Today, AWS announces the general availability of a new Local Zone in Hanoi, Vietnam, bringing AWS infrastructure closer to end users. This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon Simple Storage Service (Amazon S3) and Amazon Elastic Block Store (Amazon EBS) Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally.
AWS Local Zones are AWS infrastructure deployments that extend core services, such as compute, storage, networking, and other select services, closer to metropolitan areas worldwide. AWS Local Zones help you achieve single-digit millisecond latency for end-user workloads, meet data residency requirements, support AI/ML inference workloads, and accelerate migration and modernization of legacy applications to the cloud, all while maintaining consistent AWS APIs, tools, and services as AWS Regions. AWS Local Zones are available in more than 30 metropolitan areas worldwide.
The Hanoi Local Zone supports Amazon Elastic Compute Cloud (Amazon EC2) with C7i, M7i, and R7i instances, Amazon S3 with the One Zone-Infrequent Access storage class, Amazon EBS with Local Snapshots and volume types gp3, gp2, io1, sc1, and st1, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Virtual Private Cloud (Amazon VPC), AWS Direct Connect, and Application Load Balancer.
Today, AWS announces the general availability of a new Local Zone in Hanoi, Vietnam, bringing AWS infrastructure closer to end users. This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon Simple Storage Service (Amazon S3) and Amazon Elastic Block Store (Amazon EBS) Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally.
AWS Local Zones are AWS infrastructure deployments that extend core services, such as compute, storage, networking, and other select services, closer to metropolitan areas worldwide. AWS Local Zones help you achieve single-digit millisecond latency for end-user workloads, meet data residency requirements, support AI/ML inference workloads, and accelerate migration and modernization of legacy applications to the cloud, all while maintaining consistent AWS APIs, tools, and services as AWS Regions. AWS Local Zones are available in more than 30 metropolitan areas worldwide.
The Hanoi Local Zone supports Amazon Elastic Compute Cloud (Amazon EC2) with C7i, M7i, and R7i instances, Amazon S3 with the One Zone-Infrequent Access storage class, Amazon EBS with Local Snapshots and volume types gp3, gp2, io1, sc1, and st1, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Virtual Private Cloud (Amazon VPC), AWS Direct Connect, and Application Load Balancer.
To get started, enable the Hanoi Local Zone (ap-southeast-1-han-1a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API. For pricing information, visit the AWS Local Zones pricing page. To learn more, visit the AWS Local Zones overview page.
Amazon MSK Provisioned clusters with Express brokers now support Intelligent Rebalancing on all existing clusters, at no additional cost. Previously available only on newly created clusters, Intelligent Rebalancing is now available on all MSK Provisioned clusters running Express brokers, making it effortless for customers to benefit from automatic partition balancing when scaling their Express-based clusters up or down.
Intelligent Rebalancing maximizes the capacity utilization of MSK Express-based clusters by optimally rebalancing Kafka resources for better performance, eliminating the need for customers to manage partitions themselves or via third-party tools. Intelligent Rebalancing performs these operations up to 180 times faster compared to Standard brokers. Clusters are continuously monitored for resource imbalance or overload based on intelligent Amazon MSK defaults to maximize cluster performance. When required, brokers are efficiently scaled without affecting cluster availability for clients to produce and consume data.
Intelligent Rebalancing is now available on all MSK Provisioned clusters with Express brokers in all AWS Regions where Express brokers are available. To learn more, see the Amazon MSK Developer Guide.
Amazon MSK Provisioned clusters with Express brokers now support Intelligent Rebalancing on all existing clusters, at no additional cost. Previously available only on newly created clusters, Intelligent Rebalancing is now available on all MSK Provisioned clusters running Express brokers, making it effortless for customers to benefit from automatic partition balancing when scaling their Express-based clusters up or down.
Intelligent Rebalancing maximizes the capacity utilization of MSK Express-based clusters by optimally rebalancing Kafka resources for better performance, eliminating the need for customers to manage partitions themselves or via third-party tools. Intelligent Rebalancing performs these operations up to 180 times faster compared to Standard brokers. Clusters are continuously monitored for resource imbalance or overload based on intelligent Amazon MSK defaults to maximize cluster performance. When required, brokers are efficiently scaled without affecting cluster availability for clients to produce and consume data.
Intelligent Rebalancing is now available on all MSK Provisioned clusters with Express brokers in all AWS Regions where Express brokers are available. To learn more, see the Amazon MSK Developer Guide.
Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7 instances, accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances deliver up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6.
You can use G7 instances for AI inference workloads such as language translation, video and image analysis, speech recognition, and recommender systems. Additionally, G7 instances also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics, and game streaming, as well as data analytics workloads such as large-scale data processing pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU, custom Intel Xeon 6 processors, and up to 700 Gbps of Elastic Fabric Adapter (EFA) networking bandwidth.
You can start using Amazon EC2 G7 instances today in two AWS Regions: US East (Ohio) and US West (Oregon). You can purchase G7 instances as On-Demand Instances, as part of Savings Plans, or Spot Instances.
Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7 instances, accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances deliver up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6.
You can use G7 instances for AI inference workloads such as language translation, video and image analysis, speech recognition, and recommender systems. Additionally, G7 instances also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics, and game streaming, as well as data analytics workloads such as large-scale data processing pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU, custom Intel Xeon 6 processors, and up to 700 Gbps of Elastic Fabric Adapter (EFA) networking bandwidth.
You can start using Amazon EC2 G7 instances today in two AWS Regions: US East (Ohio) and US West (Oregon). You can purchase G7 instances as On-Demand Instances, as part of Savings Plans, or Spot Instances.
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit this blog post and the G7 instance page.
Amazon MQ for RabbitMQ now supports private networking, enabling your brokers to connect to private resources in your VPC without exposing those resources publicly.. This helps you meet your security and compliance requirements when your brokers need to reach private identity providers (such as LDAP and OAuth 2.0), other Amazon MQ for RabbitMQ brokers, or self-hosted RabbitMQ brokers. Previously, this connectivity for RabbitMQ Federation, Shovel, or authentication required Network Load Balancer and NAT Gateway workarounds.
Amazon MQ establishes this connectivity using Amazon VPC Lattice, AWS Resource Access Manager (AWS RAM), and AWS PrivateLink, and manages the underlying infrastructure on your behalf. To get started, create a VPC Lattice resource gateway, package your resource configurations into an AWS RAM resource share, and associate it with your broker.
Amazon MQ for RabbitMQ now supports private networking, enabling your brokers to connect to private resources in your VPC without exposing those resources publicly.. This helps you meet your security and compliance requirements when your brokers need to reach private identity providers (such as LDAP and OAuth 2.0), other Amazon MQ for RabbitMQ brokers, or self-hosted RabbitMQ brokers. Previously, this connectivity for RabbitMQ Federation, Shovel, or authentication required Network Load Balancer and NAT Gateway workarounds. Amazon MQ establishes this connectivity using Amazon VPC Lattice, AWS Resource Access Manager (AWS RAM), and AWS PrivateLink, and manages the underlying infrastructure on your behalf. To get started, create a VPC Lattice resource gateway, package your resource configurations into an AWS RAM resource share, and associate it with your broker. Private networking is available only for Amazon MQ for RabbitMQ brokers, in all AWS Regions where Amazon VPC Lattice is available. To learn more, see Private networking in the Amazon MQ Developer Guide and the Amazon MQ pricing page.
Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x). Faster service auto scaling also enables you to reduce baseline capacity and lower compute costs while maintaining service reliability and performance as workload demand fluctuates.
Amazon ECS service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics. With today’s launch, target tracking policies for CPU and memory utilization now support 20-second metric resolution, in addition to the default 60-second resolution, for faster scaling signal detection. To get started, use the AWS Console, CLI, CloudFormation, or AWS SDKs to configure 20-second resolution for CPU or memory utilization metrics when creating or updating your ECS service, then configure a target tracking policy selecting the corresponding high-resolution predefined metric.
This feature is available in all AWS commercial and AWS GovCloud (US) Regions, across all ECS compute options: AWS Fargate, Amazon ECS Managed Instances, and Amazon EC2. High-resolution metrics are subject to standard CloudWatch charges; for a pricing example, see Amazon CloudWatch pricing. To learn more, see our documentation and the launch blog post.
Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x). Faster service auto scaling also enables you to reduce baseline capacity and lower compute costs while maintaining service reliability and performance as workload demand fluctuates. Amazon ECS service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics. With today’s launch, target tracking policies for CPU and memory utilization now support 20-second metric resolution, in addition to the default 60-second resolution, for faster scaling signal detection. To get started, use the AWS Console, CLI, CloudFormation, or AWS SDKs to configure 20-second resolution for CPU or memory utilization metrics when creating or updating your ECS service, then configure a target tracking policy selecting the corresponding high-resolution predefined metric. This feature is available in all AWS commercial and AWS GovCloud (US) Regions, across all ECS compute options: AWS Fargate, Amazon ECS Managed Instances, and Amazon EC2. High-resolution metrics are subject to standard CloudWatch charges; for a pricing example, see Amazon CloudWatch pricing. To learn more, see our documentation and the launch blog post.
Today, AWS announced the availability of Ministral-3-14B-Instruct-2512 in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from Mistral AI delivers frontier-class multimodal capabilities in a compact 14B-parameter architecture optimized for edge deployment, enabling customers to build advanced AI assistants, agentic systems, and vision-enabled applications on AWS infrastructure.
Ministral-3-14B-Instruct excels at analyzing images and providing insights based on visual content in addition to text, agentic capabilities with native function calling and JSON output, and multilingual understanding across dozens of languages including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic.
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Today, AWS announced the availability of Ministral-3-14B-Instruct-2512 in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from Mistral AI delivers frontier-class multimodal capabilities in a compact 14B-parameter architecture optimized for edge deployment, enabling customers to build advanced AI assistants, agentic systems, and vision-enabled applications on AWS infrastructure.
Ministral-3-14B-Instruct excels at analyzing images and providing insights based on visual content in addition to text, agentic capabilities with native function calling and JSON output, and multilingual understanding across dozens of languages including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic.
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Today, AWS announced the availability of all-MiniLM-L12-v2 in Amazon SageMaker JumpStart, expanding the portfolio of models available to AWS customers. This model from Sentence Transformers maps sentences and paragraphs to a 384-dimensional dense vector space, enabling customers to build high-quality semantic search, text clustering, and sentence similarity applications on AWS infrastructure.
all-MiniLM-L12-v2 excels at encoding sentences and short paragraphs into dense vector representations that capture semantic meaning, making it ideal for information retrieval, semantic search systems, document clustering, duplicate detection, and paraphrase identification. Its compact architecture delivers fast inference while maintaining strong embedding quality, well suited for production workloads that require efficient text representations at scale.
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Today, AWS announced the availability of all-MiniLM-L12-v2 in Amazon SageMaker JumpStart, expanding the portfolio of models available to AWS customers. This model from Sentence Transformers maps sentences and paragraphs to a 384-dimensional dense vector space, enabling customers to build high-quality semantic search, text clustering, and sentence similarity applications on AWS infrastructure.
all-MiniLM-L12-v2 excels at encoding sentences and short paragraphs into dense vector representations that capture semantic meaning, making it ideal for information retrieval, semantic search systems, document clustering, duplicate detection, and paraphrase identification. Its compact architecture delivers fast inference while maintaining strong embedding quality, well suited for production workloads that require efficient text representations at scale.
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Starting today, Nested virtualization is now available on additional Intel platforms and additional Regions. Nested virtualization is now available on C7i,R7i, M7i, C7id,R7id, M7id, C7i-flex,R7i-flex, M7i-flex, I7i, C8i-flex,R8i-flex, M8i-flex,and X8i, in addition to already available support on C8i, M8i and R8i instances. This capability is also now available in US GovCloud (US-East) and US GovCloud (US-West), in addition to existing support in all commercial regions. With nested virtualization capabilities, customers can create nested environments by running KVM or Hyper-V on virtual EC2 instances. Customers can leverage this capability for use cases such as running emulators for mobile applications, simulating in-vehicle hardware for automobiles, and running Windows Subsystem for Linux on Windows workstations. To learn more see documentation .
Starting today, Nested virtualization is now available on additional Intel platforms and additional Regions. Nested virtualization is now available on C7i,R7i, M7i, C7id,R7id, M7id, C7i-flex,R7i-flex, M7i-flex, I7i, C8i-flex,R8i-flex, M8i-flex,and X8i, in addition to already available support on C8i, M8i and R8i instances. This capability is also now available in US GovCloud (US-East) and US GovCloud (US-West), in addition to existing support in all commercial regions. With nested virtualization capabilities, customers can create nested environments by running KVM or Hyper-V on virtual EC2 instances. Customers can leverage this capability for use cases such as running emulators for mobile applications, simulating in-vehicle hardware for automobiles, and running Windows Subsystem for Linux on Windows workstations. To learn more see documentation .
Amazon Connect Customer now supports the ability to interrupt an agent with a contact, overriding their usual routing configuration in case of urgent or time-sensitive work. For example, an agent may be waiting for a time-sensitive callback on their personal extension, while taking customer service calls in the meantime. When that urgent call comes in, it can now ring the agent even if the agent is currently already on another call, so the agent can decide whether to put the first caller on hold to pick up the callback as well.
You can also use this feature to directly assign certain contacts to a specific agent even though that agent has set themselves to a custom status where they normally could not be offered queued contacts. For example, you may want to ensure that a specific agent cannot take customer service calls while in “Back Office Work” but still allow calls to their personal extension to ring through, improving efficiency for urgent contacts.
Amazon Connect Customer now supports the ability to interrupt an agent with a contact, overriding their usual routing configuration in case of urgent or time-sensitive work. For example, an agent may be waiting for a time-sensitive callback on their personal extension, while taking customer service calls in the meantime. When that urgent call comes in, it can now ring the agent even if the agent is currently already on another call, so the agent can decide whether to put the first caller on hold to pick up the callback as well. You can also use this feature to directly assign certain contacts to a specific agent even though that agent has set themselves to a custom status where they normally could not be offered queued contacts. For example, you may want to ensure that a specific agent cannot take customer service calls while in “Back Office Work” but still allow calls to their personal extension to ring through, improving efficiency for urgent contacts. This feature is available in all AWS regions where Amazon Connect Customer is offered. To learn more about this feature, see the Amazon Connect Customer Administrator Guide. To learn more about Amazon Connect Customer, the AWS cloud-based contact center, please visit the Amazon Connect Customer website.
¿Por qué los medicamentos contra el cáncer no funcionan igual para todo el mundo?
Por: Susanna Ray, escritora de Microsoft.
El tratamiento del cáncer se ha vuelto más preciso con el tiempo, ya que los médicos clasificaron primero la enfermedad según su origen en el cuerpo y, de manera más reciente, por las mutaciones encontradas en las células cancerosas para ayudar a encontrar los fármacos adecuados para tratarla.
Pero, ¿por qué dos personas con cánceres en apariencia similares pueden responder de manera tan diferente al mismo medicamento? El investigador de Microsoft, Lorin Crawford, cree que la respuesta está en cómo se comportan en realidad los tumores, no solo en cómo se categorizan.
Un estudio de Crawford y su equipo, publicado . en Nature Methods, supone un paso importante para ayudar a la IA a entender cómo actúan e interactúan las células individuales con su entorno, ya que los investigadores aprovechan el poder de la tecnología para detectar patrones que los enfoques tradicionales pueden pasar por alto.
La investigación forma parte del Proyecto Ex Vivo, una colaboración entre Microsoft y el Broad Institute con el apoyo del Dana-Farber Cancer Institute. El trabajo del grupo tiene como objetivo incluir el comportamiento celular en la categorización y tratamiento del cáncer, para ayudar a combatir una de las principales causas de muerte a nivel mundial, al emparejar con mayor éxito las terapias con los pacientes.
«La complejidad de la enfermedad es muy interesante a nivel científico, pero también una en la que puedes tener un impacto casi inmediato», dice Crawford. «Siento que hago algo más grande que yo. Cualquier hallazgo parece un paso adelante de alguna manera.»
Mirar más allá de las mutaciones
Parte del reto de la investigación sobre el cáncer es que los científicos pueden perder señales clave cuando prueban fármacos fuera del cuerpo. Los modelos ex vivo — células cancerosas cultivadas en laboratorios, incluidos minitumores llamados organoides — no siempre coinciden con lo que ocurre dentro de una persona. Eso significa que un medicamento que parece prometedor en una placa de Petri puede quedarse corto en un paciente.
El equipo del Proyecto Ex Vivo se centra en el «estado celular» — cómo se comportan y responden las células cancerosas a su entorno. Los estados celulares pueden influir en a qué tratamientos es sensible un tumor, a qué velocidad se desarrolla la resistencia a los fármacos y a qué tan agresiva se vuelve la enfermedad.
En el cáncer de páncreas, por ejemplo, los investigadores han observado dos estados celulares amplios asociados con diferentes resultados y respuestas al tratamiento. Pero cuando las células tumorales se cultivan en laboratorio, dice Crawford, los modelos a menudo reflejan solo uno de esos estados, lo que puede hacer que los resultados de laboratorio no coincidan con lo que ocurre en los pacientes.
«Tú y yo podemos tener la misma mutación, pero estados celulares diferentes por completo, y eso es lo que en verdad importa más adelante», dice Crawford.
Menos desajustes, mejores apuestas
Si el estado celular puede medirse de manera fiable, Crawford cree que podría cambiar el tratamiento del cáncer de dos maneras clave.
En primer lugar, podría mejorar la forma en que los pacientes se emparejan con terapias existentes y se inscriben en ensayos clínicos. Una forma más matizada de agrupar los tumores podría aumentar las probabilidades de que un tratamiento se pruebe y en verdad tenga posibilidades de funcionar.
En segundo lugar, podría abrir un nuevo camino para el propio desarrollo de fármacos. En lugar de dirigirse a una mutación, los investigadores podrían intentar atacar —o incluso cambiar— el estado subyacente de un tumor, empujándolo hacia una forma más fácil de tratar.
«El reto como clínico es entender qué características dentro del tumor de un paciente tal vez influirán en su comportamiento y respuestas a las terapias con el tiempo», dice Srivatsan Raghavan, oncólogo médico y médico-científico en Dana-Farber y codirector del Proyecto Ex Vivo. «Esta investigación pretende representar mejor estas diversas características en los modelos de cáncer y producir pruebas que reflejen la complejidad de los comportamientos tumorales que observamos en los pacientes.»
Un estadístico en un laboratorio húmedo
Lorin Crawford. (Foto de Gregory Winter)
Crawford tomó un camino poco común en esta investigación.
Formado como matemático y estadístico, un asesor le instó a mirar más allá de las hojas de cálculo y aprender cómo se creaban los datos. Pasó la mayor parte de su trabajo de posgrado en la Universidad de Duke, donde cruzaba dos mundos, inmerso en un «laboratorio húmedo» de biología del cáncer donde los investigadores trabajaban de manera directa con células vivas, no solo con datos.
Aunque al principio parecía «estar en un país extranjero», Crawford dice que «tal vez fue la mejor decisión que he tomado nunca, desde el punto de vista profesional.»
Se dio cuenta de que su formación podría ayudar a los biólogos a gestionar la enorme cantidad de datos que se utilizan para comprender las variaciones entre pacientes y subtipos de cáncer.
«Las cosas empezaron a encajar para mí», dice.
Esa experiencia moldeó su manera de pensar hoy en día sobre el Proyecto Ex Vivo — como una manera de cerrar la brecha entre lo que funciona en el laboratorio y lo que en verdad ayuda a los pacientes. Expertos computacionales y experimentadores se sientan a resolver problemas juntos, para utilizar herramientas de IA y muestras de tumores de pacientes anónimos.
Desde un problema de laboratorio hasta la transformación del tratamiento del cáncer
Lo que comenzó como un esfuerzo de investigación enfocado en 2022 se ha expandido con rapidez, impulsado por los avances en IA. El equipo del Proyecto Ex Vivo utiliza modelos computacionales para realizar experimentos virtuales e identificar las hipótesis más prometedoras antes de invertir tiempo y dinero en el laboratorio. Las herramientas de IA pueden ayudar a predecir cómo un fármaco podría cambiar de un estado a otro, o cómo los estados se traducen entre diferentes tipos de cáncer.
Como muestran Crawford y sus colegas en el estudio de Nature Methods, los modelos de IA aprenden más al observar una amplia gama de comportamientos celulares que tan solo al recibir más datos, un hallazgo que desafía una suposición común en el campo.
«Hay una tentación real de pensar que solo ampliar los conjuntos de datos resolverá estos problemas», dice Peter Winter, codirector del Proyecto Ex Vivo e investigador principal en el Broad Institute, una institución independiente sin ánimo de lucro con estrechos vínculos con el MIT y la Universidad de Harvard. «Pero la diversidad de estados celulares en esos conjuntos de datos moldea de manera fundamental qué tipo de conocimientos pueden producir estos modelos.»
El siguiente paso es definir con claridad los estados celulares y validarlos a través de los cánceres, con el objetivo de proporcionar a los médicos mejor información para ayudar a guiar las decisiones de tratamiento.
«Hay un mundo en el que dentro de cinco años este espacio se ve muy diferente a como es ahora, lo cual me parece muy, muy alentador», dice Crawford. «La realidad de esto no está tan lejos.»
Imagen principal: Foto de Gregory Winter
Susanna Ray escribe sobre IA y tecnología, con relatos que muestran su impacto en el mundo real y examinan cómo la innovación está transformando el trabajo, los negocios y la sociedad. Antes reportó para Bloomberg News y otras grandes organizaciones internacionales de noticias en EE. UU. y en el extranjero, donde cubría temas que iban desde política y gobierno hasta negocios y aviación. Sigan su trabajo en Microsoft Source.
