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Adobe explora mais recursos de IA em seu app de fotografia Project Indigo

21 July 2026 at 14:39

A Adobe anunciou a adição de novos recursos temporários impulsionados por inteligência artificial (IA) ao Project Indigo, seu app de câmera exclusivo para iOS, com a ferramenta AI Playground.

Os quatro principais recursos de IA do Project Indigo incluem a manipulação de objetos (Object Editing, ou “edição de objeto”, em tradução direta), a estilização de uma foto em uma determinada técnica artística (Styles, ou “estilos”), o feedback por IA das suas fotos (Photo Guidance, ou “orientação fotográfica”) e a edição a partir de comandos de linguagem natural (Custom Edit, ou “edição customizável”).

Recurso Styles do Project Indigo da Adobe
Divulgação/Adobe

De forma opcional, a Adobe afirmou ainda que o acesso ao AI Playground estará restrito a uma parcela dos usuários do Project Indigo, sem menção aos critérios exatos de disponibilização. O teste ocorrerá durante algumas semanas e a ideia é compreender o uso de IA no app, embora ela reitere seus pilares de privacidade:

Quem aceitar participar precisará concordar em compartilhar apenas as interações com os botões do recurso. A Adobe afirma que não acessará os comandos digitados, nem as imagens, não fará upload delas para seus servidores e não as utilizará para treinar modelos de IA. Como não há login, todos os dados coletados são anônimos. Quem preferir não participar pode recusar e continuar usando o aplicativo normalmente.

Recursos como a estilização de fotos ou a remoção de objetos farão uso do modelo Nano Banana, do Google, ou dos próprios modelos Firefly da Adobe, mas ela reitera que o leque de modelos poderá se expandir no futuro.

Por fim, a Adobe explicou ainda que os modelos atuais de geração de imagens produzem apenas resultados com alcance dinâmico padrão (SDR 1Standard dynamic range, ou faixa dinâmica padrão.) e, para preservar a aparência das fotos capturadas com HDR 2High dynamic range, ou ampla faixa dinâmica. pelo Project Indigo, a empresa desenvolveu um modelo próprio de ML 3Machine learning, ou aprendizado de máquina. que converte automaticamente o resultado gerado pela IA para HDR, utilizando a foto original como referência, sem empregá-la para treinar modelos de IA.

via TechCrunch

Notas de rodapé

  • 1
    Standard dynamic range, ou faixa dinâmica padrão.
  • 2
    High dynamic range, ou ampla faixa dinâmica.
  • 3
    Machine learning, ou aprendizado de máquina.

A Slowly Aging Butterfly Could Help Scientists Understand Longevity

26 June 2026 at 13:25

A butterfly that survives for nearly a year, while its closest relatives live only two weeks, offers a potential model for studying how aging and lifespan can evolve in nature.

A University of Bristol-led study published in Nature Communications reports that some Heliconius butterflies have evolved both longer lifespans and slower physical decline, distinguishing them from most other butterfly species studied to date.

Heliconius butterflies inhabit the rainforests of Central and South America, where most adult butterflies live only a few weeks. In the study, Heliconius hewitsoni reached a maximum lifespan of 348 days, while a related species, Dione juno, only survived about 14 days under observed conditions. This represents a 25-fold difference in maximum lifespan between two closely related species.

Old Age Without the Decline

Heliconius butterflies not only live longer than their relatives, but also show slower physical decline with age. The team, in collaboration with the Smithsonian Tropical Research Institute in Panama, measured grip strength in aging butterflies to assess physical decline. Older Heliconius hecale performed as well as the younger individuals tested, showing no detectable age-related decline in grip strength.

The team combined butterfly house data, mark–recapture field studies, and controlled lab experiments. In these cases, Heliconius butterflies consistently showed longer lifespans, lower mortality rates, and slower rates of aging than closely related species that do not feed on pollen.

The Pollen Connection

One hypothesis for their extended lifespan focuses on their unusual adult diet. While most butterflies feed only on nectar, Heliconius also consumes pollen, which was thought to provide the nutritional basis for its extended lifespan.

To test this idea, researchers compared pollen-feeding Heliconius hecale with its non-pollen-feeding relative, Dryas iulia. H. hecale maintained body mass and muscle performance longer than D. iulia. Even without pollen in its diet, H. hecale still outlived its relative by a wide margin. This suggests that diet alone does not explain Heliconius’s extended lifespan.

Dr. Jessica Foley, the study’s lead author from the University of Bristol’s School of Biological Sciences, explained that insects already show a huge range of lifespans. Some, like mayflies, live only a few days, while ant and termite queens can survive for decades. “This represents a roughly 5,000-fold difference within the class, compared with around a 100-fold difference in lifespan observed in mammals.”

Heliconius butterflies are notable because they have evolved both longer lifespans and slower rates of aging, allowing them to outlive close relatives from which they diverged only recently in evolutionary history.

A New Model for an Old Problem

Most aging research uses a limited set of laboratory organisms, such as mice, worms, and fruit flies, to study biological aging mechanisms. The heliconius butterfly provides a unique opportunity to study a long-lived species closely related to short-lived species, allowing for direct comparison in a natural context. Foley noted that comparing these two groups “can help reveal how lifespan is extended, making them a highly promising new model for research into the biology of aging and longevity.”

If pollen feeding and longevity evolved together but are not entirely dependent on each other, the next step is to identify the genetic and physiological changes responsible for both traits. Understanding these mechanisms in Heliconius butterflies could provide insight into how aging evolves across species.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

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