Aiton English

Learning Languages for Life

Podcast – Develop culture of long-term growth

Pre-listening:

  1. How consistent are you as a leader (think of various factors / spheres such as expectations, rules, transparency, attitude etc)?
  2. How properly do you plan in terms of strategic planning?
  3. Does your larger corporate vision cascade down into individual business units?
  4. What do you think about the resident equity model?
  5. How do you develop culture within an organization?
  6. How well does it drive growth?
  7. When you take on new people, how important is it for their skills to align with the culture as opposed to the position?
  8. What are the advantages of chasing profit as opposed to revenue?
  9. How do you align strategy and the measurable goals that you set up?
  10. How effective are dashboards in motivating staff?
  11. Is fostering relationships the most important ingredient success for work and life in general?

Listening:

Preposition quiz

Python NLP Task: Scrape website to get words in bold

This is a student vocabulary list. The student copied a lot of text and put in bold the new expressions he is interested in. I only want the words in bold. Please use Python + the beautiful soup library to remove all words not in bold

TEXT BELOW….

What does this really mean in the near term, and what do we think we’ll see in a few years?

To separate the hype from the substance

I want to dive right in to the terminology for our listeners

Let me start with a couple of framing elements

We’re just getting started.

Think about that now from an enterprise business perspective

I do want to make sure we sort of simplify a little bit at least for me and for some of the listeners

At the most basic level

Jack, let me turn it over to you before we go deeper into some of these other areas.

So there’s a lot of capital that’s going to be required to realize this.

What new participants do you see emerging in the connectivity ecosystem?

We think that there’s both an offensive and defensive perspective;

from a defensive perspective they have massive customer bases and assets, ….. on the offensive side, they also have a chance to explore a plethora of new business models and develop new solutions that drive more top-line growth for their business and adjacent businesses

If we can overcome this fiber challenge, Dave, what are the new business models that we may begin to see evolving?

They’ll be able to choose to be part of the connected economy, and hopefully at price points that they can afford.

I would encourage all industries to be thinking about how they can best capitalize and take advantage of the opportunities that these higher speeds and increased access are going to enable

Consumers are without a doubt going to benefit from it

new players and technology, as well as new industries, are beginning to rewrite the rules.

5 G geopolitics

When it comes to your business and Russia, what do you think about this idea?

In the race to dominate the next generation of cellular networks, both the United States and China know there’s much more at stake than ultrafast internet.

United States believes that whoever controls 5G, the fifth generation of wireless communication, will have a global advantage for decades to come.

The fear is that China is almost there

David, tell us about what happened in Germany earlier this month.

Michael, it was a really remarkable scene at the Munich Security Conference

Mike Pence

It is my honor to join you for the 55th Annual Munich Security Conference.

Under President Donald Trump, the United States will seize every opportunity to achieve peace.

But we will approach every challenge with our eyes wide open.

We will deal with the world as it is, not as we wish it to be.

The United States has also been very clear with our security partners on the threat posed by Huawei and other Chinese telecom companies.

America is calling on all our security partners to be vigilant and to reject any enterprise that would compromise the integrity of our communications technology or our national security systems.

We must protect our critical telecom infrastructure. We cannot ensure the defense of the West if our allies grow dependent on the East.

Do not let Chinese companies and the Chinese government into your communications systems, because you will forever poison the security of your countries and perhaps your relationship with the United States.

5G overview,

This is the future and it will be powered by 5G.

·     self-driving cars,

·     smart cities,

·     fully connected homes,

·     robots.

These are the networks that will connect the internet of things,

the billions of different devices we’re now attaching to the internet to the central networks and to the cloud.

5G is the next generation of wireless service. And it may be closer than many people think.

·      It’ll be the way that our autonomous vehicles run.

·      It’ll be the way our machinery runs.

·      It’ll be the way our gas pipelines, our water systems run.

All of them connected into these networks.

Whoever dominates these fifth-generation networks will have an economic intelligence and military edge for decades to come.

Because in future conflicts, the war starts not with nuclear weapons, not with artillery. It starts with unplugging a country— their electricity. And it starts, of course, with their communication networks.

The 5G networks are getting ready to get rolled out.

It’s billions and billions of dollars of investment.

And the decisions on those investments will be made in the next 6 to 18 months.

This is the new arms race. In the old Cold War, people counted missiles. In the new era, you’re going to count who controls which networks.

it became one of the fastest growing tech companies in the world.

Well, what they’re largely offering, Michael, is a lower price.

First, there are developing countries that just don’t have very much money to go build these networks. So if the Chinese come along with incredibly good terms, that’s pretty appealing, right?

you better think twice about letting the Chinese build the core of the networks that connect the political and military leadership to the rest of the world

We don’t really want all of our messages going directly to Beijing.

Well, you know, if I strike a really good trade deal with Xi Jinping, the Chinese president, maybe we’ll just release Ms. Meng.

China and the United States are engaged in the last stages of this enormously complex set of trade talks that President Trump has escalated.

think there’s one strong argument in favor of letting Huawei compete in some of these Western countries, and maybe even compete in the United States. It’s that if they want to have their equipment and their software inside the United States, they have to show it to American authorities.

Deep learning: FIFA supervised learning video

Teach yourself

Use the internet (including “pictures / картинки”) to quickly get a general idea of the following”

  • What is deep learning and how is it connected to artificial intelligence and machine learning?
  • What is a neural network?
  • One type of neural network is an LSTM network. What do the letters “LSTM” stand for?
  • How many people in the world speak “Indian” English?

Now read the video questions then watch and answer them:

According to the speaker, why is the network better at recognizing a player then the ball?

What do the two LSTM networks do?

Why can’t the program defend well?

Speaking Grammar: Talk about the past

To increase speaking range, practice using the past grammar structures below to answer the speaking questions at the bottom of the page

grammar structures for the past

Talking about a past habit that isn’t now I used to exercise a lot but now I do nothing
Giving background before main event I had always wanted / hoped to / dreamed of
Showing disappointment by talking about what didn’t happen I had hoped / I had expected / I had anticipated that I would … but..
Showing relief (talking about something bad that didn’t happen) I could (easily) have died
Talking about a result We ended up going home.
Reminiscing about the past When I was young I would (often / always) play football (every day) after school
imagining about the past 1 If I had(n’t) done it…..careful!
imagining about the past 2 (before ) Had my parents not helped me so much, I never would have become a …
criticizing about the past 1


I (really / probably) should have started studying Egnlish ealier.
I (really / probably) shouldn’t have chosen my career.



Criticizing about the past 2 (when someone chose the wrong thing) The hospitals are bad now. The government would have been better off spending more on them and less on the army.
Critiziing about the past 3 (soft) My parents let me play too much when I was young. It would have been better if they had made me study more.
Critcizing about the past 4 (strong) Politicians are really bad. They could have done a lot more in the last 10 years to help people.


speculating about the past 1 : I am sure that (+ past) People before must have worked less before because they did not have computers.
speculating about the past 2: I am not sure / I can guess that People before might have been happier as there was less stress. me might / could / might well / could well have liked it because
speculating about the past 3: kind of sure / good guess Families might well have had an easier life before because there were less divorces.
speculating about the past 4: sure NOT People can’t have had a better life before because they died earlier than now.
speculating about the past (until now) 5: I’m sure sure Most people won’t have tried Fijian food.
speculating about the past 6: I’m logically sure You would have been a good student at school


complaining about the past The government could have invested more money in hospitals in the last 10 years. The hospitals now are really bad
regretting about the past 1 I wish I / we / the government had (n’t) done it because had I done so…
regretting about the past 2 If only I had(n’t) done it, I would (n’t)…..

questions (IELTS Part 2) about the past

Talk about a time you made a mistake

– When was it

– Why did you make the mistake

– What did you do about it

And what happened as a result

Talk about a time someone you know made a big mistake

– When was it

– Why did he/she make the mistake

– What did you do about it

And what happened as a result

Talk about a time you were late for something

– When was it

– Why were you late

– What did you and others think about this

What happened as a result

Talk about a time you made someone unhappy

– Who was the person

– What did you do

– How did you feel about it

Did the other person forget about this

Talk about a time someone made you feel bad

– Who was the person

– What did he / she do to make you feel bad

– What happened as a result

and do you still see this person

Talk about a time you forgot something important

– What was it

– Why did you forget it

– What were the consequences

And has this happened again

Talk about a time you missed a good opportunity

– When was it

– What was the opportunity

– Why did you miss it

And did you get the opportunity again

Talk about a time you had difficulty going to sleep.

– When was it

– What caused the difficulty

– What did you do about it

and what was the end result

Talk about a time you were a passenger in a car

– Whose car was it

– Where were you going

– Why

and what did you think about it

Talk about a time you were irritated or annoyed

– When was it

– How badly annoyed or irritated were you

– Why

and what was the end result

Talk about a time you needed a break

– What were you doing

– Why

– Why did you need the break

and if you took it

Tesla Artificial Intelligence

You’re going to watch a video with Elon Musk + Tesla specialist to learn how Tesla cars use artificial intelligence (neural networks) to drive by themselves. Before watching, do two things:

1 – In this video of a self-driving car, why do you think the camera has some things in blue, some in green, some in red, yellow, purple etc..

2 – Use the internet to answer these questions in 15 minutes:

a – Why is it difficult for computers to recognize images of things like animals, trees or cars?


b – What does an artificial neural network look like? What does the human brain’s neural network look like? (CLUE: use google pictures)

c -How long does it take to train a neural network to do something like recognize a dog? How long does it take to train a little child to recognize a dog?

d – Is it easier to train the car to drive in a lane in a highway, or to drive in this situation…

e – How many cars does Tesla have in its fleet? Why is this an advantage when it comes to self driving?

VIDEO:

35 minutes: Now watch the Tesla presentation from the Artificial Neural Network expert: (from 1 hour fifty one minutes to 2 hours twenty five mark – if you want you can continue watching the Question and answer session )

Python NLP Task: Clean HTML and measure speed

Problem

I recently created a task for a student learning Russian, but when I copied the text from a webpage I got the HTML as well as the text.


Here is the “raw” text with HTML..

<p>Джейме — старший сын лорда <a href=”https://gameofthrones.fandom.com/ru/wiki/Тайвин_Ланнистер“>Тайвина
Ланнистера</a>, главы дома <a href=”https://gameofthrones.fandom.com/ru/wiki/Ланнистеры“>Ланнистеров</a>,
(богатый) семьи <a href=”https://gameofthrones.fandom.com/ru/wiki/Семь_Королевств“>Семи
Королевств</a>. В детстве Джейме не любил
читать, чтение (даваться)
ему ___ трудом, и ему приходилось упражняться
(час), с (тот) пор он не очень-то любит это
занятие.
</p>


Task 1


 

Your task is to use two different libraries to clean the raw text of HTML. Library 1 = regex (import re) / Library two = Beautiful Soup (bs4 )Ci.e. you will have two different programs that do the same thing, clean the HTML from a raw text.

The “clean” text should look like this………………….

Джейме — старший сын лорда Тайвина
Ланнистера, главы дома Ланнистеров,
(богатый) семьи Семи
Королевств. В детстве Джейме не любил
читать, чтение (даваться)
ему _ трудом, и ему приходилось упражняться
(час), с (тот) пор он не очень-то любит это
занятие.

Task 2

The final task is to measure speed. Almost always in programming there is more than one way to achieve a goal, and often the one that is quicker is better. USe google to find out how to measure the time it takes the two code solutions


Python NLP Task: How many words?

My student wants to know how many words she will need to learn for the Chinese intermediate exam. Use Python to give her an answer. Each word is on a new line

拿上
你猜
听不懂
读书
走路
来接我
骑车
报纸
护士
服务员
售票员
暖和
散步
考试
成绩
开会
地址
放假
沙发
草地
超市里
变化
到处
附近
图书馆
准时到了
经常
最近

邻居
花了钱

应该

饼干
常常
盒子里面
两种糖
必须

记得
除了


葡萄
教室
打扫

干净
认真
聊天

另外
出去的时候
别忘了
窗户

西边
方便

校园
打排球
肚子都圆了

介绍
今天多云
可能会
奇怪
停电了
讨厌
游戏
打算
暑假
参观
长城
担心
安全
钢琴弹
真不错
开始
她看起来
不但。。而且
健康
年轻
她看起来不但健康,而且年轻。
或者
空调
春天
夏天
关于
一会儿
讨论

快点儿
打扰一下
地铁站
离这儿远吗
公里
在路东
准备
如果
没什么其他事儿
离开
毕业
见面

好像
声音
手表
决定
刚才
生气
厉害
熟悉
表演
大声地笑了起来
国外
电子邮件
联系
和我联系
发烧了
虽然
没有出汗
一直很不舒服
参加
比赛
紧张
心情
练习
努力
一点儿也不难
我太马虎了
做错了
重要
容易
复习
玩具
在排队
节日
爬山
有礼貌
警察
写作业
秋天
凉快
初中的知识
已经
学会了
洗手间
二楼
楼下
下课的时候
突然
明白
蝴蝶

放哪儿
手机
手机响了
句子
照相
太辣了
打网球
体育
体育馆
虫子
天气越来越热了
将来
我希望
刷牙

危险
眼镜
戴眼镜
兴趣
兴趣
对画画儿感兴趣
坚持

锻炼
身体
锻炼
坚持
饮料
非常
号码
公园
迷路了
清楚
看不清楚
安静
别害怕
新闻
然后
烤鸭
进去
放寒假
旅游

地图
在三层
打针
习惯
搬到
洗脸
刷牙
电视声音
懂礼貌
骑自行车
破了
合适
变 瘦 了
上班
累坏了
盘子
办法
受不了
注意
注意安全
记住
其他
其他人都
同意
信封
都行
冰箱
身体健康
祝您身体健康
幸福
幸福快乐
大概
离开
马虎
毕业
饭馆
尝尝
电梯
做游戏


Python NLP Task: lines in a text

Convert the text below so that is looks like in the photo below. i.e. putting the “broken” lines into normal paragraphs

Blazing Trails

Thanks to the Appen TTS engine, GuildLink was the frst company in the world to
provide audio CMIs and is still one of the only ones doing it. “There’s an increase
in demand for accurate and current medicines information and being able to
provide the information in multiple ways has helped alleviate that,” says Paonne.
“Appen’s text-to-speech conversion has been pivotal in helping us provide more
information in more ways and helping patients understand what they’re taking.”

GuildLink’s pioneering service has led to partnerships with several Australian
government-sponsored websites that use the information in the company’s
database. Those include the Therapeutic Goods Administration site, and other
distributors of medicine information.

But the company doesn’t want to stop there. “To further improve accessibility,
we’d like to look into translating the CMIs into multiple languages in the future,”
says Paonne. “It’s so important that consumers have high-quality, accurate
information they can understand. Our philosophy is the more information you
give people, the better.”

Business Machine Learning: text-to-speech

The Situation

It’s critical for patients to understand what medicine they’re taking and how to take it. Every prescription medication in Australia requires an accompanying Consumer Medicines Information (CMI) leaflet, describing what the medicine does, how to take it, and possible side effects. The trouble with the leaflets is that the information on them changes frequently, and it’s difficult to get updated information to consumers.

GuildLink, the pre-eminent provider of up-to-date medicine information for consumers and health providers, solves this problem by storing, managing and distributing CMIs electronically. They recognized the need for audio CMIs to meet the needs of consumers who can’t use the printed information for a variety of reasons—including impaired vision, a non-English-speaking background, and learning disabilities such as dyslexia

Blazing Trails

Thanks to the Appen TTS engine, GuildLink was the frst company in the world to
provide audio CMIs and is still one of the only ones doing it. “There’s an increase
in demand for accurate and current medicines information and being able to
provide the information in multiple ways has helped alleviate that,” says Paonne.
“Appen’s text-to-speech conversion has been pivotal in helping us provide more
information in more ways and helping patients understand what they’re taking.”

GuildLink’s pioneering service has led to partnerships with several Australian
government-sponsored websites that use the information in the company’s
database. Those include the Therapeutic Goods Administration site, and other
distributors of medicine information.

But the company doesn’t want to stop there. “To further improve accessibility,
we’d like to look into translating the CMIs into multiple languages in the future,”
says Paonne. “It’s so important that consumers have high-quality, accurate
information they can understand. Our philosophy is the more information you
give people, the better.”

from here

retail case

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