Can AI do abductive reasoning?
Understanding Reasoning LLMs
Methods and Strategies for Building and Refining Reasoning Models
Artificial intelligences and human scientists exhibit...
We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social science such as...
Artificial
An LLM is a computer program. We should talk about it like a computer program.
Artificial
An LLM is a computer program. We should talk about it like a computer program.
Solving a Million-Step LLM Task with Zero Errors
LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans,...
Is AI Reasoning Right for the Wrong Reasons? | Quanta Magazine
The idea that artificial intelligence can “reason” is more intuitive than ever. But intuitions can be wrong, and the science is far from settled.
My AI Skeptic Friends Are All Nuts
My smartest friends have bananas arguments about LLM coding.
My AI Skeptic Friends Are All Nuts
My smartest friends have bananas arguments about LLM coding.
Model Collapse Ends AI Hype
We Don't Understand Neural Networks At The Algorithmic Level
The largest ongoing debate about AI is “Are Large Language Models (LLMs) intelligent?” That makes sense, at least: the evidence is ambiguous and the stakes a...
AI Agents: Key Concepts and How They Overcome LLM Limitations
An AI agent is an autonomous software entity that is often used to augment a large language model. Here's what developers need to know.
MainPage8
The InfoCentral project has historically been focused more heavily upon the symbolic (human-meaningful) side of AI. This included derivatives of Semantic Web and related knowledge representation research, which were dependent on human effort to curate and categorize structured data. The symbolic approach to AI has largely lost the race to the neuro/neuro-symbolic (opaque vectors, statistical) side of AI research, notably the success of the Large Language Model (LLM) paradigm. Due to its faculties with natural language, remarkably little human involvement is needed to infer structure and meaning from traditional (mostly unstructured) information. As such, some of the approaches previously explored in the InfoCentral project have lost relevance. That said, LLM-based tools can perhaps help us finish the symbolic side of the grand AI project, by doing the tedious work that humans were reluctant to do! It has long been theorized that a combination of approaches will ultimately provide the most robust and performant systems, able to work with messy transient unstructured information while also building networks of verifiable persistent structured information that both humans and machines can cross-verify. This may prove useful in both ensuring that future AI systems remain aligned to human goals and values and that inevitable abuses and failures can be easily recovered from. To that end, some research results of this project may be re-usable in a new context.
Ethan Landes, Kathryn B. Francis & Jim A. C. Everett, People defer to AI moral advice, but not blindly - PhilArchive
As AI large language models (LLMs) become increasingly embedded in everyday technologies, should we be concerned about their capacity to influence human beliefs - particularly in the moral domain? Being persuaded ...
