Alternative AI Futures

Alternative AI Futures is a hands-on workshop series that invites students to directly experiment with, critique, and modify contemporary AI systems in order to reimage how different design choices, cultural values, and shared goals could produce radically different forms of AI. 

Each workshop will begin with a simple but provocative prompt: What if AI were built differently? More specifically, what if factors like transparency, accountability, and social responsibility were the primary values guiding AI system design? What would those systems look like? How would they behave? And how might they address commonly held concerns about the structural limitations and/or social consequences of AI? These “what if” questions will foreground the assumptions embedded in current AI systems and open space for imagining alternative futures for how AI might be built and used differently.

These 1.5-hour workshops will be split between hands-on experimentation and discussion. Students will work directly with small-scale machine learning systems and language models, using tools like Google Collab Notebooks to modify and reconfigure how these systems behave. Through guided, hands-on exploration, students will experiment with training data, embedding spaces, system instructions, and model parameters, among other elements, to see how changes at the technical level can give rise to alternative forms of AI in the form of small, working prototypes. The discussion portion of each workshop will build on this hands-on work, allowing students to reflect on how technical design choices give rise to broader cultural, social, ethical, and political impacts.

Workshop 1: AI as an Instrument

 
What if AI was designed to be engaged as a tool or instrument rather than a persona in a chat interface?

This workshop starts from a provocation most students haven't seriously considered: the chatbot interface is a business decision, not a technical necessity. When a language model greets you by name, says "I think" or "I understand," hedges its claims, and apologizes when corrected, none of that reflects how the underlying technology works. These behaviors are designed in, layered on top of a system that operates through probability and pattern rather than thought or feeling. But the design is so effective that most users engage with AI as though it were a conversational partner with beliefs, intentions, and something resembling understanding. This workshop asks students to think about what happens when/if users look past the persona and engage the technology directly, as an instrument.

Students will work with the LLM Token Prediction Explorer, a custom interface connected to a live language model that exposes the internal mechanics commercial AI products keep hidden. Through next-token prediction, sentence construction with branching, and direct manipulation of temperature, top-K, and system prompt, students will observe firsthand that the model does not compose or deliberate. It assigns probabilities across a vocabulary, selects one token at a time, and generates each word to plausibly follow the last, regardless of where the sentence is headed. By forcing the model to choose different words at any point in a sentence and watching it rebuild what follows with equal fluency and confidence, students will see that the model will complete any thought it is given, not because it believes what it is saying, but because that is what autoregressive generation does. Language like "I think," "I feel," and "I understand" does not describe what is happening inside the system. It misrepresents a process of probabilistic token selection as cognition.

  • token predictor screenshot

    Token Predictor Interface: Full Sentence Generation
  • token predictor screenshot

    Token Predictor Interface: Branching Alternative Model Responses

Token Predictor 

Students generate multi-token sequences and observe how individual predictions compound into seemingly coherent text. Each generated word is color-coded by the model's confidence, making visible the uneven terrain of certainty and uncertainty that underlies every generated sentence. Some words appear with high confidence, typically common function words and predictable continuations, while others appear with notably lower confidence, marking positions where the model could easily have gone a different direction. The sentence reads as fluent prose, but the confidence coloring reveals that it was not produced with uniform certainty. It was assembled one word at a time, with some steps nearly guaranteed and others closer to a coin toss.

Clicking any word opens a panel showing the alternatives the model considered at that step, ranked by probability. Students will often find that at low-confidence positions, several candidates were nearly as likely as the one the model chose. Verbs are particularly instructive: after a noun, students may see six different verbs among the top candidates, and the verb that was selected steers the rest of the sentence in a specific direction. But the model did not pick that verb because it planned the rest of the sentence. It picked a word, and now it is committed to a trajectory. The words that came before paint the model into a corner, constraining what can plausibly follow, and the model continues generating the next most plausible word in front of the last, regardless of where the sentence is headed. This is also the mechanism behind hallucination: when the probability distribution is relatively flat and the model is, in a sense, guessing, it can select a plausible-sounding word that commits it to a factual claim it cannot support. The self-reinforcing nature of autoregressive generation then locks the model into elaborating on that claim with the same fluency and confidence it brings to everything else.

Students can click an alternative word to branch the generation, substituting a different choice and watching the text diverge from that point forward. The model regenerates everything after the branch point, producing a new sentence that may differ subtly or dramatically from the original. Both versions are equally fluent. Both read as confident, coherent prose. But they may say entirely different things, because a single different word at an early step sent the generation down a different path. The branching mechanic makes the contingency of generated text concrete: every sentence the model produces is one path through a tree of possibilities, and the model will complete any path it is placed on with equal fluency, not because it believes what it is saying, but because that is what autoregressive generation does.

By exposing these stages and letting students manipulate them directly, the interface reframes AI-generated text not as the product of understanding or intelligence, but as the output of a specific, modifiable, and contestable technical pipeline.

 

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Go to Google Colab Notebook (next_token_explorer.ipynb).

 

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Go to Token Predictor web interface.

 

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Download instructions for Notebook and interface.

 

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Download sample exercise for Token Predictor.