The importance of the Internet: communication and information

The importance of the Internet: communication and information

Why is the Internet so important to us?

In this article we look at the importance of the Internet. For many people, a fast Internet connection has become a basic provision, just like water, electricity and a paved road. Internet is important for people, but also for business. Without it a house or company will no longer be able to meet the requirements imposed on our society and economy. In this article we discuss the general importance of the Internet.

The importance of Internet – communication and information

The Internet has ensured that people can communicate with each other in an easy and inexpensive way, no matter where in the world they are. People can explore and share ideas, and maintain social connections and networks. Social interactions can be invaluable for the development and strengthening of self-confidence and social skills. The Internet can help people develop their interests and find other people with the same interests.

It can also help broaden their horizons by introducing new ideas. The Internet has made it considerably easier to gather and distribute information in all forms. The broad and immediate availability of this makes it possible for everyone to improve their knowledge. In addition, people can put new theories or ideas online without the need for a paper publisher.

The importance of the Internet – social benefit

The Internet has enormous potential to achieve the right of freedom of expression and the right to information. The Internet can offer an accessible and powerful toolkit for emphasizing and responding to problems and causes. The Internet can be used to organize activities, events or groups to express problems and opinions and make a wider audience aware of them.

The importance of Internet – livability and sustainability

The Internet also makes it possible for companies to offer products and services at home and abroad, and to advertise them cheaply and on a large scale. In any case, the Internet can improve trade and economy. If developing countries could bridge the gap in Internet penetration, they would see a big increase in GDP growth and productivity, and improvement of health status and educational opportunities.

The possibilities concern all aspects of livability and sustainability. The Internet offers opportunities for working from home, the number of miles driven can be limited, and it contributes to pleasant living. This makes the balance between work and private life more flexible. Internet stimulates the economic development of rural areas and increases the value of homes and offices.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction |
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction

What is climate change: An introduction

What is climate change: An introduction

What is climate change?

What is climate change? In this article we answer this question. By burning fossil fuels and cutting down and burning down forests, for example, people are changing the climate in such a way that the survival of all organisms is at risk. A more sustainable way of living, working and living is needed to stop climate change.

 

What is climate change – the definition of climate change

The most general definition of climate change is a change in the statistical properties (mainly the mean and the spread) of the climate system considered over long periods of time. Fluctuations over periods shorter than a few decades are not regarded as climate change. Current climate change is caused by natural factors, such as variations in solar radiation and volcanic eruptions, but mainly by human activities. By, among other things, burning fossil fuels, and cutting down and burning down forests, people have changed the heat balance so that the average temperature has fallen outside the range that has characterized the recorded (human) history so far. Due to delays in the climate system, the impact of climate change will continue for many decades and – in some cases – many centuries.

What is climate change: An introduction

 

What is climate change – scientific evidence for climate change

Although there is still much to learn, the core phenomena, scientific questions and hypotheses surrounding climate change have been thoroughly investigated. These have survives in debates and the evaluation of alternative explanations. Evidence for climate change comes from a number of sources that can be used to reconstruct the climate in the past. For the distant past, the major part of this concerns changes in indicators that reflect the climate, such as vegetation, ice cores, sea level change and glacial geology. With regard to later periods, one can rely on archaeological evidence, oral history and historical documents.

Worldwide reports of surface temperatures are available from the late 19th century. This data was supplemented with atmospheric controls by the middle of the 20th century. Satellite data collected from the 1970s onwards also show that the amount of solar radiation has not increased since then. Heating up in the past 30 years can therefore not be attributed to an increase in solar energy. Climate models are unable to reproduce climate change when only the output of the sun and volcanic activity are taken into account. These only work if human activity is taken into account. More than 95% of the researchers are certain that the phenomenon is caused by increasing concentrations of greenhouse gases and other human activities.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction 
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI

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How do smart devices work: sensors, IoT, Big Data and AI

How do smart devices work: sensors, IoT, Big Data and AI

How do smart devices work?

In this article we discuss the chain that is formed between sensors, the Internet of Things (IoT), Big Data and Artificial Intelligence (AI). All these – at first glance – isolated technologies are connected within smart devices. In this article we briefly discuss the separate elements, but mainly go into the connections between them. When all elements are applied together, smart devices and smart solutions are created, with which we can greatly improve and enrich our (daily) life.

Smart devices – What are sensors?

A sensor is an artificial implementation of what is called a sense in biology. With a sensor, a machine observes the environment and information can be collected. A sensor measures a physical quantity and converts it into a signal. Sensors translate measurements from the real world into data for the digital domain. There is an almost infinite diversity of parameters that can be measured, such as location, displacement, movement, sound frequency, temperature, pressure, humidity, electrical voltage level, camera images, color, chemical composition, etc.

The goal is to detect events or changes in the environment. A sensor is always used with other electronics, as simple as a lamp or as complex as a computer. Advanced chip technology makes it possible to integrate all the required functions at low cost, in a small volume and with low energy consumption. The number of sensors around us is increasing rapidly. Estimates vary, but many expect that by 2020 more than 50 billion sensors will be connected to each other via the Internet of Things (IoT).

Smart devices – Why do we want to measure?

But why would you use sensors? Or in other words: why would you want to measure? A well-known (Dutch) statement is: “to measure is to know”. This refers to the great importance of carrying out measurements to make concrete, factual information available. You can compare, calculate, predict and check with figures. Measuring provides insight into things that go well and that do not go well. By measuring you check whether you have done what you intended to do and whether your goals were achieved. If you measure, you know where you are now; you know the current situation. From there you can always get better. You can therefore learn and improve by measuring.

Smart solutions – Sensors in the Interet of Things (IoT)

The devices, which together form the Internet of Things (IoT), are equipped with sensors. With these sensors, the devices collect data about the way they are used and about the environment around them. The collected data can be as simple as a measurement of the temperature or as complex as a complete video feed. But also think of sensor data in the form of location, sound or humidity, and different measurements of machines or of our bodies. These devices have built-in (wireless) connectivity, so that they can be connected to the Internet and can exchange data. Billions of connected devices are part of the IoT. A side effect of IoT is that all connected devices generate a huge amount of data (Big Data).

Smart devices – Why IoT?

IoT makes an endless supply of information available that was previously unavailable. And if sensor data was already present, it was difficult to analyze because it came from all sorts of separate devices. With IoT we record continuous measurements of various sensors, which we can easily read out. This allows us to recognize trends and make predictions.

IoT makes our lives easier (the smart thermostat), shifts our focus to efficiency (the washing machine starts when the prices of electricity drop), and helps us anticipate (body monitors that provide us with information continuously). The expectation is that IoT can offer solutions for major social problems relating to energy, the environment and crime. Over time, using IoT, for example, we will use less energy, waste fewer products and spend less money. To give a very concrete example: thanks to IoT, trash cans can let us know how full they are and thus whether they need to be emptied. Those who know how to make use of such information can work much more efficiently.

Smart devices – Big Data from the IoT sensors

The IoT is growing exponentially: there are more and more devices that collect, store and exchange data. In addition, consumers, organizations, governments and companies themselves produce more and more data, for example on social media. The amount of data is growing exponentially. People speak of Big Data when they work with one or more datasets that are too large to be maintained with regular database management systems.

More and more you hear that Big Data describes a development. It contains two components. First of all computer technology: the increasingly sophisticated hardware and software that makes it possible to collect, process and store more data. The second component is the statistic that make it possible to find meaning in a collection of separate data. Big Data in this definition refers to our possibility to analyze and use the ever increasing amount of data. Big Data is essentially about realizing added value from the processing and analysis of data. Characteristic is that it concerns unstructured, varied data from different types of sources that are processed in real time.

Smart devices – The added value of Big Data

Big Data is playing an increasingly large role. After all, these data contain a wealth of information for various purposes, such as marketing, scientific research or preventive maintenance. In order to actually use the increasing amount of data, a good and smart analysis of the data is necessary. Big Data analysis is the process of researching Big Data – to discover hidden patterns, unknown correlations, market trends, customer preferences and other useful information – to make more informed decisions.

The advantage of data analysis is that decisions can be based on knowledge gained from facts and thus to become less dependent on intuition and subjective experiences. With this knowledge, costs can be reduced, processes can be streamlined and the quality of products and services can be increased. By combining data intelligently and by interpreting / translating, new insights are created that can be used for new services, applications and markets. This information can also be combined with data from various external sources, such as weather data or demographics.

Smart devices – The relationship between IoT and Cloud

The IoT generates an unprecedented amount of Big Data, which greatly taxes the Internet infrastructure. According to an estimate, by 2020 there will be 5,200 gigabytes of data for all people on Earth. To support the billions of paired devices expected by then, we would have to deploy around 340 application servers per day (or 120,000 servers per year). Cloud computing offers a way to meet these dizzying requirements.

Cloud computing is the availability of hardware, software and data via a network on request. When you work in the Cloud you store and retrieve hardware, software and data in a different location than your own. Because this storage location is not visible and tangible, the term Cloud is used. Everything is stored on a server that is unknown to you. The Cloud stands for a network that with all the computers that are connected to it forms a kind of cloud, where the end user does not know on how much and on which computer the software runs, or where those computers are located exactly. The user has his own scalable, virtual infrastructure that is scalable. Without the possibility of scaling, an online service does not relate to cloud computing.

IoT devices sometimes run on their own embedded software or firmware, but they can also use the Cloud to process data. The data that is sent is stored and processed within the Cloudserver, ie in a data center using data analysis. As soon as the data reaches the Cloud, the software processes the data. This can be very simple, such as checking whether the temperature value is within an acceptable range. Or complex, such as the use of computer vision on video to identify objects (such as intruders in your home).

Smart devices – Big Data analysis in the Cloud

We are seeing an explosive growth in the volume, speed and variety of Big Data collected by the IoT. But how do you come up with ways to analyze large amounts of data and thereby unlock information? This problem is also called the “Big Data problem”: the collection of complex data sets that are so large that it becomes difficult to analyze and interpret them manually or using current applications. Big Data analyses require new processing forms for large data sets.

Big Data, the Cloud and the Internet of Things are all parts of a continuum. Cloud computing is the structure that supports Big Data projects. You can not think about the IoT without thinking about the Cloud, and it’s hard to think about the Cloud without thinking about the analysis of the stored Big Data. Because the faster you analyze data, the faster you get results and the greater the predictive value of data. After all, the real value of Big Data lies in the insights gained through analysis – discovered patterns, derived meanings, indicators for decisions and ultimately the ability to respond to the world with greater intelligence.

Smart devices – A future with IoT and AI

Big Data analysis consists of a series of advanced technologies designed to work with large amounts of heterogeneous data. To reap the full benefits of IoT data, we need to improve the speed and accuracy of Big Data analysis. This involves the use of advanced quantitative methods such as Artificial Intelligence (AI), including machine learning, to explore the data, and to discover connections and patterns. In order to identify potential problems, the data must be analyzed in terms of what is normal and what is not. Agreements, correlations and deviations must be quickly identified on the basis of real-time data streams. In an IoT situation, AI can help bring the billions of data points down to what really makes sense. It is impossible to assess and understand all Big Data with traditional methods. It just takes too much time.

It is generally accepted that IoT and AI are very important to each other’s future. AI will make IoT viable on a scale, and through IoT the lives of most people will be influenced daily by AI. The potential for highly individualized services is endless and will drastically change the way people live.

Smart devices – Why traditional analysis is not enough

When it comes to IoT, it is often necessary to identify correlations between input from dozens of sensors and external factors that quickly generate millions of data points. Machine learning starts with the outcome variables (e.g. energy saving) and then automatically searches for prediction variables and their interactions. Machine learning is valuable if you know what you want, but you just don’t know the important input variables to make that decision. So you give the algorithm the goal and then let it “learn” which factors are important to reach that goal.

In addition, machine learning is also valuable for accurately predicting future events. Algorithms are continuously improved as more data is captured and assimilated. This means that the algorithm can make predictions and can see what actually happens, which can be compared to make adjustments to become more accurate. The predictive analyzes made possible by machine learning are extremely valuable for many IoT applications. By collecting data from multiple sensors, algorithms can learn what is typical and then detect when something abnormal begins to happen.

Smart devices – IoT, Big Data and AI are inseparable

In essence, IoT involves sensors that are embedded in all kinds of devices and send data streams via Internet connections to one or more central (Cloud) locations. That data can then be analyzed. These results are used to improve the life of the user. All IoT devices follow these five basic steps: measuring, sending, storing, analyzing, acting. What makes an IoT application worth buying (or making) is value in the last step of that chain, “acting”. Acting can mean an infinite number of things, ranging from a physical action to providing information. Regardless of how acting looks, its value depends entirely on the “analysis”. And AI (or rather machine learning) plays a crucial role in this analysis. With machine learning, patterns can be detected in the data. When machine learning is applied to the “analyze” step, this can dramatically change what is (or is not) done in the subsequent “acting” step.

We need to improve the speed and accuracy of Big Data analysis to ensure that IoT fulfills its promise. All data in the world is completely useless if we can not use it. The only way to analyze this data generated by the IoT is with machine learning. With machine learning, patterns, correlations and anomalies can be found, from which lessons can be learned, so that ultimately, for example, better decisions can be made. The potential of Big Data can only really be realized when it is combined with AI.

Smart devices – AI and IoT form Smart Machines

The combination of IoT solutions with AI enables real-time reactions, for example via a remote video camera that reads license plates or analyzes faces. In addition, AI processes data afterwards, such as searching for patterns in data and performing predictive analyzes. AI makes the huge amounts of data from IoT devices valuable, while IoT is the best source for the real-time data that AI needs to develop. Devices transform from “smart”, i.e. connected to the Internet with a corresponding mobile app, to “intelligent”, which is characterized by the ability for devices to learn from their interactions with users and other devices, as well as the interactions with all other devices in the network. Artificial Intelligence really helps IoT devices become intelligent.

Smart devices – Big Data Intelligence

What does the future look like for Big Data Intelligence; the convergence of Big Data and AI? What seemingly impossible challenges can we tackle? Better jobs, a more sustainable environment, smarter economy, a safer world, a cure for cancer?

Thanks to Big Data, data scientists gain unhindered access to – and work with – huge data sets. Instead of relying on representative data examples, data scientists can now rely on the data itself. This is why many organizations have switched from a hypothesized approach to a “data first” approach. We can let data determine the direction and tell the story. Big Data enables an environment that stimulates the discovery through iteration. As a result, we can learn faster.

Smart devices – From IoT device back to the user

The information from IoT devices must somehow be made useful for the end user. Smart objects must be able to communicate with people. We usually have access to the results on our mobile devices or computers via apps or browsers. Information is displayed in the form of graphs or diagrams in a user-friendly interface. The user can then perform an action and influence the system. The adjustments or actions that the user makes are then sent via the system: from the user interface, to the Cloud and back to the sensors / devices to make the changes.

However, some actions are performed automatically. Instead of waiting for you to adjust the temperature, the system can do this automatically via predefined rules. And instead of calling you to warn you of an intruder, the IoT system can also automatically inform the relevant authorities. They can take measurements of the environment and use the data to change their own settings and to signal other devices to do this. Many of the objects perform actions based on algorithms, which take place either within their own processors or on Cloud servers.

IoT has various gradations: 1) you can have an object measure and, based on that, have an unambiguous action to it, 2) you can also have an object interpret certain information and have it act on it, and 3) you can let an object understand data and set new goals independently. Things and products become “intelligent” by adding computing power and are thus able to make decisions themselves. They can therefore exchange information at any time and initiate physical actions. This means that soon there will be little or no human interaction with these devices. Especially when there are more devices that can work together with other devices, we can automate many everyday tasks.

Smart devices – Added value through Big Data analysis

In the end it is not about the product itself, but about the digital, data and information-driven added value that arises. Through IoT we get much more insight in and influence on situations. Insight and influence arise because there is continuous meaningful information that can be converted into (automatic) action.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction 
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 12 – What is climate change: An introduction

What is the IoT (Internet of Things): An introduction

What is the IoT (Internet of Things): An introduction

What is the IoT (Internet of Things)?

The Internet of Things (IoT) is a network of connected objects that collect and share data about how they are used and about the environment around them. The Internet is what connects all these objects or “things”. The biggest advantage of the IoT is that a lot of manual processes can be automated. Devices may also notice differences and adapt their programs accordingly. Over time, our energy consumption decreases, we waste fewer products and improve production processes. In this article we give a definition: What is the IoT?

What is IoT – the smart devices on the internet of things

To understand what the Internet of Things is, we first look at the Internet itself. The Internet is a collection of computers, servers, smart phones and tablets connected by IP addresses. The IoT also wants devices with no (computer) screen to communicate with each other. The things of the Internet of Things have built-in wireless connectivity, so that they can be monitored, controlled and connected to the Internet (via a mobile app). There they can exchange data that is collected from the surrounding environment of things. They do this with the help of sensors, processors and communication hardware.

What is IoT – smart devices that collect data

The devices can monitor their environment, transmit statuses, receive instructions and take action themselves based on the information they receive. They can also sometimes talk to other related devices and respond to the information they get from each other. People can communicate with things to set them up, give instructions or access the data, but things do most of their work without human intervention. The IoT therefore consists of all “connected” or “smart” devices with Internet access that collect, send and act on data.

What is IoT – smart devices that make autonomous decisions

IoT makes the world around us smarter by allowing devices to communicate with each other. In 1939 Philco released a device with which you could change the channels and the volume of your radio. That, however, is not a smart device, but a remote control. Smart devices are not about operating a device, but about communication between devices without the user having to do anything. The (semi-)intelligent devices on the IoT can communicate with people and other objects, and are able to make autonomous decisions. In other words: with the IoT, everyday objects become an entity on the Internet, that can communicate with people and with other objects, and that can make autonomous decisions.

What is IoT – examples of smart devices

In essence, each device with an on / off switch can be connected to the Internet. The IoT includes things of all shapes and sizes – from smart microwaves, which cook your food automatically for the right amount of time, to autonomous cars, whose complex sensors detect objects on their path, to portable exercise machines that measure your heart rate and the number of steps you take each day and use that information to draw up personal training schedules. An estimated 1.9 billion devices are already connected to the IoT. And as IoT continues to grow in the coming years, more devices will be added to that list.

What is IoT – the difference with M2M

Some companies use machines that talk to each other without the involvement people. These machines monitor processes. This is called M2M, which stands for machine-2-machine. The terms M2M and IoT are sometimes used interchangeably. But some argue that M2M is the communication between machines without involving people. In that case IoT is the communication between machines and people.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction 
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction

What are serious games: An introduction

What are serious games: An introduction

What is a serious game?

Serious games aim to teach or train players, or change their behavior. The positive impact of serious games on the development of various skills can be seen in various application areas. In this article we answer the question: what are serious games?

The definition of serious games

Serious games have been developed for decades under various pseudonyms, including for example educational games. Serious games can come from any genre, use any technology and be developed for every platform. The difference with “normal” games concerns the goal of the game: serious games are designed to teach or train players, or change their behavior. However, it is not that serious games are by definition not entertaining, fun or fun. However, it is true that there is another goal to identify.

What are serious games? Serious games can have a positive impact on the development of various skills. Especially when the game in question is fascinating and motivating. Consider, for example, the development of spatial and strategic insight, learning and remembrance, psycho-motor skills, attention, self-monitoring, problem recognition and resolution, and social skills. One of the reasons why games are effective here is that learning and training takes place within a context that is meaningful for the game. In addition, serious games can be used to experience situations that are impossible or difficult to create in the real world. This for example in regards to safety and costs.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction 
Part 8 – What is deep learning: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction

What is deep learning: An introduction

What is deep learning: An introduction

What is deep learning?

Deep learning is connected to machine learning and neural networks; therefore we discussed these two topics in the previous articles. We conclude our series with this article about deep learning. In this introduction we give a definition. What is deep learning?

What is deep learning: Related to machine learning, neural networks and AI

Deep learning aims to bring machine learning closer to one of its original goals: Artificial Intelligence (AI). AI encompasses a wide range of technologies that enable computers to solve problems in a way that (at least superficially) resembles human thinking. Within that realm is a smaller category called machine learning, which is the name for a whole toolbox of mathematical techniques that allows computers to improve their performance by performing tasks. Finally, within machine learning there is the smaller subcategory deep learning. Deep learning focuses on a subset of machine learning tools and techniques through the use of deep neural networks.

What is deep learning: Deep neural networks

Deep neural networks are distinguished from other neural networks by their depth. A deep neural network is a neural network with multiple hidden layers between the input and output layers. The large number of layers makes complex transformations possible. In short, deep learning is the application of (deep) neural networks that contain more than one hidden layer.

Research into neural networks made the term deep learning popular, because it could be emphasized that the researchers were able to train deeper neural networks and draw attention to the theoretical importance of this depth. Many scientists therefore prefer to use the academic term deep neural networks rather than deep learning.

What is deep learning: Structures in unlabeled data

Above all, these deep neural networks are capable of discovering latent structures within unstructured and unlabeled data, or the vast majority of all data in the world. One of the problems that deep learning excels in is the processing and bundling of this raw data. In other words: To distinguish patterns in data that no person has ever organized or given a name.

What is deep learning: Pattern recognition

Deep learning techniques are currently the state of the art for identifying pattern recognition, such as objects in images and words in sounds. Deep learning enables computers to learn representations of data with multiple levels of abstraction: From properties at the lowest level to concepts at the highest level.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction 
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction

What is a neural network: An introduction

What is a neural network: An introduction

What are neural networks?

In this series we work towards deep learning. Deep learning is a complex form of machine learning, which we discussed in previous articles. Deep learning consists of the exposure of multilayer neural networks to enormous amounts of data. Deep learning is thus made possible, as it were, by neural networks. Before we discuss deep learning, we therefore first discuss neural networks. In this introduction we give a definition: what is a neural network?

What is a neural network: A definition

Neural networks are an important approach in machine learning. These networks are composed of multiple but simple processors that work in parallel to model (non-linear) systems, where there is a complex relationship between input and output. Analogous to our brains, the processors in a neural network are also called neurons.

What is a neural network: Function

A neural network usually consists of multiple layers: an input layer of neurons representing the input of a problem, an output layer of neurons representing the solution of the problem, and intermediate layers with artificial neurons that perform calculations. Each connection can transfer a signal to another neuron. Neurons have a weight that can increase or decrease the power of the transmitted signal. In other words, neurons are activated via weighed connections of previously active neurons. The receiving neuron processes the signal and then sends a signal to the next neurons. The threshold is important here: Only if the aggregate of the signal is lower (or higher) than that threshold level is the signal transmitted.

What is a neural network: It can learn

The interaction between the processors in a neural network is adaptive, so that connections between other processors in the neural network can be formed, and existing connections can be strengthened, weakened or broken again. This means that a neural network can learn. Here, ‘learning’ refers to the automatic adjustment of the parameters of the system, so that the system can generate the correct output for a given input.

What is a neural network: An example

Information that flows through the network has consequences for the structure of the neural network because it changes – or learns – on the basis of that input and output. Take the example of pattern recognition: Neural networks can identify images of cats by analyzing sample pictures that are manually tagged as ‘cat’ or ‘no cat’ and use these results to identify cats in other images. Neural networks are most useful in this type of applications, which are difficult to express in a traditional computer algorithm using rule-based programming. Applications range from optical character recognition (printed or handwritten scans to digital text) to facial recognition.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction

What is machine learning: An introduction

What is machine learning: An introduction

What is machine learning?

In this series we work towards deep learning. To paint a clear picture of deep learning, however, we first discuss machine learning and neural networks. To explain deep learning clearly, it is important to outline the connections between all these concepts. After the short introduction in the previous article, we now cover these topics in more detail. We start with machine learning. In this introduction we give a definition. What is machine learning?

What is machine learning: A definition

Machine learning is a broad research field within Artificial Intelligence (AI), which deals with the development of algorithms and techniques with which computers can learn. Machine learning refers to any system where the performance of a machine in performing a task improves by gaining more experience in performing that task. Machine learning therefore consists of algorithms that learn thanks to data. It involves using statistical / mathematical techniques to enable computers to learn without being explicitly programmed. Using algorithms that iteratively learn from data, machine learning can find hidden insights without being explicitly programmed where to look.

What is machine learning: Task T, experience E and performance measure P

A commonly used definition of machine learning is: A computer program is said to learn from experience E with respect to task T and performance gauge P, if its performance with tasks in T, as measured by P, improves with experience E. So if you want your program to make predictions, for example traffic patterns at a busy intersection (task T), you use an algorithm for machine learning with data on historical traffic patterns (experience E) and, if it has learned successfully, then it will perform better in predicting future traffic patterns (performance measure P).

What is machine learning: Learning from experience

When machine learning models are exposed to new data, they can adapt independently. They learn from previous calculations to produce reliable, repeatable decisions and results. It is important to understand that the ‘learning’ effect is twofold: learning data (known observations) and learning new events (new observations). The latter is really about learning from experience. The goal is to automate these decisions and predictions as much as possible on the basis of self-learning algorithms (i.e. without human intervention).

What is machine learning: Renewed attention thanks to Big Data

Machine learning is part of AI and is often used in the development of AI applications, such as Apple’s Siri for speech recognition. Given the attention to the concept you would almost think that it is something new. However, the first algorithms were used 50 years ago. What makes machine learning so interesting right now is that the world around us has changed: The digital era has led to an explosion of data in all forms and from all regions of the world.

This data, known as Big Data, comes from sources such as social media, internet search engines, e-commerce platforms, online cinemas, etc. This huge amount of data is easily accessible and can be shared via applications such as cloud computing. However, the amount of data that is normally unstructured is so great that it can take decades for people to understand and extract relevant information.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 7 – What is a neural network: An introduction
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction

Machine learning, neural networks and deep learning explained

Machine learning, neural networks and deep learning explained

An introduction to machine learning, neural networks and deep learning

In this series we work towards deep learning. To paint a clear picture of deep learning, however, we first discuss machine learning and neural networks. To explain deep learning clearly, it is important to outline the connections between all these concepts. In this introduction we give a brief overview of the concepts machine learning, neural networks and deep learning, and the connections between them.

Artificial Intelligence

Artificial Intelligence (A) is intelligence exhibited by machines. The term AI is often used when a machine mimics cognitive functions that are associated with humans. But where does this intelligence come from? It is difficult or even impossible to formulate formal rules with sufficient complexity, that accurately describe our world. It is therefore necessary to give AI systems the opportunity to acquire their own knowledge by extracting patterns from data. This skill is known as machine learning.

Machine learning

Machine learning refers to any system where the performance of a machine in performing a task improves by gaining more experience in performing that task. Machine learning consists of algorithms that learn thanks to data. In machine learning, a trend line is usually calculated on the basis of data. The system can recognize a pattern in a cloud with points. The system can also use that pattern to make predictions.

Neural networks

Machine learning is, in short, the design of machines that can learn from data. There are different ways to design these machines. Neural networks are an important approach to machine learning. In this case, the algorithms in machine learning are implemented by using the structure of neural networks. These neural networks model the data using artificial neurons. Neural networks thus mimic the functioning of the brain, which forms a biological neural network.

Deep learning

As mentioned, in this series of articles we work towards deep learning. Machine learning includes deep learning. Both are the techniques by which computer systems can learn with the help of experience. Deep learning, however, is a complex form of machine learning. For example, multilayer neural networks and non-linear transformations are used. In other words, deep learning consists of algorithms that make it possible to train computers by exposing multilayer neural networks to huge amounts of data (Big Data).

Machine learning, neural networks and deep learning

In summary, machine learning is the field that deals with the design of machines that can learn from data. One of the models that can be used in machine learning is the neural network. Deep learning is a complex form of machine learning in which multiple layers of neural networks are used to design machines that can learn from data.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction

What is a smart city: An introduction

What is a smart city: An introduction

What is a smart city?

This article provides a definition of a smart city: What is a smart city? In this introduction we try to show what smart cities really are.

What is a smart city: A definition of a smart city

A smart city wants to respond to the challenges of our time, such as those regarding sustainability and quality of life. This is achieved for example by improving the efficiency of urban operations and services, as well as its competitiveness. And to ensure that the city meets the needs of present and of future generations, in terms of economic, social and environmental issues. In short, a smart city should be a good place to live, with the best possible quality of life and with the most efficient use of resources.

In a smart city, people exchange information and work together on projects. In addition, smart cities are sustainable and they are economical with energy, water, raw materials, food and financial resources. Third, smart cities should encourage their residents, businesses and city services to invent new ways of organizing, sharing, communicating and producing. Furthermore, a city is only really smart when it involves its residents and companies in its projects. Finally, a smart city works on simplifying things: Everyone would rather have simple, transparent (public) services.

What is a smart city: The goals of a smart city

The main objectives of smart cities are: 1) Improving environmental quality in the urban space. Among other things, by reducing CO2 emissions, traffic and waste. And optimizing energy consumption, by creating energy efficient buildings, home appliances and electronic devices. Completed with recycling of energy and use of renewable energy. And 2) Strengthening the quality of life. And providing better public and private services, such as public transport and health services.

What is a smart city: The six aspects of a smart city

In literature, six different aspects of smart cities are defined: (Smart) economy, people, governance, mobility, environment and living.

1) Smart business includes aspects like innovation, entrepreneurship, a flexible labor market and an international network. 2) Smart people is not just about educating people in the city, but also about social interaction, public awareness and participation in public life. 3) Smart governance includes government transparency, availability of public and social services, and citizens’ participation in decision-making. 4) Transport systems and (ICT) infrastructure are part of smart mobility, with accessibility (both physically and digitally) playing a major role. 5) Smart environment focuses on sustainable management of resources, environmental protection and pollution issues. 6) The final aspect (smart living) focuses on the lives of citizens, including culture, health, safety, housing and social cohesion.

What is a smart city: The ideal smart city

An ideal smart city is a city where you can enjoy a healthy and pleasant life, where you can have reliable transport, where work is good and where it is safe. And of course it is also a sustainable city. Where the economy is spreading new successful activities and where everyone enjoys culture. An ideal smart city, as a living organism, checks its vital functions, adjusts itself and keeps itself healthy, minimizing the disadvantages of urbanization. Away with traffic congestion, housing shortage, unhealthy concentrations of fine dust, overpopulated inner cities, crumbling social structures, and noise.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction

What is AR (Augmented Reality): An introduction

What is AR (Augmented Reality): An introduction

What is AR?

This article provides a definition of AR (Augmented Reality): What is AR? In this introduction we try to show what AR really is.

What is AR – A definition of Augmented Reality

Augmented Reality (AR) adds digital components to the perceived real world. It is a live image of the real environment to which elements are added by a computer. These additional elements may include: Audio, video, animation, or (GPS) data. The idea of AR is to put real-time images, audio and other sensory enrichment on top of the true environment. In this definition AR is a mix of reality with a virtual addition.

In summary, an AR system is a system that 1) combines real and virtual objects in a real environment, 2) interacts in real time, and 3) registers/aligns real with virtual objects.

What is AR – AR versus VR

AR is related to the more general concept Mediated Reality. Hereby, the perception of reality is also changed by a computer. Information can be added as well as removed. The goal of Mediated Reality is to improve the current perception of reality. In addition, AR is often mentioned together with Virtual Reality (VR). The big difference between the technologies is that with VR the experience of the real world is replaced by the experience of simulated reality. AR, on the other hand, adds an additional information layer to the perception of reality.

What is AR – The applications of AR

Previously, AR was used mostly semantically for television broadcasting. It was used to display scores in sports, ball trajectories and quiz points that were placed over the actual images. AR applications have gained popularity in recent years. Especially for smartphones, which are widespread, have sufficient computing power and have the necessary hardware components, AR applications are increasingly being developed. For example, these apps place actual GPS data over buildings or provide information about products. More and more games are using AR as well, as Pokémon GO. Using more advanced AR technology like computer vision and object recognition, the information can be interacted with and manipulated.

What is AR – From entertainment to healthcare

It is expected that, thanks to applications such as Microsoft HoloLens and Pokémon GO, the AR market will be worth $90 billion by 2020. This market covers education and entertainment, with which you can try new home furnishings through Augmented Reality. AR will also be used in industry and healthcare, such as with the support of complex tasks, in areas such as assembly, maintenance and operations. Think about the automotive branch, both in building cars and in controlling them. Soldiers or tourists; everyone can benefit from computer-generated images in their field of view.

What is AR – The future of AR

Mobile phones are already an integral part of our lives, but AR offers great opportunities to improve the user experience. AR designers should consider the question of how traditional experiences can be improved by AR. Simply making sure the stove is suitable for computer improvements is not enough; it should provide healthier or better cooked foods before users see the importance. AR can have a great future when it can improve job efficiency or quality of output of an experience for the user.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 2 – What is VR (Virtual Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction

What is VR (Virtual Reality): An introduction

What is VR (Virtual Reality): An introduction

What is VR?

This article provides a definition of VR (Virtual Reality): What is VR? In this introduction we try to show what VR really is.

What is VR: A definition of Virtual Reality

Virtual Reality (VR) makes it possible to project images in three dimensions. For example, you can walk through an exotic landscape with VR or mimic an operation through a serious game as if it were real. Your senses are given a non-existent reality. It is not possible to look through the headset; instead of transparent glasses, there are screens that project the virtual world on your retina. This world can be made up of camera images or computer animations. Because your movements are followed, you can look or walk around, and interact with objects in the virtual world.

VR enables you create a virtual world that is barely different from reality. The feeling of being present in a virtual world is therefore central to the experience. VR offers you the ability to perform tasks and tests within this virtual world, which can be adapted to the individual’s characteristics and needs.

What is VR: The use of sensors

Through sensors in controllers and cameras, consoles and PCs allow for exergaming, so that exercises can be performed and recorded. VR systems also work with sensors. These sensor tracking systems are a crucial part of a VR system. Usually, a head tracker ensures that the user’s field of view can be registered. This causes the image to move when looking up, down or side, or when the angle of the head changes. Commonly used sensors include gyroscopes, accelerometers and magnetometers. In addition, to measure movements of the head and eyes, cameras and eye trackers can be used.

What is VR: The tracking of movements

However, the use of sensors at Virtual Reality (VR) does not stop with the head; there are motion trackers for each body part, such as hands and legs. Many of these sensors work with a combination of tracking in the headset and external sensors in the room. For this purpose, infrared sensors, controllers or cameras can be used. However, there are many more ways to track positions, as well as using magnets and acoustics. Experiments are also being carried out with, among other things, treadmills and bodysuits.

What is VR: The future of VR

Body tracking with sensors can include movements like jumping and bending, but fine motor skills can also be recorded. This allows completely new VR applications, which allows for interaction with (objects in) the virtual world. In addition, combinations with haptic feedback can be made, so the user actually feels it when he picks up a virtual object and even experience the texture of it. Using a camera or bodysuit eliminates the need for a traditional controller; the person becomes the controller as it were.

This article is part of a series.

Read more:

Part 1 – What is AI (Artificial Intelligence): An introduction
Part 3 – What is AR (Augmented Reality): An introduction
Part 4 – What is a smart city: An introduction
Part 5 – Machine learning, neural networks and deep learning explained
Part 6 – What is machine learning: An introduction
Part 7 – What is a neural network: An introduction
Part 8 – What is deep learning: An introduction
Part 9 – What are serious games: An introduction
Part 10 – What is the IoT (Internet of Things): An introduction
Part 11 – How do smart devices work: sensors, IoT, Big Data and AI
Part 12 – What is climate change: An introduction