Imagine a world where you could play DOOM—yes, the iconic 1993 first-person shooter—powered not by a traditional game engine but by a neural network. Thanks to a groundbreaking new AI system called GameNGen, developed by researchers at Google Research, Google DeepMind, and Tel Aviv University, this is no longer a futuristic dream but a reality....
Author: Martin Treiber
What is preference-driven refinement prompting?
Preference-driven refinement prompting is a technique used in AI prompt engineering to tailor the outputs of language models according to specific user preferences. This process involves iteratively refining prompts based on user feedback to achieve desired results. Here’s how it works: Initial Prompt Creation: Start with a basic prompt that outlines what you want the...
Is AI About to Make Developers Redundant?
Software developers, take note: your role might be about to evolve. That’s according to Matt Garman, head of Amazon Web Services (AWS), who recently suggested that AI could soon take over many programming tasks, reshaping the future of development. But before you panic and start brushing up on your résumé for a career change, there’s...
Why are separators important for prompt engineering?
Separators, also known as delimiters, play a crucial role in enhancing the performance and effectiveness of prompts used with Large Language Models (LLMs). The integration of separators within prompting is a strategy inspired by human cognitive processes, aimed at improving the reasoning capabilities of large language models (LLMs). This method, involves strategically placing separators in...
What is the difference between an AI bot and an AI agent?
The terms "AI bot" and "AI agent" are often used interchangeably, but there are some key differences between them. AI bots are typically designed to perform specific tasks or functions, such as responding to customer inquiries or providing information. They are often powered by machine learning and natural language processing, which allows them to learn...
What is Prompt Chaining?
Prompt chaining is a technique used in generative AI models, particularly within the realms of conversational AI and large language models (LLMs). This method involves using the output from one model interaction as the input for the next, creating a series of interconnected prompts that collectively address a complex problem or task[1][2]. This approach contrasts...
What is In-Context Learning of LLMs?
In-context learning (ICL) refers to a remarkable capability of large language models (LLMs) that allows these models to perform new tasks without any additional parameter fine-tuning. This learning approach leverages the pre-existing knowledge embedded within the model, which is activated through the use of task-specific prompts consisting of input-output pairs. Unlike traditional supervised learning that...
Do Emergent Abilities in AI Models Boil Down to In-Context Learning?
Emergent abilities in large language models (LLMs) represent a fascinating area of artificial intelligence, where models display unexpected and novel behaviors as they increase in size and complexity. These abilities, such as performing arithmetic or understanding complex instructions, often emerge without explicit programming or training for specific tasks, sparking significant interest and debate in the...
An introduction to how Large Language Models work
Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by offering unprecedented capabilities in generating coherent and fluent text[1]. The evolution of LLMs can be traced back to early language models that were limited by their simplistic architecture and smaller datasets. These initial models primarily focused on predicting the next word...
Coding in the age of AI
Artificial Intelligence (AI) has been making subtle yet significant inroads into the daily workflows of tech professionals. Despite the lack of mainstream media coverage, these transformative tools are reshaping how work is done, often with profound benefits to individual workers rather than firms. Here, we explore two illustrative accounts from Nicholas Carlini and Erik Schluntz,...
