Category: Artificial intelligence

Artificial intelligence
Alessandro Fiori

AI: engineering prompts to reduce hallucinations [part 2]

Hallucinations, i.e., responses that appear to make sense but are actually incorrect, afflict all large language models (LLMs). There are some techniques that can be used to mitigate this behavior. Let us discover some of them through examples and by analyzing the advantages and disadvantages.

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Artificial intelligence
Alessandro Fiori

AI: prompt engineering to reduce hallucinations [part 1]

Prompt engineering techniques allow us to improve the reasoning and responses provided by LLMs , such as ChatGPT. However, are we sure that the responses received are correct? In some cases no! When this happens, the model is said to have hallucinated. Let’s find out what this is and what are the techniques to reduce the probability of getting them.

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Artificial intelligence
Alessandro Fiori

Gradio: web applications in Python for AI [Part 3]

With Gradio, it is possible to create web applications for our machine learning and AI models in just a few lines of code. Through some examples, we will see the advanced features available, such as authentication, caching, and input file processing. We will also build a chatbot and an image classifier from pre-trained models. Finally we will discuss how to deploy our project in a few simple steps.

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Artificial intelligence
Alessandro Fiori

Gradio: web applications in python for AI [part2]

Gradio is a python library that allows us to create web applications quickly and intuitively for our machine learning and AI models. Our applications always require user interaction and layout customization. Let us find out, through examples, how to improve our applications.

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Artificial intelligence
Alessandro Fiori

Gradio: web applications in python for AI [part1]

Writing web applications for our machine learning and/or artificial intelligence models can take a lot of time and skills that we do not possess. To streamline and speed up this task we are helped by Gradio, a Python library designed to create web applications with just a few lines of code. Let’s discover its basic functionality with some examples.

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Artificial intelligence
Alessandro Fiori

AI: the best prompt techniques for leveraging LLMs

Prompt techniques are the basis for the use of LLMs. There are several studies and guidelines for obtaining the best results from these models. Let us analyze some of them to extract the basic principles that will allow us to obtain the desired answers according to our task.

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Artificial intelligence
Alessandro Fiori

AI: create a chatbot with your own data

ChatGPT allows us to have a virtual assistant at our complete disposal. However, it has one major limitation: it does not know our private data. How can we build our own virtual assistant, or rather a chabot, that uses our data and does not require us to invest money? Let’s find out how to build one using open LLM, free computational environments such as Colab, and Python libraries to manage PDF files and create simple and intuitive web interfaces.

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Artificial intelligence
Alessandro Fiori

Exploring artificial intelligence: deep learning project ideas

Artificial intelligence has been gaining enormous interest in recent times. The application of deep learning and AI models to real scenarios has opened up new horizons. To generate models for our applications, however, we need data on which to train these models. Let us explore some ideas that could enable us to create new applications and services.

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Artificial intelligence
Alessandro Fiori

Prompt engineering: prompting techniques [part 2]

The techniques proposed by prompt engineering allow even very complex tasks to be performed. Some of them, such as zero-shoot, few-shoot and Chain-of-Thought (CoT), manage to provide excellent results in some contexts. Where models have limitations, however, they can be remedied by even more advanced techniques such as Self-Consistency, Generated Knowledge and Tree of Thoughts.

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Artificial intelligence
Alessandro Fiori

Prompt engineering: prompting techniques [part 1]

Prompt engineering makes it possible to optimize the results obtained by acting only on what is provided to the LLMs. In recent years, several techniques have been proposed to appropriately structure prompts to solve even complex tasks without having to retrain the initial model. We will look at some simple techniques such as zero-shoot, few-shoot and Chain-of-Thought (CoT).

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