- Generative AI is becoming increasingly omnipresent — and is a growing hot topic.
- Chatbots such as OpenAI's ChatGPT are changing the way we find information, generate images, and more.
- Here are the people, companies, and words you need to know to understand AI.
It's becoming increasingly impossible to ignore AI in our everyday lives.
Since OpenAI released ChatGPT in late 2022, people have gotten used to using the chatbot in many ways. Workers are turning to AI to automate tasks, while others are using the technology to improve their personal lives.
And as AI continues to advance, there may be a greater need for everyone to understand what it is and how it may affect us.
Here's a list of the people, companies, and terms you need to know to talk about AI, in alphabetical order.
The top AI leaders and companies
Sam Altman: The cofounder and CEO of OpenAI, the company behind ChatGPT. In 2023, Altman was ousted by OpenAI's board before returning to the company as CEO days later.
Dario Amodei: The CEO and cofounder of Anthropic, a major rival to OpenAI, where he previously worked. The AI startup is behind an AI chatbot called Claude 2. Google and Amazon are investors in Anthropic.
Demis Hassabis: The cofounder of DeepMind and now CEO of Google DeepMind, Hassabis leads its AI efforts at Alphabet.
Jensen Huang: The CEO and cofounder of Nvidia, the tech giant behind the specialized chips companies use to power their AI technology.
Elon Musk: The Tesla and SpaceX CEO founded artificial intelligence startup xAI in 2023. The valuation of this new venture has risen dramatically in just 16 months — it now has an estimated valuation of $50 billion, according to recent reports. Musk also cofounded OpenAI, and after leaving the company in 2018, he has maintained a feud with Altman.
Satya Nadella: The CEO of Microsoft, the software giant behind the Bing AI-powered search engine and Copilot, a suite of generative AI tools. Microsoft is also an investor in OpenAI.
Mustafa Suleyman: The cofounder of DeepMind, Google's AI division, who left the company in 2022. He cofounded Inflection AI, before he joined Microsoft as its chief of AI in March 2024.
Mark Zuckerberg: The Facebook founder and Meta CEO who has been spending big to advance Meta's AI capabilities, including training its own models and integrating the technology into its platforms.
The AI terms you need to know
Agentic: A type of artificial intelligence that can make proactive, autonomous decisions with limited human input. Unlike generative AI models like ChatGPT, agentic AI does not need a human prompt to take action — for example, it can perform complex tasks and adapt when objectives change. Google's Gemini 2.0 focuses on agentic AI that can solve multi-step problems on its own.
AGI: "Artificial general intelligence," or the ability of artificial intelligence to perform complex cognitive tasks such as displaying self-awareness and critical thinking the way humans do.
Alignment: A field of AI safety research that aims to ensure that the goals, decisions, and behaviors of AI systems are consistent with human values and intentions. In July 2023 OpenAI announced a "Superalignment" to focus on making its AI safe. That team was later disbanded and in May the company set up a safety and security committee to advise the board on "critical safety and security decisions."
Biden's executive order on AI:Biden signed this landmark executive order, officially called the "Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence," in 2023. It does a number of things to try to regulate the development of AI, including demanding greater transparency from tech companies producing AI, setting new standards for AI safety and security, and taking steps to ensure the US stays competitive in AI research and development. President-elect Donald Trump has vowed to rescind this order.
Compute: The AI computing resources needed to train models and carry out tasks, including processing data. This can include GPUs, servers, and cloud services.
Deepfake: An AI-generated image, video, or voice meant to appear real which tends to be used to deceive viewers or listeners. Deepfakes have been used to create non-consensual pornography and extort people for money.
Effective altruists: Broadly speaking, this is a social movement that stakes its claim in the idea that all lives are equally valuable and those with resources should allocate them to helping as many as possible.And in the context of AI, effective altruists, or EAs, are interested in how AI can be safely deployed to reduce suffering caused by social ills like climate change and poverty. Figures including Elon Musk, Sam Bankman-Fried, and Peter Thiel identify as effective altruists. (See also: e/accs and decels).
Frontier models: Refers to the most advanced examples of AI technology. The Frontier Model Forum — an industry nonprofit launched by Microsoft, Google, OpenAI, and Anthropic in 2023 — defines frontier models as "large-scale machine-learning models that exceed the capabilities currently present in the most advanced existing models, and can perform a wide variety of tasks."
GPU: A computer chip, short for graphic processing unit, that companies use to train and deploy their AI models. Nvidia's GPUs are used by Microsoft and Meta to run their AI models.
Hallucinations: A phenomenon where a large language model (see below) generates inaccurate information that it presents as a fact. For example, during an early demo, Google's AI chatbot Bard hallucinated by generating a factual error about the James Webb Space Telescope.
Large language model: A complex computer program designed to understand and generate human-like text. The model is trained on large amounts of data and produces answers by scraping information across the web. Examples of LLMs include OpenAI's GPT-4, Meta's Llama 2, and Google's Gemini.
Machine learning: Also known as deep learning, machine learning refers to AI systems that can adapt and learn on their own, without following human instructions or explicit programming.
Multimodal: The ability for AI models to process text, images, and audio to generate an output. Users of ChatGPT, for instance, can now write, speak, and upload images to the AI chatbot.
Natural language processing: The umbrella term encompasses a variety of methods for interpreting and understanding human language. LLMs are one tool for interpreting language within the field of NLP.
Neural network: A machine learning program designed to think and learn like a human brain. Facial recognition systems, for instance, are designed using neural networks in order to identify a person by analyzing their facial features.
Open-source: A trait used to describe a computer program that anyone can freely access, use, and modify without asking for permission. Some AI experts have called for models behind AI, like ChatGPT, to be open-source so the public knows how exactly they are trained.
Optical character recognition: OCR is technology that can recognize text within images — like scanned documents, text in photos, and read-only PDFs — and extract it into text-only format that machines can read.
Prompt engineering: The process of asking AI chatbots questions that can produce desired responses. As a profession, prompt engineers are experts in fine tuning AI models on the backend to improve outputs.
Rationalists: People who believe that the most effective way to understand the world is through logic, reason, and scientific evidence. They draw conclusions by gathering evidence and critical thinking rather than following their personal feelings.
When it comes to AI, rationalists seek to answer questions like how AI can be smarter, how AI can solve complex problems, and how AI can better process information around risk. That stands in opposition to empiricists, who in the context of AI, may favor advancements in AI backed by observational data.
Responsible scaling policies: Guidelines for AI developers to follow that are designed to mitigate safety risks and ensure the responsible development of AI systems, their impact on society, and the resources they will consume, such as energy and data. Such policies help ensure that AI is ethical, beneficial, and sustainable as systems become more powerful.
Singularity: A hypothetical moment where artificial intelligence becomes so advanced that the technology surpasses human intelligence. Think of a science fiction scenario where an AI robot develops agency and takes over the world.