Adventurous AI Journey Ahead

  Machine learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from data without being explicitly programmed . Instead of following pre-written instructions, ML algorithms find patterns in data, make predictions or decisions, and get better at the task over time as they are exposed to more information.   More on ML

  Visit :  AI Glossary/ Dictionary (Source: MIT.edu)   This is a valuable set of AI vocabulary to start off with. It not only defines the AI terms, it also explains their importance. It is good to keep this list handy.  Learning the basic AI vocabulary can help us gain a better understanding of the complex field of AI. Also, developing AI literacy will enable us keep up with its advancement, appreciate its opportunities and challenges. Thus, putting us in a better position to balance any innovation with ethical considerations. Ultimately, we want AI to impact our society positively. Here’s another helpful glossary on basic GenAI  Visit:  Glossary of Term: Generative AI Basics (Source: MIT.edu)  (Check out their LLM definitions!)

  Visit: What is AI (Source: IBM.com) This site provides a complete guide to AI fundamentals. It relatively easy to go through its materials on, for example, machine learning, deep learning, generative AI, AI agents and agentic AI. In addition, it gives an overview of AI history, its benefits and use cases, AI ethics and governance. Overall, this is a great resource for me to start off.

Artificial intelligence (AI) is technology (applied knowledge) that enables machines to simulate human learning, comprehension / reasoning, problem solving, decision making, creativity and autonomy. AI enables us to build smart machines that can perceive, reason, and act to achieve defined outcomes. Also, by incorporating learning, they can adapt and improve their performance without explicitly reprogramming. More on AI