Artificial Intelligence: Difference between revisions

Revision as of 09:41, 6 July 2025

Artificial Intelligence is a branch of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, problem-solving, understanding natural language, recognizing patterns, and making decisions. The field of artificial intelligence (AI) encompasses various subfields, including machine learning, natural language processing, robotics, and computer vision, each of which contributes to creating intelligent behavior in machines.

History

The history of artificial intelligence dates back to ancient times, but it formally began in the mid-twentieth century. The term "artificial intelligence" was first coined in 1956 at the Dartmouth Conference, which was organized by John McCarthy and other prominent figures such as Marvin Minsky, Nathaniel Rochester, and Claude Shannon. They sought to explore the possibility of creating machines that could simulate human intelligence. Early work in AI primarily involved symbolic approaches, where researchers focused on programming computer systems to manipulate symbols and solve problems.

The Early Years

During the 1950s and 1960s, researchers developed algorithms and models that laid the groundwork for future AI advancements. Notable programs from this period include the Logic Theorist (1955) and the General Problem Solver (1957), both developed by Allen Newell and Herbert A. Simon. These early programs demonstrated that computers could solve complex mathematical problems and perform logical reasoning. However, the initial optimism waned during the 1970s due to the limitations of existing technology and inflated expectations, leading to what is known as the "AI winter."

Resurgence in the 1980s

The 1980s marked a resurgence in AI research, spurred by the development of expert systems, which were designed to mimic human decision-making in specific domains. These systems, such as MYCIN for medical diagnosis and DENDRAL for chemical analysis, showed promise and gained commercial interest, resulting in increased funding and research activity. The introduction of backpropagation algorithms for neural networks in the late 1980s also revived interest in machine learning paradigms.

The Modern Era

The 21st century has seen unprecedented advancements in artificial intelligence, driven by the availability of vast amounts of data, the expansion of computational power, and the emergence of sophisticated algorithms. Machine learning, particularly deep learning, has become a dominant approach, allowing computers to learn from large datasets without explicit programming. This period has witnessed significant breakthroughs in fields such as computer vision, natural language processing, and robotics, leading to applications in various industries, including healthcare, finance, and transportation.

Architecture

The architecture of artificial intelligence systems is a fundamental aspect that impacts their performance and efficiency. The design of AI systems can vary widely depending on the goals, data, and specific application. However, several common architectural approaches and frameworks have emerged, including rule-based systems, neural networks, and hybrid systems.

Rule-Based Systems

Rule-based systems, also known as expert systems, operate on the principle of "if-then" rules. These systems leverage domain knowledge encoded in rules to make inferences and solve problems. They are particularly effective in well-defined domains with clear rules, such as medical diagnosis or financial risk assessment. The key components of a rule-based system include a knowledge base, which contains the rules and facts, and an inference engine, which applies the rules to derive conclusions or suggestions.

Neural Networks

Neural networks have become the backbone of modern AI, particularly in machine learning tasks. Modeled after the structure and function of the human brain, neural networks consist of interconnected nodes (neurons) organized in layers, including input, hidden, and output layers. Training a neural network involves adjusting the weights of the connections based on the input data and the desired output, often utilizing backpropagation algorithms. Deep learning, a subset of machine learning, employs deep neural networks with many hidden layers to capture complex patterns in high-dimensional data.

Hybrid Systems

Hybrid systems combine multiple AI techniques to leverage their respective strengths. For instance, a system may integrate rule-based reasoning with machine learning to enhance performance and adaptability. Hybrid architectures can be particularly advantageous in applications that require both structured knowledge and the ability to learn from unstructured data. This approach has gained traction in fields such as autonomous systems, where combining various methods can improve decision-making under uncertain conditions.

Implementation and Applications

Artificial intelligence has been successfully implemented across a variety of domains, leading to transformative impacts on industries and society. The applications of AI can be classified into several key areas, including healthcare, finance, transportation, and entertainment.

Healthcare

In the healthcare sector, AI is being utilized for several applications, including medical imaging, diagnostics, and personalized treatment plans. Machine learning algorithms analyze medical images, such as X-rays and MRIs, to detect anomalies with high accuracy, often surpassing human radiologists. Additionally, AI-powered predictive analytics can identify patients at risk for certain conditions, enabling timely interventions. Natural language processing has also been applied to analyze clinical notes and research literature, facilitating knowledge discovery and improving decision-making.

Finance

The finance industry has embraced AI technologies to enhance operational efficiency and reduce risks. Algorithms equipped with machine learning capabilities are used for fraud detection, analyzing transaction patterns to identify unusual behavior. Algorithmic trading leverages AI to devise strategies that react to market changes in real-time, optimizing investment decisions. Furthermore, AI-driven chatbots provide customer support, handling queries and transactions with high levels of efficiency.

Transportation

AI plays a pivotal role in the development of autonomous vehicles, which utilize a combination of sensors, machine learning, and advanced algorithms to navigate and operate without human intervention. Self-driving cars rely on AI systems for image recognition, path planning, and decision-making processes. AI is also employed in traffic management and optimization, analyzing data from various sources to improve traffic flow and reduce congestion.

Entertainment

In the entertainment industry, AI has transformed content creation and distribution. Streaming platforms leverage AI algorithms for personalized recommendations, analyzing user preferences and behavior to suggest relevant content. Additionally, AI is utilized in video game development to create intelligent non-player characters (NPCs) that enhance user experience through adaptive behavior. Furthermore, AI-generated music and art are emerging as new forms of creative expression, raising questions about authorship and originality.

Criticism and Limitations

Despite its remarkable advances, artificial intelligence faces several criticisms and limitations that raise ethical, societal, and technical concerns. These challenges must be addressed to ensure the responsible development and deployment of AI technologies.

Ethical Concerns

The ethical implications of AI are a significant area of concern. Issues surrounding bias in AI algorithms can lead to discrimination and unfair treatment, particularly in sensitive applications such as hiring or law enforcement. Additionally, the use of AI in surveillance raises privacy concerns, with potential misuse of personal data and loss of individual freedoms. The lack of transparency in AI decision-making processes further complicates accountability and trust.

Job Displacement

The automating capabilities of AI have led to fears of job displacement across various sectors. While AI can enhance productivity and create new job opportunities, the rapid advancement of technology may outpace workforce adaptability. Low-skilled jobs in particular are at risk, as machines can perform repetitive tasks, prompting discussions about retraining and reskilling initiatives to prepare workers for the changing job landscape.

Technical Limitations

AI also confronts technical challenges that impact its effectiveness. AI systems often require large amounts of high-quality data for training, which can be difficult to obtain in certain domains. Overfitting, where models perform well on training data but poorly on unseen data, represents another technical limitation. Furthermore, AI systems can struggle to generalize knowledge across different contexts, restricting their applicability and necessitating continuous learning and adaptation.

Future Trends

The future of artificial intelligence holds tremendous potential for continued advancements and transformative applications. Upcoming trends include the integration of AI with other emerging technologies, a focus on ethical AI, and the exploration of general artificial intelligence (AGI).

Integration with Emerging Technologies

AI is anticipated to increasingly integrate with other emerging technologies, such as the Internet of Things (IoT), blockchain, and quantum computing. This integration can lead to enhanced automation, smarter devices, and improved data security. For instance, AI can process vast amounts of data collected from IoT devices to deliver actionable insights and optimize operations across various sectors, from manufacturing to smart cities.

Ethical AI Development

As public awareness of ethical AI increases, organizations will likely prioritize responsible AI development. This shift may result in the establishment of regulatory frameworks governing AI applications to ensure fairness, accountability, and transparency. Collaborations between governments, private sector entities, and civil societies will play a key role in fostering ethical guidelines and frameworks to navigate the complex landscape of AI.

Pursuit of General Artificial Intelligence

The pursuit of general artificial intelligence, which aims to replicate human cognitive abilities across diverse tasks, remains a prominent goal within the AI community. While current AI systems excel in specific tasks, achieving AGI requires advancements in understanding human cognition, learning capabilities, and emotional intelligence. Researchers continue to explore innovative approaches, including neuromorphic computing and evolutionary algorithms, to push the boundaries of machine intelligence.

References

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-'''Artificial Intelligence''' is a branch of computer science that aims to create systems capable of performing tasks that would typically require human intelligence. These tasks include visual perception, speech recognition, decision-making, language translation, and other cognitive functions. The field has origins in the mid-20th century but has exponentially grown with advances in algorithms, data availability, computing power, and interdisciplinary research. Its applications span various domains, including healthcare, finance, education, and more, leading to the ongoing evolution of both technology and society.
+'''Artificial Intelligence''' is a branch of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, problem-solving, understanding natural language, recognizing patterns, and making decisions. The field of artificial intelligence (AI) encompasses various subfields, including machine learning, natural language processing, robotics, and computer vision, each of which contributes to creating intelligent behavior in machines.
 == History ==
-=== Early Developments ===
+The history of artificial intelligence dates back to ancient times, but it formally began in the mid-twentieth century. The term "artificial intelligence" was first coined in 1956 at the Dartmouth Conference, which was organized by John McCarthy and other prominent figures such as Marvin Minsky, Nathaniel Rochester, and Claude Shannon. They sought to explore the possibility of creating machines that could simulate human intelligence. Early work in AI primarily involved symbolic approaches, where researchers focused on programming computer systems to manipulate symbols and solve problems.
-The concept of artificial intelligence dates back to ancient history, where myths and stories of intelligent automatons are found in diverse cultures. However, the formal exploration began in the 20th century. In 1950, British mathematician and logician Alan Turing introduced the Turing Test, a criterion for determining whether a machine exhibits intelligent behavior indistinguishable from that of a human. This laid the foundation for considering the philosophical and practical implications of machine intelligence.
-By 1956, at the Dartmouth Conference, the term "artificial intelligence" was officially coined. Pioneers such as John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon predicted that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." Early AI research focused on problem-solving and symbolic methods, resulting in programs that could solve algebra problems and play games like chess.
+=== The Early Years ===
+During the 1950s and 1960s, researchers developed algorithms and models that laid the groundwork for future AI advancements. Notable programs from this period include the Logic Theorist (1955) and the General Problem Solver (1957), both developed by Allen Newell and Herbert A. Simon. These early programs demonstrated that computers could solve complex mathematical problems and perform logical reasoning. However, the initial optimism waned during the 1970s due to the limitations of existing technology and inflated expectations, leading to what is known as the "AI winter."
-=== The AI Winters ===
+=== Resurgence in the 1980s ===
-Despite initial optimism, the field experienced periods known as "AI winters" during the 1970s and late 1980s when funding and interest waned. These downturns were largely attributed to unmet expectations, limitations in computing power, and the complexity of human cognitive processes. Researchers struggled to create systems that could handle the variability and ambiguity intrinsic to human intelligence.
+The 1980s marked a resurgence in AI research, spurred by the development of expert systems, which were designed to mimic human decision-making in specific domains. These systems, such as MYCIN for medical diagnosis and DENDRAL for chemical analysis, showed promise and gained commercial interest, resulting in increased funding and research activity. The introduction of backpropagation algorithms for neural networks in the late 1980s also revived interest in machine learning paradigms.
-=== Resurgence and Modern AI ===
+=== The Modern Era ===
-The resurgence of artificial intelligence began in the late 1990s, attributed to several factors, including advancements in machine learning algorithms, the availability of vast amounts of digital data, and the significant increase in computational power. The introduction of deep learning, a subset of machine learning that uses neural networks with many layers, has revolutionized the field. Breakthroughs in technologies such as computer vision, natural language processing, and reinforcement learning have enabled machines to achieve and even exceed human performance in certain tasks.
+The 21st century has seen unprecedented advancements in artificial intelligence, driven by the availability of vast amounts of data, the expansion of computational power, and the emergence of sophisticated algorithms. Machine learning, particularly deep learning, has become a dominant approach, allowing computers to learn from large datasets without explicit programming. This period has witnessed significant breakthroughs in fields such as computer vision, natural language processing, and robotics, leading to applications in various industries, including healthcare, finance, and transportation.
-== Architecture and Design ==
+== Architecture ==
-=== Types of Artificial Intelligence ===
+The architecture of artificial intelligence systems is a fundamental aspect that impacts their performance and efficiency. The design of AI systems can vary widely depending on the goals, data, and specific application. However, several common architectural approaches and frameworks have emerged, including rule-based systems, neural networks, and hybrid systems.
-Artificial intelligence can be broadly categorized into two types: narrow AI and general AI. Narrow AI, also known as weak AI, is designed to perform a specific task within a limited context. Examples include recommendation systems, virtual personal assistants like Amazon's Alexa, and image recognition software. In contrast, general AI, or strong AI, refers to a theoretical form of artificial intelligence that possesses the capability to understand, learn, and apply knowledge across a wide range of tasks at levels comparable to human intelligence.
-=== Machine Learning and Deep Learning ===
+=== Rule-Based Systems ===
-Machine learning, a subset of AI, involves training algorithms to improve automatically through experience. Traditional machine learning techniques include supervised learning, where the model is trained on labeled data, and unsupervised learning, where the model identifies patterns in unlabelled data.
+Rule-based systems, also known as expert systems, operate on the principle of "if-then" rules. These systems leverage domain knowledge encoded in rules to make inferences and solve problems. They are particularly effective in well-defined domains with clear rules, such as medical diagnosis or financial risk assessment. The key components of a rule-based system include a knowledge base, which contains the rules and facts, and an inference engine, which applies the rules to derive conclusions or suggestions.
-Deep learning extends this concept, utilizing neural networks to process data in complex ways similar to human brain functions. These networks consist of layers of interconnected nodes, or neurons, that learn to extract features from raw data. The backpropagation algorithm allows these networks to adjust weights and biases to minimize the error in predictions.
+=== Neural Networks ===
+Neural networks have become the backbone of modern AI, particularly in machine learning tasks. Modeled after the structure and function of the human brain, neural networks consist of interconnected nodes (neurons) organized in layers, including input, hidden, and output layers. Training a neural network involves adjusting the weights of the connections based on the input data and the desired output, often utilizing backpropagation algorithms. Deep learning, a subset of machine learning, employs deep neural networks with many hidden layers to capture complex patterns in high-dimensional data.
-=== Natural Language Processing ===
+=== Hybrid Systems ===
-Natural Language Processing (NLP) is another critical area of AI focused on the interaction between computers and humans through natural language. NLP enables machines to understand, interpret, and generate human language in a way that is both valuable and meaningful. Techniques involved in NLP include syntax analysis, semantic analysis, and sentiment analysis. Recent developments in NLP, particularly with transformer models such as BERT and GPT, have pushed the boundaries of what machines can comprehend and generate, facilitating conversations and improving user experiences across various platforms.
+Hybrid systems combine multiple AI techniques to leverage their respective strengths. For instance, a system may integrate rule-based reasoning with machine learning to enhance performance and adaptability. Hybrid architectures can be particularly advantageous in applications that require both structured knowledge and the ability to learn from unstructured data. This approach has gained traction in fields such as autonomous systems, where combining various methods can improve decision-making under uncertain conditions.
 == Implementation and Applications ==
+Artificial intelligence has been successfully implemented across a variety of domains, leading to transformative impacts on industries and society. The applications of AI can be classified into several key areas, including healthcare, finance, transportation, and entertainment.
 === Healthcare ===
-The implementation of artificial intelligence has shown considerable promise in the healthcare sector. AI systems are increasingly used to analyze medical data, assisting healthcare professionals in diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. Algorithms can process imaging data for radiology, detect anomalies in pathology slides, and even predict the likelihood of diseases based on genetic information.
+In the healthcare sector, AI is being utilized for several applications, including medical imaging, diagnostics, and personalized treatment plans. Machine learning algorithms analyze medical images, such as X-rays and MRIs, to detect anomalies with high accuracy, often surpassing human radiologists. Additionally, AI-powered predictive analytics can identify patients at risk for certain conditions, enabling timely interventions. Natural language processing has also been applied to analyze clinical notes and research literature, facilitating knowledge discovery and improving decision-making.
-AI-driven robots are also being utilized in surgical settings, enhancing precision and reducing recovery times. Furthermore, AI applications such as chatbots are enhancing patient engagement and streamlining administrative processes.
 === Finance ===
-In the financial industry, AI applications range from algorithmic trading to risk management. Machine learning models analyze vast amounts of financial data, allowing for faster and more accurate decision-making. Many banks and financial institutions employ AI for fraud detection, using anomaly detection to identify suspicious transactions in real-time. These systems can learn from previous transactions to improve their predictive accuracy continually.
+The finance industry has embraced AI technologies to enhance operational efficiency and reduce risks. Algorithms equipped with machine learning capabilities are used for fraud detection, analyzing transaction patterns to identify unusual behavior. Algorithmic trading leverages AI to devise strategies that react to market changes in real-time, optimizing investment decisions. Furthermore, AI-driven chatbots provide customer support, handling queries and transactions with high levels of efficiency.
 === Transportation ===
-The transportation sector is experiencing a significant transformation through the implementation of AI technologies. Autonomous vehicles, using AI algorithms for navigation and obstacle detection, are poised to revolutionize how people travel. Companies such as Tesla, Waymo, and others have invested heavily in AI-driven systems to enhance safety and efficiency on the roads. AI is also utilized in traffic management and logistics, improving route optimization and reducing congestion through predictive modeling.
+AI plays a pivotal role in the development of autonomous vehicles, which utilize a combination of sensors, machine learning, and advanced algorithms to navigate and operate without human intervention. Self-driving cars rely on AI systems for image recognition, path planning, and decision-making processes. AI is also employed in traffic management and optimization, analyzing data from various sources to improve traffic flow and reduce congestion.
-== Real-world Examples ==
+=== Entertainment ===
-=== Google DeepMind ===
+In the entertainment industry, AI has transformed content creation and distribution. Streaming platforms leverage AI algorithms for personalized recommendations, analyzing user preferences and behavior to suggest relevant content. Additionally, AI is utilized in video game development to create intelligent non-player characters (NPCs) that enhance user experience through adaptive behavior. Furthermore, AI-generated music and art are emerging as new forms of creative expression, raising questions about authorship and originality.
-One of the most prominent examples of AI research and application is Google DeepMind's work with Reinforcement Learning. The development of AlphaGo, which defeated champion Go player Lee Sedol in 2016, exemplified the potential of AI to outperform humans in complex strategic games. Following AlphaGo, DeepMind has continued to push the boundaries of AI by developing systems such as AlphaFold, which predicts protein folding with remarkable accuracy, revolutionizing the field of biology.
-=== IBM Watson ===
+== Criticism and Limitations ==
-IBM Watson's capabilities in natural language processing and machine learning have enabled it to analyze vast troves of unstructured data. In healthcare, Watson has been applied to assist oncologists in determining treatment options based on patient data and the latest medical literature. Watson’s use in other fields, such as customer service and finance, demonstrates AI's versatility and transformative potential across industries.
+Despite its remarkable advances, artificial intelligence faces several criticisms and limitations that raise ethical, societal, and technical concerns. These challenges must be addressed to ensure the responsible development and deployment of AI technologies.
-=== OpenAI GPT ===
+=== Ethical Concerns ===
-The release of OpenAI's Generative Pre-training Transformer (GPT) series has brought significant public attention to natural language generation. These models have been employed in various applications, from content creation to customer support. The ability of GPT-3 to generate human-like text has highlighted both the capabilities and challenges posed by AI in content accuracy and ethical considerations regarding automated content generation.
+The ethical implications of AI are a significant area of concern. Issues surrounding bias in AI algorithms can lead to discrimination and unfair treatment, particularly in sensitive applications such as hiring or law enforcement. Additionally, the use of AI in surveillance raises privacy concerns, with potential misuse of personal data and loss of individual freedoms. The lack of transparency in AI decision-making processes further complicates accountability and trust.
-== Criticism and Limitations ==
+=== Job Displacement ===
-=== Ethical Concerns ===
+The automating capabilities of AI have led to fears of job displacement across various sectors. While AI can enhance productivity and create new job opportunities, the rapid advancement of technology may outpace workforce adaptability. Low-skilled jobs in particular are at risk, as machines can perform repetitive tasks, prompting discussions about retraining and reskilling initiatives to prepare workers for the changing job landscape.
-As artificial intelligence continues to pervade various aspects of life, ethical concerns have arisen regarding its development and deployment. Issues including data privacy, algorithmic bias, and the accountability of AI systems are pivotal areas of discussion among researchers, policymakers, and the public. Reports of biased algorithms leading to unfair hiring practices or law enforcement profiling exemplify the need for responsible AI development.
-=== Employment Displacement ===
+=== Technical Limitations ===
-Another significant concern is the potential for AI and automation to displace jobs, particularly in sectors that rely on repetitive tasks. While AI can enhance productivity and efficiency, there is a fear that rapid development may outpace opportunities for workforce retraining and adjustment, leading to socioeconomic disparities and unemployment.
+AI also confronts technical challenges that impact its effectiveness. AI systems often require large amounts of high-quality data for training, which can be difficult to obtain in certain domains. Overfitting, where models perform well on training data but poorly on unseen data, represents another technical limitation. Furthermore, AI systems can struggle to generalize knowledge across different contexts, restricting their applicability and necessitating continuous learning and adaptation.
-=== Dependence on Technology ===
+== Future Trends ==
-The growing dependence on AI technologies raises questions about the diminishing human skills and the implications of delegating critical decision-making processes to machines. Dependence on AI tools in critical fields such as healthcare and law enforcement could lead to a lack of human oversight, potentially resulting in harmful outcomes if AI systems fail or are manipulated.
+The future of artificial intelligence holds tremendous potential for continued advancements and transformative applications. Upcoming trends include the integration of AI with other emerging technologies, a focus on ethical AI, and the exploration of general artificial intelligence (AGI).
-== Future Directions ==
+=== Integration with Emerging Technologies ===
-=== Ongoing Research and Innovations ===
+AI is anticipated to increasingly integrate with other emerging technologies, such as the Internet of Things (IoT), blockchain, and quantum computing. This integration can lead to enhanced automation, smarter devices, and improved data security. For instance, AI can process vast amounts of data collected from IoT devices to deliver actionable insights and optimize operations across various sectors, from manufacturing to smart cities.
-The future of artificial intelligence promises continual advancements and innovations. Research focuses on creating more robust AI systems that require less data, operate in real-time, and adapt to new information. Explainable AI, which prioritizes transparency and understandability of AI decision-making, is becoming increasingly important as AI systems are integrated into life-critical scenarios.
-=== Interdisciplinary Collaboration ===
+=== Ethical AI Development ===
-The evolution of AI will likely benefit from interdisciplinary approaches that incorporate insights from psychology, neuroscience, and ethics. Collaborations between computer scientists, social scientists, and ethicists will be crucial to developing AI technologies that align with human values and promote societal well-being.
+As public awareness of ethical AI increases, organizations will likely prioritize responsible AI development. This shift may result in the establishment of regulatory frameworks governing AI applications to ensure fairness, accountability, and transparency. Collaborations between governments, private sector entities, and civil societies will play a key role in fostering ethical guidelines and frameworks to navigate the complex landscape of AI.
-=== Regulatory Frameworks ===
+=== Pursuit of General Artificial Intelligence ===
-As AI technologies advance, effective regulatory frameworks are essential to mitigate potential risks while fostering innovation. Policymakers are beginning to draft legislation aimed at addressing issues like data governance, algorithmic accountability, and ethical AI deployment. The establishment of international guidelines may be necessary to ensure that AI development is conducted responsibly and equitable across global contexts.
+The pursuit of general artificial intelligence, which aims to replicate human cognitive abilities across diverse tasks, remains a prominent goal within the AI community. While current AI systems excel in specific tasks, achieving AGI requires advancements in understanding human cognition, learning capabilities, and emotional intelligence. Researchers continue to explore innovative approaches, including neuromorphic computing and evolutionary algorithms, to push the boundaries of machine intelligence.
 == See also ==
 * [[Machine Learning]]
+* [[Deep Learning]]
 * [[Natural Language Processing]]
 * [[Robotics]]
-* [[Neural Networks]]
+* [[Computer Vision]]
-* [[Ethics in Artificial Intelligence]]
-* [[Autonomous Vehicles]]
 == References ==
-* [https://www.ibm.com/artificial-intelligence IBM Watson]
+* [https://www.aaai.org/ The Association for the Advancement of Artificial Intelligence]
-* [https://deepmind.com/ Google DeepMind]
+* [https://www.oreilly.com/library/view/ai-superpowers/9780525556558/ AI Superpowers: China, Silicon Valley, and the New World Order]
-* [https://www.openai.com/ OpenAI]
+* [https://www.technologyreview.com/2021/04/13/1022279/what-is-ai-artificial-intelligence-explained-short-guide/ MIT Technology Review: What is AI?]
+* [https://www.forbes.com/sites/bernardmarr/2021/02/24/the-top-10-most-important-trends-in-artificial-intelligence-ai-in-2021/?sh=3c72a916684c Forbes: The Top 10 Most Important Trends In Artificial Intelligence (AI) In 2021]
 [[Category:Artificial intelligence]]
 [[Category:Computer science]]
 [[Category:Technology]]