The AI knowledge gap is one of the greatest challenges of our time. Artificial intelligence is revolutionizing how we work and live, but most of us are not able to harness its potential or address its risks. The AI World serves as the one-stop-shop that integrates insights on AI ecosystems, practical applications, market developments, and emerging technologies to empower business leaders, policymakers, investors, students, and citizens to not just understand the AI revolution but to actively shape its future.

The 2024 AI WORLD INDEX (AIWI) is here! Discover which countries are leading the global AI race across patents, investments, research, and jobs. The United States dominates in patents and investments, while China leads in AI research. In Europe, Germany claims the top spot for AI jobs in Europe. How is your country performing in the AI revolution? Find out now!

The job market is transforming rapidly, with AI skills becoming central in 2024. Many job roles are evolving to require proficiency in leveraging AI tools to do more, better and faster. But what specific AI skills are companies prioritizing? Based on Lightcast data, Machine Learning tops the list, with 22% of AI skill demands at the EU level. Meanwhile, cutting-edge technologies like Deep Learning and Generative AI are increasingly revolutionizing hiring trends, signaling the beginning of a new era in workforce development.

This treemap visually represents the proportion of Venture Capital funding received and patents granted by leading companies of the world in the field of Artificial Intelligence. It is mainly a composition of the United States and Asia.

The United States leads the charge in AI investments with OpenAI’s generative AI breakthroughs, Waymo’s self-driving revolution, or Elon Musk’s latest venture, xAI. In China, Moonshot AI dominates their popular chatbot Kimi. Europe’s rising star, Mistral AI, is redefining efficiency and scalability in AI development and pushing the boundaries of Open Source AI. With billions in venture funding explore how these global players are shaping the future of AI, one groundbreaking innovation at a time.

Artificial Intelligence is not just a buzzword - it's a transformative force reshaping our entire economy. This infographic illustrates how AI is revolutionizing all 25 GICS industry groups through multiple use cases. From autonomous vehicles in transportation to predictive analytics in financial services, AI is driving innovation and efficiency pretty much everywhere. As AI continues to evolve, it promises to unlock new possibilities, streamline operations, and create unprecedented value in virtually every industry, making it a critical force that businesses, policy makers, or investors can't ignore. But to thrive and empower humans, AI needs entirely new systems. You can’t introduce a new technology in outdated organizational systems.

Our platform democratizes access to AI literacy by connecting the many dots and complex layers of Artificial Intelligence. Let's start the thread with Deep Learning. This is a foundational technology that enables advanced AI models such as GPT-4. These models, in turn, power the consumer-facing products that we use daily - such as ChatGPT. And, of course, behind ChatGPT we have companies that pour billions of dollars (in this case OpenAI).

AI World showcases real-world applications of AI in areas like grading and assessment, where tasks often require human oversight. By introducing concepts like Human-in-the-Loop, AI World emphasizes the importance of human input in maintaining ethical standards and minimizing biases. Through detailed, interconnected profiles, AI World makes complex AI concepts accessible and easy to understand for everyone.

This section focuses on the large language models with the highest number of parameters, which have driven recent advancements in natural language processing. By analyzing these models, we can better understand the significant computational resources required to train them.

This plot visualizes the exponential growth in the number of parameters of the leading models over time, providing insight into the increasing complexity and computational demands of these cutting-edge AI systems.

This analysis explores the computational resources required for training machine learning models over time, measured in floating-point operations (FLOP). By plotting this data on a logarithmic scale, researchers can observe trends in the growth of computational demands as new models are developed.

This plot helps highlight how advancements in AI are increasingly reliant on greater computational power. The logarithmic scale allows for a clearer visualization of exponential increases, making it easier to track the rapid growth of training compute needs across different publication dates.