This week, I explored the fascinating evolution and current landscape of Artificial Intelligence (AI). AI began in the mid-20th century with symbolic reasoning, leading to early successes like chess-playing computers and expert systems. However, these methods struggled with real-world complexities, resulting in the AI Winter of the 1970s.

The 1980s saw a revival through expert systems and the inception of connectionism, paving the way for neural networks. The 1990s brought significant advancements, such as IBM's Deep Blue defeating a world chess champion and the rise of semantic web technologies.

In the 21st century, AI became part of everyday life with innovations like the Roomba and Google's self-driving car. The 2010s were pivotal, marked by IBM Watson's Jeopardy win and the rise of virtual assistants like Siri and Alexa, showcasing AI's ability to perform complex tasks and interact naturally with humans.

A major breakthrough in 2012 involved deep convolutional neural networks for image classification, reducing error rates. By 2015, Microsoft's ResNet achieved human-level performance in image recognition. AI continued to excel in speech recognition, autonomous driving, and reading comprehension, matching human expertise across these domains.

Recently, large language models like BERT and GPT-3 have emerged, driven by vast data availability, enhancing AI's capabilities in natural language processing.

Additionally, I participated in a Zoom meeting recognizing six teachers involved in Amazon's technology education projects. Key figures included Gavin Liu, who has been working for 20 years, and Jason Chen, an experienced cloud computing speaker. Aimee Cao excels in talent exploration, while Chen Hua leads innovation projects. Cynthina Zhang has extensive financial experience, and Julian is noted for his collaborative training efforts with Amazon. They discussed the purpose of boot camps and the importance of applying for work after the Amazon exam.

I also learned about AI topics like Knowledge Representation and Expert Systems. Symbolic technology intelligence underscores the importance of knowledge in understanding and integrating our world. The DIKW Pyramid—Data, Information, Knowledge, and Wisdom—replaces restricted knowledge concepts. Cataloging computer knowledge involves objects, values, and attributes, such as matching a Python developer's name with attributes.

Hierarchical Representation helps categorize knowledge, as seen with the example of a golden bird using known attributes like wings, flying speed, and color. Processing representation employs if-then logic for inference.

Expert systems, another intriguing subtopic, involve short-term and long-term memory, utilizing expert databases for inference. They employ forward and backward inference. Additionally, neural networks and perceptrons are introduced, discussing various models and training methods that help readers understand output processing.

Overall, this week's exploration provided a comprehensive understanding of AI's history, current capabilities, and future potential, along with insights into education and knowledge representation in technology.