Terminologia AI, wyjaśniona dla ludzi

Artificial intelligence is the hot fresh thing in tech — it feels like all company is talking about how it’s making strides by utilizing or developing AI. But the field of AI is besides so filled with jargon that it can be remarkably hard to realize what’s actually happening with each fresh development.

To aid you better realize what’s going on, we’ve put together a list of any of the most common AI terms. We’ll do our best to explain what they mean and why they’re important.

Artificial intelligence: frequently shortened to AI, the word “artificial intelligence” is technically the discipline of computer discipline that’s dedicated to making computer systems that can think like a human.

But right now, we’re mostly proceeding about AI as a technology and or even an entity, and what precisely that means is harder to pin down. It’s besides frequently utilized as a marketing buzzword, which makes its definition more mutable than it should be.

Google, for example, talks a lot about how it’s been investing in AI for years. That refers to how many of its products are improved by artificial intelligence and how the company offers tools like Gemini that appear to be intelligent, for example. There are the underlying AI models that power many AI tools, like OpenAI’s GPT. Then, there’s Meta CEO Mark Zuckerberg, who has utilized AI as a noun to mention to individual chatbots.

As more companies effort to sale AI as the next large thing, the ways they usage the word and another related nomenclature might get even more confusing. There are a bunch of phrases you are likely to come across in articles or marketing about AI, so to aid you better realize them, I’ve put together an overview of many of the key terms in artificial intelligence that are presently being bandied about. Ultimately, however, it all boils down to trying to make computers smarter.

(Note that I’m only giving a rudimentary overview of many of these terms. Many of them can frequently get very scientific, but this article should hopefully give you a grasp of the basics.)

Machine learning: device learning systems are trained (we’ll explain more about what training is later) on data so they can make predictions about fresh information. That way, they can “learn.” device learning is simply a field within artificial intelligence and is critical to many AI technologies.

Artificial general intelligence (AGI): Artificial intelligence that’s as smart or smarter than a human. (OpenAI in peculiar is investing heavy into AGI.) This could be incredibly powerful technology, but for quite a few people, it’s besides possibly the most frightening possible about the possibilities of AI — think of all the movies we’ve seen about superintelligent machines taking over the world! If that isn’t enough, there is besides work being done on “superintelligence,” or AI that’s much smarter than a human.

Generative AI: An AI technology capable of generating fresh text, images, code, and more. Think of all the interesting (if occasionally problematic) answers and images that you’ve seen being produced by ChatGPT or Google’s Gemini. Generative AI tools are powered by AI models that are typically trained on vast amounts of data.

Hallucinations: No, we’re not talking about weird visions. It’s this: due to the fact that generative AI tools are only as good as the data they’re trained on, they can “hallucinate,” or confidently make up what they think are the best responses to questions. These hallucinations (or, if you want to be completely honest, bullshit) mean the systems can make factual errors or give gibberish answers. There’s even any controversy as to whether AI hallucinations can always be “fixed.”

Bias: Hallucinations aren’t the only problems that have come up erstwhile dealing with AI — and this 1 might have been predicted since AIs are, after all, programmed by humans. As a result, depending on their training data, AI tools can show biases. For example, 2018 investigation from Joy Buolamwini, a computer scientist at MIT Media Lab, and Timnit Gebru, the founder and executive manager of the Distributed Artificial Intelligence investigation Institute (DAIR), co-authored a paper that illustrated how facial designation software had higher mistake rates erstwhile attempting to identify the sex of darker-skinned women.

AI model: AI models are trained on data so that they can execute tasks or make decisions on their own.

Large language models, or LLMs: A kind of AI model that can process and make natural language text. Anthropic’s Claude, which, according to the company, is “a helpful, honest, and harmless assistant with a conversational tone,” is an example of an LLM.

Diffusion models: AI models that can be utilized for things like generating images from text prompts. They are trained by first adding sound — specified as static — to an image and then reversing the process so that the AI has learned how to make a clear image. There are besides diffusion models that work with audio and video.

Foundation models: These generative AI models are trained on a immense amount of data and, as a result, can be the foundation for a wide variety of applications without circumstantial training for those tasks. (The word was coined by Stanford researchers in 2021.) OpenAI’s GPT, Google’s Gemini, Meta’s Llama, and Anthropic’s Claude are all examples of foundation models. Many companies are besides marketing their AI models as multimodal, meaning they can process multiple types of data, specified as text, images, and video.

Frontier models: In addition to foundation models, AI companies are working on what they call “frontier models,” which is fundamentally just a marketing word for their unreleased future models. Theoretically, these models could be far more powerful than the AI models that are available today, though there are besides concerns that they could pose crucial risks.

Well, they’re trained. Training is simply a process by which AI models learn to realize data in circumstantial ways by analyzing datasets so they can make predictions and admit patterns. For example, large language models have been trained by “reading” vast amounts of text. That means that erstwhile AI tools like ChatGPT respond to your queries, they can “understand” what you are saying and make answers that sound like human language and address what your query is about.

Training frequently requires a crucial amount of resources and computing power, and many companies trust on powerful GPUs to aid with this training. AI models can be fed different types of data, typically in vast quantities, specified as text, images, music, and video. This is — logically adequate — known as training data.

Parameters, in short, are the variables an AI model learns as part of its training. The best description I’ve found of what that actually means comes from Helen Toner, the manager of strategy and foundational investigation grants at Georgetown’s Center for safety and Emerging Technology and a erstwhile OpenAI board member:

Parameters are the numbers inside an AI model that find how an input (e.g., a chunk of prompt text) is converted into an output (e.g., the next word after the prompt). The process of ‘training’ an AI model consists in utilizing mathematical optimization techniques to tweak the model’s parameter values over and over again until the model is very good at converting inputs to outputs.

In another words, an AI model’s parameters aid find the answers that they will then spit out to you. Companies sometimes boast about how many parameters a model has as a way to show that model’s complexity.

Natural language processing (NLP): The ability for machines to realize human language thanks to device learning. OpenAI’s ChatGPT is simply a basic example: it can realize your text queries and make text in response. Another powerful tool that can do NLP is OpenAI’s Whisper speech designation technology, which the company reportedly used to transcribe audio from more than 1 million hours of YouTube videos to aid train GPT-4.

Inference: When a generative AI application actually generates something, like ChatGPT responding to a request about how to make chocolate chip cookies by sharing a recipe. This is the task your computer does erstwhile you execute local AI commands.

Tokens: Tokens mention to chunks of text, specified as words, parts of words, or even individual characters. For example, LLMs will break text into tokens so that they can analyse them, find how tokens relate to each other, and make responses. The more tokens a model can process at erstwhile (a quantity known as its “context window”), the more sophisticated the results can be.

Neural network: A neural network is computer architecture that helps computers process data utilizing nodes, which can be kind of compared to a human’s brain’s neurons. Neural networks are critical to popular generative AI systems due to the fact that they can learn to realize complex patterns without explicit programming — for example, training on medical data to be able to make diagnoses.

Transformer: A transformer is simply a kind of neural network architecture that uses an “attention” mechanics to process how parts of a series relate to each other. Amazon has a good example of what this means in practice:

Consider this input sequence: “What is the colour of the sky?” The transformer model uses an interior mathematical representation that identifies the relevancy and relation between the words color, sky, and blue. It uses that cognition to make the output: “The sky is blue.”

Not only are transformers very powerful, but they can besides be trained faster than another types of neural networks. Since erstwhile Google employees published the first paper on transformers in 2017, they’ve become a immense reason why we’re talking about generative AI technologies so much right now. (The T in ChatGPT stands for transformer.)

RAG: This acronym stands for “retrieval-augmented generation.” erstwhile an AI model is generating something, RAG lets the model find and add context from beyond what it was trained on, which can improve accuracy of what it yet generates.

Let’s say you ask an AI chatbot something that, based on its training, it doesn’t actually know the answer to. Without RAG, the chatbot might just hallucinate a incorrect answer. With RAG, however, it can check external sources — like, say, another sites on the net — and usage that data to aid inform its answer.

Nvidia’s H100 chip: 1 of the most popular graphics processing units (GPUs) utilized for AI training. Companies are clamoring for the H100 due to the fact that it’s seen as the best at handling AI workloads over another server-grade AI chips. However, while the extraordinary request for Nvidia’s chips has made it among the world’s most valuable companies, many another tech companies are developing their own AI chips, which could eat distant at Nvidia’s grasp on the market.

Neural processing units (NPUs): Dedicated processors in computers, tablets, and smartphones that can execute AI inference on your device. (Apple uses the word “neural engine.”) NPUs can be more efficient at doing many AI-powered tasks on your devices (like adding background blur during a video call) than a CPU or a GPU.

TOPS: This acronym, which stands for “trillion operations per second,” is simply a word tech vendors are utilizing to boast about how capable their chips are at AI inference.

There are many companies that have become leaders in developing AI and AI-powered tools. any are entrenched tech giants, but others are newer startups. Here are a fewer of the players in the mix:

• OpenAI / ChatGPT: The reason AI is specified a large deal right now is arguably thanks to ChatGPT, the AI chatbot that OpenAI released in late 2022. The explosive popularity of the service mostly caught large tech players off-guard, and now beautiful much all another tech company is trying to boast about their AI prowess.
• Microsoft / Copilot: Microsoft is baking Copilot, its AI assistant powered by OpenAI’s GPT models, into as many products as it can. The Seattle tech giant besides has a 49 percent stake in OpenAI.
• Google / Gemini: Google is racing to power its products with Gemini, which refers both to the company’s AI assistant and its various flavors of AI models.
• Meta / Llama: Meta’s AI efforts are all around its Llama (Large Language Model Meta AI) model, which, unlike the models from another large tech companies, is open source.
• Apple / Apple Intelligence: Apple is adding fresh AI-focused features into its products under the banner of Apple Intelligence. 1 large fresh feature is the availability of ChatGPT right inside Siri.
• Anthropic / Claude: Anthropic is an AI company founded by erstwhile OpenAI employees that makes the Claude AI models. Amazon has invested $4 billion in the company, while Google has invested hundreds of millions (with the possible to invest $1.5 billion more). It late hired Instagram cofounder Mike Krieger as its chief product officer.
• xAI / Grok: This is Elon Musk’s AI company, which makes Grok, an LLM. It late raised $6 billion in funding.
• Perplexity: Perplexity is another AI company. It’s known for its AI-powered search engine, which has come under scrutiny for seemingly sketchy scraping practices.
• Hugging Face: A platform that serves as a directory for AI models and datasets.