Artificial Intelligence Foundations: Thinking Machines

 

Just finished the course “Artificial Intelligence Foundations: Thinking Machines”! 

This was an informative overview of the history and technology behind artificial intelligence.

The course started by covering key concepts such as the General Problem Solver and symbolic reasoning. Then it covered how machine learning got a slow start but then quickly became a dominant field in AI. Finally I saw how artificial neural networks could be used with machine learning to get deeper insights and find complex patterns.

One of the top takeaway for me was understanding the distinction between strong vs weak AI.

• Strong AI (General AI): This type of AI displays behaviors akin to a full-fledged artificial person, including emotions and a sense of purpose. It is capable of learning new tasks for the joy of learning, much like a human. However, strong AI remains largely theoretical and is often depicted in science fiction.
• Weak AI (Narrow AI): This AI is designed to perform specific, narrow tasks, such as language processing or sorting pictures. Examples include virtual assistants like Siri, which convert natural language into text and perform tasks based on voice commands. Most current AI developments focus on expanding weak AI capabilities.

But now I want to dive deeper into the nitty-gritty details of these concepts and models. I want to understand what these models are, how they work, and how we can build them. 

Stay tuned for more insights and inspiration.

Course link:

• https://www.linkedin.com/learning/artificial-intelligence-foundations-thinking-machines?isLearningSubscriber=true&trk=feed-share_video_title_learning&lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_recent_activity_content_view%3B9OvDg59%2FQ%2BWq3dJEu6nScQ%3D%3D&lici=aB4K%2FiDgCW0wcZ%2B051wiIQ%3D%3D