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ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Fashion or real innovation?
As steam, electricity and internet did in the past, Artificial Intelligence is transforming our world in a definitive way, giving life to what experts already consider the Fourth Industrial Revolution.
Artificial intelligence has enthusiastically entered the agenda of companies, in academic and online discussions, in the collective imagination.
In a simplistic way we could define Artificial Intelligence as the ability of a technological system to solve problems or perform tasks and activities typical of the human mind and body. In practice, it consists of programming a computer to behave and perform a task as an intelligent agent (i.e. a person) would do.
The goal is therefore to create machines capable of “acting” autonomously, carrying out tasks and actions typical of human intelligence (planning, understanding of language, image and sound recognition, problem solving, pattern recognition, etc.). For this reason, under the label of Artificial Intelligence we find mathematical, computer and statistical disciplines.
In this context, the algorithm is recognized as being of central importance. An algorithm is a well-defined calculation procedure which, starting from certain input values, provides certain output values. So, nothing more than a finite sequence of steps that transform inputs into outputs. In the field of Artificial Intelligence, however, the algorithm acquires its own autonomy: once the training has been carried out, the system voluntarily starts the action in its environment and pursues objectives without interacting with the human agent.
How Artificial Intelligence works.
Talking about AI actually means talking about different technologies and tools. There are four key points that help us define the activity of Artificial Intelligence.
Through the simulation of cognitive abilities of data and event correlation, AI (Artificial Intelligence) is able to recognize texts, images, tables, videos, voice and extract information from them.
Through logic, the systems are able to connect the multiple information collected (through precise mathematical algorithms and in an automated way).
In this case we are talking about systems with specific functions for the analysis of data inputs and for their “correct” return in output (it is the classic example of Machine Learning systems that with machine learning techniques lead AI to learn and perform various functions).
Human Machine Interactions, that is, the methods of functioning of AI in relation to its interaction with humans. This is where Nlp – Natural Language Processing systems are strongly advancing, technologies that allow humans to interact with machines (and vice versa) by exploiting natural language.
ICG offers customized Artificial Intelligence and Machine Learning solutions for Insurance Players
ICG supports companies in exploiting the potential of Artificial Intelligence to implement sector-specific solutions. Thanks to the skills consolidated during the implementation of numerous PoCs with latest generation technologies, it can be considered at the forefront of these issues that will inevitably mark the near future of the insurance world.
Why are we talking about Artificial Intelligence in Insurance?
Automation through artificial intelligence is destined to permeate all processes in the insurance world, giving a significant opportunity to streamline and optimize processes and reduce management costs.
Carrying out repetitive and low added value activities through these technologies leads to an improvement in process efficiency and more easily ensures compliance with increasingly stringent regulatory requirements for verifiability, security, data quality and operational resilience.
Technology will drastically change the insurance workforce in the years to come: there will be less demand for resources to handle routine processing tasks, and at the same time the workforce will begin to favor creative thinkers and market strategy connoisseurs. The resources able to collaborate effectively with the technology and train it, as if it were an apprentice, will become fundamental.
In fact, these new technologies cannot ignore interaction with man: they are at his service and work in an integrated way with human intelligence. In fact, Artificial Intelligence can analyze large amounts of data very quickly and, with self-learning processes, progressively improve the performance of the systems. However, it always needs a guide and a teacher who asks the correct questions and objectives: the algorithm provides results and modifies its behavior based on human user feedback.
AI and Big Data
Every day Terabytes of data are produced and exchanged by companies, organizations and users which, for a large percentage, are not structured data, that is, they cannot be traced back to fixed and easily usable structures.
The management of such a large amount of data is not possible by humans without the aid of computer systems. The unstructured nature of the data implies a further difficulty, because it requires a capacity for abstraction and analysis that cannot be managed by traditional analytics.
In this context, only deep neural networks, or Deep Neural Networks, have a learning mode that replicates human reasoning in an algorithmic form and try to induce solutions by learning from heterogeneous and unstructured data.
The learning models of neural networks
In order for this process to be efficient, it is necessary to “train” the neural networks, that is to make them learn how to behave when an engineering problem has to be solved, such as the recognition of a human being from the analysis of images (through for example facial recognition technology).
A neural network actually looks like an “adaptive” system capable of modifying its structure (nodes and interconnections) based on both external data and internal information that connect and pass through the neural network during the learning phase and reasoning.
The revolution brought about by Deep Learning is evident on many fronts, so much so that starting from 2015 algorithms that exploit deep neural networks have exceeded the capabilities of man in simple tasks such as image recognition and transcription of audio into text.
The merits of Deep Learning are to be found in the ability to obtain ever better results by increasing the complexity of the neural network or by adding unstructured input data to the model. From the point of view of performance, the possibility of exploiting GPUs for massive computations has also favored the introduction of more complicated algorithms in production environments.
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