webenv-ml v0.1.0

WebEnv

Turn the most widely-used data interchange format (the Web) into a numeric interface (Env) for training general intelligence: learn a rich understanding of the world, then accomplish user-defined tasks with it.

Setting up an infinite loop that allows useful learning in any situation is essential for the most interesting applications of intelligence. WebEnv provides a clean interface to the real world, while discouraging practices that act as barriers to learning.

In this interface, agents interact with the Web by continuously receiving observations and sending actions.

• Reward is decided dynamically, if ever. Agents ought to be self-supervised and/or integrated with human life.
• Both observations and actions can be extremely-high-dimensional, so models must be scalable.
• The particular interface format can be defined by both initialization and web-pages, so only fully general architectures can succeed.
• One environment is one continuous bidirectional stream of data. Agents have to learn online.

Getting started

Using NPM, as you commonly do in machine learning, install the webenv-ml package:

npm install -g webenv-ml

Then, require it in JavaScript, or use a bridge to another language, and use it:

Index

• Create your own agent, via copy-modify
• Choose the static interfaces
• Choose additional datasets
• What counts as solving WebEnv
• Explanations of architectural decisions
• Contribution

Features

No constraints to make learning easier. Brush against raw generality.

• Multimodal interfaces: the Web contains text-in-pages, video, audio, interactive UI, games, and other data streams. WebEnv agents have no upper limit on their capabilities.

  • Consistent data. Web pages are made by humans for humans; large ML datasets often scrape this, breaking the intended consistencies and making data unclean. Here, challenge the shapeshifting master of data.
  • Extensible: choose the particular static interfaces that your agent can rely on, and allow web pages to dynamically establish direct links. Pre-empt the age of neural interfaces by using directLink in your website.

• Universality: WebEnv is able to include all ML datasets and environments, behavior of ML solutions in them, an agent's own behavior, and most of human ingenuity. Instead of creating a new agent for each task, re-use the same one for all. AI media typically makes it seem like different AI models constitute work on the same AI, but if one model can manage to do all that, then said media's representation will be accurate.

  • Scope: train on the RandomURL dataset to randomly sample the whole Web. When data gets too big to memorize, generality is the only solution.
  • Open-ended: one way to describe general intelligence is "good zero-shot performance on unseen tasks", and the most unseen tasks are ones that do not exist yet. Web pages can call directScore to evaluate your agent, creating an expansive set of maximization tasks, which will only get bigger with time.

• Efficiency: combining all formats introduces a non-insignificant representation overhead, so WebEnv is fast and robust to compensate.

  • Scalable: a single WebEnv instance can handle as many data streams as you want, and if that is not enough, simply have many, and write parameter-update synchronization yourself. Work on computations, not infrastructure.
  • Real-time: agents must focus on their throughput and frame-time consistency too. This presents novel challenges to many ML frameworks (namely, efficient BPTT handling is pain, easier to use synthetic gradients).

• Self-determination: under constant pressure to represent essentially-infinitely-complex interactions with data and goals, only the most complete representations will survive, creating mesa-optimizers: aware of everything, general, quickly adaptable, and learned. Research learned agency at scale.

  • Understandable: general intelligence can only be guided and judged by general intelligence, so interfaces (mostly) share a human-usable format, and observations and their predictions are easy to inspect visually.
  • Human-integrated: humans can connect their own browsing to pre-trained WebEnv agents through an extension or a JS API, and control observations and reward via directScore. AI safety can be ensured through AGI-as-a-service platforms.

Caveats

No constraints to make learning easier. Brush against raw generality.

• You can get IP-banned by some websites for running bots. (Respect the etiquette.)

• Reliant on its community: people deciding to use directLink and directScore to create experiences that they cannot currently partake in. For example, there are currently no direct-link forum/chat/comments, nor Web-controlled robots. (AGI-as-a-service should help with adoption, with absolutely no chance of anything going wrong.)

• Web code is not native code. Without an explicit bridge, it is impossible to send actions to or receive non-video observations from native applications. (In exchange, we leverage greater control over what happens, currently through navigation and visual augmentations.)

• URLs are invisible to agents. Agents cannot interact with popups, and so, for example, cannot install extensions.

License

MIT