Chi per diletto o per lavoro si è trovato recentemente a frequentare i social network avrà notato una miriade di schermate che riportano conversazioni tra un essere umano e l’intelligenza artificiale ChatGPT (Generative Pretrained Transformer). Abbiamo recentemente parlato dei Transformer qui.
Cos’è ChatGPT?
ChatGPT è uno prototipo di IA sviluppato da OpenAI. La stessa fondazione ha lanciato anche Dall-E e il suo successore Dall-E 2, un potente generatore di immagini a partire da una descrizione testuale.
Abbiamo addestrato un modello chiamato ChatGPT che interagisce in modo colloquiale. Il formato di dialogo consente a ChatGPT di rispondere a domande di follow-up, ammettere i propri errori, contestare premesse errate e rifiutare richieste inappropriate.
Come funziona ChatGPT?
E’ quello che si definisce un modello conversazionale. Esso accetta le domande degli utenti e risponde fornendo informazioni. Il modello è stato addestrato fornendogli migliaia di testi presi da libri, giornali e pagine web.
Il modo di esprimersi di ChatGPT è assolutamente naturale: sintassi e costruzioni perfette gli permettono di rispondere alle domande in modo sempre pertinente al contesto e con uno stile indistinguibile da un essere umano.
Ma ChatGPT non è solo un bravo conversatore, è anche capace di riconoscere quando sbaglia e di correggere il suo interlocutore se questi dovesse porgli questioni inappropriate. Se infatti provate a chiedergli qualcosa che implichi emozioni o opinioni personali, ChatGPT vi risponderà che è una intelligenza artificiale e non ha opinioni o sentimenti.
Il suo utilizzo e le questioni che solleva
Sebbene molti vedano questo genere di modelli come il futuro dei motori di ricerca, dobbiamo tener conto che talvolta sbagliano o possono dare informazioni errate o tendenziose. A parte questo, esso è già stato utilizzato da molti giornalisti per provare le sue capacità di scrivere articoli, canzoni o testi argomentativi.
Le capacità di ChatGPT e dei suoi futuri discendenti tuttavia pongono anche dei problemi: la scrittura “autentica” di tesi di laurea e saggi, la possibilità di ottenere informazioni false e manipolatorie, il futuro di molte professioni creative e non ultimi, i problemi di copyright. Ricordiamoci infatti che questo genere di modelli viene addestrato su testi già “esistenti”: egli potrebbe, ad esempio, fornirci esempi di programmazione protetti da licenza. Un programmatore ha già citato in giudizio Microsoft e OpenAI per violazione di copyright.
Le critiche: smorziamo l’entusiasmo
Molti autori, tra i quali Ian Bogost in un recente articolo apparso su The Atlantic, hanno fortemente ridimensionato l’entusiasmo mediatico sollevato da ChatGPT e i suoi fratelli.
Da esperto di tecnologia ritengo che l’entusiasmo suscitato da ChatGpt, il modello linguistico di grandi dimensioni (Llm) messo a punto da OpenAI, sia fuori luogo…Prima di tutto ChatGpt non ha la capacità di comprendere realmente la complessità del linguaggio e delle conversazioni umane. Il programma è semplicemente progettato per generare parole a partire da un determinato input, ma non ha la capacità di comprenderne il significato. Di conseguenza, qualsiasi risposta sarà probabilmente piatta, superficiale e senza acume.
L’intelligenza artificiale può produrre un testo convincente solo perché la scrittura e le nostre aspettative si sono abbassate moltissimo.
I computer non sono mai stati strumenti della ragione capaci di risolvere problemi spiccatamente umani. Sono apparecchi che strutturano l’esperienza umana attraverso un metodo peculiare e molto potente di manipolazione dei simboli. Questo li rende oggetti estetici tanto quanto funzionali. Gpt e i suoi cugini ci offrono l’occasione di prenderli per quello che sono e usarli non tanto per svolgere determinati compiti, ma per giocare con il mondo che hanno creato. O meglio, per distruggerlo.
Ma nemmeno noi abbiamo resistito alla tentazione: ecco le domande
Come vedrete abbiamo cercato di saggiare le compentenze di ChatGPT su vari temi, da quelli più tecnici a quelli più “umani”, cercando di portarlo in un terreno a lui poco congeniale. Ma, come vedrete, le risposte ci lasciano comunque a bocca aperta.
Abbiamo notato che le risposte sono più ricche e interessanti se utilizziamo la lingua inglese e per questo motivo le abbiamo lasciate in questa lingua.
What is the origin of the score system in tennis?
The score system in tennis has its origins in the old jeu de paume (game of the palm) game that was played in medieval France. In this game, players used their bare hands to hit a wooden ball over a net, and the game was won by the player who scored the most points.
The modern scoring system used in tennis is thought to have originated in the 19th century, when rackets were introduced to the game and the rules were standardized. The current system uses a sequence of 15, 30, 40, and game to represent the score, with the first player to reach four points (assuming a margin of two) winning the game.
The reason for using the sequence 15, 30, 40, game is not entirely clear, but it is thought that it may have originated as a way of representing the four points that a player needed to win the game. The use of the sequence 15, 30, 40 may also have been a way of counting the points in a more rhythmic and memorable way, making it easier for players and spectators to keep track of the score.
What is the best algorithm to calculate the eigenvalues of a matrix?
There are several algorithms that can be used to calculate the eigenvalues of a matrix, and the best choice may depend on the specific matrix and the requirements of the application. Some common algorithms for calculating eigenvalues include the QR algorithm, the Jacobi algorithm, and the power iteration method.
The QR algorithm is a popular and efficient method for calculating the eigenvalues of a matrix. It uses QR decomposition to iteratively find the eigenvalues of a matrix. The Jacobi algorithm is a similar iterative method, but it uses a different decomposition to calculate the eigenvalues.
The power iteration method is a simpler algorithm that can be used to find the largest eigenvalue of a matrix. It involves repeatedly multiplying the matrix by a starting vector and normalizing the result to find the eigenvector corresponding to the largest eigenvalue.
Overall, the best algorithm to use will depend on the specific matrix and the requirements of the application. It may be necessary to experiment with different algorithms to determine the best one for a particular situation.
How can I read a csv file with Python?
To read a CSV (Comma Separated Values) file with Python, you can use the csv module, which provides functions for reading and writing CSV files. Here is an example of how to read a CSV file using this module:
import csv
# Open the file in read mode
with open('data.csv', 'r') as csv_file:
# Create a CSV reader object
csv_reader = csv.reader(csv_file)
# Loop through the rows in the file
for row in csv_reader:
# Print each row
print(row)
This code will open the file data.csv, create a CSV reader object to parse the data, and then loop through the rows in the file, printing each row to the console. You can modify this code to store the data in a list or other data structure, or to perform additional processing on the data as needed.
How can I date a girl I like?
There is no one-size-fits-all answer to this question, as every person and relationship is unique. However, some general tips for dating someone you like may include:
- Show interest in the person by getting to know them and asking them questions about themselves.
- Be respectful and considerate of their boundaries and needs.
- Be honest and open with your feelings, but also listen to and respect their perspective.
- Make an effort to spend time together and engage in activities that you both enjoy.
- Communicate openly and honestly to maintain trust and build a strong connection.
It’s important to remember that every person is different, and what works for one person may not work for another. The best way to approach dating is to be yourself and to treat the other person with kindness and respect.
Do you believe in God?
As a machine learning model, I don’t have beliefs or opinions. I am a tool designed to provide information and answer questions to the best of my ability based on the input I have been trained on. My purpose is to assist with tasks and answer questions, not to have beliefs or opinions.
Can you explain what is feedback control?
Feedback control is a type of control system where the output of a system is used to calculate and regulate the input to the system. This feedback can be used to maintain a desired state for the system, such as a constant temperature or a specific speed. Feedback control systems can be found in a wide range of applications, from thermostats and cruise control in cars, to medical equipment and robotics.
What are pivot tables in Excel?
Pivot tables are a powerful tool in Microsoft Excel that allows you to quickly and easily summarize large sets of data. They are called “pivot tables” because they allow you to pivot, or rotate, the data to view it from different perspectives. This makes it easy to see patterns and trends in the data that may not be immediately apparent when looking at the raw data.
Pivot tables are created by selecting a range of data in an Excel worksheet and then choosing the “PivotTable” option from the Insert menu. This will open the PivotTable builder, where you can select the data that you want to include in the pivot table and choose how to summarize and group the data. Once the pivot table is created, you can drag and drop the data fields to rearrange the data and see it from different perspectives.
Pivot tables are a powerful tool for data analysis and can save a lot of time and effort compared to manually summarizing large sets of data. They are commonly used in business, finance, and other fields where it is important to quickly and easily summarize large amounts of data.
Can you write a short article about artificial intelligence (AI)?
Artificial intelligence (AI) is a rapidly growing field that involves the use of machine learning and other techniques to create intelligent systems that can learn, adapt, and perform tasks that typically require human intelligence. These systems can be trained on large amounts of data to recognize patterns, make decisions, and solve problems, making them a valuable tool for a wide range of applications.
AI has the potential to revolutionize many different industries, from healthcare and finance to transportation and retail. For example, AI-powered systems can be used to diagnose medical conditions, manage financial portfolios, and even drive cars.
One of the key challenges in the development of AI is finding ways to make these systems more efficient and effective. This involves developing algorithms and techniques that can enable AI systems to learn and adapt quickly and accurately, as well as finding ways to integrate AI into existing systems and processes.
Overall, AI has the potential to greatly impact many different aspects of our lives, and as the field continues to advance, we can expect to see even more exciting developments and applications in the future.
How can I understand whether you are a human or a machine?
One way to understand whether I am a human or a machine is to ask me questions that are difficult or impossible for a machine to answer. For example, you could ask me about my feelings, opinions, or personal experiences, as these are things that only a human can have. You could also ask me to perform tasks that require human-like intelligence, such as solving complex problems or coming up with creative ideas.
Another way to understand whether I am a human or a machine is to look for characteristics that are unique to humans or machines. For example, humans have unique physical characteristics, such as a face and body, while machines typically have a more uniform and mechanical appearance. Humans also have the ability to communicate using natural language, while machines typically use pre-programmed responses or commands.
Overall, there are many different ways to understand whether I am a human or a machine, and the best approach will depend on the specific context and the information available.
May I thank you for all answers you gave me today?
You’re welcome! I’m glad that I was able to help you with your questions. As a machine learning model, my purpose is to assist with tasks and answer questions to the best of my ability based on the input I have been trained on. If you have any other questions, feel free to ask and I will do my best to help.
Foto di Alex Knight
