Emerging Technology Chapter 13 Quiz

Introduction to Emerging Technology

• Artificial Intelligence (AI) refers to the capability of machines to simulate human behavior, learn, and make decisions.
• Evolution of information systems has led to devices with greater processing power than historical computers used for significant tasks, such as moon landings.
• AI technologies enable companies to better understand customer preferences through algorithms and machine learning.

AI Evolution

• Early discussions on intelligent machines began with Claude Shannon's 1950 work on a chess-playing program and Alan Turing’s exploration of machine intelligence.
• The Turing Test evaluates whether a machine can mimic human responses to a point where it is indistinguishable from a human.
• The term "AI" was first used in 1956; initial AI systems handled basic mathematical problems.
• AI development flourished between 1957 and 1974 as storage capabilities increased and algorithms improved.
• Notable milestones include IBM's Deep Blue defeating chess champion Garry Kasparov in 1997 and IBM's Watson winning "Jeopardy!" in 2011.

Machines, AI & Humans

• Current AI development focuses on emulating human behavior to perform complex tasks.
• Key differences between machines and AI include AI's ability to analyze data, learn, and adapt, while machines follow pre-defined commands.
• For example, Grammarly utilizes AI to enhance user interactions beyond simple spell-checking functionalities.
• AI operates based on algorithms, which are sets of rules to solve specific problems, enabling smarter performance with increased data.

Types of AI

• AI is classified into three main types based on ability:
  • Artificial Narrow Intelligence (A.N.I): Current AI that excels at a single task (e.g., smart assistants like Siri).
  • Artificial General Intelligence (A.G.I): Hypothetical AI that mimics human intelligence across multiple tasks.
  • Artificial Super Intelligence (A.S.I): Future AI that would surpass human intelligence in all aspects.
• AI can also be categorized based on functionality:
  • Reactive Machines: AI that performs specific tasks without learning (e.g., Deep Blue).
  • Limited Memory: AI that uses past data to inform future actions.
  • Theory of Mind: Not yet realized; involves understanding other entities' emotions and beliefs.
  • Self-Aware AI: Hypothetical future AI with consciousness and self-awareness.

Machine Learning and Deep Learning

• Machine Learning (ML) is a technique enabling AI to analyze data and improve automatically.
• Deep Learning is a subset of ML that uses large datasets for training, needing minimal human intervention.
• Types of machine learning include:
  • Supervised Learning: Machine learning with labeled data, promoting targeted outcomes like predicting house prices.
  • Unsupervised Learning: Involves unlabeled data, allowing the machine to identify patterns independently (e.g., customer clustering).
  • Reinforcement Learning: Learning through trial and error, where machines refine their actions based on feedback (e.g., robots trained to perform specific tasks).

Applications and Future of AI

• AI applications enhance businesses by automating tasks, improving customer service through chatbots, and providing personalized experiences (e.g., Best Western using AI with IBM).
• The future of AI will require careful consideration of ethical implications and the relationship between AI and human workers.### Deep Learning and Neural Networks
• Deep Learning autonomously extracts features and classifies data, eliminating the need for human intervention.
• Utilizes Artificial Neural Networks (ANN), which mimic the human brain's neural connections.
• Data is processed through layers of interconnected nodes, using mathematical calculations for decision-making.
• Applications span various fields including computer vision, natural language processing, and healthcare, aiding in disease diagnosis and management.
• Deep Learning is a subset of machine learning, which in turn is a subset of artificial intelligence.

Differences Between Deep Learning and Machine Learning

• Data Requirements: Deep Learning demands large datasets; Machine Learning performs well with small to medium datasets.
• Hardware: Deep Learning often requires powerful machines with GPUs; Machine Learning can operate on lower-end machines.
• Training Time: Training can last from hours to weeks for Deep Learning, while Machine Learning typically ranges from seconds to hours.
• Processing Speed: Deep Learning has faster processing times compared to the slower processing associated with Machine Learning.
• Algorithms: Deep Learning requires fewer algorithms; Machine Learning needs more varied algorithms.
• Data Interpolation: Deep Learning faces significant difficulties, while Machine Learning's challenges vary.

Autonomous Technology

• Combines software, sensors, and location technologies to create devices that operate independently (e.g., self-driving cars, medical nanobots).
• Nanobots: Operate on a nanoscale and show promise for treating diseases such as cancer.
• Unmanned Aerial Vehicles (UAVs): Known as drones, used in military, civil applications, and personal use.

Robots

• Robots automate tasks, increasing speed and precision, commonly used in manufacturing, medical, and educational fields.
• Advantages: Reduced risk in dangerous tasks, continuous operation, and improved precision.
• Disadvantages: High initial costs, potential job loss, and lack of creativity and emotional intelligence.

Intelligent Agents

• Include software, human, and robotic agents that perceive their environments and make decisions.
• Learning agents adapt based on experiences, enhancing autonomy in decision-making.

Natural Language Processing (NLP)

• Enables computers to understand and process human language, facilitating interaction through speech and text.
• Applications include translation services, interactive voice response systems, and grammar checking tools.

Expert Systems

• Simulate human decision-making to solve complex problems in specific contexts.
• Use if-then-else reasoning to provide reliable assistance in tasks such as fraud detection.

Extended Reality (XR)

• Encompasses augmented reality (AR), virtual reality (VR), and mixed reality (MR).
• Augmented Reality (AR): Enhances the real world with digital information (e.g., Snapchat lenses, Pokémon Go).
• Virtual Reality (VR): Creates simulated environments for training and education in high-risk fields like military and medicine.

Emerging Technology Trends

• Wearable Technology: Includes devices like smartwatches and fitness monitors; future developments may enable energy harvesting.
• Internet of Things (IoT): Connects everyday devices to the internet for automated data sharing and monitoring, impacting various sectors including manufacturing and home automation.
• Quantum Computing: Leverages quantum mechanics for high-performance computing, enabling significantly faster calculations and solving complex problems.

Ethical Considerations

• Rapid advancements in AI and emerging technologies require careful governance to address ethical and privacy concerns.### AI and Privacy
• Notable breaches of privacy include Google’s DeepMind violating laws and the Facebook-Cambridge Analytica scandal.
• Data from DeepMind and Facebook was shared with third parties without user consent.

ChatGPT and Ethical Implications

• ChatGPT, launched by OpenAI in November 2022, generates written texts and code from prompts.
• Ethical concerns have arisen regarding misinformation, bias perpetuation, and deception through impersonation.
• Academic institutions have expressed concerns about the tool's potential use for cheating, leading to bans in some schools.

Growth and Development of AI

• The Internet of Things has immensely increased data volumes as everyday devices like smartphones and voice assistants collect data.
• Upgrades in AI technology have enhanced processing speeds and computational capacity, making AI systems more efficient and accessible.

Ethical Considerations of AI

• The rapid expansion of AI necessitates careful consideration of its ethical implications in usage.

Key Takeaways on AI

• AI refers to the capability of computers to learn and think similarly to humans.
• Originating in the 1950s, AI has evolved significantly, with the Turing Test assessing machines’ ability to mimic human thinking.
• Machines with AI technology can make certain decisions but lack emotional intelligence, or "the human factor."
• AI classifications include machine learning, deep learning, and autonomous technologies such as driverless cars and UAVs.
• Robots automate processes with little human intervention, while expert systems streamline knowledge and decision-making.
• Extended Reality (XR) encompasses various real and virtual environments, including Augmented Reality (AR) and Virtual Reality (VR).
• Emerging technology trends are wearable, connected, collaborative, and advanced devices, influencing business operations and strategies.

Review Question Insights

• Understanding AI's capabilities, classifications, decision-making applications, robotics, advantages and disadvantages of robots, and diverse technological trends are essential for comprehending its implications in business contexts.

Test your knowledge on Chapter 13: Emerging Technology. This chapter covers the evolution of AI, applications of machine learning, and trends in technology. Prepare to explore the future of technology and its implications for humans and machines.