Contextual AI enables systems to interpret information the same way a human would. From analyzing wording and sentiments to recognizing cultural and environmental contexts, this “intuitive” understanding allows AI systems to produce more in-depth, relevant, and accurate outputs.
What is contextual AI?
In a sentence: contextual AI takes a human approach to processing content. It allows AI systems, like chatbots and virtual assistants, to have a real-world interpretation of language, audio, video, and images so they can behave less like traditional computers and more like humans.
It’s what helps an AI recognize when an image is upside down, whether you’re happy or frustrated by the tone of your voice, or that the right answer to the question, “where did Doc send Einstein?” is, “one minute into the future” — not, “sorry, I don’t know that one.”
This is because contextual AI is capable of analyzing the cultural, historical, and situational aspects surrounding incoming data, then using that context to determine the most meaningful outcome for the end-user.
In human to machine (H2M) conversations, this outcome can be as simple as a chatbot using your location to direct you to the nearest laptop repair shop. In human to human (H2H) conversations, it could be anything from recognizing speakers, sentiments, and buyer’s intent, to adding valuable insights to real-time sales call transcriptions.
Why does contextual AI matter?
Contextual AI creates a more collaborative partnership between humans and machines by driving dynamic conversations, providing highly relevant responses, and generating increasingly accurate predictions.
Context is a fundamental building block of machine learning (ML), and the missing puzzle piece to making your AI’s intelligence rival that of a human. So, by leveraging contextual AI, you can give your system the power to:
- Generate new knowledge: A contextual AI system can pick up patterns and features in the data, and extrapolate context clues from a few supervised learning cases to gain a deeper understanding of any situation. This allows your AI system to learn in an unsupervised manner, figuring out new scenarios on a case-by-case basis — just like a human would.
- Transfer knowledge between contexts: This means your AI system is able to take what it learned from one context and apply it to another to perform better on a similar task. For example, a contextual AI in charge of transcribing a company meeting could instantly recognize and link a project name that was once mentioned in a different meeting.
- Infer context to problem-solve: As it learns from each interaction, your AI system gets better at considering every aspect of a situation to deduce what the end-user truly needs at that moment. For example, a self-driving car could capture environmental cues, like wet roads and pedestrians ahead, then automatically reduce its speed.
Achieving this “human level” of intelligence, however, also requires a few key ingredients that you need to build in:
Domain knowledge: Contextual AI lets you train your models with business-specific data for much more detailed, accurate, and valuable results. This is a step up from the generic outputs you usually get after training your models using bulk aggregated data from AI providers, like Google Vision.
For example, in the case of automatically meta-tagging images, a generic AI would add simple tags like “hobbit” and “ring”. Whereas contextual AI would be able to add more helpful tags like, “Frodo Baggins”, “The One Ring”, “inside Mount Doom”, and “dangerous” — which give you a much better idea of the specific scene it’s referring to.
Furthermore, now that the AI understands what “dangerous” looks like, it can intelligently add that tag to completely different images it has never been specifically trained with. This means you can use smaller, more focused data sets to train your AI, then just set it loose and let it learn as it goes.
Explainability: Explainability is when a system can show what it knows, how it knows, and what it’s doing. Currently, many AI systems operate as a “black box,” where the reasoning behind their decisions is indecipherable. This lack of transparency makes the AI untrustworthy, particularly in safety-critical settings, like cancer detection or criminal facial recognition, where a bad AI prediction can be potentially life-changing.
Contextual AI adds explainability throughout the ML pipeline, from data ingestion to inference. Having this visibility into the inner workings of your AI will help you build better and safer systems that you can easily understand, improve, and steer away from any misguided decision-making.
Customization: As you know, contextual AI has the ability to adapt to situations it hasn’t been specifically trained to handle. But just as you wouldn’t instantly excel at a brand new task, AI won’t always get everything right either.
For a contextual AI system to continually improve, users need to be able to tweak its behavior so it can better meet their expectations. For example, if a music streaming service keeps suggesting questionable songs, you should be able to alter your preferences and get the AI back on track.
Where can you use contextual AI?
Contextual AI is a good idea when a more sophisticated understanding of human situations would improve the user experience. The most common scenarios involve things like self-driving cars, facial recognition, and quality control. But voice-based assistants and conversational agents are where contextual AI can really work its magic.
To give you a better idea of when it makes sense to use contextual AI, consider these two main types of conversations:
Human to machine (H2M) conversations
These involve a person typing or speaking to a conversational agent, like a chatbot or voice assistant. While they could all benefit from contextual AI, not every setting actually needs it.
For example, if you develop a closed-domain chatbot with the sole purpose of tracking food orders, it can coast by perfectly fine using a rule-based system. In contrast, a virtual assistant at an automated customer support call center will surely induce human rage if it can’t make sense of basic requests.
With contextual AI, you can make the virtual assistant recall historical data, user inputs, previous interactions, and even identify the caller’s emotional state to steer the conversation. This added intelligence would make the entire interaction considerably smarter, smoother, and more user-friendly.
The decision to use contextual AI in an H2M situation largely boils down to the complexity of the conversation you expect it to handle. The more aspects your AI system will need to consider (e.g. emotions, intent, etc.), the more impactful contextual AI would be.
Human to human (H2H) conversations
These conversations can be between two or more people, like a business conference, a sales meeting, or a telehealth video call. H2H scenarios are ideal playgrounds for contextual AI since human conversations are largely unstructured and contextually ambiguous — which will quickly overwhelm a generic AI system.
For example, an e-learning session between a teacher and a group of students could see the conversation go in any direction. The students will likely raise questions, bring up related topics, or mention things like past homework and upcoming exam dates.
If your AI had to transcribe this lesson (either from a recording or in real-time), contextual AI ensures your system can understand what’s being said with the same level of perception as a fellow student. It could then surface the most important information for later in the form of notes, listed topics and questions, or even suggested action items and follow-ups.
Without contextual understanding, much of that data would be reduced to a heap of intangible, random information, and you’d have your work cut out attempting to train your AI on all the possible topics future lessons could cover.
Contextual AI clearly adds a valuable dimension that results in more human-like behavior, more meaningful insights, and vastly better user experiences. So, now that you know what it is and why it matters, next consider learning <how to add context to your conversational AI> and what tools you need to implement it.
For more information on the fascinating subject of contextual AI, check out these links: