Shadowing Practice: What's next for mainframes and AI? - Learn English Speaking with Video
Les maken...
1
A trillion web transactions daily.
2
More than 300 billion inference requests in a single day.
3
A million plus transactions in a second.
4
The modern mainframe can handle all that and more with ease.
5
Welcome to AI Academy.
6
I'm Lou Folia, a writer and journalist.
7
And I'm here with Christian Jacoby, IBM Fellow and CTL of Systems Development at IBM.
8
On average, Visa processes around 8 ,500 global transactions every second.
9
A mainframe which can handle one million transactions per second would be able to process more than 100 times Visa's volume.
10
So, Christian, to you, what makes the mainframe remarkable?
11
And what makes them key to AI's next big breakthrough?
12
Is it the hardware?
13
Is it the software?
14
Is it somewhere in between?
15
It's really the hardware and the software together.
16
When we design the next generation of the mainframe, we're enhancing the performance, the scalability, availability features.
17
But we're doing this in conjunction as a hardware team.
18
We're working closely with the firmware team and software team to make sure
19
that we end up with a stack optimized end -to -end solution that clients can use to run their workloads.
20
IBM has been thinking about AI on mainframes for a long time now, right?
21
This isn't something we've recently come to.
22
Ten years at least.
23
And the IBM Telum processor includes an on -chip AI accelerator.
24
Why is that important, having the accelerator on chip?
25
And how does it enhance AI performance?
26
Yeah, as you're running workloads on the processor, of course, a lot of the data resides within the chip.
27
And within the chip, we can communicate between different components of the chip, literally in nanoseconds, right?
28
Nanoseconds is a tiny sliver of time, of course.
29
Versus if you go off -chip and go to a separate card, you're talking microseconds.
30
And if you go to a different system, you might be talking milliseconds, right?
31
And so by putting AI acceleration directly onto the processor chip, we could achieve the lowest possible latency for some of those traditional models.
32
And then now with the Spire acceleration card that goes into the IO subsystem,
33
we can run generative AI also with very low latency without needing to cross system boundaries and go into a separate system.
34
It matters how big that slice of the second is.
35
It matters big time.
36
Absolutely.
37
When you're running millions of transactions every second
38
and the latency requirements from a business perspective is that transactions need to finish within a handful of milliseconds,
39
then you really don't want to waste milliseconds and traveling the data between here and some other system for AI inferencing.
40
Let's drill in on that a little bit.
41
So what makes mainframes so pivotal for AI?
42
Their hardware capabilities, their software capabilities, What is it that sort of makes it perfectly suited for AI?
43
Yeah, so clients are running mission -critical central workloads for their corporations.
44
They run the central ledger of a bank or they run insurance claims processing on mainframes.
45
That's where a lot of the data resides.
46
That's where the transactions get processed.
47
And of course, you can use AI to increase the sort of business value
48
of each transaction by checking for fraud on an insurance claim
49
or on a banking transaction and being able to integrate that directly into the workload, detect fraud, for example,
50
in the instant of time where the transaction is getting processed is of huge value to clients compared to,
51
for example, doing it in a cloud a few minutes or an hour after the transaction has already completed.
52
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.
53
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
54
or in the I .O subsystem on a Spire accelerator for generative models.
55
You can maintain low latency, get the good throughput and performance that the mainframe is known for,
56
but also preserve the security capabilities by not needing to send the data to a separate system.
57
When you're sending the data out into the world, it's slowing things down and it's creating security risk.
58
Correct.
59
Exactly.
60
You're sending it somewhere else that inherently takes time and you're moving the data out of the highly secure mainframe environment.
61
So now it gets exposed to different attack surfaces that hackers could exploit.
62
What should organizations consider when choosing AI -ready infrastructure?
63
And how do mainframes address those needs better than other platforms?
64
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,
65
the readiness of the models.
66
Do they have the capabilities in the data science departments to kind of train their own data into the models
67
that they will be using?
68
And then also look at, you know, what latency requirements do you have?
69
What throughput requirements?
70
How many inferences per second do you need to run in that workload?
71
And by working with clients through those considerations early on,
72
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.
73
So we're in a room that has seen a lot of mainframe history.
74
You spent the bulk of your career working on mainframes.
75
What excites you about the future of mainframes?
76
We've been already developing the next generation and the generation after that.
77
We're always working on three mainframe generations at the same time.
78
So it's your job to think about what the future's going to look like and build a machine that serves it.
79
And that's exciting and a lot of fun.
80
Could you talk a little bit about some of the use cases that may have surprised you?
81
Yeah, I'll give you an example.
82
There's one client who's actually using mainframes to analyze imagery of seagrass, satellite images.
83
Like seagrass, like on the beach seagrass?
84
Yes, yeah.
85
And they see beach grass dying in certain areas, and they use that information
86
that they obtained from the AI models to then do ecological measures to protect more of the seagrass.
87
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?
88
That is super exciting.
89
Like we have, when we design the hardware and the software, we have use cases in mind that we're optimizing for.
90
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.
91
And that is one of the most fascinating parts to see how this sometimes evolves.
92
Yeah, totally.
93
So that's the future.
94
But even today, just think about it.
95
Every time you pay using a credit card, withdraw cash from an ATM, make reservations with any airline,
96
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.
97
Now, imagine a day without mainframes.
98
Mainframes are without a doubt integral to enterprise IT.
99
And today, with new integrations and enhancements, they are redefining their role in modern IT.
100
So, to answer the question we started with, what makes the mainframe unique?
101
It's the hardware and the software and everything in between.
102
It's how the end -to -end stack is optimized from the Silicon App for mission -critical workloads.
103
And I think that we could both agree that that's what makes the mainframe so remarkable.
104
And that's what makes it great for AI.
📺 Zelfde Kanaal
✨ Aanbevolen Video
C18
114 zinnen
AI, Machine Learning, Deep Learning and Generative AI Explained
Business EnglishUniversity StudentsWorking Adults
C12
182 zinnen
Inside The Hidden Bottleneck In The AI Chip Boom
IELTS PrepBusiness EnglishUniversity Students
C11
68 zinnen
System Design: Why is Kafka fast?
Business EnglishUniversity StudentsWorking Adults
C11
28 zinnen
Gemma 4 Just Released - Whats In? In 2026!
Business EnglishUniversity StudentsWorking Adults
C11
315 zinnen
how AI broke the SWE Career Ladder
Business EnglishUniversity StudentsWorking Adults
C11
40 zinnen
The Engineering that Runs the Digital World 🛠️⚙️💻 How do CPUs Work?
Business EnglishUniversity StudentsWorking Adults
C1320 zinnen
FULL: Elon Musk, Jensen Huang, Tom Brown Discuss the AI Revolution & What's Next - 09/29/26
Business EnglishDaily ConversationUniversity Students
C1115 zinnen
Novanta CIO Sarah Betadam on managing risk around AI
Business EnglishUniversity StudentsWorking Adults
C12
182 zinnen
We Let $200 GPT-Pro Think for 7 Days on Unsolved Math
Business EnglishUniversity StudentsWorking Adults
C11
75 zinnen
What AI can (and can't) currently do in a real lab | Jennifer Doudna
IELTS PrepBusiness EnglishUniversity Students
C11
87 zinnen
The AI Atrophy Problem How CIOs Fight It
Business EnglishUniversity StudentsWorking Adults
Woordenschat en spreektips bij deze les
Deze spreekles op niveau C1 is gebaseerd op de video “What's next for mainframes and AI?”. Deze woorden komen het vaakst terug: mainframe, transaction, latency, workload, chip. Deze video bevat 104 zinnen en 1305 woorden om na te spreken. Het gesproken deel duurt 7:15. De spreker praat snel, ongeveer 180 woorden per minuut, dus reken op verbonden en ingeslikte klanken. Slechts 79% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden, dus de woordenschat is pittig. 23 van de 104 zinnen zijn vragen, dus het is goede oefening voor vraagintonatie en korte antwoorden.
Belangrijke woorden in deze video
De 15 moeilijkste woorden uit de video, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| latency zelfstandig naamwoord | /ˈleɪ.tən.si/ | wachttijd, vertraging |
| millisecond zelfstandig naamwoord | /ˈmɪlɪˌsɛkənd/ | milliseconde |
| processor zelfstandig naamwoord | /ˈpɹɑˌsɛsɚ/ | verwerker |
| accelerator zelfstandig naamwoord | /əkˈsɛl.əˌɹeɪ.tɚ/ | versneller |
| excite werkwoord | /ɪkˈsaɪt/ | prikkelen, opwinden |
| optimize werkwoord | /ˈɑptɪmaɪz/ | optimistisch zijn/handelen |
| inference zelfstandig naamwoord | /ˈɪn.fə.ɹəns/ | afgeleid gegeven, conclusie |
| nanosecond zelfstandig naamwoord | nanoseconde | |
| reside werkwoord | /ɹɪˈzaɪd/ | wonen |
| acceleration zelfstandig naamwoord | /əkˌsɛl.əˈɹeɪ.ʃən/ | versnelling, acceleratie |
| enhance werkwoord | /ɪnˈhæns/ | vergroten, uitbreiden |
| stack zelfstandig naamwoord | /stæk/ | stapel |
| embark werkwoord | /ɪmˈbɑɹk/ | beginnen, starten |
| fascinate werkwoord | /ˈfæsɪneɪt/ | fascineren |
| microsecond zelfstandig naamwoord | microseconde |
Phrasal verbs die je zult horen
| Woord | Betekenis |
|---|---|
| go off werkwoord | afgaan |
Zinnen om te herhalen
Korte, volledige zinnen uit de video die je in alledaagse gesprekken kunt gebruiken:
- This isn't something we've recently come to.
- Let's drill in on that a little bit.
- What excites you about the future of mainframes?
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | has already completed · has seen · We've been |
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | is known · being served · is optimized |
Uitspraak om op te letten
De spreker gebruikt 19 samentrekkingen en verkorte vormen, zoals we're, you're, I'm. Spreek ze kort uit, zoals je ze hoort.
- De “th”-klanken: throughput /ˈθɹuː.pʊt/, breakthrough /ˈbɹeɪk.θɹuː/, withdraw /wɪðˈdɹɔ/
- De klanken “sh” en “zh”: acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, conjunction /kənˈd͡ʒʌŋk.ʃn̩/, reservation /ˌɹɛz.ɚˈveɪ.ʃən/, detection /dɪˈtɛk.ʃən/
- Lange woorden — let op de klemtoon: capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, millisecond /ˈmɪlɪˌsɛkənd/, accelerator /əkˈsɛl.əˌɹeɪ.tɚ/, generative /ˈd͡ʒɛnəɹətɪv/, acceleration /əkˌsɛl.əˈɹeɪ.ʃən/
Klanken die Nederlandstaligen lastig vinden:
- Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: millisecond /ˈmɪlɪˌsɛkənd/, optimize /ˈɑptɪmaɪz/, generative /ˈd͡ʒɛnəɹətɪv/, reside /ɹɪˈzaɪd/, evolve /ɪˈvɑlv/
- /æ/ — opener dan de Nederlandse “e”: seagrass /ˈsiˌɡɹæs/, enhance /ɪnˈhæns/, stack /stæk/, expanse /ɪkˈspæns/, fascinate /ˈfæsɪneɪt/
- /g/ — een harde plofklank, geen Nederlandse “g”: seagrass /ˈsiˌɡɹæs/, integrate /ˈɪn.tɪ.ɡɹeɪt/, integral /ˈɪntəɡɹəl/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als latency, millisecond, processor.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.