Here are the most tweeted papers that were uploaded onto arXiv during July 2020. Results are powered by Arxiv Sanity Preserver. Abstract: Massive language models are the core of modern NLP modeling and have been shown to encode impressive amounts of commonsense and factual information. However, that knowledge exists only within the latent parameters of the model, inaccessible to inspection and interpretation, and even worse, factual information memorized from the training corpora is likely to become stale as the world changes. Knowledge stored as parameters will also inevitably exhibit all of the biases inherent in the source materials.

The rest of the thread, Tell me about a paper that you found inspiring, from u/mitare is also quite interesting. This paper is a really comprehensive review detailing what exactly current ML techniques are unable to do that humans can do very well. It lays the groundwork that needs to be done to make human-level artificial intelligence.

Machine learning and Artificial intelligence are the new buzz words that are being thrown around more than any other trending technology today. It is starting to reshape how we think about building products. It's time we understood what it is and why it matters. Machine Learning: (ML) is an area of computational science that enables machines (computers) to undertake tasks without being explicitly programmed. The idea behind machine learning is that by training computers to analyze and interpret existing data from prior human interactions, machines are able to find patterns and structures in data.

Convolutional neural networks (CNN's) are the main deep learning tool to use for image processing. I recently used a CNN for my latest student project here at Flatiron and got to have a look at how they work and how they differ from dense neural networks, in addition to how they perform better when working with images and python. In my project, I was able to classify patient x-ray images to determine whether they had pneumonia or not. There are also many other uses for image processing in the medical field and in other fields of work and study. Next, we'll try to show the simplest, most basic breakdown of some of these steps so that you can get on your way to building a CNN for image classification with Keras.

Have you ever tried training an object detection model using a custom dataset of your own choice from scratch? If yes, you'd know how tedious the process would be. We need to start with building a model using a Feature Pyramid Network combined with a Region Proposal Network if we opt for region proposal based methods such as Faster R-CNN or we can also use one-shot detector algorithms like SSD and YOLO. Either of them is a bit complicated to work with if we want to implement it from scratch. We need a framework where we can use state-of-the-art models such as Fast, Faster, and Mask R-CNNs with ease.

Machine learning is a vast field. Thanks to the internet, there are plenty of resources available to get your hands on it -- from books to blogs to vlogs. Analytics India Magazine has been compiling learning resources for the ML community for quite some time now. In this article, we list down top machine learning books for those who want to get practical with algorithms. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

On 21th June 2018 at Buildo, Data Science Milan has organized an event on a fashion topic: Natural Language Processing (NLP). Nowadays we found many applications of NLP, such as machine translation (Google translator), question answering (chatbot), web and application search (Amazon), lexical semantics (Thesaurus), sentiment analysis (Cambridge Analytica), natural language generator (Reddit bot). What is the mean of natural language processing? Natural language processing is a branch of artificial intelligence representing a bridge between humans and computers; it can be broadly defined as the automatic manipulation of natural language, like speech and text, by software. There are many ways to represents words in NLP and you cannot use text data directly on machine learning algorithms.

The resurgence of artificial intelligence (AI) is largely due to advances in pattern-recognition due to deep learning, a form of machine learning that does not require explicit hard-coding. The architecture of deep neural networks is somewhat inspired by the biological brain and neuroscience . Like the biological brain, the inner workings of exactly why deep networks work are largely unexplained, and there is no single unifying theory.

Another reason for the excitement is that humans have always been fascinated by machines that appeared to be able to respond intelligently to what we say to them. In the mid-1960s, for example, the computer scientist Joseph Weizenbaum wanted to demonstrate the superficiality of human-machine interactions. So he wrote a program called Eliza that used pre-written scripts to respond to inputs. The most famous script, Doctor, simulated a Rogerian psychotherapist – ie, one who simply parroted back at patients what they'd just said. Poor Weizenbaum, a gentle and innocent soul, was then astonished to find people apparently having serious consultations with Eliza.

In this tutorial, you will learn how to perform video classification using FastAI, Python, and Deep Learning. FastAI is a Deep Learning library that is built on the top of Pytorch. There are freely available tutorials/courses for FastAI. I am also currently enrolled in Practical Deep Learning for Coders course.