artificial intelligence – Pagina 3

How many fields in data science?

Massimo2021-06-270460 views

Data Science is a research activity… mostly Data-driven scientific discovery is regarded as the fourth science paradigm The twenty-first century has ushered in a new age that is coined as data science and big data analytics. Data-driven scientific discovery is regarded as the fourth science paradigm. Data science has been a core driver of the new-generation science, technologies and economy, and is driving new researches, innovation, profession, applications and education across both disciplines and business domains. There are many scientific and technical challenges associated with big data, ranging from data capture, creation, storage, search, sharing, modeling, representation, analysis, learning, visualization, explanation, and decision making. Among the many data characteristics and complexities to be addressed, I mention the hybridization of heterogeneous, multisource, hierarchical, interactive, dynamic,…

Speaker at FUTURE Labs 2021

Massimo2021-05-250501 views

At FUTURE Labs 2021, the spotlight on Artificial Intelligence’s role in Research and Development underscores its pivotal contribution to shaping the laboratories of tomorrow. The conference, renowned for its diverse assembly from academia to industry giants across various sectors, including Biotech, Pharma, and more, serves as a crucible for innovation. It invites a confluence of ideas and visions, aiming to redefine laboratory operations and efficiency. With discussions spanning nine crucial themes, including AI & Machine Learning, Digital Transformation, and Data Management, the event promises a comprehensive exploration of the technological forefront, all delivered in English, facilitating a global discourse.

Speaker at the next Future Labs 2021

Massimo2021-01-290489 views

In the realm of innovation, Future Labs Live 2021 stands as a beacon, uniting over eighty global experts in a comprehensive dialogue on the forefront of digital transformation, data science, Artificial Intelligence, and Machine Learning. This event, spanning two days, is pivotal, addressing the urgent need for technological, organisational, and cultural shifts across industries.

Interpreting deep learning models

Massimo2020-07-170433 views

Interpreting deep learning models is crucial for diverse applications such as healthcare and self-driving cars. Understandably, errors can have catastrophic consequences. Thus, achieving interpretability is essential for decision-makers. Properties like fidelity, comprehensibility, and accuracy are vital for evaluating interpretability. Various methods, including visualization techniques and knowledge distillation, offer insights into complex models. However, quantifying interpretability remains a challenge. For more information, refer to research papers on refining deep neural networks and interpreting CNNs. Enhancing interpretability not only fosters trust in AI but also mitigates risks in decision-making processes.

Why Meta-learning is important

Massimo2020-03-090424 views

Meta-learning, a groundbreaking approach in AI, empowers machines to rapidly adapt and learn from minimal data. By transcending traditional machine learning, meta-learning revolutionizes various sectors like healthcare, finance, and education. This technique facilitates few-shot learning, enabling models to excel with limited examples, a paradigm shift from data-intensive methods. Meta-learning’s impact spans diverse domains, from personalized education to drug discovery in pharmaceuticals, promising accelerated innovation and optimized processes. Embracing meta-learning heralds a future where AI systems dynamically evolve and excel in novel tasks with unprecedented efficiency.