Beyond Evergrande, China’s Property Market Faces a $5 Trillion Reckoning

As many economists say China enters what is now the final phase of one of the biggest real-estate booms in history, it is facing a staggering bill: According to economists at Nomura, $ 5 trillion plus loans that developers had taken at a good time. Holdings Inc.

The debt is almost double that at the end of 2016 and last year exceeded the overall economic output of Japan, the world’s third-largest economy.

With warning signs on the debt of nearly two-fifths of growth companies borrowed from international bond investors, global markets are poised for a potential wave of defaults.

Chinese leaders are getting serious about addressing debt by taking a series of steps to curb excessive borrowing. But doing so without hurting the property market, crippling more developers and derailing the country’s economy is turning into one of the biggest economic challenges for Chinese leaders, and one that resonates globally when mismanaged. could.

Luxury Developer Fantasia Holdings Group Co. It failed to pay $206 million in dollar bonds that matured on October 4. In late September, Evergrande, which has more than $300 billion in liabilities, missed two interest-paying deadlines for the bond.

A wave of sell-offs hit Asian junk-bond markets last week. On Friday, bonds of 24 of 59 Chinese growth companies on the ICE BofA Index of Asian Corporate Dollar Bonds were trading at over 20% yields, indicating a high risk of default.

Some potential home buyers are leaning, forcing companies to cut prices to raise cash, and could potentially accelerate their slide if the trend continues.

According to data from CRIC, a research arm of property services firm e-House (China) Enterprise Holdings, overall sales among China’s 100 largest developers were down 36 per cent in September from a year earlier. Ltd.

It revealed that the 10 largest developers, including China Evergrande, Country Garden Holdings Co. and china wenke Co., saw a decline of 44% in sales compared to a year ago.

Economists say most Chinese developers remain relatively healthy. Beijing has the firepower and tighter control of the financial system needed to prevent the so-called Lehman moment, in which a corporate financial crisis snowballs, he says.

In late September, Businesshala reported that China had asked local governments to be prepared for potentially intensifying problems in Evergrande.

But many economists, investors and analysts agree that even for healthy enterprises, the underlying business model—in which developers use credit to fund steady churn of new construction despite the demographic less favorable for new housing—is likely to change. Chances are. Some developers can’t survive the transition, he says.

Of particular concern is some developers’ practice of relying heavily on “presales”, in which buyers pay upfront for still-unfinished apartments.

The practice, more common in China than in the US, means developers are borrowing interest-free from millions of homes, making it easier to continue expanding but potentially leaving buyers without ready-made apartments for developers to fail. needed.

According to China’s National Bureau of Statistics, pre-sales and similar deals were the region’s biggest funding sources since August this year.

“There is no return to the previous growth model for China’s real-estate market,” said Hous Song, a research fellow at the Paulson Institute, a Chicago think tank focused on US-China relations. China is likely to put a set of limits on corporate lending, known as the “three red lines” imposed last year, which helped trigger the recent crisis on some developers, he added. That China can ease some other restrictions.

While Beijing has avoided explicit public statements on its plans to deal with the most indebted developers, many economists believe leaders have no choice but to keep the pressure on them.

Policymakers are determined to reform a model fueled by debt and speculation as part of President Xi Jinping’s broader efforts to mitigate the hidden risks that could destabilize society, especially at key Communist Party meetings next year. before. Mr. Xi is widely expected to break the precedent and extend his rule to a third term.

Economists say Beijing is concerned that after years of rapid home price gains, some may be unable to climb the housing ladder, potentially fueling social discontent, as economists say. The cost of young couples is starting to drop in large cities, making it difficult for them to start a family. According to JPMorgan Asset Management, the median apartment in Beijing or Shenzhen now accounts for more than 40 times the average family’s annual disposable income.

Officials have said they are concerned about the risk posed by the asset market to the financial system. Reinforcing developers’ business models and limiting debt, however, is almost certain to slow investment and cause at least some slowdown in the property market, one of the biggest drivers of China’s growth.

The real estate and construction industries account for a large portion of China’s economy. Researchers Kenneth S. A 2020 paper by Rogoff and Yuanchen Yang estimated that industries, roughly, account for 29% of China’s economic activity, far more than in many other countries. Slow housing growth could spread to other parts of the economy, affecting consumer spending and employment.

Government figures show that about 1.6 million acres of residential floor space were under construction at the end of last year. This was roughly equivalent to 21,000 towers with the floor area of ​​the Burj Khalifa in Dubai, the tallest building in the world.

Housing construction fell by 13.6% in August below its pre-pandemic level, as restrictions on borrowing were imposed last year, calculations by Oxford Economics show.

Local governments’ income from selling land to developers declined by 17.5% in August from a year earlier. Local governments, which are heavily indebted, rely on the sale of land for most of their revenue.

Another slowdown will also risk exposing banks to more bad loans. According to Moody’s Analytics, outstanding property loans—mainly mortgages, but also loans to developers—accounted for 27% of China’s total of $28.8 trillion in bank loans at the end of June.

As pressure on housing mounts, many research houses and banks have cut China’s growth outlook. Oxford Economics on Wednesday lowered its forecast for China’s third-quarter year-on-year GDP growth from 5% to 3.6%. It lowered its 2022 growth forecast for China from 5.8% to 5.4%.

As recently as the 1990s, most city residents in China lived in monotonous residences provided by state-owned employers. When market reforms began to transform the country and more people moved to cities, China needed a massive supply of high-quality apartments. Private developers stepped in.

Over the years, he added millions of new units to modern, streamlined high-rise buildings. In 2019, new homes made up more than three-quarters of home sales in China, less than 12% in the US, according to data cited by Chinese property broker Kei Holdings Inc. in a listing prospectus last year.

In the process, developers grew to be much bigger than anything seen in the US, the largest US home builder by revenue, DR Horton. Inc.,

Reported assets of $21.8 billion at the end of June. Evergrande had about $369 billion. Its assets included vast land reserves and 345,000 unsold parking spaces.

For most of the boom, developers were filling a need. In recent years, policymakers and economists began to worry that much of the market was driven by speculation.

Chinese households are prohibited from investing abroad, and domestic bank deposits provide low returns. Many people are wary of the country’s booming stock markets. So some have poured money into housing, in some cases buying three or four units without the intention of buying or renting them out.

As developers bought more places to build, land sales boosted the national growth figures. Dozens of entrepreneurs who founded growth companies are featured on the list of Chinese billionaires. Ten of the 16 soccer clubs of the Chinese Super League are wholly or partially owned by the developers.

Real-estate giants borrow not only from banks but also from shadow-banking organizations known as trust companies and individuals who invest their savings in investments called wealth-management products. Overseas, they became a mainstay of international junk-bond markets, offering juicy produce to snag deals.

A builder, Kaisa Group Holdings Ltd. , defaulted on its debt in 2015, was still able to borrow and later expand. Two years later it spent the equivalent of $2.1 billion to buy 25 land parcels, and $7.3 billion for land in 2020. This summer, Cassa sold $200 million of short-term bonds with a yield of 8.65%.

By: Quentin Webb & Stella Yifan Xie 

Source: Beyond Evergrande, China’s Property Market Faces a $5 Trillion Reckoning – WSJ

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Employers Need To Tread Carefully On The Road Back To Office Working

Open plan office

In some ways the coming weeks and months are likely to be more difficult for organizations and employees than the past year or so has been. With governments increasingly intent on opening up economies effectively closed down by the pandemic, uncertainty is rife.

Employers and staff alike are caught between wanting to go back to something like normal and not wishing to take too many risks, especially since the Delta variant of the coronavirus is pushing spikes in new cases even in countries such as the U.S. and the U.K. where significant proportions of the population have been at least partially vaccinated.

One factor that could be behind the unease about rushing back to normal working habits is a feeling that, just as governments made mistakes in the handling of the crisis, so too did organizations. According to a survey just out from the finance comparison platform NerdWallet, a third of the U.K.’s business leaders are dissatisfied with the way that staff have been managed through the pandemic.

A similar proportion said that financial stability and business productivity was put ahead of staff safety. Unsurprisingly perhaps, more than half of the nearly 1,000 decision-makers questioned said they planned to carry out a review of how they had handled things. However, nearly half have already invested in new equipment designed to improve health and safety and to facilitate social distancing, while more than half have introduced greater flexibility to working hours.

Employers’ definitions of flexibility appear to be, well, flexible. An insight into the current situation is provided by the consultancy Mercer in its latest survey of working policies and practices among nearly 600 employers in the U.S.. The key findings were:

  • Hybrid working — a blend of in-person and remote working — was favoured by vast majority.
  • Predominantly office-based working was the preference of a fifth of employers.
  • Fully remote or virtual-first working was the choice of just 6% of employers
  • A distributed model making increased use of satellite campuses was likely to be adopted by just 4%.

Mercer’s research and analysis suggests that, across all industries, the proportion of the workforce working on-site full-time is likely to be about 40%. The hybrid category will probably be split, with about 29% of the workforce working remotely one or two days a week and approximately 17% doing so three or four days a week. About 14% of workers are expected to work remotely full-time.

The challenge for employers will be deciding how they can retain the employee experience and hang on to talent. Lauren Mason, principal in Mercer’s career business, and Ravin Jesuthasan, global leader of Mercer’s transformation business, suggest five principles to consider:

  1. Empower teams but set guidelines:  Nearly all employers plan to bring in changes to working policies as a result of the pandemic. Nearly half are already actively developing a strategy, while nearly a quarter of employers are in the process of implementing or have already implemented plans. Employers can and should empower teams to continue to work flexibly but they should also establish guidelines to maximize business outcomes and ensure a consistent employee experience.
  2. Keep a pulse on the market and your competition: Flexibility will likely have a high impact on an organization’s ability to retain talent. If employees are unhappy about employers’ flexible working plans, they will be likely to consider other workplaces that might better meet their needs.
  3. Don’t rush to get employees to the office: Employers should focus on returning employees in a way where co-working benefits can be maximized immediately. They should concentrate on making workers feel energized, empowered and engaged to be back together with their colleagues. This may entail phased transitions, where employees may only initially come in one or two days a week, planned team meetings or on-site social events and celebrations to make those early office days more purposeful.
  4. Stay agile: Workers do not want or need a standardized solution. Employers can demonstrate a continued trust and sense of partnership that was so valued during the pandemic by providing options that are appropriate for the work being performed. The key is to give employees some control and flexibility.
  5. Don’t limit flexibility to remote work: Flexible working is about more than remote working. Inclusive flexibility ensures that all jobs can be flexible when needed. Given the massive challenges employers are facing in attracting and retaining workers, options such as flexible schedules or compressed workweeks can be a huge differentiator. Progressive companies are not just challenging “when” and “where” work is done but also how the it is done, who does it and what the work is.
Check out my website.

I am a U.K.-based journalist with a longstanding interest in management. In a career dating back to the days before newsroom computers I have covered everything from popular music to local politics. I was for many years an editor and writer at the “Independent” and “Independent on Sunday” and have written three books, the most recent of which is “What you need to know about business.”

Source: Employers Need To Tread Carefully On The Road Back To Office Working

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Train Your Brain to Remember Anything You Learn With This Simple, 20-Minute Habit

Not too long ago, a colleague and I were lamenting the process of growing older and the inevitable increasing difficulty of remembering things we want to remember. That becomes particularly annoying when you attend a conference or a learning seminar and find yourself forgetting the entire session just days later.

But then my colleague told me about the Ebbinghaus Forgetting Curve, a 100-year-old formula developed by German psychologist Hermann Ebbinghaus, who pioneered the experimental study of memory. The psychologist’s work has resurfaced and has been making its way around college campuses as a tool to help students remember lecture material. For example, the University of Waterloo explains the curve and how to use it on the Campus Wellness website.

I teach at Indiana University and a student mentioned it to me in class as a study aid he uses. Intrigued, I tried it out too–more on that in a moment. The Forgetting Curve describes how we retain or lose information that we take in, using a one-hour lecture as the basis of the model. The curve is at its highest point (the most information retained) right after the one-hour lecture. One day after the lecture, if you’ve done nothing with the material, you’ll have lost between 50 and 80 percent of it from your memory.

By day seven, that erodes to about 10 percent retained, and by day 30, the information is virtually gone (only 2-3 percent retained). After this, without any intervention, you’ll likely need to relearn the material from scratch. Sounds about right from my experience. But here comes the amazing part–how easily you can train your brain to reverse the curve.


With just 20 minutes of work, you’ll retain almost all of what you learned.

This is possible through the practice of what’s called spaced intervals, where you revisit and reprocess the same material, but in a very specific pattern. Doing so means it takes you less and less time to retrieve the information from your long-term memory when you need it. Here’s where the 20 minutes and very specifically spaced intervals come in.

Ebbinghaus’s formula calls for you to spend 10 minutes reviewing the material within 24 hours of having received it (that will raise the curve back up to almost 100 percent retained again). Seven days later, spend five minutes to “reactivate” the same material and raise the curve up again. By day 30, your brain needs only two to four minutes to completely “reactivate” the same material, again raising the curve back up.

Thus, a total of 20 minutes invested in review at specific intervals and, voila, a month later you have fantastic retention of that interesting seminar. After that, monthly brush-ups of just a few minutes will help you keep the material fresh.


Here’s what happened when I tried it.

I put the specific formula to the test. I keynoted at a conference and was also able to take in two other one-hour keynotes at the conference. For one of the keynotes, I took no notes, and sure enough, just shy of a month later I can barely remember any of it.

For the second keynote, I took copious notes and followed the spaced interval formula. A month later, by golly, I remember virtually all of the material. And in case if you’re wondering, both talks were equally interesting to me–the difference was the reversal of Ebbinghaus’ Forgetting Curve.

So the bottom line here is if you want to remember what you learned from an interesting seminar or session, don’t take a “cram for the exam” approach when you want to use the info. That might have worked in college (although Waterloo University specifically advises against cramming, encouraging students to follow the aforementioned approach). Instead, invest the 20 minutes (in spaced-out intervals), so that a month later it’s all still there in the old noggin. Now that approach is really using your head.

Science has proven that reading can enhance your cognitive function, develop your language skills, and increase your attention span. Plus, not only does the act of reading train your brain for success, but you’ll also learn new things! The founder of Microsoft, Bill Gates, said, “Reading is still the main way that I both learn new things and test my understanding.”

By: Scott Mautz

Source: Pocket

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Critics:

Dr. John N. Morris is the director of social and health policy research at the Harvard-affiliated Institute for Aging Research. He believes there are three main guidelines you should follow when training your mind:

  1. Do Something Challenging: Whatever you do to train your brain, it should be challenging and take you beyond your comfort zone.
  2. Choose Complex Activities: Good brain training exercises should require you to practice complex thought processes, such as creative thinking and problem-solving.
  3. Practice Consistently: You know the saying: practice makes perfect! Dr. Morris says, “You can’t improve memory if you don’t work at it. The more time you devote to engaging your brain, the more it benefits.”
  4. If you’re looking for reading material, check out our guides covering 40 must-read books and the best books for entrepreneurs.
  5. Practice self-awareness. Whenever you feel low, check-in with yourself and try to identify the negative thought-loop at play. Perhaps you’re thinking something like, “who cares,” “I’ll never get this right,” “this won’t work,” or “what’s the point?” 
  6. Science has shown that mindfulness meditation helps engage new neural pathways in the brain. These pathways can improve self-observational skills and mental flexibility – two attributes that are crucial for success. What’s more, another study found that “brief, daily meditation enhances attention, memory, mood, and emotional regulation in non-experienced meditators.”
  7. Brain Age Concentration Training is a brain training and mental fitness system for the Nintendo 3DS system.
  8. Queendom has thousands of personality tests and surveys. It also has an extensive collection of “brain tools”—including logic, verbal, spatial, and math puzzles; trivia quizzes; and aptitude tests
  9. Claiming to have the world’s largest collection of brain teasers, Braingle’s free website provides more than 15,000 puzzles, games, and other brain teasers as well as an online community of enthusiasts.

 

6 Math Foundations to Start Learning Machine Learning

As a Data Scientist, machine learning is our arsenal to do our job. I am pretty sure in this modern times, everyone who is employed as a Data Scientist would use machine learning to analyze their data to produce valuable patterns. Although, why we need to learn math for machine learning? There is some argument I could give, this includes:

  • Math helps you select the correct machine learning algorithm. Understanding math gives you insight into how the model works, including choosing the right model parameter and the validation strategies.
  • Estimating how confident we are with the model result by producing the right confidence interval and uncertainty measurements needs an understanding of math.
  • The right model would consider many aspects such as metrics, training time, model complexity, number of parameters, and number of features which need math to understand all of these aspects.
  • You could develop a customized model that fits your own problem by knowing the machine learning model’s math.

The main problem is what math subject you need to understand machine learning? Math is a vast field, after all. That is why in this article, I want to outline the math subject you need for machine learning and a few important point to starting learning those subjects.

Machine Learning Math

We could learn many topics from the math subject, but if we want to focus on the math used in machine learning, we need to specify it. In this case, I like to use the necessary math references explained in the Machine Learning Math book by M. P. Deisenroth, A. A. Faisal, and C. S. Ong, 2021.

In their book, there are math foundations that are important for Machine Learning. The math subject is:

Image created by Author

Six math subjects become the foundation for machine learning. Each subject is intertwined to develop our machine learning model and reach the “best” model for generalizing the dataset.

Let’s dive deeper for each subject to know what they are.

Linear Algebra

What is Linear Algebra? This is a branch of mathematic that concerns the study of the vectors and certain rules to manipulate the vector. When we are formalizing intuitive concepts, the common approach is to construct a set of objects (symbols) and a set of rules to manipulate these objects. This is what we knew as algebra.

If we talk about Linear Algebra in machine learning, it is defined as the part of mathematics that uses vector space and matrices to represent linear equations.

When talking about vectors, people might flashback to their high school study regarding the vector with direction, just like the image below.

Geometric Vector (Image by Author)

This is a vector, but not the kind of vector discussed in the Linear Algebra for Machine Learning. Instead, it would be this image below we would talk about.

Vector 4×1 Matrix (Image by Author)

What we had above is also a Vector, but another kind of vector. You might be familiar with matrix form (the image below). The vector is a matrix with only 1 column, which is known as a column vector. In other words, we can think of a matrix as a group of column vectors or row vectors. In summary, vectors are special objects that can be added together and multiplied by scalars to produce another object of the same kind. We could have various objects called vectors.

Matrix (Image by Author)

Linear algebra itself s a systematic representation of data that computers can understand, and all the operations in linear algebra are systematic rules. That is why in modern time machine learning, Linear algebra is an important study.

An example of how linear algebra is used is in the linear equation. Linear algebra is a tool used in the Linear Equation because so many problems could be presented systematically in a Linear way. The typical Linear equation is presented in the form below.

Linear Equation (Image by Author)

To solve the linear equation problem above, we use Linear Algebra to present the linear equation in a systematical representation. This way, we could use the matrix characterization to look for the most optimal solution.

Linear Equation in Matrix Representation (Image by Author)

To summary the Linear Algebra subject, there are three terms you might want to learn more as a starting point within this subject:

  • Vector
  • Matrix
  • Linear Equation

Analytic Geometry (Coordinate Geometry)

Analytic geometry is a study in which we learn the data (point) position using an ordered pair of coordinates. This study is concerned with defining and representing geometrical shapes numerically and extracting numerical information from the shapes numerical definitions and representations. We project the data into the plane in a simpler term, and we receive numerical information from there.

Cartesian Coordinate (Image by Author)

Above is an example of how we acquired information from the data point by projecting the dataset into the plane. How we acquire the information from this representation is the heart of Analytical Geometry. To help you start learning this subject, here are some important terms you might need.

  • Distance Function

A distance function is a function that provides numerical information for the distance between the elements of a set. If the distance is zero, then elements are equivalent. Else, they are different from each other.

An example of the distance function is Euclidean Distance which calculates the linear distance between two data points.

Euclidean Distance Equation (Image by Author)
  • Inner Product

The inner product is a concept that introduces intuitive geometrical concepts, such as the length of a vector and the angle or distance between two vectors. It is often denoted as ⟨x,y⟩ (or occasionally (x,y) or ⟨x|y⟩).

Matrix Decomposition

Matrix Decomposition is a study that concerning the way to reducing a matrix into its constituent parts. Matrix Decomposition aims to simplify more complex matrix operations on the decomposed matrix rather than on its original matrix.

A common analogy for matrix decomposition is like factoring numbers, such as factoring 8 into 2 x 4. This is why matrix decomposition is synonymical to matrix factorization. There are many ways to decompose a matrix, so there is a range of different matrix decomposition techniques. An example is the LU Decomposition in the image below.

LU Decomposition (Image by Author)

Vector Calculus

Calculus is a mathematical study that concern with continuous change, which mainly consists of functions and limits. Vector calculus itself is concerned with the differentiation and integration of the vector fields. Vector Calculus is often called multivariate calculus, although it has a slightly different study case. Multivariate calculus deals with calculus application functions of the multiple independent variables.

There are a few important terms I feel people need to know when starting learning the Vector Calculus, they are:

  • Derivative and Differentiation

The derivative is a function of real numbers that measure the change of the function value (output value) concerning a change in its argument (input value). Differentiation is the action of computing a derivative.

Derivative Equation (Image by Author)
  • Partial Derivative

The partial derivative is a derivative function where several variables are calculated within the derivative function with respect to one of those variables could be varied, and the other variable are held constant (as opposed to the total derivative, in which all variables are allowed to vary).

  • Gradient

The gradient is a word related to the derivative or the rate of change of a function; you might consider that gradient is a fancy word for derivative. The term gradient is typically used for functions with several inputs and a single output (scalar). The gradient has a direction to move from their current location, e.g., up, down, right, left.

Probability and Distribution

Probability is a study of uncertainty (loosely terms). The probability here can be thought of as a time where the event occurs or the degree of belief about an event’s occurrence. The probability distribution is a function that measures the probability of a particular outcome (or probability set of outcomes) that would occur associated with the random variable. The common probability distribution function is shown in the image below.

Normal Distribution Probability Function (Image by Author)

Probability theory and statistics are often associated with a similar thing, but they concern different aspects of uncertainty:

•In math, we define probability as a model of some process where random variables capture the underlying uncertainty, and we use the rules of probability to summarize what happens.

•In statistics, we try to figure out the underlying process observe of something that has happened and tries to explain the observations.

When we talk about machine learning, it is close to statistics because its goal is to construct a model that adequately represents the process that generated the data.

Optimization

In the learning objective, training a machine learning model is all about finding a good set of parameters. What we consider “good” is determined by the objective function or the probabilistic models. This is what optimization algorithms are for; given an objective function, we try to find the best value.

Commonly, objective functions in machine learning are trying to minimize the function. It means the best value is the minimum value. Intuitively, if we try to find the best value, it would like finding the valleys of the objective function where the gradients point us uphill. That is why we want to move downhill (opposite to the gradient) and hope to find the lowest (deepest) point. This is the concept of gradient descent.

Gradient Descent (Image by Author)

There are few terms as a starting point when learning optimization. They are:

  • Local Minima and Global Minima

The point at which a function best values takes the minimum value is called the global minima. However, when the goal is to minimize the function and solved it using optimization algorithms such as gradient descent, the function could have a minimum value at different points. Those several points which appear to be minima but are not the point where the function actually takes the minimum value are called local minima.

Local and Global Minima (Image by Author)
  • Unconstrained Optimization and Constrained Optimization

Unconstrained Optimization is an optimization function where we find a minimum of a function under the assumption that the parameters can take any possible value (no parameter limitation). Constrained Optimization simply limits the possible value by introducing a set of constraints.

Gradient descent is an Unconstrained optimization if there is no parameter limitation. If we set some limit, for example, x > 1, it is an unconstrained optimization.

Conclusion

Machine Learning is an everyday tool that Data scientists use to obtain the valuable pattern we need. Learning the math behind machine learning could provide you an edge in your work. There are many math subjects out there, but there are 6 subjects that matter the most when we are starting learning machine learning math, and that is:

  • Linear Algebra
  • Analytic Geometry
  • Matrix Decomposition
  • Vector Calculus
  • Probability and Distribution
  • Optimization

If you start learning math for machine learning, you could read my other article to avoid the study pitfall. I also provide the math material you might want to check out in that article.

 

By: Cornellius Yudha Wijaya

Source: 6 Math Foundations to Start Learning Machine Learning | by Cornellius Yudha Wijaya | Towards Data Science

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Critics:

Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data“, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the “signal” or “feedback” available to the learning system:

  • Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
  • Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
  • Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that’s analogous to rewards, which it tries to maximize.

References

Want to Raise Successful Kids? Science Says These 5 Habits Matter Most

I’ve been on a mission, collecting science-based parenting advice both here in my column on Inc.com and in my continuously updated free e-book How to Raise Successful Kids, which you can download here.

Here’s a short but detailed look at five of the most useful studies that I’ve found, and the habits they suggest for successful parents.

1. Be a role model (but not their only role model).

Let’s give the plot twist up front: Kids need great role models, but one of the most important roles you can model is how you deal with failure.

Deal with it honestly, openly, and transparently. Let them see that you do sometimes try and come up short. Because, of course, they will fail at things themselves, and you want to teach them two things:

  • Don’t be afraid or ashamed of failure, especially if they’ve given it their all.
  • Rebound from it the right way.

A few years ago, researchers at the Massachusetts Institute of Technology ran experiments with children as young as 15 months old. The more their parents let them see that they struggled and failed at times, the more resilient the kids became.

“There’s some pressure on parents to make everything look easy,” one of the study’s leads said. “[T]his does at least suggest that it may not be a bad thing to show your children that you are working hard to achieve your goals.”

Beyond that? Make sure they have great role models, both in their lives and in literature.

2. Teach them to love the outdoors.

This advice seems especially timely as we emerge from the pandemic. But kids need to be outside.

Studies show that kids who spent a lot less time outdoors during the early days of the coronavirus crisis experienced a strikingly negative effect on their emotional well-being.

This almost seems like common sense, but we see it come up again and again in both children and adults.

These kinds of habits — and a lifelong appreciation for nature (or not) — can start young, and cost almost nothing.

Against this — and I’m no Luddite, and I know we live in a digital world, but — researchers have found that happiness and well-being among U.S. middle schoolers has declined steadily since 2012.

Hmmm, what happened in 2012? That’s when American kids largely started to get their own smartphones, combined with unlimited data plans.

3. Teach them to prioritize kindness.

A couple of years ago, psychologist and business school professor Adam Grant and his wife, Allison Sweet Grant, wrote a book about kids and kindness. In an article they wrote for The Atlantic around the same time, they made an interesting point:

  • More than 90 percent of U.S. parents say that “one of their top priorities is that their children be caring.”
  • But if you ask children what their parents’ top priorities are for them,  “81 percent say their parents value achievement and happiness over caring.”

There’s a disconnect. And it might stem from people not realizing one of the most fascinating paradoxes, which is that people who demonstrate kindness and caring for others are often more likely to achieve what they want as a result.

As the Grants put it:

Boys who are rated as helpful by their kindergarten teacher earn more money 30 years later. Middle-school students who help, cooperate, and share with their peers also excel–compared with unhelpful classmates, they get better grades and standardized-test scores.

The eighth graders with the greatest academic achievement, moreover, are not the ones who got the best marks five years earlier; they’re the ones who were rated most helpful by their third-grade classmates and teachers.

And middle schoolers who believe their parents value being helpful, respectful, and kind over excelling academically, attending a good college, and having a successful career perform better in school and are less likely to break rules.

We see this in negotiations, too: Develop empathy with the people you’re dealing with, care legitimately about what they want as well as what you want, and you’re more likely to reach a desirable resolution.

4. Praise them the right way.

There are at least three facets of praising kids well that I’ve found in my surveys of the research.

The first is to praise kids for their effort, not their gifts. I’ve gotten a bit of pushback on this idea recently, which I’ll address in a future column. But in short:

  • Good: I’m very proud of you. I saw how hard you studied for that test.
  • Not-so-good: I knew you’d do well on that test. You’re so smart and naturally good at math.

The second is to praise them authentically. Kids aren’t stupid (mostly). They know if you’re blowing smoke when you praise them for things that don’t really merit praise. But they also need reinforcement to know that you’re proud and think they’re doing the right things.

In one study of 300 kids, researchers found that:

When parents perceived that they over- or underpraised their children for schoolwork, children performed worse in school and experienced depression to a greater extent, as compared with children whose parents thought their praise accurately reflected reality.

Finally, however: Be generous with your praise in terms of quantity.

A three-year study out of Brigham Young University found that there’s no magic amount of praise, but it’s helpful to do so as often as possible. One trick might be to break down tasks and praise for each one specifically, as opposed to holding your positive reinforcement until the end of a task.

5. Be there for them, and then some.

This last bit of advice is perhaps the hardest because it flies in the face of one of the parenting clichés we all want to avoid: namely, becoming a helicopter parent.

That said, I’m going to combine studies here, and at least give you food for thought — if not a complete guide.

The bottom line up front is to be there, be vocal, and be involved, while still letting your kids do for themselves as much as they can.

  • Study No. 1: Researchers found that girls whose mothers “nagged the heck out of them” were less likely to become pregnant as teenagers, more likely to go to college, and less likely to have long periods of unemployment or get stuck in dead-end jobs.
  • Study No. 2: A series of studies, actually, found that parents who were quick to run to their children’s side when they faced big challenges or had setbacks — at almost any age — wound up raising kids who were more successful and had better relationships with their parents as they got older.

In short, you’re your child’s parent, and they need you to act like that: guiding them, pushing them, and showing that you’ll always be there for them. Do that much, and you’re doing quite a lot.

By: Bill Murphy Jr., http://www.billmurphyjr.com@BillMurphyJr

Source: Want to Raise Successful Kids? Science Says These 5 Habits Matter Most | Inc.com

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Critics:

Parenting or child rearing promotes and supports the physical, emotional, social, and intellectual development of a child from infancy to adulthood. Parenting refers to the intricacies of raising a child and not exclusively for a biological relationship. The most common caretaker in parenting is the father or mother, or both, the biological parents of the child in question. However, a surrogate may be an older sibling, a step-parent, a grandparent, a legal guardian, aunt, uncle, other family members, or a family friend.

Governments and society may also have a role in child-rearing. In many cases, orphaned or abandoned children receive parental care from non-parent or non-blood relations. Others may be adopted, raised in foster care, or placed in an orphanage. Parenting skills vary, and a parent or surrogate with good parenting skills may be referred to as a good parent. Parenting styles vary by historical period, race/ethnicity, social class, preference, and a few other social features.

Additionally, research supports that parental history, both in terms of attachments of varying quality and parental psychopathology, particularly in the wake of adverse experiences, can strongly influence parental sensitivity and child outcomes.

Parenting does not usually end when a child turns 18. Support may be needed in a child’s life well beyond the adolescent years and continues into middle and later adulthood. Parenting can be a lifelong process.

Parents may provide financial support to their adult children, which can also include providing an inheritance after death. The life perspective and wisdom given by a parent can benefit their adult children in their own lives. Becoming a grandparent is another milestone and has many similarities with parenting.

See also

How The Power Of Predictive Analytics Can Transform Business

Tableau analytics visual

With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:

  • How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
  • What prevents people from more deeply exploring and using data?
  • In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?

With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.

Current realities and barriers to scale smarter decision-making with AI 

With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.

Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.

At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.

Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.

The opportunity to solve data science challenges

This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:

  • Reducing data exploration and prep work
  • Empowering analyst experts to deliver data science outputs at lower costs
  • Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
  • Extending, automating, and accelerating analysis for business groups and domain experts
  • Reducing time and costs spent on deploying and integrating models
  • Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias

Business scenarios that benefit from predictive analytics 

There are several business scenarios where predictive capabilities can be immensely useful.

Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.

A solution to consider: Tableau Business Science

We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.

This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.

When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.

Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.

Source: How The Power Of Predictive Analytics Can Transform Business

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Critics:

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Predictive analytics is used in actuarial science,marketing,business management, sports/fantasy sports, insurance,policing, telecommunications,retail, travel, mobility, healthcare, child protection, pharmaceuticals,capacity planning, social networking and other fields.

One of the best-known applications is credit scoring,[1] which is used throughout business management. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.

For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.

See also

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