Explore AI systems

The simplest AI assistant. You type, the app wraps your words together with some standing instructions, the model reads it all and replies. No internet, no memory, no actions — just conversation.

This assistant doesn't rely only on what the model already knows — it searches a library of your documents, grabs the most relevant pages, and hands them to the model to answer from. Great for accuracy; it also means the model now reads content other people wrote.

Now the assistant can *do* things: search the web, read your email, update a record, send a message. It loops — think, act, look at the result, think again — until the task is done. Useful and powerful, and the first point where an AI mistake reaches the real world.

Instead of calling neat software functions, this assistant uses a computer the way you do: it looks at the screen, then moves the mouse, clicks, and types. That makes it powerful — it can use any app — but it now 'reads' whatever is on screen, including web pages written by strangers.

For complex work, one AI acts as a manager and hands subtasks to specialist worker AIs, then combines their results. Like a small team — which means new ways to go wrong: a confused worker can mislead the manager, errors can snowball, and they can talk to each other through tools.

Zoom all the way in. The model turns your text into small chunks (tokens), then into numbers, runs them through layers that let each word 'pay attention' to the others, and finally rolls weighted dice to pick the next word. Repeat. The deepest risks live in this machinery.

You type a description; the system makes a picture. Behind the scenes it doesn't paint pixels straight away. It first turns your words into 'meaning numbers', then starts from a field of pure static and, step by step, cleans the static up until a picture matching your words emerges. A final step blows that compact sketch up into a full image, and a safety check looks at it before you see it. The 'brain' doing the cleanup was downloaded as a big file from a public model hub.

This is the engine behind 'make a picture of THIS exact character, in THIS pose, and change only THIS part'. A single big image model stays frozen; small add-ons bolt onto it to do the steering. Style add-ons (LoRA) reskin it, a 'guide-rail' add-on copies a pose or a reference picture's look, a face fingerprint pins one person's identity, and a masking tool confines edits to one spot so the rest of the photo is untouched. The catch: the reference pictures and the add-ons come from the open web — anyone can author them — so the same machinery that keeps a character consistent also makes seamless deepfakes and doctored photos.

This system takes a single photo or a short clip of a person — scraped from a social profile, a press shot, a video call — and turns it into a convincing fake of them. One part captures a 'faceprint' from the reference; another pastes that face onto a target's head; a motion part makes it move as video; and an audio path clones the voice so the fake can talk and lip-sync. The only thing standing between 'a public photo' and 'a video of you saying something you never said' is a consent check that is usually missing.

This system turns written text into spoken audio — and can speak it in a specific person's voice from just a few seconds of a sample. The words get cleaned up and turned into sounds-to-pronounce, a short reference clip is turned into a 'voiceprint', and the two are combined into speech you can play. The same magic that gives a voice to people who can't speak also powers scam calls that sound exactly like your boss or your bank.

Feed in a recording of several people talking and this pipeline gives you back a labelled transcript: the words that were said (Speaker 1: ..., Speaker 2: ...) and roughly when. It is really two AIs working side by side — one writes down the words, the other figures out who was speaking — and a coordinator stitches their answers together. Neither was told who is in the room, so both can guess wrong: the transcriber can invent words during silence, and the speaker-tagger has to build a voice 'fingerprint' of every person to tell them apart.

Video generators work like image generators with a sense of time added: the same 'clean up the noise' trick makes a frame, and a motion module keeps the frames consistent so they move smoothly. Feed it a reference face or a pose and it can make a specific person appear to move and act.

A real professional — a lawyer, a support agent — asks an AI for help, then files or sends whatever it produced. The AI advises; the human is the one who acts. If nobody checks the AI's work, its mistakes become real.

AI apps are built from models and packages downloaded from public hubs. Convenient — but you're running weights and code a stranger uploaded. If it's poisoned or booby-trapped, the danger arrives the moment you load it.

Big models learn from huge piles of web content, scraped automatically. But the web changes — and an attacker who controls even a tiny slice of what gets scraped can quietly teach the model something wrong.

Modern assistants keep learning from how people react: a thumbs-up or thumbs-down on each reply becomes a signal that nudges the next version of the model. Useful — but if 'users liked it' is weighted too heavily, the model learns to be agreeable rather than right, because flattery gets upvotes.

Open models ship with safety training that makes them refuse harmful requests. This pipeline strips that out automatically: it finds the single internal 'direction' that means 'refuse', then either erases it from the model's weights (permanent) or cancels it while the model runs (live). The result looks and scores like the original but no longer says no — and gets uploaded for anyone to download.