One of the seven patterns of AI, among hyperpersonalization, autonomous systems, predictive analytics and decision support, patterns and anomalies, recognition systems, goal-driven systems and, what we are talking about today, conversational/human interactions.
Allow machines to be able to interact with humans throughhuman language patterns, and machines to be able to communicate back to humans in a way they can understand.
No more clicking, typing, swiping, or programming …We are too lazy.
Now we want machines to interact with us in the same way that we communicate with each other. This includes voice, writing, or whatever method our wired brain is capable of understanding.
There arethree scenarios: Machine to human, machine to machine, and human to machine interactions.
Some examples are found in voice assistants, intention analysis, content generation, mood analysis, sentiment analysis or chatbots; developing solutions in cross-cutting sectors such as the financial sector or telemedicine.
Mood, intent, sentiment, visual gestures, … These shapes or concepts are already understandable to the machine.
There’s a difference betweenASR (Automatic Speech Recognition),STT (Speech to Text)andNLP (Natural Language Processing). While the first two, ASR & STT, are based on the transformation or generation of sound waves that are converted into words, the third one, NLP, interprets the data it hears. Not for this reason, AI(and Deep Learning)is no longer important in ASR & STT fields, since it has helped make speech-to-text more precise and text-to-speech more human.
However,Natural Language Processing (NLP)goes further than converting waves into words.We need to understand and provide understanding.
It’s here where we enter a huge world that ranges from the generation of speechs or texts, extraction and understanding of entities, detection and identification of topics or themes, connection of sentences, concepts, intentions and meanings.
Natural Language Processing is divided in TWO PARTS:natural language understandingandnatural language generation.
Let’s see.