Information Technology

Here's how to pre-order Roku's new streaming devices

USATODAY - Tech Top Stories

If you're into tech at all, you've heard of Roku: the streaming device guru responsible for some of our favorite streaming devices. Today, Roku unveiled the latest additions to its lineup of streaming-forward devices: a new Roku Ultra and Roku Streambar. The new Roku Ultra starts at $99.99, while the Streambar starts at $129.99. Here's everything we know about these new Roku devices--and how to pre-order them. The new Roku Ultra is available for pre-order from today.

Roku's new $129 soundbar offers Dolby Audio and 4K streaming


A soundbar can be a good addition to your living room if you want to up the audio game of your movie nights. But in addition to being quite expensive, soundbars can be hard to buy if you don't already know exactly what you're looking for. Roku hopes to make the decision easier for most people with its newest audio device, the Roku Streambar. It's a soundbar that's only slightly larger than a carton of eggs and priced competitively at $129 -- oh, and it also has Roku's 4K streaming technology inside of it. The company launched a couple of smart speakers, a subwoofer and a full-sized soundbar last year.

Video streaming device leader Roku debuts new soundbar, player and Roku Channel app

USATODAY - Tech Top Stories

The newest Roku products include a streaming device promising improved video delivery throughout the home, a smaller soundbar that also streams, and an updated mobile app for viewing on the go. The nation's leading streaming platform, Roku said it had about 43 million monthly active accounts at the end of June 2020. Research firm eMarketer estimates Roku captures about 33% of U.S. internet users and 47% of connected TV users. Roku's lineup of devices includes the Roku Express ($29.99) and Roku Streaming Stick ($49.99). But its marquee standalone player – it also markets Roku TVs with built-in streaming capability – is the Roku Ultra ($99.99).

This interactive VR 'film' lets you play god to an evolving artificial intelligence


If you could play god to an emerging artificial intelligence, would you? That's the moral dilemma at the heart of Agence, an interactive "dynamic film" that blends virtual reality, gaming, and cinematic storytelling to let audiences influence a handful of evolving, three-legged AI creatures, known as agents. The project, which recently debuted at the Venice International Film Festival, is a co-production between Toronto-based indie studio Transitional Forms and the National Film Board (NFB) of Canada. Think of it as Tamagotchi for the 2020s, but with real consequences on the development of digital life. "I think the core artistic vision of this is to cause people to question humans' role in artificial intelligence," says Pietro Gagliano, creator of Agence and founder of Transitional Forms, "both in its creation and interaction right now. These are virtually living creatures that are learning. This is a moment that I hope that we look back on in time as, you know, we made the right choices. And we decided to empathize with these creatures that didn't ask to be born."

Why some artificial intelligence is smart until it's dumb


Starfleet's star android, Lt. Commander Data, has been enlisted by his renegade android "brother" Lore to join a rebellion against humankind -- much to the consternation of Jean-Luc Picard, captain of the USS Enterprise. "The reign of biological life-forms is coming to an end," Lore tells Picard. "You, Picard, and those like you, are obsolete." In real life, the era of smart machines has already arrived. They haven't completely taken over the world yet, but they're off to a good start.

Top 10 Applications of Machine Learning in Healthcare - FWS


Healthcare is an important industry which offers value-based care to millions of people, while at the same time becoming top revenue earners for many countries. Today, the Healthcare industry in the US alone earns a revenue of $1.668 trillion. The US also spends more on healthcare per capita as compared to most other developed or developing nations. Quality, Value, and Outcome are three buzzwords that always accompany healthcare and promise a lot, and today, healthcare specialists and stakeholders around the globe are looking for innovative ways to deliver on this promise. Technology-enabled smart healthcare is no longer a flight of fancy, as Internet-connected medical devices are holding the health system as we know it together from falling apart under the population burden.

Classification Algorithms Explained in 30 Minutes -


In the Machine Learning terminology, the process of Classification can be defined as a supervised learning algorithm that aims at categorizing a set of data into different classes. In other words, if we think of a dataset as a set of data instances, and each data instance as a set of features, then Classification is the process of predicting the particular class that that individual data instance might belong to, based on its features. Unlike regression where the target variable (i.e., the predicted value) belongs to a continuous distribution, in case of classification, the target variable is discrete. It can only be one of the various target classes in a given problem. For example, let's say you are working on a cat-dog-classifier model that predicts whether the animal in a given image is a cat or a dog.

SAP BrandVoice: AI-Fueled Startup Turns Disrupted Supply Chains Into Last Mile Opportunity


When Shamir Rahim, founder and CEO of VersaFleet, transformed his bio-medical startup into an AI-powered transportation management system, he never imagined being at the epicenter (in a good way) of a supply chain revolution during a worldwide pandemic. As anyone desperately searching for toilet paper discovered earlier this year, the last mile is the crucial link in every supply chain. VersaFleet's SaaS-based cloud platform relies on AI to meet one of the toughest supply chain challenges: last mile delivery. "We wanted to provide our customers with a command center view of last mile product delivery with cost and time savings," said Shamir Rahim, founder and CEO of VersaFleet. "As our customers slowly open up again, VersaFleet is providing greater agility so they can quickly adjust logistics for maximum efficiency, whether people are out sick or returning to work, quarantines are lifted or imposed again, and operational hours shift at any time."

Trustworthy ML


As machine learning (ML) systems are increasingly being deployed in real-world applications, it is critical to ensure that these systems are behaving responsibly and are trustworthy. To this end, there has been growing interest from researchers and practitioners to develop and deploy ML models and algorithms that are not only accurate, but also explainable, fair, privacy-preserving, causal, and robust. This broad area of research is commonly referred to as trustworthy ML. While it is incredibly exciting that researchers from diverse domains ranging from machine learning to health policy and law are working on trustworthy ML, this has also resulted in the emergence of critical challenges such as information overload and lack of visibility for work of early career researchers. Furthermore, the barriers to entry into this field are growing day-by-day -- researchers entering the field are faced with overwhelming amount of prior work without a clear roadmap of where to start and how to navigate the field. Provide a platform for early career researchers to showcase and disseminate their work.

CHIRPS: Explaining random forest classification


Modern machine learning methods typically produce "black box" models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path Snippets (CHIRPS); a novel algorithm for explaining random forest classification per data instance. CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification.