Word embeddings have become one of the most used tools and main drivers of the amazing achievements of Artificial Intelligence tasks that require processing natural languages like speech or texts. In…

The path to becoming a front-end developer, as looked back upon by anyone who self-identifies that way, is likely a very windy one full of thorn bushes and

Artificial Intelligence Overview AI refers to ‘Artificial Intelligence’ which means making machines capable of performing intelligent tasks like human beings. AI performs automated tasks using intelligence. The term Artificial Intelligence has two key components – Automation Intelligence For More Information you visit Original Blog Goals of Artificial Intelligence Stages of Artificial Intelligence Stage 1 –… Read More »Overview of Artificial Intelligence and Role of Natural Language Processing in Big Data

Deep learning models contributed to reaching unprecedented results in prediction and classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable progress, they do not provide human-understandable insights on how a specific result was achieved. In contexts where the impact of AI on human life is relevant (e.g., recruitment tools, medical diagnoses, etc.), explainability is not only a desirable property, but it is -or, in some cases, it will be soon-a legal requirement. Most of the available approaches to implement eXplainable Artificial Intelligence (XAI) focus on technical solutions usable only by experts able to manipulate the recursive mathematical functions in deep learning algorithms. A complementary approach is represented by symbolic AI, where symbols are elements of a lingua franca between humans and deep learning. In this context, Knowledge Graphs (KGs) and their underlying semantic technologies are the modern implementation of symbolic AI—while being less flexible and robust to noise compared to deep learning models, KGs are natively developed to be explainable. In this paper, we review the main XAI approaches existing in the literature, underlying their strengths and limitations, and we propose neural-symbolic integration as a cornerstone to design an AI which is closer to non-insiders comprehension. Within such a general direction, we identify three specific challenges for future research—knowledge matching, cross-disciplinary explanations and interactive explanations.

My first steps into the world of A.I. (Part 2)

Artificial Intelligence Overview AI refers to ‘Artificial Intelligence’ which means making machines capable of performing intelligent tasks like human beings. AI performs automated tasks using intelligence. The term Artificial Intelligence has two key components – Automation Intelligence For More Information you visit Original Blog Goals of Artificial Intelligence Stages of Artificial Intelligence Stage 1 –… Read More »Overview of Artificial Intelligence and Role of Natural Language Processing in Big Data

There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.