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What will happen to language if you spend more time talking to an AI than you do other people?

Curriculum learning is an interesting area of study within machine learning. However, it is often applied in a somewhat hand-wavy fashion. What does it mean for a particular training example to be harder than another one?

Even something as seemingly definitive as time can wiggle a little from different perspectives. If you set yourself a goal of writing every day, do you have until midnight in your time zone to meet the goal for the day, or until the day is done across the world? Perhaps more importantly, does it matter?

Define helpful. In a way that can be replicated.

Knowledge work can be very open ended. You have tasks, to be sure. But they aren’t as cut and dry as you might think. You can always iterate, or ship the next version. What does done look like? If you aren’t careful done will elude you.

What is novelty?

Consider the following three queries. Something behind the scenes is triggering Gemini. Should the end user have a say about whether they want vintage Google (web search) or a GenAI output?

How much have your workflows and routines changed in the last year? The last 5 years? The last 10 years? What part of those changes would you attribute to technology? What part would you attribute to maturity, learning, and personal development? Are the two areas intertwined?

Are the resources required for AI fundamentally different? Or do they exist on a continuum?

Maintenance is hard.