تدريب Shadowing: What is AI Ethics? - تعلم التحدث بالإنجليزية عبر الفيديو
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I want to start off with talking to you about three things that keep me up at night, right?
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Three things.
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The first, and it may be, you know, very common for you too, is climate change.
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Climate change absolutely keeps me up at night.
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The second thing that keeps me up at night is
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that people may have no idea that an artificial intelligence is making a decision
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that directly impacts their lives what percentage interest rate you get on your loan,
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whether you get that job that you applied for, whether your kid gets into that college that they really want to go to.
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Today, AI is making decisions that directly impact you.
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The third thing that keeps me up at night is even
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when When people know that an AI is making a decision about them,
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they may assume that because it's not a fallible human with bias,
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that somehow the AI is going to make a decision that's morally or ethically squeaky clean.
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And that could not be farther from the truth.
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So if you think about organizations and what happens, over 80 % of the time,
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proof of concepts associated with artificial intelligence actually gets stalled in testing.
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And more often than not, it is because people do not trust the results from that AI model.
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So we're going to talk a lot about trust.
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And when thinking about trust, I'm going to switch colors here, there's actually five pillars.
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Okay.
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When you're thinking about what does it take to earn trust in an artificial intelligence
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that's being made by your organization or being procured by your organization, five pillars.
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The first thing to be thinking about is fairness.
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How can you ensure that the AI model is fair towards everybody, in particular historically underrepresented groups?
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The second is explainable.
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Is your AI model explainable such that you'd be able to tell somebody,
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an end user, what data sets were being used in order to curate that model?
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What methods, what expertise, what's the data lineage and provenance associated with how that model was trained?
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The third, robustness.
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Can you assure end users that nobody can hack such an AI model such
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that a person could disadvantage willfully other people and or make the results of that model benefit one particular person over another?
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The fourth is transparency.
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Are you telling people right off the bat than the AI model is indeed being used to make that decision?
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And are you giving people access to a fact sheet or metadata so that they can learn more about that model?
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And the fifth one is, are you assuring people's data privacy?
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So those are the five pillars, okay?
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Now, IBM has come up with three principles when thinking about AI in an organization.
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The first being that the purpose of artificial intelligence is really meant to be to augment human intelligence,
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not to replace it.
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The second is that data and the insights from those data belong to the creator alone.
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And the third is that AI systems, and I would opine the entire AI lifecycle, really should be transparent and explainable right?
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So those are the five pillars.
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Now, the next thing I want you to remember
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as you're thinking about this space of earning trust and artificial intelligence is that this is not a technological challenge.
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It can't be solved with just throwing tools and tech over some kind of a fence.
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This is a socio -technological challenge.
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Socio meaning people, people, people.
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Socio -technological challenge.
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And because it's a socio -technological challenge.
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It must be addressed holistically, okay?
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Holistically, meaning there's three major things that you should think about.
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I mentioned people, people, the culture of your organization, right?
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Thinking about the diversity of your teams, you know, your data science team who is curating that data to train that model.
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How many women are on that team?
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How many minorities are on that team, right?
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Think about diversity.
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I don't know if you've ever heard of the the wisdom of crowns that's actually a proven mathematical theory
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The more diverse your group of people, the less chance for error.
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And that is absolutely true in the realm of artificial intelligence.
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The second thing is process or governance, right?
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What is it that you as your organization,
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what are you going to promise both your employees as well as the market with respect to what standards
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you're going to stand by for your AI model in terms of things like fairness and explainability, accountability, etc., right?
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And the third area is tooling, right?
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What are the tools, AI engineering,
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methods, frameworks that you can use in order to ensure these things, ensure those five pillars?
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And we're going to do a deep dive into that as well.
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But the next show that I'm going to be running with you, we're actually going to be talking about this one, about people and culture.
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So stay tuned.
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If you like this video and series, please comment below.
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Stay tuned for more videos that are part of this series.
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And to get updates, please like and subscribe.
تنزيل التطبيق
كل ما تحتاجه للتحدث بطلاقة
تقييم النطق بالذكاء الاصطناعيتقييم كل جملة
تدريب IPAأتقن كل صوت
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المفردات وملاحظات التحدث لهذا الدرس
هذا الدرس في المحادثة مبني على الفيديو “What is AI Ethics?”. أكثر الكلمات تكرارًا في الفيديو: model, intelligence, artificial, organization, thinking. يحتوي هذا الفيديو على 21 جملة و859 كلمة للتدرب على الترديد. مدة الكلام 6:09. يتكلم المتحدث بسرعة ثابتة، نحو 140 كلمة في الدقيقة، وهي سرعة مريحة للترديد. 86% من الكلمات ضمن أكثر 3,000 كلمة شيوعًا في الإنجليزية، ويُستحسن مراجعة الباقي قبل البدء. 13 من أصل 21 جملة هي أسئلة، لذلك يناسب التدرب على نبرة السؤال والإجابات القصيرة.
أهم المفردات في هذا الفيديو
15 كلمة من الفيديو تستحق التعلّم، مع النطق والمعنى:
| الكلمة | النطق | المعنى |
|---|---|---|
| artificial صفة | /ˌɑː.tɪˈfɪʃ.əl/ | مُتَصَنَّع, صِنَاعِيّ |
| pillar اسم | /ˈpɪlɚ/ | عمود |
| earn فعل | /ɝn/ | كَسَبَ |
| diversity اسم | /daɪˈvɜː(ɹ)sɪti/ | تَنَوُّع, تَعَدُّدِيَّة |
| tune اسم | /tjuːn/ | لَحْن |
| assure فعل | /əˈʃʊɹ/ | أَكَّدَ |
| fairness اسم | /ˈfɛɚ.nəs/ | إِنْصَاف |
| percentage اسم | /pə(ɹ)ˈsɛntɪd͡ʒ/ | النِّسْبَة الْمِئَوِيَّة |
| solve فعل | /sɒlv/ | ذَابَ, حَلَّ |
| minority اسم | /maɪˈnɒɹ.ɪ.ti/ | أَقَلِّيَّة |
| sheet اسم | /ʃit/ | وَرَقَة, وَرَق |
| belong فعل | /bɪˈlɒŋ/ | اِنْتَمَى |
| privacy اسم | /ˈpɹaɪ.və.si/ | سِرِّيَّة, خَلْوَة |
| principle اسم | /ˈpɹɪn.sɪ.pəl/ | مَبْدَأ, قاعِدَة |
| wisdom اسم | /ˈwɪzdəm/ | حِكْمَة |
الأفعال المركبة التي ستسمعها
| الكلمة | المعنى |
|---|---|
| come up with فعل | جَاءَ بِـ, أَتَى بِـ |
القواعد في هذا الفيديو
أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:
| التركيب | في الفيديو |
|---|---|
| المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعله | being made · was trained · is really meant |
| المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآن | has come · you've ever heard |
نطق يحتاج إلى انتباه
يستخدم المتحدث 13 من الصيغ المختصرة، مثل we're, you're, I'm. انطقها بصيغتها القصيرة كما تسمعها.
- أصوات “th”: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
- أصوات “sh” و“zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
- الكلمات الطويلة — انتبه لموضع النبر: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/
أصوات يجدها متحدثو العربية صعبة:
- /p/ — ليس /b/: انطقه مع نفخة هواء: pillar /ˈpɪlɚ/, percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, privacy /ˈpɹaɪ.və.si/, principle /ˈpɹɪn.sɪ.pəl/, expertise /ˌɛkspɜːˈtiːz/
- /v/ — ليس /f/: مع اهتزاز الأوتار الصوتية: diversity /daɪˈvɜː(ɹ)sɪti/, solve /sɒlv/, privacy /ˈpɹaɪ.və.si/, diverse /daɪˈvɜːs/, dive /daɪv/
- تتابع الصوامت — لا تضف حركة بينها: tune /tjuːn/, solve /sɒlv/, privacy /ˈpɹaɪ.və.si/, principle /ˈpɹɪn.sɪ.pəl/, framework /ˈfɹeɪm.wɜːk/
كيف تتدرب بهذا الفيديو
- استمع إلى الفيديو كاملًا مرة واحدة دون أن تتكلم، ودوّن الكلمات التي لا تعرفها.
- ردّد جملة جملة بالسرعة العادية، وكرّر كل جملة حتى يتطابق إيقاعك مع المتحدث.
- سجّل صوتك وقارنه بالأصل، مع الانتباه إلى كلمات مثل artificial, pillar, earn.
ما هي تقنية التظليل الصوتي؟
التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.