Kylian Mbappé Ya no pensó en eso Paris Saint Germain. El delantero francés no estará en la convocatoria luis enriquePara visitar Metz. De esta forma, será el segundo partido consecutivo en el que el astro francés estará ausente. Tampoco estuvo en Niza el pasado miércoles tras perderse el entrenamiento del pasado martes por unas molestias en el bíceps femoral que no parecen ceder. Así que si no pasa nada extraño Lo normal es verle en la final de la Copa de Francia del 25 de mayo contra el Lyon.
Por este motivo, el internacional francés aprovechó para irse de vacaciones unos días con el equipo Hoyuelo de OthmanTambién descartó viajar a Cannes para disfrutar del festival en la ciudad del sur de Francia. En estas “mini” vacaciones las fotografías de ambos invadieron las redes sociales, pero una de ellas no pasó desapercibida para los internautas. El delantero francés se prepara para almorzar con Dembel y mientras charla continuamente, el delantero nota una presencia Mujer misteriosa El que ondea la bandera. Al principio Mbappé no nota su presencia, su presencia, pero al cabo de unos segundos vuelve a girar la cabeza y vuelve la cara. El gesto de sorpresa y deleite se difunde rápidamente.. Brondy parece estar enganchado a la mujer desconocida.
Mbappé y su gesto viral en una fiesta en Cannes con Dembel: ¿quién es la mujer misteriosa?
Parejas de Mbappé
El futbolista francés guarda un gran hermetismo respecto a su vida privada. Actualmente, Mbappé no tiene ninguna relación reconocida, aunque hay varias mujeres que han pasado por su vida o al menos se rumorea que mantienen una relación. Alicia Ellis El era uno de ellos. La bella modelo, Miss Francia 2017, no ocultó su amor por el francés, y fue vista en varias ocasiones dentro de algunos estadios apoyándolo en su camino hacia el título. A su llegada a París, Saint-Germain se vio envuelto en una relación de parentesco Emma Smith. Comenzaron a aparecer juntos en muchos eventos deportivos adicionales. La actriz nunca negó su relación con el “asesino” y no ocultó su deseo de verlo triunfar con el equipo parisino en 2021.
Kylian Mbappé también fue vinculado con la modelo belga, Estela Maxwell. A la rubia siempre le encantó ser el centro de atención, porque no andaba con él en vano. Rose Depp, Miley Cyrus y Kristen Stewart. La única vez que se les vio juntos fue en un evento privado en Cannes. Otra modelo belga ha sido vinculada a Mbappé. Rosa Bertram Es una de las últimas personas con las que estuvo vinculado, pero el foco mediático le causó serios problemas y por eso explotó en Instagram.
Pero fue quizás el más controvertido y famoso de todos. Olvídate de Rao. La modelo transgénero nunca confirmó la relación, pero muchos paparazzi los fotografiaron juntos en vacaciones y en diversos eventos como el Festival de Cine de Cannes en 2022.
Las personas adaptan su comportamiento a su entorno de manera tan fluida que es casi inconsciente. Ajustamos nuestra zancada cuando caminamos con tenis o tacones altos, o cambiamos nuestro peso cuando aterrizamos sobre un guijarro. Como estudiante de posgrado, Mackenzie Mathis quería saber cómo nuestro cerebro y nuestras extremidades coordinan este cambio.
En 2013, Mathis, una neurocientífica que entonces estaba en la Universidad de Harvard en Cambridge, Massachusetts, pasó meses entrenando ratones para que usaran pequeños joysticks, para poder comprender cómo el cerebro procesa señales externas sutiles. Una vez que los ratones estuvieron entrenados, introdujeron fuerzas para empujar el joystick fuera de su trayectoria y observaron cómo los dedos de los animales compensaban. Pero las herramientas computacionales disponibles sólo podían rastrear el joystick o, en el mejor de los casos, las extremidades del roedor, no sus diminutas garras.
Mientras tanto, en un laboratorio cercano de la Universidad de Harvard, el neurocientífico computacional Alexander Mattes instaló una cinta de correr especial y vertió leche con chocolate sobre su superficie. Cualquier rata que siguiera el rastro del azúcar sería recompensada y las ratas no tuvieron ningún problema. Pero le costó analizar datos sobre el movimiento de las narices sensibles de los animales, especialmente porque no podía usar tintes o marcadores que pudieran alterar su sentido del olfato. No le faltaban datos, pero analizarlos para obtener resultados significativos era una tarea desalentadora.
Cinco formas en que el aprendizaje profundo ha transformado el análisis de imágenes
Dos investigadores, dos conjuntos de datos y un problema: los algoritmos para rastrear la posición de partes del cuerpo generalmente requieren que los investigadores etiqueten miles de imágenes de entrenamiento antes de que el software pueda aplicarse a datos reales. “Al finalizar mi doctorado, me obsesioné un poco con la idea de poder hacer esto mejor”, dice Mackenzie Mathis. Los dos investigadores se conocieron en un pasillo de Harvard en 2013, cuando el asesor postdoctoral de Alexander los presentó. Se casaron en 2017 y colaboraron para solucionar su problema científico poco después. Desarrollaron este método en colaboración con colegas de otros laboratorios. Corte Profundoun conjunto de herramientas computacionales que combina una interfaz de usuario simple con un algoritmo de inteligencia artificial (IA) de aprendizaje profundo que los investigadores pueden usar para estudiar el movimiento y la postura de los animales en videos sin el uso de tintes y otros marcadores intrusivos (a. Matisse et al. naturaleza nerviosa. 21, 1281-1289; 2018). Luego, los investigadores pueden medir pequeñas diferencias en el movimiento en respuesta a estímulos para comprender mejor cómo el entorno del animal desencadena cambios de comportamiento. “Verlo en acción por primera vez fue nuestro '¡Eureka!'” “Un momento”, dice Mackenzie Mathis. “Fue uno de esos momentos inolvidables de la vida”.
Según los estándares científicos, DeepLabCut se ha generalizado. Aproximadamente dos semanas después de enviar el manuscrito, Mackenzie y Alexander estaban tomando un café en la sala de descanso cuando entró el asesor de Alexandre Matisse. ¿Viste las reseñas?”, recuerda Alexander Mathis. Un crítico calificó el manuscrito como “su artículo favorito de la década”, dice Mackenzie Mathis. “La broma es que probablemente nunca en mi vida recibiré mejores críticas para un artículo”.
Pero no fueron sólo los críticos los que estaban entusiasmados. Los investigadores también publicaron el manuscrito en el servidor de preimpresión bioRxiv. A los pocos días, notaron que docenas de usuarios lo probaban en bailarinas, geckos y calamares. En la plataforma de redes sociales Twitter (ahora llamada X), Mackenzie Mathis quedó encantada con los vídeos en escala de grises de animales con superposiciones esqueléticas con manchas de arcoíris. Los puntos de colores identifican puntos clave en los fotogramas de vídeo que los investigadores marcan para entrenar el algoritmo. DeepLabCut utiliza esas coordenadas para rastrear partes del cuerpo a lo largo del tiempo.
Más de cinco años después, el artículo DeepLabCut del equipo ha obtenido casi 3.000 citas. El propio DeepLabCut tiene más de 670.000 descargas. Fue cubierto, junto con el equipo de DeepLabCut en general, por una revista estadounidense. océano Atlánticoagencia de noticias Noticias de negocios de Bloomberg Y otros medios, incl. naturaleza (ser visto naturaleza574, 137-138; 2019). El año pasado, el trabajo de la pareja fue reconocido con el premio Eric Kandel de 100.000 dólares (108.000 dólares estadounidenses) para jóvenes neurocientíficos. El jurado del premio reconoció el trabajo como un “gran avance en las ciencias de la vida”. Esta es la primera vez que el premio se otorga a una pareja y no a un individuo, dice Mackenzie Mathis. “En junio daremos juntos la Conferencia del Premio, que será nuestra primera charla conjunta”, añade.
Mackenzie y Alexandre Mathis.Crédito: Cassandra Cerrar
Para Alexandre Matisse, la publicación supuso un cambio en el enfoque de la investigación: del seguimiento de olores a la neurociencia computacional y el aprendizaje automático. “Lo curioso es que el artículo principal sobre ese experimento no se ha publicado hasta el día de hoy”, dice. “DeepLabCut descarriló mi carrera en ese sentido; en realidad, todas nuestras carreras”.
Sin embargo, el gran éxito de DeepLabCut es, en cierto modo, sólo un eco de la carrera de Mackenzie Mathis.
Buen comienzo
Mackenzie Mathis pasó sus primeros años en el Valle Central de California, a unos 250 kilómetros al sureste de San Francisco. “El clima es cálido, hermoso y está lleno de vacas y naranjas”, dice riendo. “Fue una infancia muy interesante”. Entrenar y montar a caballo de forma competitiva fue gran parte de su adolescencia. Y también los perros. “Siempre tuve muchos perros y siempre quise enseñarles trucos”, recuerda. “Creo que siempre hubo ese entrenador de animales en mí”.
Esta instalación con animales jugará un papel importante en su carrera científica. Mathis comenzó su trabajo doctoral con una pregunta de investigación en mente. Su asesor, el neurobiólogo Naoshige Uchida de Harvard, recuerda haber quedado impresionado por su madurez científica desde la primera entrevista. “No fue una conversación entre el estudiante y el profesor, fue más bien como dos científicos hablando entre sí”, dice Uchida. La capacidad de Mathis para entrenar animales también resultó útil: era uno de los pocos miembros del laboratorio que podía entrenar ratones en la tarea del joystick, dice Uchida.
El aprendizaje profundo impulsa la revolución del seguimiento del movimiento
En 2016, mientras Mackenzie Mathis todavía realizaba su investigación doctoral, Uchida la animó a postularse para el Programa de becas Rowland, un programa altamente competitivo que la ayudaría a establecer un laboratorio independiente, sin ninguna experiencia en investigación posdoctoral, en el Instituto Rowland de la Universidad Rowland. . Universidad de Harvard, aceptada en noviembre. Después de graduarse, Mathis pasó cuatro meses en la Universidad de Tübingen en Alemania antes de regresar a Harvard para abrir su propio laboratorio en septiembre de 2017. DeepLabCut fue producto de unos meses de trabajo creativo entre abril y agosto de ese año.
Casi inmediatamente después de eso naturaleza Al publicar, los investigadores comenzaron a hacer preguntas y posibles colaboraciones. Un científico quería rastrear guepardos en una reserva natural de Sudáfrica. Los colores de los animales y el complejo entorno supusieron un desafío para DeepLabCut: era difícil reconocer los hombros, las patas y las extremidades de los animales camuflados en los bosques. Pero con algunas modificaciones para ayudar al algoritmo a reconocer el aspecto del animal, funcionó.
Los investigadores ahora han aplicado DeepLabCut a una asombrosa variedad de especies, incluidas moscas de la fruta, anguilas, ratas y caballos. Mackenzie Mathis rara vez participa en estos estudios y sólo se entera de nuevas aplicaciones cuando se publican los artículos. Pero esos avistamientos “en la naturaleza” son especialmente estimulantes, dice, porque son “pruebas de buena documentación”. Algunos de sus ejemplos favoritos han utilizado el instrumento para estudiar el comportamiento de lagartos, geckos, calamares y pulpos. Los animales que se camuflan, como los pulpos, plantean desafíos únicos para el software de seguimiento de movimiento. “Hubo un estudiante que tuiteó algunos vídeos de un pulpo en el Mar Rojo; como ser humano, ni siquiera puedes ver el pulpo hasta que se mueve”, dice Mackenzie Mathis. “Fue increíble verlo”.
Pagalo despues
A lo largo de su carrera, Mackenzie Mathis ha sido muy consciente de cuán pocas mujeres y personas de comunidades históricamente marginadas continúan en la neurociencia computacional. Para abordar esto, en 2022, los laboratorios de Mattis, actualmente en el Instituto Federal Suizo de Tecnología en Lausana, organizaron la primera Residencia de IA DeepLabCut, un curso de ocho semanas diseñado para ayudar a los investigadores principiantes de grupos subrepresentados a adquirir experiencia en DeepLabCut.
Centro Tecnológico de la Naturaleza
Una de los residentes de 2022, la neurocientífica Sabrina Peñas, que enseña en la Fundación Instituto Leloir en Buenos Aires, utilizó DeepLabCut para estudiar la memoria de objetos, que los mamíferos utilizan para explorar objetos desconocidos. Ella dice que el programa le permitió enriquecer su comprensión del programa. Pero conocer a Matisse también le dio un nuevo modelo a seguir. “Su confianza es realmente asombrosa”, dice Peñas. “Yo también quiero eso.”
Otro participante, el neurocientífico Konrad Danielowski del Instituto Nienke de Biología Experimental de Varsovia, desarrolló un caso de síndrome del impostor durante su estancia en 2023, temiendo no poder seguir el ritmo de sus compañeros. Después de ser seleccionado entre cientos de candidatos, “sientes una especie de presión para hacer lo mejor que puedas, para obtener algún resultado final de la residencia”, dice. El primer día, los hermanos Mathis llevaron a los estudiantes a almorzar. En ese momento, Mackenzie Mathis enfatizó la importancia de cada contribución, grande o pequeña, al código fuente abierto de DeepLabCut. “Muchas veces, cuando trabajas en ciencia, piensas que tu trabajo va a ser en vano”, dice Danielowski. Pero trabajar con la familia Mathis le ayudó a darse cuenta de que “también se trata de ser parte de la comunidad y ayudar a la gente. Te hace querer esforzarte”.
En última instancia, esto es lo que esperan los Mathis. Para Mackenzie Mathis, la residencia en DeepLabCut fue una forma de avanzar en el apoyo que hizo posible su carrera. Mackenzie Mathis dice que Uchida y sus otros mentores la ayudaron a desarrollar su confianza durante los primeros años de su carrera. Ahora quiere hacer lo mismo con quienes la admiran. “Realmente aprecio a las personas que alientan a los demás”, explica. “También traté de hacer esto tanto como pude”.
Eddie Winslowun sello Asuntos familiares sigue siendo una tendencia Q estrella de Sydney Recientemente publicó un video sexy de ella y Darius McCrary de 2021. Aparentemente, es posible que Darius se haya unido a Onlyfans, como “Cantante transgénero de 35 años Recurrió a X (anteriormente Twitter) para promocionar su página azul y dijo: “¿Quieres ver qué pasa después entre Eddie Winslow y yo? Suscríbete a mis únicos fans ahora.“.
La estrella de Trans OnlyFans, Sidney Starr, se ha vuelto viral por un video obsceno con el actor Darius McCrary, Ed
La publicación de Sydney Star incluye un video de 2021 que la muestra a ella y a Darius McCrary detrás del escenario en una sesión de fotos, bailando y besándose íntimamente, mientras la canción de Tank “When We” suena de fondo. Escribió en una publicación de blog en ese momento: “Aquí a mi lado está un actor legendario con experiencia en la industria del entretenimiento @dariusmccry. Un actor negro heterosexual se enfrenta a una mujer trans controvertida como yo… Somos grandes amigos y hemos hecho un gran esfuerzo para hacer de esto una lección para el mundo de que todos somos humanos, ¡pase lo que pase!
McCrary negó que estuviera saliendo con Starr y dijo que solo eran amigos, pero no está claro si había algo más en el clip que lo que bromeaba en las redes sociales. Los usuarios de las redes sociales están sorprendidos por las crecientes interacciones del actor de Family Matters con Sydney Starr. Cuando empezaron a aparecer más vídeos de esa época.. En general, Darius McCrary y Sydney Star están a punto de lanzar algún tipo de proyecto Onlyfans y las redes sociales se están preparando para ese momento.
conejito malo Una vez más se convirtió en el centro de la polémica en las redes sociales. En esta ocasión, el cantante se convirtió en la comidilla de los usuarios cuando se viralizó un impactante video en el que se puede ver al puertorriqueño disfrutando de la música. La compañía de otro hombre en un lugar amigable.. En el vídeo se ve a Bad Bunny bailando e intercambiando abrazos y besos con otra persona del mismo género en un bar de Nashville, Tennessee (EE.UU.).
Por ser una figura de talla internacional, Bad Bunny no pasa desapercibido allá donde va. De su vida amorosa destaca un fugaz romance con una modelo Kendall JennerTiene 28 años, pero con este video se puso en boca de todos debido a las especulaciones, las cuales no se hicieron esperar, pues muchos inmediatamente se preguntaron quién era el desconocido.
Bad Bunny y el video viral donde se muestra muy cariñoso con otro hombre: ¿quién era?
Sin embargo, en las redes sociales fueron los fanáticos de Bad Bunny quienes salieron a aclarar que el hombre misterioso es el hermano del cantante, Bysael. Algo que se puede comprobar En la foto que Bissell publicó en su cuenta de Instagram. Donde se le vio vistiendo una camiseta de Nashville.
¿Cuántas veces has leído los términos y condiciones, EULA y políticas de privacidad? Aunque sabemos que debemos examinar los detalles más finos, es algo que pocos de nosotros nos molestamos en hacer, ciertamente no del todo.
La organización sin fines de lucro Tax Policy Associates quería demostrar lo inútiles que eran esos documentos, por lo que en febrero de 2024 añadió una línea a su política de privacidad, ofreciendo una “botella de buen vino” a la primera persona que descubriera y se pusiera en contacto con la oferta.
Después de tres meses sin que nadie se diera cuenta de la adición, la recompensa finalmente fue encontrada por alguien que la encontró después de mirar varios ejemplos de políticas de privacidad en línea para tener una idea de cómo crear las suyas propias.
No es la primera vez
El presidente de la organización, Dan Needle, compartió la historia en X y le contó al canal bbc Fue mi “protesta infantil de que todas las empresas deberían tener una política de privacidad y nadie la lee. Cada pequeño café debería tener una política de privacidad en su sitio web, eso es una locura. Es dinero que se desperdicia”.
En su cobertura, que fue la noticia más leída del sitio, el bbc Señaló que cualquier empresa que posea datos personales, “incluidas las pequeñas empresas y las organizaciones benéficas”, debe tener una política de privacidad según el Reglamento General de Protección de Datos de 2018 (GDPR) del Reino Unido.
En realidad, esta es la segunda vez que Tax Policy Associates realiza una adición engañosa a su política de privacidad. La primera vez fueron necesarios cuatro meses para encontrarla. “Lo hicimos de nuevo para ver si la gente prestaba más atención”, dijo Needle a la BBC.
Desde entonces, la política de privacidad de la compañía cambió después del descubrimiento y ahora dice: “Sabemos que nadie está leyendo esto, porque agregamos en febrero que enviaríamos una botella de buen vino a la primera persona que se comunicara con nosotros, y solo Obtuve una respuesta en mayo.” “.
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Si se pregunta qué se consideraría una “buena” botella de vino en esta situación, la respuesta, según la BBC, es: Castillo de Sales 2013/14, Pomerol.
Nuestra experiencia continua continúa en cuanto a si alguien lee los términos y condiciones de nuestro sitio web. Ponemos esto en nuestros términos en febrero. Se acaba de reclamar. pic.twitter.com/N7k3weTuA99 de mayo de 2024
Apple today announced that it is tweaking the terms of the 0.50 euro Core Technology Fee (CTF) that apps distributed using the new EU business terms must pay, introducing a solution that would keep small apps that go viral from being bankrupt.
First, independent and small developers who earn no revenue at all will not have to pay the CTF. Students, hobbyists, and freeware app developers who distribute free apps and earn no money will not be charged the fee. Developers will need to declare their non-commercial status on an annual basis, and to maintain this status, developers must have no revenue in or out of the App Store for their app product.
Second, to address fears of the CTF causing outrageous fees for an app that suddenly goes viral, Apple has implemented a three year on-ramping process for small developers. The three year period begins when a developer agrees to the new App Store business terms, and during this time, if an app goes viral and exceeds the one million annual install threshold that triggers the CTF, the CTF won’t be charged if the developer earns less than 10 million euros in global business revenue, and the fee is reduced after that.
Under 10 million euros: No CTF during the three year period.
Between 10 million and 50 million euros: CTF must be paid, but it is capped at one million euros per year for the three year period.
Beyond 50 million euros: Benefit is no longer available, and the full CTF has to be paid.
After three years: Developers will pay for each first annual install after the initial one million first annual installs per year.
Note that this ramp up period is only available to small developers who have not previously exceeded one million first annual installs, and it is calculated based on global business revenue rather than just App Store revenue.
Apple says that 99 percent of developers will not be subject to the CTF to begin with, but the new ramp up period will go further to make sure that small developers who get a breakout hit will have time to scale their businesses before having to pay fees.
Back in March, developer Riley Testut spoke with Apple officials at a workshop on the Digital Markets Act, and he asked what would happen if a young developer had an app go viral and unwittingly racked up millions in fees. Testut asked the question because when he was a high school student, he released GBA4iOS outside of the App Store. It was unexpectedly downloaded more than 10 million times, and that would have bankrupted him had he been subject to the Core Technology Fee.
In response, Apple VP of regulatory law Kyle Andeers said that Apple was working on a solution because the company is not trying to stifle innovation. Apple believes that a free app going viral and being subject to exorbitant fees will be a rare occurrence, but the changes will keep that from happening. The CTF update will also be a welcome change for those who want to release entirely free apps outside of the App Store.
The CTF is only applicable to apps that have opted in to the new App Store business terms in the European Union. Apps in the EU are now able to be distributed through alternative app stores and developer websites without having to rely on the App Store.
A new interview with the director behind the viral Sora clip Air Head has revealed that AI played a smaller part in its production than was originally claimed.
Revealed by Patrick Cederberg (who did the post-production for the viral video) in an interview with Fxguide, it has now been confirmed that OpenAI‘s text-to-video program was far from the only force involved in its production. The 1-minute and 21-second clip was made with a combination of traditional filmmaking techniques and post-production editing to achieve the look of the final picture.
Air Head was made by ShyKids and tells the short story of a man with a literal balloon for a head. While there’s human voiceover utilized, from the way OpenAI was pushing the clip on social channels such as YouTube, it certainly left the impression that the visuals were was purely powered by AI, but that’s not entirely true.
As revealed in the behind-the-scenes clip, a ton of work was done by ShyKids who took the raw output from Sora and helped to clean it up into the finished product. This included manually rotoscoping the backgrounds, removing the faces that would occasionally appear on the balloons, and color correcting.
Then there’s the fact that Sora takes a ton of time to actually get things right. Cederberg explains that there were “hundreds of generations at 10 to 20 seconds a piece” which were then tightly edited in what the team described as a “300:1” ratio of what was generated versus what was primed for further touch-ups.
Such manual work also included editing out the head which would appear and reappear, and even changing the color of the balloon itself which would appear red instead of yellow. While Sora was used to generate the initial imagery with good results, there was clearly a lot more happening behind the scenes to make the finished product look as good as it does, so we’re still a long way out from instantly-generated movie-quality productions.
Sora remains tightly under wraps save for a handful of carefully curated projects that have been allowed to surface, with Air Head among the most popular. The clip has over 120,000 views at the time of writing, with OpenAI touting as “experimentation” with the program, downplaying the obvious work that went into the final product.
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Sora is impressive but we’re not convinced
While OpenAI has done a decent job of showcasing what its text-to-video service can do through the large language model, the lack of transparency is worrying.
Air Head is an impressive clip by a talented team, but it was subject to a ton of editing to get the final product to where it is in the short.
It’s not quite the one-click-and you-‘re-done approach that many of the tech’s boosters have represented it as. It turns out that it is merely a tool which could be used to enhance imagery instead of create from scratch, which is something that is already common enough in video production, making Sora seem less revolutionary than it first appeared.
Since ChatGPT burst onto the scene in November 2022 we’ve seen generative AI make some some startlingly human-like artistic creations – and the latest tool to go viral is Suno, an AI-powered song generator.
We’ve seen AI music generators before, from Adobe’s Project Music GenAI to YouTube’s Dream Track and Voicify AI (now Jammable). But the difference with Suno is that it can create everything, from song lyrics to vocals and instrumentation, from a simple prompt. You can even steer it towards the precise genre you want, from Delta Blues to electronic chillwave.
(Image credit: Suno)
In Suno’s new V3 model, you can now create full two-minute songs with a free account. The results can be varied, depending on which genre you choose, but Suno is capable of some seriously impressive results.
But how exactly does Suno work, who actually owns the rights to its generated music, and how can you start making your own robo-rock? We’ve answered all of this and more so you can stage-dive into the strange world of AI-generated music…
What is Suno?
Suno is a web-based, text-to-music generator that can whip up full songs in seconds from a simple text prompt. For example, tell it to make a ‘psychedelic UK garage song about a friend with a Nokia obsession’, and you’ll get a couple of two-minute songs complete with vocals, instrumentation, lyrics, a song title and even artwork.
This is all possible with the free version of Suno, although those accounts naturally come with limitations. You get a maximum of 50 credits per day, which is enough for ten songs. You also can’t use the songs commercially with a free account, so it’s very much for dabbling and or writing songs for your dog.
(Image credit: Suno)
Shell out for the Pro plan ($8 a month, around £6.30 / AU$12.20) and you get enough credits to generate 500 songs a day. You can also use the songs commercially, for example on YouTube or even uploading them to Spotify or Apple Music.
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The Premier Plan ($24 a month, around £20 / AU$38) bumps your limit up to 2,000 songs a day, which makes Bob Dylan look positively lazy. But whichever plan you’re on, you get access to all of Suno’s tools – including a ‘custom’ mode where you write your own lyrics and an ‘instrumental’ mode for crafting some new work music.
How does Suno work?
Like most generative AI tools, the precise mechanics of how Suno works are a little hazy. It isn’t yet clear what data or music the tool has been trained on – we asked Suno for clarification on this and are yet to hear back.
But more broadly, Suno works in a similar way to large language models (LLMs) like ChatGPT. Lots of training data (which in Suno’s case, includes recordings of speech) help it construct original songs and lyrics based on your prompts. With text, LLMs typically work by predicting what words are most likely to come next in a given sequence, but this is far more challenging for music.
(Image credit: Suno)
This is why Suno also uses so-called diffusion models (which power the likes of Midjourney) alongside transformer models. In an interview with Lightspeed Venture Partners Suno’s CEO and Co-Founder, Mikey Shulman, said: “Not all audio is done with transformers, there’s a lot of audio that’s done with diffusion – and these two methods have pros and cons”.
Whatever the algorithmic rumblings that are going on under Suno’s hood, it’s one of the best AI music generating engines we’ve seen (or heard) so far. Sure, the results are heavily compressed and it’s stronger at aping some genres than others, but it’s also the perfect project for a rainy weekend afternoon…
How do you use Suno?
Suno is ridiculously easy to use – perhaps worryingly so, if you currently make your money from music. Just go to the Suno website, make a free account and head to the ‘Create’ section to get started.
Here you’ll find a small box to write the description for your song. The main thing to remember is to describe the style of the music you want (in other words, the genre) plus the topic you want the song to be about. You can’t ask Suno to write something in the style of a particular artist – which is understandable, as Suno doesn’t (yet) have any licenses with labels.
We asked Suno to write a TechRadar theme song celebrating gadgets and technology in the genre of electronic chillwave – you can listen to the resulting ‘Future frequencies’ song below (or by opening the song on Suno, where you can also read its lyrics).
Not bad for a first try. It won’t win any Grammys, with its generic EDM synth sound and echoes of The Weeknd, but it’s also one of the few times where Suno pronounced the TechRadar name correctly.
Challenging Suno with more stripped-down genres produces slightly more mixed results. Our attempt at making a solo acoustic song about a ‘sad AI that yearns to be human’ sounds like a robot Phoebe Bridgers who’s been forced to write a Eurovision ballad. Suno also really struggled to write a birthday song for our friend in the style of psychedelic 90s rock.
But we have also heard some surprisingly impressive results with blues music – Rolling Stone magazine, for example, managed to whip up a delta blues track called ‘Soul of the Machine’ (below) that’s got nearly 40,000 plays on Soundcloud and sounds very much like a lo-fi recording from the Deep South.
One of the touted benefits of Suno’s latest V3 model, which was launched on March 21, is “more styles and genres”, so its versatility should start to improve over time.
It’s also possible to polish Suno’s results using other applications, like Band in a Box, to help improve the sound quality and instrumentation. Just go to the three dots in your song title, then go to ‘download’ then ‘audio’ to get the file. To extend a song, choose ‘Continue from this clip’, generate a new section, then select ‘Get whole song’ to stitch it all together.
You obviously can’t monetize the results unless you’re on one of the paid plans and you need to attribute the song to Suno as well. Of course, this opens up a bigger discussion about copyright and ownership…
Who owns the songs made with Suno?
The short answer is that you own the songs generated using Suno, as long as you’re shelling out for its Pro or Premier plans. If you’re a free user, Suno says it retains ownership of the songs you generate.
But this is different from copyright ownership. As Suno’s FAQ section says: “the availability and scope of copyright protection for content generated (in whole or in part) using artificial intelligence is a complex and dynamic area of law, which is rapidly evolving and varies among countries”.
In the US, for example, creative works that are made by AI without human involvement currently can’t be copyrighted. Text-to-music tools like Suno muddy these waters, though, which is why Suno recommends consulting an attorney if you really need the latest legal guidance on your AI-generated masterpieces.
(Image credit: Suno)
There’s also a wider debate around AI-generated content looming in the background right now. For example, the New York Times is suing OpenAI and Microsoft because it claims ChatGPT was trained on millions of its articles without its permission. Is training an AI model on someone else’s content infringing on its copyright? That’s the big unanswered question.
You may also remember the viral ‘Heart on my sleeve’ in May 2023, which was supposedly made by Drake and The Weeknd and racked up nine million views on TikTok, before it was revealed that it’d been made using AI by a user called Ghostwriter977. Cue a takedown notice from the artists’ record label, Universal Music Group, and a copyright debate that’s still rumbling on.
This is why Suno understandably doesn’t let you ask it to generate songs in the style of specific artists or use real artists’ voices. According to a Rolling Stone, Suno’s backers are aware that music labels and publishers could one day sue them, but the labels are currently staying quiet on the matter. In other words, this area is very much a case of ‘watch this space’ (while wearing a large pair of noise-cancelling headphones, if you’re Suno).
What’s next for Suno?
A glimpse of where Suno could be going is Google‘s Dream Track (below), which has collaborated with artists to allow its small number of early users to generate AI soundtracks for their YouTube Shorts.
If Suno gets the music labels on board, it could use your favorite artist as a spark to create a new AI-generated track in their style. As Suno’s CEO Mikey Shulman said in an interview with Lightspeed Venture Partners: “Let’s fast forward a few years to where the licensing climate is a little less uncertain, maybe we can let you prompt the model with a Taylor Swift song.”
The idea would be for you to pay an artist in a similar way to how sampling works now – only you’d instead be using their music as a template for a new AI-generated track.
But it’s still very early days – and with those licensing issues a long way from being ironed out, Suno is currently more a fun way to create an original birthday song for your friend rather than a fully-blown robot musician.
It also has plenty of competition from the likes of Google, Adobe and OpenAI. For now, though, Suno is one of the best tools we’ve tried for making full-blown songs, and with V4 on the horizon, we’re looking forward to seeing how it evolves.
Since Apple announced plans for the 0.50 euro Core Technology Fee that apps distributed using the new EU App Store business terms must pay, there have been ongoing concerns about what that fee might mean for a developer that suddenly has a free app go viral.
Apple’s VP of regulatory law Kyle Andeers today met with developers during a workshop on Apple’s Digital Markets Act compliance. iOS developer Riley Testut, best known for Game Boy Advance emulator GBA4iOS, asked what Apple would do if a young developer unwittingly racked up millions in fees.
Testut explained that when he was younger, that exact situation happened to him. Back in 2014 as an 18-year-old high school student, he released GBA4iOS outside of the App Store using an enterprise certificate. The app was unexpectedly downloaded more than 10 million times, and under Apple’s new rules with Core Technology Fee, Testut said that would have cost $5 million euros, bankrupting his family. He asked whether Apple would actually collect that fee in a similar situation, charging the high price even though it could financially ruin a family.
In response, Andeers said that Apple is working on figuring out a solution, but has not done so yet. He said Apple does not want to stifle innovation and wants to figure out how to keep young app makers and their parents from feeling scared to release an app. Andeers told Testut to “stay tuned” for an answer.
What we are trying to do is tear apart a model that has been integrated for 15 years. And so for 15 years, the way we’ve monetized everything was through the commission. It covered everything from technology to distribution to payment processing, and the beauty of that model is that it allowed developers to take risks. Apple only got paid if the developer got paid, and that was an incredible engine for innovation over the last 15 years. We’ve seen it go from 500 apps to more than 1.5 million.
To your point, we’ve seen kids everywhere from 8-year-olds, 9-year-olds, 10-year-olds, to teenagers come up with some amazing applications and it’s been one of the great success stories of the App Store. In terms of the Core Technology Fee and our business model, we had to change. The mandates of the DMA forced us to tear apart what we had built and price each component individually. And so we now have a fee associated with technology, tools, and services, we now have a fee associated with distribution and the services we provide through the App Store, and then we have a separate fee for payment processing if a developer wants to use it.
To your point – what is the impact on the dreamer, the kid who is just getting started. It could be a kid, it could be an adult, it could be a grandparent. We want to continue to encourage those sorts of developers. We build a store based on individual entrepreneurs, not so much catering to large corporate interests. And so we really wanted to figure out how do we solve for that.
We haven’t figured out that solution here. I fully appreciate that. We looked at the data. We didn’t see many examples of where you had that viral app or an app just took off that incurred huge costs. That said, I don’t care what the data said. We don’t care what the data said. We want people to continue to feel… and not be scared… some parents… hey, I’ve got four kids who play around with this stuff. I don’t have five million euros to pay. This is something we need to figure out, and it is something we’re working on. So I would say on that one, stay tuned.
It is not clear when Apple might come up with a solution or what that solution might be, but it sounds like the company might soon have some kind of option for these rare fringe cases when an app goes unexpectedly viral.
The 0.50 euro Core Technology Fee (CTF) that Apple is charging applies to all apps created under Apple’s new business terms, both those distributed in the App Store and those distributed outside of the App Store in the European Union. The CTF must be paid for every “first” app install over one million installs.
A free app that is distributed outside of the App Store and downloaded over a million times will owe 0.50 euros for every subsequent “first” install, aka the first time a customer downloads an app on a device each year. The fee is incurred whether or not an app charges, creating a situation where an app developer could owe Apple money without ever making a dime.
As it stands, the CTF is a major unknown for any kind of freemium or free app built under the new business terms that might go viral, effectively making it very risky to develop a free or freemium app outside of the App Store. A free or freemium app that gets two million annual “first installs” would need to pay an estimated $45,290 in fees per month, or more than half a million dollars per year, even with no money earned. That’s not a sustainable model for free apps, and freemium apps would need to earn at least 0.50 euros per user to break even.
App developers are able to continue to use Apple’s current App Store business terms instead of adopting the new terms, paying just 15 to 30 percent commission to Apple with no change. That prevents distribution outside of the App Store, and it prevents developers from using third-party alternative payment solutions in the App Store. Adopting any of the new features that Apple has implemented because of the Digital Markets Act requires opting in to the updated business terms.
Apple has been tweaking the app ecosystem rules that it introduced in the European Union based on developer feedback. Developers can now opt back in to the current App Store rules after trying out the new rules, though this is only available one time. Apple also recently did away with an app marketplace restriction that required alternative marketplaces to offer apps from any third-party developer that wanted to participate.
Third-party app stores are now able to offer apps only from their own catalog, and developers will soon be able to distribute apps directly from their websites as long as they meet Apple’s requirements. Note that all of these changes are limited to the European Union, and the App Store is operating as before in the United States and other countries.
The world of videography is currently experiencing a significant transformation, thanks to the rise of AI video tools. These tools are not just changing how videos are made; they’re also making it possible for more people to become creators. If you’re interested in the media industry, you might have noticed that AI-generated videos are bringing a new dimension to storytelling and content creation.
AI technologies in video production are becoming as important as the large language models that have reshaped text-based AI. These video AI technologies are trained on huge amounts of data, which allows them to understand and predict video sequences with amazing accuracy. For creators, this means being able to put together complex visual stories with much less effort.
Consider Runway’s Gen 2 model, which is leading the way in innovation. It can turn a single image into a full video sequence. Plus, its selective animation feature lets you animate just parts of an image, making still pictures come alive. This level of control is groundbreaking and opens up new possibilities for your creative projects.
How to make viral videos using artificial intelligence
AI video tools have also evolved to work offline, recognizing that not everyone has access to fast internet all the time. Stable Diffusion’s open-source video model is a great example of this, allowing you to work on video projects without needing to be online. With the option to change frame rates, your creative process becomes more flexible and accessible.
Here are some other articles you may find of interest on the subject of creating videos and animations using artificial intelligence:
The development of web interfaces and tools has made it easier to create AI video clips. These platforms help you produce and extend video clips with ease, making high-quality video content available to more people, not just industry professionals with expensive equipment.
However, improving video quality is still a challenge, similar to the law of diminishing returns in professional video production. Despite this, there’s a constant effort to push AI capabilities further to achieve higher resolution, frame rate, and more realistic videos.
The release of Pika Labs 1.0 model has been a milestone, much like ChatGPT’s impact in the text domain. This model allows for the creation of professional-looking AI-generated video content, enabling your videos to stand out without the need for costly resources.
Pika Labs offers a unique and powerful platform that transforms text into captivating videos. This comprehensive guide will walk you through the steps to harness the full potential of Pika, ensuring that your creative ideas are brought to life with ease and precision. Getting Started To begin, you’ll need a Discord account.
One of the most exciting things about AI video tools is how they’re making storytelling available to all. These tools give you the power to create complex visual narratives that used to be possible only for film studios. This change is making the media landscape more inclusive and diverse.
Additional factors to consider when trying to create viral videos :
However using AI is not the only consideration when making videos that you hope will go viral you also need to take into consideration other options such as :
Audience Understanding: Know your target audience. What are their interests, online behaviors, and preferences? Tailoring content to resonate with your intended viewers increases the chance of it being shared.
Content Originality and Relatability: Original content that viewers can relate to, either emotionally or through shared experiences, often has a higher chance of going viral. It should either entertain, inform, inspire, or evoke strong emotions.
Platform Specifics: Different platforms have different audiences and norms (e.g., YouTube, TikTok, Instagram). Understanding the nuances of each platform, like optimal video length and format, is crucial.
Trends and Timeliness: Capitalizing on current trends or events can boost the relevance of your video. However, ensure your content remains unique and adds a new perspective to the ongoing conversation.
High-Quality Production: While not every viral video is professionally made, ensuring clear audio and visual quality can help. The first few seconds are crucial to retain viewer interest.
Engaging and Compelling Storytelling: A clear, engaging narrative or a unique storytelling approach can make your video more memorable and shareable.
Strong Opening: Capture attention in the first few seconds. The initial part of your video should be compelling enough to hook viewers immediately.
Emotional Connection: Videos that evoke emotions, whether laughter, joy, surprise, or even outrage, are more likely to be shared.
Incorporate a Call to Action: Encourage viewers to share, like, comment, or follow. Sometimes a direct appeal can significantly increase engagement.
Optimization for Sharing: Make it easy to share your video across various platforms. Ensure the video format is compatible with different social media platforms.
Collaborations and Influencers: Collaborating with influencers or other creators can help your content reach a broader audience.
Consistency: If you’re building a channel or a brand, consistent posting can help build an audience over time, which in turn can help your videos go viral.
Analytics and Feedback: Use analytics to understand what works and what doesn’t. Viewer feedback, through comments and shares, can also provide valuable insights.
Promotion Strategy: Beyond the organic reach, consider promoting your video through ads or social media to boost its visibility.
Luck and Timing: Sometimes, virality is a matter of being in the right place at the right time with the right content.
For those working in videography, it’s crucial to adapt and learn how to use AI video tools. These tools are not just a passing trend; they’re becoming the foundation of future media creation. By adding AI to your skill set, you can stay competitive in an industry that’s evolving quickly.
The advancements in AI video tools in 2023 mark a key moment for the media industry. As you explore these tools, you’ll find new ways to express your creativity, tell stories, and connect with audiences. The future of videography is here, and AI is driving it forward.
Filed Under: Guides, Top News
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