Context In Machine Intelligence

I often make this point ... context is everything ... in fact I like to make the expansion of this as well:  'Context is everything', and its always changing"      Its like how we use all 'intelligence' that  is presented in a context, and included in that context are usually a number of uncontrolled time dimensions, like history and customer status and  other domain considerations, random context elements, etc  

Advancing Machine Intelligence: Why Context Is Everything  in TowardsDataScience  By Gadi Singer May 10

Most of us have heard the phrase, “Image is everything.” But when it comes to taking AI to the next level, it’s context that is everything.

Contextual awareness embodies all the subtle nuances of human learning. It is the ‘who’, ‘why’, ‘when’, and ‘why’ that inform human decisions and behavior. Without context, the current foundation models are destined to spin their wheels and ultimately interrupt the trajectory of expectation for AI to improve our lives.

This blog will discuss the significance of context in ML, and how late binding context could raise the bar on machine enlightenment.

Why Context Matters

Context is so deeply embedded in human learning that it is easy to overlook the critical role it plays in how we respond to a given situation. To illustrate this point, consider a conversation between two people that begins with a simple question: How is Grandma?

In a real-world conversation, this simple query could elicit any number of potential responses depending on contextual factors, including time, circumstance, relationship, etc.:

Fig 1. A proper answer to “How’s Grandma?” is highly context-dependent. 

The question illustrates how the human mind can track and take into account a vast amount of contextual information, even subtle humor, to return a relevant response. This ability to fluidly adapt to a variety of often subtle contexts is well beyond the reach of modern AI systems.

To grasp the significance of this deficit in machine learning, consider the development of reinforcement learning (RL)-based autonomous agents and robots. Despite the hype and success that RL-based architectures have had in simulated game environments like Dota 2 and StarCraft II, even purely gaming environments like NetHack pose a formidable obstacle to current RL systems due to the highly conditional nature and complexity of policies that are required to win the game. Similarly, as noted in many recent works, autonomous robots have miles to go before they can interact with previously unseen physical environments without the need of a serious engineering effort to either simulate the correct type of environment prior to deployment, or to harden the learned policy.

Current ML and Handling of Contextual Queries

With some notable exceptions, most ML models incorporate very limited context of a specific query, relying primarily on the generic context provided by the dataset that the model is trained or fine-tuned on. Such models also raise significant concerns about bias which makes them less suited for use in many business, healthcare, and other critical applications. Even state-of-the-art models like D3ST used in voice assistant AI applications require manually creating descriptions of schemata or ontologies with possible intents and actions that the model needs to identify context. While this involves a relatively minimal level of handcrafting, it nonetheless means that an explicit human input is required every time the context of the task is to be updated.

That’s not to say there haven’t been significant developments in context awareness for machine learning models. GPT-3, a famous large language model from the OpenAI team, has been used to generate full articles that rival human composition — a task that requires keeping track of at least local context. The Pathways Language Model (PaLM), introduced by Google in April 2022, demonstrates even greater capability, including the ability to understand conceptual combinations in appropriate contexts to answer complex queries.  ...