Stuff we find interesting

Large language models (LLMs) improve their ability to mimic reasoning as they scale by recognizing patterns in vast amounts of data, but they don’t form explicit, rule-based logic like humans. Human reasoning uses symbolic systems that apply consistent rules, while LLMs rely on probabilistic guesses, which can lead to errors. As AI advances, hybrid models that combine LLMs with symbolic reasoning offer the potential for more accurate and reliable reasoning by blending pattern recognition with formal logic.

In recent years, the rapid advancements in artificial intelligence (AI) have sparked widespread speculation about the future of coding. Some believe that the era of manually writing code is coming to an end, soon to be replaced by simply instructing AI through natural language. While this vision holds a kernel of truth, the reality might be far more nuanced. I expect the future of software development will lie in a symbiotic relationship between human coders and AI, where a blend of natural language and precise coding can unlock unprecedented potential.

I believe that a profound transformation is underway, one that promises to reshape our understanding of how services are delivered. This transformation heralds the arrival of what I’m calling "Service as Software," a shift that challenges the traditional Software as a Service (SaaS) model. At its core, Artificial Intelligence (AI) is the driving force behind this change, enabling service companies to transition to a world where software not only supports organisations in service delivery, but actually delivers the service.

Robots spinning pens as skill-fully as any human. NVIDIA Research's latest AI agent, Eureka, has made this a reality.This skill, depicted in a recent video (for those who can't view it, here's a link), is just a glimpse of Eureka's capabilities. To date, robots under Eureka's training have mastered nearly 30 complex tasks, ranging from the casual act of opening drawers to the playful toss and catch of balls.

Alexa, Amazon's voice assistant, recently rolled out some advanced features that promise a significant leap in how we converse with AI.

Artificial Intelligence (AI) is no longer just a tool for human use; it's evolving into systems that can converse and interact with each other. This shift towards AI-to-AI communication has far-reaching implications for various business domains, including marketing, branding, search, sales, and customer support.

The rise of LLMs like ChatGPT and GPT-3 has generated buzz across various industries, from email composition to software code generation. However, with their growing popularity, concerns about their limitations also surface. Here's a look at some challenges LLMs face and the ongoing research to mitigate them

Announced at Google I/O 2023 by CEO Sundar Pichai, Google's upcoming AI system, Gemini, is poised to be a game-changer. Developed by Google DeepMind, it promises to rival and possibly surpass existing language models like OpenAI's ChatGPT

The advent of Large Language Models (LLMs) like GPT-3 and ChatGPT has opened a plethora of possibilities, from drafting emails to coding software. But despite their potential, LLMs aren't psychic; they require skillfully crafted prompts to deliver meaningful outputs. This blog post explores the fascinating world of prompting techniques, spotlighting the intriguing "Chain of Thought" approach.