The Enormous Rise of Artificial Neural Networks

Posted by Andreas Christodoulou

Description :

Artificial Neural Networks, which are also known as ANNs have received a lot of attention recently. The increasing awareness of Artificial Intelligence has contributed a lot towards their popularity.

The Artificial Neural Networks can simply be defined as biologically inspired networks. In other words, these networks work similar to the human brain. As is probably well known, the human brain consists of billions of special cells named as neurons. To be precise, it has been estimated that a typical human brain consists of about 150 billion neurons. These neurons are inter-connected with each other to form a network. As a result, the human brain can be considered as a collection of neural networks.

The Concept of Neural Computing

The application of Artificial Neural Networks is known as Neural Computing. Neural computing uses a limited amount of concepts that are borrowed from biological neural systems. The main objective of Artificial Neural Networks is to simulate massively parallel processes that are associated with interconnected processing elements in network architecture. An artificial neuron would be the building block of an Artificial Neural Network.

A neuron would receive inputs from the other neurons that are connected to it. And conversely, the output of a neuron will be passed to several other connected neurons. The same mechanism takes place within the human brain as well. Likewise, the artificial signals processed by the neurons can be changed. How Artificial Neural Networks process information entirely depends on its structure. The algorithm that is being used to process information can also create an impact on it.

Application and Benefits of Artificial Neural Networks

Artificial Neural Networks can be applied to a variety of scenarios. The benefits that are associated with these applications have contributed a lot towards their proliferation over the past decade. The most popular areas where ANNs are being applied include learning, pattern recognition and interpretation of noise and incomplete inputs.

Humans follow a different approach when solving problems when compared to computers. The objective of Artificial Neural Networks is to make computers think and solve issues like human beings. This can help computers to solve complex problems, which cannot be solved with a rule based approach. In other words, Artificial Neural Networks are being used to solve complex problems that cannot be simulated using analytical or logical techniques. They have the potential to solve issues, which cannot be solved even with expert systems. Pattern recognition is a perfect example to prove the aforementioned fact.

Artificial Neural Networks are in a position to analyze large amounts of data in an effective manner. After the analysis, it can establish characteristics and patterns, where rules or logic are not known. Loan applications are a perfect example for such a situation. After going through a large number of historical cases, the questionnaire of the applicant is either accepted or rejected. Only Artificial Neural Networks have the ability to automate this process of approving loans.

Artificial Neural Networks also have the ability to create profiles or patterns of applications that need to be denied or approved. Then a new application is matched against a pattern by the computer. The computer would gain the intelligence to classify whether it is a “yes” or “no”. Otherwise, it would go for the decision of humans. That’s the main reason why neural networks are being used for a variety of financial applications such as predicting exchange rates, predicting bankruptcy and determining when to purchase and sell stock.

Artificial Neural Networks and Smart Computers

Computer systems have gone through significant development throughout the past few years. Artificial Neural Networks have contributed a lot towards this evolution. As a result, people in the 21st century prefer to use computers to accomplish most of their day to day needs.

A traditional computer has the potential to perform millions of operations within a second. However, this type of computer is not in a position to think like a human and make decisions by themselves. In other words, the traditional computers do not have the ability to perform certain tasks on their own. You will need to program those computers to make them perform certain tasks, but you will not be able to make them solve problems in the way humans do. That’s where Artificial Neural Networks come into play.

Artificial Intelligence can simply be defined as a set of techniques, which are entirely based on the behavioral patterns of the human brain. It can learn on its own and make decisions like humans do. Most of the living beings that exist on this planet are biological systems that learn on their own. They are capable of making useful decisions as well. Generally these decisions are based on the instinct of survival.

Human beings need to learn before they can make decisions. The same principle applies for machines as well. Machines differ from biological systems though in that they will have to learn new things through mathematical algorithms. However, Artificial Neural Networks have given machines the opportunity to learn new things like human beings. This has given life to a brand new aspect in computing as well.

The use of Artificial Intelligence and Artificial Neural Networks are being implemented mostly in the software industry at the moment. This has the potential to emulate the parallel nature of an Artificial Neural Network into a linear system. As mentioned earlier, Artificial Neural Networks are being used on a variety of applications such as image reconstruction, character recognition and voice recognition.

Neural networks can also be found in a variety of applications such as robotics. People however are extremely concerned about automation and the danger of robots taking over their lives as a result of advances in the field of Artificial Intelligence and Machine Learning. These fears derive from the fact that whilst Artificial Intelligence is helping the humans to achieve a better standard of living, it is also important to keep in mind that transferring the biological capabilities of humans to computers can make us more dependent on them.

In this respect, special attention has been attributed towards Artificial Neural Networks. This is because Artificial Neural Networks have the ability to make computers think like human beings and solve complex problems which in effect means that it can humanize computers. Such a prospect creates all sorts of anxiety and extreme scenarios. Nevertheless, for the time being, research is focused on different medical applications of ANNs with beneficial results for humans. What remains to be seen is how future research and practice in this exciting area will continue to grow.

READ THIS: Real Life Super Humans Via Robotic Software

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6 Learning Trends Driving Education in a Post-Pandemic Scenario

Posted by Dr Stephania Savva

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Following the surge of the Covid19 pandemic around the globe and with the new school year kicking off in September, the talk around the current and future educational landscape globally is resurfacing once more. Here we have compiled the top 6 learning trends driving education in a post-pandemic scenario and beyond. 

The constantly evolving learning demands and needs of a globalized new millennium challenge the teaching practices and strategies for researchers, policy makers and educators; the mission is to reinvent schools for the 21st century. Working within these realizations, several learning trends for education have emerged in the past few years in an attempt to re-conceptualize education. 

Most of these trends are based in the belief that schooling in the 21st century should be connected to real life experiences, becoming meaningful and relevant for students. In this perspective, students as learners are actively engaged in collaborative project-based learning processes integrated in a curriculum that derives from students’ interests, experiences, talents and diverse skills. The learning trends identified below address these understandings and lead the wind of change in education for the years to come.

1. Blended Learning

One of the buzzwords in the educational community worldwide now is blended learning. The term refers to an instructional strategy where the student learns through at least partly online delivery of instruction and content. Teaching within a blended learning environment implies that there are elements of student control over time, place, path and/or pace. There have been six models identified as characteristic of blended learning: Face-to-Face Driver Model, Rotation Model, Flex Model, Online Lab Model, Self-Blend Model and Online Driver Model. The beneficial attributes of any of these models of blended learning are found in the potential to address students’ needs and variant learning styles through differentiated instruction. Such an approach to teaching allows for teachers to make decisions regarding their teaching strategies that will result in better student performance.

2. Game Based Learning

Another trend in education nowadays is game based learning (GBL), which has been cited as a revolutionized way of utilizing games for learning purposes. In particular, GBL is a teaching approach where students undertake work in a game based learning context often in a virtual environment designed by teachers. The intention is for teachers and students to be immersed in the game, collaborate and take action towards a particular learning goal. The aim is to make learning fun, liven up the classroom by engaging and motivating the students. As this is a growing field with increasing interest, the video game industry should consider pairing with researchers to create games that could be employed by formal learning institutions and align with goals of the curriculum. Theorists stress that there is still a need for developing new technology that supports specific pedagogical functions (e.g. group regulation or project management) for schooling in the postmodern era. What we should be aware however is to find a balance between technological or technical aspects and teaching/learning processes.

3. Maker Education

Student work has been taken to a whole new level with the introduction and gradual implementation of maker education or maker movement in schools, a trend starting from the US. An evolution of experiential learning and hands on learning which has been around for more than half a century, the maker education is an approach that adopts a culture of learning by doing which utilizes current and emerging technological tools such as 3D printing, wearable computing, robotics, and coding tools to drive creativity. The intention is to create makerspaces in classrooms where students are given the expressive freedom and digital tools to invent, build and share their work during the learning process allowing for their talents to nourish. 

4. Deeper Learning

Deeper learning is part of a developing movement in the US for education that emphasizes on the knowledge that students need to successfully engage in 21st century jobs and civic life. The term refers to a set of student educational outcomes that stem from a set of core competencies, which allow for students to develop higher order skills and metacognitive understandings. Such an example of a teaching and learning approach to schooling acknowledges students’ multiple and diverse identities, experiences and capabilities while also equipping them to become the flexible and critical learners required in our contemporary societies. Even these students, who have been characterized as multimodally literate (Davies 2006) or digital natives (Prensky 2001), are not ‘per se’ capable to critical reflect and make advanced cognitive processes in their everyday activities. Using the deeper learning theory could empower students’ critical awareness of and uptake of learning tasks.

5. Design Thinking

Design thinking is an emerging trend for education that seems promising in relation to other approaches such as the maker education movement. Stemming from product design processes, design thinking refers to a creative process where you employ problem solving to reach to meaningful solutions in the classroom, school or community. Environments such as schools that implement design thinking use it as a basis for teaching different subjects through workshops where students become design thinkers by engaging in problem solving, brainstorming and other activities that aim to bring solution to real life situations. At the core of this learning approach is the intention to empower students to feel confident to create for solving realistic problems.

6. Technology Enhanced Learning 

Technology has taken the education world by storm in recent years. Technology enhanced learning is an umbrella term used to describe the use and implementation of technological tools such as smartphones and mobile devices including touchscreen and tablet computers for learning, also known as educational technology. The impact of using mobile digital technologies for example through adoption of BYOD (Bring Your Own Device) movement has altered the learning experience for students. Regardless of their background and interests, students nowadays seem to engage in learning more easily when interacting with these devices while they seem relevant to their daily lives where exposure to technology is constant. The interest in educational technology through technology enhanced learning will continue to grow in the coming years. This is driven by the calls for personalized learning and how to encourage a technologically and digitally diverse learning experience which empowers our students. 

The learning trends mentioned in this article are gradually implemented in mainstream education worldwide. The premise is that a more systematic implementation of the above learning theories and practices could transform education for the 21st century.

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