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Make to Stock & Order in Software development…

Ever wondered why I can get what I want from McDonald or Starbucks so quickly while it takes months and years to get a report that actually works for you in time from your IT department.  Well your prayers are answered … thatAsthu !!! (a word from Hindu mythology where the seeker’s wish is granted by the learned or Amen) .
The world is soon going to see an end to millions of dollars spent on building complex software using sophisticated models and frameworks (these days we hear a lot about Agile Framework) . The entire software development life cycle is so broken , time consuming and error prone that many a times the cost to have an application build is so out of line with reality. Business users are facing real time challenges and expect their problems solved today or better NOW. But  companies are working on maintaining a huge laundry list of software issues faced by businesses today and have them setup processes to help resolve these issues by their tomorrow , but the reality is Tomorrow is quite different from today and comes with its own set of challenges. Traditionally problems faced today get evaluated three months later by their leadership team who are clueless of the entire issue (or approve with their set of assumptions). Managers are then asked to plan and budget to resolve these issues , hire a talented  team who would later work on hypothetical understanding of the problem. Once budgets are approved and when the issue makes it to their release pipeline , a solution is built and delivered to a Testing team which is an incredibly long cycle in the SDLC process.Once build meets the satisfaction of the tester , it is passed to the user  as part of one of their releases and they are trained to handle this unique scenario. (Whoa… and in the process are built thousands of spreadsheets and word documents) (more…)

As Wikipedia puts it , Learning is the act of acquiring new, or modifying and reinforcing, existing knowledge, behaviors, skills, values, or preferences and may involve synthesizing different types of information. Living beings have a natural tendency to learn and adapt to the environment they survive in. Humans started training machines in a hope to build Expert Systems and succeeded well in line with Turing’s Test.

The Turing test is a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.-Alan Turing

True intelligence known to human’s is the one exhibited by children and their ability to know, learn and understand their surroundings and react to situations , human like. We mainly try to define it in two major terms Intelligence Quotient (IQ) and Emotional Quotient (EQ) . Simply put IQ is the ability to understand, comprehend, discover, and become aware of information. It also measures how we register or retrieve, how creative we are , how we comprehend and solve problems and also the ability to evaluate options. On the other hand EQ is the ability to put the solution to Action.

Humans have overtime built a complex structure in terms of the way the communicate , judge and respond . The likes of Watson that now having successfully solved the problem of translating the problem of having machines do the same. They have spent enormous time in building a platform and deliver hard assets enabling applications built on these utilities.

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In the past two decades an immense interest in the use of computers for language teaching and learning is evident. ESL/EFL software programs intend to develop language skills for the incorporation of Communicative Language Teaching (CLT) principles. The following article explores how new technologies utilizing self-programming can increase the opportunities for students of English as a foreign language (EFL) to receive input outside the classroom.

What does self-programming represents?

A computer system consists of a sequence of operations. Scientists distinguish between conventional systems and intelligent systems based on whether their operations follow predetermined programs developed by human programmers (conventional), or they are capable of ‘self-programming’ which means that to a varying degree operationalization is the result of decisions by the system itself (intelligent) (IIIM, 2011). The latter relate to Artificial Intelligence in that a lot of functionalities performed such as abilities for searching, planning, producing, inductive logic programming, adaptive and reactive agent/robot is the result of self -programming to an extent. A cognitive system is thus modifying the fundamental “program code” that guides its thinking, instead of modifying the knowledge that’s used by this program code in the course of its thinking (Goertzel, 2011).

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Integrated technology processes are thus increasingly used in aerospace industry to improve efficiency in different sectors by enhancing process automation, information concurrency and avoiding data recopy. What follows is a description of how each of these areas are evolving in relation to technology integration.

A notable shift in mentality

The aerospace industry is known to be of the most conservatives with little flexibility in implementing emerging technological advances. Rather the common approach has been for companies to use successful assembly methods and processes. This reluctance to embrace integrated technology processes however is changing in recent years with companies like Boeing and Lockheed Martin showing a complete mentality shift.

Process automation

As more and more aircrafts phase out for more energy efficient new aircrafts, the need arises for aircraft production. To respond to this backlog of orders, aerospace manufacturers need to automate their factories to improve quality and boost productivity. Therefore, industrial automation has been deemed as a necessary step for reducing manufactural and assembly costs of aircraft structural components as well as for maintenance processes. At the same time automation saves time and enables more productivity and accuracy.

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Our constantly evolving world is characterized by technological advances that aim to facilitate our living conditions. Among the different technologies available, smart systems have emerged as innovative solutions. This article looks deeper into the concept of smart systems integration and what it entails.

Defining smart systems

Smart systems is a term used to describe different technological systems that are autonomous or collaborative and have combine functionalities including the ability to sense, actuate, and control a given situation in order to describe and analyze it. These systems are capable of predicting, deciding or assisting to make decisions based on available data, thereby performing smart actions using highly sophisticated interfaces between systems and users (EC, 2011). The indication of ‘smartness’ of the system derives from autonomous operation based on the elements of closed loop control, energy efficiency and networking capabilities. The previous result in different functionality and excellence in self-reliance and adaptability (EC, 2011).

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The advantage of the emotions is that they lead us astray, and the advantage of science is that it is not emotional. —Oscar Wilde, 1891, The Picture of Dorian Gray

Although there has been diachronically great interest in examining the ability of individuals to recognize emotions at different rates of precision from different modes or channels (Banziger et al. 2009), now more than ever much discussion exists on the role and influence of emotional intelligence in all aspects of our lives.

What is emotional intelligence?

Emotional intelligence has been identified as more important than IQ in succeeding in life and career. The term coined in 1990 by Salovey and Mayer refers to the “accurate appraisal and expression of emotions in oneself and others and the regulation of emotion in a way that enhances living” (Mayer, DiPaolo & Salovey, 1990, p. 772). Encompassing a set of interrelated skills and processes (Elfenbein et al. 2002), developing emotional intelligence is crucial for understanding, empathize and negotiate with other people. We must be able to read other people’s signals and react appropriately to them, which means to exhibit emotional recognition ability.

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It is claimed that hydrogen fuel cells could be of the most important alternative technologies of future transport with current applications already visible. In fact, some scientists suggest that in 20 or 30 years from now hydrogen-powered, fuel efficient vehicles will be mass produced and utilized to respond to energy demands of the 21st century.

Why hydrogen?
Hydrogen is the simplest and most abundant element in the universe. However, it does not come as an existing energy source like fossil fuels, but it is an energy carrier as it is produced first and following this process it is stored, similar to a battery. Since 2014, 95% of hydrogen is made from methane. The process to produce it involves using renewable sources, however it is expensive. Integrated wind-to-hydrogen plants, and using electrolysis of water to break down the H2O molecules have been amongst the technologies explored to deliver low enough costs and great enough qualities to compete with traditional energy sources. An alternative source to extract hydrogen is natural gas, which incorporates naturally occurring hydrocarbons. The procedure to separate the hydrogen in the gas from the carbon is called steam reformation and it is at present the most common method of large scale production of hydrogen, making it the most optimum solution for getting the hydrogen for fuel cell vehicles in any mass production. On the downside of this process however is that it uses fossil fuels — the natural gas – therefore it makes it the worst possible source for hydrogen if we intend to avoid depleting fossil fuel reserves.

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Until now, silicon has been the main material used in microchips for its ease to extract and efficiency. However, more recent research suggests that material such as carbon graphene which is used for carbon nanotubes, can outperform silicon and indicate the future of electronics becoming of the most significant products of fullerene research. In this sense, it is no wonder that in the recent United Nations Climate Change Conference in Paris on November 30th, Russian President Vladimir Putin said that new technologies for producing carbon nanotubes will help reduce carbon dioxide emissions in Russia by roughly 160 to 180 million tons by 2030.

What are carbon nanotubes

Carbon nanotubes (CNTs) are seen as a revolutionary material. In their essence, they are an allotrope of carbon, consisting of a complicate arrangement of carbon graphitic layers of atoms wrapped in microscopic cylindrical tubes, redefining the meaning of “reinforcements”. United, they form a “nanotube forest” that can be used in a tough surface, bearing the strength and permeability of fiberglass and carbon fiber, as well as sharing great flexibility to stretch. Carbon nanotubes are not found in nature but rather have to be synthesized artificially and can be woven into a thread like material. It is remarkable how they feature only a few nanometers in diameter –with a structure 10,000 times smaller than a human hair- yet up to a millimeter long, carbon nanotubes are excellent conductor of electricity bearing thermal conductivity.

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With the proliferation of technologies and the emergence of machine learning, much talk has arisen in recent years over the transition from human neurons to artificial neurons. In this article the term Artificial Neural Networks is introduced alongside a discussion of their potential use for human language learning.

What is it all about?

Artificial Neural Networks (ANN) refers to a novel information processing system that takes inspiration by the way different biological nervous systems like the brain, process information. The distinct characteristic of ANN is how it is a software simulation of a bunch of highly interconnected processing elements (neurons) working united to solve problems. An ANN learns by doing, which means that it is configured for specific applications, to perform operations such as pattern recognition through a learning process (Stergiou and Siganos, ). The learning process involves adjustments to the synaptic connections that exist between the neurons. There is a fix number or “layers” of these neurons and the output is produced after moving through them all.

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With the proliferation of technologies and the unprecedented progress, smart navigation systems have emerged as one of the most promising solutions for developing better and more sustainable cities, Smart Cities as they are commonly called. The following article addresses six future trends in utilization of Smart Navigation for Smart Cities.

Smart Navigation Systems: What is it?

The term navigation system refers to a certain system that assists in navigation and is located usually on board a vehicle or vessel, or could be elsewhere and communicate through signals, or even combine all of the above. Smart Navigation resulted from the evolution in the technological capabilities of navigation systems over time, also referred to as intelligent navigation technologies. Depending on their use navigation systems may contain maps in a human readable format, determine a vehicle or device’s location, provide directions to a human via text or speech, provide directions directly to an autonomous device such as a robot, share information on nearby vehicles, devices, or objects, and even on traffic conditions and suggest alternative routes.

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The face of 21st century education is shifting to embrace challenges and needs for incorporating the technological innovations and globalization imperatives, revised conceptualizations of learning and the need to provide with meaningful and relevant to students’ lives experiences. Deeper learning is proposed as an approach that might bring a fundamental change in education.

What the core concept entails

Deeper Learning is “an umbrella term for the skills and knowledge that students must possess to succeed in 21st century jobs and civic life” (Hewlett Foundation, 2013). It was first introduced by the Hewlett Foundation and at its core is a set of competencies that students must posit so that they develop a keen understanding of academic content and solve problems in the classroom and on their future workplace.

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As the usage of surveillance and personal video capturing devices is more evident in our lives, enormous amount of video data is being captured every day. Resulting from this, video based facial recognition has received unprecedented attention.

What is face recognition?

Face recognition has been an active yet challenging topic in the field of biometrics for the past two decades. The terms describes a special form of pattern recognition from human faces which is employed to identify individuals and pick up information from their expressions. Although identifying faces feels effortless for humans, it is actually a challenging computational problem (Reddy, et al. 2014). The difficulty lies in that the general impression is that human faces share overall similar configuration, and in that sense, face images can be described by a relatively low dimensional subspace (Patel, et al. ref).Based on this perception, holistic dimensionality reduction subspace methods have been employed for face recognition (Patel, et al. ref).

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It is widely acknowledged that there is an extraordinary amount of unstructured clinical data referred to as “the text blob” in healthcare today. Although there have been numerous incentives to support the adoption of electronic medical records (EHRs), evidence that these systems improve the efficiency or quality of care has been scarce. A promising solution for the problem seems to be natural language processing (NLP).

What does Natural Language Processing mean?

Natural language processing is a method that utilizes computational techniques for the purpose of learning, understanding, and producing human language content. In particular, NLP programmes process text to extract information based on the rules of linguistics using sophisticated algorithms. The process in one where NLP breaks down sentences and phrases into words, and assigns each word a part of speech, for instance a noun or adjective. It should be noted that natural language processing is a lot more that a computer or program recognizing a list of words. Mark Kumar posits that, “when it comes to data variety, a large part of the challenge lies in putting the data into the right context.”

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Artificial intelligence is a bustling concept that is emerging in the last few decades in ways we could not have imagined before. The face of modern AI, is diverse, machine centric, with wildly complex intelligent systems, promising to change every aspect of human life. These applications are found mostly in robot innovations, fueling a fascinating future for the field of robotics.

Demystifying the term

Although increasingly becoming part of our lives, John McCarthy’s term “Artificial Intelligence” in 1956 still sounds like a mythical future prediction more than a reality for the most of us. Perhaps this relates to the difficulty in defining the term. Artificial Intelligence is a theory that its ultimate form would entail the recreation of human thought process, operating through a man-made machine that shares our intellectual abilities. These would include features such as the ability to learn pretty much everything, the ability for reasoning, using language and formulating original ideas.

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In the present world and technology, differential equations are used almost everywhere. Ordinary differential are most commonly used for population growth and over-population, over-use of natural resources leading to extinction of animal populations and the depletion of natural resources, genocide, and the spread of diseases.

Finding solutions for these differential equations has been a difficult and interesting task. Around 1900, two mathematicians named C Runge and M W Kutta developed different techniques to find out the approximate solutions of ordinary differential equations. These Techniques are thus named after them as Runge-Kutta methods. Most commonly used method for finding solutions of an ordinary differential equation is RK4 (Runge-Kutta 4^th order).

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