People tend to accept robots with humanlike characteristics up to a point. Then, things get strangely uncomfortable. Robots have appeared in film for more than 100 years, with the first depiction occurring in the silent film "The Master Mystery," starring magician-turned-wannabe-actor Harry Houdini. Previously referred to as "automatons" before "robot" became commonplace, these metal machines have been portrayed as delightful helpers à la C-3PO and WALL-E and as villains, like the T-800 from "Terminator" or VIKI from "I, Robot." Whether a robot is "good" or "bad" isn't the ultimate indicator of whether we fear them or not.

Global Predictive Analytics Market (2020 to 2025) – Advent of Machine Learning and Artificial Intelligence is Driving Growth.

With movie theaters largely off the table and new game systems on the way it's the perfect time to upgrade to your home theater. Whether you're looking for a $50 stocking stuffer, a big-ticket new TV or something in between there are plenty of ways to improve the room where you spend a lot of your time. Faster apps, bigger screens and more control are right at your fingertips, so let's take a look at some of the best devices available right now. If you're looking for the simplest upgrade for your TV, the Streaming Stick fits the bill. It's USB-powered so you may not even need to find a nearby outlet and it's small enough to fit in a pocket when you're taking a trip (or staying with relatives) so you can bring your own apps and queues along for the ride.

Last August I decided to hire a journalist to produce content for a project. This person would help me with social media posts, newsletters and reports, with the goal of providing other journalists with scientific knowledge coming from social media. After a few weeks, having worked on the budget and the targets I had in mind, I pivoted the whole plan and hired two part-time developers to create a Twitter bot and an automated report delivery system. In one stroke, I solved two problems: feeding my project's Twitter timeline and providing newsletter content for the users. After the launch, no human hands needed to touch those two streams of content. It was a classic scenario in which automation literally stole a journalist's job.

Feature extraction is an important process of machine learning and deep learning, as the process make algorithms function more efficiently, and also accurate. In natural language processing used in deception detection such as fake news detection, several ways of feature extraction in statistical aspect had been introduced (e.g. N-gram). In this research, it will be shown that by using deep learning algorithms and alphabet frequencies of the original text of a news without any information about the sequence of the alphabet can actually be used to classify fake news and trustworthy ones in high accuracy (85\%). As this pre-processing method makes the data notably compact but also include the feature that is needed for the classifier, it seems that alphabet frequencies contains some useful features for understanding complex context or meaning of the original text.

Modern neural architectures for classification tasks are trained using the crossentropy loss, which is widely believed to be empirically superior to the square loss. In this work we provide evidence indicating that this belief may not be wellfounded. We explore several major neural architectures and a range of standard benchmark datasets for NLP, automatic speech recognition (ASR) and computer vision tasks to show that these architectures, with the same hyper-parameter settings as reported in the literature, perform comparably or better when trained with the square loss, even after equalizing computational resources. Indeed, we observe that the square loss produces better results in the dominant majority of NLP and ASR experiments. Cross-entropy appears to have a slight edge on computer vision tasks. We argue that there is little compelling empirical or theoretical evidence indicating a clear-cut advantage to the cross-entropy loss. Indeed, in our experiments, performance on nearly all non-vision tasks can be improved, sometimes significantly, by switching to the square loss. Furthermore, training with square loss appears to be less sensitive to the randomness in initialization. We posit that training using the square loss for classification needs to be a part of best practices of modern deep learning on equal footing with cross-entropy. Modern deep neural networks are nearly universally trained with cross-entropy loss in classification tasks.

The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier's output. The human interpretable rule is defined as an axis-aligned hyper-cuboid containing the instance for which the classification decision has to be explained. The proposed procedure finds the largest (high \textit{coverage}) axis-aligned hyper-cuboid such that a high percentage of the instances in the hyper-cuboid have the same class label as the instance being explained (high \textit{precision}). Novel approximations to the coverage and precision measures in terms of the parameters of the hyper-cuboid are defined. They are maximized using gradient-based optimizers. The quality of the approximations is rigorously analyzed theoretically and experimentally. Heuristics for simplifying the generated explanations for achieving better interpretability and a greedy selection algorithm that combines the local explanations for creating global explanations for the model covering a large part of the instance space are also proposed. The framework is model agnostic, can be applied to any arbitrary classifier, and all types of attributes (including continuous, ordered, and unordered discrete). The wide-scale applicability of the framework is validated on a variety of synthetic and real-world datasets from different domains (tabular, text, and image).

When Apple unveiled the HomePod mini last month, the company showed off a long-awaited feature: the ability to use it with third-party music services. And as of now, Pandora is the first third-party music app to work with Apple's existing HomePod as well as the upcoming HomePod mini. An update to Pandora's iOS app is rolling out that lets you add the app the HomePod. If you've updated the Pandora app, you'll find a new item in the settings menu: Connect with HomePod. From there, it just takes a few taps to give Pandora permission to work with the speaker. This means you can ask Siri to do things like "play New Indie Radio on Pandora" and it'll start playing your station directly on the speaker.

The team developed this machine learning technique through algorithms that learn transcriptome information from artificial organoids derived from …

Integrating machine-learning technology with high-resolution imaging helps identify plant species. The external morphology of a pollen grain of the …