At a time when many versions of AI rely on pre-established data sets for image recognition, Facebook has developed SEER (Self-supERvised) – a deep learning solution able to register images on the Internet independent of curated and labeled data sets. With major advances already underway in natural language processing (NLP) including machine translation, natural language interference and question answering, SEER uses an innovative billion-parameter, self-supervised computer vision model able to learn from any online image. Thus far, the Facebook AI team has tested SEER on one billion uncurated and unlabeled public Instagram images. The new program performed better than the most advanced self-supervised systems as well as self-supervised models on downstream tasks such as low-shot, object detection, image detection and segmentation. In fact, exposure to only 10 percent of the ImageNet data set still resulted in a 77.9 percent recognition rate by SEER.
IMAGE: SMU Assistant Professor Sun Qianru says highly diverse training data is critical to ensure the machine sees a wide range of examples and counterexamples that cancel out spurious patterns. SMU Office of Research and Tech Transfer - Artificial Intelligence, or AI, makes us look better in selfies, obediently tells us the weather when we ask Alexa for it, and rolls out self-drive cars. It is the technology that enables machines to learn from experience and perform human-like tasks. As a whole, AI contains many subfields, including natural language processing, computer vision, and deep learning. Most of the time, the specific technology at work is machine learning, which focuses on the development of algorithms that analyses data and makes predictions, and relies heavily on human supervision.
Artificial Intelligence, or AI, makes us look better in selfies, obediently tells us the weather when we ask Alexa for it, and rolls out self-drive cars. It is the technology that enables machines to learn from experience and perform human-like tasks. As a whole, AI contains many subfields, including natural language processing, computer vision, and deep learning. Most of the time, the specific technology at work is machine learning, which focuses on the development of algorithms that analyzes data and makes predictions, and relies heavily on human supervision. SMU Assistant Professor of Information Systems, Sun Qianru, likens training a small-scale AI model to teaching a young kid to recognize objects in his surroundings.
Generative Pre-trained Transformer 3 is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation of language prediction model in the GPT-n series created by OpenAI. GPT-3 is an extension and scaled-up version of GPT-2 model architecture -- It includes the modified initialization, pre-normalization, and reversible tokenization and shows strong performance on many NLP tasks in the zero-shot, one-shot, and few-shot settings. In the above graph, it is clearly visible how GPT-3 dominates all the small models and gets substantial gains on almost all the NLP tasks. It is based on the approach of pretraining on a large dataset followed by fine-tuning or priming for a specific task.
Imagine if your digital marketing tools had the capacity to predict the future. What would you do with that crystal ball? Or providing each user a set of search results that have shown to be the most likely to yield a conversion? Recommending a product through a web campaign that can be most effective to prompt an engagement? This is where artificial intelligence is most effective for digital marketers.
This article is part 2 of my Popular Machine Learning Interview questions. Here I feature more questions I usually see asked during interviews. I shall note that this isn't an interview prep guide nor a conclusive list of all questions. Rather, you should use this article as a refresher for your Machine Learning knowledge. I suggest reading the question then try to answer it yourself before reading the answer.
Researchers at Google Brain have open-sourced the Switch Transformer, a natural-language processing (NLP) AI model. The model scales up to 1.6T parameters and improves training time up to 7x compared to the T5 NLP model, with comparable accuracy. The team described the model in a paper published on arXiv. The Switch Transformer uses a mixture-of-experts (MoE) paradigm to combine several Transformer attention blocks. Because only a subset of the model is used to process a given input, the number of model parameters can be increased while holding computational cost steady.
Artificial intelligence – AI – was a mere computational theory back in the 1950s when Alan Turing designed the first Turing Test to measure a machine's intelligence. Today, AI inhabits consumer electronics in the form of Siri, Cortana, Alexa and Google Assistant – it lives behind our internet browsers, within the relative confines of wireless networks and circuit boards. We interact with AI all the time – Google's auto-suggest function, customer service bots and YouTube's search algorithm are all examples of AI. In just half a century, AI's role in society has become firmly established. Developments in software programming and IT have facilitated important innovations in AI.
AbbVie is a research-based biopharmaceutical company that serves more than 30 million patients in 175 countries. With its global scale, AbbVie partnered with Intel to optimize processes for its more than 47,000 employees. This whitepaper highlights two use cases that are important to AbbVie's research. The first is Abbelfish Machine Translation, AbbVie's language translation service based on the Transformer NLP model, that leverages second-generation Intel Xeon Scalable processors and the Intel Optimization for TensorFlow with Intel oneAPI Deep Neural Network Library (oneDNN). AbbVie was able to achieve a 1.9x improvement in throughput for Abbelfish language translation using Intel Optimization for TensorFlow 1.15 with oneAPI Deep Neural Network Library when compared to TensorFlow 1.15 without oneDNN.1
For Data Science, Machine Learning, and AI Created by Lazy Programmer Inc. English [Auto], Italian [Auto], Preview this Udemy Course GET COUPON CODE Description *** NOW IN TENSORFLOW 2 and PYTHON 3 *** Learn about one of the most powerful Deep Learning architectures yet! The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world! This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing). You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself. This course will teach you: The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!) How to model image data in code How to model text data for NLP (including preprocessing steps for text) How to build an CNN using Tensorflow 2 How to use batch normalization and dropout regularization in Tensorflow 2 How to do image classification in Tensorflow 2 How to do data preprocessing for your own custom image dataset How to use Embeddings in Tensorflow 2 for NLP How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition) All of the materials required for this course can be downloaded and installed for FREE.