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Data in industrial systems

Previous articles have pointed out that databases form the foundation of industrial systems and that their effectiveness depends on being properly tailored to the nature of the data and processes.

The next step in the development of such systems is to use data not only for monitoring or analysis, but also to create digital representations of reality and support decision-making processes.

In this context, it is important to distinguish between the concepts of Digital Shadow and Digital Twin, which are often used interchangeably, even though they describe different levels of maturity for data-driven systems.

Digital Shadow vs. Digital Twin – A Key Distinction

In industrial practice, a digital representation of a system can take various forms, depending on the degree of data integration and the possibilities for its use.

Digital Shadow refers to a situation in which data from a physical system is mapped to the digital world, but without actively influencing its operation. This means that:

  • data flows from the physical system to the digital system,
  • analysis and visualization of the system’s state are possible,
  • there is no feedback mechanism influencing the process.

This approach is characteristic of monitoring and reporting systems, where the goal is to understand the status and history of processes.

In contrast, a Digital Twin represents a more advanced form of integration, in which there is a two-way relationship between the physical system and its digital representation. In this case:

  • data is not only analyzed but also used to model the system’s behavior,
  • it is possible to simulate scenarios and predict events,
  • the digital system can influence operational decisions or directly affect control.

In practice, this means a shift from observation to actively supporting or optimizing processes.

The role of the data layer in both approaches

Regardless of the level of sophistication, both Digital Shadow and Digital Twin are based on the same foundation: a coherent and well-designed data architecture.

The data layer is responsible for:

  • integrating information from sensors, automation systems, and master systems,
  • ensuring its timeliness and availability,
  • storing the history necessary for analysis and modeling.

The difference lies not in the data itself, but in how it is used. In the case of Digital Shadow, data is used primarily for observation and analysis, whereas in Digital Twin, it serves as the basis for building models and autonomous decision-making.

This distinction is not merely conceptual but is also reflected in standards for digital twins, such as ISO 23247, which outline different levels of integration between the physical system and its digital representation—ranging from one-way data flow to feedback loops that enable influence over the process

From monitoring to decision support

The development of industrial systems does not end with the creation of digital representations. Solutions that help users make decisions based on available data are becoming increasingly important.

The graphic shows a breakdown of digital solutions, classified from shadow to twin

In this context, the concept of the Digital Advisor emerges—an analytical system positioned between Digital Shadow and Digital Twin solutions that:

processes data from multiple sources,

identifies relationships and patterns,

suggests possible actions or recommendations.

A key element of this approach is the presence of a human in the decision-making process (the so-called “human in the loop”). This means that the system does not make decisions autonomously, as in the case of a Digital Twin, but supports the operator or engineer by providing recommendations and key information for decision-making.

This approach is particularly important in industrial settings, where decisions have a direct impact on safety, operational continuity, and operating costs.

Consistency with the normative approach and the direction of development of industrial systems

The development of data-driven industrial systems is neither random nor purely technological. It reflects a broader normative approach, as described, for example, in standards on digital twins, such as ISO 23247. These standards indicate that the construction of modern systems should be based on consistent data integration, the ability to map the system’s structure and behavior, and the ability to use models for decision-making.

In practice, this means that the development of such solutions should be treated as a gradual process, rather than a one-time implementation of a specific technology. Industrial systems mature alongside the organization, and subsequent functional layers are built upon the existing data infrastructure.

This approach is directly reflected in the observed direction of system development:

  • from monitoring systems (Digital Shadow), which enable data observation and analysis,
  • through decision-support systems (Digital Advisor), in which data and predictions are transformed into recommendations,
  • to modeling and predictive systems with feedback (Digital Twin), capable of automatically making decisions and controlling the physical system.

The transition between these levels is not abrupt. Organizations develop their systems evolutionarily, expanding their capabilities as the maturity of data, models, and operational processes increases.

As a result, data architecture becomes not only an element of infrastructure but a long-term foundation for the development of systems that support and automate decision-making in industry.

Summary

Data-driven industrial systems are evolving toward greater integration and the use of information in decision-making processes. In this context, it is crucial to distinguish between maturity levels—from Digital Shadow, through Digital Advisor, to Digital Twin systems.

Regardless of the stage of development, the foundation remains a properly designed data layer that enables the integration, analysis, and practical application of information in operations.

It is this layer that determines whether data will remain merely a record of events or become a real tool supporting the functioning of modern industry.

Types of Databases in Industrial Systems

The previous article (“Databases in Industry”) noted that a database serves as the foundation of industrial systems, enabling the integration, analysis, and use of data in decision-making processes.

In practice, however, simply implementing a database does not guarantee the effectiveness of the entire solution. The key lies in selecting the right database—one tailored to the nature of the data and how it will be used within the system. Industrial environments generate information with highly varied structures and dynamics, which directly influences how it is stored and processed.

Data diversity as a starting point

To choose the right approach to data storage, it is essential to understand the nature of the data. In industrial systems, we most often deal with several basic categories:

  • data (e.g., temperature, vibration, pressure),
  • data from automation systems (states, control signals, process variables),
  • event data (alarms, failures, state changes),
  • structural and configuration data (system description, relationships between components).

Each of these types serves a different function in the system. Measurement data is responsible for observing reality, automation data for control, event data for recording significant moments, and structural data for providing context.

In practice, this means that the data architecture must account for different methods of storing and processing data, rather than attempting to unify everything into a single model.

Basic approaches to data storage

In response to the diversity of data in industrial systems, several main approaches are used to organize it:

  • relational – used where data consistency and clearly defined relationships are essential,
  • time-series – used for measurement data recorded continuously,
  • event-driven – focused on recording and processing events in a specific order,
  • flexible – designed for data with a variable structure.
The chart shows the types of data stored in databases, broken down into groups

Each of these approaches addresses different needs. Structured data requires precision and integrity checks, while measurement data requires performance and the ability to handle large volumes. Event data, on the other hand, requires maintaining sequence and temporal context, and data with a variable structure requires flexibility.

Applying a single approach to all data types typically leads to limitations—either in terms of performance or analytical capabilities.

Hybrid architecture as a practical approach

In practice, modern industrial systems are based on a hybrid architecture in which different approaches coexist and complement one another.

This means that data is not stored in a single location in a uniform manner, but is distributed according to its nature. Measurement data goes to structures optimized for time and volume, configuration data to structures ensuring consistency, and event data to mechanisms enabling real-time analysis.

This approach not only increases system performance but also better reflects the company’s operational reality. As a result, it is possible to simultaneously conduct monitoring, historical analysis, and respond to current events.

The Importance of Industrial Automation Data

Data from automation systems plays a particularly important role in the overall architecture. This data directly reflects the logic of technological processes and how equipment operates.

Measurement data alone only shows the system’s state, whereas only when combined with control data can we answer the question of why that state changed. In practice, this means that different types of data must be correlated and analyzed in the context of time and the relationships between system components.

This approach enables:

  • the identification of the causes of events,
  • the analysis of dependencies between control and the state of devices,
  • the reconstruction of the course of technological processes.

Without taking this layer into account, it is impossible to move from simple monitoring to a true understanding of processes.

Challenges in choosing the right approach

Data storage in industrial systems is a complex and multifaceted process. It is not merely a technological decision but requires consideration of the specific nature of operational processes, the nature of the data, and how it is used within the organization.

One of the main challenges is the need to reconcile conflicting requirements. On the one hand, the system must efficiently handle large volumes of measurement data generated in real time; on the other, it must ensure the consistency and integrity of structured data. Additionally, there is a need for rapid response to events and the ability to perform historical analyses.

Another significant challenge is the integration of data from various sources. Automation systems, sensors, and business applications inherently operate in separate environments, using different data models. Integrating them requires not only the right architecture but also an understanding of the relationships between the data and their operational context.

In practice, the decision-making process is influenced by, among other things:

  • the volume and frequency of data generation,
  • requirements regarding access and processing time,
  • the need for real-time and historical analysis,
  • the complexity of technological processes,
  • integration with existing system infrastructure.

Industrial systems evolve alongside the organization—the number of devices, measurement points, and the scope of analysis all increase. The data architecture should therefore allow for gradual expansion without the need to rebuild the entire system.

As a result, selecting the right approach requires viewing the system as a whole—not only through the lens of technology, but above all through the lens of the processes it is designed to support.

Summary

Selecting the right approach to data storage is one of the key elements of industrial system design. It does not involve choosing a single solution, but rather consciously combining different models into a coherent architecture.

It is precisely this architecture that allows for the effective processing of diverse data, which can then be used for analysis, optimization, and decision-making.

The next article in this series will explain how a properly designed data layer enables the creation of digital twins and predictive systems in modern industry.

Databases in Industry

Modern industry increasingly relies on data derived from real-world technological processes. The development of IoT systems, the growing number of sensors, and the need for continuous infrastructure monitoring mean that companies are processing ever-larger volumes of information.

However, this data has no value in and of itself—its significance only becomes apparent once it can be organized, analyzed, and used to make decisions. In this context, databases play a key role, serving as the foundation for systems of monitoring, diagnostics, and optimization of industrial processes.

What is a database in an industrial context?

In the traditional sense, a database is a system that enables the storage, management, and sharing of information. In an industrial environment, however, its role is much broader.

A database becomes a central element of the system infrastructure that:

  • integrates data from multiple sources (sensors, SCADA systems, business applications),
  • ensures its consistency and availability,
  • enables real-time and historical analysis,
  • serves as the foundation for predictive and reporting systems.

In practice, this means that the database is not merely a “data warehouse,” but an active component of the decision-making system.

What data is processed in industry?

Industrial systems handle various types of data, which differ in nature, frequency, and intended use.

1.Measurement (sensor) data

This is the most common type of data:

  • temperature,
  • humidity,
  • vibration,
  • pressure,
  • sound and vibration levels.

It is characterized by high recording frequency and large volume.

2.Data from industrial automation systems

Data originating directly from automation systems, such as PLCs, SCADA, or DCS, are a significant source of information. They include, among others:

  1. states of digital inputs and outputs,
  2. values of registers and process variables,
  3. control signals,
  4. control logic and process sequences.

Unlike raw measurement data, automation data reflects the actual course of technological processes and the operation of control systems.

Their analysis enables:

  • identification of inefficiencies in processes,
  • detection of anomalies in equipment operation,
  • correlation of technological events with measurement
  • data,reconstruction of process sequences (so-called traceability).


In practice, this means a transition from simple monitoring to a full understanding of the behavior of the production system.

3. Event Data
They describe specific situations within the system:

  • alarms,
  • failures,
  • threshold exceedances,
  • changes in device statuses

They are essential for diagnostic and reactive systems.

4. Configuration and Structural Data

It contents:

  1. system structure,
  2. device configuration,
  3. relationships between components.

They form the basis for interpreting measurement data.

5. Historical Data
Used for:

  • trend analysis,
  • reporting,
  • building predictive models.

It is precisely on this basis that it is possible to move from reaction to prediction.

Why are databases essential in industrial systems?

In an industrial environment, the database serves as a central hub for integrating information from both sensors and automation systems (PLC, SCADA, DCS). It is at this level that measurement data can be linked to the actual course of technological processes, which forms the basis for further analysis.

The graphic illustrates the aggregation of data from multiple industrial facilities into a single centralized database, after which the data is processed using artificial intelligence


From an operational perspective, a well-designed database ensures:

  • data consistency and a single source of truth for the entire organization,
  • real-time availability of information and its resilience to failures,
  • scalability as the number of devices and data volume increases,
  • the ability to integrate data from different system layers (sensors, automation, business systems).

The ability to analyze data is also of key importance, particularly in the context of industrial automation. Only by correlating measurement data with control data can one fully understand processes and make informed decisions.
In practice, this enables:

  • the identification of anomalies and inefficiencies in technological processes,
  • root cause analysis of events and failures,
  • the development of predictive models to support maintenance,
  • the development of monitoring systems, operational dashboards, and digital twins.

As a result, the database ceases to be merely a layer for storing information and becomes the foundation of analytical and decision-making systems in modern industry.

Databases as the Foundation of Digital Transformation

Digital transformation in industry is not merely about implementing new technologies, but above all about shifting the decision-making process from intuitive to data-driven.
Databases play a fundamental role in this process, enabling:

  • system integration,
  • real-time data analysis,
  • the development of predictive models,
  • the advancement of concepts such as Industry 4.0 and digital twins.

Summary

In an industrial setting, a database is not merely a component of IT infrastructure, but a key element of an organization’s entire operational system. It is the database that enables the transformation of raw data into information and, subsequently, into tangible business value.
In the upcoming articles in this series, we will take a closer look at the various types of databases used in industry and how to select them based on specific applications.

Fight against pollution

Modern urban environmental management is based on precise measurements and their reliable analysis. Air pollution, traffic noise, vibration impacts, and the growing problem of light pollution are measurable phenomena – and therefore subject to analysis, modeling, and optimization. However, this requires the right measurement methodology and expertise in interpreting the results.

The WHO Global Air Quality Guidelines (2021) indicate that even low concentrations of PM2.5 have a significant impact on the health of the population. This document recommends an annual PM2.5 level of 5 µg/m³ as the value that minimizes health risks [1]. These guidelines remain the current global benchmark for air quality assessment.

In terms of environmental noise, the applicable reference document is the Environmental Noise Guidelines for the European Region (2018), which clearly indicate a link between long-term exposure to noise and sleep disturbances and an increased risk of cardiovascular disease [2].

The Environmental Noise in Europe 2020 report shows the extent to which EU citizens are exposed to traffic noise and its health consequences [3].

Fig. 1 Air pollution measurements

Air quality – European data

Air quality monitoring primarily covers:

  • PM2.5 and PM10
  • NO₂
  • O₃
  • SO₂ and CO

In its briefing “Europe’s air quality status 2024,” the EEA points out that a significant proportion of the EU’s urban population continues to breathe air with concentrations exceeding WHO recommendations [4].

Additionally, the publication “Harm to human health from air pollution in Europe: burden of disease 2024” updates data on the burden of disease and premature deaths associated with exposure to pollution [5].

In expert practice, not only the reading of the value itself is crucial, but also:

  • calibration and periodic verification of sensors,
  • validation of measurement data,
  • analysis of seasonality and correlation with traffic and meteorological conditions,
  • modeling the spread of pollution.

Only by combining measurement data with spatial and statistical analysis can effective emission reduction measures be designed.

In Poland, the primary reference document is the Annual Assessment of Air Quality in Zones in Poland for 2024, published by the Chief Inspectorate for Environmental Protection [6].

This report covers:

  • classification of 46 zones,
  • information on exceedances of PM10, PM2.5, NO₂, and benzo(a)pyrene standards,
  • assessment of compliance with EU requirements,
  • trend analysis.

This document provides the basis for developing air protection programs and corrective measures at the regional and local levels.

Fig. 2 Noise and light pollution measurements

Environmental noise

The following indicators are used in environmental acoustics:

  • LAeq – equivalent sound level,
  • Lden – daily indicator,
  • Ln – nighttime noise level.

The EEA report Environmental Noise in Europe 2020 indicates that road traffic noise remains the dominant source of exposure in European cities [3].

In accordance with Directive 2002/49/EC [7], this data forms the basis for the creation of strategic noise maps in EU Member States. However, the mere creation of a map is not the goal—its use in infrastructure design, traffic planning, and investment impact assessment is key.

In expert practice, the analysis includes, among other things:

  • identification of dominant emission sources,
  • sound propagation modeling,
  • simulation of noise reduction options,
  • assessment of the effectiveness of technical measures (e.g., noise barriers or changes in traffic organization).

Light pollution

Light pollution (artificial light at night) is increasingly being identified as a growing environmental pressure. In its materials on pressures on ecosystems, the EEA points to the impact of artificial light on biodiversity and the functioning of organisms [8].

From an urban perspective, this means the need to:

  • measurements of luminance and illuminance,
  • light spectrum analysis,
  • lighting design in accordance with the principle of minimizing unnecessary light emissions.

Local actions – the example of Małopolska and Krakow

The Air Protection Program for the Małopolska Province and the anti-smog resolution for Krakow are examples of a systemic approach to reducing emissions.

At the same time, Krakow is preparing for further measures aimed at reducing various environmental pressures that residents encounter on a daily basis — not only in terms of air quality, but also noise and traffic management, among other things.

This is an example where regulations, monitoring, and reporting form a coherent environmental management system.

Expert publications and knowledge development

In response to growing regulatory and technological requirements, BFirst.Tech develops expert studies and industry publications, including white papers. These documents are technical in nature and focus on issues such as:

  • measurement methodologies in environmental acoustics,
  • standardization and validation of measurement data,
  • integration of sensor systems with analytics and modeling,
  • practical application of environmental regulations for infrastructure and industrial investments.

The aim of the publication is to organize knowledge in the field of environmental monitoring and to present an approach based on data, analysis, and methodological consistency. These materials are addressed to specialists, public administration, designers, and entities responsible for planning and implementing investments.

The development of expert knowledge also includes educational activities. Recently, BFirst.Tech conducted training on combating noise pollution at the headquarters of UN Global Compact Network Poland in Warsaw. The meeting focused on:

  • interpretation of acoustic indicators,
  • practical aspects of noise mapping,
  • methods of reducing emissions in urban and industrial projects,
  • the role of environmental data in achieving ESG goals.

At the same time, BFirst.Tech implements a proprietary measuring station integrated with the BFirst.Tech Ecosystem, enabling data collection, monitoring, validation, and analysis in a single operating environment.

The scope of measurements and details will be presented in the next article devoted to the system and its urban applications.

Summary

Urban pollution requires an approach based on:

  • precise measurements,
  • consistent indicators,
  • data analysis,
  • practical use of results in planning and investments.

Reports by the WHO [1][2], EEA [3][4][5], and GIOŚ [6] show that both air quality and environmental noise remain significant health risks in Europe and Poland.

An expert approach combining monitoring, modeling, and data interpretation forms the foundation of responsible urban environmental management.

Sources

[1] WHO (2021), WHO Global Air Quality Guidelines 
https://www.who.int/publications/i/item/9789240034228 

[2] WHO Regional Office for Europe (2018), Environmental Noise Guidelines for the European Region 
https://www.who.int/europe/publications/i/item/9789289053563 

[3] EEA (2020), Environmental Noise in Europe 2020 
https://www.eea.europa.eu/publications/environmental-noise-in-europe 

[4] EEA (2024), Europe’s air quality status 2024 
https://www.eea.europa.eu/en/analysis/publications/europes-air-quality-status-2024 

[5] EEA (2024), Harm to human health from air pollution in Europe: burden of disease 2024 
https://www.eea.europa.eu/en/analysis/publications/harm-to-human-health-from-air-pollution-2024 

[6] GIOŚ (2025), Roczna ocena jakości powietrza w strefach w Polsce za rok 2024 
https://powietrze.gios.gov.pl/pjp/content/show/50015113 

[7] Dyrektywa 2002/49/WE 
https://eur-lex.europa.eu/legal-content/PL/ALL/?uri=celex%3A32002L0049 

[8] EEA, Zero pollution – ecosystem pressures (light pollution context) 
https://www.eea.europa.eu/en/analysis/publications/zero-pollution/ecosystems/signals/biodiversity-signals

Smart City

With increasing urbanisation around the world and increasingly important social issues such as air pollution, urban litter, the fight against climate change or over-reliance on car transport, the need to manage cities more efficiently is emerging. Modern technologies can be used to achieve this. The idea of Smart Cities is to use communication technologies to create a more interactive and efficient urban infrastructure, as well as to raise citizens’ awareness of its operation [1]. Smart Cities therefore represent a wide range of solutions that, in combination, improve the lives of residents and help combat the problems of today’s world. In the following article, we will present some of the Smart City solutions. The role of data collection in the Smart City, Smart City technologies for transport, smart energy management, as well as for combating environmental and noise pollution will be discussed.

Data collection and analysis in a Smart City  

A fundamental role in the functioning of a Smart City is the collection of data through all sorts of measurement tools such as sensors, probes and cameras. The collection of real and up-to-date data on the operation of the city is crucial to the proper functioning of Smart City solutions, as their analysis allows real-time decision-making, significantly reducing resource consumption without compromising the standard of living of the inhabitants [2]. The proper collection and analysis of the vast amount of data needed for the proper operation of Smart City systems is a huge challenge. 

BFirst.Tech specialises in the implementation of IoT technology, providing advanced solutions for smart monitoring, data analysis and optimisation of urban infrastructure. As a member of the United Nations Global Compact Network Poland and co-author of the Recommendations for Cities by the World Urban Forum 11 Business Council, the company actively supports the development of sustainable technologies, focusing on innovative diagnostics, environmental acoustics and data engineering systems. 

Smart City in transport   

One of the main areas of use of Smart City solutions is in transport. Today’s cities are able to collect far more transport data using smart tools in public transport vehicles, at important points on the road such as intersections, or through public monitoring.    

The data collected in this way can then be processed accordingly and used to improve the efficiency of the city’s transport system.  The collected information can be used to display timetable information and the current position of public transport vehicles with the estimated time of arrival at the stop, making public transport a very attractive alternative to the car. 

Rys. 1. Using the Smart City in Transport. Source: https://www.digi.com/blog/post/introduction-to-smart-transportation-benefits 

Data flowing into traffic management systems allows real-time optimisation of urban traffic to improve safety and reduce emissions. Smart parking systems make use of data on parking spaces, monitoring them and informing drivers of their availability, and allow payment for parking to be collected, improving driver comfort and also reducing pollution by reducing the time used to find a parking space [3]. 

Smart City solutions also help to solve the so-called first and last kilometre problem – the first and last part of a journey in a city, usually being considerably shorter than the public transport journey itself, while possibly taking a similar amount of time. Smart City systems can allow the linking of the public transport network with the use of lightweight short-distance transport modes such as bicycles or electric scooters. Properly placed hubs for such transport, combined with ease of use, can significantly facilitate urban travel and even encourage some drivers to use public transport [4]. 

Smart energy management  

With the increasing demand for electricity, due in part to the need to decarbonise the economy as much as possible, there is a growing emphasis not only on increasing the production of energy from renewable sources, but also on using it more efficiently. The use of intelligent energy management solutions leads to less energy consumption and therefore less energy production, which can have a major impact on environmental protection. 

Rys. 2. Green energy in the city. Source: https://leadersinternational.org/sme4smartcities-insights/revolutionising-urban-life-how-smart-technologies-and-sustainable-energy-are-creating-the-cities-of-the-future/ 

Among the Smart City systems that support better management are smart grids that monitor energy distribution and consumption, efficient systems for storing cheaply produced energy at peak production times, and smart sensors able to regulate the use of lighting according to the amount of natural light. All these solutions in combination also make it possible to create programmes that optimise when energy is used, using it mainly during the periods of lowest production costs, which is used, among other things, in the charging of electric vehicles [5].  

In addition to the above-mentioned ways of using electricity more efficiently, less energy consumption can also be influenced by technical developments and new regulations for the construction and renovation of buildings so that they use as little energy as possible. This can be done, among other things, by using efficient and environmentally friendly materials, by designing buildings to minimise heat loss while allowing as much natural light as possible, or by using intelligent systems to optimise heating and lighting consumption. 

Efficient energy management is one of the key aspects of the energy transition and the fight against progressive climate change. The transformation of cities into smart cities will require large amounts of electricity, which must be produced efficiently to contribute to better environmental protection [6].  

BFirst.Tech has become a member of the Business Council at PRECOP29, which produced a “White Paper” providing a Polish perspective on climate issues, including energy management ahead of the United Nations Climate Change Conference 2024. BFirst.Tech offers end-to-end solutions for monitoring, diagnostics and management of big data, including energy. To learn more, explore our solutions under this link. 

Smart City in the fight against pollution and noise  

One of the biggest problems facing modern cities is air pollution, resulting from a number of factors, such as the burning of solid fuels in cookers and urban planning. High levels of pollution affect the health of city dwellers, reducing their productivity, occupying the raw materials of health services and reducing attractiveness for business and tourists.  

In order to effectively combat air pollution, it is necessary to have accurate information on its levels and spatial distribution provided by a large number of sensors across the city. The information gathered in this way helps to make appropriate decisions on measures to improve the state of the air. In addition, properly presented information on the state of the air to residents can strengthen public awareness of the problem and increase pressure to find appropriate solutions to combat pollution [7]. 

In addition to air pollution, the problem of urban noise is also increasingly discussed. Traffic jams, renovations, construction of new buildings and other sources of noise in cities can sometimes pose a serious threat to human health [8], further worsening levels of concentration and focus, lowering the standard of living of residents. 

Rys. 3. Sources of noise for urban residents. Source: https://www.hseblog.com/noise-pollution/ 

Smart sensors that are able to estimate not only the level of noise recorded but also the source of the noise can be used to combat this problem. This data can then be processed and used by experts to prepare a plan to mitigate noise levels, thus improving the lives of residents [9]. 

BFirst.Tech is a company with many years of expert experience in implementing solutions to combat noise pollution. BFirst.Tech offers a modern and advanced approach in the field of noise reduction, in line with the needs not only of smart cities but also of modern industry. Explore our products and solutions under this link. 

Summary

Smart Cities make use of today’s advanced data acquisition, processing and storage techniques. Through their use, our cities are gaining new tools and techniques to combat the increasingly pressing problems of the modern world. These technologies can help not only with the problems of public transport, air pollution, noise and energy management mentioned in the article, but also with many others, among which are better prevention and crisis management, public safety or waste management. Which cities make the best use of them could be a key factor in their further development and the key to better meeting the needs of their inhabitants. 

References

[1] https://uclg-digitalcities.org/en/smart-cities-study/2012-edition/ 

[2] https://www.oecd.org/en/publications/smart-city-data-governance_e57ce301-en.html 

[3] https://www.teraz-srodowisko.pl/aktualnosci/przyszlosc-transport-smart-city-forum-11962.html 

[4] https://smartride.pl/przyszlosc-transportu-w-smart-city-komfort-podrozy-i-czyste-powietrze/ 

[5] https://energy-floors.com/10-smart-city-energy-solutions-kinetic-floors/ 

[6] https://www.teraz-srodowisko.pl/aktualnosci/inteligentne-technologie-zarzadzanie-energia-miasta-efektywnosc-energetyczna-13055.html 

[7] https://www.innovationnewsnetwork.com/the-development-of-the-smart-city-waste-management-and-air-quality-monitoring/39990/ 

[8] https://pmc.ncbi.nlm.nih.gov/articles/PMC6878772/ 

[9] https://newsroom.axis.com/blog/noise-pollution-smart-cities 

The effect of technological illusions on people’s perception of reality

Computerisation, which began in the 1990s, has propelled humanity into an era where working and interacting with technology on a daily basis is common and natural. Artificial Intelligence answers our questions, and the Internet is seen as an endless source of information. While one may think that the development of technology helps us to understand the world around us, there are phenomena that show how often our intuitions fail. Technologies, which at first glance are simple and obvious, can hide paradoxes and illusions, which may be more difficult to spot, as well as to understand, than it seems. This article will explore three interesting phenomena: the ELIZA effect, the Moravec’s paradox and the Streisand effect. Each of these shows how technology can change our perception of reality, affecting how we see machines, data and information. Exploring these phenomena will provide a different perspective on the development of technology and help us to use it more consciously. 

ELIZA effect 

In the 1960s, Joseph Weizenbaum at the Massachusetts Institute of Technology developed the ELIZA programme [1]. This programme was one of the first chatbots – it naturally mimicked a normal conversation. Despite the simplicity of the algorithm, which created responses based on the keywords entered by acting according to predetermined patterns, many users of the programme reported that they got the impression that Eliza really understood them. Thanks to the clever selection of answers, users were able to be highly engaged in the conversation, satisfied that the interviewer understood them and was paying attention. The creator himself was surprised at how convinced people were that Eliza was a human being, not a machine.  

It is from this chatbot that the ELIZA effect, the phenomenon of the tendency for humans to attribute to machines, programmes (including AI) the capabilities of understanding, empathy and intelligence, i.e. to anthropomorphise them [2], got its name. Examples of this phenomenon include the appearance of “hello” or “thank you” messages on ATM and self-checkout displays, which are pre-defined texts to be displayed rather than an expression of gratitude by the machine; or communication with voice assistants – thanking them, saying “she” about the Alexa assistant, which despite using a female voice still remains a genderless algorithm. The reason behind this effect can be attributed to our nature – everything that is human seems familiar, closer, less frightening, which can be seen, for example, in the way ancient deities are depicted and compared to humans and animals, attributing weather phenomena or elements to them [3].  

Such bonding with, sometimes very complex, technologies allows one to overcome fear of novelty, encourages interaction and builds attachment to the product being used. At the same time, this effect can cause an overestimation of the capabilities of a given algorithm (due to the assumption that the machine knows and understands more than it actually does), excessive trust in the information received, or an inappropriate treatment of the creation as a human being, e.g. by treating a chatbot like a therapist or marrying AI [4]. 

Moravec’s paradox 

Another interesting phenomenon takes its name from the Canadian scientist Hans Moravec, author of works on technology, futurology and transhumanism. In 1988, he, together with Rodney Brooks and Marvin Minsky, formulated the statement: “It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility” [5]. It implies that tasks that are considered difficult, requiring knowledge, intelligence and logical thinking, are relatively easy to solve using AI, while those activities that we consider simple and natural – walking, recognising faces and objects or motor coordination – are very challenging and difficult to implement in machines.  

Researchers attribute the reason for this paradox to the human evolutionary process. Human motor skills developed over millions of years and were essential for survival and slowly but continuously improved by natural selection. The human brain has had plenty of time to assimilate and adapt to activities such as grabbing tools, recognising faces and emotions, walking or motor coordination, so they are automated at a deep level; we perform them without conscious effort. On the other hand, abstract thinking, mathematics, logic are relatively new abilities, not rooted so deeply and requiring conscious intellectual effort. Because these abilities are not ingrained so deeply in human beings, it is easier to apply reverse engineering to them and implement them in the form of a programme. In addition, computers are most effective at mapping logical, schematic processes with specific steps. For these reasons, we already have programs that are superior to humans when it comes to complex calculations, chess, simulations, but when it comes to mobility, coordination, object and face recognition, or other “basic” activities that we consider natural and simple for a child of just a few years old – the development is very slow. It is only recently that the amount of data and technology has allowed a gradual development in this area, as shown, for example, by the robotics design company Boston Dynamics [6].

Streisand effect 

Another phenomenon presented in this paper is the Streisand effect. According to this phenomenon, the more one tries to remove or censor a piece of information on the Internet, the more publicity and interest it receives. The effect owes its name to Barbra Streisand and the situation that occurred in 2003, when photographer Kenneth Adelman took photographs of the California coastline to document the progressive erosion [7]. These photographs were made public on a website dedicated to the subject of coastal erosion. Coincidentally, one of the photographs showed Barbra Streisand’s residence. She sued the photographer for invasion of privacy, demanding damages and the removal of the photograph as she did not want anyone to see it. However, it turned out to be quite the opposite – she lost the lawsuit and had to reimburse the photographer, and not only was the photograph not removed, but it received even more publicity and many more views than before the whole situation.  

This effect can be attributed to several factors, mainly based on human psychology, the role of social media and the general mechanisms of online information circulation. People are very reluctant to endure any restrictions imposed on their freedom, including access to information. Often, in situations of enforced censorship, people deliberately act out of spite – they want to get as much news about the “forbidden” information as possible, are willing to share it and spread it further. The “forbidden fruit” effect works in a similar way – by attempting to hide the information, it appears even more interesting and intriguing, even though without the attempt the message would probably have been disregarded. Nowadays, because of the ease of access to information and the multitude of different media, news is widespread and can quickly become viral, attracting huge audiences. The Internet has also changed the perception of various content. In theory, the fact that any user can save and share content makes it impossible to remove something from the Internet once it has been posted there. Given also how quickly the media seize on and publicise instances of censorship, it becomes quite obvious why an attempt to hide or cover up something usually ends up having the opposite effect. 

There are many examples of the occurrence of the Streisand effect. In 2013, after her Super Bowl performance, singer Beyonce’s publicist deemed one of the photos particularly unfavourable and attempted to remove it from the Internet. The effect was exactly the opposite; the photograph became considerably more popular than it had originally been and also began to serve as a template for internet memes. There are also many examples of the Streisand effect from the world of technology. In 2007, a user of the Digg website revealed that the Advanced Access Content System (AACS) copyright protection system used in HD DVD players could be cracked with a string known as 09 F9. Representatives of the industry using this protection demanded that the Digg post be removed and threatened legal consequences. As a result, a great deal of discussion took place on the Internet, and information about the code (which for a while was referred to as “the most famous number on the Internet”) spread heavily and was reproduced in the form of videos, t-shirt prints or even songs [8]. 

Summary

The phenomena discussed in the article show that although technologies such as Artificial Intelligence and the Internet are powerful tools, they have the potential to distort human perception and create misleading impressions. It is easy to fall into the various traps related to technology, which is why awareness of the phenomena mentioned is important, as it allows for a more critical approach towards interaction with technology and information, a better use of their potential and their healthy and sensible application.  

References

[1] https://web.stanford.edu/class/cs124/p36-weizenabaum.pdf 

[2] https://modelthinkers.com/mental-model/eliza-effect 

[3] https://builtin.com/artificial-intelligence/eliza-effect 

[4] https://www.humanprotocol.org/blog/what-is-the-eliza-effect-or-the-art-of-falling-in-love-with-an-ai  

[5] https://www.researchgate.net/publication/286355147_Moravec%27s_Paradox_Consideration_in_the_Context_of_Two_Brain_Hemisphere_Functions  

[6] https://www.scienceabc.com/innovation/what-is-moravecs-paradox-definition.html  

[7] https://www.forbes.com/2007/05/10/streisand-digg-web-tech-cx_ag_0511streisand.html  

[8] https://web.archive.org/web/20081211105021/http://www.nytimes.com/2007/05/03/technology/03code.html 

ESG

In the face of climate change, growing social awareness and the need for ethical governance, there is an emerging need to set new standards for how companies operate. ESG is the actions implemented by a company through the lens of its environmental (E), social (S) and corporate governance (G) impacts. The aim of this initiative is to promote sustainability and social responsibility in the wider business community. In line with this, companies seek to strike a balance between generating profits and caring for the environment. The obligation for companies to report their ESG activities will be gradually extended, depending on the size of the entity and the specifics of its operations. Starting in 2024, the obligation will cover companies with more than 250 employees and by the end of 2027, it will also cover small and medium-sized enterprises with more than 10 employees. The purpose of this article is to show that the importance of sustainability continues to grow and ESG issues are becoming a key area of focus in business [1].

ESG indicators 

The three ESG areas mentioned above—namely environment, society and corporate governance—are an integral element necessary to be taken into account by companies that care about their image as socially responsible organisations. Effective management of each of these areas, through companies taking specific actions related to them, is key to achieving this goal. A fundamental action to be taken is monitoring, which allows awareness of the intensity of the impact exerted in each area. 

  1. Environment 

To effectively monitor the impact on nature, consideration should be given to areas such as: 

  • greenhouse gas emissions, 
  • energy consumption, 
  • carbon footprint, 
  • hazardous waste production, 
  • emissions (such as substances or noise) to the environment, 
  • emissions to the aquatic environment. 
  1. Society 

In order to effectively monitor relations with employees, customers, investors and the local community, it is important to consider areas such as: 

  • supporting diversity, 
  • minimising disparities, 
  • ensuring work-life balance, 
  • respecting employee rights, 
  • ensuring employee safety. 
  1. Corporate governance 

In order to effectively monitor how the management board operates, the following areas should be taken into account: 

  • fiscal transparency, 
  • countering corruption, 
  • structure of the management board, 
  • remuneration for the management board and employees, 
  • respect for shareholder rights [2]. 
Fig. 1. Graphic showing ESG indicators
Source: https://www.iberdrola.com/about-us/esg-responsible-management

Impact of ESG on companies’ operations

ESG issues have a significant impact on the actions taken by companies and their strategies. Operating a sustainable and conscious business is now a necessity in order to maintain a leading position in the market. Implementing an ESG strategy brings with it a number of valuable values for a company, as outlined below. 

  • Increased customer loyalty 

Companies that actively engage with environmental, social and corporate issues build a bond of trust with their customers. Customer loyalty increases as consumers are more likely to support companies that take action for social and environmental good. 

  • Improving the image 

ESG-compliant actions build a positive corporate image in local communities, among customers, investors and business partners. A company that cares about the environment, supports local communities and applies high ethical standards is seen as a responsible actor and a reliable partner that cares equally about social and environmental well-being. 

  • Stable market position 

Companies that effectively implement ESG strategies can enjoy a more stable position in the market. By integrating environmental, social and corporate factors into their operations, the company minimises the risk of reputational crises, which translates into operational stability and long-term growth [3]. 

Innovative Technologies and the Achievement of Sustainable Development Goals 

Technologies such as Artificial Intelligence, Big Data and Blockchain are effective tools for monitoring and understanding an organisation’s social and environmental impact. Artificial Intelligence, used in data analysis, enables the identification of consumers’ needs, which allows companies to understand them better and plan in advance the necessary actions to be taken. Big Data analytics, on the other hand, makes it possible to process and analyse extensive data sets, enabling more “targeted” business decisions based on the non-obvious information contained in this data. Blockchain technology, on the other hand, ensures the security and immutability of data, which is key to ensuring transparency in business processes. By using these technologies, companies can develop effective sustainability strategies, taking advantage of the opportunity to digitise and automate business processes. As a result, companies can create business models that not only generate profits but are also socially responsible and environmentally friendly. Moving towards the use of advanced technologies in the area of ESG is becoming not only a trend, but also a necessity for companies wishing to be leaders in sustainability [3, 4]. 

ESG at BFirst.Tech 

BFirst.Tech considers sustainability one of the most essential elements of the company’s strategy. With many innovative, proprietary and environmentally friendly products, we are able to meet our customers’ needs. For the second decade BFirst.Tech has been setting the standard for solutions in reducing noise pollution in working environments and urban agglomerations, generating, aggregating and providing management information (including data for non-financial ESG reporting) and monitoring and analysing information on the state of the industrial infrastructure of companies.  As we are aware of the climate changes taking place, it is environmental activities that are particularly important to us, which is why we focus on them when building the company’s strategy.  

Summary 

ESG is a key element in building the long-term value of companies in contemporary business. Implementing an effective ESG strategy allows companies to positively influence environmental protection, stakeholder relations and governance within the organisation. It also carries a number of precious values for the company, such as a more lasting relationship with customers, an increase in the company’s reputation, or a strengthening of its position in the industry. Thus, by implementing ESG, a company can become an indispensable part of the environment positively affecting the quality of life of the community. 

References

[1] About ESG—Polish ESG Association 

[2] ESG co to jest? Kogo dotyczy i jaki ma wpływ na przedsiębiorstwo? (ESG—what is it? Who does it apply to and what impact does it have on a company?) (sterrn.pl) 

[3] Zrozumieć ESG: Definicja, Znaczenie i Wpływ na Biznes (Understanding ESG: Definition, Importance and Impact on Business) (boringowl.io) 

[4] ESG, Blockchain, and AI – Oh My! | Barnes & Thornburg (btlaw.com) 

Proteus Effect – How an Avatar Influences the User

The relationship between man and technology has been a subject of philosophical interest for some time. Over the years, a number of theories have emerged that attempt to explain the reciprocal influence of man on technology and technology on man, or entire societies. Although debates between determinists (who claim that technology shapes humans) and constructivists (who argue that humans shape technology) will likely never be resolved, this article examines the Proteus effect, which may be closer to one of these perspectives.

What is the Proteus effect?

The Proteus effect is a phenomenon first described by Yee and Bailenson in 2007. It is named after the myth of the god Proteus, who could change his appearance in any way he wished. He was said to use this power to conceal his knowledge of past and future events. Yee and Bailenson noted that individuals using virtual avatars change their behaviour based on the observed traits of these characters while playing them in the virtual world. The researchers argue that players infer from the appearance and characteristics of their avatars how they should adjust their behaviour and overall attitude to meet the expectations set by their virtual representation. There are also grounds to believe that this effect can extend beyond digital worlds and influence behaviour and attitudes in the real world [1].

Proteus Effect – Example of Occurrence

To illustrate how the Proteus effect works with a real-world example, I will refer to a study in which the authors investigated the presence of the Proteus effect during matches played with various characters in the popular MOBA game, League of Legends. Participants in the game are divided into two teams of five players each, who then engage in battle on a map. Before starting, each player must choose a so-called champion. League of Legends allows players to play a match with one of over 140 champions [2], each characterised by different appearances and abilities. The authors of this study analysed how players communicate with each other, considering the champion they play.

The presence of the Proteus effect was measured using the game’s chat. Researchers established indicators such as vocality (“acting more vocal”), toxic behaviour (“acting more toxic”), and positive or negative valence. Valence is a form of sentiment analysis aimed at depicting the emotional state of a player. The analysis results confirmed the presence of the Proteus effect, but not for every champion or type of champion. It was primarily observed through valence and toxicity of speech. The most significant finding of this study was proving that the way players communicated via chat indeed changed with the champion they selected. Depending on the chosen character, a player did not necessarily speak more or less but could exhibit more toxic behaviour and be in a worse mood [3].

Utilising the effect

The Proteus effect is a phenomenon that particularly draws our attention to the relationship between people and virtual worlds. It clearly demonstrates that technology, in one way or another, exerts a direct influence on us, even altering our behaviour. Some researchers have attempted to explore whether this effect can be practically applied, for example, in performing certain jobs. Let’s delve into their studies.

Impact on strength

A group of five German researchers hypothesised that using a suitably matched avatar would cause the person controlling it to perform tasks better than if they embodied a different, non-distinctive character or themselves. In this case, the researchers decided to investigate whether a person whose virtual appearance suggests they are stronger than the subject would lead the subject to exert more effort in physical exercises. In addition to tracking the movements of participants wearing VR equipment, grip strength was also measured.

During the study, participants were assigned avatars according to their gender. They were subjected to a series of physical tasks, such as lifting weights of varying heaviness and squeezing a hand as hard as possible for five seconds. According to the results, the authors conclude that the study cannot be considered representative. No increase in grip strength was observed in women, though such results were evident in men. Thus, it can be partially inferred that a more muscular avatar may influence the strength of men [4].

Stimulating creativity 

The following study examined whether an avatar, as a representation of an individual in the virtual world, stimulates creativity. As part of the study, creativity sessions were organised during which participants brainstormed while embodying a particular character. Prior to the sessions, the researchers selected several avatars that were perceived as creative and neutral. Participants were divided into three groups: a control group (brainstorming in the real world), a group using neutral avatars, and a group using creative avatars, defined as inventors.  All groups held creative sessions in the same rooms—the control group gathered around a round table, while the others used equipment in the same room in separate cubicles. They then sat at a round table in a recreated space in virtual reality. 

The left part shows a room with a round table and chairs around it in a virtual space. The right part shows the prototype in the real world.
Figure 2. On the left, the virtual space with a round table and workstations recreated in virtual reality. On the right, its real-world counterpart. [5]

The researchers avoided any contact between the participants in the avatar groups before and after the main part of the brainstorming session took place; the subjects never met each other outside the experiment. A key finding, particularly relevant for the future of remote collaboration, is that the groups using non-creative avatars achieved the same results as those sitting at the table in the real world. However, the most important result is the demonstration that individuals embodying an inventor avatar consistently achieved better results for each creativity indicator used in the experiment [5].

Assistance in improving communication

Another study was conducted to explore the potential for training effective communication skills among physicians in the preoperative stage. Communication with patients can be ineffective, partly because doctors may use jargon or phrases from their professional environment. This study utilised two virtual reality experiences. During the experience, participants played the role of a patient. This enabled the researchers to describe the development and impressions that the subjects experienced.

During the experiment, participants experienced negative or positive communication styles in a situation where they were about to undergo surgery. Interviews conducted at the next research stage revealed that participants recognised the importance of good communication skills. Overall, the participants learned and adjusted their communication style in their subsequent work. Virtual reality, in which participants embodied a patient in one of the two experiences, proved effective in providing a fully immersive experience. As participants stated, they felt as if they were the patient. It can be further concluded from this study that the Proteus effect is also useful for educational purposes, improving communication, and increasing empathy towards others [6].

Summary

In the face of continuous technological development, we constantly discover new phenomena that can shape our future approach to technology. The Proteus effect demonstrates that its impact can be much more direct than we may assume. Although this phenomenon is largely harmless, it indicates how we can be influenced by our virtual representation. People have already begun exploring applications of this effect in various areas, such as mental enhancement of strength, supporting creative processes, and improving communication skills. However, to ascertain whether the Proteus effect will become a permanent aspect of our daily lives, we will need to wait and see. Additionally, it is worth noting that Microsoft has begun organising international conferences in virtual reality, utilising avatars for participation. Polish entrepreneur Gryń—former owner of Codewise—has established a company in London to scan people for such purposes. At BFirst.Tech, leveraging its expertise in Data Architecture & Management—specifically through its Artificial Intelligence Adaptations product—a project has been completed for the Rehasport clinic network, enabling surgeries to be conducted in augmented reality (AR).

References

[1] The Proteus Effect: The Effect of Transformed Self‐Representation on Behavior: https://academic.oup.com/hcr/article-abstract/33/3/271/4210718?redirectedFrom=fulltext&login=false 

[2] Number based on description at: https://www.leagueoflegends.com/en-us/champions/ (accessed 23 June 2024) 

[3] Do players communicate differently depending on the champion played? Exploring the Proteus effect in League of Legends: https://www.sciencedirect.com/science/article/abs/pii/S0040162522000889

[4] Flexing Muscles in Virtual Reality: Effects of Avatars’ Muscular Appearance on Physical Performance: https://www.academia.edu/77237473/Flexing_Muscles_in_Virtual_Reality_Effects_of_Avatars_Muscular_Appearance_on_Physical_Performance 

[5] Avatar-mediated creativity: When embodying inventors makes engineers more creative: https://www.sciencedirect.com/science/article/pii/S0747563216301856 

[6] Patient-embodied virtual reality as a learning tool for therapeutic communication skills among anaesthesiologists: A phenomenological study: https://www.sciencedirect.com/science/article/pii/S0738399123001696 

Artificial Intelligence — a tool for breaking human records

Go, originating from China with a history spanning over 2500 years, and chess, originating from India and dating back approximately 1500 years, are the most popular strategic board games in the world. Significant not only in terms of entertainment but also culturally. The rules are clear and precise, making the entry threshold for new players for both of these games very low. It is this simplicity that gives rise to the invention of original solutions. It also about tactics and, above all, enormous human intellectual effort. All that to defeat the opponent. 

Artificial Intelligence is not only the subject of serious applications such as Intelligent Acoustics in industry, Artificial Intelligence Adaptation in development research or Data Engineering. These and other algorithms are also used in various fields of entertainment. They are used to create models, artificial players to beat human players in board games and even in e-sports. 

At the turn of the 20th and 21st centuries, chess and Go lived to see their digital versions. Computer games also emerged, with players vying for first place on the board and e-sports titles. In parallel with these, several artificial intelligence models with appropriately implemented rules have emerged to search for better plays and beat human players. In this post, I am going to describe how board games, computer games and artificial intelligence complement and inspire each other. I am also going to show how a properly trained artificial intelligence model has defeated not only individual modern grandmasters, but also entire teams. 

Artificial intelligence conquers board games

How artificial intelligence defeated a chess grandmaster has its roots in the Deep Blue project led by IBM. The main goal of the project was to create a computerised chess system. Deep Blue was the result of years of work by scientists and engineers. The first version of Deep Blue was developed in the 1980s. It used advanced algorithms, i.e.: 

  • Tree Search based on a database of chess moves and positions, 
  • Position Evaluation, 
  • Depth Search. 

In 1996, the first match between Deep Blue and Garri Kasparov took place. This match was experimental and was the first official meeting of its kind. Kasparov won three games, drawing and losing one. In May 1997, they clashed again in New York. This time, Garii Kasparov fell in a duel with artificial intelligence. Deep Blue won twice and lost only once. A draw was declared three times. 

Fig. 1 Garii Kasparov during a game against Deep Blue in May 1997. 

Source: https://www.scientificamerican.com/article/20-years-after-deep-blue-how-ai-has-advanced-since-conquering-chess/ 

Less is more

An equally interesting case is a programme created by DeepMind called AlphaGo. This artificial intelligence was designed to play Go, as the world found out when it beat Go grandmaster Lee Sedol. Go is much more difficult than other games, including chess. This is due to the much larger number of possible moves. It makes it difficult to use traditional AI methods such as exhaustive search [1, 2]. DeepMind started work on the AlphaGo programme in 2014. The aim was to create an algorithm that could compete with the masters. It used advanced machine learning techniques:

  • Deep Learning, 
  • Reinforcement Learning (RL), 
  • Monte Carlo Tree Search. 

AlphaGo’s first significant achievement was beating European competitor Fan Hui in October 2015. The engine from DeepMind completely dominated each game, thus winning five to zero [3]. The next step was to defeat grandmaster Lee Sedol. During the matches, artificial intelligence surprised not only its opponent but also experts with its unconventional and creative moves. The programme demonstrated its ability to anticipate strategies and adapt to changing conditions on the board. As a result, after games played from 9-15 March 2016, AlphaGo claimed a historic victory over Lee Sedol, winning the five-match series 4-1. 

Competition on digital boards 

In 2018, OpenAI created a team of artificial players, the so-called bots, dubbed the OpenAI Five. The bot team faced professional players in Dota 2, one of the most complex MOBA (Multiplayer Online Battle Arena) games. Two teams of five players battle against each other to destroy the opponent’s base. Several advanced machine learning techniques and concepts were used to ‘train’ OpenAI Five:   

  • Reinforcement Learning – bots learned to make decisions by interacting with the environment and receiving rewards for certain actions, 
  • Proximal Policy Optimisation (PPO) – this is a specific RL technique that, according to the developers, was crucial to its success [5]. This method optimises the so-called policy (i.e. decision-making strategy) in a way that is more stable and less prone to oscillations compared to earlier methods such as Trust Region Policy Optimisation (TRPO) [6]. 
  • Spontaneous learning – artificial players played millions of games against each other. This allowed them to develop increasingly sophisticated strategies, learning from their mistakes and successes. 

In August 2018, artificial intelligence beat the semi-professional Pain Gaming team at the annual world championship ‘The International’. In 2019, at the OpenAI Five Finals event, the bots defeated a team made up of top players. It included members of the OG team, winners of The International in 2018. DeepMind, on the other hand, decided not to stop with AlphaGo and turned its focus towards StarCraft II, one of the most popular real-time strategy (RTS) games, by creating the AlphaStar programme. AI went into one-on-one duels with professional StarCraft II players in 2019. In January, it defeated the strategy’s top players — Gregory “MaNa” Komincz twice — and also won over Dario “TLO” Wünsch. AlphaStar thus proved its capabilities. 

Artificial Intelligence in e-sports

Artificial intelligence is playing an increasingly important role in the training of professional e-sports teams. Especially in countries such as South Korea, where the League of Legends is one of the most popular games. Here are some key areas where AI is being used for training in professional organisations such as T1, and Gen.G. 

Analytics teams use huge amounts of collected data from league and friendly matches. They analyse match statistics such as number of assists, gold won, most frequently taken paths and other key indicators. This allows coaches to identify patterns and weaknesses in both their players and opponents. 

Advanced training tools using artificial intelligence, such as ‘AIM Lab’ or ‘KovaaK’s’, help players develop specific skills. Such tools can personalise training programmes that focus on improving reactions, aiming, tactical decisions and other key aspects of the game. 

They are also used to create advanced simulations and game scenarios while mimicking various situations that may occur during a match. This allows players to train under conditions closely resembling real-life scenarios. This allows players to better prepare for unexpected events and make better decisions faster during actual matches. 

AI algorithms can be used to optimise team composition by analysing data on individual player skills and preferences. The results of such studies can suggest which players should play in which positions. They can also help select line-ups to maximise team effectiveness. 

Conclusions

This article shows how artificial intelligence has dominated board games and made a permanent presence in e-sports. It has defeated human champions in chess, Go, Dota 2 and StarCraft II. The successes of projects such as Deep Blue, AlphaGo, OpenAI Five and AlphaStar show the potential of AI in creating advanced strategies and improving gaming techniques. Future development opportunities include its use in creating more realistic scenarios, developing detailed and personalised player development paths, and predictive analytics that can revolutionise training and strategy across industries. 

References

[1] Google achieves AI ‘breakthrough’ by beating Go champion, “BBC News”, 27 January 2016 

[2] AlphaGo: Mastering the ancient game of Go with Machine Learning, “Research Blog” 

[3] David Larousserie et Morgane Tual, Première défaite d’un professionnel du go contre une intelligence artificielle, “Le Monde.fr”, 27 January 2016, ISSN 1950-6244 

[4] https://openai.com/index/openai-five-defeats-dota-2-world-champions/ accessed 13 June 2024 

[5] https://openai.com/index/openai-five/ accessed 13 June 2024 

[6] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347. 

Uncanny valley 

The uncanny valley is a term used to refer to the familiar, disturbing impression people have when a robot resembles a human being very closely but is not convincingly realistic [1]. The phenomenon first emerged in the 1970s. Japanese roboticist Masahiro Mori observed that robots became more interesting the more they resembled humans in appearance. However, this tendency holds only up to a certain point. He then described this phenomenon as bukimi no tani (English: uncanny valley). After ‘reaching’ bukimi no tani, interest turns into alienation, anxiety or even fear [2].

Fig. 1. Diagram illustrating the uncanny valley phenomenon. 

Source: https://www.linkedin.com/pulse/uncanny-valley-personalization-mac-reddin-/  

Why do we experience the uncanny valley?

We have yet to find one concrete answer to this question. However, several theories help us better understand why it occurs. These reasons are divided as follows:

  • Neurological

In a 2019 study, Fabian Grabenhorst and a team of neuroscientists analysed the neurological aspect of the uncanny valley. They investigated brain patterns in 21 people using functional magnetic resonance imaging (fMRI), a technique that measures changes in blood flow in different brain areas. During the tests, participants determined their confidence level towards humans and robots with varying levels of human similarity. The results showed that some specific parts of the brain were particularly important for the uncanny valley. Two parts of the medial prefrontal cortex, responsible for attention and senses, showed unusual activity. One of them transformed the ‘human resemblance signal’ into a ‘human detection signal’ and overemphasised the boundary between human and non-human. On the other hand, the other correlated this signal with a sympathy rating. This combination formed a mechanism that closely resembles the uncanny valley phenomenon.

  • Psychological

It turns out that as early as 1919, Sigmund Freud observed a phenomenon he described as ‘a strange emotion felt by people which is aroused by certain objects’. He suggested that the feeling we then experience may be related to doubts about whether something inanimate has a ‘soul’. Interestingly, at the time, his observation obviously referred not to robots but realistic dolls or wax figures. He suggested that the phenomenon may be older than we think and pertain to more things than just machines. Today, the film industry uses a similar mechanism. Many horror films give human characteristics to characters that are not human.

  • Evolutionary

The uncanny valley can also be linked to evolution. The robots we classify in the uncanny valley look like humans but also have features that are clearly not human. Some of these features, such as lifeless skin, unnatural facial features or a voice that does not match their appearance, can make us associate them with something outside the norm or even dangerous. This, in turn, creates aversion or fear in us. When we are confronted with something that is human, but unrealistic, not ‘like a living thing’, it evokes a feeling similar to the one we experience when we come into contact with something that is dead.

  • Cognitive

The uncanny valley may also stem from an existential fear of robots replacing humans. The sight of a robot that resembles a human in appearance but is not human disrupts our expectations of what a human looks like versus what a robot looks like. It raises doubts about who humans are, what they should look like, and how they should behave. It is worth noting that the anxiety does not stem from the mere existence of robots but from the existence of such robots that combine elements that do not usually occur together. For example, robots that ‘sound like robots’ are not a problem for us, while robots with a human voice are [2, 3].

The uncanny valley in reality

The uncanny valley is present in many different areas. Outside robotics, it can also be observed in computer games or films that use computer-generated imagery (CGI). This effect goes beyond technology and can be caused by objects such as realistic dolls, mannequins or wax figures.

  • Sophia

Photo 1. Photo of the Sophia robot. 

Source: https://pl.wikipedia.org/wiki/Plik:Sophia_%28robot%29.jpg  

Sophia is the most advanced humanoid robot yet developed. Created by Hanson Robotics, it was first activated in 2016. Sophia was granted citizenship of Saudi Arabia, thus becoming the world’s first robotic citizen. The robot was awarded the title of Innovation Champion of the United Nations Development Programme. Sophia has also gained recognition through appearances on TV programmes such as Good Morning Britain and The Tonight Show [4]. Sophia can express various complex emotions, assume human facial expressions and interact with others. The robot is equipped with the ability to process and use natural language, facial recognition and visual tracking [5]. Sophia’s ‘skin’ is made of a special material developed by researchers at Hanson Robotics, which has been named Frubber. It is a type of rubber that resembles human skin’s texture and elasticity [6]. Because of its appearance and behaviour, which are very close to those corresponding to humans, it is still too unnatural. Sophia is the case of the uncanny valley and can thus arouse discomfort and anxiety.

  • The Polar Express

Fig. 2. Computer-generated shot from The Polar Express. 

Source: https://collider.com/worst-cases-of-uncanny-valley-movies/ 

The Polar Express is a 2004 animated film directed by Robert Zemeckis. This film was made using CGI, which many believe was misused. The producers of the film adaptation themselves had conflicting visions of how the film should be made. In an interview with Wired, Robert Zemeckis said that ‘live action would look awful, and it would be impossible – it would cost $1 billion instead of $160 million.’ In contrast, Tom Hanks, who played seven characters in the film, argued that the film should not have been made as animation [7]. The filmmakers found a kind of consensus by combining the two approaches. They used motion capture, a method of recording actors’ movements and then transferring them to a computer. However, critics argue that the filmmakers failed to represent the characters well, making them seem insufficiently realistic. The characters lack human emotions and facial expressions; they move unnaturally, and their gaze seems constantly ‘absent’.

Consequences of the uncanny valley

The uncanny valley significantly impacts the future of many different areas of our lives. With the existing knowledge of the unwanted feelings it can cause, roboticists, filmmakers and video game designers can factor this problem into their work. It is clear now that there is value in developing robots that do not create mistrust between the machine and the user. Otherwise, they will be exposed to poor reception and less usefulness in achieving their intended purpose.

In films, on the other hand, overly realistic computer-generated characters can, at best, elicit a lack of sympathy from the viewer and, at worst, feelings such as anxiety or even fear. This is why filmmakers often overemphasise certain characters’ physical characteristics. Giving characters distinctive traits such as outsized eyes, unnatural skin colour, or overly dynamic movements is one way of dealing with avoiding the effect caused by the uncanny valley. Similar mechanisms are used in computer games; designers may want to create characters that are not overly realistic to avoid an unfavourable reception from players. However, there are also exceptions; in some cases, filmmakers or game designers may want to get characters that deliberately fit into the uncanny valley. In this way, they can control, for example, how villains will be perceived. A protagonist who exhibits some unnatural and overly realistic characteristics will create a sense of resentment among the audience [8, 9].

The uncanny valley and UX

A very interesting issue in the uncanny valley is its impact on user interface design. Adding certain realistic elements to the interface design can have positive effects. For example, light and shadow lend a sense of being able to press an item, and sound can provide a counterpart to a particular sound that we would also hear in real life. However, adding too much realism can lead to too thin a line between the virtual and the real. For example, a highly detailed calendar application whose texture resembles natural paper. The fact that we cannot touch it but only ‘scroll’ through it on a computer or smartphone screen can give us the impression of something strange, ‘not right’. This is why it is so important not to strive for elements that completely mirror real objects. By striking the right balance between realism and fiction, the user experience becomes enjoyable and dilemma-free [10].

Fig. 3. A very realistic Google Chrome logo from 2008 and its upgraded, much less realistic version from 2011. 

Source: https://bpando.org/2011/03/17/the-new-chrome-logo/  

Conclusions

People experience anxiety when encountering almost realistic-looking but still insufficiently realistic human-like entities; this phenomenon is called the uncanny valley. It is critical in various areas. Some examples include advanced robots, computer-generated characters or even forms beyond the realm of technology, such as dolls or wax figures. The implications of the uncanny valley can significantly affect the acceptance and usability of technology. In the context of UX, awareness of the uncanny valley is crucial for designers who seek to minimise undesirable effects by designing interfaces appropriately so that users feel comfortable and engaged in their interactions with products.

References

[1] https://www.techtarget.com/whatis/definition/uncanny-valley  

[2] https://spectrum.ieee.org/what-is-the-uncanny-valley  

[3] https://www.sciencefocus.com/news/uncanny-valley-what-is-it-and-why-do-we-experience-it  

[4] https://aidriven.pl/ai/etyka-i-prawo/robot-sophia-jak-humanoidy-zmieniaja-nasze-postrzeganie-ai/ 

[5] https://robotsguide.com/robots/sophia 

[6] https://www.hansonrobotics.com/the-making-of-sophia-frubber/ 

[7] https://faroutmagazine.co.uk/the-disturbing-valley-robert-zemeckis-polar-express/  

[8] https://www.techtarget.com/whatis/definition/uncanny-valley  

[9] https://www.verywellmind.com/what-is-the-uncanny-valley-4846247 

[10] https://cassidyjames.com/blog/uncanny-valley-curve/