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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. 

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/

The idea behind Society 5.0 is to create a super-intelligent society in which various social challenges are solved by implementing innovations of the fourth industrial revolution — such as IoT, Big Data, Artificial Intelligence (AI), robotics, or the sharing economy — into every industry and social life. In such a world, people, machines and their environment are interconnected and able to communicate with each other [1]. In practice, Society 5.0 will, among other things, seek to provide better care for seniors — in Japan, the population is ageing rapidly, and if there were ever to be a shortage of hands to care for the elderly in the future, it is the new quality of computing that will be able to raise the standard of healthcare for retirees [2]. Society 5.0 is a term that refers to a new society in which technological developments are human-centred and seek valuable solutions for the lives of people around the world.

Fig. 1. Illustration of Japan’s social transformation plan — Society 5.0. 
Source: https://www.japan.go.jp/abenomics/_userdata/abenomics/pdf/society_5.0.pdf

[Accessed: 7 March 2024]. 

History of the Development of Society

Society 5.0 is the result of nothing more than an evolution spanning five stages of social development: 

• Society 1.0: Gatherer-hunter society (the way of life of the first humans, which lasted until about 12,000 years ago) — a society that based its lifestyle on hunting for animals and searching for wild vegetation and other types of nutrients [3]. 
• Society 2.0: Agricultural society (first appears around 10,000–8,000 years ago) — a society that focuses its economy primarily on agriculture and the cultivation of large fields [4]. 
• Society 3.0: Industrial society (from the late 18th century onwards) — a society in which the dominant way of organising life is through mass production technologies, used to produce immense quantities of goods in factories [5]. 
• Society 4.0: Information society (since the second half of the 20th century) — a society in which the creation, dissemination, use, integration and management of information is an essential aspect of economic, political or cultural activities [6]. 

Technological Integration for a Better Quality of Life

The concept of collecting data from the world around us, processing it by computers and putting it to practical use is not new in today’s world. The operation of air conditioners, for example, is based on exactly this principle. They regularly measure the temperature in a room and then compare the reading with a pre-programmed temperature. Depending on whether the measured temperature is higher or lower than the one originally set, the device pauses or starts the airflow. This mechanism uses automated computer systems. The term ‘information society’ (Society 4.0) therefore refers to a society in which each such system acquires data, processes it and then uses it in its own specified environment.

Now, knowing exactly what the idea of Society 4.0 is, we can understand what distinguishes it from Society 5.0. The fundamental difference is that Society 5.0, instead of using systems that operate in a defined, limited way, will use systems that operate in an integrated way, affecting the life of society as a whole. Data will be processed by advanced information systems, such as Artificial Intelligence, as these systems are adapted to process such large amounts of data. The main purpose of using the collected data will be to ensure everyone’s happiness and comfort [7]. At BFirst.Tech, we also see these needs and respond to them with specific tools. Our areas — Data Engineering and Data Architecture & Management use innovative technological solutions to collect, analyse and manage data to support efficient and sustainable process management. This type of management has a significant impact on security, data reliability and strategic decision-making, which contributes to the prosperity of society.

The New Era of Prosperity and the Challenges It Faces

Society 5.0 aims to use state-of-the-art technology in such a way as to ensure the well-being of all people. The idea is that technological development can be a tool to address social inequalities, improve quality of life and create a more sustainable community. The main objectives it envisages are:

• reducing social inequalities, 
• speeding up medical services and increasing the precision of medical procedures and operations, 
• increasing food production while reducing waste 
• improving public safety 
• solving problems caused by natural disasters, 
• promoting public participation in the development of ideas and projects, 
• ensuring transparent access to data and ensuring information security. 

Society 5.0 aims to create a harmonious balance between technological development and societal needs, but this brings its own challenges. One of the most crucial conditions for this vision’s successful implementation is the commitment and leadership of governments. This is because governments are responsible for aspects such as funding, the implementation of technology in public life or the creation of new security-related legislation. Cybersecurity risks are another significant challenge. It is important to bear in mind that the actions of hackers, or issues related to data theft, can effectively hinder the development of innovation, so it is crucial to ensure a sound level of data protection [8].

The United Nations Sustainable Development Goals

Society 5.0 and the United Nations Sustainable Development Goals are two separate initiatives that are moving in a very similar direction. Indeed, these two innovative approaches share one common goal — to eliminate social problems sustainably. It can be said that Society 5.0 will, in a way, realise the Sustainable Development Goals, through specific actions. These actions, matched with specific goals, are:

• aiming for more accurate and efficient diagnosis of diseases through the use of advanced technologies (such as Big Data and Artificial Intelligence),

Fig. 2. Illustration of UN Sustainable Development Goal 3. 

Source: https://www.un.org.pl/download 

• disseminating e-learning and making education more accessible,

Fig. 3. Illustration of UN Sustainable Development Goal 4. 

Source: https://www.un.org.pl/download 

• creation of new jobs related to fields such as robotics, Artificial Intelligence or data analytics,

Fig. 4. Illustration of UN Sustainable Development Goal 8. 

Source: https://www.un.org.pl/download 

• promoting innovation and investing in new infrastructure (such as smart networks or high-speed internet),

Fig. 5. Illustration of UN Sustainable Development Goal 9. 

Source: https://www.un.org.pl/download 

• creating smart cities that use sensors and data analysis to optimise traffic flow, reduce energy consumption and improve safety, 

Fig. 6. Illustration of UN Sustainable Development Goal 11. 

Source: https://www.un.org.pl/download 

• Reducing greenhouse gas emissions and promoting sustainable transport.

Fig. 7. Illustration of UN Sustainable Development Goal 13.

Source: https://www.un.org.pl/download 

Common Direction

It is crucial that the benefits of Society 5.0 are equally available to everyone, so that everyone has the same opportunity to benefit from their potential. Only with such an approach can Society 5.0’s contribution to the Sustainable Development Goals have a chance of an effective outcome [9]. BFirst.Tech, as a substantive partner of the United Nations Global Compact Network Poland (UN GCNP), is also concerned with the implementation of the Sustainable Development Goals, through the specific activities it undertakes. In areas that focus on data processing, design and management, namely Data Engineering and Data Architecture & Management, our company implements goals that overlap with those targeted by Society 5.0, such as Goal 9 — on securing, aggregating and analysing big data, optimising and managing and controlling the quality of processes using AI; Goal 11 — on securing critical information that impacts on improving the lives of urban residents; and Goal 13 — on reducing resource consumption and waste emissions by increasing production efficiency.

Changes Affecting Numerous Areas

With the implementation of the Society 5.0 concept, many various facets of society can be modernised. As mentioned earlier, one of these is healthcare. With Japan’s ageing population, the country is currently grappling with rising expenses and the need to care for seniors. Society 5.0 solves this problem by introducing Artificial Intelligence, which collects and then analyses patient data to provide the highest level of diagnosis and treatment. Remote medical consultations, in turn, positively impact the convenience of the elderly, giving them the possibility of contacting a doctor even from their own place of residence.

Another facet is mobility. Most rural areas of Japan do not have access to public transport, influenced in part by a declining population contributing to an increasingly sparsely populated area. The growing shortage of drivers, linked to the ever-expanding e-commerce sector, is also a problem. The solution that Society 5.0 proposes to these issues is the implementation of autonomous vehicles such as taxis and buses. What is also worth mentioning is the area of infrastructure. In Society 5.0, it will involve sensors, AI and robots that will autonomously control and maintain roads, tunnels, bridges and dams. The final area worth mentioning is financial technology (FinTech). In Japan, the majority of monetary transactions are still carried out using cash or banking procedures, which can take far too long. Society 5.0 proposes the implementation of Blockchain technology for monetary transactions and the introduction of universal smartphone payments available everywhere [10]. 

Summary

Society 5.0 is the concept of a society that uses advanced technologies to create a society based on sustainability, social innovation and digital transformation. The aim of Society 5.0 is not only to achieve economic growth, but also to improve the quality of life of citizens. There are also some challenges behind the development of this idea, mainly related to data security, or the introduction of appropriate regulations to ensure a transition that will be smooth and comfortable for all. Society 5.0 largely shares a vision of the future with the Sustainable Development Goals (SDGs) announced by the United Nations — many of the SDG targets can be achieved through the implementation of this concept. Society 5.0 encompasses a wide range of areas of society, including healthcare, mobility, infrastructure and financial technology. Through advanced technologies in these areas, the aim is to create a sustainable and innovative society that will positively impact citizens’ quality of life.

References

[1] https://www.japan.go.jp/abenomics/_userdata/abenomics/pdf/society_5.0.pdf [Accessed: 7 March 2024]. 

[2] https://sektor3-0.pl/blog/japonski-czlowiek-nowej-ery-czyli-spoleczenstwo-5-0/ 

[3] https://education.nationalgeographic.org/resource/hunter-gatherer-culture/ 

[4] https://www.thoughtco.com/agrarian-society-definition-3026047 

[5] https://www.thoughtco.com/industrial-society-3026359 

[6] https://www.techtarget.com/whatis/definition/Information-Society 

[7] Atsushi Deguchi, Chiaki Hirai, Hideyuki Matsuoka, Taku Nakano, Kohei Oshima, Mitsuharu Tai, Shigeyuki Tani “What is Society 5.0?” 

[8] https://www.sydle.com/blog/society-5-0-5fc163e1725a642683ed9230 

[9] https://media.inti.asia/read/society-50-and-the-sustainable-development-goals-a-roadmap-for-a-better-future 

[10] https://medium.com/@jacobprakoso/japan-super-smart-society-5-0-9b9e8ba49a7 

What makes some websites appear immediately after entering a search query, while others disappear in the midst of other sites? How can we make it easier for users to find our website? SEO is responsible for these and other aspects, and it has nothing to do with randomness.  Whether you are just starting your journey with running a website or have been doing it for a long time, whether you handle everything yourself or delegate it to someone else, it’s important to know the basic principles of SEO. After reading this article, you will learn what SEO is, what it consists of, and how to use it properly. 

What is SEO?

Let’s start with what SEO actually is and what it consists of. SEO (Search Engine Optimization) is a set of activities undertaken to improve the positioning of a website in search results [1]. It consists of various practices and strategies, such as proper text editing and building a link profile. SEO also involves adapting the website to algorithms used by search engines. These algorithms determine which pages will be displayed on the first page of search results and in what order. Through optimization, a website can gain a better position in the search results, which increases its visibility.

It is important to remember, of course, that SEO tools are only one way to improve the popularity of a website. It doesn’t produce results as quickly as, for example, paid advertising, but it’s relatively inexpensive. Furthermore, the achieved effect will last longer and won’t disappear after a subscription expires, as is the case with many other marketing techniques.

On-site positioning

We can divide SEO into two types: on-site and off-site. On-site SEO includes all activities that take place on a specific website. These are all editorial, technical, or other issues that affect content loading speed. By taking care of these aspects, the website is more readable for both the user and Google’s robots. Good on-site SEO requires attention to:

• Metadata and ALT description – even if a page is readable for users, what about search engine algorithms? To make it readable for them as well, it’s worth taking care of meta titles and descriptions, which will help search engines find our website. In addition, it is also worth taking care of ALT descriptions, also known as alternative text. Algorithms don’t understand what’s in images. With this short description, they will be able to assign its content to the searched phrase and improve positioning. 
• Header – this is another thing that affects more than just human perception. Proper distribution of headers and content optimization in them can significantly contribute to improved positioning. 
• Hyperlinks – the set of links, also known as the link profile. Here we can distinguish between external and internal linking. External linking refers to links coming from websites other than our own and is considered off-site SEO. On the other hand, internal linking refers to links within a single website that redirect users to other tabs or articles. 

Off-site positioning

Off-site SEO refers to all activities undertaken outside the website to increase its visibility and recognition on the web. This helps generate traffic to the site from external sources. Such activities include:

• Hyperlinks – again, a link profile that builds a site’s popularity and recognition on the web. Off-site SEO includes external linking, i.e. from other sources. It is worth ensuring that these are of good quality, i.e. from reliable sources. Gone are the days when only quantity mattered. Nowadays, search engine algorithms pay much more attention to value.
• Internet marketing – this includes activities such as running profiles on social media, engaging in discussions with users on forums, or collaborating with influencers. These aspects do not directly affect search results but can indirectly contribute a great deal to boosting the number of queries about our website. 
• Reviews – after some time, opinions about a website or business naturally appear on the web. It’s worth taking care of them and responding to users who leave them. Maintaining a good customer opinion is one aspect of building a trustworthy brand image [3].

Link building and positioning

Link building is the process of acquiring links that will lead to our website. These can be links from external sources (so-called backlinks) or internal linking. In that case, we are talking about links that will redirect us within a given website. A well-built link profile significantly affects positioning, as discussed above [4]. However, how has the significance of such practices changed? 

For many years, Google allowed SEO practitioners a lot of leeway in this regard. It was commonplace to encounter sites that had hundreds of thousands of links leading to them because the number of links had a significant impact on positioning, and their quality was not as crucial. The vast majority of these were low-quality links, which were posted online in forums, guestbooks, directories, comments, etc. This was often not handled by a human, but special applications were used that did it automatically. This approach brought significant results and could be carried out relatively inexpensively. But not for long. This all changed in April 2012. There was a kind of revolution back then – Google introduced a new algorithm called Penguin.

How did Penguin change SEO?

What is Penguin? It is an algorithm created by Google and introduced on 24th April 2012, to combat unethical SEO practices. SEO specialists tried to trick Google’s script by buying links and placing them in inappropriate places, but Penguin effectively caught them. 

Let’s try to answer how Penguin works. This script analyses the links leading to a particular website and decides on their value. If it deems them to be of low quality, it will lower the rankings of the sites they lead to. Such links include purchased ones (also from link exchanges) or those created by bots. It will also do the same for spam links, such as those placed in forum comments or on completely unrelated websites. However, its action is not permanent – when low-quality links are removed, a given website can regain its position. It’s worth mentioning that Penguin was not created only to detect fraud and reduce the visibility of websites in search results. Its role is also to reward honestly conducted websites. If it deems the link profile valuable, it will increase the visibility of such sites [6].

Ethical and unethical positioning

Depending on what we base our SEO techniques on, a distinction can be made between White Hat SEO and Black Hat SEO. These terms allude to the good and evil characters in western tales. According to culturally accepted convention, the characters usually wore white and black hats respectively, hence the association. But what do they mean and how do these techniques differ?

White Hat SEO is ethical SEO, applied according to guidelines recommended by search engines. It involves procedures such as creating good quality content (free of duplicates). Using headings, bullet points and ensuring paragraphs are the right length is also important. Black Hat SEO, on the other hand, is characterized by unethical behavior aimed at artificially boosting popularity. These include practices such as overusing key phrases out of context, hiding text or buying links. Such actions can result in a decrease in trust in the site and the imposition of filters lowering its position. Even exclusion from search results is possible[7].

Summary

The key to increasing traffic to a website and improving its positioning is the skilful use of SEO tools. These are both on-site and off-site techniques that can significantly increase reach. When using SEO, it is important to remember to do it properly. By following the recommendations of search engines and adapting the content to both the user and the algorithms, we can count on positive results and improved statistics. Unethical practices, on the other hand, can lead to the opposite effect.

References

[1] https://searchengineland.com/guide/what-is-seo 

[2] https://www.semstorm.com/pl/blog/seo-and-ppc/czym-sie-rozni-on-site-seo-od-off-site-seo 

[3]https://www.semrush.com/blog/off-page-seo/?kw=&cmp=EE_SRCH_DSA_Blog_EN&label=dsa_pagefeed&Network=g&Device=c&utm_content=676606914923&kwid=dsa-2185834089536&cmpid=18361923498&agpid=157305243831&BU=Core&extid=105138960331&adpos=&gad_source=1&gclid=CjwKCAjw7-SvBhB6EiwAwYdCAQvsJcp7q2JoIQMf2RzGg_HVRjTFb7AB2sTcZ2khQdIN3qvCREr9GhoCzOIQAvD_BwE 

[4] https://greenparrot.pl/blog/co-to-jest-off-site-seo/ 

[5] https://1stplace.pl/blog/algorytm-google-pingwin/  

[6] https://www.business2community.com/infographics/history-google-penguin-infographic-01468714 

[7]https://www.semrush.com/blog/black-hat-seo/?kw=&cmp=EE_SRCH_DSA_Blog_EN&label=dsa_pagefeed&Network=g&Device=c&utm_content=683809340380&kwid=dsa-2264710307245&cmpid=18361923498&agpid=156456448517&BU=Core&extid=105138960709&adpos=&gad_source=1&gclid=CjwKCAjw7-SvBhB6EiwAwYdCAZln5MkdcE3R2XZq-FUhanEKkDWUbpUoZxIowWHslE3ETaNFW88vPBoCJ5sQAvD_BwE

Artificial intelligence is one of the most exciting technological developments of recent years. It has the potential to fundamentally change the way we work and use modern technologies in many areas. We talking about text and image generators, various types of algorithms or autonomous cars. However, as the use of artificial intelligence becomes more widespread, it is also good to be aware of the potential problems it brings with it. Given the increasing dependence of our systems on artificial intelligence, how we approach these dilemmas could have a crucial impact on the future image of society. In this article, we will present these moral dilemmas. We will also discuss the problems associated with putting autonomous vehicles on the roads. Next we will jump to the dangers of using artificial intelligence to sow disinformation. Finaly, it will come to te concerns about the intersection of artificial intelligence and art.

The problem of data acquisition and bias

As a rule, human judgements are burdened by a subjective perspective; machines and algorithms are expected to be more objective. However, how machine learning algorithms work depends heavily on the data used to teach the algorithms. Therefore, data selected to train an algorithm with any, even unconscious bias, can cause undesirable actions by the algorithm. Please have a look at our https://bfirst.tech/problemy-w-danych-historycznych-i-zakodowane-uprzedzenia/earlier article for more information on this topic.

Levels of automation in autonomous cars

In recent years, we have seen great progress in the development of autonomous cars. There has been a lot of footage on the web showing prototypes of vehicles moving without the driver’s assistance or even presence. When discussing autonomous cars, it is worth pointing out that there are multiple levels of autonomy. It is worth identifying which level one is referring to before the discussion. [1]

• Level 0 indicates vehicles that require full control of the driver, performing all driving actions (steering, braking acceleration, etc.). However, the vehicle can inform the driver of hazards on the road. It will use systems such as collision warning or lane departure warnings to do so. 
• Level 1 includes vehicles that are already common on the road today. The driver is still in control of the vehicle, which is equipped with driving assistance systems such as cruise control or lane-keeping assist. 
• Level 2, in addition to having the capabilities of the previous levels, is – under certain conditions – able to take partial control of the vehicle. It can influence the speed or direction of travel, under the constant supervision of the driver. The support functions include controlling the car in traffic jams or on the motorway. 
• Level 3 of autonomy refers to vehicles that are not yet commercially available. Cars of this type are able to drive fully autonomously, under the supervision of the driver. The driver still has to be ready to take control of the vehicle if necessary. 
• Level 4 means that the on-board computer performs all driving actions, but only on certain previously approved routes. In this situation, all persons in the vehicle act as passengers. Although, it is still possible for a human to take control of the vehicle. 
• Level 5 is the highest level of autonomy – the on-board computer is fully responsible for driving the vehicle under all conditions, without any need for human intervention. [2] 

Moral dilemmas in the face of autonomous vehicles

Vehicles with autonomy levels 0-2 are not particularly controversial. Technologies such as car control on the motorway are already available and make travelling easier. However, the potential introduction of vehicles with higher autonomy levels into general traffic raises some moral dilemmas. What happens when an autonomous car, under the care of a driver, is involved in an accident. Who is then responsible for causing it? The driver? The vehicle manufacturer? Or perhaps the car itself? There is no clear answer to this question.

Putting autonomous vehicles on the roads also introduces another problem – these vehicles may have security vulnerabilities. Something like this could potentially lead to data leaks or even a hacker taking control of the vehicle. A car taken over in this way could be used to deliberately cause an accident or even carry out a terrorist attack. There is also the problem of dividing responsibility between the manufacturer, the hacker and the user. [3]

One of the most crucial issues related to autonomous vehicles is the ethical training of vehicles to make decisions. It is expecially important in the event of danger to life and property. Who should make decisions in this regard – software developers, ethicists and philosophers, or perhaps country leaders? These decisions will affect who survives in the event of an unavoidable accident. Many of the situations that autonomous vehicles may encounter will require decisions that do not have one obvious answer (Figure 1). Should the vehicle prioritise saving pedestrians or passengers, the young or the elderly? How important is it for the vehicle not to interfere with the course of events? Should compliance with the law by the other party to the accident influence the decision? [4]

Fig. 1. An illustration of one of the situations that autonomous vehicles may encounter. Source: https://www.moralmachine.net/  

Deepfake – what is it and why does it lead to misinformation?

Contemporary man using modern technology is bombarded with information from everywhere. The sheer volume and speed of information delivery means that not all of it can be verified. This fact enables those fabricating fake information to reach a relatively large group of people. This allows them to manipulate their victims into changing their minds about a certain subject or even attempt to deceive them. Practice like this has been around for some time but it did not give us such moral dilemmas. The advent of artificial intelligence dramatically simplifies the process of creating fake news and thus allows it to be created and disseminated more quickly.

Among disinformation techniques, artificial intelligence has the potential to be used particularly effectively to produce so-called deepfakes. Deepfake is a technique for manipulating images depicting people, relying on artificial intelligence. With the help of machine learning algorithms, modified images are superimposed on existing source material. Thereby, it is creating realistic videos and images depicting events that never took place. Until now, the technology mainly allowed for the processing of static images, and video editing was far more difficult to perform. The popularisation of artificial intelligence has dissolved these technical barriers, which has translated into a drastic increase in the frequency of this phenomenon. [5]

Video 1. Deepfake in the form of video footage using the image of President Obama.

Moral dilemmas associated with deepfakes

Deepfake could be used to achieve a variety of purposes. The technology could be used for harmless projects, for example educational materials such as the video showing President Obama warning about the dangers of deepfakes (see Figure 2). Alongside this, it finds applications in the entertainment industry, such as the use of digital replicas of actors (although this application can raise moral dilemmas), an example of which is the use of a digital likeness of the late actor Peter Cushing to play the role of Grand Moff Tarkin in the film Rogue One: A Star Wars Story (see Figure 2).

Fig. 2. A digital replica of actor Peter Cushing as Grand Moff Tarkin. Source: https://screenrant.com/star-wars-rogue-one-tarkin-ilm-peter-cushing-video/ 

However, there are also many other uses of deepfakes that have the potential to pose a serious threat to the public. Such fabricated videos can be used to disgrace a person, for example by using their likeness in pornographic videos. Fake content can also be used in all sorts of scams, such as attempts to extort money. An example of such use is the case of a doctor whose image was used in an advertisement for cardiac pseudo-medications, which we cited in a previous article [6]. There is also a lot of controversy surrounding the use of deepfakes for the purpose of sowing disinformation, particularly in the area of politics. Used successfully, fake content can lead to diplomatic incidents, change the public’s reaction to certain political topics, discredit politicians and even influence election results. [7]

By its very nature, the spread of deepfakes is not something that can be easily prevented. Legal solutions are not fully effective due to the global scale of the problem and the nature of social network operation. Other proposed solutions to the problem include developing algorithms to detect fabricated content and educating the public about it.

AI-generated art

There are currently many AI-based text, image or video generators on the market. Midjourney, DALL-E, Stable Diffusion and many others, despite the different implementations and algorithms underlying them, have one thing in common – they require huge amounts of data which, due to their size, can be obtained only from the Internet – often without the consent of the authors of these works.  As a result, a number of artists and companies have decided to file lawsuits against the companies developing artificial intelligence models. According to the plaintiffs, the latter are illegally using millions of copyrighted images retrieved from the Internet. Up till now, he most high-profile lawsuit is the one filed by Getty Images – an agency that offers images for business use – against Stability AI, creators of the open-source image generator Stable Diffusion. The agency accuses Stability AI of copying more than 12 million images from their database without prior consent or compensation (see Figure 3). The outcome of this and other legal cases related to AI image generation will shape the future applications and possibilities of this technology. [8]

Fig. 3. An illustration used in Getty Images’ lawsuit showing an original photograph and a similar image with a visible Getty Images watermark generated by Stable Diffusion. Source: https://www.theverge.com/2023/2/6/23587393/ai-art-copyright-lawsuit-getty-images-stable-diffusion  

In addition to the legal problems of training generative models on the basis of copyrighted data, there are also moral dilemmas about artworks made with artificial intelligence. [9]

Will AI replace artists?

Many artists believe that artificial intelligence cannot replicate the emotional aspects of art that works by humans offer. When we watch films, listen to music and play games, we feel certain emotions that algorithms cannot give us. They are not creative in the same way that humans are. There are also concerns about the financial situation of many artists. These occur both due to not being compensated for the created works that are in the training collections of the algorithms, and because of the reduced number of commissions due to the popularity and ease of use of the generators. [10]

On the other hand, some artists believe that artificial intelligence’s different way of “thinking” is an asset. It can create works that humans are unable to produce. This is one way in which generative models can become another tool in the hands of artists. With them they will be able to create art forms and genres that have not existed before, expanding human creativity.

The popularity and possibilities of generative artificial intelligence continue to grow. Consequently, there are numerous debates about the legal and ethical issues surrounding this technology. It has the potential to drastically change the way we interact with art.

Conclusions

The appropriate use of artificial intelligence has the potential to become an important and widely used tool in the hands of humanity. It has the potential to increase productivity, facilitate a wide range of activities and expand our creative capabilities. However, the technology carries certain risks that should not be underestimated. Reckless use of autonomous vehicles, AI art or deepfakes can lead to many problems. These can include financial or image losses, but even threats to health and life. Further developments of deepfake detection technologies, new methods of recognising disinformation and fake video footage, as well as new legal solutions and educating the public about the dangers of AI will be important in order to reduce the occurrence of these problems.

References

[1] https://www.nhtsa.gov/vehicle-safety/automated-vehicles-safety

[2] https://blog.galonoleje.pl/pojazdy-autonomiczne-samochody-bez-kierowcow-juz-sa-na-ulicach

[3] https://www.forbes.com/sites/naveenjoshi/2022/08/05/5-moral-dilemmas-that-self-driving-cars-face-today/

[4] https://www.bbc.com/news/technology-45991093

[5] https://studiadesecuritate.uken.krakow.pl/wp-content/uploads/sites/43/2019/10/2-1.pdf

[6] https://www.medonet.pl/zdrowie/wiadomosci,kolejny-lekarz-ofiara-oszustow–zostal-twarza-pseudolekow–dr-sutkowski–to-jest-kradziez,artykul,26668977.html

[7] https://businessinsider.com.pl/technologie/nowe-technologie/deepfakes-historia-falszywych-filmow-i-pomysly-na-walke-z-nimi/s17z2p0

[8] https://apnews.com/article/getty-images-artificial-intelligence-ai-image-generator-stable-diffusion-a98eeaaeb2bf13c5e8874ceb6a8ce196

[9] https://www.benchmark.pl/aktualnosci/dzielo-sztucznej-inteligencji-docenione.html

[10] https://businessinsider.com.pl/technologie/digital-poland/sztuczna-inteligencja-w-sztuce-szansa-czy-zagrozenie/7lq70sx