シャドーイング練習: What's next for mainframes and AI? - 動画で英語スピーキングを学ぶ
レッスンを作成中...
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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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このレッスンについて
このレッスンでは、メインフレームとAIの未来についての会話を通じて、専門的な英語表現や論理的な会話の流れを練習します。特に、技術的な用語の使い方、質問に対する応答の構造、リアルなディスカッションのリズムを身につけることができます。「shadowspeak」や「英語シャドーイング」の技法を使って、ネイティブスピーカーの発音やイントネーションを模倣し、スピーキング力を向上させましょう。
重要な語彙とフレーズ
- Mainframe：メインフレーム（大型コンピュータ）
- Inference：推論（AIがデータから結論を出すこと）
- Latency：レイテンシ（遅延）
- Throughput：スループット（単位時間あたりの処理能力）
- Mission-critical：ミッションクリティカル（事業に不可欠な）
- Generative AI：生成AI
- Seagrass：海草
練習のヒント
このビデオの会話は専門的な内容が多いため、shadowing（シャドーイング）を行う際はまずスピードを落として練習しましょう。最初は1.5倍速ではなく0.75倍速で聞き、発音やアクセントを注意深く観察します。特に「latency」や「throughput」などの技術用語は正確に発音することが重要です。
会話の流れを理解するため、質問と答えのペアを重点的に練習します。例えば、「メインフレームがAIにとって重要なのはなぜか？」という質問に対する回答を繰り返し模倣することで、論理的な応答の構造を身につけることができます。
「shadow speak」の技法を使い、発言のリズムやポーズを再現しましょう。ネイティブスピーカーは単語間に自然なポーズを入れることで、意味を明確にします。これを真似ることで、より自然な英語表現ができるようになります。
最後に、実際の使用例（例えば海草の画像分析）を使って、専門用語を文脈の中で覚えましょう。これは語彙の定着にも役立ち、スピーキングの柔軟性を高めます。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。