, courts are unlikely to view its use of copyrighted material as fair. This process turns a chaotic data ecosystem into something that can be queried with precision.
Why does AI art screw up hands and fingers?.
Posted: Wed, 15 Jan 2025 08:00:00 GMT [source]
You can invoke multiple AI personas and use just the one from the dataset as the core baseline. Another equally fine approach consists of describing the overall nature of a persona that you want to have invoked. On one side, it invites us to celebrate innovation and the expansion of creativity; on the other, it forces us to confront the limits of our definition of what creation itself means. Perhaps it’s not about determining whether all this is good or bad but about learning to live with a future where these questions will remain open.
And lastly, the biggest concern is that some fear that generative AI might replace human jobs in creative fields. A commonly referenced method of custom-model training is creating LoRAs, which refers to low-rank adaptation. Sources suggested that an IP or specific project could involve creating and applying a set of distinct LoRAs, such as one for a specific character and another for the animation style. I am going to look at one called FinePersonas and another dataset known as PersonaHub. The datasets that provide AI personas are pretty much all relatively similar. The typical format is a spreadsheet-like structure that houses the AI persona descriptions.
In the film and gaming industries, generative AI creates realistic characters, landscapes, and animations. AI-generated music is also used for background scores and soundtracks. Generative AI meaning can be defined as a type of artificial intelligence that is used to create content. It differs from traditional AI models, which are typically used to recognise patterns or make predictions.
Governments and organizations will likely establish regulations to address ethical and legal concerns. The term “generative” comes from the word “generation,” meaning the creation or production of something. Essentially, generative AI enables machines to simulate creativity and produce outputs that closely resemble human-made content. Companies face a variety of complex challenges in designing and optimizing their supply chains. Increasing their resilience, reducing costs, and improving the quality of their planning are just a few of them.
Conventional spreadsheet skills are usually all that you need to know. While fair use—a legal framework allowing limited use of copyrighted material without permission—has long been a pillar of creativity and innovation, applying it to generative AI is fraught with legal and ethical challenges. We can use retrieval + generative technology; grounded on our ontologies and known prior knowledge, to assist in this interrogation. We can begin to identify gaps in our knowledge, areas of contradiction, or create focus and reduce unnecessary duplication.
This technology can help synthesise information into insights you can use, making sense of your data, connecting dots and highlighting patterns that would be impossible for humans to identify alone. Data Engineering is the discipline that takes raw, unstructured data and transforms it into actionable, high-value insights. Without a strong data foundation, the $10M average that 1 in 3 enterprises are spending on AI projects next year alone, are setting themselves up for failure. Generative AI is a new and cutting-edge technology that is changing the way we create and consume content.
Fair use traditionally applies to specific, limited uses—not wholesale ingestion of copyrighted content on a global scale. Yet even with the positives described above, fine-tuning for content creation still holds a plausible degree of ethical and legal risk for studios. Likewise, even as a few AI studios and independent creators pursue new methods, sources told VIP+ the major traditional studios still see legal and consumer backlash risks as reasons not to use AI for consumer-facing content. These studio teams see fine-tuning as a way of executing on original IP developed in-house. Sources reflected that training custom models speeded and scaled artistic output while remaining visually consistent with the original IP or project.
Sources described this process being done and seen as creatively viable for animation. In-house artists or animators develop a “core set” of original concept art representative of the original character or project. These assets form the dataset used to train any foundation image or video model the studio prefers (e.g., Stable Diffusion). The resulting fine-tuned model can then be used to drive subsequent content creation, whether producing outputs that replicate the studio’s specific characters or an aesthetic style present in the art assets. Generative AI is powered by advanced algorithms and machine learning techniques.
For others, if you are conducting a subject-based study and want to have a swath of AI personas, or if you are unsure of what AI persona you want to invoke, these datasets can be quite valuable. Indeed, any kind of large-scale testing of AI or using AI to generate lots of outputs of synthetic data can be streamlined by leveraging an AI persona dataset. That being said, I don’t want to seemingly diminish the heroic and thankful effort of those who put together these datasets. There is admittedly more elbow grease and hard work that goes into establishing a useful and usable personas dataset.
The use cases for generative range over various topics, from writing to art and marketing to healthcare. One important thing to keep in mind is that it must be used responsibly, like any other AI tool. We can make the most of generative AI by understanding its meaning, workings, and implications. “No scraped data will be part of the pipeline once that becomes available,” said Trillo.
Everyone is enamoured with generative AI and state-of-the-art model releases, often overlooking that it’s the data foundation that will make or break your use case (& the relative investment you’ve made). In today’s column, I showcase a novel twist on the prompting of personas when using generative AI and large language models (LLMs). You conventionally enter a prompt describing the persona you want AI to pretend to be (it’s all just a computational simulation, not somehow sentience). Well, good news, you no longer need to concoct a persona depiction out of thin air.
• Automated writing tools might undercut opportunities for professional writers. • AI-generated text might reorganize or paraphrase existing content without offering unique insights or value. While these factors have worked well in traditional scenarios like criticism, parody or education, generative AI presents unique challenges that stretch these boundaries. Generative AI has been making headlines for it’s potential to revolutionise the way we think,work and solve problems, with McKinsey projecting it will contribute up to $4.4 trillion dollars to the global economy annually.
Yet the prospect of using generative AI for animation still poses bigger-picture ethical and legal challenges for the industry. No need to derive AI personas from scratch when you can leisurely and conveniently lean into an AI persona dataset. Of course, this is based simply on the numerous speeches, written materials, and other collected writings that suggest what he was like. The AI has pattern-matched computationally on those works and mimics what Lincoln’s tone and remarks might be.
In an amazing flair, the AI seemingly responds as we assume Lincoln might have responded. These cases underscore the difficulty of applying traditional fair use principles to generative AI’s large-scale, automated processes. The answer depends on whether the AI’s use of copyrighted material satisfies the fair use criteria, and in most cases, it does not. • An AI art generator might create an image resembling a copyrighted painting. Generative AI has emerged as a transformative force in technology, creating text, art, music and code that can rival human efforts.
Why AI art will always kind of suck.
Posted: Thu, 23 May 2024 07:00:00 GMT [source]
In those two examples, I used first a physics teacher and then an art teacher. I might want to run through a wider range of teachers that cover a variety of academic specialties. I then used that text in a prompt and got AI to pretend to be that persona.