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...
Category: AI
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...
Women are less interested in AI than men, but using it would help them advance at work
Women use generative artificial intelligence tools less than men do. The World Economic Forum recently published an article on the subject. It reported that 59 per cent of male workers aged between 18 and 65 use generative artificial intelligence at least once a week, compared with 51 per cent of women. Among young people aged...
AIs encode language like brains do − opening a window on human conversations
Language enables people to transmit thoughts to each other because each person’s brain responds similarly to the meaning of words. In our newly published research, my colleagues and I developed a framework to model the brain activity of speakers as they engaged in face-to-face conversations. We recorded the electrical activity of two people’s brains as...
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,...
What is quantization of LLMs?
Quantization, a compression technique, has long been utilized in various fields to map high precision values to lower precision ones, thus making data more manageable and less memory-intensive[1][2]. The advent of Large Language Models (LLMs) has necessitated the adoption of such techniques due to the exponential increase in model parameters and the associated computational demands....
What is AI model collapse?
AI model collapse is a phenomenon in artificial intelligence (AI) where trained models, especially those relying on synthetic data or AI-generated data, degrade over time. This degradation is characterized by increasingly limited output diversity, a tendency to stick to “safe” responses, and a reduced ability to generate creative or original content[1]. The phenomenon has significant...
