“Advancing AI: Human-like reasoning & decision-making”
Discover how advanced AI systems replicate human-like reasoning and decision-making using the Q* Algorithm for efficient problem-solving and inference.
This article discusses the development of an AI Inference System that aims to replicate human-like reasoning and decision-making. The system utilizes the Q* Algorithm to enhance the efficiency of searching within a logical inference system, prioritizing the most promising paths and increasing the speed and accuracy of problem resolution. The article also highlights the usefulness of theorem proving in generative AI, as it enhances the system’s ability to infer new information based on existing knowledge. Additionally, it explores the concepts of linear resolution with selection functions, semantic tree structures, and axiom generation. The article concludes by discussing the importance of path optimization algorithms in creating a graph representation that captures the logical structures and relationships within the system, thereby improving efficiency.
Analysis: This article presents an ambitious vision for AI systems that can reason and make strategic decisions like humans. It emphasizes the importance of the Q* Algorithm for efficient problem-solving and theorem proving within logical systems. The article provides insights into the usefulness of theorem proving in generative AI and highlights the benefits of linear resolution and semantic tree structures. It also touches upon the significance of path optimization algorithms for improving the efficiency of navigating complex logical structures. Overall, the article showcases the ongoing efforts to push the boundaries of AI and create systems that can approach human-level intelligence.
Why customer tools are organized wrong
This article reveals a fundamental flaw in how customer support tools are designed—organizing by interaction type instead of by customer—and explains why this fragmentation wastes time and obscures the full picture you need to help users effectively.
Infrastructure shapes thought
The tools you build determine what kinds of thinking become possible. On infrastructure, friction, and building deliberately for thought rather than just throughput.
Server-side dashboard architecture: Why moving data fetching off the browser changes everything
How choosing server-side rendering solved security, CORS, and credential management problems I didn't know I had.
The work of being available now
A book on AI, judgment, and staying human at work.
The practice of work in progress
Practical essays on how work actually gets done.
The silence that ships
Three projects independently discovered the same bug pattern today — code that reports success when something important didn't happen. The most dangerous failures don't look like failures at all.
When your work moves faster than your rules can keep up, governance quietly becomes theater
I want to talk about something that happened this week that looks like a technical problem but is actually a management problem. And I think it maps onto something most organizations are going to f...
Junior engineers didn't become profitable overnight. The work did.
We've been celebrating that AI made junior engineers profitable. That's not what happened. AI made it economically viable to give them access to work that actually builds judgment, work we always knew
It’s going to take a century for artifical intelligence to be able to perform most human jobs. But there are going to be some key developments during the next decade.
Explore how AI will transform jobs in the next decade, from enhancing security to automating coding, reshaping the future of work.
Many businesses are not yet prepared to fully reap the benefits of AI.
Unlock AI's true potential for your business by integrating it into your strategy, boosting productivity, and enhancing customer experiences.
Rose-tinted predictions for artificial intelligence’s grand achievements will be swept aside by underwhelming performance and dangerous results.
Explore the reality of generative AI in 2024 as hype fades, revealing limitations, job displacement, and the need for regulation.