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From Teams to PowerPoint: 10 ways Azure AI enhances the Microsoft Apps we use everyday

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Azure AI is driving innovation and improving experiences for employees, users, and customers in a variety of ways, from increasing workday productivity to promoting inclusion and accessibility. The success of Azure AI--featuring Azure Cognitive Services, Azure Machine Learning, and Azure OpenAI Service--is built on a foundation of Microsoft Research, a wide range of Azure products that have been tested at scale within Microsoft apps, and Azure customers who use these services for the benefit of their end users. As 2023 begins, we are excited to highlight 10 use cases where Azure AI is utilized within Microsoft and beyond. Speech transcription and captioning in Microsoft Teams is powered by Azure Cognitive Services for Speech. Microsoft achieved human parity in conversational speech recognition when it reached an error rate of 5.9 percent.


The future of artificial intelligence -- explained by a robot

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While Paul Budde is an expert in the field of technology, this week he's letting artificial intelligence speak for itself to discuss the current state of AI. Recently, a new AI service was launched called ChatGPT. I have used it now several times and I am amazed at the accuracy of this AI tool. ChatGPT is a large language model developed by OpenAI. It is a variant of the Generative Pre-trained Transformer (GPT) model, which uses deep learning techniques to generate human-like text. The model is trained on a massive dataset of text from the internet, allowing it to generate a wide range of text on various topics.


Top Artificial Intelligence (AI) Books to Read in 2023 - MarkTechPost

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The ability of a machine to reason, learn, and solve problems in the same ways that people do constitutes artificial intelligence. The beautiful thing about artificial intelligence is that you can construct a computer with pre-programmed algorithms that can function with its own intelligence, so you don't need to pre-program a machine to perform something. One of the most frequently used buzzwords in technology today is artificial intelligence. We're learning more and more about how computers can mimic human thought processes and even complete jobs that were once thought too complex for machines to complete, thanks to innovations like Siri and Alexa. For a while now, the concept of artificial intelligence has occupied the minds of philosophers, technologists, and science fiction writers.


Quantum machine learning (QML) poised to make a leap in 2023

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Check out all the on-demand sessions from the Intelligent Security Summit here. Classical machine learning (ML) algorithms have proven to be powerful tools for a wide range of tasks, including image and speech recognition, natural language processing (NLP) and predictive modeling. However, classical algorithms are limited by the constraints of classical computing and can struggle to process large and complex datasets or to achieve high levels of accuracy and precision. Enter quantum machine learning (QML). QML combines the power of quantum computing with the predictive capabilities of ML to overcome the limitations of classical algorithms and offer improvements in performance.


Want to Build A Career In AI? Here Are The Five Skills You Need - Clover Infotech

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Industries and processes across the globe are embracing new technologies to increase efficiency and deliver faster and accurate outcomes. Artificial Intelligence (AI) and Machine Learning (ML) have recently taken the world by storm with their advancements in delivering impactful and insightful results. Today, the recruitment sites are swarmed with AI-based jobs. Organizations across the world are looking for skilled resources in AI to help them to accelerate data analytics, research, and intelligence in operations. From robots serving food to self-driving cars to home listening devices, AI can be witnessed in our day-to-day lives.


Progress in the field of Spoken language understanding part1

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Abstract: Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling. To improve the performance of these two sub-tasks, we propose to use consistency regularization based on a hybrid data augmentation strategy. The consistency regularization enforces the predicted distributions for an example and its semantically equivalent augmentation to be consistent. We conduct experiments on the MASSIVE dataset under both full-dataset and zero-shot settings. Experimental results demonstrate that our proposed method improves the performance on both intent detection and slot filling tasks.


Progress in the field of Spoken language understanding part2

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Abstract: Most spoken language understanding systems use a pipeline approach composed of an automatic speech recognition interface and a natural language understanding module. This approach forces hard decisions when converting continuous inputs into discrete language symbols. Instead, we propose a representation model to encode speech in rich bidirectional encodings that can be used for downstream tasks such as intent prediction. The approach uses a masked language modelling objective to learn the representations, and thus benefits from both the left and right contexts. We show that the performance of the resulting encodings before fine-tuning is better than comparable models on multiple datasets, and that fine-tuning the top layers of the representation model improves the current state of the art on the Fluent Speech Command dataset, also in a low-data regime, when a limited amount of labelled data is used for training.


Top 16 Artificial Intelligence Applications: 14 Uses of AI

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Artificial intelligence (AI) is hailed as the disruptive technology that is set to revolutionize the 21st century. The function and popularity of this technology are soaring by the day. It has the potential to solve many of humanity's most pressing problems. AI is an umbrella term for technologies that can display some kind of intelligence such as machine learning computer vision, natural language processing, etc… These intelligent agents are algorithms trained using vast amounts of data to give machines some kind of reasoning ability. Instead of purely logical processing that computers usually perform, intelligent agents are designed around human thinking patterns and problem-solving skills. Artificial intelligence technologies create intelligent systems capable of self-learning and adapting to any new challenge. These specification has led to the rapid adoption of AI across different fields and industries around the world. Artificial intelligence has been around for decades, but its applications are only now opening up as more and more resources are dedicated to it. Over the last few years, AI has significantly evolved and is being extensively used in different aspects of human life and industry. Companies have begun using intelligent machines to mine data to optimize just about everything within their business operations. Artificial intelligence is a branch of computer science that aims to create intelligent machines that work and react like humans. It is the broad term for any device that is capable of performing a task normally restricted to human intelligence. AI combines several disciplines such as computer science, cognitive psychology, and neuroscience. The concept underlying AI is to get a non-human entity to make decisions just as an intelligent human would. Artificial intelligence research is about the creation of computer systems capable of visual perception, speech recognition, decision-making, and translation between languages. So many fields are using AI nowadays, from research and home automation to data processing and analysis. The technology is used to solve problems in many different ways and it is found in many types of systems such as household appliances, automobiles, financial systems, medical applications, and many other common tools. AI has undergone rapid development over the past decades, fueled by significant research and trail-blazing technological advancements. Nowadays, it is often used to make computer programs better than humans at perception and cognition tasks. AI technologies are on an exponential level of development and are becoming so advanced that it is entering nearly every field of modern life.


Hardening ML Classifiers. A Brief Review

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Machine learning (ML) classifiers are a fundamental component of ML and are widely used in a variety of applications, including image and speech recognition, natural language processing, and bioinformatics. They are models that are trained to make predictions about the class or category of an input data point. However, classifiers are also subject to adversarial attacks, which can cause misclassifications and potentially lead to abuse. In this article, we will discuss the various ways in which classifiers can be exploited, and methods that can be used to harden classifiers against these attacks. Adversarial attacks on classifiers involve manipulating the input data, such as images or speech, in order to cause the classifier to make a misclassification. These attacks can be performed by adding small, carefully crafted perturbations to the input data, called adversarial examples, that are designed to confuse the classifier.


The Evolution of Deep Learning: Past, Present, and Future

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Deep learning is a subset of machine learning that is based on the use of neural networks. It has been used to achieve state-of-the-art results in a variety of applications, including image and speech recognition, natural language processing, and computer vision. In this article, we will provide an in-depth overview of deep learning and neural networks, including their history, how they work, the types of neural networks, popular applications, advantages, challenges, future of deep learning and conclusion. First, let's start with the brief history of deep learning and neural networks. The concept of neural networks dates back to the 1940s, when Warren McCulloch and Walter Pitts proposed a model of the brain that could process information in a similar way to the way that humans do.