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Think you can spot content written by AI? The truth is you've probably already read a lot of it


Two years ago this weekend, GPT-3 was introduced to the world. You may not have heard of GPT-3, but there's a good chance you've read its work, used a website that runs its code, or even conversed with it through a chatbot or a character in a game. GPT-3 is an AI model -- a type of artificial intelligence -- and its applications have quietly trickled into our everyday lives over the past couple of years. In recent months, that trickle has picked up force: more and more applications are using AI like GPT-3, and these AI programs are producing greater amounts of data, from words, to images, to code. A lot of the time, this happens in the background; we don't see what the AI has done, or we can't tell if it's any good.

Free Machine Learning Courses From Top Companies And University


If you are learning machine learning to get your first job or trying to change the industry, this article is for you. I am going to tell you about the free courses or almost free courses to learn machine learning. The course is developed by Facebook artificial intelligence team. This course is one of the best courses to learn deep learning algorithms. It has easy-to-understand explanations with amazing visuals.

DeepMind: Why is AI so good at language? It's something in language itself


Can the frequency of language, and qualities such as polysemy, affect whether a neural network can suddenly solve tasks for which it was not specifically developed, known as "few-shot learning"? How is it that a program such as OpenAI's GPT-3 neural network can answer multiple choice questions, or write a poem in a particular style, despite never being programmed for those specific tasks? It may be because the human language has statistical properties that lead a neural network to expect the unexpected, according to new research by DeepMind, the AI unit of Google. Natural language, when viewed from the point of view of statistics, has qualities that are "non-uniform," such as words that can stand for multiple things, known as "polysemy," like the word "bank," meaning a place where you put money or a rising mound of earth. And words that sound the same can stand for different things, known as homonyms, like "here" and "hear." Those qualities of language are the focus of a paper posted on arXiv this month, "Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers," by DeepMind scientists Stephanie C.Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya Singh, Pierre H. Richemond, Jay McClelland, and Felix Hill.



The last decade has seen the increasingly important, even dominant, application of deep learning (DL) in the field of various applications. Conventional machine learning methods have been the focus of intense investigations for years; however, they have limited capabilities, are biased to dataset selection, and are faced with an overwhelming challenge to integrate large, heterogeneous data sources. On the other hand, recent advancements in deep learning architectures, coupled with high-performance computing, have demonstrated significant breakthroughs in dealing with complexities by radically changing research methodologies toward a data-oriented approach. This Special Issue encourages authors, from academia and industry, to submit new research results about positioning and navigation models based on machine learning for complex systems. Manuscripts should be submitted online at

Research Papers based on Gated RNN'S(Deep Learning)


Abstract: Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developing a noise robust acoustic-to-articulatory speech inversion system, we have shown the importance of noise augmentation to improve the performance of speech inversion in noisy speech. In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data. We also propose a Bidirectional Gated Recurrent Neural Network as the speech inversion system instead of the previously used feed forward neural network. The inversion system uses mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and six vocal tract-variables (TVs) as the output articulatory features.

4 questions to ask before building a computer vision model – TechCrunch


It's since been an exciting time for startups as entrepreneurs continue to discover use cases for computer vision in everything from retail and agriculture to construction. With lower computing costs, greater model accuracy and rapid proliferation of raw data, an increasing number of startups are turning to computer vision to find solutions to problems. However, before founders begin building AI systems, they should think carefully about their risk appetite, data management practices and strategies for future-proofing their AI stack. TechCrunch is having a Memorial Day sale. You can save 50% on annual subscriptions for a limited time.

Artificial Intelligence in App Creation: Beginners Edition - Coursemetry


Note: 4.1/5 (122 notes) 39,729 students Welcome to experience the course "Artificial Intelligence in App Creation: Beginners Edition". Today, Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are used in diverse fields as part of the daily life of large organizations across the globe. The rapid speed of AI growth demonstrates that it is a groundbreaking technology designed to transform the way people use devices and conduct business: achievements in unmanned aerial vehicles, the ability to beat people in chess and sporting games, automated customer service, and analytical systems – of course. Talking about the business, development, or marketing field, for instance, it is worth noting that Artificial Intelligence does not apply in a pure form to real self-aware intelligence machines in this sense. Instead, it can be considered a generic term for the number of software powered by automation that is being used by developers of websites and smartphone apps. They include the recognition of images and speech, cognitive computing, automated processing, and machine learning – for that matter.

Machine Learning: Natural Language Processing in Python (V2)


Welcome to Machine Learning: Natural Language Processing in Python (Version 2). In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe. You'll then apply what you learned for various tasks, such as: Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization. You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.

GitHub - jason718/awesome-self-supervised-learning: A curated list of awesome self-supervised methods


Self-Supervised Learning has become an exciting direction in AI community. Predicting What You Already Know Helps: Provable Self-Supervised Learning. For self-supervised learning, Rationality implies generalization, provably. Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? FAIR Self-Supervision Benchmark [pdf] [repo]: various benchmark (and legacy) tasks for evaluating quality of visual representations learned by various self-supervision approaches.

Artificial Intelligence in China


In the fifth of a series of blogs from our global offices, we provide a overview of key trends in artificial intelligence in China. What is China's strategy for Artificial Intelligence? In March 2021, the Chinese government released the Outline of the 14th Five-Year Plan of the National Economic and Social Development of the People's Republic of China and Vision 2035. This includes more than 50 references to "[artificial] intelligence", reflecting China aims to develop of a new generation of information technology powered by artificial intelligence. Specifically, China intends to drive industry through science and technology projects to develop cutting-edge fundamental theories and algorithms, create specialized chips and build open-source algorithm platforms such as deep learning frameworks.