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Il termine intelligenza artificiale potrebbe far pensare a macchine dotate di vera conoscenza, in grado di ragionare e consapevoli di ciò che stanno facendo; le cose, invece, sono molto diverse: il fatto che un software impari a riconoscere se in una foto sono presenti dei gatti non significa che sappia che cosa sia un gatto; allo stesso modo, il computer che ha battuto Lee Sedol, il maestro di Go, non aveva la più pallida idea di che cosa stesse facendo (e lo stesso vale per lo storico esempio riguardante le partite a scacchi tra Deep Blue di IBM e Gary Kasparov). Leggi tutto:  https://andreadanielesignorelli.com/2020/10/31/intelligenza-artificiale-come-impara-apprende/

What are analytics? The dictionary tells us the word entered English in the late sixteenth century from Aristotle’s analytiká , an Ancient Greek root meaning “sports talk for nerds who’ve never won a tackle.” Since formal syllogisms aren’t much help in scouting left backs, in soccer we usually reserve the term for data analytics—you know, stats. Tables and figures. Messi vizzes that’ll do numbers on Twitter. Anything that sets out to ruin the beautiful game by turning it into math class, that’s analytics. The best reason to try to measure the sport is the same reason people used to say it couldn’t be measured: soccer is hard. Even coaches and analysts and scouts who’ve spent their lives learning to watch it won’t see games quite the same way. There are too many moving parts, too many possibilities to hold in your head at once. Had we but world enough and time, you might rewatch each match over and over to pause and study it and it’d still be impossible to see and remember it all. And ...

Intervista a Mark Thompson, presidente e CEO di New York Times Company, sulla transizione dal modello cartaceo ad un modello digitale https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/building-a-digital-new-york-times-ceo-mark-thompson

If you're new to data analytics in soccer and want to learn about data sources and datasets available to you, how to get to them and what to expect, you might find the below article of interest. It is meant to give you a quick primer and introduction to the most popular sources known to me at this point, different types of data and their basic applications. Read more:  https://www.linkedin.com/pulse/soccer-analytics-data-beginners-guide-christian-kotitschke/

Artificial intelligence (AI) seems to be everywhere. We experience it at home and on our phones. Before we know it—if entrepreneurs and business innovators are to be believed—AI will be in just about every product and service we buy and use. In addition, its application to business problem solving is growing in leaps and bounds. And at the same time, concerns about AI’s implications are rising: we worry about the impact of AI-enabled automation on the workplace, employment, and society. A reality sometimes lost amid both the fears and the headline triumphs, such as Alexa, Siri, and AlphaGo, is that the AI technologies themselves—namely, machine learning and its subset, deep learning—have plenty of limitations that will still require considerable effort to overcome. This is an article about those limitations, aimed at helping executives better understand what may be holding back their AI efforts. Read more:  https://www.mckinsey.com/capabilities/quantumblack/our-insights/what-ai-ca...

Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information. Analyses of team tactics require however detailed data from various sources including technical skill, individual physiological performance, and team formations among others to represent the complex processes underlying team tactical behavior. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer. In parts, this has also been due to the lack of available data. Increasingly however, detailed game logs obtained through next-generation tracking technologies in addition to physiological training data collected through novel miniature sensor technologies have become available for research. This leads however to the opposite problem where the shear amount of data becomes an obstacle in itself as methodological guidelines as well as theoretical modelling of tactical decision making in team sports...

Artificial intelligence is rapidly reshaping entire industries, from finance to media to healthcare, with significant breakthroughs accelerating within the past few months alone. The art world will be no exception, as AI could offer boundless potential, from helping art businesses operate more efficiently to artistic creation. While artists using AI, such as Refik Anadol, Mario Klingemann, and Sougwen Chung, have gained institutional recognition and market traction, the technology remains both a powerful tool and a point of contention across the commercial art ecosystem. ... That tension is a key takeaway from our inaugural 2026 Artsy AI Survey, the first poll of its kind. With responses from more than 300 gallery professionals, we found that while AI is now widely used for day-to-day administrative and operational tasks, skepticism persists around its legitimacy as an artistic medium and its long-term impact on the art market. Read more: https://www.artsy.net/article/artsy-editoria...