The hottest Reasoning Substack posts right now

And their main takeaways
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Top Technology Topics

The latest open artifacts (#6): Reasoning models, China's lead in open-source, and a growing multimodal space

Democratizing Automation β€’ 261 implied HN points β€’ 27 Jan 25
πŸ•Ή Technology AI Models Open Source Datasets Reasoning Geopolitics
  1. Chinese AI labs are now leading the way in open-source models, surpassing their American counterparts. This shift could have significant impacts on global technology and geopolitics.
  2. A variety of new AI models and datasets are emerging, particularly focused on reasoning and long-context capabilities. These innovations are making it easier to tackle complex tasks in coding and math.
  3. Companies like IBM and Microsoft are quietly making strides with their AI models, showing that many players in the market are developing competitive technology that might not get as much attention.

Do OpenAI's New Reasoning Models (o1 Series) Differ Politically from Their Predecessors?

Rozado’s Visual Analytics β€’ 150 implied HN points β€’ 28 Jan 25
πŸ•Ή Technology AI Models Data Analysis Reasoning Political Bias Model development
  1. OpenAI's new o1 models are designed to solve problems better by thinking through their answers first. However, they are much slower and cost more to run than previous models.
  2. The political preferences of these new models are similar to earlier versions, despite the new reasoning abilities. This means they still lean left when answering political questions.
  3. Even with their advanced reasoning, these models didn't change their political views, which leads to questions about how reasoning and political bias work together in AI.

How Does "Reasoning" Work?

In My Tribe β€’ 379 implied HN points β€’ 04 Feb 25
πŸ•Ή Technology AI Computer Science Philosophy Reasoning
  1. Reasoning in AI often involves finding and using analogies to solve problems. Just like a chess program cuts down on bad moves, AI looks for the best comparisons to answer a question.
  2. Human thought relies heavily on metaphors, which are used to understand new ideas. These metaphors can be good or bad depending on how well they fit the situation.
  3. Both humans and AI have strengths and weaknesses in reasoning. AI can be quicker but may miss the deeper meaning in a question, while humans can make creative leaps but might take longer.

Gain of Function.

News Items β€’ 471 implied HN points β€’ 18 Jan 24
πŸ”¬ Science AI Mathematics Reasoning Data generation Training
  1. AlphaGeometry AI system solves complex geometry problems as well as a human Olympiad gold-medalist.
  2. AlphaGeometry combines neural language model with a rule-bound deduction engine for reasoning.
  3. Development of AlphaGeometry highlights AI's logic reasoning progress and ability to discover and verify new knowledge.
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Meta-rational practices: Overview 1

Meaningness β€’ 219 implied HN points β€’ 09 Mar 24
πŸ“– Philosophy Meta-rationality Norms Practices Reasoning
  1. Meta-rationality is different from traditional rationality and requires an open-ended inquiry and responsiveness to various contexts and purposes.
  2. Meta-rationality involves ongoing consideration of when and how to apply rationality, recognizing situations where rational methods may not be sufficient.
  3. The norms of reasonableness, rationality, and meta-rationality differ in terms of accountability, formal rules adherence, and responsiveness to context and purpose.

Chain of Thought Prompting for LLMs

Deep (Learning) Focus β€’ 294 implied HN points β€’ 24 Apr 23
πŸ•Ή Technology AI Deep Learning Language Models Reasoning Prompting
  1. CoT prompting leverages few-shot learning in LLMs to improve their reasoning capabilities, especially for complex tasks like arithmetic, commonsense, and symbolic reasoning.
  2. CoT prompting is most beneficial for larger LLMs (>100B parameters) and does not require fine-tuning or extensive additional data, making it an easy and practical technique.
  3. CoT prompting allows LLMs to generate coherent chains of thought when solving reasoning tasks, providing interpretability, applicability, and computational resource allocation benefits.

The Liar Paradox (and Russell's Paradox) for Non-Logicians (UNLOCKED)

Philosophy for the People w/Ben Burgis β€’ 399 implied HN points β€’ 22 Jan 23
πŸ“– Philosophy Logic Consistency Reasoning
  1. The Liar Paradox questions whether statements can be both true and false, challenging fundamental logical principles like Bivalence and the Law of the Excluded Middle.
  2. Russell's Paradox, on the other hand, questions the existence of sets based on self-referential properties, leading to contradictions like a set that contains itself and doesn't.
  3. The debates around these paradoxes highlight the importance of classical logic principles like the Law of Non-Contradiction and Disjunctive Syllogism in everyday reasoning and understanding the world.

3 Tests AGI Must Pass

Insight Axis β€’ 237 implied HN points β€’ 27 Aug 23
πŸ•Ή Technology AI Computing Testing Reasoning Creativity
  1. Computers must excel at calculations to form the foundation for any further intelligence programming.
  2. After calculation, computers need to progress to reasoning - the ability to evaluate information and use it to make value-based decisions.
  3. The ultimate test for artificial intelligence is creativity - the capability to acknowledge rules but break them intuitively to create something new.

Program-Aided Language Models

Deep (Learning) Focus β€’ 196 implied HN points β€’ 22 May 23
πŸ•Ή Technology AI Programming Reasoning Language Models Deep Learning
  1. LLMs can struggle with tasks like arithmetic and complex reasoning, but using an external code interpreter can help them compute solutions more accurately.
  2. Program-Aided Language Models (PaL) and Program of Thoughts (PoT) techniques leverage both natural language and code components to enhance reasoning capabilities of LLMs.
  3. Decoupling reasoning from computation within LLMs through techniques like PaL and PoT can significantly improve performance on complex numerical tasks.

#108 It's a process

Design Lobster β€’ 299 implied HN points β€’ 02 May 22
πŸͺ‘ Design Process Reasoning Events Quotations
  1. The design process can sometimes feel like magic when a solution comes together, often due to abductive reasoning that brings out novel ideas.
  2. Creativity thrives in spaces outside of formal work processes, like in unscheduled moments or unconventional events like 'unconferences'.
  3. Design work is a continuous journey of developing new understandings and appreciations as you navigate through the stages, emphasizing the importance of flexibility in thinking.

Reasonableness: An Introduction

The End(s) of Argument β€’ 39 implied HN points β€’ 10 Jun 23
🚌 Education Beliefs Reasoning Misinformation Evidence Argumentation
  1. Two primary accounts of the relation between evidence and belief in misinformation research are naive and non-naive models, both with limitations.
  2. People's pursuit of reasonableness influences how they collect and share evidence to support their beliefs, aiming to seem rational to others.
  3. Beliefs are often maintained through a balance of evidence and perceived reasonableness, impacting how individuals process and adopt new information.

Understanding human stupidity in a post-truth era

Klement on Investing β€’ 9 implied HN points β€’ 05 Jan 24
πŸ“– Philosophy Human nature Reasoning Social influence Education Satire
  1. Human stupidity involves a temporary inability to properly reason, plan, or learn.
  2. Stubbornness often accompanies stupidity, making people hold on to disproven beliefs.
  3. In a post-truth era, combating human stupidity requires strong institutions, satire, education, and sometimes allowing people to face the consequences of their beliefs.

Can Large Language Models Reason?

AI: A Guide for Thinking Humans β€’ 4 HN points β€’ 10 Sep 23
πŸ”¬ Science Reasoning Language Models Evaluation Pattern matching
  1. There is a debate about whether large language models have reasoning abilities similar to humans or rely more on memorization and pattern-matching.
  2. Models like CoT prompting try to elicit reasoning abilities in these language models and can enhance their performance.
  3. However, studies suggest that these models may rely more on memorization and pattern-matching from their training data than true abstract reasoning.

GRANTED: Reasoning with unreasonable people

Granted β€’ 0 implied HN points β€’ 31 Jan 21
🚌 Education Reasoning Learning Debate Teaching
  1. The primary goal of a university is more about promoting the pursuit of knowledge rather than just teaching skills.
  2. In arguments, rather than preaching or prosecuting, try treating it like an interview by asking questions to help others consider their reasons for change.
  3. Recognize the 'I'm-not-biased' bias by understanding that knowing what you don't know is wisdom.

post hoc ergo propter hoc

ThΓ‘i | Hacker | Kα»Ή sΖ° tin tαΊ·c β€’ 0 implied HN points β€’ 17 May 08
πŸ“– Philosophy Logic Reasoning Ethics Metaphysics
  1. Post hoc ergo propter hoc is a logical fallacy that assumes because event A happened before event B, then A caused B. It's important to provide clear evidence to support conclusions rather than relying on chronological order.
  2. Critically analyzing the logical errors in arguments, such as post hoc ergo propter hoc, is crucial for effective reasoning and debate. It helps avoid making unfounded assumptions and faulty conclusions.
  3. Blaming external factors like the field of study, educational programs, or instructors for personal failures is a common mistake. Taking responsibility for one's actions and attitude towards learning is essential for success.

An Introduction to the Philosophies of Baruch Spinoza

Wayne's Earth β€’ 0 implied HN points β€’ 15 Dec 22
πŸ“– Philosophy God Ethics Reasoning Morality History
  1. Spinoza believed that God and nature are one and the same, a view known as pantheism, which suggests that all things in nature are expressions of a single divine force. This unity prompts us to appreciate and honor nature.
  2. Spinoza advocated for an ethical system grounded in reason rather than emotions or religious doctrines. He emphasized judging actions by their consequences and thinking critically about decisions to prevent harm to others.
  3. Even centuries later, Spinoza's ideas on God, nature, ethics, and reason are influential. His insights are appreciated by modern philosophers, highlighting his timeless wisdom and significant impact on philosophical thought.

RAT β€” Retrieval Augmented Thoughts

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots β€’ 0 implied HN points β€’ 13 Mar 24
πŸ•Ή Technology AI Generative Machine Learning Reasoning Applications
  1. RAT combines two methods: Chain-of-Thought (CoT) prompting and retrieval augmented generation (RAG). It helps improve complex reasoning tasks by revising thoughts step-by-step.
  2. Finding a balance between efficiency and accuracy is important when using AI tools. Too many checks can slow down the process, but having high accuracy is crucial for user satisfaction.
  3. Using RAT shows better performance in tasks like coding and creative writing compared to other methods. This approach helps avoid mistakes and ensures more accurate responses.