Talks
Speakers
Events
Topics
Sign in
Home
Talks
Speakers
Events
Topics
Leaderboard
Use
Analytics
Sign in
Suggest modification to this talk
Title
Description
@mseckington HAL, Skynet, KITT… we've always been intrigued by artificial intelligence, but have you ever stopped to considered the un-artificial? Most developers are familiar with the basics of AI: how do you make a computer, an algorithm, a system learn something? How do you model real world problems in such a way that an artificial mind can process them? What most don't realize though is that the same principles can be applied to people. This talk looks at some of the theories behind how machines learn versus how people learn, and maps it to real life examples of how specifically our users learn their way around interfaces and how designers and developers apply learning methodologies in their day-to-day actions. Talk given at GORUCO 2015: http://goruco.com
Date
Summarized using AI?
If this talk's summary was generated by AI, please check this box. A "Summarized using AI" badge will be displayed in the summary tab to indicate that the summary was generated using AI.
Show "Summarized using AI" badge on summary page
Summary
Markdown supported
In the video titled "Un-Artificial Intelligence," Melinda Seckington explores the parallels between artificial intelligence (AI) and human learning, emphasizing the concept of 'un-artificial' intelligence. She begins by defining intelligence, not just as the accumulation of knowledge but as the ability to acquire, reason, and apply that knowledge effectively. Key Points Discussed: - **Definitions of Intelligence**: Seckington refers to a Dungeons and Dragons manual to define intelligence in terms of the ability to learn and reason, suggesting that true intelligence involves practical application of knowledge. - **Artificial Intelligence Overview**: AI can refer to both the intelligence exhibited by machines and the field of study focused on creating such intelligence. She introduces the concept of intelligent agents—systems designed to achieve optimal outcomes in given circumstances. - **Human vs. Machine Learning**: The speaker compares the behavior of intelligent agents to human learning, employing an analogy based on sensors and effectors. Seckington utilizes Pavlov’s classical conditioning to illustrate how humans and animals alike learn through associations. - **Learning Mechanisms**: Seckington presents three types of learning algorithms in AI—supervised learning, unsupervised learning, and reinforcement learning—paralleling them with human learning processes that involve directive, conversational, and assessment-based learning. - **Distinctions Between Humans and Machines**: While machines operate within a specific framework, humans learn contextually, develop social skills, and possess emotional understanding, enabling rich and diverse learning experiences. - **Future of AI and Human Collaboration**: Seckington posits that as AI develops, there will be a need for collaboration between humans and machines. The future may see educational environments evolving to benefit both parties, blurring the lines between artificial and human intelligence. Significant Examples: - Seckington shares an anecdote about training her cats to respond to an alarm sound, illustrating conditioning and the formation of habits in both animals and humans. - She mentions various learning styles, such as collaborative and assessment-based learning, emphasizing how these mirror AI's learning strategies. Conclusions: - A truly intelligent machine must encompass a broad range of learning abilities akin to human cognitive functions. The ongoing development of AI presents opportunities for collaboration rather than competition, inviting a future where both humans and machines learn from each other, with implications for technology and education.
Suggest modifications
Cancel
