Speaker
Description
Physical and Natural sciences in general have entered the domain of Big Data and the quantity, complexity and rate at which data are produced, analyzed or needed to be simulated,
is imposing considerable stress on conventional computing to extract relevant scientific information in a timely manner. Novel, disruptive techniques such as Artificial Intelligence
and, at a different level Quantum Computing, open the window for entirely new paradigms in computing.
Artificial Intelligence (AI) allows to analyze huge amounts of data, uncovering hidden correlations even without pre-existing knowledge. Among AI methods, the so-called generative models are a set of incredibly powerful techniques that learn an internal representation of a data set and use it to uncover processes and correlations that characterize the system opening novel insights and high precision.
Generative modelling will change the way of how we are doing science nowadays: “a third approach between observation and simulation”. The number of generative modelling applications in science, industry and society at large is increasing at an incredible speed.
In recent years, also Quantum Computing (QC) has developed from the stage of laboratory experiments to an amazing and rapidly evolving research field. With the increasing availability of quantum computers in the range of 10-100 qubits, a large variety of practical applications is being explored, targeting both near-term noisy quantum computer hardware employing error correcting methods as well as future fault-tolerant quantum computers.
This presentation will outline how techniques, such as AI and QC, are changing the way we formulate problems, validate solutions, and design the computing models for Natural
Sciences.
The rapid evolution in AI as well as in QC asks for technology synergies with industry at the earliest stage. Examples on how the CERN Openlab is cooperating and jointly developing
applications with partners in industry is lined out.
