Sunday idiom on Tuesday
Title: Urban “facing-the-bottom” coloured with gold
Prendere per oro colato (take something for cast gold)
Take something at face value
Etwas für bäre Münze nehmen (take something for cash)
Prendre pour argent comptant (take something for cash)
Andare a fondo di qualcosa (get to the bottom of something)
Get to the bottom of something
Einer Sache auf den Grund gehen (get to the bottom of something)
Aller au fond des choses (get to the bottom of something)
Meaning and origin of “To take at face value”: it means to accept something as it appears, without looking for a hidden meaning or an ulterior motive. To take what someone says at face value means you accept the truthfulness and sincerity of the sentiment.
The idiom dates to the 1850s.
(Source: Grammarist)
Definition of get to the bottom of:
to find out the true reason for or cause of (something)
(Source: Merriam-Webster)
Meaning
– to find out the truth relating to a situation
– to uncover what exactly happened
– to reveal facts through investigation
The speculation around the origin lies in unearthing architectural artefacts relating to other eras to be able to find out more about that time. To get to the bottom would literally require to dig things out of the bottom. This helps archaeologists find out the truth about the way of life and make estimates about how advanced the race was. The literary origin is, however, not traceable. (Source: theidioms.com)
Now that we experience infodemic* in journalism and mass communication I almost miss storytelling*.
Still, I know that all is not lost.
In a world where human intelligence and Artificial Intelligence* (AI) interact daily; where machine learning* and deep learning*/artificial neural networks* have been inspired by the biological neural network and designed to continually process information and analyse data with a logic structure similar to how a human would draw conclusions: we more than ever need to use our adaptive skills and behaviours.
And, as inspiring human beings, we at least have the responsibility to set an example when we use our neural networks.
At this point in history we should also be aware of “the danger of a single story“**. Even though this single story is so captivating and rewarding for our way of thinking and it makes our lives apparently easier, it should never be taken at face value.
* Meanings hereunder in English and Italian
**Il pericolo di una storia unica – The danger of a single story – Die Gefahr einer einzigen Geschichte – Le danger d’une histoire unique (Chimamanda Ngozi Adichie at TEDGlobal 2009)
Infodemic
A surfeit of information about a problem that is viewed as being a detriment to its solution. (Source: Lexico)
Infodemia s. f. Circolazione di una quantità eccessiva di informazioni, talvolta non vagliate con accuratezza, che rendono difficile orientarsi su un determinato argomento per la difficoltà di individuare fonti affidabili.
(Source: Treccani – neologismi 2020)
Storytelling
The activity of telling or writing stories.
Storytelling (story-telling)
Affabulazione, arte di scrivere o raccontare storie catturando l’attenzione e l’interesse del pubblico. (Source: Treccani – neologismi 2008)
Artificial Intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. (Source: Britannica)
Intelligenza Artificiale (IA) Disciplina che studia se e in che modo si possano riprodurre i processi mentali più complessi mediante l’uso di un computer. Tale ricerca si sviluppa secondo due percorsi complementari: da un lato l’i. artificiale cerca di avvicinare il funzionamento dei computer alle capacità dell’intelligenza umana, dall’altro usa le simulazioni informatiche per fare ipotesi sui meccanismi utilizzati dalla mente umana. (Source: Treccani)
Machine learning
in artificial intelligence (a subject within computer science), discipline concerned with the implementation of computer software that can learn autonomously. (Source: Britannica)
More in detail: “Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions”
An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences with other listeners who have a similar musical taste. This technique, which is often simply touted as AI, is used in many services that offer automated recommendations.
Machine learning fuels all sorts of automated tasks that span across multiple industries, from data security firms that hunt down malware to finance professionals who want alerts for favorable trades. The AI algorithms are programmed to constantly be learning in a way that simulates as a virtual personal assistant—something that they do quite well.
Now, the way machines can learn new tricks gets really interesting (and exciting) when we start talking about deep learning and deep neural networks. (Source)
Machine Learning
Branca dell’Intelligenza Artificiale che si occupa dello sviluppo di algoritmi e tecniche finalizzate all’apprendimento automatico mediante la statistica computazionale e l’ottimizzazione matematica. (Source: Treccani – neoligismi 2019)
Deep learning is considered an evolution of machine learning. It uses a programmable neural network that enables machines to make accurate decisions without help from humans.
Deep learning loc. s.le inv. Nell’Intelligenza Artificiale, classe di algoritmi di apprendimento automatico che utilizza livelli multipli per estrarre progressivamente caratteristiche di livello superiore dall’input grezzo. (Source: Treccani – neologismi 2019)
Neural network, a computer program that operates in a manner inspired by the natural neural network in the brain. The objective of such artificial neural networks is to perform such cognitive functions as problem solving and machine learning.
Rete neurale artificiale locuz. sost. f. – Modello semplificato del sistema nervoso, formato da unità che corrispondono alle cellule nervose (neuroni) collegate tra loro da connessioni unidirezionali che corrispondono alle sinapsi tra i neuroni. In ogni determinato istante ciascuna unità della rete ha un livello quantitativo di attivazione corrispondente al ritmo con cui un neurone inivia gli impulsi nervosi che viaggiano lungo l’assone del neurone stesso e vanno a influenzare il livello di attivazione dei neuroni collegati. (Source: Treccani – Lessico del XXI Secolo – 2013)
You find two interesting and simple articles concerning the above AI related terms here and here.