What is Realtalk’s relationship to AI?
If we take “AI” to mean the current trend of deep-learning models trained on large datasets, there are a number of ways in which these techniques are incompatible with our values.
Visibility. To empower people to understand and have full agency over the systems they are involved in, we aim for a computing system that is fully visible and understandable top-to-bottom — as simple, transparent, trustable, and non-magical as possible. When it works, you learn how it works. When it doesn’t work, you can see why. Because everyone is familiar with the internals, they can be changed and adapted for immediate needs, on the fly, in group discussion.
Current AI systems do not share these priorities.
Agency. A Realtalk system “knows” as little as possible. One of our most surprising discoveries has been: to maximize agency, minimize what the computer knows. Every time we make the computing system less aware of what’s going on, every time we remove user interfaces in favor of noticing simple spatial relations between objects, every time we remove programmed rules in favor of socially-agreed-upon practices, our systems become more flexible and composable, and new dimensions open up for improvisational modification.
We often take a “human-in-the-loop” approach: Instead of a complex program which accomplishes a task entirely automatically, we design for people and simple programs working cooperatively. Instead of a computer making decisions and taking actions, we prefer people making decisions and taking actions within a rich environment of realtime feedback and analysis.
Most current AI systems rely on enormous datasets to know as much as possible about their domain and make the most autonomous decisions. Failures and biases are addressed by further enlarging the dataset. These systems rarely have the property that reducing their knowledge makes them perform better.
Human connection. Realtalk is about people connecting with people — working side-by-side with other people, learning from other people, developing relationships with other people, trusting other people. Computation takes the form of physical tools and materials that people use in conversation with other people. People do not have conversations with their tools.
Many current AI systems adopt an agent-like posture, where users are encouraged to converse directly with the system as if it were an “assistant” or “servant”. In our view, reliance on agents detracts from opportunities to deepen relationships with real people. We are also uncomfortable with the mindset that accompanies owning servants.
Human growth. Our view of a “humane dynamic medium” extends from what made the medium of print-based literacy so powerful. A book cannot do something for you. Instead, reading a book can change you into someone who can do something for yourself. The role of a great medium is not to help people get things done, but to help people become deeper people — by providing a context in which they grow their skills and knowledge, broaden their context and perspective, and deepen their awareness and discernment.
One of our oppositions to product consumption is that it is a form of “outsourcing understanding”. Instead of learning a practice, one uses a product which insulates them from the underlying knowledge. The “smarter” the product, the less the user needs to understand. While each product may be convenient individually, the cumulative result is an almost universal ignorance, helplessness, dependence, and fragility. [more]
We see many current AI products, particularly so-called generative tools, as “outsourcing understanding” in its most virulent form. [more]
On the other hand, there are some “non-semantic” uses of AI techniques, such as text recognition and speech transcription, which can be used in ways that don’t displace learning and development. We are still opposed to their complexity, opacity, and enormous training sets, but we may be more willing to cautiously admit them for peripheral purposes, and seek ways to enhance their visibility.
Reality. Realtalk is focused on the real world. It is of the utmost importance that our models reflect reality, and that everyone understands how much they reflect reality and why we believe so. Realtalk’s rules form explicit chains of inference, and Realtalk’s statements can be seen as the supporting evidence.
Many current AI systems have a loose regard for reality, and readily produce results that appear real but are not.
What is Realtalk’s relationship to STEPS?
The STEPS project from Viewpoints Research Institute pursued order-of-magnitude reductions in the complexity of desktop computing by leveraging specialized languages, metalanguages, and architectures. Realtalk pursues order-of-magnitude reductions in complexity by leveraging the physical world to avoid simulating a desktop in the first place (along with a flexible reactive architecture for the computing that remains).
We see physicality and metalanguages as complementary forms of leverage, and we are eager to combine them as we continue down the path that STEPS laid out. Realtalk is already multilingual, and many non-textual notations (both symbolic and diagrammatic) are more comfortably and communally drawn on paper than on a screen. Future Realtalks will be even more oriented around defining bespoke interpreters — for sentences, for notations, for diagrams, for scenes — and integrating all of these modes to achieve the most compact and understandable representations.
What are Realtalk’s predecessors and influences?
Some of our general inspirations for the dynamic medium are on the shelf. [more]
The technical design of Realtalk was deeply inspired by Sketchpad, Smalltalk, Lisp machines, Self, HyperCard, Etoys, Inform 7, Linda, and Datalog, and we’ve also drawn ideas from Erlang, Clojure, Haskell, Forth, and many others.
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