Author Archives: Beiyue Wang

How will Artificial Intelligence Change the HR Industry?

Beiyue Wang

Abstract

Artificial intelligence (AI) is increasingly pervasive in HR industry and brings lots of potentials. This article is focused on deblackboxing the ways that Artificial Intelligence change the HR industry and trying to find out its advantages and limitations through analyzing the design principle and algorithm, and then provide business advices of future AI use in HR industry.

  1. Introduction

Nowadays, AI and ML technologies are advancing at a phenomenal rate and immersing into different kinds of industries. To some extent, this is a world of “ubiquitous computing’. The English Oxford dictionary defines AI as “The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” Three core components — high-speed computation, a huge amount of quality data and advanced algorithms differentiate AI from ordinary software (The new age: artificial intelligence for human recourse opportunities and functions, 2018).

AI technologies offer great opportunities to business. More than 40% of employers across the globe already use AI in some way, a Deloitte study in 2018 found, with “exponential” growth expected over the next five years. One of the changes brought by AI is improving HR functions, such as recruiting and talent acquisition, providing real-time information to employees, training services, etc. According to Forbes Human Recourse, AI may revolutionize and redefine the recruiting and hiring process. (The Rise of AI In HR: Nine Notable Developments That Will Impact Recruiting And Hiring, 2018) Not only the employment-oriented service business, like LinkedIn, many other companies — from Allstate to Hilton to Five Guys Burgers and Fries — are also using AI to help recruiters and hiring manager screen, review and interview job candidates.

This article is focused on deblackboxing the ways that Artificial Intelligence change the HR industry and trying to find out its advantages and limitations through analyzing the design principle and algorithm. The purpose of the article is to explain AI application in HR industry to the business and ordinary people, and then provide business recommendations when using AI system. The entire article could be divided into three main parts: 1) introduce the ways that AI applied in HR industries and its technical components; 2) present the limitations and challenges of AI development in HR industry; 3) provide business advices of future AI use in HR industry.

  1. The ways that AI applied in HR industries

In general, there are main three ways that AI can be applied in HR industries, including staff forecasting, hiring talents and personalizing employee experience. Then, I will introduce the three ways separately in the following article.

2.1 Staff Forecasting

Machine learning and prediction is possible because the world has regularities. Things in the world change smoothly. We are not beamed from point A to point B, but we need to pass through a sequence of intermediate locations (Machine Learning Chapter 2&3, 2017). Today, utilizing machine learning to predict human behavior has become very commonplace. Through learning from huge amounts of data, we are able to get a general model or pattern, and then complete some prediction.

Take employee’s turnover prediction as an example. There is no doubt that every year, the company need to face inevitable turnover and attrition. Now AI is able to inform the HR department of their employee’s decision before they leave the company, so that the company could make some preparation in advance. Using employee engagement data, whether it be from pulse surveys, brand advocacy or performance gamification, artificial intelligence could determine an employee’s level of interest, match the current model that made by previous training data, and then give a prediction on whether they are trying to change positions.

In addition, a stronger digital IQ will bring a business deeper into what is referred to as an “unconscious level” of information. ( (The new age: artificial intelligence for human resource opportunities and functions, 2018) By analyzing people’s statements, mood and intentions on social media, along with other public- data sources, human behavior can be simulated by autonomously learning machines. This makes it possible to validate the employee experience on a day-to-day basis. HR performance and succession data provide information on which employees are engaged and challenged. That gives a new dimension to strategic workforce planning to reduce employee attrition.

Not only predict whether the staff decide to leave company, AI can also predict the following important things (How Artificial Intelligence Impacts HR: Incorporating intelligent assistants in the workplace, 2016)

  • Which job candidates will make the best hires
  • Which employees are most likely to leave the organization
  • What kind of compensation packages are most likely to lead to employee retention
  • What the need and availability will be for employees with certain skill sets
  • Which benefit packages are most likely to appeal to employees

However, AI prediction cannot be used in the the HR departments of small companies, because in problems where data are limited, machine learning often is not an ideal solution (Deep Learning: A Critical Appraisal). Human beings can learn abstract relationships in a few trials, but machine learning needs thousands, millions or even billions of explicit training examples. The employee’s digital data in those small companies is not enough to implement deep learning.

2.2 Hiring Talents

Selecting talents from huge amounts of resumes is a really tiring and tedious work. Owing to lack of time and energy, the interviewers usually take less than a minute to decide whether an interviewee is an ideal match for the job or not. Based on the candidate’s appearance, speech, experience and the way they present themselves, random decision to hire people is made without the help of data, so HR department are very likely to make a wrong selection. According to ‘Artificial Intelligence in HR’, hiring managers complain about getting 30 to 40 percent of their candidates’ wrong.

Nowadays, Artificial Intelligence is able to free HR staff from boring resume scanning work by utilizing Natural Language Processing. Natural Language Processing (NLP) is a wide area of research where the worlds of artificial intelligence, computer science, and linguistics collide. It includes a bevy of interesting topics with cool real-world applications, like named entity recognition, machine translation or machine question answering. Each of these topics has its own way of dealing with textual data.

Take us take Google Hire to see how AI to scan resumes by Natural Language Processing. Launched in 2018, Google Hire is an applicant tracking system developed by Google that helps small to medium businesses (SMBs) to distribute jobs, identify and attract candidates, build strong relationships with candidates, and efficiently manage the interview process (Wikipedia). Machine learning makes it possible to identify the skill and capabilities required for a certain open position. Based on the job requirements, the app will select the strongest matches after analyzing each candidate’s skills, education, location, salary preferences, etc. This capability doesn’t have to be restricted to the pool of individuals who have applied for a specific job. The search could be extended to individuals who have previously submitted their resume or who are regularly looking for new positions within the organization.

Besides, an AI system could even help company predict a certain level of culture fit based on those attributes or other skills that the prospect listed on their resume.
Among other processes, some AI platforms could also schedule interviews by finding optimal availability for all parties involved.

According to Business Insider, about 90% of big companies in America use AI system to scans resumes for keywords and then forwards on “qualified” candidates to the company’s HR team. In a market where you’ve got hundreds, and even thousands, of applicants for a single job, it’s the most efficient use of a company’s time.

Source: https://business.linkedin.com/content/dam/business/talent-solutions/global/en_us/blog/2017/11/AI-recruiting-chart-hero.jpg

2.3 Personalizing Employee Experience

Artificial intelligence has undergone a paradigm shift from logic-based to interactive (agent-oriented) models paralleling that in software engineering. Interactive models provide a common framework for agent-oriented AI, software engineering, and system architecture (Why Interaction Is More Powerful Than Algorithms).

Artificial Intelligence in HR industry could support employees to find the right information, with lower costs, in less time and in a secure manner. Every day, the HR staff need to deal with different kinds of questions from the employees, so sometimes they are unable to provide very patient and nice solution for everyone. Besides, some new employees are not familiar the departments in the company and puzzled with whom to ask for help. According to a report by Deloitte, nearly 80 percent of executives rated employee experience very important or important, but only 22 percent reported that their companies were excellent at building a differentiated employee experience (EX) (How AI Chatbots Can Help Transform The Employee Experience, 2014). The conversational AI solution uses a machine learning capability — Natural Language Processing can solve all the problems mentioned above.

Humans and learning machines are working together to produce an ever-increasing amount of HR data in the computing cloud, and the use of artificial intelligence analyses can automatically offer answer based on the cloud database to the employee question. AI will help to efficiently automate many back-office functions for reliable HR transactions and service delivery. This document is focused on conversational AI capabilities for HR transactions and provides insight about intelligent automation via the technology-agnostic Chatbot (The new age: artificial intelligence for human resource opportunities and functions, 2018).

Source: The new age: artificial intelligence for human resource opportunities and functions

The above diagram depicts a high-level technology landscape for an HR solution. It shows how to access and maintain HR transactions, via Chatbots, using conversational AI.

For new employees, conversational AI for the HR system will help get them up to speed fast. For example, an AI-powered program could provide the names, locations and contact information for people they should connect with during their first week. New employees could also be advised by AI engines to check out a new-hire web page containing useful
information, including training modules and business-conduct guidelines. For department managers, they can also utilize conversational AI to schedule a meeting and book a meeting room, or even check their employees’ performance within one week. Additionally, chatbots also send personalized notifications to employees about company policies, rewards, holidays and so on. This way, your employees are kept informed about the latest updates without having to access or navigate the mails to find them. The real-time chatbot can significantly improve people’s working efficiency.

  1. Limitations and Challenges

Artificial intelligence is increasingly influence HR industry, but there is a growing awareness of the effect of bias problem and data privacy in machine learning. In this section, we will discuss them separately.

3.1 Bias in Artificial Intelligence

Last year, Amazon.com Inc’s (AMZN.O) machine-learning specialists uncovered a big problem: its new system was not rating candidates for software developer jobs and other technical posts in a gender-neutral way. That is because Amazon’s computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period. Most came from men, a reflection of male dominance across the tech industry, just like the following image:

Source: Latest data available from the companies, since 2017. By Han Huang | REUTERS GRAPHICS

Amazon tries to revise the the program, but that was no guarantee that the machines would not devise other ways of sorting candidates that could prove discriminatory.

Every AI system is dedicated to map human mind and the reality into binary structure and computation system. Actually, this is not the only case that found AI has bias on gender, races, class, etc. Over centuries, humans developed critical theory to inform decisions and avoid basing them solely on personal experience. However, machine intelligence learns primarily from observing data that it is presented with. While a machine’s ability to process large volumes of data may address this in part, if that data is laden with stereotypical concepts of gender, the resulting application of the technology will perpetuate this bias. (Gender Bias in Artificial Intelligence: The Need for Diversity and Gender Theory in Machine Learning, 2018)

3.2 Data Privacy

Deep learning, as it is primarily used, is essentially a statistical technique for classifying patterns, based on sample data, using neural networks with multiple layers. In problems where data are limited, deep learning often is not an ideal solution. (Deep Learning: A Critical Appraisal) Therefore, AI application in HR industry requires huge amounts of human data to operate, including the data from questionnaire, business file, interaction with Chatbots, resume, or even their personal social media. To some extent, employee are surveilled by their company all the time.

Now some companies have made relative rules to protect people’s data privacy, such as not collecting Personally identified information (PII), which was defined in OMB Memorandum M-07-1616 refers to information that can be used to distinguish or trace an individual’s identity. It includes our name, personal identification number, address information, personal facial characteristics, etc. The table below is the DPI (Department of Public Instruction) PII examples (not all inclusive).

Nowadays, technology is developing so fast that law regulation could not catch up its development speed and always lag out. Our PII is very valuable asset and it belongs to us, but some companies are collecting and tracking our personal data and selling our data without our consent and knowledge. The rules of Internet privacy could not just be conducted by those Internet giant. This principle prescribes that any matter which is essential because it either concerns fundamental rights of individuals or is important to the state must be dealt with by a parliamentary, democratically legitimized law. (Paul Nemitz)

  1. Conclusion

Through the article, we could conclude that artificial intelligence really bring some changes in HR industry, including staff forecasting, hiring talents and personalizing employee experience. By learning from huge amounts of data in the company cloud dataset, HR managers are able to get a general model or pattern, and then do some prediction, such as the turnover of the employees, the packages which are most likely to appeal to employees, etc. Artificial Intelligence is also able to free HR staff from boring resume scanning work by utilizing Natural Language Processing. Using Artificial Intelligence for resume scanning can not only target the employees who are most fit the companies’ requirements, but also save time of HR staff so that they can make more effort on human service work. Besides, Artificial Intelligence in HR industry could support employees to find the right information, with lower costs, in less time and in a secure manner by using Chatbots. The conversational AI solution uses a machine learning capability — Natural Language Processing can personalize workers’ user experience and make working easier and more efficient.

However, the world is moving faster with new technologies, and it is easy for organizations to make missteps. Artificial intelligence is not the perfect solution, and it also has lots of challenges and limitations. The major problems are data privacy and bias issue. If that data is laden with stereotypical concepts of gender, the resulting application of the technology will perpetuate this bias. In addition, some companies use employee’s data without any consent. Law regulation could not catch up its development speed and always lag out. We could also find that it is sometimes hard to use AI in small companies, because there are not enough training data to be fed into the AI system.

My advice to the company using AI in HR industry is listed below:

1、Eliminate bias. AI applications are capable of processing speed, but they can go wrong due to biased learning input. An AI solution can be a catalyst for positive change if it has been used in the correct way.

2、Data protection. Companies have the responsibility to protect their employee data privacy. All of the regulation and procedure of using employees’ data must be very transparent and democratic.

3、Keep moderate expectations, with a scalable solution. Don’t make business decision only based on artificial intelligence. Human participation must be involved in the AI system.

AI-based HR applications have strong potential to raise employee productivity, but we also need to be cautious to use it.

References:

 

Deep Learning Challenge in Healthcare

Deep learning, as it is primarily used, is essentially a statistical technique for classifying patterns, based on sample data, using neural networks with multiple layers. (Deep Learning: A Critical Appraisal)

There is no doubt deep learning benefits our lives a lot. However, through this week’s reading, we know that deep learning and AI has lots of limitations. Take health care sector as an example.

According to Healthcare Tech Outlook, AI applications making waves in healthcare today, including disease detection at early stages, drug creation etc. The potential of artificial intelligence for making healthcare better seems to be indisputable, but the medical limitations of present-day AI has to be acknowledged.

First, there is the risk of feeding the computer with underlying bias. The outcomes of deep learning only depend on previous income data, so the forecasting and predictive abilities of smart algorithms are anchored in previous case. Therefore, deep learning might be useless in novel cases of drug side-effects or treatment resistance.

Second, this can be especially problematic since machine learning apps usually run as a “black box” where the machinations of its decision-making aren’t open to inspection. If a clinician can only judge a prediction based on a system’s final outcome, it may either undermine the human opinion or simply prove worthless.

Third, in problems where data are limited, deep learning often is not an ideal solution. (Deep Learning: A Critical Appraisal) Human beings can learn abstract relationships in a few trials, but deep learning thousands, millions or even billions of explicit training examples. Nowadays, patients’ digital data is not enough to implement deep learning.

AI works best in closed-end classification problems given that there is enough data available and the test set closely resembles the training set. However, our world is always changing and unstable.

Therefore, AI is not as a universal solvent, but simply as one tool among many. Not only in health care, when we implement AI to run a conclusion, we need to be very careful about the results.

 

https://blog.pokitdok.com/ai-in-healthcare/index.html

https://www.beckershospitalreview.com/artificial-intelligence/6-ways-ai-is-changing-healthcare.html

Zachary C. Lipton and Jacob Steinhardt, “Troubling Trends in Machine Learning Scholarship,” ArXiv:1807.03341 [Cs, Stat], July 9, 2018.

Gary Marcus, “Deep Learning: A Critical Appraisal,” ArXiv.Org, January 2, 2018.

Big Data in Education Industry and Its Challenges

The revolution is happening at the convergence of two trends: the expansion of the internet into billions of computing devices, and the digitization of almost everything. (Big Data, Digitization, and Social Change) Big data means not only large volume amount and high speed data exchange. The most important characteristic of big data is the connection and digitization of everything. There is no doubt digital data have enormous latent value. We can take an example of big data in education industry.

Online education Industry is flooding with a huge amount of data related to students, faculties, courses, results and what not. It was not long before we realized that the proper study and analysis of this data can provide insights that can be used to improve the operational effectiveness and working of educational institutes. For example, MOOC platforms now collect and analyze every keystroke and gesture of every student, enabling the system to adjust its pace and style to individual learners. (Big Data, Digitization, and Social Change) Customized programs and schemes for each individual can be created using the data collected on the bases of a student’s learning history to benefit all students. This improves the overall student results. An increasing number of companies now confirming the online courses’ certificates. Maybe in one day, online course can take place of traditional courses.

Now, it is the age of attention economy. There are too much information and news online trying to catch us attention. Business needs to learn about what their target users think and want to provide the most attractive information. The best way to learn their users is to get insight from their digital data.

Although digital data has huge latent value, extracting that value is becoming increasingly difficult. (Big Data: Big Data or Big Brother? That Is the Question Now) The process includes finding relevant data, data preparation, data cleaning, data analysis, and data visualization. Data Scientists need not only hard computing knowledge but also soft presentation and communicating skills.

Big data is a two-edged weapon, serving crime and terror with the same indifference that it serves democracy and freedom. (Big Data, Digitization, and Social Change) Therefore, we need to take care of big data usage to avoid possible misuses and manipulations.

references:

Jeffrey   Johnson, Peter Denning, et al., Big Data, Digitization, and Social  Change (Opening Statement), Ubiquity 2017, (December 2017).

Bernardo A. Huberman, “Big Data and the Attention Economy: Big Data,” Ubiquity 2017, (December 2017): 2:1–2:7.

Jeffrey Johnson, Peter Denning, et al., “Big Data: Big Data or Big Brother? That Is the Question Now (Concluding Statement),” Ubiquity 2018, no. August (August 2018): 2:1–2:10.

Cloud Computing in Chatbot and Its Shortcoming

Cloud computing is a paradigm that allow on-demand network access to shared computing resources. It is a model for managing, storing, and processing data online via the internet. (Cloud Computing)

Cloud computing is a growing market. According to a study by Forbes, Cloud computing is projected to increase from $67B in 2015 to $162B in 2020 attaining a CAGR of 19%. The examples of cloud computing are everywhere from the messaging apps to audio and video streaming services.

For example, chatbots, such as siri, Alexa and google assistant, all are cloud-based natural-language intelligent bots. Capacity of the cloud enables business to store information about user preferences and provide customized solutions, messages and products based on the behavior and preferences of users. These chatbots leverage the computing capabilities of the cloud to provide personalized context-relevant customer experiences.

Cloud computing is still in its infancy. (The basics of cloud computing) Although cloud computing benefits in both our ordinary lives and business, it can still cause lots of problems if we don’t regulate it in the right way. One of the problems with cloud computing is that technology is frequently light years ahead of the law. There are many questions that need to be answered.

Security and privacy is one of the biggest issue for our data. Does the user or the hosting company own the data? Can the host deny a user access to their own data? Data is kind of valuable asset for both individual and companies. How can the “big four” companies (Google, AWS, IBM, Microsoft) ensure users’ data privacy? In fact, these years more and more news pop up related to data leakage of business and individuals by the Internet companies.

Besides, owing to developing in very short time, lack education in public is also a problem. Cloud computing is a kind of service, not a product, so it is very hard to define and communicate in public. Due to a lack of normal communication between the cloud computing industry and its customers, users have very little control over or even knowledge of who may be sharing the same systems as them and the principle of system charge.

To some extent, regulation of the business customers of the cloud services providers is urgently needed.

References

https://www.newgenapps.com/blog/top-10-cloud-computing-examples-and-uses

Nayan B. Ruparelia, Cloud Computing (Cambridge, MA: MIT Press, 2016). Selections.

Derrick Roundtree and Ileana Castrillo. The Basics of Cloud Computing: Understanding the Fundamentals of Cloud Computing in Theory and Practice. Amsterdam; Boston: Syngress / Elsevier, 2014. 2.

Boudewijn de Bruin and Luciano Floridi, “The Ethics of Cloud Computing,” Science and Engineering Ethics 23, no. 1 (February 1, 2017): 21–39.

Our Personally Identified Information (PII) are Being Robbed

Personally identified information (PII) as defined in OMB Memorandum M-07-1616 refers to information that can be used to distinguish or trace an individual’s identity. It includes our name, personal identification number, address information, personal facial characteristics, etc. The table below is the DPI (Department of Public Instruction) PII examples (not all inclusive).

There is no doubt that our PII is very valuable asset and it belongs to us, but the world seems to forgot that. The Internet giants, such as Facebook, Instagram, Google and etc., are all collecting and tracking our personal data and selling our data to the advertisers without our consent and knowledge to create a more completed business empire. For instance, when I was doing internship in a Japanese commercial company, my work was to design personal push content for social media users. The company are able to get very important users’ PII from WeChat, such as location, age, skin condition, salary and etc., and divide these users into different groups based on their PII information. Different group users are received different product recommendation and brand contents, like people living northern areas are likely to receive moisturizer product recommendation.

To some extent, our personal data assets are being robbed. Although some Internet companies take some action to “protect” personal data privacy, it seems that there are very little effects. For example, when we create an account of ITunes, we need to agree the Apple’s terms and conditions, but nobody will read these 36-page complicated words seriously. Most of us just skip the terms and click “agree”. Therefore, the rules of Internet privacy could not just be conducted by one-side. This principle prescribes that any matter which is essential because it either concerns fundamental rights of individuals or is important to the state must be dealt with by a parliamentary, democratically legitimized law. (Paul Nemitz)

References:

Paul Nemitz, “Constitutional Democracy and Technology in the Age of Artificial Intelligence,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, no. 2133 (November 28, 2018): 20180089.

https://dpi.wi.gov/sites/default/files/imce/wisedash/pdf/PII%20list%20of%20Examples.pdf

De-blackbox the Algorithms of Netflix Recommendation

What makes me most interest in this week’s reading is “recommendation system” because it has been very commonplace in a variety of areas, such as Facebook news, Instagram, music App, etc. According to the recent survey conducted by Pew Research Center, most U.S. people confirmed that their social media could accurately define their key characteristics, such as hobbies and interests and etc. In fact, I am increasingly surprised by how my cell phone knows me so well by recommending me new videos and music that amazingly fit my taste.

According to the Wikipedia, recommendation systems typically produce a list of recommendations in one of two ways – through collaborative filtering or through content-based filtering. I would like to use Netflix as an example to explain how recommender systems works. Actually, Netflix combines the two kinds of recommendation system method. The website makes personal recommendations by comparing the watching and searching habits of similar users (i.e., collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based filtering). (Wikipedia)

The video below gives more detailed explanation to how Netflix recommendation system works. In Netflix, a huge matrix factorization was created based on 2000 users’ previous rating and 1000 movies features with a kind of training model so that recommend every user’s fittest movie based on their movie preference. Many math’s calculation and error correction were continuously conducted in the process. To some extent, Netflix might know our movie preference better than ourselves.

reference:

https://en.wikipedia.org/wiki/Recommender_system

https://help.netflix.com/en/node/100639

 

Google Translate: Bi-directional Recurrent Neural Network

I often use Google Translate when reading, but I have no idea about how it works at all until this week’s study. Google Translate is based on something called “statistical machine translation”. The hidden principle and procedures is not magic, but a series training and processing based on statistics.

Machine translation was always regarded to be inaccurate and full of mistakes until recent years with the development of machine learning. In fact, machine translation is not easy at all. Translation requires fully understanding the sentence to be translated and having an even better knowledge of the target language. (machine translation,62) One of the biggest challenge is that language itself is ambiguous. Different people may understand one same sentence in different ways. For instance, when I once translated Trump’s Twitter related to recent government shutdown, I was puzzled with his meaning. In his Twitter, he said that “Every nation has not only the right but the absolute duty to protect its borders and its citizens. A nation without borders is a nation not at all. Without borders we have the reign of chaos, crime, cartels and believe it or not coyotes.” The word “coyote” can be understood in two ways——one is its self-meaning, the other one is illegal migrants (a kind of metaphor). I don’t know which one to choose to translate.

There are two architectures in Google Translation——encoder and decoder. First, convert a sentence that need to be translate into a sector (a series of number that can be readable by computers) with the help of bi-directional recurrent neutral network, which is called encoding process. Second, convert the sector into a translated sentence with another bi-directional recurrent neutral network, which is called decoding process. There are 8 layers of LSTM-RNN that have residual connections between layers with some tweaks for accuracy and speed between encoder and decoder. In the process, Google Translate continuously identify the best possible alignment and find correspondent at word level by learning pattern and data from thousands of transaction examples. Therefore, Google Translate is also called example based translations.

Karen Hao, “The Technology Behind OpenAI’s Fiction-Writing, Fake-News-Spewing AI, Explained,” MIT Technology Review, February 16, 2019.

Thierry Poibeau, Machine Translation (Cambridge, MA: MIT Press, 2017). Selections.

https://blog.statsbot.co/machine-learning-translation-96f0ed8f19e4

Cloud Database

Big data are often defined in terms of the three Vs: the extreme volume of data, the variety of the data types, and the velocity at which the data must be processed. (data science) Big data is very valuable to some extent. If we utilize bid data in the right way, it is able to provide us with predictive pattern to help us make better decision and strategy. The key to success is getting the right data and finding the right attributes. (data science)

Because of these traits of big data, it is difficult for both individuals and organizations to keep and process their all data on in-house computer servers. Therefore, we need stronger data management system for us to store and process data–cloud database.

A cloud database is a collection of content, either structured or unstructured, that resides on a private, public or hybrid cloud computing infrastructure platform. The examples of cloud database are Amazon Relational Database, Microsoft Azure SQL Database etc. Actually, cloud computing is very commonplace in our ordinary lives. Most people use many cloud computing applications without realizing they are Gmail, google drive and even our Facebook and Instagram.

Cloud databases can be divided into two broad categories: relational and non-relational. A relational database, typically written in structured query language (SQL), is composed of a set of interrelated tables that are organized into rows and columns. Non-relational databases, sometimes called NoSQL, do not employ a table model. Instead, they store content, regardless of its structure, as a single document, which often used for social media.

For example, I once helped a company manage their CRM database. It is a kind of relational cloud database. I can access customer information via cloud-based CRM software from my computer or while traveling, and can quickly share that information with other authorized parties anywhere and anytime.

The video below shows how one of the cloud relational database–Amazon RDS works:

John D. Kelleher and Brendan Tierney, Data Science (Cambridge, Massachusetts: The MIT Press, 2018).

Michael Buckland, Information and Society (Cambridge, MA: MIT Press, 2017).

The Shannon’s Information Theory: Why converted all messages into binary digits?

After 20th century, our society has evolved from Industrial Age into Information Age, which means not only people can access information and knowledge very easily, but also the whole world can be regarded as a cosmic information-processing machine. According to Gleick, the bit is irreducible kernel and the information forms the very core of existence. Nowadays, almost very discipline is associated with computer and information. For instance, finance is recognizing itself as an information science because money itself is completing a developing step from matter to bits, stored in computer memory. Besides, our online blogs, pictures and videos are all kind of information in the form of bits (0 and 1) stored in computers.

The transmission of information among systems is dependent on E-information. Systems process information without regard to its meaning and simultaneously generate meaning in the experience of their users (Great principle of computing, 57).

This week’s reading mainly focuses on Shannon’s Information Theory. Based on the reading, we know that the main questions motivating the theory is how to design telephone system to carry the maximum account of information and how to correct for distortion on the lines.

To solve the question, Shannon converted all messages into binary digits known as bits. If we send messages in long distance, binary digits can be transmitted more completed because they can be read and repeated exactly.

Shannon also found that information strongly depends on the context. The rarer the information within the context, the more information it has, which means the first fraction of the message is far more important than others. It reminds me of the theory of time perception. As we get old, we often feel our time pass away much faster than before. The younger we are, the fresher experience we will have, and then the more information will be stored in our brain, which means we feel time pass away slower.

Nowadays, we require transmitting information in long distance, faster and more accurate. Shannon’s Information Theory plays a very important role in our real life, which can help us communicate in a better way: like avoiding entropy and disorder.

Use AI to Predict the Future

Many years ago, I read a book called Predictive Analytics written by Eric Siegel who is the founder of Predictive Analytics World and executive editor of The Predictive Analytics Times. The book introduces many cases studies showing how to use quantitative data and computation to predict human’s behavior. What impressed me most is a study that presents the relationship between stock price and public anxiety degree reflected in social media. The researchers collected netizens’ words and judged each world by machine learning. They try to get the average degree of public anxiety, then to predict the future stock price. The conclusion shown in the picture was drawn that the higher public anxiety degree is, the lower the stock price will be after two days.

Note: The dotted line is the change of anxiety degree and the black line is the change of stock price.

In the book, the author just tells us the use to machine learning very basically, not the hidden principle. So machine learning was a kind magic in my mind until I read this week’s articles, which de-blacks the box of AI and MI.

Today, utilizing machine learning to predict human behavior has become very commonplace in both academic and marking field. It is possible because the world has regularities. Things in the world change smoothly. We are not beamed from point A to point B, but we need to pass through a sequence of intermediate locations. (machine learning, 41). So we are able to get a general model or pattern through learning from huge amounts of data and predict the future.

However, prediction can also cause many problems. For example, the police now use machine learning to get a demographic pattern of criminals so they are likely to watch those people more than others, which causes some discrimination problems.

Today’s AI has much more abilities than we imagine. In the past, we regarded language, creativity and emotion as intelligence that only human beings belong to, but now Al has, too and sometimes they are stronger than us. For instance, AI technology can generate many ideas that are historically new, surprising, and valuable in designing engines, pharmaceuticals, and various types of computer art. (AI Its Nature and Future, 68)

Owing to AI’s strong ability, many people are afraid of it. We need to regulate AI carefully and always remember AI was created and designed by humans and human actors and human behavior are always the most important part of AI systems.

reference:

Margaret A. Boden, AI: Its Nature and Future (Oxford: Oxford University Press, 2016).

Ethem Alpaydin, Machine Learning: The New AI. Cambridge, MA: The MIT Press, 2016.

Deborah G. Johnson and Mario Verdicchio, “Reframing AI Discourse,” Minds and Machines 27, no. 4 (December 1, 2017): 575–90.