跟读练习: What's next for mainframes and AI? - 通过视频学习英语口语
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A trillion web transactions daily.
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More than 300 billion inference requests in a single day.
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A million plus transactions in a second.
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The modern mainframe can handle all that and more with ease.
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Welcome to AI Academy.
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I'm Lou Folia, a writer and journalist.
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And I'm here with Christian Jacoby, IBM Fellow and CTL of Systems Development at IBM.
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On average, Visa processes around 8 ,500 global transactions every second.
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A mainframe which can handle one million transactions per second would be able to process more than 100 times Visa's volume.
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So, Christian, to you, what makes the mainframe remarkable?
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And what makes them key to AI's next big breakthrough?
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Is it the hardware?
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Is it the software?
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Is it somewhere in between?
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It's really the hardware and the software together.
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When we design the next generation of the mainframe, we're enhancing the performance, the scalability, availability features.
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But we're doing this in conjunction as a hardware team.
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We're working closely with the firmware team and software team to make sure
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that we end up with a stack optimized end -to -end solution that clients can use to run their workloads.
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IBM has been thinking about AI on mainframes for a long time now, right?
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This isn't something we've recently come to.
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Ten years at least.
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And the IBM Telum processor includes an on -chip AI accelerator.
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Why is that important, having the accelerator on chip?
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And how does it enhance AI performance?
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Yeah, as you're running workloads on the processor, of course, a lot of the data resides within the chip.
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And within the chip, we can communicate between different components of the chip, literally in nanoseconds, right?
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Nanoseconds is a tiny sliver of time, of course.
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Versus if you go off -chip and go to a separate card, you're talking microseconds.
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And if you go to a different system, you might be talking milliseconds, right?
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And so by putting AI acceleration directly onto the processor chip, we could achieve the lowest possible latency for some of those traditional models.
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And then now with the Spire acceleration card that goes into the IO subsystem,
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we can run generative AI also with very low latency without needing to cross system boundaries and go into a separate system.
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It matters how big that slice of the second is.
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It matters big time.
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Absolutely.
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When you're running millions of transactions every second
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and the latency requirements from a business perspective is that transactions need to finish within a handful of milliseconds,
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then you really don't want to waste milliseconds and traveling the data between here and some other system for AI inferencing.
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Let's drill in on that a little bit.
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So what makes mainframes so pivotal for AI?
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Their hardware capabilities, their software capabilities, What is it that sort of makes it perfectly suited for AI?
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Yeah, so clients are running mission -critical central workloads for their corporations.
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They run the central ledger of a bank or they run insurance claims processing on mainframes.
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That's where a lot of the data resides.
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That's where the transactions get processed.
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And of course, you can use AI to increase the sort of business value
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of each transaction by checking for fraud on an insurance claim
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or on a banking transaction and being able to integrate that directly into the workload, detect fraud, for example,
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in the instant of time where the transaction is getting processed is of huge value to clients compared to,
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for example, doing it in a cloud a few minutes or an hour after the transaction has already completed.
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By doing it directly in the mainframe box itself, you cut out all that latency and that's how you get to the milliseconds of latency for, for example, fraud detection.
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You already have all the data, all the transaction processing happening in the mainframe system by doing the AI directly in the processor for traditional models
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or in the I .O subsystem on a Spire accelerator for generative models.
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You can maintain low latency, get the good throughput and performance that the mainframe is known for,
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but also preserve the security capabilities by not needing to send the data to a separate system.
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When you're sending the data out into the world, it's slowing things down and it's creating security risk.
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Correct.
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Exactly.
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You're sending it somewhere else that inherently takes time and you're moving the data out of the highly secure mainframe environment.
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So now it gets exposed to different attack surfaces that hackers could exploit.
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What should organizations consider when choosing AI -ready infrastructure?
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And how do mainframes address those needs better than other platforms?
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When clients embark on AI journeys in the context of the mission -critical workloads, We often talk to them about the readiness of the data,
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the readiness of the models.
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Do they have the capabilities in the data science departments to kind of train their own data into the models
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that they will be using?
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And then also look at, you know, what latency requirements do you have?
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What throughput requirements?
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How many inferences per second do you need to run in that workload?
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And by working with clients through those considerations early on,
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we can make sure that clients are actually successful in deploying AI solutions, not just developing them, and then kind of getting surprised about how hard it is to scale them to actual deployment.
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So we're in a room that has seen a lot of mainframe history.
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You spent the bulk of your career working on mainframes.
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What excites you about the future of mainframes?
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We've been already developing the next generation and the generation after that.
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We're always working on three mainframe generations at the same time.
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So it's your job to think about what the future's going to look like and build a machine that serves it.
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And that's exciting and a lot of fun.
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Could you talk a little bit about some of the use cases that may have surprised you?
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Yeah, I'll give you an example.
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There's one client who's actually using mainframes to analyze imagery of seagrass, satellite images.
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Like seagrass, like on the beach seagrass?
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Yes, yeah.
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And they see beach grass dying in certain areas, and they use that information
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that they obtained from the AI models to then do ecological measures to protect more of the seagrass.
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So when you start designing something, and then you see where it goes, like protecting seagrass, like how cool is that for you to see the expanse?
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That is super exciting.
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Like we have, when we design the hardware and the software, we have use cases in mind that we're optimizing for.
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And then once it's in the hands of our customers, their innovation starts and how they can use all these capabilities for use cases we would have never imagined.
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And that is one of the most fascinating parts to see how this sometimes evolves.
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Yeah, totally.
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So that's the future.
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But even today, just think about it.
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Every time you pay using a credit card, withdraw cash from an ATM, make reservations with any airline,
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file your taxes or access your medical records or basically do a thousand other things, it's likely you are being served by a mainframe.
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Now, imagine a day without mainframes.
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Mainframes are without a doubt integral to enterprise IT.
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And today, with new integrations and enhancements, they are redefining their role in modern IT.
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So, to answer the question we started with, what makes the mainframe unique?
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It's the hardware and the software and everything in between.
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It's how the end -to -end stack is optimized from the Silicon App for mission -critical workloads.
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And I think that we could both agree that that's what makes the mainframe so remarkable.
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And that's what makes it great for AI.
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对话背景与场景
这段视频是关于大型机与人工智能未来发展的访谈，主持人与IBM专家探讨了大型机的性能、AI加速技术及实际应用。对话中包含专业术语与技术讲解，语速适中，适合提升科技类英语听力与口语能力。通过学习这段内容，不仅能了解前沿技术，还能掌握商务访谈中的表达逻辑。
日常交流高频短语5则
- end-to-end solution：端到端解决方案。例：我们致力于提供优化的端到端解决方案。
- mission-critical workloads：关键任务工作负载。例：企业需确保关键任务工作负载的稳定运行。
- latency requirements：延迟要求。例：金融交易对延迟要求极高。
- attack surfaces：攻击面。例：数据传输会增加系统的攻击面。
- scale to deployment：扩展至部署。例：AI模型需能扩展至实际部署场景。
影子跟读分步指南（针对本视频）
本视频涉及科技术语，跟读时需注意专业词汇的发音与连读。以下是 proven 技巧：
第一步：拆分片段 将视频按话题分段（如大型机性能、AI加速、应用案例），每段30秒左右。使用“shadowspeak”技巧，逐句跟读，重点模仿重音与语调。
第二步：攻克术语 预先熟悉“mainframe”（大型机）、“inference”（推理）等词汇的发音，确保跟读时流畅度。可结合“看视频学英语”的方式，边看画面边理解术语含义。
第三步：提升反应速度 视频中对话节奏较快，可采用“shadow speak”训练法，滞后1-2秒跟读，锻炼听力与口语的同步反应。反复练习直至能跟上原速。
第四步：模仿语气 注意访谈中专家的自信语气与主持人的提问节奏，学习商务沟通中的表达技巧。这对“英语口语练习”至关重要。
通过以上步骤，结合专业的“shadowing site”资源，可有效提升科技英语的听说能力。坚持练习，你会发现应对复杂对话的能力显著增强。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。