Compare types of decision making and thinking with a focus on emotional versus logical and literal versus abstract.
Describe types of machine learning (ML).
Describe the challenges and limitations of artificial intelligence (AI) systems.
Compare and contrast AI, ML, and deep learning (DL).
Differentiate between the major branches of AI (ML, neural networks, expert systems, natural language processing, robotics, and fuzzy logic).
Examine the role of AI in software.
Examine the role of AI in software and physical systems.
Explain how computers model intelligent behavior (as found in robotics, speech and language recognition, and computer animation).
Test a machine-learning algorithm using human input data.
Write a machine-learning algorithm.
Hypothesize the future of technology and technology-related careers.
Categorize careers by the role of computing.
Explain the impact of computing on 21st-century careers.
Describe careers created by innovation in computing and technology.
Describe careers to which computing is central.
Recognize the role of computing in a wide spectrum of careers.
Decision making and thinking can be categorized based on emotional versus logical and literal versus abstract approaches. Emotional decision making involves making decisions based on feelings and gut instincts, while logical decision making involves using reasoning and evidence to make decisions. Literal thinking involves taking things at face value and focusing on concrete facts, while abstract thinking involves looking at the bigger picture and considering possibilities beyond what is immediately apparent.
Machine learning (ML) is a subset of artificial intelligence that involves creating algorithms and models that enable computers to learn from and make predictions or decisions based on data. There are different types of machine learning approaches, such as supervised learning, unsupervised learning, and reinforcement learning, each of which has specific applications and methods.
The challenges and limitations of artificial intelligence (AI) systems include issues related to data quality, bias in algorithms, and the inability of AI to fully replicate human intelligence. AI systems may struggle with tasks that require common sense reasoning or emotional intelligence, and there are concerns about the ethical implications of AI systems making decisions that impact human lives.
AI, ML, and deep learning (DL) are closely related but distinct concepts. AI is the overarching field of computer science that aims to create machines capable of intelligent behavior. ML is a subset of AI that focuses on algorithms and models that learn from data. Deep learning is a subfield of ML that involves artificial neural networks with many layers, enabling the processing of complex patterns and data representations.
The major branches of AI include machine learning, neural networks, expert systems, natural language processing, robotics, and fuzzy logic. Each branch has specific applications and techniques that contribute to the overall field of artificial intelligence.
AI plays a significant role in software development, enabling the creation of intelligent systems that can automate tasks, make predictions, and learn from data. AI algorithms are used in various software applications, such as recommendation systems, fraud detection, and image recognition.
In addition to software, AI is increasingly being integrated into physical systems, such as autonomous vehicles, smart home devices, and industrial robots. These AI-powered systems can perform complex tasks, adapt to changing environments, and interact with humans in more intuitive ways.
Computers model intelligent behavior through various techniques, such as robotics, speech and language recognition, and computer animation. Robotics involves creating machines that can perform physical tasks, while speech and language recognition enable computers to understand and respond to human speech. Computer animation involves simulating lifelike movements and behaviors in virtual environments.
Testing a machine-learning algorithm using human input data involves training the algorithm on a dataset of human-generated input and evaluating its performance in making predictions or decisions. This process helps improve the algorithm's accuracy and effectiveness in real-world applications.
Writing a machine-learning algorithm involves designing and coding an algorithm that can learn from data and make predictions or decisions. This process involves selecting appropriate features, defining a model architecture, and optimizing the algorithm's parameters to achieve the desired outcome.
The future of technology and technology-related careers is likely to be shaped by advances in artificial intelligence, machine learning, and other cutting-edge technologies. Careers in computing are diverse and span a wide range of industries, from software development and data science to robotics and cybersecurity.
Careers in computing can be categorized based on the role of computing in the job, such as computer programming, data analysis, system administration, and network security. Computing skills are increasingly becoming essential in various fields, as technology continues to play a critical role in modern workplaces.
The impact of computing on 21st-century careers is significant, as technology is driving innovation and automation across industries. Jobs in computing are in high demand, with opportunities for growth and advancement in areas such as AI, cybersecurity, and software engineering.
Innovation in computing and technology has created new careers in fields such as artificial intelligence, data science, and cloud computing. These emerging roles require specialized skills and knowledge in cutting-edge technologies, offering exciting opportunities for professionals in the industry.
Computing is central to many careers, as technology is increasingly becoming a fundamental component of modern workplaces. Jobs in fields such as IT, computer science, and engineering rely on computing skills to design, develop, and maintain systems and software that drive innovation and productivity.