Tokens—numbers assigned to words and subwords—are the basic units of data that AI tools process to generate outputs that mimic language without understanding the text itself.

Transformer architecture can recognize and predict patterns in language

The underlying software powering text generation in AI tools associates each word or subword—called a token—with a set of values corresponding to how often it appears near other tokens. By recognizing these associations in prompts, the LLM can infer meaning.

Visualize how transformer architecture generates a large language model's outputs by running words and subwords assigned to specific numbers through multiple rounds of mathematical processing.

Find out how important you are to AI tools when they generate text responses with this tool, which estimates the weights of people's names.

Unlike generative AI systems, such as chatbots that do not take follow-up actions after generating their output, agentic AI systems proactively take steps to achieve goals without continuous user input.

LLMs are the backbone of AI tools that process and generate natural language

Large language models identify patterns in massive datasets of books, code, and unlabeled text from which to generate coherent responses. GPT-3 was trained on about 45 terabytes of data and uses 175 billion parameters—each parameter being a value the model adjusts as it learns.

Transformer architecture was the breakthrough that made AI chatbots possible

It changed how text was translated from a sequential, "one word at a time" method to one where every word in a text is processed in parallel. Advances in positional encoding and self-attention also helped models recognize context and word order better.

In 2023, OpenAI spent $520 million on ChatGPT

The ongoing development and refinement of new AI models requires larger datasets and processing more parameters between words and subwords to improve the accuracy of generated content. New startups are expected to harness this technology to provide unthought-of products and services.

AI gained mainstream attention with tools like IBM Watson and Apple’s Siri

With the release of ChatGPT in 2022, which drew over 100 million weekly users in just two months, natural language processing and understanding could be achieved at scale via machine learning. Unlike earlier artificial intelligence that could pull stored knowledge, generative AI produces text, images, or sounds.

Because they are built on content produced by humans, who possess biases, generative AI tools inherit prejudices and stereotypes from training data, which later appear in their outputs.

Graphics processing units, which process large amounts of data in parallel, have become the foundational hardware for AI systems that rely on hardware capable of processing millions of text parameters simultaneously.

Anthropic's interpretability team discusses how AI models' thinking mimics biology

Just as humans engage in complex behaviors to survive and reproduce, teams of experts in neuroscience, virology, mathematics, and other disciplines argue that large language models develop complex mechanisms to achieve their goals. These mechanisms can be manipulated to assess how they modify outputs—much like stimulating individual neurons—to uncover how LLMs "think."

Generative AI tools excel at pattern recognition, not contextual accuracy

Large language models are trained on vast amounts of unstructured data, from which they develop parameters for grammar and associations between words. These connections can introduce errors due to inapplicable reasoning when used on new data in unfamiliar contexts.

EOS and maximum length tokens tell an LLM when to stop generating a response

Tokens are words or subwords (e.g., prefixes) that a large language model processes to generate a response. During training, the model is taught that if it ever generates a special token called end-of-sequence, its response is complete.

LLMs explained simply and quickly

Large language models generate responses by predicting one token at a time, based on patterns they’ve learned from massive datasets. This explainer covers their core components: data, architecture, training, and how transformer models use context to simulate conversation.

How ChatGPT learns to read

The introduction of ChatGPT last fall jumpstarted public interest in the future capabilities of artificial intelligence and what it means for society. Of many somewhat "jargony" terms thrown around, the phrase "Large Language Model" is commonly seen—but also incredibly unclear as to what it means. In the simplest sense, the programs comb through mountains of internet data in response to a question and guess at what should come next (subscription needed).

The AI that creates any picture you want and how it works

Since January 2021, advances in AI research produced a plethora of deep-learning models capable of generating original images from simple text prompts. Researchers at OpenAI, Google, Facebook, and others have developed yet to be released text-to-image tools, and similar models have proliferated online. This video is a primer on how we got here, how the technology works, and its implications.

Experiments involving AI agents operating in virtual societies for 15 days produced diverse outcomes, ranging from every agent dying to the creation of a functioning democracy, depending on the AI model used.

Through prompt injection attacks, bad actors provide deceptive prompts to generative AI systems in order to manipulate their outputs and acquire sensitive data.

Understanding how Claude Code and other AI coding agents function

Under human oversight, a supervising large language model interprets user tasks and delegates work to subordinate LLMs, which can generate code, fix bugs, and run tests, often most effectively for proofs of concept. Incremental backups and versioning are crucial when using such agents, which can lose details during their work as a result of compressing context history to work around memory limitations.

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.

Computer systems create artificial voices by stitching together phonemes—individual sounds that make up speech, such as 't,' 'long a,' and 'k' to say 'take.'

Technologies labeled “AI” have historically lost that title after widespread adoption

Generative AI is the latest entry in a recurring cycle where emerging tools start as "AI" until they become common software, like databases or machine learning. Generative AI and large language models may be the next platform shift after smartphones and the Web.

Just as large language models are trained on text to enable generative AI systems to mimic natural language, automated AI robotic systems are trained in virtual environments to help them 'learn' about the properties of reality.

'Context rot' explains why AI conversations degrade the longer they run

Research shows that as a large language model's context window fills up, performance declines, and models begin over-indexing on content at the start and end of a conversation while losing track of what's in the middle. The practical fix is simpler than you'd think: start a fresh chat more often.

Studies show AI tools can result in passive learning with less retention

Although ChatGPT, Google's AI overviews, and other similar software can save time, a study of more than 10,000 adults showed that reliance on them yielded work products that were more generic and included fewer facts than those produced solely by Google search. Weaker brain connectivity has also been observed when users write using AI.

Create professional-quality spoken audio from text

Generative AI now makes it possible to create everything from computer-generated written content to beautiful imagery and hyper-realistic videos in seconds from a user’s simple text prompt. Add to that list: High-quality spoken audio, which the AI software company ElevenLabs specializes in. Its natural-sounding speech synthesis and text-to-speech tech can produce human-like speech that mimics a variety of different tones, styles, and accents.

ChatGPT is trained on most of the world's digitally accessible text

As with other AI chatbots, ChatGPT is primarily trained through non-supervised pretraining, which identifies underlying structure and patterns within input data. Then, human intervention addresses harmful or biased patterns that should not be propagated to generated outputs.

Try OpenAI's ChatGPT for free

In late 2022, OpenAI launched a chatbot that quickly became one of the most viral tech products of all time. The software uses code to interpret natural language to produce sophisticated content based on simple user prompts.

Neural networks are modeled after an idealization of the brain

These networks—including the backbone of ChatGPT-3, which has approximately 175 billion parameters—naturally discover the implicit rules of grammar and syntax within language by identifying patterns. Deviations in word patterns add creativity to the text generation.

The Anthropic Economic Index shows AI adoption is highest in computer science roles.

AI models incorporate a randomness parameter when generating responses to prompts

AI models use this parameter to prevent repeated outputs by sometimes choosing less likely next words during sequential generation. However, this randomness—alongside insufficient data and training—may cause hallucinations of incorrect results.

Researchers found that by mid-2025, around 35% of newly published websites utilized AI for language generation

The study also found that pages using AI narrow the range of ideas and perspectives and skew more positively, though the measurable impacts on factual accuracy and writing style variation remain unclear.

Artificial general intelligence equals or surpasses human intelligence

AGI incorporates sensory perception, memory, and advanced logical inference systems to move beyond the narrow tasks seen in large language models and chatbots. One of the most significant hurdles in developing AGI is designing systems that learn and can flexibly apply learning across domains.

Decoding the hype around AI

2022 was the year AI broke onto the scene, with applications like ChatGPT amazing users with its ability to generate realistic-sounding answers. Technologist Arvind Narayanan presents a clear-eyed view of what the technologies can (and can't) do.

As of 2025, increasing numbers of people turn to AI companions for emotional connection

These chatbots provide endlessly supportive, one-sided, and highly customizable relationships to users, but may hinder the development of real-world connections and emotional growth. About 72% of teens have tried AI companions, with more than half of them using one regularly.

Since 2025, AI data center growth has created shortages and price hikes in graphics processing units and their supporting hardware, including high-speed memory.

When users prompt AI chatbots, both cooling systems that support data centers and power plants that rely on steam cycles to generate electricity for data centers contribute to water use.

AI excels at assisting with structured tasks involving logistics and planning

Tools like ChatGPT, Gemini, and Perplexity can help organize chaotic calendars, troubleshoot bedtime routines, and assist with salary planning and moving. However, specific quotes, like insurance, and local cost comparisons can be flawed since AI draws from nonlocalized datasets.

During a job interview with an AI agent, applicants are evaluated against a rubric that considers factors such as tone, clarity, and the use of specific words or phrases aligned to the job.

Scientists increasingly can't predict what AI will do

Most AI systems are so-called "black box" models, where we only see inputs and outputs while the inner workings remain obscure. This piece, written prior to the launch of ChatGPT, cautions developers to know why and how a system produces its results, and not to sacrifice explainability for accuracy.

Chat with famous, historical, and fantasy figures with this chatbot

Character.ai—a generative AI-based chatbot platform—allows you to have free-flowing conversations with a wide range of people. From celebrities to famous figures from the past, the platform relies on a neural language model to process huge amounts of text, learning from conversations with users to develop responses. A number of observers have referred to the platform as "making AI fun," with user time on site surpassing a whopping two hours per day.

Stephen Wolfram on AI's rapid progress & the "Post-Knowledge Work" era

AI, as a concept, has been around since at least the 1950s, but it's only in the last year or so that the technology has been able to equal humans at many tasks -- and in many cases surpass humans. In this podcast, mathematician and computer scientist Stephen Wolfram provides an AI 101, explaining how we got here, how highly advanced chatbots like ChatGPT work, and what our world is in store for next.

OpenAI initially focused on novelty projects, like training AI bots to win video games, before becoming known for its AI chatbot ChatGPT.

According to the International Energy Agency, hyperscale data centers consume about as much electricity annually as 400,000 electric vehicles to provide 24/7 access to online applications, services, and information.

The rise of generative AI could exacerbate anxiety because people may turn to chatbots for constant reassurance.

Google invented the foundation of the AI movement—but ChatGPT was a shock

For years, Google was the cutting-edge of the AI world, including the development of the Transformer—the basis of contemporary AI chatbots. But since OpenAI debuted ChatGPT in 2022, the company was had to play catch-up. This is a four-hour deep dive into Google's AI history.

AI is not good at understanding satire

Social media has become rife with misinformation. In response, some organizations have deployed AI-assisted programs trying to curb the spread of this inaccurate content. The problem is that misinformation and satire, rhetorically speaking, often use the same kind of language, something AI struggles to understand.

LLMs are trained via modeling, imitation learning, and reinforcement learning

Training large language models begins with pretraining the model to predict the next word in a sequence based on finding patterns in massive amounts of text. Patterns are then fine-tuned to model human-written dialogue and to align responses with user preferences.

How AI is revolutionizing banking

Generative artificial intelligence has left no industry untouched, and banking isn’t an exception. Experts estimate that gen AI could add as much as $340B in value to the banking sector each year. For consumers, this involves new features to prevent fraud and customer service chatbots. This report dives into the revolution underway and seeks to define a “full stack” AI bank of the future. Read more here.

Sam Altman acknowledges the fears around creativity, copyright, and misuse of AI

In 2022, OpenAI's CEO, Sam Altman, popularized the use of large language models with the release of ChatGPT. He has emphasized the importance of involving society in shaping AI’s safety frameworks before models begin to act independently online.