Category: Contextual Artificial Intelligence

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Contextual AI adds explainability to different stages of machine learning pipelines – data, training, and inference – thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.

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.

At one time or another, we’ve all heard a famous quote misconstrued or taken out of context. And though Robert Frost might actually tell you to just pick any old path and not mourn “The Path Not Taken,” the paths you take toward understanding your customers and their broader contexts really do make all the difference.

According to Gartner analyst Melissa Davis:

In human discourse, people often emphasize the ‘context’ or ‘contextual explanation’ to develop a better understanding over the specific conversation. In many cases, context is vital in decision-making or reacting to the actual situation. Nowadays, data is like a goldmine for researchers, service providers and technology vendors. At this juncture, data-driven models are extensively used in intelligent buildings to monitor the whole building performance along with forecasting services.

Random forests are a supervised Machine learning algorithm that is widely used in regression and classification problems and produces, even without hyperparameter tuning a great result most of the time. It is perhaps the most used algorithm because of its simplicity. It builds a number of decision trees on different samples and then takes the majority vote if it’s a classification problem.

I am assuming you have already read about Decision Trees, if not then no need to worry we’ll read everything from start. In this article, we’ll figure out how the Random Forest algorithm works, how to use it, and the math intuition behind this simple algorithm.

Before learning this algorithm let’s first see what are Ensemble techniques.

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