The socio-technical planning is the arrangement or process of developing plans that involves human and technology in an organization (Long, 2013). This learner has the socio-technical planning related to robotics in education. The designing plan will optimize the interaction between the robotics and the participants including students, teachers, and school staffs to obtain effective results and efficient performance.  

Based on New Media Consortium (2016), education in the next five years will be different and improved because educational institutions apply robotics into the curriculum. The scope of this socio-technical plan will focus on the humanoid robotics that can help people who disability to improve their communication skills and social skills. Three important features of robotics: teaching, learning, and creative inquiry capability.

• Teaching: With robotics, autistic children can improve their communication, emotions, appropriate action in social situations and self-motivation. For example, an elementary school in Dallas, TX implemented the program in helping autism kids through robotics called Milo. Children are observed to enhance their peer communication by eliminating confrontation. They also learned to act in their social situations, and maintain their self-discipline (Rowley, 2017).
• Learning: programmers will plant the machine learning algorithm allowing robotics to take the input in a form of data learning to improve its accuracy and understanding of the students better.
• Creative inquiry: robotics can be programmed to include many creative tools and inquiries for the students to improve their skills and knowledge, especially when it can be applied to specific children based on their traits.

 

On contrary, there are limitations of developing and using robotics in education. Firstly, The idea of using robotics to mimic real human behavior like teaching style is incredibly hard because of the complexity in the human intellectual system. Secondly, the hardware and software of the robotic industrials are still in an early stage of development where these components are not standardized.

Fields like healthcare, defense, securities, and manufacturing will be able to benefit from the socio-technical planning of robotics applying in education by making robotics less clumsy, more sophisticated, and humanlike, it will be a powerful tool to boost the training and skill human labors. The studies and development of this planning by incorporating machine and human behavior together so that robotics can reach its fullest potentials. Specifically, education, robotics can be a great teacher to students and all the information and knowledge are within reach for the students to grasp at a given time.
A short video made to explain the concept of this article:

https://s3.amazonaws.com/embed.animoto.com/play.html?w=swf/production/vp1&e=1544323742&f=DlrkSIzJYHkWJ6eyD5s10w&d=0&m=p&r=360p&volume=100&start_res=undefined&i=m&asset_domain=s3-p.animoto.com&animoto_domain=animoto.com&options=

Reference:

Long, S. (2013). Socioanalytic methods: discovering the hidden in organizations and social systems. Karnac Books.

New Media Consortium, (2016). NMC horizon. Retrieved April 18, 2016, from
http://www.nmc.org/nmc-horizon/go.nmc.org/airtraffic

Office of Student Programs, Mount Holyoke College (2011). Skill building – group
decision making. Retrieved on April 18, 2016, from https://www.mtholyoke.edu/sites/default/files/studentprograms/docs/skillbuilding_groupdecisionmaking.pdf

Rowley, M. J. (2017, August 22). The rise of robot teachers. https://newsroom.cisco.com/feature-content?articleId=1873531

Serendipity is the discovery of something that is least expecting, yet it is satisfying or beneficial. For example, Alexander Fleming was a Scottish biologist and pharmacologist, who went on a research journey to discover penicillin by mere serendipity in 1928. He was in fact at that time working on the investigation of staphylococci, a kind of bacteria that usually found on the skin, hair, and nose of the animal or people. He then went on to find the fungus contaminating the bacteria and destroy the colonies of staphylococci. Without any expectation, Fleming discovered penicillin on March 07, 1929, when he experimented a new bacteria with a growing mold and found that the disease-causing bacteria could be killed by the new substance the mold produced. This new substance is the penicillin, which later on gave Fleming the chance to win the Nobel Prize in Physiology or Medicine in 1945, which was also the mark of a new start in modern antibiotics (Colbrook, 1956).

An error is a mistake or a fault condition caused by wrong judgment or conduct. In this case, errors can lead to a new discovery even after the miscalculation or inaccuracy. For instance, in 2014, a team of chemists at an IBM laboratory discovered a new material out from an error made by one of the members, and her name is Jeannette M. Garcia. She was supposed to follow a simple formula that produces stronger and easily recyclable plastics. Unfortunately, she left out one chemical due to misstep during the procedure, which turned out to be a great invention of a new material that is strong and light, and has self-healing and recyclable properties (Markoff, 2014).

According to Gould (1982), the exaptation is defined as the changing function of a trait during evolution. For example, the exaptation of the birds’ feathers which at first transform the trait to adapt temperature regulation, and later birds use them to fly. Another example is the evolution of skull bone structure. The vertebrates have sutures between bones which allow the skulls to grow, but it is an additional help to women when giving births because these sutures allow the bones to compress through the birth canal (Parry, 2013).

 

Reference:

Colbrook, L. (1956). Alexander Fleming 1881-1955. Biographical Memoirs of Fellows of the Royal Society 2, 117–126. doi:10.1098/rsbm.1956.0008

Markoff, J. (2014, May 15). Error at IBM lab finds new family of materials. Retrieved from https://www.nytimes.com/2014/05/16/science/error-leads-ibm-researchers-to-a-new-family-of-materials.html.

Parry, W. (2013, September 16). Exaptation: How evolution uses what’s available. Retrieved from https://www.livescience.com/39688-exaptation.html

In this assignment, we examine a case study related to the application of planning and forecasting. The involving forces and impacts from the application will be analyzed and explored as well as the graphics model of the process. The two techniques for planning and forecasting discussed in this project are the traditional forecasting and scenario planning. The organization of the project will be divided into three section: 1) Definition of traditional forecasting and scenario planning, 2) Case study and discussion, and 3) The force and impact.

1. Definition

Traditional forecasting is the approach to estimate the occurrence of an event in the future. According to Seeman (2002), the technique is used in many areas such as weather, economics, healthcare…etc. Many people get confused when comes to distinguish between prediction and forecasting as in certain cases, these terms can be interchangeable. However, to be more specific, prediction refers to a general estimate of future events of how many times it occurs, whereas forecasting is the estimation of certain values (Ogilvy, 2015).

Another important technique in planning and forecasting is the scenario planning, which usually focuses on the strategic side of the business at the top level. Based on Wade (2014), businesses use scenario planning as a compliment to the traditional forecasting with the flexibility to different situations such as increased competition, or a recession.

2. Case study and discussion

The case study we bring to the discussion is Microsoft. Microsoft is a software company that in 1990, it used the standard forecasting process that based on three steps: business strategy, systems strategy, and IT strategy (McNurlin, Sprague, & Bui, 2009). The company also used the outside-in approach as the traditional forecasting method. Figure 1 shows the outside-in approach that has the core and frontline as the middle portion. Under this approach, the company has several assumptions which are quite conservative and old-fashioned. These assumptions include that the future event is predictable, and the executive levels are the smartest person to make an overall decision and they are also the commanders with orders that the subordinators need to adhere.

Figure 1: Outside-in Technique for Traditional Forecasting

Since the assumption and the forecasting did not work because Microsoft did not gain any market shares and did not earn revenue enough for the company to be successful. Then, Microsoft began to use the scenario planning with pragmatic assumptions. These assumptions are the opposite of the traditional forecasting especially the fact that the future is far from being predictable and the company should not ignore the lower level management because they are the first one to notice any small changes in the business environment. With this new planning technique, Microsoft started many varieties of products (.NET platform, Xbox 360), pushed higher revenues, and won a lot of different market throughout the year.

In this section, we will take a deeper examination of the scenario planning process and how Microsoft turned its business around with this technique. According to Figure 2, the scenario planning process has eight steps: 1) focal issue, 2) key factors, 3) external forces, 4) critical uncertainties, 5) scenario logic, 6) scenarios, 7) implications and options, and 8) early indicators (Ogilvy, 2015). With this process in mind, the company will have to test its strategy based on each of the scenarios with any possible outcome in the future (Ogilvy, 2015).

Figure 2: 8 Steps for Scenario Planning Process

Since the scenario planning creates a 3D future outcome rather than just only 2D future outcome like the traditional forecasting, the company can project and use the strategy accordingly which can offer unique opportunities like taking advantage of a new market. The scenario planning also adapts sophisticated techniques in quantitative data like time series, exponential smoothing regression to estimate the future values (GBN, 2008).

3. The force and impact

There are certainly many forces that influence the application of planning and forecasting. Firstly, the competition force can either threatening and rewarding if the company approaches it correctly. The scenario planning can help the company win over its competition by producing differently in terms of product design or quality. Secondly, economic force is also important because once the country economy is improving, the purchasing power is also increasing, thereby driving more revenues for the company. Lastly, technological force allows firms to find new products with better technologies and quality (McNurlin et al., 2009).

The way the scenario planning can impact the social changes is through the leadership the company uses. If the company adopt a more flexible approach to the business future, then the company likely has a forward-thinking, therefore cultivating new lines of knowledge for innovation that ordinary customers can enjoy.

 

 

Reference:

Global Business Network (GBN, 2008). Introduction to scenario planning. Retrieved May 2, 2016, from https://www.mwcog.org/uploads/committee-documents/aV5eWFtX20080731094534.pdf

McNurlin, B. C., Sprague, R. H., & Bui, T. X. (2009). Information systems management in practice. Prentice-Hall International.

Ogilvy, J. (2015). Scenario planning and strategic forecasting. Retrieved May 2, 2016, from http://www.forbes.com/sites/stratfor/2015/01/08/scenario-planning-and-strategic-forecasting/#661a1e006b7b

Seeman, S. (2002). Traditional forecast techniques. Retrieved May 2, 2016, from http://speedy.meteor.wisc.edu/~swetzel/winter/methods.html

In this post, we will focus on the concept of forecasting and prediction method in the business context. According to Ogilvy (2015), forecasting is more about the estimate of a certain value in the future, whereas the prediction is the general estimate of what will happen in a period of time. Other people define forecasting as the basis of analysis of the past, and prediction as more judgemental and subjective to the future.

We will discuss a story about an American civil engineer known as John Elfreth Watkins who predicted a number of future technology and event in 1900 that turned out true today. Among those predictions, he was correct about the wireless technology we have today including instant access to photographs across continent especially in news reports and wireless telephone (Geoghegan, 2012). He said “Photographs will be telegraphed from any distance. If there be a battle in China a hundred years hence, snapshots of its most striking events will be published in the newspapers an hour later…. photographs will reproduce all of nature’s colors.” The idea of having an instant photograph share to another part of the world seem impossible during the time he wrote his prediction. In addition, he wrote this about the wireless telephone, “Wireless telephone and telegraph circuits will span the world. A husband in the middle of the Atlantic will be able to converse with his wife sitting in her boudoir in Chicago. We will be able to telephone to China quite as readily as we now talk from New York to Brooklyn.” International phone calls were not possible until 15 years later and it was not as easily as Watkins wrote in his prediction. 

The two forces behind the growth of wireless communication are changing market dynamics and the expansion of application and users. Firstly, wireless technology allows firms to expand their new services and products across continents. For example, software licensing was possible because wireless technology allows firms to specify users to operate. Other examples of market expansion are the broadcast of television and ratio, satellite communication and cellular telephone systems. Secondly, human wants the newest technology that can help them through each day easier with less headache, thus pushing the technology to improve every day like how we transited from using the communication from wired to fixed place-to-place to wireless mobile communication. The numbers of people using wireless communication have grown dramatically, and today we have approximately 300 million cell phone subscribers in the U.S. and 5 billion subscribers worldwide (“Introduction: Trends and forces reshaping the wireless world”, n.d.).

Reference:

Geoghegan, T. (2012, January 11). Ten 100-year predictions that came true. Retrieved from https://www.bbc.com/news/magazine-16444966

Introduction: Trends and forces reshaping the wireless world. (n.d.) Retrieved from https://www.nap.edu/read/13051/chapter/3#17

In this post, we explore the concept of planning and forecasting and we will discuss the traditional forecasting and scenario planning.

Traditional forecasting is the traditional way of predicting the result of an event or a future state based on historical observations; the examples are the forecast of inventory requirement or budgets. The traditional forecasting has two methods: the qualitative method and quantitative method (Daniel Research Group, 2011). Just like the research method, the qualitative method conducts surveys or interview of the participants on their opinions and judgments, whereas the quantitative method uses statistical analysis such as the causal and multivariate techniques, or time series analysis based on empirical data. For example, the finance department uses the quantitative method to forecast next quarter revenue based on the empirical relationships observed from previous quarters (Seemann, 2002).

On the other hand, scenario planning focuses more on the strategic side of the organization by studying the possibility and plausibility of the forecast (Wade, 2014). Firstly, scenario planning helps the firms to simulate alternatives views, therefore managing any unknown risk and uncertainty especially winning the competitions. According to Frum (2013), for business to prosper, the management teams have to recognize and adapt the changes in the economy, financial, and market environment, and scenario planning is the technique that will do just that.

The advantages of using a traditional forecast method are the inclusion of science in the planning. Both the mathematical and scientific approach will produce the result based on the empirical data and is independent of human involvement which means no personal biases in the model. In contrast, the drawback of the traditional forecast is the predictive outcome having a higher rate of failure because it cannot respond quickly enough to a sudden change in the event.

The greatest benefit of using scenario planning method is the flexibility if it offers to the strategic planning responding to immediate changes in the future state. Another advantage is the outcome of the forecast has a high rate of success and how the technique allows firms to an organizational framework based on a set of scenarios, thus making the strategic immune to uncertainty and risks (Ogilvy, 2015). However, the technique also has some shortcomings. The ideas of having many scenarios based on the changes of the future state can cause a high stress to the employees to handle the controllable factors. Also, executing the scenario planning method is more about art than sciences due to its flexibility, therefore making the method harder to manage.

Reference:

Daniel Research Group (2011). Traditional forecasting and modeling methods. Retrieved from
http://www.danielresearchgroup.com/WhatWeDo/ForecastsandMarketModels/TraditionalForecasting.aspx

Frum, R. (2013, August 6). Word association of newspapers scenario planning. Retrieved from http://personalexpertsystem.blogspot.com/2013/08/world-association-of-newspapers.html

Ogilvy, J. (2015). Scenario planning and strategic forecasting. Retrieved from http://www.forbes.com/sites/stratfor/2015/01/08/scenario-planning-and-strategic-forecasting/#661a1e006b7b

Seeman, S. (2002). Traditional forecast techniques. Retrieved from
http://speedy.meteor.wisc.edu/~swetzel/winter/methods.html

Wade, W. (2014). “Scenario Planning” – thinking differently about the future. Retrieved from http://e.globis.jp/article/000363.html

In this project, we will discuss the inventions as a game-changing idea that comes from an accident or error. The invention of X-Rays and microwaves were discovered by accidents and their stories will be presented in a separate section.

X-Rays Invention

X-Ray invention is the story of discovering something amazing by mistake and it can change the world completely from a medical perspective. Back in 1895, a scientist known as Wilhelm Rontgen studied the Crookes Tube, a peculiar device that was a mystery for many physicians at the time. The tube is a sealed glass cylinder with no oxygen inside and it has two electrodes, the anode, and the cathode. What’s fascinating about this device is that it produces a greenish-yellowish glow that scientists attempted to figure out what the light is. The light is, in fact, the reaction of the electron charge of atoms. Prior to this knowledge, physicians tried to put the glass wall on the other side of the tube, and then the light goes through the wall casting a shadow. The light that goes through the wall is called the cathodic rays, which is described as invisible rays (Levi, 2016).  

As Rontgen worked in his lab in 1895, he discovered something that no one ever noticed before about what the device can do. He noticed the board that was a few behind him started to glow because of the electric current flowing through the tube. Rontgen started to cover the tube with a cover cloth but still, the board continued to glow. He did not know that this light could do this. Unlike most scientists who were interested to find out what happened inside the tube, Rontgen was passionate to explore the light that went out from the tube. Later, Rontgen named this unknown radiation as X-Rays.

In Rontgen’s experiment report, he wrote “A plate of aluminum about [an inch] thick, though it enfeebled the action seriously, did not cause the fluorescence to disappear entirely. Sheets of hard rubber several inches thick still permit the rays to pass through them. If the hand is held between the discharge-tube and the screen, the darker shadow of the bones is seen within the slightly dark shadow-image of the hand itself..” Out of curiosity, he allowed his wife to put her hand in front of a photographic plate and turned on the Crookes tube. The amazing shadow cast through her hand allowing them to see the bone of her hand include a distinct shadow of her wedding ring. With this discovery, he believed he had found a way to look inside a human body.

Microwave Discovery

A simple kitchen appliance that is in 90% of American homes was once discovered by accident 70 years ago by Percy Spencer (Blitz, 2016). In fact, Spencer did not have a formal education and he had to work his way by working with the different organizations and read a lot of books himself. In 1925, he worked in a manufacturing company that made missiles, military training systems, and electronic warfare products. He was also the brightest engineer in the company. He described the accident as a gooey and sticky mess. It was during World War II that Spencer was given a task to solve the radar technology for the allied forces. He was also the inventor of the proximity fuses or detonators that can trigger artillery shells in the mid-air before reaching the target. At that time, Spencer wanted to improve the power level of the radar magnetrons which vibrated electromagnetic waves.

One day when Spencer was testing his magnetrons, he instead discovered something different from what he intended. He found that his snake, the peanut cluster bar was melted during the experiment of the electromagnetic waves. With this shocking discovery, he made another experiment by putting an egg on the tube of magnetrons, and then the egg exploded in front of him. That was how the idea of the microwave was found and invested.

In 1947, the first commercial microwave was put on sale in the market, but it weighed nearly 750 pounds and the price tag was $2000. Due to its weight and price, the microwave was not popular until 1967 when a compact version of it introduced to the market.

 

Reference:

Blitz, M. (2016). The Amazing true story of how the microwave was invented by accident. Retrieved from https://www.popularmechanics.com/technology/gadgets/a19567/ how-the-microwave-was-invented-by-accident/

 

Levi, R. (2016, February 11). How X-Rays were discovered – by mistake. Retrieved from https://medium.com/@ranlevi/how-x-rays-were-discovered-by-mistake-aea9c4a83c4a

In this discussion, we will talk about group decision-making methods such as the Delphi technique. A group decision-making is a way to gather all the knowledge in one place to make an optimal decision without relying on the knowledge of one person. It is also a method to filter out any bad decisions among the group. The Delphi technique, also known as Delphi procedure, is a repeatable method to ask the discussion group with questionnaires to gather all the best opinions until a group consensus is reached (Hasson F, Keeney, & McKenna, 2000). The main feature of this method is the anonymity factor. The members who participate in the decision-making process will not share their identities, thus making them feel comfortable sharing their honest opinions and avoiding any appeals to authority later on. The disadvantages of the Delphi technique is that the procedure required the participants to have continued commitments to answer similar questions multiple times. 

Another method is Hoy-Tarter model which focuses on the selection process of who should be included in the decision-making process. In a business, making the right decision is vital to the owners and he or she can use Hoy-Tarter model to choose who or what should include in the process. The drawback for this method is the mistake of making the bad selection and it can ruin the decision entirely. Looking at another decision-making method is the Modified Borda Count model, which is the extension of the multi-voting model. The participants will be given a ballot to vote for ideas that they support. The weighted value of each idea is determined by the points from the voters. The winning idea will be the one that got the highest numbers of points. Due to its voting characteristic, the method can lead to tactical voting, when a voter supports another candidate more strongly than their sincere preference in order to prevent an undesirable outcome (“Seven Methods for Effective Group Decision-Making,” 2018). 

In a competitive market, quality decision making is essential to the success of a company. Therefore, having a plan in mind to make the right decision is parts of the decision-making process.

Reference:

Hasson F, Keeney S, and McKenna H. Research guidelines for the Delphi survey technique. Journal of Advanced Nursing , 32 (4), 1008–1015. doi:10.1046/j.1365-2648.2000.t01-1-01567.x

 

Seven Methods for Effective Group Decision-Making. (2018, January 19). Retrieved from http://www.free-management-ebooks.com/news/effective-group-decision-making/

In this discussion board, we will talk about machine learning and artificial intelligence (AI) as an emerging technology and the trend we will continue to see further adoption in the next four to five years. Machine learning is defined as a computing algorithm that takes a huge amount of data and learns from this data to make behavior predictions. This data can be the experiences and knowledge that human shares on a daily basis. Applications of machine learning can be seen from self-driving cars, speech and text recognition, and semantic applications. In China, huge volumes of data being collected from approximately 700 million internet users can be a perfect gate toward a huge potential in the AI field. For example, the government can use machine learning to predict the pollution levels and formulate policies accordingly that can affect health and climate in the country (Knight, 2016). Not only the machine learning that is seen as an emerging field but the whole industry that involves with AI like robotics, which is estimated to be $135 billion industry in 2019 (Vanian, 2016).

The two driving forces that have made machine learning and AI became available in today’s application are the increase of computing power and digital data boom. In fact, AI has existed for more than 60 years, but because of the computing power that we have today allowing the engineers to build a high-functioning system with the right hardware and infrastructure to support AI systems. With faster computers, a large volume of data can be processed and perform at a higher caliber. For example, Google introduced the TPU (tensor processing units that is believed to perform 15 times faster than a normal GPU. Secondly, the data is increasing at an exponential rate and keep accumulating every day through many channels like social media. As big data continue to grow, more advanced processing application of the data is being created every day so that we can have an increasing number of sensor, systems, and device to transmit this data. The International Data Corporation (IDC) estimated that we will have 163 zettabytes (a trillion gigabytes) of data in 2025 (“The 3 forces that brought AI to life,” 2017).

Reference:

 

Horizon Report for Higher Education. (2018). Retrieved from http://cdn.nmc.org/media/2017-nmc-technology-outlook-for-chinese-higher-education-EN.pdf

 

Knight, W. (2016, March 28). Can machine learning help lift China’s smog? Retrieved from https://www.technologyreview.com/s/600993/can-machine-learning-help-lift-chinas-smog/

 

The 3 Forces that Brought AI to Life (And Why it’s Only Now Changing the World). (2017, December 15). Retrieved from https://blog.cloudfactory.com/3-forces-brought-ai-to-life

 

Vanian, J. (2016, February 24). The multi-billion dollar robotics market is about to boom. Retrieved from http://fortune.com/2016/02/24/robotics-market-multi-billion-boom/

Hello, my name is Keang Cheang Ung, a doctoral student at Colorado Technical University (CTU). I have a background in data science, economics, and the stock market. I graduated from Texas A&M University in 2017 and moved to Denver to continue my doctoral program in computer science. I also have a strong passion in the equity market and day-trading, and I started day-trading at home using my personal account in April.

The purpose of creating this blog is to 1) provide news and recent developments in artificial intelligence, and 2) to share knowledge and lessons related to AI, deep learning and data science in general. Also, another reason to start this blog is to fulfill an individual project in my class, the Futuring and Innovation at CTU. This class opens the door to new innovative ideas that can affect organizations and how the new trends in technology like AI and big data can change the future.

The theme in the blog will cover theories and applications of AI and data science. As a doctoral student in computer science focusing on big data, I have a research passion for building a predictive algorithm to forecast the direction of the stock price given the duration of the investment and risk involved. In order to conduct this research, I have to study prior literature that has been carried out with similar objectives of predicting the stock market movement. For example, Lachiheb and Gouider (2018) used four years of price data to forecast the future 5-minute price. The strength of this study was the fact that the researcher used the deep neural network (DNN) with dimension reduction technique, variable selections, and correlation factors incorporating into the model making it outperforms the traditional neural network like artificial neural network (ANN). Also, the price input data was long history that allows the models to learn and train to adapt patterns better compared to models using a short period of price data (Lachiheb, & Gouider, 2018).

 

Another interesting approach to forecasting the stock price direction is to use pattern recognition through neural networks and point reduction techniques. Chen and Chen (2016) built an intelligence model for analyzing and predicting the stock market using the perceptual important point (PIP) to reduce the unnecessary points in the time series data and then feed them to neural networks. The intelligence parts of this algorithm were to capture the bull-flag pattern in the technical chart with the least running time as possible and to predict the direction of the stock price with the high accuracy. The results showed that the intelligent algorithm proposed in this study shows a better return than the rough set theory (RST), genetic algorithms (GAs), and their hybrids. By correctly predicting the bull flag turning point, the investors could benefit huge returns by executing the stock orders in a timely manner.

In conclusion, I hope this blog will give values to you as someone is interested in AI and big data. We will apply machine learning and deep learning together to solve real-world issues, especially in the investment arena.

 

 

Reference:

Chen, T. L. & Chen, F. Y. (2016). An intelligent pattern recognition model for supporting investment decisions in stock market. Information Sciences, 346–347, 261-274. doi: 10.1016/j.ins.2016.01.079.

Lachiheb, O., & Gouider, M. S. (2018). A hierarchical deep neural network design for stock returns prediction. Procedia Computer Science, 126, 264-272. doi: 10.1016/j.procs.2018.07.260.