Conversations between Robots and Humans
Natural Language Processing is a branch of Artificial Intelligence that deals with designing systems that are able to analyse, understand and interpret human language, as well as to produce responses in natural language.
NLP technology analyses data and extracts its contents: concepts, key words, categories and emotions using the comprehension of the natural language. Its applications range from virtual assistants to automatic translators, chatbots and voice solutions that enable verbal or written interaction.
A constantly improving technology
Humable’s solutions facilitate the machine’s self-learning abilities using machine learning technologies. Our software communicate directly with the interlocutor through Natural Language Processing solutions by stimulating and facilitating the self-learning of the machine in order to address more and more complex tasks.
Humable helps companies use artificial intelligence to create new models of customer interaction. The texts, verbal expressions and the tone of voice are analyzed in an intelligent and effective way, drawing useful data to understand business phenomena and trends.
Artificial intelligence goes beyond the limits of human attention, thus eliminating accidental errors and speeding up responses.
Better motivated staff can focus on activities of higher value that can facilitate the company’s growth.
Turns data into value
Greater process efficiency, better data organization and monitoring.
Get more value from your data with Natural Language Processing
The development process
The implementation of Natural Language Processing solutions requires continuous interaction with the client to collect necessary information, in a continuously evolving state. For this reason, Humable uses an “Agile” methodology that enables the optimization of the processes and the creation of valuable solutions.
The process starts with a workshop with the client followed by biweekly sprints to check the progress of the proposed solution. Each sprint serves to discuss a new feature and to verify the satisfaction of the customer, to whom is shown the work done up to that point. Thanks to this approach, it is possible to make changes to the project easily and to identify new improvement needs for the solution.
Amazon Web Service
AWS offers the largest and deepest set of tools for businesses to create machine learning solutions faster. AWS offers services for a wide range of applications including processing, archiving, database building, networking, analysis, machine learning and artificial intelligence (AI), Internet of Things (IoT), security and development, implementation and application management, etc.
Azure offers the most advanced machine learning features. Machine learning models can be built and deployed quickly and easily using Azure Machine Learning, Azure Databricks and ONNX.
– Azure Machine Learning: A Python-based machine learning service with automated machine learning and edge deployment capabilities
– Azure Databricks: A big data service based on Apache Spark with an Azure Machine Learning integration
– ONNX: An open source and runtime model format for machine learning that allows you to easily move between the frameworks and hardware platforms of your choice.
GCP: Google Cloud Platform
Google Cloud Platform reduces the gap in complexity and offers solutions for storage space, analysis, big data, machine learning, and application development. Thanks to the training provided by Google and its resources, it is possible to start using these tools with high security.
– App Engine is a platform-as-a-service (PaaS) that can be used to distribute your code and then let the platform do the rest. App Engine automatically creates multiple instances for handling larger volumes of a high-usage app.
– Compute Engine is an IaaS (Infrastructure-as-a-service) platform that provides highly customizable virtual machines with the ability to distribute the code directly or via containers. Although it requires a greater degree of configuration and customization, Compute Engine offers greater flexibility and lower costs than App Engine.
– Kubernetes Engine enables you to use Kubernetes clusters fully managed to distribute, manage and orchestrate large-scale containers.
– Kubernetes Engine consente di utilizzare cluster Kubernetes completamente gestiti per distribuire, gestire e orchestrare container su larga scala.