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 Roku.com today.
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.
This one had me do a double take. If you really want to feel like your home is some sort of impenetrable fortress complete with roving security drones. Amazon's Ring has a new product for you. Ring latest home security camera is taking flight -- literally. The new Always Home Cam is an autonomous drone that can fly around inside your home to give you a perspective of any room you want when you're not home. Once it's done flying, the Always Home Cam returns to its dock to charge its battery.
These and other insights are from LinkedIn's Top Startups 2020: The 50 U.S. companies on the rise published today. This is the 4th annual LinkedIn list of the hottest startups to work for. The list is determined by the billions of actions taken by LinkedIn's 706 million members. The annual list is a reflection of how business and work is evolving through the pandemic, what industries are emerging and growing and where people want to work now, reflecting the current state of the economy and the world. Even in the face of Covid-19, the startups on this year's list are all still innovating and experiencing growth and the majority of the companies on the list are currently hiring, with 3,000 jobs now open on LinkedIn. To be eligible for the list, a company must be independent and privately held, have at least 50 employees, be seven years old or younger, be headquartered in the country on the list which they appear and have a minimum of 15% employee growth over the time period. The top 50 U.S. startups include the following: Full-time headcount: 4,000 Headquarters: New York City Year founded: 2016 What you should know: While the U.S. economy quickly sank into a recession at the start of the pandemic, one of its engines has been roaring: housing.
A way of monitoring household appliances by using machine learning to analyse vibrations on a wall or ceiling has been developed by researchers in the US. Their system could be used to create centralized smart home systems without the need for individual sensors in each object. What is more, the technology could help track energy use, identify electrical faults and even remind people to empty the dishwasher. "Recognizing home activities can help computers better understand human behaviours and needs, with the hope of developing a better human-machine interface," says team member and information scientist Cheng Zhang of Cornell University. The system, dubbed VibroSense, comprises two core parts: a laser Doppler vibrometer and a deep learning model, which is a type of machine learning system.
Hostile and hateful remarks are thick on the ground on social networks in spite of persistent efforts by Facebook, Twitter, Reddit and YouTube to tone them down. Now researchers at the OpenWeb platform have turned to artificial intelligence to moderate Internet users' comments before they are even posted. The method appears to be effective because one third of users modified the text of their comments when they received a nudge from the new system, which warned that what they had written might be perceived as offensive. The study conducted by OpenWeb and Perspective API analysed 400,000 comments that some 50,000 users were preparing to post on sites like AOL, Salon, Newsweek, RT and Sky Sports. Some of these users received a feedback message or nudge from a machine learning algorithm to the effect that the text they were preparing to post might be insulting, or against the rules for the forum they were using.
Infer Genetic Disease From Your Face - DeepGestalt can accurately identify some rare genetic disorders using a photograph of a patient's face. This could lead to payers and employers potentially analyzing facial images and discriminating against individuals who have pre-existing conditions or developing medical complications.
I'm Vegard, and I currently work as the Lead Data Scientist in a software company called Axbit. In addition to that I also have a part-time position as an Associate Professor in machine learning at Molde university college. Today I am happy to answer a couple of questions related to data science, what data science is all about and how working within this field is like. Transcript: How did I become a data scientist? First of all, I think my background is probably a bit different compared to a lot of other data scientists.
Finally, AI is ready for the mainstream. When your enterprise is handling transactions between 25 million sellers and 182 million buyers, supporting 1.5 billion listings, manual decision-making processes just won't cut. Such is the case with eBay, the mega commerce site, that has been employing artificial intelligence for more than a decade. As Forbes contributor Bernard Marr points out, eBay employs AI across a broad range of functions, "in personalization, search, insights, discovery and its recommendation systems along with computer vision, translation, natural language processing and more." As part of a massive operation with so much experience with AI, Mazen Rawashdeh, CTO of eBay, has plenty to say about the current state of enterprise AI.
Tesla may be introducing machine-learning training as a web service with its upcoming'Dojo' supercomputer, CEO Elon Musk said on Twitter. Project Dojo was initially revealed by Musk last year and is a supercomputer which Tesla has been working on. The supercomputer has been designed to ingest massive amounts of video data and perform massive levels of unsupervised training on the visual data. The goal of Dojo will be to be able to take in vast amounts of data and train at a video level and do massive unsupervised training of vast amounts of video data. Dojo uses our own chips & a computer architecture optimized for neural net training, not a GPU cluster. Could be wrong, but I think it will be best in world.