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Artificial Intelligence and Augmented Intelligence have entered the business realm, been the subject of academic and online discussions and captured the collective imagination with great enthusiasm.
The terms Artificial Intelligence and Augmented Intelligence are often used interchangeably. However, although they do share many methodological and technological similarities, these two approaches are based on two different assumptions, one placing machines and the other humans at the heart of the decision making process.
Artificial Intelligence is the collection of mathematics, computing and statistics that makes it possible to perform tasks once deemed exclusively human in nature, tackling problems typically resolved by humans through intelligence. Within this context, it is acknowledged that the algorithm has an element of independence: once the training has been completed, the system voluntarily initiates the action within its environment and pursues the objectives set without interacting with the human agent.
Conversely, Augmented Intelligence integrates and supports human thinking, analysis and planning, maintaining, however, the intent of a human player at the centre of the human-machine interaction.
During the development of Augmented Intelligence, the methods by which computer systems learn have evolved, starting with traditional statistics and moving towards the modern Deep Learning and Augmented Intelligence approaches.
The machines learn from humans: the field of traditional statistics and the first steps in supervised learning. In this case the goal is known and Artificial Intelligence is used to improve system performance, with the algorithm helping to predict the link between inputs and outputs.
The machines learn as humans: the similarity level with human learning grows and artificial intelligence elements are transformed to accommodate the change. Deep Neural Networks mimic the behaviour of the human brain and attempt to induce solutions learning from heterogeneous and unstructured data.
The machines learn with humans: the learning process becomes collaborative and the Augmented Intelligence realm is introduced. Machine learning and the human expert come together: the algorithm produces results and modifies its behaviour based on the feedback received from the human user.
Target Reply operates on many aspects: it supports companies through an end-to-end consulting process that extends from identifying customer requirements to designing and implementing concrete solutions, working with some of the most advanced technologies in the Data Integration, Data Modelling, Predictive Analytics, Machine Learning and Data Intelligence realms. Target Reply provides customers with a complete and up-to-date skill set encompassing all Data Science technologies, both commercial and Open Source. Target Reply analyses and meets customer requirements from building the architecture of the data infrastructure, to the structuring of analysis dashboards, providing customers with clear insights, applicable to their specific business in a tangible way.
Every day, terabytes of data are produced and exchanged by companies, organisations and users, a large percentage of which is not structured data and it therefore cannot be attributed to fixed structures nor is it easily usable. Managing such a large amount of data is not possible for human beings without the support of computer systems. However, the unstructured nature of the data signifies a capacity for abstraction and analysis that is not readily manageable by traditional analytics.
Target Reply’s Data Scientists team has successfully implemented various projects, applying Augmented Intelligence logic and technologies in different business contexts and highlighting the flexibility and value of this approach. Some examples: