Create A Discussion Thread With Your Name And Answer 912368

Create A Discussion Thread With Your Name And Answer The Following Q

Create a discussion thread (with your name) and answer the following question: Discussion 2 (Chapter 2): Discuss the process that generates the power of AI and discuss the differences between machine learning and deep learning. There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author's words and continue to use in-text citations.

Paper For Above instruction

Artificial Intelligence (AI) has become a transformative force across various industries, driven by its ability to analyze vast amounts of data and perform tasks that typically require human intelligence. The power of AI derives mainly from processes that enable systems to learn from data, adapt, and improve over time. At its core, AI generates its strength through complex algorithms and models that imitate cognitive functions, such as learning, reasoning, and problem-solving. These algorithms process input data to identify patterns, make decisions, and execute tasks with increasing accuracy and efficiency. The development of AI is rooted in advancements in computational power, data availability, and sophisticated algorithms, which collectively enable machines to perform tasks that were once thought exclusive to humans (Russell & Norvig, 2016).

The primary process that fuels AI's power is machine learning (ML), a subset of AI focused on developing systems that can learn from data without explicit programming for each task. Machine learning involves training algorithms on large datasets, allowing the system to recognize patterns and make predictions or decisions based on new, unseen data. This process involves several stages: data collection, feature extraction, model training, and evaluation. During training, the algorithm iteratively adjusts itself to minimize errors, improving its accuracy as it learns. Over time, these models can be deployed to perform a variety of tasks, from image recognition to language processing, with enhanced performance due to continual learning.

Deep learning, a subset of machine learning, delves even further into layered neural networks that mimic the structure of the human brain. Deep learning models consist of multiple layers of interconnected nodes, called artificial neurons, which process data with increasing abstraction at each level. This architecture enables deep learning systems to automatically extract features from raw data, significantly reducing the need for manual feature engineering and leading to improved performance in complex tasks such as speech recognition, natural language understanding, and autonomous driving (LeCun, Bengio, & Hinton, 2015). The difference between machine learning and deep learning primarily lies in their complexity and the scale of data they handle. While traditional machine learning models rely on manual feature extraction and are suitable for smaller datasets, deep learning thrives on large datasets and complex architectures, often achieving superior results in tasks such as image and speech recognition.

In summary, the power of AI stems from sophisticated learning processes, with machine learning and deep learning playing pivotal roles. Machine learning offers algorithms that improve over time through data-driven training, whereas deep learning leverages neural networks with multiple layers to handle vast and complex datasets more efficiently. The ongoing evolution in these fields continues to expand AI's capabilities, driving innovation and transforming industries worldwide (Goodfellow, Bengio, & Courville, 2016).

References

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
• Russell, S., & Norvig, P. (2016). Artificial Intelligence: A modern approach (3rd ed.). Pearson.