level statistical information on firms has been collected since the early 1950s. back then, only a few aggregate statistics were available. we began by surveying the balance sheets of a small group of firms, an activity that was progressively expanded until it became what is now the survey on industrial and service firms. these days, we collect detailed data on about 5, 000 firms. at the beginning of the 1960s, the bank began surveying household finance. nowadays, this survey allows us to evaluate income and wealth distribution at the micro level in order to analyse individual preferences and the determinants of their economic decisions. in recent years, we have interviewed a sample of more than 8, 000 households in each survey ; about 50 per cent of them were part of a panel. in 2016, banca d ’ italia started its project on big data and machine learning applications. we wanted to fine - tune the accuracy of our economic analysis by combining all available skills ranging from economics to statistics, from computer science to engineering, and from mathematics to applied physics. this year we have stepped up our action by strengthening the team with new resources from universities and by widening our data collection from private companies. 1 3. the covid - 19 pandemic has radically transformed our lives in a number of different ways. direct personal contacts have been partly replaced by online events. most meetings around the world are now organized via multiple it applications. this has allowed many of us to keep working in safety. it has also reduced both travelling cost and carbon emissions. these changes have been made possible by the progress of it in many areas, from core technology advances – like further doublings of basic computer power every 18 months – to successful investment in complementary innovations, like cloud infrastructure. one of the most important developments is the wave of recent improvements in artificial intelligence, especially machine learning. such as, for example, immobiliare. it, mutuionline. it, twitter, indeed, dow jones, etc. machine learning represents a paradigm shift from the first wave of computerization. historically, most computer programs were created by accurately codifying human knowledge, mapping inputs to outputs as prescribed by the programmers. in contrast, machine - learning systems use categories of general algorithms ( e. g. neural networks, random forest and gradient boosting ) to figure out mappings and are typically driven by voluminous amounts of data. by employing huge data sets and big data processing resources, machines have made impressive progress. for instance, error rates

us to act intelligently, as well as boldly and courageously if necessary. will the euro one day be as important a currency as the us dollar? that is not the aim ; the euro was not created for this purpose. my predecessor wim duisenberg and i have always said that the euro was not created to either compete with or replace another currency. we are not campaigning for the international use of the euro. the euro was created in order to achieve the single european market. if we can achieve this goal, we will provide an important basis for prosperity, stability and peace in europe.