How models will learn to think fast or slow on demand — and what it means for the future of intelligent systems Introduction When you’re driving and a red light flashes, you react instantly.When you’re making an investment decision, you stop, analyze, and deliberate. Humans instinctively shift between two modes of thought: System 1 —… Continue reading The Next Frontier of AI: Dynamic Thinking Speed
If you’re a non-technical founder, leading a software team can feel like navigating in the dark. You’re investing time, money, and trust—yet weeks can go by without seeing tangible progress. Updates sound like gibberish. Timelines shift. You’re told something was “almost done”… again and again. It’s easy to feel like the engineering process is a… Continue reading No More Black Boxes: How to Gain Visibility Into Your Dev Team’s Progress
Alright, buckle up, because the future of coding is becoming more expressive and creative! After listening to the discussion from Lex Fridman with the Cursor team, here’s where we’re headed: What’s Possible Today: Where We’re Headed in the Future: Why Coding is Still Necessary: In a nutshell: Coding isn’t going away, it’s evolving. AI is… Continue reading Unleashing the Future of AI-Powered Coding: Build Smarter, Faster, and Without Limits
tldr; The development of ChatGPT is a testament to the remarkable progress made in natural language processing (NLP), a subfield of artificial intelligence. Through a series of groundbreaking innovations, NLP models have evolved from simple word counting to sophisticated systems that can understand and generate human-like language. ChatGPT is the culmination of these advancements, leveraging… Continue reading How ChatGPT Became So Intelligent: Unpacking the Legacy of Natural Language Processing
Below I attempt to paraphrase the following paper in plain in English: Better Fine Tuning by Reducing Representational Collapse by Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, and Sonal Gupta from Facebook submitted to Arxiv on August 6th, 2020. tldr Today NLU is about leveraging pre-trained models on large datasets like RoBERTa… Continue reading NLU: A search for a generalizable model that consistently performs well across multiple tasks
The paper End-to-end Differentiable Proving describes a break through way for computers to reason, aka learn first order logic from a knowledge base. Learning to answer questions such as what country is next to Germany or who was George Washington’s nephew? All without being explicitly programmed to do so. The technique is called Neural Theorem… Continue reading Computers Learning to Reason
Intelligence comes in many flavors from kinesthetic intelligence in the sports all star, logical processing in the math genius, or interpersonal skills found in the charismatic sales person. No matter the domain, intelligence is often measured by the information people know or what people can do. Similarly, in order for artificial intelligence to become a… Continue reading Knowledge a necessary but not sufficient ingredient for artificial intelligence
