Ollama is a tool to run Large Language Models locally, without the need of a cloud service. Its usage is similar to Docker, but it's specifically designed for LLMs. You can use it as an interactive shell, through its REST API or using it from a Python library. Read more here - https://www.andreagrandi.it/posts/ollama-running-llm-locally/#:~:text=Ollama%20is%20a%20tool%20to,it%20from%20a%20Python%20library

According to Subbarao Kambhampati, a professor at Arizona State University, the recent claims that large language models (LLMs) like GPT-3, GPT-4, and ChatGPT possess reasoning and planning abilities are unfounded. Prof Kambhampati conducted experiments testing these LLMs on standard planning tasks and found their empirical performance was poor, especially when object and action names were obfuscated. While fine-tuning the models on planning data can boost performance, he argues this merely converts the task to approximate retrieval rather than true reasoning. The practice of having humans provide "chain of thought" prompting to steer LLMs is susceptible to the human unintentionally guiding the model, Kambhampati claims. He also expresses skepticism about papers claiming LLMs can self-critique and iteratively improve their own plans and reasoning. While LLMs excel at extracting general planning knowledge and generating ideas, Kambhampati found they struggle to assemble that knowledge into executable plans that properly handle subgoal interactions. Many papers making planning claims either ignore such interactions or rely on human prompting to resolve them, he says. Instead, Kambhampati proposes using LLMs to extract approximate domain models, which human experts then verify and refine before passing to traditional model-based solvers. This resembles classic knowledge-based AI systems, with LLMs replacing human knowledge engineers – while employing techniques to reason with incomplete models. Overall, the AI expert argues that despite their impressive capabilities, LLMs fundamentally lack true autonomous reasoning and planning abilities as traditionally understood. However, he believes they can productively support these tasks by combining their knowledge extraction and idea generation strengths with external solvers and human oversight. https://cacm.acm.org/blogcacm/can-llms-really-reason-and-plan/

https://youtu.be/wjZofJX0v4M?si=UV_tB_3rwz4qzJoC

This is (unfortunately paywalled) interesting work by Bloomberg: They essentially created 1,000 resumes that were by and large identical in terms of experience, grades, qualifications etc and beyond that only changed the names to fall equally into 1 of 4 groups: Asians, Blacks, Hispanics, Whites, and for each of these 125 men and 125 women. They then asked GPT3.5 (and also GPT4) to rank the candidates on most to least suitable for 4 different roles: HR manager, software engineer, retail and financial analyst. They ran this 1,000 times. Open AI's GPT showed heavy racial bias, mostly significantly against black men (ranked as often as 32% as least suitable, which is an almost 2x deviation from the 'objective' 12.5%) and women, and also against white men. None of this is truly surprising by my count, as the models are mere reflections of the data on which they are trained, which of course is as biased, because they are essentially trained on the internet, which hardly serves as a heaven of unbiased equality. https://www.bloomberg.com/graphics/2024-openai-gpt-hiring-racial-discrimination/

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.