In many ways, the use of artificial intelligence to personalize software experiences by contextualizing those experiences has been going on for many decades, albeit with coarser tools. We started with rule-based systems (preferences and profiles) then moved to collaborative filtering (if you like “A” you might like “B”) then content-consumption-based filtering (what have you consumed or used before).
Also, software and devices began to use location, time, device type or workflow as personalization tools.
The objective has been to make software more user-friendly and relevant by adapting to individual users and their specific situations. Back in the early days of personal computing, personalization largely meant sers were given explicit tools to tailor their software and hardware.
That took the form of allowing customization of themes, fonts, keyboard shortcuts, default directories, and window layouts.
Macros and scripting for word processing and spreadsheets also were examples.
User profiles on computers also were examples of personalization.
In the earlier days of the World Wide Web, we saw the use of cookies and other data-driven approaches to personalization. HTTP cookies sustained user preferences across sessions. Websites could "remember" a user's login status, shopping cart contents, or even rudimentary language preferences.
Websites such as My Yahoo! Enabled creation of customized homepages (news categories, stock tickers, weather, widgets).
Collaborative Filtering then was used to make recommendations based on what "similar" users had purchased or liked.
Location-based services became more important as users shifted to use of the mobile web. Though perhaps not so oriented to content as hardware performance, sensor data, connectivity management (only conduct some operations if connected using Wi-Fi) and app design formatted for mobile screens became important.
Social media popularized the idea of the "social graph," where content relevance was heavily influenced by your friends' activities and interests.
Personalized search based on a user's search history, location, and even implied interests became possible, as well as behavioral targeting. Websites started "relevant" advertisements based on user behavior.
AI adds new layers for recommendations and makes predictive personalization (recommendations, for example) easier. And we are getting to the point where software can react dynamically to a multitude of real-time contextual signals, including emotional state (inferred from tone or facial expressions), environmental factors (noise, light).
Proactive information delivery also will be more common.
The point is that software personalization and contextualization have been sought for many decades, just at a more mechanical level. AI will make all that easier and more dynamic.
For example, traditional search engines primarily relied on matching keywords to find relevant pages. AI aims to understand the meaning and context behind a query, rather than just the words themselves. AI-powered search should handle ambiguity better, as well as operate more semantically, taking into account relationships between concepts or entities.
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