Japan is struggling to keep covid-19 at bay at the Olympics

世界最高のアスリートが4年ごとにオリンピックに集まるとき、彼らは走ったり、ジャンプしたり、泳いだりする以上のことをします。前回の東京大会の後に発表された回想録で、1964年、オーストラリアの水泳選手であるドーンフレーザーは、オリンピックのバブルの中での生活の幕を閉じました。 「オリンピックのモラルは、部外者が予想するよりもはるかに緩いです」と彼女は書いています。堕落に対する村の評判はそれ以来成長してきました。主催者は1988年にアスリートにコンドームを配り始め、表面上はHIVについての意識を高めました。 2016年にリオデジャネイロで開催された最後の夏季オリンピックで、彼らは記録的な45万人を配りました。元オリンピックスキーヤーの1人がアメリカのスポーツ出版物であるespnThe Magazineに書いたように、オリンピック村は「「不思議の国のアリス」のように、すべてが可能な魔法のようなおとぎ話のような場所です。金メダルを獲得でき、本当に熱い男と一緒に寝ることができます。」

https://connect.isa.org/blogs/full-free/2021/07/17/watch-kgf-chapte…

https://connect.isa.org/blogs/full-free/2021/07/17/watch-space-jam-…

https://connect.isa.org/blogs/full-free/2021/07/17/watch-escape-roo…

今年のオリンピックでは、雰囲気はより暗く、鈍く、貞淑になります。アスリートにとっては、70ページの禁止事項の本に記載されているように、村での生活は制限されます。彼らは、できるだけ遅く（イベント開始の5日前までに）日本に到着し、できるだけ早く（イベント終了後2日以内に）日本を離れるように求められています。彼らは、日本に向けて出発する前の4日間に行われた2つのテストで否定的な結果を提示し、到着時に別の否定的なテスト結果を提示する必要があります。アスリートの80％以上がワクチン接種を受けると予想されていますが、彼らは毎日テストを受け、確認されたケースは失格の可能性につながります。マスクは、睡眠、食事、競技の場合を除いて必須です。つまり、選手村のジムで運動しているときでも、それまでに到達した場合は、表彰台に立ってメダルを受け取るときでも、マスクを着用する必要があります。宿泊施設と競技会場以外に行くことはできません。すべての食事は、村のカフェテリアで混ざり合うことなく、すばやく食べる必要があります。村ではアルコールは提供されず、グループや公共の場所での飲酒は禁止されます。

開会式のちょうど1週間前に、感染の拡大は、スポーツ会場に観客がいなくても、パンデミック中に世界最大のスポーツイベントを開催するリスクを浮き彫りにしました。

東京の南西にある浜松市のホテルの7人のスタッフがコロナウイルスの検査で陽性だったと市当局者は語った。

しかし、柔道選手を含む31人の強力なブラジルのオリンピック代表団は、ホテル内の「バブル」にあり、他のゲストから分離されており、感染していません。

ロシアの女性の7人制ラグビーチームも、マッサージ師がCOVID-19の検査で陽性を示した後、孤立していたと、RIAの通信社はモスクワから報告しました。これは、南アフリカの男性のラグビーチームの一部でした。

伝染性の高いウイルス変異体が最近の感染の波を煽っており、人々へのより迅速な予防接種の失敗は日本の人口を脆弱なままにしています。

8月8日の大会終了後まで非常事態宣言が発令された東京では、水曜日に1,149件の新しいCOVID-19症例が記録され、1月22日以来最も多い。[nL1N2OQ0I3]

当局はCOVID-19を防ぐためにオリンピックの「バブル」を課しましたが、医療専門家は、オリンピックにサービスを提供するスタッフの移動が感染の機会を生み出す可能性があるため、完全にタイトではないかもしれないと心配しています。

昨年、ウイルスが世界中に蔓延したため延期されたオリンピックは、感染の急増を引き起こす恐れがあるため、日本では多くの国民の支持を失った。

国際オリンピック委員会（IOC）のトーマス・バッハ会長は、パンデミックの真っ只中にイベントを開催したことで主催者と日本人を称賛しました。

菅義偉首相と会談した後、バッハ氏は記者団に対し、「これらは歴史的なオリンピックになるだろう…ここ数年で日本人がこれほど多くの課題を克服した方法について」と語った。

2013年に日本が大会を受賞したとき、2011年の致命的な地震、津波、原発事故からの回復を祝うことが期待されていました。

日本の指導者たちはまた、今年再スケジュールされた大会がコロナウイルスに対する世界的な勝利をマークするのに役立つことを望んでいましたが、多くの国は現在、感染症の新たな急増に苦しんでいます。

MUTED GLOBAL INTEREST

多くのオリンピック代表団がすでに日本におり、数人のアスリートが到着時に陽性を示しています。

国際オリンピック委員会によると、難民オリンピックチームは、チーム関係者がカタールで陽性反応を示した後、日本への旅行を延期した。続きを読む

チームを主催している鹿児島市によると、南アフリカのラグビーチームの21人のメンバーは、飛行中に事件と密接に接触していたと考えられているため、孤立していた。

彼らは水曜日から市内に滞在する予定だったが、保健当局からのさらなる助言があるまでその計画は中止された、と市当局者の梶原毅氏は述べた。

東京オリンピックへの世界的な関心は薄れている、2のイプソス世論調査

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https://connect.isa.org/blogs/full-free/2021/07/17/watch-escape-roo…
https://ameblo.jp/pamgym/entry-12687010203.html
https://vocus.cc/article/60f2caacfd8978000161f78a
https://blog.goo.ne.jp/pamgym/e/7290907bf292f13f9f81b7333bc1d0b8
https://pamgyms.exblog.jp/28726816/
https://pamgym.at.webry.info/202107/article_2.html
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http://www.wdir1.com/link/985138/ahead-of-tokyo-olympics-japan-vacc…
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Here How AI is Changing the Way People Use Coupons

Coupons are the small vouchers or tickets issued by the sellers to offer customers discounts when they shop from them. Coupons have been used for many years to increase customer traffic at stores by attracting them with multiple discounts and offers. They are not just beneficial for customers but also for the sellers. Coupons have proved themselves time and again to be an important aspect of the eCommerce industry. 

Importance of coupons in digital marketing

Coupons are an integral component of online shopping. They not only reduce the cost of orders for the customers, but they also attract more customers to your online store. According to the latest coupon statistics, 90% of consumers use them at least once in their life. This data reveals that every second customer tends to use an online coupon while making an online purchase. 

There are reports that suggest that people are more likely to complete their online orders when they use coupons. And more than 50% of new customers are likely to visit your shop when they get a welcoming discount. Digital marketing is thriving on online coupons at the moment and it will become more dependent on digital coupons in the coming years. 

Considering the importance of coupon marketing, there should be some procedures and means to effectively use them. The most important technology impacting this mode of marketing is artificial intelligence AI.

Artificial intelligence and machine learning in coupon marketing

It is the age of artificial intelligence that has been driving all the businesses at a noticeable place and in no time will take over most of the industries. Artificial intelligence has been transforming the domain of digital marketing. AI offers instant personal customer services that help customers find the right products and discounts. 

The internet is brimming with data from people all over the world and AI is utilizing this (with the user’s permission) to bring the best products and offers to them. AI provides this information to the sellers and with its machine learning techniques, this information is used in the future as well. This makes it easy for businesses to organize their coupon marketing campaigns according to the likes and dislikes of their shoppers.

Backing up customers data for future coupons

AI is a complex system of programs that work by extracting patterns and insights from a large collection of data and uses it to make decisions. It collects data from the websites people use, unify it, and makes assumptions about their interests. This information, predicts what type of products the people would be interested in and provides them with coupons to shop for them.

This data is very useful for online businesses to attract their customers with their desired deals. The more data your AI acquires, the better it’ll be able to bring people to their desired page and desired discounts. 

AI using coupons for customer retention

It is not a hard task to attract people to business by giving them coupons. The real task is to make them stay after their first purchase. Many customers tend to avail their first order coupons and never return. It is tiresome for them to look for coupons, so they don’t spend more time in your store. Many businesses fail to retain their customers after the first purchases. Some customers even compare the coupons with other coupon offering websites to get maximum discounts. 

AI is helping businesses solve this problem. Instead of making customers search for the best coupons on their orders, AI automatically provides the best suitable coupons at the time of check out at one platform and makes it easier for customers. In this way, they become returning customers.

Predict worthwhile targets to send coupons

It is important for online businesses to send coupons to customers that are worth sending them. By repeatedly sending coupons to people that don’t benefit the business can actually result in loss. AI predicts customers that will use coupons but that is not enough. Coupons should be sent to the customers that will return to the store. 

If not, you will end up sending them to “coupon hungry” customers and it will create negative feedback on your business. Instead, the focus of your coupon campaign should be those loyal customers that will return to your store after getting a discount. That’s why AI intelligence analyzes the customer’s information using machine learning to find those “coupon-worthy” customers.

Purchase history to focus coupon marketing campaign

It is important to know the purchase history of the customers targeted in your coupon campaign. Specifically, their history regarding their behavior before and after they receive a coupon. The important details about the purchase history of a customer that AI collects are:

• The number of purchases that a customer has made
• Sum of money the customer has spent on the products
• How many products the customer has bought without coupons and discounts
• How often the customer visits the store
• How often does the customer use coupons
• How much money the customer has saved through coupons

This amount of data from various customers helps AI analyze the onsite behavior of the customers. AI predicts the moves of these customers and filters out the ones that are worth sending coupons to and increases the business’s revenue. This will reduce the amount the business spends on coupon marketing campaigns.

Make hesitant buyers your regular customers

One of the best features of artificial intelligence is that it can analyze the customer’s on-site behavior by the time they spend on the store, the pages they move around, and the time they spend on particular sections. In this way, the AI can predict the hesitant buyers who spend their time browsing through the online stores, “window shopping”, wondering if they should buy a particular item or not. 

Whatever the reason might be for their not completing the orders, these buyers can generate great revenues if they are encouraged to make the purchase. With the help of AI, these hesitant buyers can receive occasional coupons which will encourage them to shop from the store. This is just another way how AI can help you build efficient digital coupon marketing campaigns.

Automation of coupon marketing:

To extend the business and make it available for a larger audience, using and searching coupons should be made easier. Just like the logistics business, coupon searching should be a point on. With the implementation of AI in digitalized coupon marketing, the coupons should be available with a simple search of a query. 

There should be a self-learning algorithm that will update itself regularly according to the changing trends in discounts to let the customers avail of the maximum discounts. An enhancement would be to have the most suitable coupon according to the order automatically added at the time of check-out.

Chatbots helping in coupon marketing

One of the most significant advances brought by AI in customer-facing business is the emergence of chatbots. These chatbots have altered the outlook on customer-seller interactions. These chatbots reduce the human slack up to 90% as they provide 24/7 customer service. They instantly respond to the questions and queries of the customers. 

But in addition to that, they perform another vital task. They analyze the behavior of their customers and extract intricate information about them. This could lead to the creation of personable digital coupons in the e-commerce marketing industry. The chatbots will interact with their customers and will be able to create personable coupons specifically made for them. In this way, the old physical coupons with online codes will be in the past and the chatbots created, QR code scannable coupons, privately sent to customers will take charge of the digital coupon marketing.

Voice-activated assistants in coupon marketing

In the era of artificial intelligence, coupon marketing has become more conversational. The emergence of voice-activated assistants will prove to be a great incentive towards the digitalization of coupons. In 2018, Target issued 15$ voice-activated coupons to the customers who ordered through google express and said “spring into Target” into the voice-activated coupon option at the time of checkout. This opened gates for new possibilities in the field of online coupon marketing. 

With the emergence of voice-activated coupons, a larger audience would be able to benefit from them. It is estimated that by the end of next year, almost 50% of all the searches would be done through voice. It means AI providing us voice-generated coupons will be inevitable.

Wrapping up

The introduction of AI to the e-commerce business not only has sprouted it to grow but it has been providing new and innovative ideas. It has been proved by various points how AI has strengthened the coupon marketing campaign especially by the use of chatbots and voice searches. Now that you have the right information about building more efficient coupons with AI, it is now your job to utilize them efficiently and explore new horizons in business technologies.

Source Prolead brokers usa

How AI is Changing the Way People Use Coupons

Coupons are the small vouchers or tickets issued by the sellers to offer customers discounts when they shop from them. Coupons have been used for many years to increase customer traffic at stores by attracting them with multiple discounts and offers. They are not just beneficial for customers but also for the sellers. Coupons have proved themselves time and again to be an important aspect of the eCommerce industry. 

Importance of coupons in digital marketing

Coupons are an integral component of online shopping. They not only reduce the cost of orders for the customers, but they also attract more customers to your online store. According to the latest coupon statistics, 90% of consumers use them at least once in their life. This data reveals that every second customer tends to use an online coupon while making an online purchase. 

There are reports that suggest that people are more likely to complete their online orders when they use coupons. And more than 50% of new customers are likely to visit your shop when they get a welcoming discount. Digital marketing is thriving on online coupons at the moment and it will become more dependent on digital coupons in the coming years. 

Considering the importance of coupon marketing, there should be some procedures and means to effectively use them. The most important technology impacting this mode of marketing is artificial intelligence AI.

Artificial intelligence and machine learning in coupon marketing

It is the age of artificial intelligence that has been driving all the businesses at a noticeable place and in no time will take over most of the industries. Artificial intelligence has been transforming the domain of digital marketing. AI offers instant personal customer services that help customers find the right products and discounts. 

The internet is brimming with data from people all over the world and AI is utilizing this (with the user’s permission) to bring the best products and offers to them. AI provides this information to the sellers and with its machine learning techniques, this information is used in the future as well. This makes it easy for businesses to organize their coupon marketing campaigns according to the likes and dislikes of their shoppers.

Backing up customers data for future coupons

AI is a complex system of programs that work by extracting patterns and insights from a large collection of data and uses it to make decisions. It collects data from the websites people use, unify it, and makes assumptions about their interests. This information, predicts what type of products the people would be interested in and provides them with coupons to shop for them.

This data is very useful for online businesses to attract their customers with their desired deals. The more data your AI acquires, the better it’ll be able to bring people to their desired page and desired discounts. 

AI using coupons for customer retention

It is not a hard task to attract people to business by giving them coupons. The real task is to make them stay after their first purchase. Many customers tend to avail their first order coupons and never return. It is tiresome for them to look for coupons, so they don’t spend more time in your store. Many businesses fail to retain their customers after the first purchases. Some customers even compare the coupons with other coupon offering websites to get maximum discounts. 

AI is helping businesses solve this problem. Instead of making customers search for the best coupons on their orders, AI automatically provides the best suitable coupons at the time of check out at one platform and makes it easier for customers. In this way, they become returning customers.

Predict worthwhile targets to send coupons

It is important for online businesses to send coupons to customers that are worth sending them. By repeatedly sending coupons to people that don’t benefit the business can actually result in loss. AI predicts customers that will use coupons but that is not enough. Coupons should be sent to the customers that will return to the store. 

If not, you will end up sending them to “coupon hungry” customers and it will create negative feedback on your business. Instead, the focus of your coupon campaign should be those loyal customers that will return to your store after getting a discount. That’s why AI intelligence analyzes the customer’s information using machine learning to find those “coupon-worthy” customers.

Purchase history to focus coupon marketing campaign

It is important to know the purchase history of the customers targeted in your coupon campaign. Specifically, their history regarding their behavior before and after they receive a coupon. The important details about the purchase history of a customer that AI collects are:

• The number of purchases that a customer has made
• Sum of money the customer has spent on the products
• How many products the customer has bought without coupons and discounts
• How often the customer visits the store
• How often does the customer use coupons
• How much money the customer has saved through coupons

This amount of data from various customers helps AI analyze the onsite behavior of the customers. AI predicts the moves of these customers and filters out the ones that are worth sending coupons to and increases the business’s revenue. This will reduce the amount the business spends on coupon marketing campaigns.

Make hesitant buyers your regular customers

One of the best features of artificial intelligence is that it can analyze the customer’s on-site behavior by the time they spend on the store, the pages they move around, and the time they spend on particular sections. In this way, the AI can predict the hesitant buyers who spend their time browsing through the online stores, “window shopping”, wondering if they should buy a particular item or not. 

Whatever the reason might be for their not completing the orders, these buyers can generate great revenues if they are encouraged to make the purchase. With the help of AI, these hesitant buyers can receive occasional coupons which will encourage them to shop from the store. This is just another way how AI can help you build efficient digital coupon marketing campaigns.

Automation of coupon marketing:

To extend the business and make it available for a larger audience, using and searching coupons should be made easier. Just like the logistics business, coupon searching should be a point on. With the implementation of AI in digitalized coupon marketing, the coupons should be available with a simple search of a query. 

There should be a self-learning algorithm that will update itself regularly according to the changing trends in discounts to let the customers avail of the maximum discounts. An enhancement would be to have the most suitable coupon according to the order automatically added at the time of check-out.

Chatbots helping in coupon marketing

One of the most significant advances brought by AI in customer-facing business is the emergence of chatbots. These chatbots have altered the outlook on customer-seller interactions. These chatbots reduce the human slack up to 90% as they provide 24/7 customer service. They instantly respond to the questions and queries of the customers. 

But in addition to that, they perform another vital task. They analyze the behavior of their customers and extract intricate information about them. This could lead to the creation of personable digital coupons in the e-commerce marketing industry. The chatbots will interact with their customers and will be able to create personable coupons specifically made for them. In this way, the old physical coupons with online codes will be in the past and the chatbots created, QR code scannable coupons, privately sent to customers will take charge of the digital coupon marketing.

Voice-activated assistants in coupon marketing

In the era of artificial intelligence, coupon marketing has become more conversational. The emergence of voice-activated assistants will prove to be a great incentive towards the digitalization of coupons. In 2018, Target issued 15$ voice-activated coupons to the customers who ordered through google express and said “spring into Target” into the voice-activated coupon option at the time of checkout. This opened gates for new possibilities in the field of online coupon marketing. 

With the emergence of voice-activated coupons, a larger audience would be able to benefit from them. It is estimated that by the end of next year, almost 50% of all the searches would be done through voice. It means AI providing us voice-generated coupons will be inevitable.

Wrapping up

The introduction of AI to the e-commerce business not only has sprouted it to grow but it has been providing new and innovative ideas. It has been proved by various points how AI has strengthened the coupon marketing campaign especially by the use of chatbots and voice searches. Now that you have the right information about building more efficient coupons with AI, it is now your job to utilize them efficiently and explore new horizons in business technologies.

Source Prolead brokers usa

What Are the Top Mathematical Technologies That Traders Use Today

Algorithmic formulas are allowing quant trading to take over the financial capitals of the world. Math technologies are helping even the most novice traders conquer the stock market. But with so many trading software in the market, how do you choose the best one to use? The programming language on which the software is built is an excellent place to start.

Which Programming Language Is Best for Trading Software

The best programming language for trading is, by and large, determined by the transparency and ready-made features that the software built on it makes possible. Other things to consider are its strategy parameters, resiliency, general performance, and cost.

Excellent algorithmic trading software should include second-to-none research tools, execution engine, risk manager, and portfolio optimizer. Faulty software or one lacking the necessary features could be the reason you incur huge losses of your hard-earned cash. The following are some of the most preferred programming languages for trading software.

R

R programming language has been a choice language for statisticians and academics for over two decades. It is often the go-to programming language for statistical analysis. Primarily, R does what spreadsheets do, but faster and with greater ease.

What makes it stand out as a trading software is its benefits in data wrangling (tidying up data for use), data transformation (creating custom data sets), data analysis (executing statistical models), and practically all machine learning and visualization forms. R makes algorithmic trading a somewhat straightforward undertaking. That said, some intrinsic limitations show up as a trader’s needs increase.

Python

Python easily stands out as a trading programming language because of the ease with which it can be deployed in automated trading systems and machine learning. It is quite easy and straightforward for beginners to learn. It also has exclusive library functions that make it easy to code strategies in algorithmic trading.

Many traders prefer Python over C because it is faster in evaluating mathematical models. Given the centrality of speed in high-frequency trading, the less trading strategy time that Python affords the trader is a big part of its allure. However, it is somewhat slower than C++.

MATLAB

Like R, MATLAB is a programming language of choice for quantitative traders and researchers. Because of its focus on technical computing, MATLAB is an excellent choice for automated trading. What’s more, MATLAB is an integrated development platform with a user-friendly interface and debugger.

MATLAB easily stands out in backtesting compared to Visual Basic or Excel because it has an extensive database of built-in functions that are extremely helpful mathematical computations. For traders analyzing a large number of stocks simultaneously, MATLAB’s matrix manipulation and processing make such calculations as easy as analyzing a single stock. That said, it can be restrictive and somewhat risky when it comes to availability.

MQL5

MetaQuotes Language 5 (MQL5) is designed for algorithmic trading and is supported by a powerful community of helpful, highly skilled developers. It is an excellent programming language for creating utility applications, trading robots, and technical indicators that automate financial trading. Unlike other programming languages, MQL5 is designed primarily for financial trading. It, therefore, comes with an impressive list of built-in technical analysis functions and trade management features.

On top of its ease of use and extensive features, MQL5 is also fully compatible with R and Python. What that means is you can leverage the power of the most advanced programming languages within the MQL5 development environment. With that, you have the best of both worlds.

MQL5 makes it relatively easy to create automated financial trading and market analysis applications. Through the use of MLQ5 and other languages like R and Python, you can perform practically any type of data analysis and trade operation you can think of. On top of that, it makes it easy for traders to carry out trading operations and technical analysis in stock and forex exchange markets.

How to Choose the Best Trading Software

The best automated algorithmic trading software makes it easy to trade and increases profitability. Instead of creating custom trading software and platforms, the better approach is finding a trading application that checks all the necessary boxes to turn a profit. Here are some of the things to check for in trading software:

Supports all markets

The software you choose for financial trading should accept feeds in different formats, including FIX, Multicast, and TCP/IP. Go for algorithmic trading applications with the ability to process aggregated market feeds from an array of exchanges.

The best trading software should allow you to trade in different markets over multiple accounts while leveraging several strategies simultaneously. For instance, MetaTrader 5 enables hedge funds to diversify their trades and, as a result, spread their risk over many instruments and markets.

Offers data analysis tools

Traders have to keep tabs on the goings-on in the market in real-time. Without up-to-date information, the decisions you make as a trader could result in losses that could have been avoided. Go for trading software with data analysis tools that give you insights into what’s happening on trading floors live. Well-designed trading software like MetaTrader 5 goes a notch further; they show you visual trading representations through bars, broken lines, and Japanese candles.

Full transparency

Any trading software whose market and company data is not readily available for you to review is a no-go-zone. Algorithmic trading uses your hard-earned cash. You must ensure you know enough about the software you’re about to use for trading as well as the company that built it.

Go for trading software that values transparency. Work with a company that explains how they invest their money and the profit your investment is making—the ability to see the company data should be a built-in feature in the software.

Automated and fully prepared robots

The whole idea of leveraging mathematical technologies in trading is to make an otherwise tricky trading process possible and easy even for novice traders. Therefore, working with a trading platform with automated robots that do all the heavy lifting makes sense. For instance, MetaTrader 5 trading robots can analyze financial instruments quotes and execute trade operations on exchange and Forex markets.

The software also allows hedge funds to create, test, debug, implement, and optimize trading robots. And if the robots available are falling short of your requirements, there’s an option to order a trading robot to be custom-built for you.

One-click communications

Few things are as dreadful as investing money in an opaque trading platform where it’s impossible to tell where your money is and whether it’s returning a profit. To avoid the worry and uncertainty that such scenarios can bring, use trading software with one-click communications.

Trade with applications that make it easy to ask questions and get answers back. MetaTrader 5 goes a step further: it eliminates uncertainty by offering real-time fund performance with detailed reports to help you keep tabs on your algorithmic trading round the clock.

Final Thoughts

Building your own trading software is complex and often overwhelming, and most algorithmic trading software is costly. Yet, there are excellent options that live up to the pressures of exchange and forex trading and increase the chance that you’ll turn a profit. Few algorithmic trading software can match the power of MetaTrader 5 for hedge funds.

Users of this trading software can harness its capabilities by integrating it into practically any brokerage account. And with expert robotic advisors that implement automated strategies, the trading floor is open even for people with little or no programming and trading experience. In this highly competitive world, choosing the right mathematical technologies for trading is often the difference between making lots of money and wiping out your investment.

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Central Limit Theorem for Non-Independent Random Variables

The original version of the central limit theorem (CLT) assumes n independently and identically distributed (i.i.d.) random variables X1, …, Xn, with finite variance. Let Sn = X1 + … + Xn. Then the CLT states that

that is, it follows a normal distribution with zero mean and unit variance, as n tends to infinity. Here μ  is the expectation of X1.

Various generalizations have been discovered, including for weakly correlated random variables. Note that the absence of correlation is not enough for the CLT to apply (see counterexamples here). Likewise, even in the presence of correlations, the CLT can still be valid under certain conditions.  If auto-correlations are decaying fast enough, some results are available, see here.  The theory is somewhat complicated. Here our goal is to show a simple example to help you understand the mechanics of the CLT in that context. The example involves observations X1, …, Xn that behave like a simple type of time series: AR(1), also known as autoregressive time series of order one, a well studied process (see section 3.2 in this article).

1. Example

The example in question consists of observations governed by the following time series model: Xk+1 = ρXk + Yk+1, with X1 = Y1, and Y1, …, Yn are i.i.d. with zero mean and unit variance. We assume that |ρ|  <  1. It is easy to establish the following:

Here “~” stands for “asymptotically equal to” as n tends to infinity. Note that the lag-k autocorrelation in the time series of observations X1, …, Xn is asymptotically equal to ρ^k (ρ at power k), so autocorrelations are decaying exponentially fast. Finally, the adjusted CLT (the last formula above) now includes a factor 1 – ρ. If course if ρ = 0, it corresponds to the classic CLT when expected values are zero.

1.2. More examples

Let X1 be uniform on [0, 1] and Xk+1 = FRAC(bXk) where b is an integer strictly larger than one, and FRAC is the fractional part function. Then it is known that Xk also has a uniform distribution on [0, 1], but the Xk‘s are autocorrelated with exponentially decaying lag-k autocorrelations equal to 1 / b^k. So I expect that the CLT would apply to this case. 

Now let  X1 be uniform on [0, 1] and Xk+1 = FRAC(b+Xk) where b is a positive irrational number. Again, Xk is uniform on [0, 1]. However this time we have strong, long-range autocorrelations, see here. I will publish results about this case (as to whether or not CLT still applies) in a future article.

2. Results based on simulations

The simulation consisted of generating 100,000 time series X1, …, Xn as in section 1.1, with ρ = 1/2, each one with n = 10,000 observations, computing Sn for each of them, and standardizing Sn to see if it follows a N(0, 1) distribution. The empirical density follows a normal law with zero mean and unit variance very closely, as shown in the figure below. We used uniform variables with zero mean and unity variance to generate the deviates Yk.

Below is one instance (realization) of these simulated time series, featuring the first n = 150 observations. The Y-axis represents Xk, the X-axis represents k. 

It behaves quite differently from a white noise due to the auto-correlations.

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About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). He recently opened Paris Restaurant, in Anacortes. You can access Vincent’s articles and books, here.

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Why saying “We accept the Null Hypothesis” is wrong. – An Intuitive Explanation

We often come across YouTube videos, posts, blogs, and private courses wherein they say “We accept the Null Hypothesis” instead of saying “We fail to reject the Null hypothesis”.

If you correct them, they would say what s the big difference? “The opposite of ‘Rejecting the Null’ is ‘Accepting’ isn’t it ?”.

Well, it is not so simple as it is construed. We need to rise above antonyms and understand one crucial concept. That crucial concept is ‘opperian falsification.

This concept or philosophy also holds key to why we use the language “Fail to reject the Null”.

Basically, the Popperian falsification implies that ‘Science is never settled’. It keeps changing or evolving. Theories held sacrosanct today could be refuted tomorrow.

The Popperian falsification implies that ‘Science is never settled’. It keeps changing or evolving. Theories held sacrosanct today could be refuted tomorrow.

So under this principle, scientists never proclaim “X theory is true”. Instead what they try to prove that “the theory X is wrong”. This is called the principle of falsification.

Now having tried your best and you still could not prove the theory X is wrong, what would you say? You would say “I failed to prove theory X is wrong”. Ah.. now can you see the parallels between “I failed to prove theory X is wrong” and “We fail to reject the Null ”.

Now let’s come to why you can’t say “we accept the Null hypothesis”.

We could not prove theory X is wrong. But does that really mean theory X is correct? No, somebody smarter in the future could prove theory x is wrong. There always exists that possibility. Remember above that we said, “science is never settled”.

A more classic example is that of the ‘Black Swan’. “Suppose a theory proposes that all swans are white. The obvious way to prove the theory is to check that every swan really is white — but there’s a problem. No matter how many white swans you find, you can never be sure there isn’t a black swan lurking somewhere. So, you can never prove the theory is true. In contrast, finding one solitary black swan guarantees that the theory is false.”

Note: The post is merely to drive home the point of how the language “we fail to reject” came about. It is not a post favoring inductive reasoning over deductive reasoning or vice versa. Neither it is an effort to prove or disprove Karl Popper’s falsification principle.

Reference (Black swan example): https://www.newscientist.com/people/karl-popper/#ixzz70d4aPeIj

Your comments are welcome. You can reach out to me on

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PLM as a backbone for Disruptive Digital Thread

Product complexity is on the rise. Manufacturers need to understand customer requirements, define, and design products, collaborate with global designers and suppliers, ensure material feed and recipe integration, use varied manufacturing techniques, seek regulatory approvals, make processes and products sustainable, keep pace with shorter product lifecycles…there are too many moving parts in manufacturing making it extremely challenging to meet quality, cost and time-to-market goals while staying competitive. What modern manufacturing needs is a giant can of WD-40 to make all the parts interact and work smoothly. That can is Product Lifecyle Management (PLM).

PLM is designed so that the domains of engineering, manufacturing, and distribution do not have to work in siloes. Industry 4.0 technologies such as IoT, AR, VR, and 3D Printing have become a catalyst to reduce the gap between these domains even further. Modern PLM integrates Industry 4.0 technologies, effectively stitching together once-isolated clusters of knowledge: PLM becomes the digital thread running across domains, like a system-of-systems, binding the value chain of development, manufacturing, and distribution.

Some of the world’s leading manufacturers know how difficult it can be to propagate changes made to one system or product configuration across domains. Every department, from supplies to manufacturing, marketing to sales, and distribution to service, needs up-to-the-minute information on product changes so that it can continue to meet its KPIs efficiently.

Achieving efficiency in a digital environment lies in channeling feedback from design, manufacturing, sales, service, recycling, etc., to rapidly evolve products and portfolios. As the clusters of knowledge grow, from design to end of life, PLM should allow the complex changes to flow flawlessly across the lifecycle of the product. Industry forecasts show that the demand for these capabilities, coupled with rapid digital adoption, will see the market for PLM grow from US$50.7 billion in 2019 to US$73.7 billion by 2024.[i]

While PLM has done well in discrete manufacturing, it is about to make a huge dent in process industries such as oil and gas, paper products, textiles, and chemicals. PLM is no longer constrained by on-premise infrastructure—which has traditionally taken long to get off the ground. Today, PLM has become available in the cloud, with attractive cost and time-to-implement models.

PLM will become central in process manufacturing to optimize operations and take rapid decisions. Manufacturers adopting PLM will also be able to examine the previously isolated clusters of wisdom—say, design, sourcing, and costing—with a right click of the mouse. They will be able to tell when a component in a plant is about to fail, the impact of the failure on downstream processes, and how they can avoid shutdowns. They will be able to navigate changes, from design and build to operations and recycle, in an instant. And they will be able to automate their decisions, change recipe cards and plant arrangements to meet dynamic demand changes with the least possible downtime. Manufacturers hoping to maximize ROI from their digital investments and industry 4.0 technologies will make PLM their ticket to success.

[i] https://www.marketsandmarkets.com/Market-Reports/product-lifecycle-…

Authors:

Adnan Ghauri

Director Enterprise Architect-PLM

Baker Hughes

Akhil Jain

Vice President-PLM

ITC Infotech

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DSC Weekly Digest 13 July 2021

I talk to a lot of people involved in the data science and machine learning space every week – some vendors, some company CDOs, many just people in the trenches, trying to build good data models and make them monetizable.

When I ask what part of the data science pipeline they have the hardest part with, the answer is almost invariably “We can’t get enough good data.”

This is not just a problem with machine learning, however. Knowledge Graph projects have run aground because they discover that too much of the data that they have lacks sufficient complexity (read, connectivity) to make modeling worthwhile. The data is often poorly curated, poorly organized, and lacking in semantic metadata. Some data, especially personal data, is heavily duplicated, has keys that have been lost in context, and in many cases cannot in fact be collected without a court order. Large relational databases have been moved into data lakes or enterprise data warehouses, but the data within them often heavily reflects operational rather than contextual information, made worse by the fact that many programmers have at best only limited training in true data modeling practices.

What this means is that the content that drives the initial training of the data model is noisy, with the signal so weak that any optimizations made in the model itself may put the data scientist into a position where they are able to reach the wrong conclusions faster.

Effective data strategy involves assessing the acquisition of the data from the beginning, and recognizing that this acquisition will require the expenditure of money, time, and personnel. There are reasons why data aggregators usually tend to benefit heavily from being early adopters – they discovered this truth the hard way, and made the investment to make their businesses data scoops, with effective data acquisition and ingestion strategies rather than just assuming that the relational databases in the back office actually had worthwhile grist for the mill.

As data science and machine learning pipelines become more pervasive in organizations and become more automated, through MLOps and similar processes, this need for good source data is likely to be one that every organization’s CDO needs to attend to as soon as possible. After all, garbage in can only mean garbage out.

In media res,

Kurt Cagle
Community Editor,
Data Science Central

To subscribe to the DSC Newsletter, go to Data Science Central and become a member today. It’s free! 

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13 Chatbot trends and statistics you can’t afford to miss in 2021!

Machine learning is the technology behind the development of chatbots which involves:

• A computer program that learns and evolves
• Natural language processing (NLP) to mimic human-generated text and language

Before the advent of the internet, face-to-face meetings and phone calls dominated the communication landscape. Years later, online forms, mobile apps, social media, and emails have taken over as modern forms of communication.

A multitude of industries employ chatbots that help visitors navigate through a website or answer their questions. Given the highly competitive market, customers have increased expectations from brands.

Incorporating chatbots help meet those expectations. Also, AI is constantly evolving, which means that chatbots will become more sophisticated in the future. Hence, we will discuss AI chatbot trends in 2021 that you should consider adopting in your strategy. 

Let’s brush up on your knowledge of chatbot statistics 2021.

Key Chatbot Statistics

Before going into the chatbot statistics for 2021 and beyond, it’s essential to look at the state of the conversational AI market overall.

Conversational AI Market 

Research suggests that the spending on will surpass $77 Bn by 2022. The forecast for 2019 was $35.8 Bn, which means that it will likely double by next year. A third of that spending will be incurred on software.

Besides, a significant chunk of software spending will go towards AI applications such as chatbots and personal assistants (nearly $14.1 billion). 

Apart from that, the market is anticipated to experience a stellar growth at 30.5% CAGR (compound annual growth rate) between 2017-2022. In terms of market size, North America leads the global conversational AI market.  

However, when it comes to CAGR, Asia Pacific (APAC) will experience the fastest growth during the forecast period. Besides, the adoption of conversational AI software is widespread in North America.

An increasing government spending, large number of chatbot companies, and rising investments in artificial intelligence and machine learning are responsible for the adoption.

Chatbot Forecasts & Predictions

Listed below are key chatbot forecasts and predictions, divided by year:

2021

• About 50% of businesses will increase their annual expenditure on Chabot creation compared to conventional mobile app development.
• Chatbots are expected to see a whopping investment of $4.5 billion by 2021.
• The implementation of artificial intelligence in customer service interactions is anticipated to rise by 400% between 2017-2021.
• AI will manage approximately 85% of customer interactions by 2021. 

2022

• Businesses across different domains would save nearly $0.70 per customer interaction, thanks to chatbots (CNBC). 
• About 90% of customer interactions with banks would be automated using chatbots by 2022 (Juniper Research). 
• Chatbots are projected to save almost $8 billion annually for businesses by 2022. 
• About 70% of white-collar employees would regularly interact with chatbots.

2023

• Businesses and customers will save 5 billion hours on interactions because of chatbots by 2023. 
• Chatbot eCommerce transactions are projected to surpass a value of $112 billion by 2023.
• The banking, retail, and healthcare sectors will save nearly $11 billion a year by employing chatbots in customer service.
• The global chatbot market is expected to observe a double-digit growth at 34.64% CAGR between 2017-2023. 

2024

• The global conversational AI market is forecasted to reach $15.7 billion at 30.2% CAGR by 2024. 
• AI will redefine 50% of user experiences using natural language, computer vision, augmented reality, and virtual reality. 
• The global chatbot market is predicted to observe an impressive growth at 34.75% CAGR between 2019-2024. Moreover, it will cross $7.5 billion by the end of 2024.

2025

• Nearly 95% of customer interactions (online conversations and live telephone) will be taken over by artificial intelligence by 2025. However, at this point, it will be pretty tricky for customers to differentiate a chatbot from a human agent. 
• Businesses that leverage AI to automate their customer engagement will observe a 25% increase in their operational efficiency. 
• The annual revenue of the global AI software market will cross $118 billion by 2025. 

2026

• By 2026, the global chatbot market will touch $10.8 billion with an impressive CAGR of 30.9% during the 2018-2026 forecast period. 

These statistics indicate that chatbots are the future. However, with further advancements in AI, only capable and intelligent conversational AI platforms shall persist. 

In the next section, we will look at the chatbot trends more closely.

Chatbot Trends in 2021

Given the many advantages of chatbots, rising chatbot trends, and the ever-increasing popularity of machine learning (ML) and artificial intelligence, adopting new technologies has become business-critical.

Here are the top 13 chatbot trends in 2021 that you should consider embracing:

1. Payments via Chatbots

First on our list of chatbot trends is payment gateways. Several payment services, including Paypal, have incorporated chatbots in their payment gateways and digital wallets in 2020.

This shows that the application of chatbots is no longer limited to just customer service. For example, users simply have to type “pay electricity bill” in this application.

The  would walk them through the process until the final payment is made. 

2. Voice Bots

Voice-driven search, thanks to the advent of conversational AI, is taking the world by storm. It is the next big thing. According to a report by Accenture, most consumers prefer voice-based interfaces over text-based interfaces on messaging platforms.

The trend of utilizing conversational bots to assist consumers over both voice and text is on the rise. In sectors such as education, travel, and insurance, voice bots would be of great help. 

It’s all about providing a seamless user experience!

3. Chatbots with Human Touch

Chatbots use artificial intelligence to suggest options when a customer types in their inquiry. Year after year, they are becoming increasingly sophisticated in terms of mimicking human conversation. 

They will identify the right intent behind a query and provide an accurate response to that query. By learning from interactions, chatbots are beginning to pick up patterns in user behavior. 

Hence, chatbots are becoming indispensable for high-impact conversations.

4. Chatbots with Emotional Intelligence

This is slowly becoming a reality with newer, groundbreaking technology. For instance, facial feature detection AI software can detect the feelings of a person.

Similarly, chatbots with emotional intelligence can figure out your mood (happy, sad, or angry) by looking for patterns in your text. It could be capitalization or punctuation or your voice to predict your emotions.

Conversational AI with soft skills will further humanize the interaction between businesses and their customers. 

5. Chatbots based on Natural Language Processing

Chatbots driven by NLP are groundbreaking for businesses that have more excellent customer service scenarios. They can determine the user intent and generate responses accordingly.

They learn from past conversations with the customer to provide accurate answers. The Royal Bank of Scotland (RBS), for instance, has already incorporated NLP-powered chatbots to enhance their customer experience.

Check out this blog to learn more about how custom chatbots can help you improve customer satisfaction.

In 2021, you will observe more and more organizations adopting  to improve conversations. 

6. Analytics and Insights with Chatbot

To measure your performance, you need to track and evaluate data. And for that, you need chatbot analytics and insights. The good news is most chatbots today offer analytics so that you can continue to improve your strategies.

They keep a record of:

• every question asked
• every answer delivered
• every query transferred to agents

Use this information to improve your product or service. Therefore, chatbots allow you to foster strong customer relationships. 

7. Multilingual Capabilities

Next on our list of chatbot trends is multilingual capabilities. Did you know that only 20% of the world’s population speaks English? So if you plan to expand your business globally or already cater to a global customer base, you need chatbots with multilingual capabilities. 

This will not only offer a boost to your localization efforts but also amplify your reach. But, again, it’s because customers prefer to use their native tongue to communicate with their trusted brands. 

Multilingual bots enable you to tap into new markets and personalize the experience for your audience.

8. Supporting Remote Workforce

A recent report by Gartner says that about 75% of business leaders would allow 5% of their employees to work from home post-pandemic permanently.

Apart from that, 25% agreed to permanently shift 10% of their employees to the remote work setting. Hence, it’s safe to say that such a work culture is here to stay.

In such a scenario, chatbots would become pivotal in answering typical employees’ concerns. 

They can:

• answer FAQs on remote work policies
• track employee health
• notify employees of the latest changes in policies
• offer work from home tips

9. Chatbots on Social Media

Social media is the hub of much social interaction in present times. We have moved on from just making friends on social media to voicing opinions, ordering products and services, offering reviews, and even getting in touch with businesses. Thus, it becomes a necessity for businesses to use chatbots to facilitate interaction on these platforms.

Many industry leaders in various sectors have already employed chatbots to use this vital resource to better understand the customer needs and even improve ways that the business can help the consumers.

Facebook already has a chatbot feature, but it is very limited in its capabilities; perhaps it was only a test to whether chatbots would fare well on the platform. The answer is a resounding yes. Facebook has now become a trendsetter in equipping businesses with the ability to use customized chatbots made by other parties to help in this process. Every social media platform is likely to follow suit.

10. Chatbots Built with Low Code Platforms

The pandemic forced companies to become more agile in their processes. This, in turn, led to the development of low-code chatbot platforms that allow businesses to deploy apps at a much faster rate.

Even less experienced users can build chatbots using these low-code development platforms.

Many organizations have deployed chatbots for sales support, customer support, service desk management, and more with this approach.

11. Recruiting and HR Chatbots

A recruiting chatbot can filter candidates, answer their basic questions, and schedule them for interviews. As a result, they speed up the entire recruitment process.

These chatbots are helpful even after the hiring process is over. During onboarding, they can answer the most basic HR questions. This is undoubtedly a boon for large organizations with massive workforces.

12. Self-Learning Chatbots

To stay ahead in the race, it is of utmost importance to train bots with new data and keep them up-to-date. In 2021, you can expect companies to make chatbots that are self-learning.

This means companies do not have to spend time feeding new data to bots. They will analyze the pattern in every interaction and train themselves to keep the users or customers engaged. In other words, bots will learn to improve their response capabilities based on user feedback.

13. Text Messaging with Chatbot

While every other mode of communication, like email and phone calls, still holds ground, the most personal still remains – a personal message. SMS and WhatsApp are the go-to apps that people regularly check and are comfortable conversing over.

In 2021 and beyond, chatbots are going to be leaning onto this opportunity to better connect with audiences. Many companies like Yatra and MakeMyTrip already use certain chatbot features to send flight tickets directly to Whatsapp and the details via SMS. This has made the process convenient for users, and any progress would only make things easier.

In 2021, you will see SMS and create a personalized experience and facilitate open-ended conversations.

The Future of Chatbots

In a rapidly maturing conversational AI market, haphazardly placed chatbots will vanish. Only the ones that are strategically developed and implemented across channels shall survive.

Across all digital platforms, millions of users will prefer voice-enabled conversational AI to interact with an enterprise. Such is the future of chatbots. Moreover, various chatbot applications will no longer remain siloed.

Businesses would be able to create an intranet of chatbots that can share information and work together seamlessly. In the meantime, chatbots will continue to boost user engagement and enhance customer support. 

This article was originally published here

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