What is the Industrial Internet of Things (IIoT)?
The term Industrial Internet of Things (IIoT for short) refers to the so-called Internet of Things in the industrial environment. The Internet of Things (IoT) is understood to mean Objects such as sensors, actuators or other physical objects which are networked with other systems and devices via the Internet..
Industrial Internet of Things applies this type of networking and connectivity in an industrial environment, which is why it is also often referred to as part of the Industry 4.0 is seen. Alongside machine-to-machine communication, cyber-physical systems and cognitive computing, the Industrial Internet of Things is seen as an integral part of Industry 4.0, which are also interconnected and interdependent.
How does the IIoT work?
Since IIoT describes a network that is used for systems and devices to communicate with it, at least the following components are necessary. On the one hand, the devices must be able to independently collect, store and communicate information. Secondly, the necessary network infrastructure must be available so that data can also be transmitted. Finally, applications are needed that can process the collected data in order to generate usable information that can ultimately be used for decision-making. The transmission of information between the device and the application or Data analytics for further processing usually works via gateways.
What is the benefit of the Industrial Internet of Things?
The use of the Industrial Internet of Things gives rise to some Advantages and opportunities in the industrial environment:
- AutomationThrough technology and data processing, certain processes in production can be automated, flexibly designed and adapted at short notice. This can lead to increased efficiency of processes.
- Failure forecast: With the help of Predictive Maintenance defects or malfunctions can be predicted and preventively remedied before they lead to damage or interruptions.
- Customer satisfactionAnother benefit of the Industrial Internet of Things is the increase in customer satisfaction by collecting data on customer usage, which can be used in future product design.
In addition to the positive aspects, IIoT is also associated with some Disadvantages and challenges confronted:
- EffortThe operation of the network and the devices leads, among other things, to a high effort in administration, as the devices must always be kept up to date with the latest software and security gaps must be closed immediately.
- CybersecurityIn the area of security, it must be ensured that the transmitted data is secured against unauthorised access. In addition, it must also be prevented that IIoT devices can be taken over by unauthorised persons.
- ProprietaryLack of uniform standards can lead to dependencies on certain manufacturers, as IIoT devices from different manufacturers are often not compatible with each other.
Which companies are the largest providers of IIoT solutions?
Some large companies have expanded their portfolio of offerings to include IIoT solutions. Examples include ABB Ability, Cisco IoT System or Siemens MindSphere.
What are examples of IIoT products or IIoT software solutions?
The basis for the operation of an IIoT solution is hardware such as Sensors, actuators, man-machine interfaces or other measuring instrumentswhich generate the necessary data. In addition, there must also be a wired or mobile network infrastructure that ensures the transmission of the data.
3. Edge computing approach the data is processed as close as possible to the source of origin, which means that the results of the processed data are available quickly. On the other hand, the data is processed Cloud computing approach stored centrally and processed there with great potential computing power, which also allows the use of computationally intensive tasks of the Machine Learnings enables. The Processing of data and the use of analytics or other Business Intelligence Solutions is another success factor in the field of the Industrial Internet Of Things.
What is the difference between IIoT and IoT?
While certain commonalities between IIoT and the Internet of Things (IoT) such as sensors, networked devices, connectivity or communication, there are also some distinguishing features. For example, the Focus on the Industrial Internet of Things in the precision and reliability of the devices in the industrial environmentwhereas IoT mainly in a private environment is used.
In addition, the Industrial Internet of Things interaction with humans tends to be the exceptionThe approach is designed to control and monitor production processes and systems, which sometimes have to function in adverse conditions without increasing the risk of failure.
Another noteworthy The difference is in the amount of data and the complexity of the data. With the Industrial Internet of Things, considerably higher and continuous amounts of data are collected, which are ultimately also processed.
What is Internet of Things (IoT)?
The Internet of Things (IoT for short) is a Network in which physical objects are connected to other systems or devices via the internet and communicate over it. can. The devices (things) are equipped with the necessary components such as sensors, actuators or microcontrollers to generate and forward the required data.
How does the Internet of Things work?
Certain components are necessary for the functioning of an IoT network. These can be assigned to the process of data collection, data transfer and the Data analysis or the taking of measures for further data processing.
Go to Data collection web-enabled smart devices are needed that are able to collect data, forward it and react to received data. These so-called smart devices often use sensors, antennas, actuators or microcontrollers to collect data.
The Data transfer is established via an IoT hub or an IoT gateway, whereby further data processing can either take place via a cloud or the data can be transmitted via a Edge computing approach can be analysed and processed locally.
At Analysis step the transmitted data is processed and thus serves as a basis for further decisions or measures. This step can be carried out by means of specific analytics and with the aid of artificial intelligence or Machine learning support or other back-end systems can be implemented.
What are the benefits and risks of IoT?
The success and popularity of the Internet of Things can be attributed, among other things, to the following Opportunities and benefits back:
- NetworkingThrough the possibility of networking, individual digital processes can be coordinated and automated. This can result in time and resource optimisation.
- ControlThe use of IoT devices can increase the level of automation of control systems by controlling lighting, air conditioning or other elements. Security systems can also be implemented, leading to an increase in safety.
- MonitoringWith IoT technology, it is possible to permanently monitor and, if necessary, counteract devices and systems. This can be useful primarily for power consumers or high-maintenance devices by suggesting and carrying out maintenance intervals before possible damage.
- SecurityIoT can also contribute to increasing safety, for example by installing assistance systems in vehicles that support the driver while driving.
Besides the positive effects, the IoT is also associated with some Risks and challenges confronted:
- Data security: Due to the constant exchange of data between the IoT devices and the network or the Cloud the data is exposed to potential cyber threats, which must be countered by appropriate protective measures. In this respect, particular emphasis should be placed on cybersecurity when implementing IoT.
- ProprietaryLack of compatibility standards means that systems from different manufacturers are sometimes not compatible with each other and thus cannot be used together. This can result in a limitation in the use of IoT.
- Data volumeWhile the continuous communication and data transfer can be seen as an advantage in terms of availability, it also leads to the challenge of managing and organising these large amounts of data. With the connection of each additional IoT device, the amount of data and the management effort increases.
What are examples of IoT products or IoT software solutions?
The use of IoT solution is diverse and can be implemented in some areas. For example, Munich Re offers Insurance companies a IoT ecosystem in which companies can analyse data from an insurance perspective and consequently better assess and price hazards and risks. This means that customers can be offered more individualised products.
Another area of application for IoT products is in the Automotive industry. Bosch Mobility Solutions offers IoT solutions for vehicle manufacturers, which Driving assistance systems and infotainmentbut also the functionalities of the autonomous drivingindividualised service offerings and support personalisation.
Also in the Healthcare IoT solutions are increasingly being used. The company ScienceSoft offers products that can be used for the Monitoring of vital signs of patients, but also of medical equipment in facilities. In addition, this can improve processes and reduce bottlenecks.
What is the difference between IoT and IIoT?
Besides some commonalities between IoT and the Industrial Internet of Things (IIoT) there are also differences that separate the two technologies. While the Focus on IIoT on the precision and reliability of components and is located in the industrial environment, aims at IoT more focused on use cases with human involvement as of.
Moreover, IoT often focuses on a business-to-customer connection. IIoT solutions, on the other hand, aim to control and monitor production processes and systems. Furthermore, IIoT use cases are usually confronted with a much higher and continuous amount of data to process than IoT solutions.
What is Ithaca?
Ithaca is a software or an algorithm developed by the British company DeepMind, which uses Machine learning is intended to complete fragmentary ancient texts. To this end, the programme is primarily used in epigraphy, the science of inscriptions on various materials. Currently, the algorithm is aimed at the analysis of ancient Greek texts, but is In future, the application is also planned for other languages.
In addition to filling in the gaps, Ithaca should also provide information about the place of origin as well as the date of composition of the texts. The dating of these texts was previously not possible with common methods such as the radiocarbon method, because for this application the texts had to be written on carbonaceous materials.
The algorithm was developed by the company DeepMind (AlphaGo, AlphaZero, AlphaFold) was developed in cooperation with several companies such as Google and universities such as the University of Oxford, the Athens University of Economics and Business or the Università Ca'Foscari di Venezia. The name Ithaca was chosen as a homage to the homonymous home island of Odysseus from Homer's epics.
In order to make the algorithm accessible to as many people as possible, DeepMind has published the source code on the Open Source version management platform GitHub, which can be used and further developed there.
What are the functions of DeepMind's new ML model?
The algorithm for Text completion works - like most of the algorithms which artificial intelligence Apply - on the basis of probabilities. To do this, Ithaca uses the largest digital database for ancient texts from the Packard Humanities Institute in California to calculate the words that most likely fit into the gaps. The Database comprises almost 180,000 inscriptions, all of which are provided with metadata such as place and time.
Based on this, the calculation result is presented to experts for final determination of the text gaps, who evaluate the original text with their expertise. In an experiment conducted by DeepMind, Ithaca alone was able to recover single words with an accuracy of 62 %, but historians were only able to do so with an accuracy of 25 %. By collaborating the assessment of the programme and the experts, the accuracy to recover missing words increased to 72 %.
The "Lexicon of Greek Personal Names" (LGPN), a lexicon of Greek names from the British University of Oxford, supports the dating and classification of the region in which the respective writing was produced. With this data, it can be analysed when which names were most frequently represented in which region, thus generating insights into the geographical and temporal classification of the ancient texts. In this way, Ithaca should achieve an accuracy of 71 % for the date and lie within a time span of less than 30 years for the proposed date of origin.
How can researchers access Ithaca from DeepMind?
The algorithm can be accessed in several ways:
- On the open source version control platform GitHub is the source codeThe libraries and other integration options for using Ithaca are also listed.
- Ithaca was founded in a Contribution of the trade journal Nature published. In the publicly available contribution, reference is made to the The working principle of the neural network. In addition, the methods used are described in detail and examples are presented. The connection to the previous text retrieval based on neural networks from DeepMind called Pythia is also formed.
- The algorithm is furthermore about the Ithaca website of the DeepMind company retrievable and is made freely and publicly available to researchers. There, the ancient Greek text can be entered using a text box, in which the missing passages are restored. In addition, a dating and a localisation of the place of publication is carried out.
What is an Intelligent Agent?
An intelligent agent (IA) in the Artificial Intelligence (AI) is a programme that can make decisions or perform a service based on the environment, input and experience. Intelligent agents are used to autonomously collect information on a regular programmable schedule or on demand by the user in real time.
Such agents are also called bots. This technique, where information is delivered by an agent, is called push technology.
There are different types of intelligent agents, such as Reflection agents, model-based agents, goal-based agents, utility-based agents and learning agents. These types of intelligent agents are practically defined by their range of capabilities and functions. Examples of these agents are Alexa and Siri. These use sensors to communicate with the user.
The Intelligent Agent Architecture has a combination of agent functions, architecture and agent programmes. This architecture is a machinery on the basis of which the agent performs its actions. Essentially, it is a device in which there are embedded actuators and sensors. For example, autonomous vehicles exist with motion and GPS sensors. There are also actuators based on inputs that support actual driving.
Where is IA used in artificial intelligence?
Widespread techniques in which AI is successfully used include industrial robots and automated production facilities, quality assurance, with Automatic image recognition, and with Speech recognition and Speech extractionand also in weather or stock market forecasts and in knowledge-based expert systems.
Types of Intelligent Agents
Types of IA include:
- modal adaptive,
- and social agents.
Intelligent agents are characterised by knowledge, the ability to learn and the ability to make inferences. These agents also have the ability to change behaviour.
An intelligent software agent can act flexibly. It is reactive, proactive and social. Such an IA can act autonomously in its environment. It performs its tasks on behalf of a user or other agents.
An Intelligent Agent exhibits the ability to use task-oriented problem solving through an autonomous, reactive and goal-oriented application of appropriate Artificial Intelligence methods. It uses a corresponding knowledge representation in its environment with the associated actions and goals. The IA uses logical reasoning and heuristic solution seeking when planning. He uses machine learning and can handle uncertain knowledge. Intelligent interaction is possible with it, with visualisation and natural language dialogue.
There are simple reactive agents, status-based reflex agents, practical reasoning (planning) agents and learning agents. In social agents, there are robust and distributed collaborations with various other agents for individual or shared tasks and goals. And in a multi-agent system, interacting agents pursue distributed problem solving. There is hierarchical task distribution and emergent solution behaviour (with swarm intelligence). There is also coordination in the system, with communication between the agents and with different cooperation models.
What is ImageNet?
ImageNet is a database that is used especially in research. It contains images that are assigned to nouns and arranged in a hierarchy. There is one noun for every 500 images, and over 14 million images are integrated in the database. Furthermore, there are well over 20,000 different English-language categories.
Areas of application
The project enjoys great popularity in various research projects. Already in 2009, this was brought out and is used for training purposes in the field of the Convolutional Neutral Networks applied. The ILSVRC software competition, which has been held since 2010, is used to correctly classify or determine scenes and various objects. Especially when it comes to machine learning, ImageNet should not be missing as a mention in this context.
Images and term database
In principle, it is a matter of displaying the correct symbol or image for a certain term. In order for the system to recognise which noun or which term belongs to the correct image, programmers can create associations and comparisons, always in connection with the respective class. In a normal standard search engine, the system is similar. Users only find information on the term they have previously entered. To ensure that pictures of the noun searched for are always displayed, the Database always be up to date.
ImageNet as Artificial Intelligence
As an independent database system, users can access a complex service that displays various results for a single search term, but all of which are related. When smelling a certain odour, it generates and activates several mental images in the human brain, as well as associations and memories that were learned, acquired and made at some time in the past life. This then results in a certain reaction.
It is similar to this with ImageNet as artificial intelligence. By feeding in various images and associated information, as well as the frequency with which certain search terms are entered, the system can not only determine the popularity of these, but also create its own ranking as well as links to similar words.
ImageNet is a platform and database that never stays at one and the same level. Constant updates and extensions ensure that images and nouns or search terms are always being added that the system can work with. The database expands, i.e. continues to develop. Similar to the human brain, structures and the intelligence network are constantly being expanded. Even if the database is dependent on additional input, it can hope for a rosy future due to the great popularity and demand from industry and research.
At the same time, normally large data collector services may also pose a danger. Everything you publish and use there will somehow and somewhere be evaluated by someone and used and analysed for purposes of various kinds. The collection of statistics and market research purposes can be just some of the aspects. Major search engines work according to a similar principle. Unfortunately, there is often a lack of transparency for laypeople and normal users as to what happens with the data.