From Fiction to Reality: The Evolution of Artificial Intelligence

Introduction

Artificial Intelligence (AI) has come a long way since its inception in the 1950s. From simple rule-based systems to complex neural networks capable of beating humans at complex games, AI has seen remarkable progress in recent years. The advances in AI have led to transformative changes in various fields such as healthcare, finance, and transportation. As the world is becoming increasingly reliant on AI, it’s essential to understand its evolution and where it’s headed. In this article, we explore the evolution of AI following structured approaches. It is important to note from the very beginning that the future is always uncertain and subject to change based on various factors. However, we can explore scenarios of evolution based on the understanding the patterns of evolution from past to present and combinations of instances of the key influence factors.

That being said, it is likely that AI will continue to play an increasingly significant role in society in the coming years, with advancements in areas such as natural language processing, computer vision, robotics, and machine learning. In the next 10 years, we may see significant progress in areas such as autonomous vehicles, personalized medicine, and virtual assistants that can carry out a wider range of tasks. We may also see continued advancements in AI-powered chatbots and digital assistants, making them more sophisticated and able to perform a wider range of tasks.

In 20 years, we could see major breakthroughs in areas such as quantum computing and brain-computer interfaces, which could dramatically enhance the capabilities of AI and open up new possibilities in areas such as neuroscience and space exploration. In 30 years, the potential advancements in AI are virtually limitless. We may see the emergence of new types of AI systems that are capable of carrying out tasks beyond what we can currently imagine, and advancements in areas such as energy storage, climate change, and food production that are enabled by AI. It is important to note that AI also poses potential risks and challenges, including issues such as privacy, bias, and the potential displacement of human labor. It is therefore important for researchers, policymakers, and society as a whole to carefully consider the potential benefits and risks of AI as it continues to evolve in the coming years.

Factors Shaping the Future of AI: A 10, 20, and 30-Year Outlook

There are a number of factors that could potentially generate a new lifecycle curve for AI and lead to the reinvention of the field. Here are a few possibilities:

Breakthroughs in hardware: The development of new, more advanced computer architectures and technologies could provide the foundation for new types of AI systems that are more powerful, efficient, and versatile than anything we have today.
Advances in neuroscience: A deeper understanding of how the brain works and processes information could inspire new AI architectures that are modeled after biological neural networks, or could provide new insights into the design of more effective learning algorithms.
New data sources: The emergence of new data sources and types of data (such as data generated by the Internet of Things, or new forms of biometric or sensor data) could enable the development of new types of AI systems that are better adapted to specific use cases or contexts.
Breakthroughs in algorithmic research: Advancements in machine learning theory, optimization, and other areas of AI research could lead to the development of new learning algorithms that are more efficient, effective, and adaptable than current methods.
New applications and use cases: As AI becomes more widespread and is applied to a broader range of problems, new opportunities and challenges will emerge that could inspire the development of new types of AI systems or approaches.

The reinvention of AI is likely to be driven by a combination of scientific and technological breakthroughs, new data sources, and evolving societal needs and priorities. The precise shape of the next lifecycle curve for AI is difficult to predict.

Charting the Evolution of AI through TRIZ’s Laws of Evolution

As AI systems continue to rapidly evolve, we can observe five fundamental laws of evolution driving their progress: the Law of Completeness and Completeness of Parts, the Law of Transition to a Higher Level, the Law of Increasing Ideality, the Law of Harmonization, and the Law of Transition to Micro-Level. The laws of evolution are a set of principles or trends that describe the ways in which systems and technologies tend to evolve over time. Applying the laws of evolution from TRIZ to AI, we can make some predictions about the potential direction and trends of AI development over the next 10, 20, and 30 years. Initially we apply these laws as they act in siloses. The results are presented below:

The Law of Completeness and Completeness of Parts: As AI systems continue to evolve, they will likely become more complex and complete, with more subsystems and components added to enhance their functionality. In the next 10 years, we can expect AI to become more specialized and domain-specific, with more advanced applications in fields such as medicine, finance, and logistics. In the next 20 and 30 years, we may see the integration of AI with other emerging technologies, such as brain-computer interfaces, to create more complex and powerful AI systems.
The Law of Transition to a Higher Level: Over time, AI systems will likely continue to evolve towards higher levels of functionality and performance, with more advanced capabilities and applications. In the next 10 years, we may see significant progress in areas such as natural language processing, computer vision, and robotics, enabling AI to carry out a wider range of tasks. In the next 20 and 30 years, we may see AI systems become more capable of creative problem-solving and decision-making, with applications in areas such as scientific research and space exploration.
The Law of Increasing Ideality: AI systems will likely become more ideal over time, with fewer drawbacks and greater benefits for users. In the next 10 years, we may see significant progress in areas such as personalized medicine, autonomous vehicles, and virtual assistants, which could greatly enhance the quality of life for many people. In the next 20 and 30 years, we may see AI systems become more efficient and environmentally friendly, with applications in areas such as energy storage and climate change mitigation.
The Law of Harmonization: Over time, AI systems will likely become more harmonious and integrated with their environment, including other systems and users. In the next 10 years, we may see continued progress in areas such as social robotics and human-AI collaboration, enabling AI to work more seamlessly with humans in a wider range of contexts. In the next 20 and 30 years, we may see the emergence of new types of AI systems that are more empathetic and emotionally intelligent, enabling more natural and effective interactions with users.
The Law of Transition to Micro-Level: Over time, AI systems will likely become smaller and more efficient, with the miniaturization of components and the use of new materials and technologies. In the next 10 years, we may see the development of more compact and energy-efficient AI systems, enabling greater deployment in mobile and edge devices. In the next 20 and 30 years, we may see the emergence of new types of AI systems that are based on novel computing architectures, such as quantum computing, enabling significant improvements in speed and efficiency.

Thus, we might say that the evolution of AI over the next few decades will be driven by the interplay of these five laws of evolution, leading to more capable, efficient, and integrated AI systems that can significantly enhance our lives and address some of the world’s most pressing challenges.

AI Evolution through the Lens of the Concept of Ideality

The concept of ideality refers to the ratio between useful functions on one side and costs, harmful functions, and energy consumption on the other side. When the costs, energy, and harmful functions tend to zero, and the useful functions increase exponentially, the ideality of AI is enhanced. Looking at the current trend, we can expect the evolution of AI to become increasingly improved in the coming decades.

By 2030, we may see significant progress in personalized medicine, autonomous vehicles, and virtual assistants, which could greatly enhance the quality of life for many people. The reduction of costs, energy, and harmful functions will enable AI to become more efficient and environmentally friendly, with applications in areas such as energy storage and climate change mitigation. We can expect AI systems to evolve towards ideality the next decade, with a continued trend towards more efficient and cost-effective systems. Advances in AI hardware, such as neuromorphic computing and quantum computing, may enable significant improvements in speed and energy efficiency, while the integration of AI with other emerging technologies, such as blockchain and the Internet of Things, could create more seamless and secure systems. Additionally, the development of new AI applications and business models may help to drive down costs.

Moving towards 2040, we may see AI systems become more capable of creative problem-solving and decision-making, with applications in areas such as scientific research and space exploration. With continued progress in miniaturization and the use of new materials and technologies, we can expect AI systems to become smaller and more energy-efficient, enabling greater deployment in mobile and edge devices. We may see significant progress in the AI systems by 2040, with more advanced and sophisticated applications in a wide range of fields. AI systems may become more specialized and domain-specific, with greater accuracy and reliability, while the development of explainable AI may enable greater transparency and accountability. Additionally, advances in renewable energy and sustainable materials may help to reduce the environmental impact of AI systems. However, as AI systems become more sophisticated and complex, there may be increased costs associated with research, development, and maintenance.

By 2050, AI systems may become more harmonious and integrated with their environment, including other systems and users. This will allow AI to work more seamlessly with humans in a wider range of contexts, such as social robotics and human-AI collaboration. Moreover, the emergence of new types of AI systems that are more empathetic and emotionally intelligent will enable more natural and effective interactions with users. We may see the emergence of highly advanced AI systems, with greater levels of automation and autonomy, and more natural and intuitive interactions with humans. AI may play a key role in addressing some of the world’s most pressing challenges, such as climate change, healthcare, and global security. The development of new forms of AI, such as artificial general intelligence (AGI) and artificial superintelligence (ASI), may enable even more advanced applications and capabilities. The use of AI in fields such as healthcare, transportation, and energy management may help to reduce costs and energy consumption, while the development of new AI-based technologies, such as smart cities and autonomous vehicles, may help to reduce carbon emissions and improve energy efficiency.

Looking even further ahead, towards 2100, we may see the integration of AI with other emerging technologies, such as brain-computer interfaces and quantum computing, to create even more complex and powerful AI systems. This could lead to a significant increase in useful functions and further reduction in harmful functions and energy consumption, resulting in even higher performance, closer to ideality for AI. The use of advanced AI-based technologies, such as AGI and ASI, may help to reduce costs and energy consumption across a wide range of fields, while also enabling new applications and capabilities that are currently beyond our imagination. However, it will be important to manage the potential environmental impacts of these technologies, such as increased demand for energy and materials. In addition, the development of such advanced AI systems may also raise important ethical and societal questions, and may require careful management and regulation.

It is difficult to predict what will happen beyond 2100, but it is possible that AI could continue to evolve and make significant progress. However, it’s also possible that there may be limits to how far AI can advance, and that further progress will be limited by fundamental physical, computational, or philosophical constraints. However, new breakthroughs and discoveries could change our understanding of what is possible.

Notes:

AGI stands for Artificial General Intelligence, which refers to an AI system that is capable of performing a wide range of cognitive tasks that are typically associated with human intelligence, such as reasoning, learning, problem-solving, perception, and natural language understanding. AGI is sometimes also referred to as “strong AI” or “human-level AI” because it aims to replicate the general intelligence of human beings. Unlike narrow AI – that is what we have now and call AI, which is designed to perform a specific task or set of tasks, AGI would be able to learn and adapt to new tasks and situations, and would be able to reason and make decisions based on abstract concepts and general principles, rather than just responding to specific inputs or stimuli. AGI is seen by many researchers as a major milestone in the development of AI, as it would represent a major step towards creating machines that can match or exceed human intelligence in a wide range of domains. While AGI has not yet been achieved, there has been significant progress in the development of more sophisticated and versatile AI systems that can perform a broader range of tasks and operate with greater autonomy and flexibility. There are still significant challenges to be overcome in order to achieve AGI, such as improving the robustness and reliability of AI systems, developing more effective learning algorithms, and enabling AI systems to reason and learn from natural language and other forms of unstructured data.

ASI stands for Artificial Super Intelligence, which refers to an AI system that is capable of surpassing human intelligence in virtually every domain or task. ASI is considered by some researchers to be the ultimate form of AI, as it would be capable of performing tasks that are currently beyond the reach of human intelligence, such as solving complex scientific problems, developing new technologies, and predicting the outcomes of complex social and economic systems. ASI is still largely a hypothetical concept, as we have not yet developed AI systems that can match or exceed human intelligence across all domains. However, some researchers believe that ASI could be achieved in the future through the development of advanced machine learning algorithms, combined with more powerful and efficient computing technologies, such as quantum computing. There are many potential benefits to developing ASI, such as improving scientific research and discovery, solving major societal challenges, and advancing human knowledge and understanding. However, there are also significant risks associated with the development of ASI, such as the potential for unintended consequences and unforeseeable outcomes, as well as the risk of creating an AI system that is beyond our ability to control or understand. Therefore, the development of ASI will require careful consideration and planning, and a commitment to ethical and responsible AI development and deployment.

AI Evolution Towards Ideality: The Future of Bias and Data Collection

The issue of bias and data requirements in AI models is an important consideration for the evolution of AI towards ideality. The ideality formula, which measures the ratio of useful functions to costs, harmful functions, and energy consumption, suggests that for AI to become closer to ideal, we need to reduce the costs and harmful functions associated with AI, while increasing the usefulness of AI.

One way to reduce the costs and harmful functions of AI is to address the issue of bias in AI models. Bias can lead to unfair and inaccurate predictions and decisions, and can perpetuate and amplify existing social and economic inequalities. To reduce bias, we need to ensure that AI models are developed and trained using diverse and representative data, and that they are evaluated using appropriate metrics and methods.

Collecting large and diverse datasets can be expensive and time-consuming, and may require significant resources and expertise. In addition, there are concerns about privacy and security when collecting and storing large amounts of data. To address these challenges, researchers are exploring new methods for data collection and synthesis, such as synthetic data generation and federated learning, that can help to reduce the costs and risks associated with data collection.

Another way to increase the usefulness of AI is to develop more efficient and effective AI models that require less data. This can be achieved through the development of new algorithms and architectures that are more robust, adaptive, and scalable, and that can learn from smaller and more diverse datasets. For example, deep learning models can be augmented with unsupervised and reinforcement learning methods, which can help to reduce the amount of labeled data required for training.

So, we can claim that the evolution of AI towards ideality will require a concerted effort to address the issues of bias and data requirements, while also developing more efficient and effective AI models. This will require collaboration between researchers, developers, and policymakers, and a commitment to ethical and responsible AI development and deployment.

By 2030, we can expect significant progress in reducing bias and improving the quality and diversity of data used to train AI models. This will be supported by advances in data collection, data synthesis, and data sharing technologies. Additionally, we can expect the development of new algorithms and architectures that are more efficient and effective, and that require less data for training.

In the next 20 years, by 2040, we may see even more progress in reducing bias and data requirements, as AI technologies become more sophisticated and mature. This may be supported by the development of new machine learning techniques, such as transfer learning and meta-learning, that can help to generalize AI models to new tasks and domains. We may also see the emergence of more sophisticated AI systems that can reason and learn from natural language, and that can operate with greater autonomy and adaptability.

By 2050, we may see significant improvements in the usefulness of AI systems, as they become more integrated into our daily lives and more capable of solving complex and diverse problems. This may be supported by the development of advanced AI technologies, such as AGI and ASI, that can learn and reason at a human-level or beyond. We may also see the emergence of new AI applications and systems that can help to address major societal challenges, such as climate change, global health, and social inequality.

Looking further ahead, to 2100, we may see the full realization of AI’s potential to achieve ideality. This may be supported by the development of new computing technologies, such as quantum computing and biological computing, that can enable faster and more energy-efficient processing. We may also see the emergence of new AI-based systems and technologies that are fully integrated into our physical and social environments, and that can help to transform the way we live, work, and interact with each other. However, as AI becomes more powerful and pervasive, it will be important to ensure that it is developed and used in an ethical and responsible manner, and that it serves the best interests of humanity.

AI Scoring: A Historical and Future Perspective

It is not easy to provide precise numerical estimates for the gap of AI to ideality over time, as this is a complex and multifaceted concept that depends on a wide range of factors. However, I can offer some general observations and predictions based on the ideality formula, and the evolution of AI over time.

1950: At this time, AI was in its infancy, with the development of the first AI programs and systems. The ideality of AI at this time was likely quite low, as the costs, energy consumption, and harmful functions associated with these early systems were relatively high, while the useful functions were quite limited. I would estimate the progress of AI towards ideality in 1950 to be around 1-2 on a scale from 0 to 100.
1990: By this time, AI had made significant progress, with the development of expert systems, neural networks, and other advanced AI technologies. The performance of AI in 1990 was likely higher than in 1950, as the useful functions had increased significantly, while the costs, energy consumption, and harmful functions had decreased somewhat. I would estimate the progress of AI towards ideality in 1990 to be around 4-5.
2000: At the turn of the millennium, AI had made further progress, with the development of more advanced machine learning algorithms, natural language processing, and other advanced AI applications. The performance of AI in 2000 was likely higher than in 1990, as the useful functions had continued to increase, while the costs, energy consumption, and harmful functions had continued to decrease. I would estimate the progress of AI towards ideality in 2000 to be around 9-10.
2010: By this time, AI had made significant strides in areas such as image and speech recognition, autonomous vehicles, and recommendation systems. The performance of AI in 2010 was likely higher than in 2000, as the useful functions had continued to increase, while the costs, energy consumption, and harmful functions had continued to decrease. I would estimate tthe progress of AI towards ideality in 2010 to be around 20.
2020: In the past few years, AI has continued to make significant progress, with breakthroughs in areas such as natural language processing, reinforcement learning, and generative models. The move towards ideality of AI was positive, as the useful functions had continued to increase, while the costs, energy consumption, and harmful functions had continued to decrease. I would estimate the performance of AI relative to ideality in 2020 to be around 30.
2022: As of 2022, AI has continued to evolve and advance, with more sophisticated and versatile AI systems being developed for a wide range of applications. I would estimate the move towards ideality of AI in 2022 to reach the score 35 on the scale from 0 to 100.
2023, 2025, 2030: It is difficult to score the exact progress of AI towards ideality in the coming years, as this will depend on the pace of technological progress and the development of new AI applications and systems. We could see a level of 37 in 2023, 40 in 2025, and maybe 45 in 2030.
Beyond 2030: The progression of AI will depend on various factors, such as technological advancements, ethical considerations, and regulatory frameworks. Considering three scenarios influenced by these factors, we might consider the following evolution: 2040 – pessimistic 48-50, moderate 55-65, optimistic 70-80; 2050 – pessimistic 55-60, moderate 67-75, optimistic 85-90; 2070 – pessimistic 65-70, moderate 80-85, optimistic 90-92; 2090 – pessimistic 72-75, moderate 87-90, optimistic 93-95; 2100 – pessimistic 80-85, moderate 90-94, optimistic 96-98.

It’s important to note that these scores are based on hypothetical scenarios and may not accurately reflect the actual evolution of AI.

Vectorial Analysis of AI

Sub-system: What are the subsystems that make up the overall AI system, and how are they related to one another? AI can be broken down into various subsystems, such as natural language processing, image and video recognition, decision-making, and robotics. These subsystems are often interrelated, with advances in one area often leading to improvements in others. For example, improvements in natural language processing can lead to better decision-making, and advances in robotics can benefit from improvements in image and video recognition.
Super-system: What is the larger system of which the AI system is a part, and how does it interact with that larger system? AI is part of a larger system of technology and society. It interacts with other technologies, such as cloud computing and big data, and with a wide range of societal and economic factors, such as education, employment, and privacy.
Time: How does the AI system change over time, and what factors influence those changes? AI has undergone significant changes over time, from the early rule-based expert systems of the 1980s to the deep learning neural networks of today. Factors that have influenced these changes include advances in computer hardware, improvements in algorithms and models, and the availability of large amounts of data.
Space: What physical or spatial constraints does the AI system operate within, and how do those constraints impact its performance? AI operates within various physical and spatial constraints, such as the processing power of computers and the availability of data. These constraints can limit the speed and accuracy of AI systems, and can also impact their energy consumption and cost.
Matter and field: What materials or substances are involved in the AI system, and what kinds of energy or fields are present? AI involves a wide range of materials and substances, from the hardware components of computers to the data and models used to train AI systems. It also involves various forms of energy, such as electricity and heat.
Ideality: How closely does the current AI system meet the ideal requirements for its intended purpose, and what changes could be made to improve ideality? The ideal requirements for AI vary depending on its intended purpose, but generally include factors such as accuracy, speed, energy efficiency, and interpretability. While AI has made significant progress in these areas, there is still room for improvement. Possible changes to improve ideality include developing new algorithms and models, reducing energy consumption, and improving interpretability.
Control: How is the AI system controlled or regulated, and how could control mechanisms be improved or optimized? AI is currently controlled and regulated through a combination of industry standards, government regulations, and ethical guidelines. Control mechanisms could be improved or optimized through the development of new standards and regulations that address emerging issues such as bias, privacy, and security.
Error: What types of errors or malfunctions can occur in the AI system, and how can they be prevented or mitigated? AI can suffer from a wide range of errors and malfunctions, including bias, overfitting, and adversarial attacks. These errors can be prevented or mitigated through various techniques, such as data augmentation, model regularization, and adversarial training.
Cooperation: How does the AI system interact with other systems or stakeholders, and what opportunities exist for collaboration or cooperation? AI interacts with a wide range of other systems and stakeholders, including other technologies, users, and societal institutions. Opportunities for collaboration or cooperation include the development of standards and best practices, the sharing of data and models, and the involvement of stakeholders in the development and deployment of AI systems.

System Operator to Analyse the Patterns of AI Evolution

In this section we explore the use of TRIZ system operator in analyzing the patterns of evolution in the field of artificial intelligence, and how this approach can help us to identify new opportunities for innovation and progress. System operator is based on the idea that there are patterns of evolution that can be observed across many different industries and fields. By understanding these patterns, innovators and problem-solvers can identify common solutions that have worked in the past and apply them to new challenges. We will use system operator from various angles such that to create a more comprehensive picture of AI evolution. The first application considers the subsystem-system-supersystem paradigm, where in the context of AI, some possible examples of supersystems could include:

The economic and political systems that impact the funding and development of AI technology
The social and cultural factors that shape the public perception and acceptance of AI
The legal and regulatory frameworks that govern the use and deployment of AI
The global trends and technological advances that drive the development of AI and its applications.

These supersystems may not be as tangible or concrete as the hardware or software technologies that make up the system itself, but they can still have a significant impact on the evolution and direction of AI as a field.

	2010	2022	2028
Supersystem	AI was still in its early stages of development, and many people were skeptical about its potential	AI has become an important topic in public discourse, with concerns raised about its impact on privacy, employment, and ethical considerations	AI will continue to be a major topic in public discourse, with efforts to regulate its development and use to ensure ethical and responsible practices. There will also be ongoing research into new AI technologies and their potential impact on society
System	AI was being developed and used by tech companies, governments, and research institutions	AI has become an integral part of many industries, including healthcare, finance, transportation, and manufacturing	AI will become even more integrated into industries, with the potential to disrupt traditional job roles and create new ones
Subsystem	At this time, AI was primarily being used in narrow applications, such as voice recognition and image processing	AI has become more advanced and is now being used in a wide variety of applications, such as natural language processing, image and speech recognition, and predictive analytics	AI will continue to become more advanced, with the potential to be used in even more applications, such as autonomous driving, personal assistants, and decision-making

Below is the application of the system operator method to AI evolution, considering the supersystem as the hardware where AI is running, the system as the AI platforms and user experience, and the subsystem as data availability, AI models, and other features.

	2010	2022	2028
Hardware where AI is running	The hardware available for AI was relatively limited, with GPUs being the primary technology used for training AI models	The hardware available for AI has become more powerful and specialized, with the rise of dedicated AI chips and specialized hardware architectures like TPUs and FPGAs	The hardware available for AI will continue to evolve and become more specialized, with the potential for new architectures and technologies to emerge. There will also be ongoing efforts to make AI hardware more energy-efficient and sustainable
AI platforms and user experience	AI platforms were primarily being developed by tech companies and research institutions	AI platforms have become more user-friendly and accessible, with the rise of low-code and no-code platforms making it easier for non-experts to build and deploy AI applications	AI platforms will become even more user-friendly and accessible, with the potential for AI to be integrated into a wider range of applications and devices. The user experience will become increasingly important, with the potential for AI to enable more intuitive and natural interaction
Data availability, AI models, and other features	At this time, there was limited availability of large datasets, and AI models were relatively simple	There is now a wealth of data available for training AI models, with techniques like transfer learning allowing models to be trained with smaller datasets. AI models have also become more complex and capable of performing a wider range of tasks	The availability of data for AI will continue to grow, with new techniques for data collection and analysis emerging. AI models will become even more complex and capable, with the potential to perform tasks that are currently difficult or impossible for humans

In the next table is the application of the system operator model for AI evolution, with mathematical algorithms as subsystem, programming technologies and libraries as system, and global trends and technological advances as supersystem.

	2010	2022	2028
Supersystem (global trends and technological advances)	AI was not yet seen as a major area of investment or innovation, and was not yet being widely used in many industries	AI has become a major area of investment and innovation, with a growing number of applications in industries such as healthcare, finance, and transportation	AI will continue to transform industries and society at large, with increasing focus on the ethical, legal, and social implications of its use
System (programming technologies and libraries)	AI programming was still a niche field, with limited availability of specialized tools and libraries	AI programming has become more accessible and mainstream, with many more tools and libraries available to developers	AI programming will become even more user-friendly and accessible, with greater emphasis on ease-of-use and automation
Subsystem (mathematical algorithms)	Many of the mathematical algorithms used in AI today were already in existence in 2010, but were not yet widely adopted	Newer and more sophisticated algorithms have been developed, with a greater focus on deep learning and neural networks	Even more sophisticated algorithms will likely be developed, with a greater emphasis on explainability, interpretability, and ethical considerations
Supersystem (global trends and technological advances)		Current limitations and challenges in global trends and technological advances: The rapid growth of AI has led to concerns about its impact on society, such as the displacement of jobs, bias and discrimination, and the potential for misuse. Addressing these challenges will require a greater emphasis on ethical, legal, and social considerations in the development and deployment of AI, as well as increased collaboration and transparency across different stakeholders	Future possibilities and opportunities in global trends and technological advances: The continued growth of AI will drive innovation and transformation across a wide range of industries and fields These trends may include the rise of quantum computing, the development of new hardware technologies, the expansion of the Internet of Things (IoT), and the emergence of new AI applications in domains such as education, entertainment, and more
System (programming technologies and libraries)		Current limitations and challenges in programming technologies and libraries: Despite the availability of many programming tools and libraries, there are still challenges in developing and deploying AI models at scale. Addressing these limitations will require continued development of more powerful and efficient programming technologies, as well as standardization and interoperability across different AI platforms	Future possibilities and opportunities in programming technologies and libraries: Improvements in programming technologies and libraries will enable developers to create and deploy AI models more easily and efficiently, with greater scalability and interoperability New tools and platforms may emerge that simplify and streamline the AI development process even further, making it more accessible to a wider range of users
Subsystem (mathematical algorithms)		Current limitations and challenges in mathematical algorithms: The mathematical algorithms used in AI still have limitations and challenges, such as interpretability, bias, and the ability to learn from smaller data sets Addressing these limitations will require continued research and development in the field of AI	Future possibilities and opportunities in mathematical algorithms: The development of new mathematical algorithms and techniques will enable new applications and capabilities in AI, such as improved natural language processing, better image and speech recognition, and more advanced predictive modeling Continued investment and research in these areas will be key to unlocking the full potential of AI

System Operator to Analyse the Patterns of Small Data AI Evolution

In the next table we will analyse the AI patterns of evolutions from the perspectives of the paradigm of small data sets AI.

	2015	2022	2030
Supersystem	The emergence of big data and cloud computing technologies provided a new paradigm for data processing and storage	The rapid growth of data and the emergence of new technologies, such as edge computing, IoT, and blockchain, are driving the evolution of small data AI	The integration of AI with other emerging technologies, such as quantum computing, 5G, and augmented reality, will lead to the creation of new applications and use cases for small data AI
System	Small data AI was still in its early stages, with basic tools and techniques for processing and analyzing data	Small data AI has evolved significantly with the development of advanced analytics tools, machine learning algorithms, and statistical models	Small data AI will continue to evolve with the development of more efficient and accurate algorithms, as well as better visualization and user interfaces
Subsystem	Limited access to data and computational resources, simple algorithms and models	Increased access to data and computational resources, improved algorithms and models, but still limited by the scale and complexity of data	Access to vast amounts of high-quality data, sophisticated algorithms and models, and real-time analytics capabilities

The Evolution of Small Data AI and Its Interactions with Subsystems, Systems, and Supersystems

	The supersystem for small data AI includes the larger technological and scientific trends that drive the development of AI as a whole, such as advances in hardware, data storage, and data collection	The system for small data AI includes the programming languages, libraries, and tools used to develop and implement algorithms for small data sets	The subsystem for small data AI is the algorithms and techniques used to analyze and process data with limited or small amounts of data
The supersystem for small data AI includes the larger technological and scientific trends that drive the development of AI as a whole, such as advances in hardware, data storage, and data collection
The system for small data AI includes the programming languages, libraries, and tools used to develop and implement algorithms for small data sets	The interaction between the system of programming technologies and libraries and the supersystem of technological trends and advances drives the evolution and adoption of new AI platforms and tools
The subsystem for small data AI is the algorithms and techniques used to analyze and process data with limited or small amounts of data	The interaction between the subsystem of small data AI and the supersystem of technological trends and advances drives the development of new algorithms and techniques for data analysis	The interaction between the system of programming technologies and libraries and the subsystem of algorithms and techniques leads to the development of new AI applications and improved performance

Small Data AI: Contradictions, Trends, and Solutions Across Different Scales of View

In the next paragraphs we will see the system operator applied with respect to contradictions, trends and solutions.

Past:

Contradictions: With limited data availability, there were challenges in training accurate models.
Trends: The trend was towards the development of algorithms and techniques to extract more value from smaller datasets.
Initial solutions: Early solutions for small data AI focused on techniques such as transfer learning and active learning.

Present:

Contradictions: Despite advancements in techniques, small datasets continue to pose challenges for AI models.
Trends: The trend is towards the development of more efficient and effective small data AI algorithms that can work with limited labeled data.
Current solutions: Current solutions include techniques such as meta-learning, one-shot learning, and generative models that can synthesize new data.

Future:

Contradictions: As more data becomes available, there may be a temptation to rely on big data approaches rather than refining small data algorithms.
Trends: The trend is likely to continue towards more effective small data algorithms that can extract value from limited datasets.
Potential solutions: Potential solutions may include new techniques for data augmentation, active learning, and transfer learning that can help AI models learn more efficiently from limited data. There may also be developments in unsupervised learning that can reduce the need for labeled data.

Subsystem (Mathematical algorithms):

Contradictions: Traditional statistical approaches may not work as effectively with limited data, while more complex deep learning models may be too resource-intensive.
Trends: The trend is towards developing more efficient and effective algorithms that can work with small datasets, such as meta-learning, few-shot learning, and generative models.
Current solutions: Current solutions include techniques such as Bayesian optimization, ensemble methods, and probabilistic graphical models.

System (Programming technologies and libraries):

Contradictions: Current programming technologies and libraries may not be optimized for small data scenarios.
Trends: The trend is towards the development of more efficient programming technologies and libraries that can handle small datasets more effectively.
Current solutions: Current solutions include libraries such as PyTorch and TensorFlow, which provide more efficient and scalable implementations of small data algorithms.

Supersystem (Global trends and technological advances):

Contradictions: Technological advances may make it easier to collect and analyze large datasets, reducing the focus on small data algorithms.
Trends: The trend is towards the continued growth and evolution of AI, with more emphasis on making AI accessible and applicable to a wider range of scenarios.
Potential solutions: Potential solutions may include the development of more efficient algorithms and techniques for small data scenarios, as well as the integration of AI into more specialized domains such as healthcare and finance. There may also be a focus on developing more transparent and explainable AI models that can be trusted by users.

Perspectives and Considerations in Small Data AI

In the next lines we have another perspective of system operator with respect to small data AI: various views relative to past (2015), present (2022) and future (2030).

Substance: In the context of small data AI, the substance is the data itself. In the past (before 2015), the amount of data available for training AI models was limited, and often of low quality. However, with the increasing prevalence of sensors and other data-gathering technologies, as well as advances in data cleaning and preprocessing techniques, the quantity and quality of small data available for AI applications has been steadily increasing. In the present (2022), small data AI is becoming more accessible and popular, particularly in areas where large amounts of data are not available or practical. In the future (2030), we can expect even more sophisticated methods for collecting and processing small data, as well as AI models that are more adept at working with limited amounts of information.
Function: The function of small data AI is to make accurate predictions or classifications based on small datasets. In the past, the accuracy of small data AI models was limited by the amount and quality of data available. In the present, small data AI is becoming more accurate and reliable thanks to advances in algorithms, feature engineering, and ensemble methods. In the future, we can expect even more powerful small data AI models that are able to make accurate predictions with even less data.
Cause and effect: Small data AI models rely on causality to make accurate predictions or classifications. In the past, limited data made it difficult to identify causal relationships, but with the increasing availability of data and advances in causality analysis, small data AI models are becoming more adept at identifying cause and effect relationships. In the present, small data AI models are able to identify causal relationships with high accuracy and precision. In the future, we can expect small data AI models to become even better at identifying and utilizing causal relationships.
Time: Small data AI models are often trained on historical data and used to make predictions about future events. In the past, the limited availability of data made it difficult to make accurate predictions about future events. In the present, small data AI is becoming more accurate at making predictions about future events, particularly in fields such as finance, healthcare, and transportation. In the future, we can expect small data AI to become even more accurate at predicting future events, thanks to advances in algorithms and data collection.
Space: Small data AI models are often trained on data that is specific to a particular location or region. In the past, limited data made it difficult to make accurate predictions about events in different locations. In the present, small data AI is becoming more accurate at making predictions in different locations, thanks to advances in spatial analysis and geospatial data. In the future, we can expect small data AI to become even better at making predictions in different locations and across different spatial scales.
Scale: Small data AI is often used in applications that require predictions or classifications at small scales, such as individual patients, companies, or households. In the past, the limited amount of data made it difficult to make accurate predictions at small scales. In the present, small data AI is becoming more accurate at making predictions at small scales, thanks to advances in feature engineering and ensemble methods. In the future, we can expect small data AI to become even more accurate at making predictions at small scales, as well as to be applied in new areas where large amounts of data are not available.
Dynamics: Small data AI models are often used to make predictions in dynamic systems, where the underlying processes change over time. In the past, the limited amount of data made it difficult to make accurate predictions in dynamic systems. In the present, small data AI is becoming increasingly useful in dynamic systems, as new techniques like recurrent neural networks allow for more accurate predictions. In the future, we can expect further advancements in small data AI to better handle dynamic systems, with new algorithms and techniques developed to analyze and interpret the complex and changing data.
The Anti-Problem: Small data AI can be used to solve problems that are difficult or impossible to address with traditional methods. For example, small data AI can be used to identify patterns and relationships that are not apparent to human observers, enabling more accurate and effective decision-making. As small data AI continues to advance, it is likely to be increasingly applied to areas such as personalized medicine, fraud detection, and cybersecurity.
The Ideal Final Result: The ideal final result for small data AI is to be able to analyze and interpret data from small datasets with limited resources, while avoiding overfitting and generating meaningful insights and predictions accurately and efficiently. This would enable the development of robust, reliable AI systems that can be deployed across a wide range of applications.
The Transition: The transition from traditional methods to small data AI involves a shift in the way that data is collected, managed, and analyzed. It requires the development of new algorithms and techniques for working with small datasets, as well as the creation of new tools and platforms to support small data AI development. The transition also involves addressing the challenges of data quality, bias, and privacy, which are particularly important in the context of small data.
The Macro-World: The macro-world in which small data AI operates is characterized by rapid technological change and the increasing importance of data-driven decision-making. As small data AI continues to advance, it is likely to have a significant impact on a wide range of industries, from healthcare and finance to transportation and manufacturing. It is also likely to play an increasingly important role in addressing societal challenges such as climate change, food security, and public health.

Scenario Management in the AI Evolution for the Next 30 Years

There are several core factors that can influence the evolution of AI over the next 10, 20, and 30 years. Some of the most significant factors include:

Advancements in Computing Power: One of the most significant factors that will influence the evolution of AI is the continuous advancements in computing power. The increasing speed and processing capabilities of computers will enable more complex and sophisticated AI systems.
Data Availability and Quality: The availability and quality of data are critical to the success of AI systems. As more data becomes available and more advanced techniques are developed for processing and analyzing data, AI will become more accurate and effective.
Research and Development: Ongoing research and development in AI will continue to push the boundaries of what is possible. New algorithms, architectures, and applications will emerge, leading to more advanced AI systems.
Investment and Funding: The level of investment and funding available for AI research and development will play a crucial role in the evolution of AI. Increased investment can lead to faster advancements and breakthroughs in AI technology.
Regulation and Ethics: As AI becomes more prevalent, there will be an increasing need for regulation and ethical considerations. Governments, companies, and organizations will need to work together to ensure that AI is developed and used in a responsible and ethical manner.
Collaboration and Knowledge Sharing: Collaboration and knowledge sharing between researchers, companies, and organizations will help to accelerate the pace of AI development. Open-source initiatives and partnerships can help to foster innovation and progress.
Adoption and Integration: The speed and extent of AI adoption and integration into various industries and sectors will also impact the evolution of AI. The more widely and effectively AI is integrated, the more rapidly it will evolve.
Societal Acceptance: Finally, societal acceptance of AI will play a crucial role in its evolution. The more people understand and trust AI, the more they will be willing to adopt and use it, which will drive further advancements and innovation.
Government policies and regulations: Governments may set policies and regulations that could either facilitate or hinder the development and adoption of AI technologies.
Competition among industry players: Competition among companies and other organizations in the development and deployment of AI technologies can also drive progress and innovation in the field.

We discuss the evolution of these factors from three perspectives: pessimistic, moderate and optimistic. Our perspective is shown below.

Advancements in Computing Power:

Pessimistic: The rate of advancement in computing power slows down due to limitations in materials and manufacturing processes, resulting in slower progress in the development of AI systems.
Moderate: The rate of advancement in computing power continues at a steady pace, resulting in incremental improvements in AI systems.
Optimistic: Advancements in computing power accelerate, resulting in breakthroughs in AI systems that can solve previously unsolvable problems.

Data Availability and Quality:

Pessimistic: The quality of available data remains poor, and efforts to improve it are ineffective. This leads to limited progress in the development of AI systems.
Moderate: The quality of available data improves, and more data becomes available, leading to gradual improvements in AI systems.
Optimistic: The quality and quantity of available data increase significantly, leading to the development of highly accurate and effective AI systems.

Research and Development:

Pessimistic: Funding for research and development in AI decreases, resulting in slower progress in the development of AI systems.
Moderate: Research and development in AI continues at a steady pace, resulting in incremental improvements in AI systems.
Optimistic: Funding for research and development in AI increases, resulting in breakthroughs in AI technology and the development of highly advanced AI systems.

Investment and Funding:

Pessimistic: Investment in AI research and development decreases, resulting in limited progress in the development of AI systems.
Moderate: Investment in AI research and development continues at a steady pace, resulting in incremental improvements in AI systems.
Optimistic: Investment in AI research and development increases significantly, resulting in breakthroughs in AI technology and the development of highly advanced AI systems.

Regulation and Ethics:

Pessimistic: Little regulation is put in place, resulting in the development and deployment of AI systems that are not ethical and have negative societal impacts.
Moderate: Appropriate regulations and ethical considerations are put in place, resulting in responsible development and deployment of AI systems.
Optimistic: Robust regulations and ethical considerations are put in place, resulting in the development and deployment of highly ethical and beneficial AI systems.

Collaboration and Knowledge Sharing:

Pessimistic scenario: Companies and researchers become increasingly protective of their work and are reluctant to share knowledge or collaborate with others. This leads to slower progress and fewer breakthroughs in AI development.
Moderate scenario: Collaboration and knowledge sharing continue to occur, but there are some limitations and challenges. Some companies may be more protective of their intellectual property, while others may struggle to find common ground for collaboration.
Optimistic scenario: Collaboration and knowledge sharing become more widespread and streamlined, leading to rapid progress and frequent breakthroughs in AI development. Open-source initiatives and partnerships become more prevalent, allowing for greater innovation and progress.

Adoption and Integration:

Pessimistic scenario: Resistance to change and fear of job loss prevent many industries and sectors from adopting AI technology. This leads to slower progress and limited integration of AI in society.
Moderate scenario: Adoption and integration of AI continue to occur, but there are some limitations and challenges. Some industries may be slower to adopt AI, while others may struggle with the cost of implementation or lack of skilled workers.
Optimistic scenario: AI adoption and integration become widespread and seamless, leading to significant advances in many industries and sectors. The benefits of AI become increasingly apparent, driving further adoption and innovation.

Societal Acceptance:

Pessimistic scenario: Fear and mistrust of AI technology prevent widespread adoption and use. Public opinion turns against AI, leading to increased skepticism and decreased investment.
Moderate scenario: Societal acceptance of AI continues to grow, but there are some concerns and criticisms. Some people may be wary of AI technology, while others may question its impact on society and jobs.
Optimistic scenario: Societal acceptance of AI becomes the norm, with people trusting and relying on AI in their daily lives. Public opinion supports continued investment and development in AI technology, driving further progress and innovation.

Government Policies and Regulations:

Pessimistic scenario: Governments impose strict regulations and restrictions on AI development and use, hindering progress and innovation in the field.
Moderate scenario: Governments enact some regulations and policies to ensure responsible development and use of AI, but there may be some limitations and challenges. Some policies may be overly restrictive or not provide enough guidance.
Optimistic scenario: Governments enact policies and regulations that support responsible and ethical AI development and use. These policies foster innovation and progress in the field while ensuring that AI is developed and used in a responsible and beneficial manner.

Competition Among Industry Players:

Pessimistic scenario: Companies engage in cutthroat competition and sabotage each other’s AI development efforts, leading to slower progress and limited innovation.
Moderate scenario: Competition among industry players continues, but there may be some limitations and challenges. Some companies may prioritize short-term gains over long-term innovation, while others may struggle to keep up with larger players in the field.
Optimistic scenario: Competition among industry players drives rapid progress and frequent breakthroughs in AI development. Companies prioritize long-term innovation and collaboration, leading to significant advances in the field.

Now we aggregate all the 10 factors on three macro-scenarios. The first one consider the pessimistic evolution of all factors, the second one is related to the moderate evolution of the factors, and the last one describes the optimistic evolution of the influence factors. The synthetic results are:

Pessimistic scenario:
In this scenario, there is limited progress and innovation in AI over the next 10, 20, and 30 years. Advancements in computing power are slow, and data availability and quality are limited. Research and development are hindered by limited investment and funding. Government policies and regulations are overly restrictive, and competition among industry players is cutthroat. Collaboration and knowledge sharing are minimal, and there is little adoption and integration of AI in society due to resistance to change and fear of job loss. Societal acceptance of AI is low, with widespread fear and mistrust of the technology.

Moderate scenario:
In this scenario, there is some progress and innovation in AI over the next 10, 20, and 30 years. Advancements in computing power and data availability and quality are moderate, and research and development continue at a steady pace with some investment and funding available. Government policies and regulations are in place to ensure responsible and ethical AI development and use, and competition among industry players drives some progress and innovation. Collaboration and knowledge sharing occur, but there are some limitations and challenges. Adoption and integration of AI in society continue to occur, but at a slower pace. Societal acceptance of AI grows, but there are still some concerns and criticisms.

Optimistic scenario:
In this scenario, there is significant progress and innovation in AI over the next 10, 20, and 30 years. Advancements in computing power and data availability and quality are rapid, and research and development are well-funded and drive frequent breakthroughs in AI technology. Government policies and regulations support responsible and ethical AI development and use, and competition among industry players drives rapid progress and innovation. Collaboration and knowledge sharing are widespread and streamlined, fostering innovation and progress. Adoption and integration of AI in society are seamless and widespread, with people trusting and relying on AI in their daily lives. Societal acceptance of AI becomes the norm, with public opinion supporting continued investment and development in AI technology.

The evolution of AI by 2050 in the pessimistic scenario:

In the pessimistic scenario, progress and innovation in AI will be slow and limited over the next 10, 20, and 30 years. With advancements in computing power being slow, AI algorithms and systems will remain relatively basic and unsophisticated. Data availability and quality will also be limited, making it difficult to develop and improve AI systems.

Research and development in AI will be hindered by limited investment and funding, resulting in fewer breakthroughs in AI technology. Government policies and regulations will be overly restrictive, making it difficult for companies to develop and deploy AI systems. Competition among industry players will be cutthroat, resulting in limited collaboration and knowledge sharing between companies and researchers.

The lack of adoption and integration of AI in society due to resistance to change and fear of job loss will continue, leading to slower progress and innovation. Societal acceptance of AI will remain low, with widespread fear and mistrust of the technology, further hindering its development and integration.

By 2030, AI will have made some progress, but its impact will still be limited due to the slow pace of development. AI systems will be used primarily in research and development, with limited integration in industry and society. By 2040, AI systems will have improved slightly, but they will still be unsophisticated compared to the advancements that could have been made in a more favorable scenario. By 2050, the progress in AI technology will still be slow, and its impact on society will be limited. Overall, the pessimistic scenario will lead to a slow and limited evolution of AI, with little improvement in AI systems and their integration in society.

The evolution of AI by 2050 in the moderate scenario:

In the moderate scenario, progress and innovation in AI will be moderate over the next 10, 20, and 30 years. Advancements in computing power and data availability and quality will continue at a steady pace, enabling more sophisticated AI systems. Research and development in AI will continue to advance, with some investment and funding available to drive breakthroughs in AI technology.

Government policies and regulations will be in place to ensure responsible and ethical AI development and use, enabling companies to develop and deploy AI systems in a regulated environment. Competition among industry players will drive some progress and innovation, and collaboration and knowledge sharing will occur, though there may still be some limitations and challenges.

The adoption and integration of AI in society will continue, but at a slower pace, with some resistance and challenges due to concerns about job loss and other societal impacts. Societal acceptance of AI will grow, but there will still be some concerns and criticisms regarding the ethical and societal implications of AI.

By 2030, AI will have made significant progress, with more sophisticated AI systems being developed and deployed across various industries and sectors. By 2040, AI systems will have become more widespread and integrated, with more advanced applications and technologies being developed. By 2050, AI will have transformed many aspects of society, from healthcare to transportation, and its impact will be significant. Overall, the moderate scenario will lead to a moderate evolution of AI, with steady progress and innovation, but some limitations and challenges that could slow down its evolution.

The evolution of AI by 2050 in the optimistic scenario:

By 2030, AI has become a pervasive technology, used in various aspects of daily life. Advancements in computing power and data availability have led to the development of more advanced AI systems that can perform complex tasks and solve difficult problems. AI is being used in fields such as healthcare, transportation, and finance to improve efficiency and accuracy.

By 2040, AI has become even more sophisticated, with the emergence of AI systems that can learn and adapt in real-time, and work alongside humans in collaborative settings. The integration of AI with other emerging technologies such as blockchain and quantum computing has also led to new and innovative applications. Governments and organizations have worked together to establish regulations that ensure responsible and ethical development and use of AI.

By 2050, AI has revolutionized many aspects of society, from healthcare to education to transportation. Self-driving cars are the norm, and AI-powered virtual assistants are ubiquitous. AI has also played a crucial role in addressing global challenges such as climate change and resource scarcity. The level of collaboration and knowledge sharing among researchers, companies, and organizations has led to rapid advancements in AI, with new breakthroughs occurring regularly. Societal acceptance of AI has become universal, with people trusting and relying on AI to improve their lives in countless ways.

Conclusions

The analysis of AI evolution is valuable for various stakeholders, including governments, businesses, and individuals. For governments, understanding the potential impacts of AI can inform policy decisions related to investment, regulation, and ethical considerations. Businesses can benefit from understanding the potential opportunities and challenges posed by AI, including its potential to disrupt traditional industries and create new markets. Individuals can benefit from understanding the potential impacts of AI on their daily lives, including potential job displacement and changes in social and economic structures.

However, it is important to acknowledge the limitations of AI evolution analysis, such as the unpredictability of technological advancements and the complexity of societal and economic factors that influence AI development and adoption. Therefore, stakeholders should approach AI evolution analysis with a degree of caution and recognize the potential for unforeseen consequences.

TRIZ system operator and scenario management can provide a valuable framework for analyzing the evolution of AI and developing strategies to navigate potential challenges and opportunities. However, other means of estimation, such as expert elicitation, simulation modeling, and scenario planning, can also be useful tools for analyzing the potential impacts of AI. Each approach has its strengths and limitations, and stakeholders should carefully consider which methods are best suited to their particular needs and circumstances.

Other means of estimating the evolution of AI in the next three decades include surveys and expert opinions, data analysis, and trend analysis. Surveys and expert opinions can provide valuable insights into how various stakeholders perceive AI’s development, but they can be subjective and biased. Data analysis can provide objective information on past trends, but it cannot account for unexpected events that may impact AI’s development. Trend analysis can help to identify potential future developments, but it is also limited by unforeseen events and the complexity of AI’s development.

As AI continues to evolve and impact society, it is crucial to approach its development and deployment in a responsible and ethical manner, ensuring that it benefits everyone and reduces potential risks and harms. With continued collaboration, innovation, and consideration of ethical and societal implications, AI can be a transformative and beneficial technology in the coming decades.

———————-

Credits: Stelian Brad

Back to "Postari | Posts"