[영어/회화] Social Impacts of AI and Machine Learning

Vocabulary

Preemptive

If something is pre-emptive, it is done before other people can act, especially to prevent them from doing something else:

The Treasury has decided to raise interest rates as a pre-emptive measure against inflation.

 

Deploy

to use something or someone, especially in an effective way:

The company is reconsidering the way in which it deploys its resources/staff.

My job doesn't really allow me fully to deploy my skills/talents.

 

Configuration

the particular arrangement or pattern of a group of related things:

We tried the furniture in different configurations to see which fit best.

 

Passersby

someone who is going past a particular place:

A passer-by saw smoke and called the fire department.

 

Automate

to make a process in a factory or office operate by machines or computers, in order to reduce the amount of work done by humans and the time taken to do the work:

Massive investment is needed to automate the production process.

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Q1

What contributions have AI and machine learning made recently in terms of social impact?

 

Request

I once experienced an AI educational system. When I prepared for an English exam with an app, AI analysed my response and evaluated my speaking based on the sentences extracted from my voice. Also, it automatically recommended relevant questions for improvement. As the app can substitute for a personal teacher, it's cost-effective using the system rather than hiring a labor. However, the accuracy for recommendation wouldn't be sufficient if the machine learning doesn't collect significant date amount.

 

Tutor's Correction

I once experienced an AI educational system when I was preparing for an English exam. AI analyzed my response and evaluated my speaking based on the sentences extracted from my voice. Also, it automatically recommended relevant questions for improvement. As the app can substitute for a personal teacher, it's cost-effective using the system rather than hiring a labor. However, the accuracy for recommendations wouldn't be sufficient when the machine learning doesn't have a significant amount of data.

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Tutor's Editing Place

Pronunciation is awesome! Vocab and native phrases are being used correctly! 

Grammatical Accuracy: https://www.perfect-english-grammar.com/future-perfect-exercise-1.html

Word order: https://www.npr.org/transcripts/855791740 (will help with syntax)

 

Clarity and Coherence:

1. “Which get people have preemptive” → Which permits/informs preemptive actions
2. “We have been growing up with the high strains of society” → We grew (GRUUUU) up exposed to the prejudices of the society around us
3. “Automatically have some discriminational” → automatically have some prejudices (thoughts/ideas) and discrimination (actions) tendencies
4. “Reduce this unpleasant situation” → avoid this unpleasant situation; reduce the occurrence of this unpleasant situation; to take precautions, preemptive actions
   

Lexical Resource:

1. “” → AI is becoming increasingly relevant to our everyday lives, and so I wanted to learn a little bit about it.
2. “” → AI has recently been very helpful in providing comprehensive crisis responses in countries around the world. AI allowed for effective responses to the Australian wildfires and the COVID pandemic by developing and running predictive models. Additionally, on a smaller scale, AI is also useful in medical and legal settings as it permits doctors and lawyers to collect vast amounts of pertinent (relevant) data. 
3. “Understanding of voice” → speech comprehension/detection
4. “” → used image search

Grammar Range and Accuracy:

1. “I’m more focusing on” → I’m focusing on/ Recently, I have been focusing on this more
2. “I sometimes met up with” → I sometimes meet up with
3. “Doctor can get good information for diagnose(verb)”[DIuhg-NOSE] → Doctors can get good information for diagnosis(noun) plural v. diagnoses {diuhg-NOSEES}
4. “In legal field” → in the legal field
5. “In the educate(verb)” → in the education (noun) sector  
6. “The provider have to” → the provider has to

Fluency and Pronunciation:

1. “workers” → (sounded like walkers) WUR-kurs
2. “technology” → (don’t cut off the last syllable) teck-naw-luh-GEEEEE