Artificial IInntteelllliiggeennccee TThhrroouugghhtthheeAAggeess:: PAST, PRESENT, AND FUTURE
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Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to mimic human actions and cognitive processes. INTRODUCTION OF ARTIFICIAL INTELLIGENCE (AI) Definition of AI CHAPTER 1
This includes learning, reasoning, problem-solving, perception, and language understanding. AI systems are designed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Definition of AI
1950s: The term "artificial intelligence" was coined at the Dartmouth Conference in 1956, where early AI pioneers discussed the potential of creating machines that could simulate human intelligence. 1950s-1960s: The initial focus was on symbolic AI, which used rules and logic to simulate human problem-solving. 1970s-1980s: Expert systems emerged, which were AI programs that specialized in a specific domain, such as medicine or finance. However, progress was limited due to computational constraints and the inability to handle uncertainty. Timeline and History: Present: AI is ubiquitous in everyday life, powering virtual assistants like Siri and Alexa, recommendation systems, autonomous vehicles, and many other applications.
Timeline and History: Present: AI is ubiquitous in everyday life, powering virtual assistants like Siri and Alexa, recommendation systems, autonomous vehicles, and many other applications. 1990s: The rise of machine learning, particularly neural networks, revitalized interest in AI. Neural networks, inspired by the structure of the human brain, showed promise in solving complex problems like pattern recognition and language understanding. 2000s-2010s: Advances in computing power, big data, and algorithms led to significant breakthroughs in AI. Deep learning, a subfield of machine learning, became increasingly popular and enabled remarkable achievements in areas such as computer vision, natural language processing, and robotics.
Applications of AI: Natural Language Processing (NLP): AI systems can understand, interpret, and generate human language. Applications include chatbots, sentiment analysis, language translation, and speech recognition.
Computer Vision: AI enables machines to interpret and understand visual information from the world. Examples include facial recognition, object detection, autonomous vehicles, and medical image analysis. Applications of AI:
Machine Learning: This involves algorithms that improve their performance over time without being explicitly programmed. Applications range from predicting customer behavior to medical diagnosis. Applications of AI:
Robotics: AI-powered robots can perform tasks autonomously in various domains, such as manufacturing, healthcare, and space exploration. Applications of AI:
01 02 WELL KNOW APPLCIATION USING AI IN SPORTS Hawk-Eye: Ball Tracking in tennis and cricket. IBM Watson: Analytics for tennis tournaments Catapult Sport: Wearable performance trackers STATSport: GPS Tracking for team sports Second Spectrum: Video analytics for basketball and soccer 03 Data privacy concern High costs Over-reliance on Technology Bias in data Technical challenges SPORTS HOW AI SUPPORTS THE AREA OF SPORT Improved performance Injury prevention Strategics insights enhanced fan experience Efficient Recruitment ADVANTAGES DISADVANTAGES Performance Analysis Injury Prevention Tactical Analysis Fan Engagement Recruitment and scouting AI in Sports: Knowledge Elicitation
Semantic Network of AI in Sports
References: Ahramovich, A. (2024, June 18). AI in Sports: Use Cases, Examples, and Challenges. https://www.itransition.com/ai/sports Arastey, G. M. (2021, February 28). Artificial Intelligence (AI) in Sports | Sport Performance Analysis. Sport Performance Analysis. https://www.sportperformanceanalysis.com/article/artificialintelligence-ai-in-sports AI in Sports: Exploring the Benefits and Best Practices. (2024, January 12). Forbytes. https://forbytes.com/blog/ai-insports-and-fitness-its-benefits/ Peranzo, P. (n.d.). How Artificial Intelligence is Transforming the Sports Industry? Imaginovation | Top Web & Mobile App Development Company Raleigh. https://imaginovation.net/blog/ai-in-sports-industry/ Avcontentteam. (2024, February 27). How is AI Powering the Future of Sports? Analytics Vidhya. https://www.analyticsvidhya.com/blog/2023/05/how-is-aipowering-the-future-of-sports/? social=google&next=https%3A%2F%2Fwww.analyticsvidhya.co m%2Fblog%2F2023%2F05%2Fhow-is-ai-powering-the-futureof-sports%2F
References: Sharma, R. (2024, June 3). AI in Sports – How is AI Transforming the Sports Industry? Markovate. https://markovate.com/blog/ai-insports/#:~:text=1.,prevention%20and%20personalized%20traini ng%20schedules Srivastava, S. (2024, June 24). AI in Sports – How is artificial intelligence redefining the sports industry? Real-world examples. Appinventiv. https://appinventiv.com/blog/ai-insports/ Gillis, A. S. (2024, January 18). semantic network (knowledge graph). Content Management. https://www.techtarget.com/searchcontentmanagement/defi nition/semantic-network-knowledge-graph IBM Study: Fan Engagement and Consumption of Sports Shifting, Reveals New Opportunities for Technology Integrations including AI. (n.d.). Australian Associated Press. https://www.aap.com.au/aapreleases/cision20240626ae48509/ How AI is taking the production of sports content to a new level. (2024, June 18). ISPO.com. https://www.ispo.com/en/sportstech/ai-sport-artificialintelligence-production-sports-content