In a world where over 7,000 languages are spoken, the ability of machines to understand and generate language is no longer a luxury—it’s a necessity. As Large Language Models (LLMs) like ChatGPT, GPT-4, Gemini, and Claude become integral to how we search, write, and interact online, the question arises: Are these models truly multilingual? And if not, what does it mean for global users?

In the fast-moving world of artificial intelligence, few names have sparked as much attention and admiration as Mira Murati. If you’ve followed the rise of OpenAI, the company behind ChatGPT, DALL·E, and other cutting-edge AI tools, then you’ve likely heard her name. But Mira is not just another tech executive. She’s a symbol of what it means to lead with vision, ethics, and humility in an era where technology is changing our lives faster than ever.

In today's rapidly evolving technological landscape, where artificial intelligence and machine learning are transforming industries, the critical factor separating merely functional teams from truly groundbreaking ones isn't found in algorithms or infrastructure—it's embedded in organizational culture.

Artificial Intelligence (AI) has been making remarkable strides in recent years, from voice assistants to self-driving cars, and one of the driving forces behind these advancements is the improvement in machine learning models. But as these models get larger and more complex, they also face challenges related to efficiency and resource consumption. That's where a fascinating concept called Mixture-of-Experts (MoE) comes into play.

In a rapidly evolving field where breakthroughs are measured not only by technological leaps but also by the real‐world impact on communities, Sara Hooker has emerged as a leader who combines technical expertise with a strong commitment to ethical and inclusive AI. 

Artificial Intelligence (AI) is rapidly transforming many areas of our lives. From recommending movies to diagnosing health conditions, AI systems are becoming more powerful and integrated into daily routines. One of the key developments in AI over recent years has been the rise of large language models (LLMs), such as OpenAI’s GPT (like ChatGPT).

Large Language Models (LLMs) are powerful AI tools trained on massive amounts of text data from books, websites, and other sources. They have broad general knowledge, but what if you need them to perform a highly specialized task—like diagnosing diseases, analyzing legal contracts, or generating specific programming code? This is where fine-tuning comes in.

Imagine a world where the discovery of new medicines is faster, more efficient, and more accessible, bringing life-saving therapies to patients in record time. This vision is becoming a reality with the development of TxGemma, a promising new collection of open-source artificial intelligence models specifically designed to accelerate drug and therapy discovery. Built on the powerful Gemma foundation by Google DeepMind, TxGemma is poised to transform the way researchers approach therapeutic development.

In a significant shift within the AI landscape, no-code and low-code AI platforms have emerged as powerful enablers, breaking down the technical barriers that once limited AI adoption.

Large Language Models (LLMs) are trained on vast amounts of text data, making them powerful tools for generating human-like responses. However, they can also reflect and amplify societal biases, leading to unfair or even harmful outputs.

Large Language Models (LLMs) like GPT-4, Gemini, and Claude have transformed AI-driven conversations, coding assistants, and creative writing. But there’s a catch—these models are huge, expensive to run, and slow to respond under heavy load.

Large Language Models (LLMs) like ChatGPT, Claude, and Gemini have revolutionized how we interact with AI. But behind the seamless user experience lies a complex infrastructure that enables these models to handle millions of requests in real time.

Imagine you’ve built a new AI model that generates text. How do you know if it’s any good? Does it write clear and coherent sentences? Can it summarize an article accurately? Does it produce biased or misleading information?

Imagine you’re teaching a child to read. You provide them with books filled with stories, facts, and rules of grammar, helping them develop their understanding of language, the world, and even basic reasoning. Large Language Models (LLMs) like GPT or BERT go through a similar process, but instead of bedtime stories, they are fed massive amounts of text—trillions of words! This phase, known as pre-training, lays the foundation for everything an LLM knows and can do.

The way we work has transformed drastically over the past few years, with remote work becoming the new normal for millions worldwide. As this trend continues, Artificial Intelligence (AI) is emerging as a game-changer in enhancing productivity, collaboration, and overall work experience.

If you’ve ever wondered how AI models like ChatGPT or Google’s Bard generate such human-like responses, the answer lies in the transformer architecture—a revolutionary deep learning model that changed the field of Natural Language Processing (NLP). At the heart of this architecture is a powerful mechanism called self-attention, which enables AI to understand the context of words in a sentence like never before.

Ever wondered how AI understands text? Unlike humans, who read whole words and sentences naturally, AI breaks everything down into tiny pieces before making sense of it. This process is called tokenization, and it’s like translating language into a form that computers can work with.

Every smart AI system, from chatbots to recommendation engines, starts with one crucial thing—data. But have you ever thought about where all that information comes from? How does an AI model “learn” to understand and generate human-like responses? Well, it all starts with gathering and curating massive amounts of text from various sources.

Two AI outsiders just won tech’s highest honor. Richard Sutton and Andrew Barto – once on the fringes of AI – have clinched the Turing Award (often called the “Nobel Prize of Computing”) for their groundbreaking work on reinforcement learning (RL). Why should you care? Because their breakthrough in learning by doing is changing how we make decisions in business and leadership.

Imagine texting with a friend who seems to know just the right thing to say. Now, picture that friend as someone who’s read thousands of books, articles, and stories. That’s a bit like how LLMs work. They’re built to understand and generate language by learning from a massive amount of text.