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Advancing anomaly detection with AIOps--introducing AiDice

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This blog post has been co-authored by Jeffrey He, Product Manager, AIOps Platform and Experiences Team. In Microsoft Azure, we invest tremendous efforts in ensuring our services are reliable by predicting and mitigating failures as quickly as we can. In large-scale cloud systems, however, we may still experience unexpected issues simply due to the massive scale of the system. Given this, using AIOps to continuously monitor health metrics is fundamental to running a cloud system successfully, as we have shared in our earlier posts. First, we shared more about this in Advancing Azure service quality with artificial intelligence: AIOps.


CheXzero: Detect Pathologies From Unannotated X-ray Images

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This article was published as a part of the Data Science Blogathon. Working on a task involving the interpretation of chest X-ray medical images and no labeled data at your disposal? Researchers from Harvard Medical School and Stanford University have devised an artificial intelligence diagnostic tool that can detect diseases from natural language descriptions of chest X-rays without needing the labeled data. This is a major step toward significant advancement in clinical AI design because most existing models require vast amounts of annotated data before that data can be fed into a model for training. This research paper will look at the proposed method in further detail.


Technology industry artificial intelligence venture financing deals total $211.1m in Europe in August 2022

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Copy and paste the image source into your website to display the chart. The value marked a decrease of 62.1% over the previous month of $557m and a drop of 67.7% when compared with the last 12-month average of $653.54m. Europe held an 8.38% share of the global technology industry artificial intelligence venture financing deal value that totalled $2.52bn in August 2022. Sweden was the top country in Europe's artificial intelligence venture financing deal value across technology industry. In terms of artificial intelligence venture financing deal activity, Europe recorded 43 deals during August 2022, marking an increase of 7.50% over the previous month and a drop of 17.31% over the 12-month average.


Get ready for the next generation of AI

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Is anyone else feeling dizzy? Just when the AI community was wrapping its head around the astounding progress of text-to-image systems, we're already moving on to the next frontier: text-to-video. Late last week, Meta unveiled Make-A-Video, an AI that generates five-second videos from text prompts. Built on open-source data sets, Make-A-Video lets you type in a string of words, like "A dog wearing a superhero outfit with a red cape flying through the sky," and then generates a clip that, while pretty accurate, has the aesthetics of a trippy old home video. The development is a breakthrough in generative AI that also raises some tough ethical questions.


10 Best Machine Learning & AI Newsletters (October 2022)

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There are numerous machine learning & AI newsletters, below we feature the best. These enable you to keep up with the latest industry news, important developments, etc. AI Business by Unite.AI – This is our bi-weekly newsletter featuring the latest shake-ups, acquisitions, fund raises and more in the business world of AI. Check your inbox or spam folder to confirm your subscription. AI Disruption – Written by our very own Alex McFarland. Artificial intelligence (AI) will disrupt nearly every aspect of society.


State of Self-Supervised Learning in 2022 part1

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Abstract: Self-supervised learning (SSL) has achieved remarkable performance on various medical imaging tasks by dint of priors from massive unlabeled data. However, for a specific downstream task, there is still a lack of an instruction book on how to select suitable pretext tasks and implementation details. Then, we conduct extensive experiments to explore four significant issues in SSL for medical imaging, including (1) the effect of self-supervised pretraining on imbalanced datasets, (2) network architectures, (3) the applicability of upstream tasks to downstream tasks and (4) the stacking effect of SSL and commonly used policies for deep learning, including data resampling and augmentation. Based on the experimental results, potential guidelines are presented for self-supervised pretraining in medical imaging. Abstract: Lyrics recognition is an important task in music processing.


AI Data Laundering: How Academic and Nonprofit Researchers Shield Tech Companies from Accountability - Waxy.org

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Yesterday, Meta's AI Research Team announced Make-A-Video, a "state-of-the-art AI system that generates videos from text." We're pleased to introduce Make-A-Video, our latest in #GenerativeAI research! With just a few words, this state-of-the-art AI system generates high-quality videos from text prompts. Have an idea you want to see? Reply w/ your prompt using #MetaAI and we'll share more results. Like he did for the Stable Diffusion data, Simon Willison created a Datasette browser to explore WebVid-10M, one of the two datasets used to train the video generation model, and quickly learned that all 10.7 million video clips were scraped from Shutterstock, watermarks and all.


Do Not Learn Data Science

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The title of this story is what destroys a lot of people, the title was from someone who advised another who instead of helping him, destroyed him, the responder of the advice stopped the learning process but someday woke up and ask himself "why I stopped, I got a lot of effort to learn the data science domain?" the answer was someone advised me, then I was directly decided to stop without trust in myself and without giving myself a chance, then after I got the answer on my question I decided to get in the data science domain again and learn from zero to hero to reach my dream and work with the IBM, Apple, Google, Netflix, …, etc. Today I will give you a summary of what I learned and understood about What is Data Science? the course that was the first unit in the IBM Data Science Professional Certificate. Data Scientists are people who are curious about asking adorable questions that clarify the business need, they can help the organization to solve their problems upon the given data using the appropriate tools, so the organization would take a good decision based on the good findings reached by data scientists. You said why I defined the data scientist before data science, simply because if you "understood what do data scientists do, you can understand what is data science", we said they solve problems of organizations and firms, and they used suitable tools to solve that problems, so I will give you simple definition that I am very happy to know it, I learned that from the Professor Murtaza Haider: "Data Science is what data scientists do", so simple and comprehensive definition. This is an additional definition I made for you. Data Science (DS) is a major that is interested in the data and how to apply the science to the data, in DS we need to define the problem and gather the related data from different sources, then getting understand the data, find the key questions that will help to solve the problem by analyzing that gathered data using appropriate tools then communicate the findings to stakeholders.


Creating Emergent Behaviors with Reinforcement Learning and Unreal Engine

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In the following article I discuss how to generate emergent behavior in AI characters using Unreal Engine, Reinforcement Learning, and the free machine learning plugin MindMaker. The aim is that the interested reader can use this as a guide for creating emergent behavior in their own game project or embodied AI character. Emergent behavior refers to behaviors that are not pre-programmed but develop organically in response to some environmental stimuli. Emergent behavior is common to many if not all forms of life, being a function of evolution itself. It is also more recently a feature of embodied artificial agents. When one employs emergent behavior methods, one does not rigidly program specific actions for the AI, but instead allows them to "evolve" through some adaptive algorithm such as genetic programming, reinforcement learning, or Monte Carlo methods.


Voicebots and Chatbots: the key to customer service

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The creation of voice-based and chat-based tools (Voicebots and Chatbots) has allowed companies to put in a place a 24/7 customer service system that meets the required quality standards and can help with customer queries and problems as well as with with sales. Bots were first invented in the 1960s but advances in automatic natural language processing (NLP) and automation have seen Chatbots and Voicebots develop the capacity to hold real, human-sounding conversations. A conversational Chatbot or Voicebot is a computer programme developed using Natural Language Processing (NLP) and artificial intelligence (AI) that is capable of holding a conversation. Thanks to machine-learning technology, Bots can understand people's intentions and emotions and provide a fast and empathetic response to any query, all the while learning from interactions and improving and humanising processes. Chatbots and Voicebots can communicate by phone or via digital channels, such as websites, apps, instant messaging platforms like WhatsApp, social media, and more.