You need datasets to practice on when getting started with deep learning for natural language processing tasks. It is better to use small datasets that you can download quickly and do not take too long to fit models. Further, it is also helpful to use standard datasets that are well understood and widely used so that you can compare your results to see if you are making progress. In this post, you will discover a suite of standard datasets for natural language processing tasks that you can use when getting started with deep learning. I have tried to provide a mixture of datasets that are popular for use in academic papers that are modest in size.

No matter what terminology you prefer – driverless car, autonomous vehicle, autonomous driving, self-driving car or something else – the future of human and non-human driving is a hot topic. And we kicked off 2018 with two major consumer events that had the media abuzz about it. PublicRelay analyzed traditional and social media around autonomous vehicles at both events and here are some of the interesting nuggets that we uncovered. CES hands down "wins" for sheer volume of coverage at 7X the number of articles written on the subject. Autonomous drive was among the top five products discussed at CES 2018, along with gaming products, computers, and smart home technology.

Beyond the technical and engineering limitations we're faced with, AGI also brings many moral and societal questions that haven't yet been answered. Would it be right to create and control a new sentient being? For a great dive into this, listen to Sam Harris interview Max Tegmark, an MIT Physicist and AI thinker. This talk reflects on the nature of intelligence, the risks of superhuman AI, the idea of a non biological definition of life, the substrate independence of minds, the relevance and irrelevance of consciousness for the future of AI, near-term breakthroughs in AI, and other topics.

Make your agents' lives easier - Right now, chatbots can only handle somewhere between 10 to 35 percent of customer problems. Humans need to intercede for the rest. Why not let a computer handle the low-hanging fruit? This frees up your agents from the dull, repetitive questions a machine can answer and allows them to focus on the customers with complex issues. Happier customers - Research shows consumers feel customer service is excellent when it's "personalized, always on and real-time, consistent and omnichannel."

A new technology uses artificial intelligence to generate synthetic images that can pass as real. The technology was developed by a team led by Hiroshi Ishikawa, a professor at Japan's Waseda University. It uses convolutional neural networks, a type of deep learning, to predict missing parts of images. The technology could be used in photo-editing apps. It can also be used to generate 3-D images from real 2-D images.

Replacing People Isn't Revolutionary, or Is It? While the thought of some AI program doing the work of a real person isn't new or revolutionary, there are definitely some interesting benefits from removing humans from the employment screening process. For example, an AI isn't likely to make an assumption about an individual based on the perceived race or gender a person might deduce from an applicant's name. Unconscious, implicit bias is real. And while the obvious benefit comes from a reduction in labor costs associated with hiring as a result of replacing a person with an AI, another potential positive result could be an improved quality of candidates.

The key weakness of the Microsoft Cortana-powered Harman/Kardon Invoke smart speaker was that it just wasn't a great smart-home controller, lacking many of the services and IFTTT support of its rivals. Today, Microsoft said, that changes. Microsoft said Friday that it has added support for IFTTT, the conditional platform that triggers various connected services, to Cortana. And those services have expanded, too: Cortana can now control products from Ecobee, Honeywell Lyric, Honeywell Total Connect Comfort, LIFX, TP-Link Kasa, and Geeni. Because Cortana offers essentially the same capabilities across various platforms, that means the new smart-home controls are available from the Invoke, but also from your Windows 10 PC or even the Cortana apps for Android and iOS.

A recent helicopter crash in South Carolina may have been caused by a civilian drone. The National Transportation Safety Board is investigating the incident that resulted in a crash landing. The case would be the first known aircraft accident to be caused by a drone, though the United States Federal Aviation Administration (FAA) has long expressed concern about the possibility of such interference from consumer devices. A drone may be responsible for a recent helicopter crash. The crash occurred on Wednesday afternoon when a helicopter being piloted by a student and instructor came into contact with a small drone.

Dave Singhal is a scholar at Stanford University. He is exploring neuro-inspired machine learning approaches on multi-modal image data sets and evaluating hardware and software constructs to improve machine learning efficiency. With 10 patents under his belt, Dave is also a principal at Light Field Interactive--dealing with imaging, graphics, optics, and perception. His aim is to advance this area through academic program development, industry collaboration events, and multi-site research projects. Prior to this, Dave served as Director of Engineering at Cisco Systems.

A popular statistical technique to predict binomial outcomes (y 0 or 1) is Logistic Regression. Logistic regression predicts categorical outcomes (binomial / multinomial values of y), whereas linear Regression is good for predicting continuous-valued outcomes (such as weight of a person in kg, the amount of rainfall in cm). The predictions of Logistic Regression (henceforth, LogR in this article) are in the form of probabilities of an event occurring, ie the probability of y 1, given certain values of input variables x. As shown in Figure1, the logit function on the right- with a range of - to, is the inverse of the logistic function shown on the left- with a range of 0 to 1. Estimating the values of B0,B1,..,Bk involves the concepts of probability, odds and log odds. The example dataset here is sourced from the UCLA website. The task is to predict which students graduated with honours or not (y 1 or 0), for 200 students with fields female, read, write, math, hon, femalexmath .