Variation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architecture, in which attention is divided among several basis models, depending on the author's position in the social network. This has the effect of smoothing the classification function across the social network, and makes it possible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model significantly improves the accuracies of sentiment analysis on Twitter and on review data.

Neuralink Corp., the technology start-up aiming to link computers to human brains founded by billionaire Elon Musk, has raised $27 million from a dozen investors and plans to raise as much as $100 million, according to financial documents filed Friday. The San Francisco company is at the forefront of so-called neural lace technology, which implants electrodes into the brain with the goal of allowing people to upload and download thoughts and information. The financial disclosures about the fundraising were made to the Securities and Exchange Commission. However, in a series of tweets Friday, Musk denied that the company was seeking investment. "Neuralink is not raising money," Musk wrote in a reply to a tweet from Wall Street Journal reporter Rolfe Winkler.

With recent progress in graphics, it has become more tractable to train models on synthetic images, poten- tially avoiding the need for expensive annotations. How- ever, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we pro- pose Simulated Unsupervised (S U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simula- tor. We develop a method for S U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifi- cations to the standard GAN algorithm to preserve an- notations, avoid artifacts, and stabilize training: (i) a'self-regularization' term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images.

Machine learning is increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this course, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the course, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

Some white-faced capuchin monkeys stick their fingers deep into the eye sockets of their friends, and others will use an ally's body parts to whack a common enemy. A new study found that older, sociable capuchins are prone to inventing more new types of social behaviors, many of which seem to function as tests of friendship or displays against enemies. However, younger monkeys are more innovative with their behavior in different categories - for example, ways to interact with the physical environment, such a flipping over cow pies to use as see-saws. Some white-faced capuchin monkeys stick their fingers deep into the eye sockets of their friends. A new study found that older, sociable capuchins are prone to inventing more new types of social behaviors, many of which seem to function as tests of friendship or displays against enemies.

Artificial intelligence (AI) is reinventing the creative landscape for marketers. One big leap: Brands are no longer merely seen as objects, but entities with personalities that can interact dynamically with people, according to Winston Binch, chief digital officer for Deutsch North America, the ad agency behind Taco Bell's award-winning taco-ordering chatbot, the Tacobot. Binch spoke to Catharine Hays, executive director of the Wharton Future of Advertising Program, on the Marketing Matters show, which airs on Wharton Business Radio, SiriusXM channel 111. An edited transcript of the conversation follows. Catherine Hays: You are one of the true leaders in this space between AI and creativity.

If you are interested in meta-tutorials on a variety of data science topics, you have come to the right place. Of the six 7-step tutorials included herein, the first 3 tutorials cover, in order, the machine learning process from data preparation through to several different types of machine learning tasks, including both theoretical understanding and practical implementation using Python libraries. The fourth tutorial covers deep learning, mainly from an "understanding" perspective, while the final 2 cover database topics: SQL for data science, and understanding NoSQL databases. And so with a nod to Douglas Adams, and the answer to life, universe, and everything, let's have a look at 42 steps to mastering data science. Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities.

Adjusted R² measures the goodness of fit of a regression model. Higher the R², better is the model.

Six-year-old Jamil starts school on September 11. There will be no ISIS fighters in his first grade class in Ulm, Germany, but Jamil, haunted by nightmares, is still fighting the ISIS demons. The boy's ordeal began in northern Iraq on August 3, 2015. Then four-years-old, he was one of many Yazidis captured by ISIS, crammed into a bus, and taken to Mosul, the second largest city in Iraq, then under ISIS control. The Yazidi people are an ancient, non-Muslim religious community regarded by radical Islamists as infidels worthy of death.

Mixing artificial intelligence with climate science helps researchers to identify previously unknown atmospheric processes and rank climate models. Many of the latest climate models seek to increase the detail in simulations of cloud structure. As Earth-observing satellites become more plentiful and climate models more powerful, researchers who study global warming are facing a deluge of data. Some are now turning to the latest trend in artificial intelligence (AI) to help trawl through all the information, in the hope of discovering new climate patterns and improving forecasts. "Climate is now a data problem," says Claire Monteleoni, a computer scientist at George Washington University in Washington DC who has helped to pioneer the marriage of machine-learning techniques with climate science.