The future of artificial intelligence in the ski industry

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1.1. A Brief History of AI Development

Artificial intelligence (AI) is a computer science field that studies how to empower computer systems with the ability to learn, make decisions, and perform actions characteristic of human thinking. AI lies at the intersection of mathematics, biology, psychology, cybernetics, and linguistics. The main goal of this technology is to understand the nature of the human mind and teach the machine to think.

The history of artificial intelligence began in 1950 with Alan Turing’s publication of a test to check a machine’s ability to exhibit behavior indistinguishable from that of a human. He believed that machines could make decisions based on available information, just as humans do. In 1956, mathematician John McCarthy first used the term “artificial intelligence” at a conference at Dartmouth College dedicated to the “mechanization of intelligence” possibilities. That day became the official date of birth of this field. In 1958, McCarthy developed the LISP programming language, which became a major tool in early AI research.

With the computers becoming more widely accessible in the 1960s, there began the development of the first expert systems and perceptrons, i.e., models simulating the work of neurons. However, the high expectations led to disappointment. In 1969, an article by Marvin Minsky and Seymour Papert showed the limitations of perceptrons, which contributed to the first decline in AI interest, the so-called “winter of artificial intelligence.” In the same decade, Joseph Weizenbaum created ELIZA, the first chatbot that mimics the behavior of a psychotherapist.

In the 1970s and 1980s, funding for AI research declined. The renaissance began owing to the development of new algorithms and the efforts of Japan, which launched an ambitious program to create fifth-generation computers. This provoked the implementation of similar projects in the United States and Great Britain.

During the 1990s and 2000s, as computing capabilities surged, more advanced machine learning algorithms emerged, capable of tackling complex real-world challenges. In 1997, IBM’s Deep Blue supercomputer defeated grandmaster Garry Kasparov in a series of chess games, which became a symbol of AI’s breakthrough in strategic games. In 2002, iRobot introduced the Roomba, the first mass-produced robotic vacuum cleaner using AI elements for navigation.

The 21st century has witnessed an explosion in AI development, fueled by three key factors: the accumulation of big data, particularly from social media and digital platforms, advancements in cloud computing infrastructure, and the creation of innovative neural network designs. In 2011, IBM’s Watson computer won the Jeopardy! television quiz, demonstrating its ability to analyze natural language. In the same year, Apple intoduced Siri, its first mass-scale AI assistant. In 2012, the AlexNet system demonstrated a breakthrough in image recognition at the ImageNet competition, which marked the beginning of the era of deep learning.

The year 2016 saw the global spotlight fall on Sofia, an android granted Saudi Arabian citizenship, capable of facial recognition, engaging in conversations, and displaying a range of emotions.

In 2020, OpenAI released the GPT-3 model, which demonstrated a qualitative leap in text generation. Based on it, the browser version of the ChatGPT AI assistant was launched in November 2022, which gained more than 100 million users in just two months, becoming the fastest-growing consumer application in history. The year 2023 saw the emergence of the GPT-4 model, followed in 2024 by a surge of multimodal AI, a new generation of AI capable of processing text, images, code, and audio.

Figure 1.2

In addition to advancements in technology, the current phase of AI development is marked by a growing focus on safety, ethics, and regulation. In order to reduce risks and use technology responsibly, international standards and regulatory frameworks are being developed.

1.2. Stages of Artificial Intelligence Operation

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The development and operation of AI systems include several successive stages:

– Data collection. To train an AI model, a significant amount of information is required from various sources, such as open databases, Internet resources, sensors, and other channels.

– Data preparation. The data obtained is cleaned, structured, and converted into a format suitable for subsequent machine learning.

– Model training. The prepared data is used to create algorithms that can recognize patterns, classify information, provide predictions, and perform other cognitive functions.

– Testing and optimization. New, unutilized data is used to test the model’s efficacy. If necessary, algorithms are refined to improve accuracy and stability.

– Real-time application. After successful testing, the model becomes suitable for solving practical tasks, including text analysis, decision-making, event forecasting, and other forms of intelligent information processing.

Example: The ChatGPT model

The ChatGPT large language model’s working principle exemplifies the modern approach of artificial intelligence learning. At the first stage, the model was trained on extensive arrays of texts from various open sources (websites, digital publications, and encyclopedic databases). It was able to learn the language’s grammatical constructions, semantic relationships, and common patterns as a result.

At the next stage, the model underwent additional training (fine-tuning) in specialized tasks, including generating answers to questions, translating text, and summarizing information. This has improved its ability to perform specific functions.

When interacting with users, ChatGPT analyzes the input data, determines the context of the request, and generates the most relevant response based on previously learned language patterns. The results of user interaction are applied to further improve the model within the supervised learning.

1.3. What is Included in Artificial Intelligence

Artificial intelligence is not a single technology but an extensive field that unites many areas, each with its own specialization and scope of application:

– Machine learning. It is a method of data-based training of a model. Instead of giving clear instructions, the model is taught by example, so that it could find patterns on its own. Such models are used in forecasting, text recognition, image analysis, and even in the diagnosing.

– Neural networks. This is a special approach within machine learning. Neural networks are designed by analogy with the human brain: they consist of artificial neurons connected to each other. Such models are particularly good at image recognition, text generation, and voice information tasks. All neural networks are related to AI, but not all AI is based on neural networks. There are other methods.

– Natural Language Processing (NLP). These are technologies that help machines understand, analyze, and generate human speech. It is thanks to NLP that chatbots, voice assistants, automatic translators and search engines work.

– Computer vision. This area of focus allows AI to “see,” i.e., to recognize objects in photographs and videos, analyze images, and measure parameters in images. These technologies are employed in such spheres as manufacturing, autopilots, security systems, and medicine.

Figure 1.4

– Robotics. In this area, AI connects with physical devices to create robots that can move, navigate in space, and perform various tasks. Combining AI and mechanics has produced everything from industrial manipulators to robotic vacuum cleaners.

– Recommender systems. They tell you what you might like: movies, products, music, and news. Such systems analyze user behavior and offer personalized content.

Specialists of various profiles work in all these areas. For example, a machine learning engineer is engaged in “teaching” a computer to analyze data, identify patterns, and draw conclusions. Being a scientist isn’t always necessary to accomplish this. These days, a lot of tools make it possible to use AI without extensive programming experience. AI is becoming more and more integrated into daily life; it helps businesses automate procedures, enhances medical diagnosis, provides tailored recommendations, translates text, and even creates paintings and music. All this is the result of the work in many areas within one big concept – artificial intelligence.

1.4. Application Domains for Artificial Intelligence

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Today, artificial intelligence is used in a wide variety of domains, from medicine to entertainment. AI is already actively helping people in the following areas:

– Medicine. AI analyzes MRI and X-ray images, helps diagnose diseases at an early stage, predicts how treatment will proceed, and even selects individual therapy regimens.

– Finance. Banks and investors use AI to analyze financial flows, forecast market movements, spot suspicious transactions, and automate decision-making processes, like loan issuance.

– Transport. Artificial intelligence is at the heart of self-driving cars. AI also helps to build optimal routes and manage traffic flows in real time.

– Industry. AI is used in manufacturing to predict breakdowns, manage equipment, monitor product quality, and increase process efficiency.

– Agriculture. AI helps manage seeding and irrigation, calculates when it is better to harvest crops, and suggests how to use resources (water, fertilizers, and machinery) more efficiently.

– Education. AI systems can create personalized training programs, track student progress, and even help teachers review assignments and organize the learning process.

– Safety. AI is used for video surveillance, detecting suspicious behavior, analyzing large amounts of data on offenses, and preventing threats.

– Entertainment. In addition to helping platforms choose tailored suggestions for users, artificial intelligence is used in the production of movies, music, games, and other types of content.

– Energy. It can be used to predict electricity demand, manage power grids, and improve energy efficiency, both across the city and for individual buildings.

– Ecology. AI helps to monitor the state of the environment, simulate climate change, and develop strategies to reduce harmful effects on nature.

1.5. AI Geography: Countries Shaping the Future

In the 20th century, artificial intelligence (AI) was mainly an academic field at the nexus of logic, cybernetics, and cognitive psychology. However, in the 21st century, AI has emerged as a major force behind global change. Modern AI technologies, especially generative models, are leaving the laboratory and penetrating into all spheres of life: from medicine and education to the media, defense, and public administration.

The scope of this technological revolution has expanded to include both scholarly and geopolitical dimensions. AI has become a subject of competition between countries, a powerful tool for economic growth, strategic influence, and national security. In an effort to keep pace, every state creates its own policies, making investments in R&D, building infrastructure, enforcing regulations, and attracting talent.

This section provides an overview of ten countries that are currently setting the standard for the development of artificial intelligence, primarily generative AI. Let’s examine their strategic priorities, research and commercial accomplishments, and investment amounts, as well as the unique political and cultural perspectives on artificial intelligence. Let’s start with the leader who sets the trends of technological development for the whole world – the United States of America.

1st place – USA: Global Leader in Generative AI

Figure 1.6

The United States holds a leading position in the field of artificial intelligence. This is where key technological innovations appear, global trends are formed, and the most advanced models of generative AI are being developed. American companies have created such iconic models as OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini. These developments determine the course of the industry’s growth and set a high standard of quality.

Investments and Financial Support

In the US, private investment in AI is still expanding quickly. More than $33 billion of the $109 billion raised by AI startups in 2024 went toward generative AI technologies, accounting for nearly half of all AI investments made worldwide during that year.

The sector is also supported by the government, which allocated $3.3 billion for infrastructure and research in 2023. The CHIPS and Science Act has increased investments in critical technologies, including AI and semiconductors.

The Ecosystem and the Stakeholders

The American AI ecosystem unites the largest technology corporations (OpenAI, Google, Microsoft, Meta, and Nvidia) and hundreds of fast-growing startups such as Anthropic, Stability AI, and Character.ai. The scientific basis is formed by leading universities, such as Stanford, MIT, UC Berkeley and Harvard. Research laboratories at universities and companies actively cooperate and form the core of the global AI scientific community. According to the Stanford AI Index, in 2023—2024, the United States became the author of 40 of the 51 leading generative models in the world.

Science and Commercialization

Universities focus on fundamental research to develop new neural network architectures, improve learning algorithms, and study ethical aspects.

In response, businesses work to expand innovations and implement them into goods. They are integrating AI in office applications, cloud platforms, search engines, marketing, and media. For example, Microsoft has integrated the GPT model into its Office and Azure products, while Google uses its own language models in the search engine and corporate services.

Policy and Regulation

The United States promotes a strategic approach to AI development. Since 2019, there has been a national strategy aimed at interagency cooperation, education, and innovation development. In 2023, the White House published a Blueprint for an AI Bill of Rights, which outlined the principles of safe and fair use of technology.

State and local governments are putting their own plans into action. For example, New York is implementing the AI Action Plan, while San Francisco is establishing itself as the global hub for AI, offering startups infrastructure and advantages.

International Influence

The United States is actively involved in shaping global standards and regulations. They initiate and support cooperation within the G7, OECD, and GPAI, as well as discuss a unified international code for AI development companies.

At the same time, the country is tightening export regulations on cutting-edge chips, preventing competitors, mostly China, from obtaining them. This approach helps the United States not only maintain technological leadership but also shape the rules of the game on a global level.

A Look into the Future

By 2030, the United States expects to maintain leadership in the field of generative AI. The main drivers are a powerful private sector, high academic potential, and a steady flow of investments. The primary challenges are safeguarding technology, upholding human rights, and preserving competitiveness in a global marketplace that is becoming more and more competitive.

2nd place – China: a Strategic and Ambitious Opponent

Figure 1.7

Influence and Ambition

In the global hierarchy of artificial intelligence, the People’s Republic of China firmly holds the second position and actively challenges the US for technological supremacy. Beijing sees AI not just as a promising industry but as a strategic foundation for national development. According to the official program published in 2017, by 2030 China plans to become a world leader in AI, surpassing other powers not only in terms of implementation but also in terms of achievements in basic sciences.

Large-Scale Investments

The government relies on centralized investments, stimulating both basic research and commercial technology adoption. Between 2013 and 2024, private investment in Chinese AI reached $119 billion, second only to the United States. The government also provides additional funding. For instance, the National Investment Fund was established with an initial budget of $8.2 billion to support AI, and this is just one such fund. According to IDC’s forecast, by 2027, China’s annual spending on AI implementation will amount to about $38 billion, which confirms the desire for large-scale application of technology in the economy and public administration.

Generative AI: National Analogues

Amid the growing popularity of ChatGPT in the world, China reacted quickly by offering its own analogues. In 2023, Baidu released ERNIE Bot, Tencent released the Hunyuan model, Huawei released the Pangu line, and Alibaba released the Tongyi Qianwen family of language models. These systems are already used in corporate products, digital assistants, and e-commerce services.

Chinese models are showing increasingly high quality. According to the results of international tests, the gap between the best American and Chinese generative systems has narrowed to a minimum, especially in tasks in Chinese. The country is confidently reaching the level of technological self-sufficiency.