The main discussion points were:

- What an AI is and What AI isn't:
To put their answer in an statement :

AI is like using math to give machines some of the amazing abilities humans are blessed with. But not everything that runs on its own is actually AI. Real AI learns from experience, getting better as it goes, though the way machines think and learn is different from how we do.

- The Difference between AGI and AI

AI is typically narrow and domain-specific, excelling in specialized tasks within a particular field
AGI, or General AI, is conceptual and designed to operate across a wide range of domains. But, AGI remains theoretical and it is not practical yet.

- What are the foundation's required for an AI to become applicable :

1. Data : Well Collected, labeled, cleaned and Visualized data is necessary because AI is nothing without DATA
2. Work Force : People with the skills and the eagerness to learn about that field of study. hence awarness must be created regarding that
3. High Computing Powers: the concept of AI was thought about since in the late 40's and early 50's but couldn't become because of this one and specifically essential thing that is computing powers. at that time the computing powers of computers were slow and even the idea of GPU started in the 80's hence High computing powers is as equally essential as a DAT

- AI is becoming Centralized and becoming Open source and available will this generate an opportunity or will it have a bad side?:

Generally: There are both good and bad sides to think about. The downside is that we don’t always know what kind of data the AI is being trained on—it could be biased or flawed. On the other side, making AI open-source gives enthusiasts a chance to experiment and work on it, possibly leading to breakthroughs that rival even the big, closed-off AI projects. It’s a balance that needs to be considered carefully.

- AI in the Context of Ethiopia and the current status :

We’re definitely making progress in AI, but we're still in the early stages—not quite at the "toddler" level yet, more like infants. It’s crucial to raise awareness and get more people involved in the field. To move forward, we need to focus on three key areas, working together with the government, startups, and initiatives. For example, when it comes to data, we can’t just sit back—we need to be active in gathering and managing it properly. As for the workforce, we should start educating the younger generation early, even from elementary school, to prepare them for the future of AI.