Revision as of 07:46, 6 July 2025

Artificial Intelligence

Introduction

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. The term can also refer to any machine (or computer) that exhibits traits associated with a human mind such as learning and problem-solving. AI research has developed various methodologies and technologies, and it is a branch of computer science that seeks to create systems capable of performing tasks typically requiring human intelligence, including visual perception, speech recognition, decision-making, and language translation.

History

Early Developments

The concept of artificial intelligence dates back to ancient history, with myths and stories featuring automatons. However, the formal study of AI began in the mid-20th century. In 1950, British mathematician and logician Alan Turing proposed the Turing Test, a criterion of intelligence that measures a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.

In 1956, the term "artificial intelligence" was coined at a conference at Dartmouth College, which is often considered the birth of AI as a field. Early research focused on symbolic methods, problem-solving, and reasoning. During this period, programs like the Logic Theorist and the General Problem Solver were developed.

The Rise and Fall of AI

The initial excitement surrounding AI led to ambitious projects in the following decades, but progress was soon hampered by limitations in computing power, a lack of data, and overly optimistic predictions. This led to the first "AI winter," a period from the mid-1970s to the mid-1980s characterized by reduced funding and interest in AI research.

Resurgence and Advances

The late 1980s and 1990s saw a revival of interest in AI, fueled by advances in machine learning and increased computing capabilities. The development of algorithms, recognition systems, and the availability of large datasets allowed researchers to make significant strides in areas such as natural language processing, robotics, and computer vision.

The advent of the internet and cloud computing in the 2000s further accelerated AI research, leading to breakthroughs in deep learning methods. The success of systems like Google’s AlphaGo, which defeated the world champion Go player in 2016, reignited public and governmental interest in AI technologies.

Design and Architecture

Types of AI

AI can be classified into three main types:

Narrow AI (or Weak AI) refers to systems that are designed and trained for a specific task. Examples include virtual assistants like Siri and Alexa, recommendation systems, and autonomous vehicles.
General AI (or Strong AI) refers to systems that possess the ability to perform any intellectual task that a human can do. This level of AI remains theoretical and has not yet been realized.
Superintelligent AI refers to a hypothetical AI that surpasses human intelligence across virtually all areas of interest. This concept raises both scientific curiosity and philosophical concerns regarding its implications for humanity.

Architecture of AI Systems

AI systems can be built using various architectures. Common architectures include:

Rule-Based Systems use predefined logic and rules to make decisions. These systems are prevalent in expert systems used for diagnostics in medical or engineering fields.
Neural Networks are computational models inspired by the human brain, consisting of interconnected layers of nodes (neurons). They excel in pattern recognition tasks and are foundational to deep learning.
Reinforcement Learning involves training agents to make a sequence of decisions by rewarding them for good actions and punishing them for bad ones. This approach is pivotal in robotics and game-playing AI.
Natural Language Processing (NLP) entails the interaction between computers and humans using natural language. It enables applications such as chatbots, language translation, and sentiment analysis.

Usage and Implementation

Applications of AI

AI has found applications across various industries, including:

Healthcare - AI is used for predictive analytics in patient care, diagnostics, treatment recommendations, and personalized medicine.
Finance - In financial services, AI algorithms analyze market trends, detect fraud, and assist in risk management and compliance.
Transportation - The automotive industry utilizes AI for the development of autonomous vehicles and traffic management systems.
Manufacturing - AI enhances operational efficiency through predictive maintenance and automation of supply chain processes.
Entertainment - AI powers content recommendation systems, video game AI, and automated content creation in film and music.

Implementation Challenges

Implementing AI systems poses several challenges:

Data Quality and Quantity - Effective AI models require large volumes of high-quality data, which can be difficult and expensive to obtain.
Bias in AI - AI systems can inherit biases present in their training data, leading to unfair or unethical outcomes. Addressing bias in algorithms remains a significant concern.
Explainability and Transparency - Many AI models, especially deep learning systems, function as "black boxes," making it challenging to understand their decision-making process. This lack of transparency is problematic in critical sectors like healthcare and criminal justice.
Ethical and Legal Concerns - The deployment of AI raises ethical questions around privacy, security, and accountability, leading to discussions about regulation and governance.

Real-world Examples

Successful AI Implementations

Numerous organizations have successfully implemented AI technologies:

IBM Watson - IBM's AI platform gained fame for winning the quiz show Jeopardy! in 2011. It has since been applied in various fields, including healthcare, finance, and customer service.
Google DeepMind's AlphaGo - AlphaGo's victory over the world Go champion in 2016 demonstrated the capabilities of deep reinforcement learning and strategic planning.
Self-driving Cars - Companies like Tesla, Waymo, and Uber are pioneering the development of autonomous vehicles that leverage AI for navigation, obstacle detection, and route optimization.

Comparisons with Human Intelligence

AI excels in specific tasks (narrow AI) but struggles with general cognitive abilities that humans possess. For instance, while AI can process vast datasets at unprecedented speeds and achieve superhuman performance in certain games, it lacks common sense reasoning and the contextual understanding that humans intuitively possess. This limitation underscores the significance of human oversight in critical areas where AI is deployed, as relying solely on AI can lead to unpredictable outcomes.

Criticism and Controversies

Ethical Dilemmas

AI's rise has generated significant ethical concerns, including the potential for job displacement due to automation, privacy invasions through surveillance technologies, and the moral implications of autonomous weaponry. Critics argue that the design and deployment of AI systems must consider their effects on human welfare and societal structures.

Algorithmic Bias

Instances have arisen where AI systems have demonstrated biases against certain groups, particularly in criminal justice predictive tools and hiring algorithms. These biases stem from historical data and systemic inequalities. Researchers in AI ethics advocate for equitable AI systems that promote justice and fairness across all demographics.

Surveillance and Privacy

The application of AI in surveillance technology has sparked extensive debate. Governments and organizations are increasingly adopting AI for monitoring citizens, leading to concerns about civil liberties and privacy rights. The balance between security and privacy remains a contentious issue.

Regulation and Governance

As AI technology continues to evolve, the need for effective regulation becomes increasingly apparent. Policymakers worldwide are grappling with how to establish boundaries for AI deployment that safeguard public interest while still fostering innovation. Discussions revolve around creating ethical guidelines, establishing accountability frameworks, and considering the societal implications of AI technologies.

Influence and Impact

Economic Impact

AI technologies are projected to contribute trillions of dollars to the global economy over the coming decades. They have the potential to boost productivity, reduce operational costs, and create new opportunities for economic growth. However, this economic impact also raises questions about the future of work and the changing landscape of employment.

Social Impact

The integration of AI into daily life has the potential to transform social interactions, healthcare access, education, and personal relationships. AI-driven platforms have enabled greater access to information and services, but they also pose challenges regarding social isolation and reliance on technology.

Future of AI

The future of AI holds promise and uncertainty. Continued advancements may yield significant breakthroughs in various fields, such as healthcare and education, leading to enhanced human capabilities. However, the potential risks, including the prospect of superintelligent AI, necessitate thoughtful discourse and precautionary measures to ensure that AI development aligns with ethical principles and human values.

Conclusion

As a multifaceted and rapidly evolving discipline, artificial intelligence stands at the forefront of technological advancement. Its ability to transform industries and impact daily life is remarkable, yet the challenges it poses regarding ethics, bias, surveillance, and regulation must be addressed. The ongoing dialogue within academic, industry, and governmental circles regarding the responsible development and deployment of AI will shape its trajectory in the years to come.

References

This article aims to provide a comprehensive overview of artificial intelligence, its historical context, methodological approaches, applications, and the societal challenges it faces. It serves as an entry point into the vast world of AI for both general readers and professionals in the computer science community.

@@ Line 1: / Line 1: @@
 = Artificial Intelligence =
-Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, problem-solving, perception, language understanding, and even social intelligence. AI has become an integral part of modern technology, influencing various sectors such as finance, healthcare, transportation, and entertainment.
 == Introduction ==
-Artificial Intelligence can be classified into two broad categories: '''Narrow AI''' and '''General AI'''. Narrow AI is designed and trained for a specific task, such as facial recognition or internet searches, while General AI would entail a more general purpose, having the ability to understand and perform any intellectual task that a human can do. As of now, most of the progress in AI has been in the domain of Narrow AI, with General AI still being a theoretical concept.
+'''Artificial Intelligence''' (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. The term can also refer to any machine (or computer) that exhibits traits associated with a human mind such as learning and problem-solving. AI research has developed various methodologies and technologies, and it is a branch of computer science that seeks to create systems capable of performing tasks typically requiring human intelligence, including visual perception, speech recognition, decision-making, and language translation.
-AI technologies are often powered by algorithms and require significant computational resources. Achievements in AI have surged dramatically in the 21st century, driven by advancements in machine learning, neural networks, and vast amounts of data.
+== History ==
-== History or Background ==
+=== Early Developments ===
-The concept of artificial intelligence dates back to ancient history with myths and stories of intelligent automatons. However, the formal establishment of AI as a field began in the mid-20th century. Notable milestones in the development of AI include:
+The concept of artificial intelligence dates back to ancient history, with myths and stories featuring automatons. However, the formal study of AI began in the mid-20th century. In 1950, British mathematician and logician Alan Turing proposed the '''Turing Test''', a criterion of intelligence that measures a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.
-* 1950: Alan Turing proposed the Turing Test, a criterion for determining whether a machine exhibits human-like intelligence.
-* 1956: The term ”artificial intelligence” was coined at the Dartmouth Conference, which is often considered the birth of AI as a field of study.
-* 1960s - 70s: The development of early AI programs like ''ELIZA'', a natural language processing computer program that mimicked conversation.
-* 1980s - 90s: The advent of expert systems, which are computer programs that mimic human expertise in specific domains.
-* 2000s: The rise of machine learning techniques that enabled computers to learn from data and improve over time.
-* 2010s: Deep learning algorithms began to show remarkable results in tasks such as image recognition, speech recognition, and natural language processing.
-The field experienced several cycles of hype and disillusionment, often referred to as "AI winters," but has seen significant growth and acceptance in recent years.
+In 1956, the term "artificial intelligence" was coined at a conference at Dartmouth College, which is often considered the birth of AI as a field. Early research focused on symbolic methods, problem-solving, and reasoning. During this period, programs like the '''Logic Theorist''' and the '''General Problem Solver''' were developed.
-== Design or Architecture ==
+=== The Rise and Fall of AI ===
-AI systems can be built using a range of architectures, each suited for different tasks and applications. The prominent architectures include:
+The initial excitement surrounding AI led to ambitious projects in the following decades, but progress was soon hampered by limitations in computing power, a lack of data, and overly optimistic predictions. This led to the first "AI winter," a period from the mid-1970s to the mid-1980s characterized by reduced funding and interest in AI research.
-=== Neural Networks ===
+=== Resurgence and Advances ===
-Inspired by the human brain's structure, neural networks consist of interconnected processing elements called neurons. They can learn to perform tasks by adjusting the connections based on the input data. Neural networks can be simple or deep (''deep learning''), with many layers enabling them to model complex patterns.
+The late 1980s and 1990s saw a revival of interest in AI, fueled by advances in machine learning and increased computing capabilities. The development of algorithms, recognition systems, and the availability of large datasets allowed researchers to make significant strides in areas such as natural language processing, robotics, and computer vision.
-=== Machine Learning ===
+The advent of the internet and cloud computing in the 2000s further accelerated AI research, leading to breakthroughs in deep learning methods. The success of systems like Google’s [[AlphaGo]], which defeated the world champion Go player in 2016, reignited public and governmental interest in AI technologies.
-Machine learning algorithms enable computers to learn from data without being explicitly programmed. There are three main types of machine learning:
-* Supervised Learning: The model learns from labeled data, allowing it to make predictions based on new, unseen data.
-* Unsupervised Learning: The model finds patterns and relationships in unlabeled data, often used for clustering and association tasks.
-* Reinforcement Learning: The model learns by receiving feedback from its actions through rewards or penalties, often used in robotics and game AI.
-=== Natural Language Processing ===
+== Design and Architecture ==
-Natural Language Processing (NLP) involves the interaction between computers and humans through natural language. NLP applications enable machines to understand, interpret, and respond to human language, making it critical for chatbots, translation programs, and voice-activated assistants.
-=== Computer Vision ===
+=== Types of AI ===
-Computer vision enables machines to interpret and understand visual information from the world. By employing deep learning methods, computer vision systems can recognize objects, faces, and scenes, contributing significantly to advancements in autonomous vehicles and security systems.
+AI can be classified into three main types:
+* '''Narrow AI''' (or Weak AI) refers to systems that are designed and trained for a specific task. Examples include virtual assistants like Siri and Alexa, recommendation systems, and autonomous vehicles.
+* '''General AI''' (or Strong AI) refers to systems that possess the ability to perform any intellectual task that a human can do. This level of AI remains theoretical and has not yet been realized.
+* '''Superintelligent AI''' refers to a hypothetical AI that surpasses human intelligence across virtually all areas of interest. This concept raises both scientific curiosity and philosophical concerns regarding its implications for humanity.
-=== Expert Systems ===
+=== Architecture of AI Systems ===
-Expert systems are AI applications that use a knowledge base and inference rules to solve complex problems within a specific domain. These systems are used in fields such as medicine for diagnosis and in finance for risk assessment.
+AI systems can be built using various architectures. Common architectures include:
+* '''Rule-Based Systems''' use predefined logic and rules to make decisions. These systems are prevalent in expert systems used for diagnostics in medical or engineering fields.
+* '''Neural Networks''' are computational models inspired by the human brain, consisting of interconnected layers of nodes (neurons). They excel in pattern recognition tasks and are foundational to deep learning.
+* '''Reinforcement Learning''' involves training agents to make a sequence of decisions by rewarding them for good actions and punishing them for bad ones. This approach is pivotal in robotics and game-playing AI.
+* '''Natural Language Processing (NLP)''' entails the interaction between computers and humans using natural language. It enables applications such as chatbots, language translation, and sentiment analysis.
 == Usage and Implementation ==
-Artificial Intelligence technology is implemented across various industries, transforming traditional operations and creating new efficiencies.
-=== Healthcare ===
-AI is improving diagnostics, tailoring treatment plans, and conducting medical imaging analysis. For instance, algorithms that analyze X-ray or MRI scans help in early detection of diseases like cancer.
-=== Finance ===
-In finance, AI enhances fraud detection systems, automates trading, and offers personalized banking experiences through chatbots and virtual assistants.
-=== Transportation ===
-Self-driving vehicles utilize sophisticated AI systems to perceive their surroundings and make decisions, with ongoing research aimed at increasing safety and efficiency in transportation networks.
-=== Manufacturing ===
-AI technologies are utilized in manufacturing for predictive maintenance, quality control, and optimizing supply chains, significantly lowering operational costs.
-=== Entertainment ===
-Streaming services use AI algorithms for content recommendation, improving user engagement and retention. Additionally, game development benefits from AI in creating more realistic non-player characters (NPCs).
-== Real-world Examples or Comparisons ==
+=== Applications of AI ===
-Several companies and organizations utilize AI technologies, yielding significant results. These examples showcase the diverse applications of AI.
+AI has found applications across various industries, including:
+* '''Healthcare''' - AI is used for predictive analytics in patient care, diagnostics, treatment recommendations, and personalized medicine.
+* '''Finance''' - In financial services, AI algorithms analyze market trends, detect fraud, and assist in risk management and compliance.
+* '''Transportation''' - The automotive industry utilizes AI for the development of autonomous vehicles and traffic management systems.
+* '''Manufacturing''' - AI enhances operational efficiency through predictive maintenance and automation of supply chain processes.
+* '''Entertainment''' - AI powers content recommendation systems, video game AI, and automated content creation in film and music.
-=== Google AI ===
+=== Implementation Challenges ===
-Google has integrated AI into various products such as its search engine, smart assistants, and photo recognition services. Google AI Research has made advances in language understanding (like BERT) and image processing (like TensorFlow).
+Implementing AI systems poses several challenges:
+* '''Data Quality and Quantity''' - Effective AI models require large volumes of high-quality data, which can be difficult and expensive to obtain.
+* '''Bias in AI''' - AI systems can inherit biases present in their training data, leading to unfair or unethical outcomes. Addressing bias in algorithms remains a significant concern.
+* '''Explainability and Transparency''' - Many AI models, especially deep learning systems, function as "black boxes," making it challenging to understand their decision-making process. This lack of transparency is problematic in critical sectors like healthcare and criminal justice.
+* '''Ethical and Legal Concerns''' - The deployment of AI raises ethical questions around privacy, security, and accountability, leading to discussions about regulation and governance.
-=== IBM Watson ===
+== Real-world Examples ==
-IBM Watson is known for natural language processing and machine learning capabilities. It gained fame after winning the quiz show Jeopardy! against human champions. Watson has since been applied in healthcare for oncological research and patient treatment plans.
-=== OpenAI ===
+=== Successful AI Implementations ===
-OpenAI, a research organization focused on developing and promoting friendly AI, created models like GPT-3, which can generate human-like text. This technology has been integrated into services like chatbots and writing assistants.
+Numerous organizations have successfully implemented AI technologies:
+* '''IBM Watson''' - IBM's AI platform gained fame for winning the quiz show [[Jeopardy!]] in 2011. It has since been applied in various fields, including healthcare, finance, and customer service.
+* '''Google DeepMind's AlphaGo''' - AlphaGo's victory over the world Go champion in 2016 demonstrated the capabilities of deep reinforcement learning and strategic planning.
+* '''Self-driving Cars''' - Companies like Tesla, Waymo, and Uber are pioneering the development of autonomous vehicles that leverage AI for navigation, obstacle detection, and route optimization.
-=== Tesla Autopilot ===
+=== Comparisons with Human Intelligence ===
-Tesla's Autopilot feature utilizes AI to provide semi-autonomous driving capabilities. It processes data from cameras and sensors for safe navigation, showcasing the potential of AI in transportation.
+AI excels in specific tasks (narrow AI) but struggles with general cognitive abilities that humans possess. For instance, while AI can process vast datasets at unprecedented speeds and achieve superhuman performance in certain games, it lacks common sense reasoning and the contextual understanding that humans intuitively possess. This limitation underscores the significance of human oversight in critical areas where AI is deployed, as relying solely on AI can lead to unpredictable outcomes.
-== Criticism or Controversies ==
+== Criticism and Controversies ==
-Despite its potential, the rise of artificial intelligence has ignited various criticisms and controversies, especially concerning ethical implications and societal impacts.
-=== Job Displacement ===
+=== Ethical Dilemmas ===
-One of the most discussed criticisms is the impact of AI on employment. As automation becomes more prevalent, concerns arise about significant job displacement in industries such as manufacturing, customer service, and transportation.
+AI's rise has generated significant ethical concerns, including the potential for job displacement due to automation, privacy invasions through surveillance technologies, and the moral implications of autonomous weaponry. Critics argue that the design and deployment of AI systems must consider their effects on human welfare and societal structures.
-=== Privacy Concerns ===
+=== Algorithmic Bias ===
-AI technologies, especially those reliant on big data analytics, raise privacy concerns. Surveillance systems and data collection practices can infringe on individual privacy rights, leading to potential misuse of personal information.
+Instances have arisen where AI systems have demonstrated biases against certain groups, particularly in criminal justice predictive tools and hiring algorithms. These biases stem from historical data and systemic inequalities. Researchers in AI ethics advocate for equitable AI systems that promote justice and fairness across all demographics.
-=== Bias and Fairness ===
+=== Surveillance and Privacy ===
-AI algorithms can inadvertently perpetuate biases present in training data, leading to discrimination in decisions made about hiring, lending, and law enforcement. Ensuring fairness in AI systems is a critical area of ongoing research and ethical consideration.
+The application of AI in surveillance technology has sparked extensive debate. Governments and organizations are increasingly adopting AI for monitoring citizens, leading to concerns about civil liberties and privacy rights. The balance between security and privacy remains a contentious issue.
-=== Autonomous Weapons ===
+=== Regulation and Governance ===
-The development of autonomous weapons that utilize AI raises moral and ethical questions surrounding warfare. The potential for such technologies to make life-and-death decisions without human intervention is a significant concern for global security.
+As AI technology continues to evolve, the need for effective regulation becomes increasingly apparent. Policymakers worldwide are grappling with how to establish boundaries for AI deployment that safeguard public interest while still fostering innovation. Discussions revolve around creating ethical guidelines, establishing accountability frameworks, and considering the societal implications of AI technologies.
-== Influence or Impact ==
+== Influence and Impact ==
-The impact of artificial intelligence on society is vast and multifaceted, affecting several aspects of daily life, business, and innovation.
-=== Economic Growth ===
+=== Economic Impact ===
-AI has the potential to significantly contribute to economic growth by improving productivity, enhancing customer personalization, and automating routine tasks. According to various studies, the integration of AI across industries could add trillions of dollars to the global economy over the next few decades.
+AI technologies are projected to contribute trillions of dollars to the global economy over the coming decades. They have the potential to boost productivity, reduce operational costs, and create new opportunities for economic growth. However, this economic impact also raises questions about the future of work and the changing landscape of employment.
-=== Healthcare Advancement ===
+=== Social Impact ===
-AI technologies in healthcare promise advancements in disease management, personalized medicine, and streamlined operations, thus enhancing patient outcomes. The pandemic showcased how AI can analyze data rapidly for effective vaccine development and public health responses.
+The integration of AI into daily life has the potential to transform social interactions, healthcare access, education, and personal relationships. AI-driven platforms have enabled greater access to information and services, but they also pose challenges regarding social isolation and reliance on technology.
-=== Educational Transformation ===
+=== Future of AI ===
-AI applications in education support personalized learning experiences, helping students learn at their own pace. AI-driven tools assist educators in identifying struggling students and optimizing curriculum delivery.
+The future of AI holds promise and uncertainty. Continued advancements may yield significant breakthroughs in various fields, such as healthcare and education, leading to enhanced human capabilities. However, the potential risks, including the prospect of superintelligent AI, necessitate thoughtful discourse and precautionary measures to ensure that AI development aligns with ethical principles and human values.
-=== Research and Development ===
+== Conclusion ==
-AI accelerates research across various fields by identifying patterns in data that humans might overlook. It plays a significant role in drug discovery, climate modeling, and material science.
+As a multifaceted and rapidly evolving discipline, artificial intelligence stands at the forefront of technological advancement. Its ability to transform industries and impact daily life is remarkable, yet the challenges it poses regarding ethics, bias, surveillance, and regulation must be addressed. The ongoing dialogue within academic, industry, and governmental circles regarding the responsible development and deployment of AI will shape its trajectory in the years to come.
-=== Ethical and Regulatory Frameworks ===
-The rapid development of AI technology has necessitated discussions on regulatory frameworks and ethical standards. Policymakers, technologists, and ethicists are increasingly collaborating to ensure responsible AI deployment that aligns with societal values.
 == See also ==
 * [[Machine Learning]]
 * [[Deep Learning]]
+* [[Neural Networks]]
+* [[Turing Test]]
 * [[Natural Language Processing]]
-* [[Robotics]]
 * [[Artificial General Intelligence]]
-* [[Ethics of Artificial Intelligence]]
-* [[Turing Test]]
 == References ==
-* [https://www.aaai.org AAAI - Association for the Advancement of Artificial Intelligence]
+* [https://www.aaai.org/ Association for the Advancement of Artificial Intelligence]
-* [https://www.ai.gov AI.gov - U.S. Government's official AI resource]
+* [https://www.turing.org.uk/ Alan Turing Institute]
-* [https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780198816260.001.0001 The Oxford Handbook of Ethics of Artificial Intelligence]
+* [https://www.ibm.com/watson IBM Watson]
-* [https://www.mitpressjournals.org/doi/abs/10.1162/daed_a_00598 Daedalus - AI and Its Impact on Society]
+* [https://deepmind.com/ DeepMind Technologies]
-* [https://www.nature.com/articles/s41586-020-2022-5 Nature - The future of artificial intelligence and its societal implications]
+* [https://www.researchgate.net/ Research Gate]
-* [https://www.weforum.org/reports/the-global-risks-report-2022 World Economic Forum - The Global Risks Report 2022]
+This article aims to provide a comprehensive overview of artificial intelligence, its historical context, methodological approaches, applications, and the societal challenges it faces. It serves as an entry point into the vast world of AI for both general readers and professionals in the computer science community.
 [[Category:Artificial intelligence]]
 [[Category:Computer science]]
-[[Category:Cognitive science]]
+[[Category:Technology]]