The launch of ChatGPT in late 2022 has revolutionized the way people perceive and interact with technology. This groundbreaking AI chatbot allows users to engage in meaningful conversations, posing questions in natural language and receiving responses that resemble those of a human. The transformative nature of this technology has prompted major tech giants such as Google, Meta, Microsoft, and Apple to swiftly integrate AI into their suite of products. While the conversational capabilities of AI chatbots are just one facet of the broader AI landscape, the potential of generative AI holds the power to reshape economies on a global scale.
According to the McKinsey Global Institute, the annual value of generative AI to the global economy could reach a staggering $4.4 trillion. This projection underscores the immense significance of artificial intelligence and highlights why it will continue to dominate discussions in the coming years. The applications of AI are vast and diverse, with a myriad of products already making their mark. Notable examples include Google’s Gemini, Microsoft’s Copilot, Anthropic’s Claude, the Perplexity AI search tool, as well as innovations from Humane and Rabbit. Our AI Atlas hub provides comprehensive reviews, hands-on evaluations, news updates, explainers, and how-to posts on these and other AI products.
As society becomes increasingly accustomed to a world intertwined with AI, new terms and concepts are emerging at a rapid pace. Whether you aim to impress during a job interview or engage in knowledgeable conversations over drinks, familiarizing yourself with key AI terms is essential. To assist you in this endeavor, we have compiled a regularly updated glossary of important AI terms. This glossary covers a wide range of topics, including Artificial General Intelligence (AGI), agentive systems, AI ethics, AI safety, algorithms, alignment, anthropomorphism, artificial intelligence, autonomous agents, bias, chatbots, cognitive computing, data augmentation, deep learning, diffusion, emergent behavior, end-to-end learning (E2E), ethical considerations, foom, generative adversarial networks (GANs), generative AI, Google Gemini, guardrails, hallucination, large language models (LLMs), machine learning (ML), Microsoft Bing, multimodal AI, natural language processing, neural networks, overfitting, paperclips, parameters, prompt, prompt chaining, stochastic parrot, style transfer, temperature, text-to-image generation, tokens, training data, transformer model, and the Turing test.
The field of AI is rapidly evolving, and its implications extend far beyond the realm of technology. Ethical considerations surrounding AI’s impact on privacy, data usage, fairness, and safety are of paramount importance. Additionally, the potential for unintended consequences, such as the emergence of superintelligence or the perpetuation of biases, necessitates ongoing research and vigilance.
While AI continues to push boundaries and unlock new possibilities, it is crucial to maintain a balanced understanding of its capabilities. AI models, including large language models, operate based on patterns and data rather than possessing a comprehensive understanding of the world or the meaning behind language. It is essential to exercise caution and critical thinking when interpreting AI-generated content.