One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
A Japanese medical advice app provider is making a limited time offer of a free app that allows users to seek advice from doctors about the coronavirus. The free service, in Japanese only, is provided by Agree, a company based in Tsukuba, Ibaraki Prefecture. It also operates a medical advice app called Leber. Users are asked to send information such as whether they have traveled to any places where COVID-19 has been confirmed or whether they have developed a fever. With about 120 doctors registered for the service, users receive advice in about 30 minutes about the urgency of their condition, such as if they are suspected of having pneumonia and if they should seek advice from a public health center.
Every Marvel fan must have at some point of time in his fandom read or watched Ironman and wish if he had Jarvis at his disposal. I went through the same crisis once and that is where it all began. I started exploring how feasible developing my own Virtual Assistant was, and that is how MEERA was born. MEERA stands for Multifunctional Event-driven Expert in Real-time Assistance. It started as a general purpose scalable virtual assistant backed by the mystic power of machine learning and artificial intelligence.
Natural language models typically have to solve two tough problems: mapping sentence prefixes to fixed-sized representations and using the representations to predict the next word in the text. In a recent paper, researchers at Facebook AI Research assert that the first problem -- the mapping problem -- might be easier than the prediction problem, a hypothesis they build upon to augment language models with a "nearest neighbors" retrieval mechanism. They say it allows rare patterns to be memorized and that it achieves a state-of-the-art complexity score (a measure of vocabulary and syntax variety) with no additional training. As the researchers explain, language models assign probabilities to sequences of words, such that from a context sequence of tokens (e.g., words) they estimate the distribution (the probabilities of occurrence of different possible outcomes) over target tokens. The proposed approach -- kNN-LM -- maps a context to a fixed-length mathematical representation computed by the pre-trained language model.
Automated translation, including translating one programming language into another one (for instance, SQL to Python - the converse is not possible) Spell checks, especially for people writing in multiple languages - lot's of progress to be made here, including automatically recognizing the language when you type, and stop trying to correct the same word every single time (some browsers have tried to change Ning to Nong hundreds of times, and I have no idea why after 50 failures they continue to try - I call this machine unlearning) Detection of earth-like planets - focus on planetary systems with many planets to increase odds of finding inhabitable planets, rather than stars and planets matching our Sun and Earth Distinguishing between noise and signal on millions of NASA pictures or videos, to identify patterns Automated piloting (drones, cars without pilots) Customized, patient-specific medications and diets Predicting and legally manipulating elections Predicting oil demand, oil ...
As we have seen before, the Information Extraction step consists mainly of classifying words (tagging), the output can be stored as key-value pairs in a computer-friendly file format (e.g.: JSON). The data extracted can then be efficiently archived, indexed and used for analytics. If we compare OCR to young children training themselves to recognize characters and words, then Information Extraction would be like children learning to make sense of the words. An example of IE would be when you stare at your credit card bill trying to find the amount due and the due date. Suppose you want to build an AI application to do it automatically; OCR could be applied to extract the text from the image, converting pixels into bytes or Unicode characters, and the output would be every single character printed in the bill.
Maps are at the heart of Uber's services and core to the experience for millions of users. Our cutting-edge cartography makes it easy for drivers to locate passengers, delivery people to quickly transport meals via Uber Eats, and JUMP users to hop on the closest scooter or bike. Maps aren't always flashy (although many of our Uber Movement data visualizations are quite striking), but they're incredibly important. In fact, the nature of our maps technology means that if our users take them for granted, we're doing a good job. Underlying the graphical representation of streets and places exists a complex set of data allowing algorithms to calculate optimal routes based on traffic, speed limits, and other properties.
Later in the years after new technology had established itself in society, we started to witness technology become smarter. Automation started taking place wherever possible, virtual reality technologies started to emerge, and new forms of AI such as natural language processing (NLP) started to become a part of our daily lives, whether we knew it or not. Tools like Siri and Alexa have started to become more popular, and the once-believed notion of "big brother" watching over us has started to fade as youth have become more amenable to giving these technologies access to their personal data. Not only are these technologies present in our casual lives, but they have also allowed organizations to help us shop smarter and more efficiently as AI systems track our every click.
Many imbalanced classification tasks require a skillful model that predicts a crisp class label, where both classes are equally important. An example of an imbalanced classification problem where a class label is required and both classes are equally important is the detection of oil spills or slicks in satellite images. The detection of a spill requires mobilizing an expensive response, and missing an event is equally expensive, causing damage to the environment. One way to evaluate imbalanced classification models that predict crisp labels is to calculate the separate accuracy on the positive class and the negative class, referred to as sensitivity and specificity. These two measures can then be averaged using the geometric mean, referred to as the G-mean, that is insensitive to the skewed class distribution and correctly reports on the skill of the model on both classes. In this tutorial, you will discover how to develop a model to predict the presence of an oil spill in satellite images and evaluate it using the G-mean metric. Develop an Imbalanced Classification Model to Detect Oil Spills Photo by Lenny K Photography, some rights reserved. In this project, we will use a standard imbalanced machine learning dataset referred to as the "oil spill" dataset, "oil slicks" dataset or simply "oil."
Because of the exponential growth of text data, enterprises need to work shifting from numeric towards text information. Making sense of text information is becoming a key asset for businesses. Take an insurance company for instance: its whole business is dependent on text data since all its products are defined verbosely. All customer interactions happen in natural language. At the moment, the only way to deal with this mass of textual information is to use a human understanding of language.
Although technically a product breakout, the session on OpenText's Digital Accelerants product collection was presented to the entire audience as our last full-audience session before the afternoon breakouts. This was split into three sections: cloud, AI and analytics, and process automation. Jon Schupp, VP of Cloud GTM, spoke about how information is transforming the world: not just cloud, but a number of other technologies, a changing workforce, growing customer expectations and privacy concerns. Cloud, however, is the destination for innovation. Moving to cloud allows enterprise customers to take advantage of the latest product features, guaranteed availability, global reach and scalability while reducing their operational IT footprint.