Below are a few examples of what AI can accomplish today.

Natural Language Understanding and Generation. AI can process natural language, and it can read and comprehend what the intent is in documents, news articles, blogs, books, and emails. Furthermore, it can generate summary information from processing the text and label the text with topics. The technology can detect a question in text and group similar questions together. Note that humans have a lot of variation in speech, as someone can ask the same question differently. Similarly, AI can use existing unstructured data to find answers to these questions. The ability to process unstructured text and respond to the text has further been helped by the technology to hear using state-of-the-art pattern recognition speech-to-text (STT) techniques. STT application programming interfaces can convert spoken words by humans into text in real-time. Generating human-like speech is an excellent accomplishment of speech synthesis techniques. Speech synthesis is the artificial creation of human speech where a text to speech (TTS) system converts natural language text into speech. This synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database, and the quality of these speech synthesizers has been becoming more and more similar to the human voice every year. Text to speech technologies leverage Neural Network models to deliver a human-like, engaging, and personalized user experience. This capability of using existing unstructured databases and grouping questions and answers facilitates responding to those questions being asked in real-time. So not only is AI understanding, but it also generates natural language and appropriately customizes responses into a specific answer based on the context by identifying the sentiment of the question. These capabilities result in creating an engaging user experience in achieving a great degree of containment in virtual voice assistants with interactions that mimic a human conversational style. The ability to be empathetic is critical. For example, "I would like to reschedule my flight" and "I just broke with my girlfriend, I'm rebooking my flight to leave early" would be different because the contexts in both these situations are different.

Advanced Inference Engines. AI facilitates incorporating rapid and advanced inferences, and inference engines are part of the decision systems that apply logical rules to the knowledge base to deduce new information. For example, inference engines based on Bayesian Belief Networks are used for automated breast cancer detection support tools. AI is a viable option for computer-aided detection by representing the relationships between diagnoses, physical findings, laboratory test results, and imaging study findings. In the example above, the work brings essential roles such as Radiologists, Image Processing Scientists, Database Specialists, and Applied Mathematicians on a common platform. By exploiting conditional independencies entailed by influence chains, it is possible to represent extensive cause-effect relationships using little space. It is often possible to perform probabilistic inference among the features in an acceptable amount of time. The inference engines are critical in recommendation systems, such as suggesting which movie to watch next and deciding which price point will maximize the revenue.