正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software,... hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.show more

Amazon

5,918,580 subscribers

47,421 次观看 • 2 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

6 条评论

Amazon 的头像

Amazon2 年前

⬇️ Read more about Amazon's AI leadership:

Amy Shannon 的头像

Amy Shannon2 年前

My goodness… I buy stuff from you and then I find out this company is antisemite. Disgusting!

Naturalized Deer 🇮🇱 的头像

Naturalized Deer 🇮🇱2 年前

@ajassy your employees mocking hostages. In Arabic Hudhaifa wrote: "She went to bring her father's drum and came back pregnant. -- Egyptian proverb" This is beyond disgusting.

Anita C BRENG ZE THUIS 🇮🇱 🎗️ 的头像

Anita C BRENG ZE THUIS 🇮🇱 🎗️2 年前

Bring him home!!!

Ellakay 的头像

Ellakay2 年前

You guys temporarily locked my account for no reason and no way of contacting customer service and I think it’s high time I give up on Amazon

UAPKer 的头像

UAPKer2 年前

WTF? Does my family need to stop ordering from Amazon every day?

相关视频

The AI economy is not just software. It is energy. Chips. Infrastructure. Models. Applications. And every layer needs people to build it. At NVIDIA’s Five-Layer AI event, co-founder Chris Malachowsky, former secretary of commerce Gina Raimondo and Mike Rowe discussed why the future of AI means opportunity for workers across the entire stack.

The AI economy is not just software. It is energy. Chips. Infrastructure. Models. Applications. And every layer needs people to build it. At NVIDIA’s Five-Layer AI event, co-founder Chris Malachowsky, former secretary of commerce Gina Raimondo and Mike Rowe discussed why the future of AI means opportunity for workers across the entire stack.

NVIDIA

28,786 次观看 • 6 个月前

We just launched some exciting new #GenAI tools to accelerate seller growth and enhance the customer shopping experience. What are they? First up is Project Amelia, a GenAI assistant that acts as a personal Amazon expert and partner to sellers, is in beta to an initial set of U.S.-based sellers. Project Amelia learns seller’s unique businesses and provides immediate access to tools and services they use every day, in addition to tons of personalized recs and insights.

We just launched some exciting new #GenAI tools to accelerate seller growth and enhance the customer shopping experience. What are they? First up is Project Amelia, a GenAI assistant that acts as a personal Amazon expert and partner to sellers, is in beta to an initial set of U.S.-based sellers. Project Amelia learns seller’s unique businesses and provides immediate access to tools and services they use every day, in addition to tons of personalized recs and insights.

Amazon

40,695 次观看 • 2 年前

Our co-founder Pratyush Kumar highlighted Sarvam's "contrarian bet" of being a full-stack AI platform. That means we build every layer: the models, how they run (inference), how agents work with each other (orchestration), and the AI applications people use. This lets us build AI that actually works for India's unique context and diverse language needs.

Our co-founder Pratyush Kumar highlighted Sarvam's "contrarian bet" of being a full-stack AI platform. That means we build every layer: the models, how they run (inference), how agents work with each other (orchestration), and the AI applications people use. This lets us build AI that actually works for India's unique context and diverse language needs.

Sarvam AI

16,310 次观看 • 9 个月前

If your chips and models aren't built in lockstep, you're already behind. At VivaTech, Amazon SVP Peter DeSantis—who leads a new org uniting foundation models and custom chips—explained why they have to develop together, not in sequence. 🤝 "If the chips are not telling the model designers what capabilities are coming and where they can optimize, then we're not doing the science necessary to take advantage of those capabilities until the chips are available—and then you're waiting months and months." Amazon has been doing this since day one. That’s why Trainium is a chip of choice for AI companies building applications from large language models to world models and more.

If your chips and models aren't built in lockstep, you're already behind. At VivaTech, Amazon SVP Peter DeSantis—who leads a new org uniting foundation models and custom chips—explained why they have to develop together, not in sequence. 🤝 "If the chips are not telling the model designers what capabilities are coming and where they can optimize, then we're not doing the science necessary to take advantage of those capabilities until the chips are available—and then you're waiting months and months." Amazon has been doing this since day one. That’s why Trainium is a chip of choice for AI companies building applications from large language models to world models and more.

Amazon

74,099 次观看 • 2 个月前

We’re excited to partner with OpenAI to launch their new open source models natively on Databricks! gpt-oss sets a new standard of quality for open language models, supporting advanced reasoning with the transparency, flexibility and control enterprises need. Running on Databricks, the gpt-oss models connect securely to your data and scale with built-in governance, and expand what you can build and do with GenAI. Try both the 20B and 120B today in the Mosaic AI Playground.

We’re excited to partner with OpenAI to launch their new open source models natively on Databricks! gpt-oss sets a new standard of quality for open language models, supporting advanced reasoning with the transparency, flexibility and control enterprises need. Running on Databricks, the gpt-oss models connect securely to your data and scale with built-in governance, and expand what you can build and do with GenAI. Try both the 20B and 120B today in the Mosaic AI Playground.

Databricks

10,132 次观看 • 1 年前

We’re excited to introduce Taste Labs. Our mission is to end AI slop. We’re building the data and infrastructure layer to give AI models and agents taste. And today we’re coming out of stealth, announcing our $18.5M seed funding, co-led by CRV and Amplify Partners AI has nailed objective domains and made it easy to generate anything. But it still feels off. Now, the challenge is judgement. What fits, what feels like you, what’s GREAT. This requires turning a fuzzy, subjective domain into something we can measure and codify. We’re starting with design. There are two sides to cracking this, the foundation model layer and the agent layer: - We’ve already been working with the top frontier labs to evaluate and improve their models, crafting the right post-training data and RL environments. - We’ve also been working with app-layer companies to build the context and verification tools for their agents to produce better, more on-brand, more creative outputs. We want a future where AI feels right. If you’re passionate about this mission, join us!

Thais Castello Branco

2,706,149 次观看 • 2 个月前

We asked James Wang why Cerebras can run large models so quickly. His answer: Inference speed is mostly a memory problem. Instead of constantly pulling weights from external memory, Cerebras splits the model across multiple wafers and pipelines the layers together. “Inference is all about memory bandwidth.” “You don’t want to store weights external to the wafer, because the second it’s outside, it becomes much slower.” “We put the weights on the inside.” “The team built a new software stack that basically lets us split the models by layer and store them layer by layer across multiple wafers.” “That allows us to never have to read from external memory, and that’s what makes it so fast.” “For context, Claude is about 100 tokens per second.”

We asked James Wang why Cerebras can run large models so quickly. His answer: Inference speed is mostly a memory problem. Instead of constantly pulling weights from external memory, Cerebras splits the model across multiple wafers and pipelines the layers together. “Inference is all about memory bandwidth.” “You don’t want to store weights external to the wafer, because the second it’s outside, it becomes much slower.” “We put the weights on the inside.” “The team built a new software stack that basically lets us split the models by layer and store them layer by layer across multiple wafers.” “That allows us to never have to read from external memory, and that’s what makes it so fast.” “For context, Claude is about 100 tokens per second.”

MTS

27,935 次观看 • 3 个月前

“These models and applications are very power hungry, but there are things we can do together.” – Amazon CEO Andy Jassy on offsetting the energy impacts of Gen AI to remain power performant. Listen to find out 👇 : Why using Amazon Web Services is more energy efficient than on-premises Why Tranium helps (by nearly 30%!) Why Inferentia is so much more energy efficient What we’re doing about our 2030 water promise

“These models and applications are very power hungry, but there are things we can do together.” – Amazon CEO Andy Jassy on offsetting the energy impacts of Gen AI to remain power performant. Listen to find out 👇 : Why using Amazon Web Services is more energy efficient than on-premises Why Tranium helps (by nearly 30%!) Why Inferentia is so much more energy efficient What we’re doing about our 2030 water promise

Amazon

46,611 次观看 • 1 年前

Excited to be with the team in NYC today rolling out the new Alexa+. Across Amazon, we’re harnessing the transformative power of GenAI to reimagine the experiences we offer customers, and Alexa+ is the latest example. She’s smarter, more capable, more personalized, and unlike chatbots, also takes action to help you get things done—which is at the heart of our mission at Amazon to make customers’ lives better and easier every day. Can’t wait to get Alexa+ into the hands of customers.

Excited to be with the team in NYC today rolling out the new Alexa+. Across Amazon, we’re harnessing the transformative power of GenAI to reimagine the experiences we offer customers, and Alexa+ is the latest example. She’s smarter, more capable, more personalized, and unlike chatbots, also takes action to help you get things done—which is at the heart of our mission at Amazon to make customers’ lives better and easier every day. Can’t wait to get Alexa+ into the hands of customers.

Andy Jassy

107,893 次观看 • 1 年前

NeurochainAI is the all-in-one platform for developers to build AI dApps including GPU DePIN, Data Layer, and AI model layer. But what does it actually mean? AI is a $1.8 trillion industry, and NeurochainAI is tapping into 1% of that - as an infrastructure of interconnected GPUs, AI models, and data. Even simpler? Think of us as Ethereum for AI🧠🌐

NeurochainAI is the all-in-one platform for developers to build AI dApps including GPU DePIN, Data Layer, and AI model layer. But what does it actually mean? AI is a $1.8 trillion industry, and NeurochainAI is tapping into 1% of that - as an infrastructure of interconnected GPUs, AI models, and data. Even simpler? Think of us as Ethereum for AI🧠🌐

Neurochain.AI

105,995 次观看 • 2 年前

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum

The Futurum Group

75,859 次观看 • 4 个月前

🚨 Big news for AI innovation: Claude Opus 4 and Claude Sonnet 4, Anthropic's most advanced models, are now available in Amazon Bedrock. These powerful models offer hybrid reasoning, 200K token context windows, and are designed for AI agents. From financial analysis to high-quality writing, to enhanced reasoning, coding, agentic capabilities and more—all with the enterprise-grade security of Amazon Web Services.

🚨 Big news for AI innovation: Claude Opus 4 and Claude Sonnet 4, Anthropic's most advanced models, are now available in Amazon Bedrock. These powerful models offer hybrid reasoning, 200K token context windows, and are designed for AI agents. From financial analysis to high-quality writing, to enhanced reasoning, coding, agentic capabilities and more—all with the enterprise-grade security of Amazon Web Services.

Amazon

123,056 次观看 • 1 年前

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Amazon

27,151 次观看 • 3 个月前

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.

Melvin

34,306 次观看 • 2 个月前

As AI becomes more capable and agentic, models themselves become more of a commodity, and all value gets created by how you steer, ground, and finetune these models with your business data and workflow — and how they compose with the UI layer of human to AI to human interaction. That’s why today we are announcing wave two of Microsoft 365 Copilot.

As AI becomes more capable and agentic, models themselves become more of a commodity, and all value gets created by how you steer, ground, and finetune these models with your business data and workflow — and how they compose with the UI layer of human to AI to human interaction. That’s why today we are announcing wave two of Microsoft 365 Copilot.

Satya Nadella

624,449 次观看 • 2 年前