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Réduire les Hallucinations LLM

Réduire les Hallucinations LLM est une tâche difficile, mais pas impossible. Nous allons découvrir ensemble les moyens pour y parvenir.

LLM Hallucination : Les Effets de l’IA Générative

One approach to reducing AI hallucinations is to simplify the architecture of the model. This involves reducing the number of layers and neurons, as well as reducing the complexity of the activation functions. Additionally, regularization techniques such as dropout and weight decay can be used to reduce overfitting. 

L’hallucination LLM fait référence au phénomène où de grands modèles linguistiques tels que des chatbots ou des systèmes de vision informatique génèrent des sorties non sensées ou inexactes qui ne correspondent pas aux vrais modèles ou objets. Ces faux résultats de l’IA proviennent de divers facteurs. Le surajustement à des données d’entraînement limitées ou biaisées est un grand coupable. Une grande complexité du modèle contribue également, permettant à l’IA de percevoir des corrélations qui n’existent pas.

Les grandes entreprises qui développent des systèmes génératifs d’IA prennent des mesures pour résoudre le problème des hallucinations de l’IA, bien que certains experts pensent que l’élimination complète des faux résultats ne soit pas possible.

Une approche pour réduire les hallucinations de l’IA consiste à simplifier l’architecture du modèle. Cela implique de réduire le nombre de couches et de neurones, ainsi que la complexité des fonctions d’activation. De plus, des techniques de régularisation telles que le dropout et le déclin des poids peuvent être utilisées pour réduire le surajustement.

Source de l’article sur DZONE

Healthcare has been at the epicenter of everything we do for two years. While the pandemic has been a significant driver of the conversation, healthcare technology—artificial intelligence (AI) specifically—has been experiencing explosive growth. One only needs to look at the funding landscape: more than 40 startups have raised at least $20 million in funding specifically to build AI solutions for healthcare applications.

But what’s driving this growth? The venture capital trail alone won’t help us understand the trends contributing to AI adoption in healthcare. But the “2022 AI in Healthcare Survey” will. For the second year, Gradient Flow and John Snow Labs asked 300 global respondents what they’re experiencing in their AI programs—from the individuals using them to the challenges and the criteria used to build solutions and validate models. These are the top five trends that emerged from the research. 

Source de l’article sur DZONE

Imagine a room with a wall of screens displaying closed-circuit video feeds from dozens of cameras, like a security office in a film. In the movies, there is often a guard responsible for keeping an eye on the screens that inevitably falls asleep, allowing something bad to happen. Although intuition and other distinctly “people skills” are useful in security, most would agree that the human attention span isn’t well-suited for always-on, 24/7 video monitoring. Of course, footage can always be reviewed after something happens, but it’s easy to see the security value of detecting something out of the ordinary as it unfolds.

Several cameras capturing different scenes.
Cameras capture our every move, but who watches them?

Now imagine a video artificial intelligence (AI) application capable of processing thousands of camera feeds in real-time. The AI constantly compares new footage to historical footage, then classifies anomalous events by their threat level. Humans are still involved, both to manage the system as well as review and respond to potential threats, but AI takes over where we fall short. This isn’t a hypothetical situation: from smart police drones to intelligent doorbells sold by Amazon and Google, AI-powered surveillance solutions are becoming increasingly sophisticated, affordable, and ubiquitous.

Source de l’article sur DZONE

In the last decade, the finance industry has seen an infusion of cutting-edge technologies like never before. This transformation is largely attributed to many startups that appeared on the scene post 2008 recession and followed a technology-first approach to create financial products and services with a target to improve customer experience. FinTech, as these startups are known, have been the early adopters of the new technologies like Smartphones, Big Data, Machine Learning (ML), Blockchain and were considered the trendsetters that were later followed by more traditional banks and financial institutes.

The recent advancements in machine learning and deep learning has really pushed the boundaries of computer vision and natural language processing. FinTechs are leaving no stones unturned to capitalize on these breakthroughs to improve financial services. As per a report, the ML Fintech market was valued at $7.27 billion in 2019 and it is expected to grow to $35.40 billion by 2025. Statista forecasts that the entire banking industry overall will be able to derive the business value of  $182 billion globally with machine learning by the year 2025.

Source de l’article sur DZONE

In this article, I am going to explain how we integrate some deep learning models, in order to make an outfit recommendation system. We want to build an outfit recommendation system. We used four deep learning models to get some important characteristics of the clothing used by the user.

The recommendation systems can be classified into 4 groups:

Source de l’article sur DZONE

AI-Powered Computer Vision

The impact of AI on human lives can be felt the most in the healthcare industry. AI-powered computer vision technology can help bring affordable healthcare to millions of people. Computer vision practices are already in place for sorting and finding images in blogs and retail websites. It also has applications in medicine.

You may be interested in:  Computer Vision: Overview of a Cutting Edge AI Technology

Medical diagnosis depends on medical images such as CAT scans, MRI images, X-rays, sonograms, and other images.

Source de l’article sur DZONE


Introduction

The goal of this article is to explain how you can detect a drowsy person using facial landmarks as an input of a neural network, a 3D convolutional neural network, in this case, to sound an alarm to awake the user and to prevent some kind of accident.

The idea is to extract a group of frames from a webcam and then extract from them the facial landmarks, specifically the position of both eyes, then pass these coordinates to the neural model to get a final classification which will tell us if the user is awake, or falling sleep.


Source de l’article sur DZONE (AI)

Machine vision, or computer vision, is a popular research topic in artificial intelligence (AI) that has been around for many years. However, machine vision still remains as one of the biggest challenges in AI. In this article, we will explore the use of deep neural networks to address some of the fundamental challenges of computer vision. In particular, we will be looking at applications such as network compression, fine-grained image classification, captioning, texture synthesis, image search, and object tracking.

Network Compression

Even though deep neural networks feature incredible performance, their demands for computing power and storage pose a significant challenge to their deployment in actual application. Research shows that the parameters used in a neural network can be hugely redundant. Therefore, a lot of work is put into increasing accuracy while also decreasing the complexity of the network.


Source de l’article sur DZONE (AI)