Today, Amazon EBS announced a new latency injection action in AWS Fault Injection Service (FIS), a fully managed service for running fault injection experiments. You can now use this action to inject I/O latency on your volumes as part of a controlled testing experiment to understand how your mission-critical applications respond to storage faults. With the new fault action, you can test your architecture against elevated storage latency, allowing you to observe application behavior and fine-tune your monitoring and recovery processes to ensure high availability.

EBS volumes are designed to meet the needs of highly available, latency-sensitive applications such as Oracle, SAP HANA, and Microsoft SQL Server. The latency injection action simulates degraded I/O performance on your volume to replicate the real-world signals, such as Amazon CloudWatch alarms and operating system timeouts, that occur during storage performance issues. Using this action, you can build confidence that your application can withstand and quickly recover from disruptions that cause high I/O latency on your EBS volume. To get started, you can directly use the pre-defined latency injection experiment templates available in the EBS and FIS consoles. Alternatively, you can customize these experiment templates or create your own experiment templates to meet your application-specific testing needs. You can integrate these latency injection experiments into your existing chaos engineering tests, continuous integration, and release testing, as well as combine multiple FIS actions in one experiment.

This new action is available in all AWS Regions where AWS FIS is available. To learn more, visit the EBS FIS actions user guide.

​Today, Amazon EBS announced a new latency injection action in AWS Fault Injection Service (FIS), a fully managed service for running fault injection experiments. You can now use this action to inject I/O latency on your volumes as part of a controlled testing experiment to understand how your mission-critical applications respond to storage faults. With the new fault action, you can test your architecture against elevated storage latency, allowing you to observe application behavior and fine-tune your monitoring and recovery processes to ensure high availability. EBS volumes are designed to meet the needs of highly available, latency-sensitive applications such as Oracle, SAP HANA, and Microsoft SQL Server. The latency injection action simulates degraded I/O performance on your volume to replicate the real-world signals, such as Amazon CloudWatch alarms and operating system timeouts, that occur during storage performance issues. Using this action, you can build confidence that your application can withstand and quickly recover from disruptions that cause high I/O latency on your EBS volume. To get started, you can directly use the pre-defined latency injection experiment templates available in the EBS and FIS consoles. Alternatively, you can customize these experiment templates or create your own experiment templates to meet your application-specific testing needs. You can integrate these latency injection experiments into your existing chaos engineering tests, continuous integration, and release testing, as well as combine multiple FIS actions in one experiment. This new action is available in all AWS Regions where AWS FIS is available. To learn more, visit the EBS FIS actions user guide.  

Amazon S3 now supports conditional deletes in S3 general purpose buckets, which verify that an object is unchanged before deleting it. This helps you to prevent accidental deletions in high-concurrency, multiple-writer scenarios.

You can now perform conditional deletes using the HTTP if-match header with an ETag value. Amazon S3 will only allow your delete request to succeed if the Etag provided matches that of the object. Additionally, you can use the s3:if-match condition key in your S3 bucket policies to enforce conditional delete operations. For example, you can require clients to use the HTTP if-match header in both S3 DeleteObject and S3 DeleteObjects API requests, helping you to minimize the risk of accidentally deleting objects in your bucket.

Conditional deletes are available in S3 general purpose buckets at no additional cost in all AWS Regions. You can use the Amazon S3 API, SDKs, and CLI to perform conditional deletes. To learn more, visit the S3 User guide.

​Amazon S3 now supports conditional deletes in S3 general purpose buckets, which verify that an object is unchanged before deleting it. This helps you to prevent accidental deletions in high-concurrency, multiple-writer scenarios. You can now perform conditional deletes using the HTTP if-match header with an ETag value. Amazon S3 will only allow your delete request to succeed if the Etag provided matches that of the object. Additionally, you can use the s3:if-match condition key in your S3 bucket policies to enforce conditional delete operations. For example, you can require clients to use the HTTP if-match header in both S3 DeleteObject and S3 DeleteObjects API requests, helping you to minimize the risk of accidentally deleting objects in your bucket. Conditional deletes are available in S3 general purpose buckets at no additional cost in all AWS Regions. You can use the Amazon S3 API, SDKs, and CLI to perform conditional deletes. To learn more, visit the S3 User guide.  

Today, Amazon AppStream 2.0 announces support for Graphics G6 instances with fractionalized GPU sizes, which are built on the EC2 G6 family, designed to cater to graphics applications that need smaller GPU fractions.

Graphics G6 instances with fractionalized GPU sizes (G6f and Gr6f) allow users to utilize only the GPU resources they need, rather than provisioning full GPU instances. This approach helps enable better resource optimization through shared GPU capacity, offering flexibility to choose smaller GPU fractions (such as 1/2, 1/4, or 1/8) that align with specific workload requirements. Organizations can benefit from reduced costs by avoiding over-provisioning while maintaining access to GPU capabilities for applications that don’t require full GPU power.

These new instance types are available in 10 AWS Regions, including US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Frankfurt, London), Asia Pacific (Tokyo, Mumbai, Sydney), and South America (Sao Paulo). AppStream 2.0 offers pay-as-you-go pricing, see Amazon AppStream 2.0 Pricing for more information.

To get started, select an AppStream 2.0 Graphics G6 instances with fractionalized GPU sizes when launching an image builder or creating a new fleet. You can launch Graphics G6 instances with fractionalized GPU sizes using either the AWS management console or the AWS SDK. To learn more, see AppStream 2.0 Instance Families.

​Today, Amazon AppStream 2.0 announces support for Graphics G6 instances with fractionalized GPU sizes, which are built on the EC2 G6 family, designed to cater to graphics applications that need smaller GPU fractions. Graphics G6 instances with fractionalized GPU sizes (G6f and Gr6f) allow users to utilize only the GPU resources they need, rather than provisioning full GPU instances. This approach helps enable better resource optimization through shared GPU capacity, offering flexibility to choose smaller GPU fractions (such as 1/2, 1/4, or 1/8) that align with specific workload requirements. Organizations can benefit from reduced costs by avoiding over-provisioning while maintaining access to GPU capabilities for applications that don’t require full GPU power. These new instance types are available in 10 AWS Regions, including US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Frankfurt, London), Asia Pacific (Tokyo, Mumbai, Sydney), and South America (Sao Paulo). AppStream 2.0 offers pay-as-you-go pricing, see Amazon AppStream 2.0 Pricing for more information. To get started, select an AppStream 2.0 Graphics G6 instances with fractionalized GPU sizes when launching an image builder or creating a new fleet. You can launch Graphics G6 instances with fractionalized GPU sizes using either the AWS management console or the AWS SDK. To learn more, see AppStream 2.0 Instance Families.  

Today, Amazon Elastic Kubernetes Service (EKS) announced a new catalog of community add-ons that includes metrics-server, kube-state-metrics, cert-manager, prometheus-node-exporter, fluent-bit, and external-dns. This enables you to easily find, select, configure, and manage popular open-source Kubernetes add-ons directly through EKS. Each add-on has been packaged, scanned, and validated for compatibility by EKS, with container images securely hosted in an EKS-owned private Amazon Elastic Container Registry (ECR) repository.

To make Kubernetes clusters production-ready, you need to integrate various operational tools and add-ons. These add-ons can come from various sources including AWS and open-source community repositories. Now, EKS makes it easy for you to access a broader selection of add-ons, providing a unified management experience for AWS and community add-ons. You can view available add-ons, compatible versions, configuration options, and install and manage them directly through the EKS Console, API, CLI, eksctl, or IaC tools like AWS CloudFormation.

This feature is available in all AWS GovCloud (US) Regions. To learn more visit the EKS documentation.

​Today, Amazon Elastic Kubernetes Service (EKS) announced a new catalog of community add-ons that includes metrics-server, kube-state-metrics, cert-manager, prometheus-node-exporter, fluent-bit, and external-dns. This enables you to easily find, select, configure, and manage popular open-source Kubernetes add-ons directly through EKS. Each add-on has been packaged, scanned, and validated for compatibility by EKS, with container images securely hosted in an EKS-owned private Amazon Elastic Container Registry (ECR) repository. To make Kubernetes clusters production-ready, you need to integrate various operational tools and add-ons. These add-ons can come from various sources including AWS and open-source community repositories. Now, EKS makes it easy for you to access a broader selection of add-ons, providing a unified management experience for AWS and community add-ons. You can view available add-ons, compatible versions, configuration options, and install and manage them directly through the EKS Console, API, CLI, eksctl, or IaC tools like AWS CloudFormation. This feature is available in all AWS GovCloud (US) Regions. To learn more visit the EKS documentation.  

Aurora PostgreSQL Limitless Database, now available in AWS GovCloud (US-East, US-West) Regions, makes it easy for you to scale your relational database workloads by providing a serverless endpoint that automatically distributes data and queries across multiple Amazon Aurora Serverless instances while maintaining the transactional consistency of a single database. Aurora PostgreSQL Limitless Database offers capabilities such as distributed query planning and transaction management, removing the need for you to create custom solutions or manage multiple databases to scale. As your workloads increase, Aurora PostgreSQL Limitless Database adds additional compute resources while staying within your specified budget, so there is no need to provision for peak, and compute automatically scales down when demand is low.

Aurora PostgreSQL Limitless Database is available with PostgreSQL 16.6, 16.8, and 16.9 compatibility in these regions.

For pricing details and Region availability, visit Amazon Aurora pricing. To learn more, read the Aurora PostgreSQL Limitless Database documentation and get started by creating an Aurora PostgreSQL Limitless Database in only a few steps in the Amazon RDS console.

​Aurora PostgreSQL Limitless Database, now available in AWS GovCloud (US-East, US-West) Regions, makes it easy for you to scale your relational database workloads by providing a serverless endpoint that automatically distributes data and queries across multiple Amazon Aurora Serverless instances while maintaining the transactional consistency of a single database. Aurora PostgreSQL Limitless Database offers capabilities such as distributed query planning and transaction management, removing the need for you to create custom solutions or manage multiple databases to scale. As your workloads increase, Aurora PostgreSQL Limitless Database adds additional compute resources while staying within your specified budget, so there is no need to provision for peak, and compute automatically scales down when demand is low. Aurora PostgreSQL Limitless Database is available with PostgreSQL 16.6, 16.8, and 16.9 compatibility in these regions. For pricing details and Region availability, visit Amazon Aurora pricing. To learn more, read the Aurora PostgreSQL Limitless Database documentation and get started by creating an Aurora PostgreSQL Limitless Database in only a few steps in the Amazon RDS console.  

Amazon Lex now allows you to leverage large language models (LLMs) to improve the natural language understanding of your deterministic conversational AI bots in eight new languages: Chinese, Japanese, Korean, Portuguese, Catalan, French, Italian, and German. With this capability, your voice- and chat-bots can better handle complex utterances, maintain accuracy despite spelling errors, and extract key information from verbose inputs to fulfill the customer’s request. For example, a customer could say ‘Hi I want to book a flight for my wife, my two kids and myself’, and the LLM will properly identify to book flight tickets for four people.

This feature is available in 10 commercial AWS Regions where Amazon Connect is available: Europe (Ireland), Europe (Frankfurt), US East (N. Virginia), Asia Pacific (Seoul), Europe (London), Asia Pacific (Tokyo), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central). To learn more about this feature, visit Amazon Lex documentation or to learn how Amazon Connect and Amazon Lex deliver cloud-based conversational AI experiences for contact centers, please visit the Amazon Connect website.

​Amazon Lex now allows you to leverage large language models (LLMs) to improve the natural language understanding of your deterministic conversational AI bots in eight new languages: Chinese, Japanese, Korean, Portuguese, Catalan, French, Italian, and German. With this capability, your voice- and chat-bots can better handle complex utterances, maintain accuracy despite spelling errors, and extract key information from verbose inputs to fulfill the customer’s request. For example, a customer could say ‘Hi I want to book a flight for my wife, my two kids and myself’, and the LLM will properly identify to book flight tickets for four people.
This feature is available in 10 commercial AWS Regions where Amazon Connect is available: Europe (Ireland), Europe (Frankfurt), US East (N. Virginia), Asia Pacific (Seoul), Europe (London), Asia Pacific (Tokyo), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central). To learn more about this feature, visit Amazon Lex documentation or to learn how Amazon Connect and Amazon Lex deliver cloud-based conversational AI experiences for contact centers, please visit the Amazon Connect website.  

Today, Amazon announced the availability of detailed performance statistics for Amazon EC2 instance store NVMe volumes. This new capability delivers real-time visibility into the performance of your AWS Nitro System-based EC2 instance store NVMe volumes, making it easier to monitor storage health and quickly resolve application performance issues.

With EC2 detailed performance statistics, you can access 11 comprehensive metrics at one second granularity to monitor input/output (I/O) statistics of your locally attached NVMe volumes, including queue length measurements, IOPS, throughput, and detailed I/O latency histograms. These metrics are similar to the detailed performance statistics available for EBS volumes, providing a consistent monitoring experience across both storage types. The granular visibility provided by these metrics helps you identify specific workloads affected by performance variations, and optimize your application’s IO patterns for maximum efficiency. Additionally, the metrics include latency histograms broken down by IO size, providing even more detailed insights into performance patterns.

Detailed performance statistics for EC2 instance store NVMe volumes are available by default for all Nitro-based EC2 instances with locally attached NVMe volumes across all AWS Commercial and China Regions, at no additional charge.

To learn more about the EC2 instance store NVMe detailed performance statistics and how to access them, please visit the documentation here.

​Today, Amazon announced the availability of detailed performance statistics for Amazon EC2 instance store NVMe volumes. This new capability delivers real-time visibility into the performance of your AWS Nitro System-based EC2 instance store NVMe volumes, making it easier to monitor storage health and quickly resolve application performance issues.
With EC2 detailed performance statistics, you can access 11 comprehensive metrics at one second granularity to monitor input/output (I/O) statistics of your locally attached NVMe volumes, including queue length measurements, IOPS, throughput, and detailed I/O latency histograms. These metrics are similar to the detailed performance statistics available for EBS volumes, providing a consistent monitoring experience across both storage types. The granular visibility provided by these metrics helps you identify specific workloads affected by performance variations, and optimize your application’s IO patterns for maximum efficiency. Additionally, the metrics include latency histograms broken down by IO size, providing even more detailed insights into performance patterns. Detailed performance statistics for EC2 instance store NVMe volumes are available by default for all Nitro-based EC2 instances with locally attached NVMe volumes across all AWS Commercial and China Regions, at no additional charge.
To learn more about the EC2 instance store NVMe detailed performance statistics and how to access them, please visit the documentation here.  

AWS Storage Gateway announces Internet Protocol version 6 (IPv6) support for AWS Storage Gateway endpoints, APIs, and gateway appliance interfaces. This enhancement enables both IPv6 and IPv4 access to our new dual-stack endpoints. The existing AWS Storage Gateway endpoints supporting IPv4 only will remain available for backwards compatibility.

AWS Storage Gateway provides on-premises access to data stored in AWS storage. With this launch, customers can standardize their applications and workflows for managing their AWS Storage Gateway resources on IPv6 while maintaining backward compatibility with IPv4 clients. By using the new dual-stack capabilities in the Storage Gateway appliances, service endpoints, and APIs, customers can transition from IPv4 to IPv6 gradually without needed to switch all their networking at once.

AWS Storage Gateway support for IPv6 is available in all AWS Regions where the service is offered. To learn more, visit the AWS Storage Gateway user guide.

​AWS Storage Gateway announces Internet Protocol version 6 (IPv6) support for AWS Storage Gateway endpoints, APIs, and gateway appliance interfaces. This enhancement enables both IPv6 and IPv4 access to our new dual-stack endpoints. The existing AWS Storage Gateway endpoints supporting IPv4 only will remain available for backwards compatibility. AWS Storage Gateway provides on-premises access to data stored in AWS storage. With this launch, customers can standardize their applications and workflows for managing their AWS Storage Gateway resources on IPv6 while maintaining backward compatibility with IPv4 clients. By using the new dual-stack capabilities in the Storage Gateway appliances, service endpoints, and APIs, customers can transition from IPv4 to IPv6 gradually without needed to switch all their networking at once. AWS Storage Gateway support for IPv6 is available in all AWS Regions where the service is offered. To learn more, visit the AWS Storage Gateway user guide.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8i and R8i-flex instances are available in the Asia Pacific (Malaysia, Singapore) and Europe (Frankfurt) regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The R8i and R8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver 20% better performance than R7i instances, with even higher gains for specific workloads. They are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to R7i.

R8i-flex, our first memory-optimized Flex instances, are the easiest way to get price performance benefits for a majority of memory-intensive workloads. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources.

R8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. R8i instances offer 13 sizes including 2 bare metal and the new 96xlarge size for the largest applications. R8i instances are SAP-certified and deliver 142,100 aSAPS, the highest among all comparable machines in on-premises and cloud environments, delivering exceptional performance for mission-critical SAP workloads.

To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information about the new R8i and R8i-flex instances visit the AWS News blog.

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8i and R8i-flex instances are available in the Asia Pacific (Malaysia, Singapore) and Europe (Frankfurt) regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The R8i and R8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver 20% better performance than R7i instances, with even higher gains for specific workloads. They are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to R7i. R8i-flex, our first memory-optimized Flex instances, are the easiest way to get price performance benefits for a majority of memory-intensive workloads. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. R8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. R8i instances offer 13 sizes including 2 bare metal and the new 96xlarge size for the largest applications. R8i instances are SAP-certified and deliver 142,100 aSAPS, the highest among all comparable machines in on-premises and cloud environments, delivering exceptional performance for mission-critical SAP workloads. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information about the new R8i and R8i-flex instances visit the AWS News blog.  

• Categoría: Noticias de Microsoft

septiembre 16, 2025

Aplicabilidad frente a desplazamiento laboral: más notas sobre nuestra investigación reciente sobre IA y ocupaciones

Por: Kiran Tomlinson, investigador principal; Sonia Jaffe, investigadora principal; Will Wang;  Scott Counts, gerente principal senior de investigación; Siddharth Suri, investigador principal senior.

De manera reciente, publicamos un artículo (Trabajar con IA: Medición de las implicaciones ocupacionales de la IA generativa) que estudió qué ocupaciones podrían encontrar útiles los chatbots de IA y en qué grado. El documento provocó una discusión significativa, lo cual no es una sorpresa, ya que las personas se preocupan de manera importante por el futuro de la IA y los empleos, esa es parte de la razón por la que creemos que es esencial estudiar estos temas.

Por desgracia, no toda la discusión fue precisa en su descripción del alcance o las conclusiones del estudio. En específico, nuestro estudio no saca ninguna conclusión sobre la eliminación de empleos; en el documento, advertimos de manera explícita contra el uso de nuestros hallazgos para llegar a esa conclusión.

Dada la importancia de este tema, queremos aclarar cualquier malentendido y proporcionar un resumen más digerible del artículo, nuestra metodología y sus limitaciones.

¿Qué encontró nuestra investigación?

Nos propusimos comprender mejor cómo las personas usan la IA, destacamos dónde la IA podría ser útil en diferentes ocupaciones. Para hacer esto, analizamos cómo las personas usan en la actualidad la IA generativa, en específico Microsoft Bing Copilot (ahora Microsoft Copilot), para ayudar con las tareas. Luego comparamos estos conjuntos de tareas con la base de datos O*NET, un sistema de clasificación ocupacional utilizado de manera amplia, para comprender la posible aplicabilidad a varias ocupaciones.

Descubrimos que la IA es más útil para tareas relacionadas con el trabajo del conocimiento y la comunicación, en particular tareas como escribir, recopilar información y aprender.

Aquellos en ocupaciones con estas tareas pueden beneficiarse al considerar cómo se puede usar la IA como una herramienta para ayudar a mejorar sus flujos de trabajo. Por otro lado, no es sorprendente que las tareas físicas como realizar cirugías o mover objetos tuvieran una aplicabilidad menos directa de chatbot de IA.

Entonces, para resumir, nuestro artículo trata de identificar las ocupaciones en las que la IA puede ser más útil, al ayudar o realizar subtareas. Nuestros datos no indican, ni sugerimos, que ciertos trabajos serán reemplazados por IA.

Se reconocen las limitaciones metodológicas, y son importantes

El documento es transparente sobre las limitaciones de nuestro enfoque.

Analizamos las conversaciones anónimas de Bing Copilot para ver con qué actividades los usuarios buscan ayuda de IA y qué actividades pueden realizar la IA cuando se asignan a la base de datos O*NET. Si bien O*NET proporciona una lista estructurada de actividades asociadas con diversas ocupaciones, no  captura el espectro completo de habilidades, contexto y matices requeridos en el mundo real. Un trabajo es mucho más que la colección de tareas que lo componen.

Por ejemplo, una tarea puede implicar «escribir informes», pero O*NET no reflejará el juicio interpersonal, la experiencia en el dominio o las consideraciones éticas que se necesitan para hacerlo bien. El documento reconoce esta brecha y advierte contra la sobreinterpretación de los puntajes de aplicabilidad de la IA como medidas de la capacidad de la IA para realizar una ocupación.

Además, el conjunto de datos se basa en las consultas de los usuarios de Bing Copilot (de enero a septiembre de 2024), que pueden verse influenciadas por factores como el conocimiento, el acceso o la comodidad con las herramientas de IA. Diferentes personas usan diferentes LLM para diferentes propósitos y también es muy difícil (o casi imposible) determinar qué conversaciones se realizan en un contexto laboral o por ocio.

Por último, solo evaluamos el uso de chatbots de IA, por lo que este estudio no evalúa el impacto o la aplicabilidad de otras formas de IA.

¿A dónde vamos desde aquí?

Dado el intenso interés en cómo la IA dará forma a nuestro futuro colectivo, es importante que sigamos con el estudio y la mejor comprensión de su impacto social y económico. Al igual que con todas las investigaciones sobre este tema, los hallazgos tienen matices y es importante prestar atención a este matiz.

El interés público en nuestra investigación se basa, en gran parte, en el tema de la IA y el desplazamiento laboral. Sin embargo, es poco probable que la metodología actual para este estudio conduzca a conclusiones firmes al respecto.  La IA puede resultar una herramienta útil para muchas ocupaciones, y creemos que el equilibrio adecuado radica en encontrar cómo usar la tecnología de una manera que aproveche sus habilidades al tiempo que complementa las fortalezas humanas y tiene en cuenta las preferencias de las personas.

Para obtener más información de Microsoft sobre el futuro del trabajo y las habilidades de IA, consulten el Índice anual de tendencias laborales de Microsoft y Microsoft Elevate.

Etiquetas:

• IA
• Vida y Trabajo

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