In the rapidly evolving world of technology, artificial intelligence (AI) is the frontier garnering significant attention and interest. From the powerful AI-chatbot, ChatGPT by OpenAI, which has been likened to an autocomplete function on steroids due to its ability to answer any question, to the creation of fascinating images by Midjourney, AI has become an integral part of our daily lives. These chatbots, however, represent merely one facet of the vast AI landscape that continues to grow, with potential implications that could dramatically reshape economies, potentially contributing an estimated $4.4 trillion to the global economy annually, according to the McKinsey Global Institute.
As we become more accustomed to a world interwoven with AI, new terms and concepts are constantly emerging. Whether you’re looking to impress in a job interview, or just trying to sound up-to-date over drinks, it’s important to familiarize oneself with these key terms. From ‘Artificial General Intelligence’ (AGI), a concept that proposes an advanced version of AI that surpasses human capabilities, to ‘AI Ethics’, which focuses on preventing AI from causing harm to humans, this comprehensive glossary offers a deep dive into the complex world of AI. As technology continues to advance at an unprecedented pace, this glossary will be continuously updated to keep you in the know.
Making Sense of the AI Landscape: Key Terms to Know
Artificial Intelligence (AI), once a concept confined to the realms of science fiction, has now permeated our everyday lives. From AI chatbots like OpenAI’s ChatGPT that can answer any question, to AI models that create fascinating images, the potential of AI is vast. According to McKinsey Global Institute, AI’s potential could be worth $4.4 trillion to the global economy annually. As we continue to embrace this tech revolution, it’s important to familiarize ourselves with the key terms in this field.
A New Lexicon for the AI Era
Artificial general intelligence (AGI) refers to an advanced version of AI that can perform tasks far better than humans, while also teaching and advancing its own capabilities. On the other hand, AI ethics are principles designed to prevent AI from harming humans, achieved through means such as determining how AI systems should collect data or deal with bias. AI safety is an interdisciplinary field that’s concerned with the long-term impacts of AI and how it could suddenly progress to a superintelligence that could be hostile to humans.
Algorithm is a term used to describe a series of instructions that allows a computer program to learn and analyze data in a particular way, such as recognizing patterns. Alignment refers to tweaking an AI to better produce the desired outcome. Anthropomorphism is when humans tend to give nonhuman objects humanlike characteristics. In AI, this can include believing a chatbot is more humanlike and aware than it actually is.
The Intricacies of AI Models
Generative adversarial networks (GANs) are a form of generative AI model composed of two neural networks – a generator and a discriminator. The generator creates new content, and the discriminator checks to see if it’s authentic. Generative AI is a technology that uses AI to create text, video, computer code or images. Large language model (LLM) is an AI model trained on mass amounts of text data to understand language and generate novel content in human-like language.
Multimodal AI is a type of AI that can process multiple types of inputs, including text, images, videos and speech. Neural network is a computational model that resembles the human brain’s structure and is meant to recognize patterns in data. Overfitting is an error in machine learning where the model functions too closely to the training data and may only be able to identify specific examples in said data but not new data.
Navigating AI’s Potential Pitfalls
In the world of AI, there are several potential pitfalls and ethical considerations. Bias can result in errors in large language models, falsely attributing certain characteristics to certain races or groups based on stereotypes. Guardrails refer to policies and restrictions placed on AI models to ensure data is handled responsibly and that the model doesn’t create disturbing content.
Hallucination is a term used to describe an incorrect response from AI, such as generative AI producing answers that are incorrect but stated with confidence as if correct. Foom, also known as fast takeoff or hard takeoff, is the concept that if someone builds an AGI, it might already be too late to save humanity.
Takeaways
As we continue to integrate AI into our lives, understanding the language of AI becomes increasingly important. The potential of AI is vast, but so too are the ethical considerations and potential pitfalls. It is crucial to stay informed about this rapidly developing field to leverage its benefits while mitigating its risks. AI is not just about advanced technology; it’s about shaping our future in a responsible and ethical manner. The more we understand AI, the better we can navigate this new landscape.
