Reflectoputer

The primary reason why we don't keep camera, audio, screen recorders and key-loggers on all the time, is that, while it is readily possible today, we lack disk space and processing power to compress the data collected, and it requires extra energy use, while we have limited battery life.

However, it is orders of magnitude cheaper to use pre-trained neural nets than it is to train them from scratch, and those models can cheaply compress complex pictures, videos, audios, etc. and store them as sequences of features (feature vectors) on very tiny amount of disk space to keep a rich life-log about its user.

The primary reason why computers don't proactively ask us questions, is that they don't have a directive to model us. Reflectoputer would have a goal to build maximally accurate model of your self. So, additionally, -- use psychological research to build a generic human model, which captures most of what humans are like. (Human diversity is arguably quite limited (like the diversity of cats is), or like what MBTI tests show, covering broad set of aspirations with just 16 different core types of personalities with a few abstract dimensions to plot them), and all humans share a large bit of common traits that could be redundant, and so, could be used to compress model about humans (and get computers optimize for searching for something like a "sufficient statistic" for that model). Use the remaining peculiarities of the user's personality that doesn't fit the model -- as something that reflectoputer proactively asks questions in order to fill the gaps in the parametrization (or fitting) of human model (with user data). For example, certain questions from personality tests devised by psychologists can be quite useful to fill those gaps.

Then, observing repetitive works done by user, try to simulate inputs based on the model to gain confidence in automating user actions. If the sequence of actions produced by user coincides closely with the sequence predicted by the model built from past data, automatically ask user to permit automation of the user. (E.g., when confidence level exceeds certain threshold.) Do all that with local compute and data, without ever using cloud services.

This way, personal reflectoputer builds model of the user self by observing, recommending and asking, as well as helping user to build their life story.

Reflectoputers would be useful, as they would learn to automate you as you use them. Additionally, if you were to die, others could query your model. You wouldn't even have to write an autobiography.

While this may be quite WKTE in science fiction, the particular path of using pre-trained models to cheaply compress data to save space, in combination with building user's personality model through asking in addition to listening (e.g., current intelligent assistants do not proactively and randomly ask things about you, and they don't try to learn you and help you without explicit commands, but reflectoputers would), are the parts the combination of which may have not been readily pursued by academic research in the particular way I describe here. So, this idea is a concrete idea for a research paper and a computer program. I may be wrong, and it all may have been already done. Search is in progress.

Credits: Mindey of HalfBakery.