A student reached out to me for a school project and asked me the following 5 questions: 1. What areas of research in AI deserve more attention? 2. Would it be possible to have AI in an educational setting as an omnipresent way of monitoring/grading students? 3. What would you say to someone with a pessimistic opinion about AI? 4. What are the main ethical considerations and challenges regarding the use of AI? 5. How do you think the relationship between machines and humans will be in the future? My answers are below. Feel free to reply with your own answers! ## What areas of research in AI deserve more attention? Building on the impressive capabilities of large language models (LLMs), an emerging trend in AI research is the development of agent-based AI systems. These systems enhance LLMs with additional capabilities, such as the ability to use tools, learn from interactions, and work autonomously on complex tasks. Examples of such systems include Devin, CrewAI, Autogen, and ChatDev, each designed to autonomously solve problems in various domains. Devin, developed by Cognition Labs, is an agent-based AI system focused on software engineering tasks. It can autonomously plan and execute complex development workflows, learn new technologies, find and fix bugs, and contribute to open-source projects. Similarly, CrewAI, Autogen, and ChatDev are autonomous agent-based frameworks that orchestrate teams of AI agents to collaborate on a wide range of tasks. The potential applications of agent-based AI are vast, extending to all sorts of complex, multi-step problems that could benefit from enhanced autonomous AI systems. However, while many research groups are working on developing these systems, there has been insufficient systematic study comparing the different approaches to each other. Typically, when a new agent-based system is announced, researchers compare its performance to unenhanced LLMs like GPT-4. However, this is not an apples-to-apples comparison, as the agent-based systems have additional capabilities beyond the base LLM. For example, Devin's performance on software engineering benchmarks was compared against raw LLMs, showing a significant advantage. This comparison is misleading, as it doesn't account for Devin's additional tools and capabilities.