Today's digital assistants can sometimes fool you into believing they are human, but vastly more capable digital helpers are on their way. Behind the scenes, Siri, Alexa and their ilk use sophisticated speech-recognition software to figure out what you are requesting and how to provide it, and they generate natural-sounding speech to deliver scripted answers matched to your questions. Such systems must first be "trained"--exposed to many, many examples of the kinds of requests humans are likely to make--and the appropriate responses must be written by humans and organized into highly structured data formats. That work is time-consuming and results in digital assistants that are restricted in the tasks they can perform. The systems can "learn"--their machine-learning capabilities allow them to improve their matching of incoming questions to existing answers--but to a limited extent. Even so, they are extremely impressive.

Google's next voice-activated device could be a smart speaker with a screen. Leaked photos claim to show a smart display, called the'Google Home Hub,' which features a 7-inch display mounted on top of a speaker. The photos give credence to previous rumors that Google may soon launch its own smart display. Google's next voice-activated device could be a smart speaker with a screen. For all of 2017, 35 million smart speakers were shipped worldwide, Canalys data reveals.

On October 9th, Google will reveal its latest hardware lineup. Rumors have spread for some time that the company is preparing to unveil a smart display at the event, and a leak unearthed by MySmartPrice corroborates the existence of the device, indicating that it will be called Home Hub. Meanwhile, a separate leak of a retail listing suggests the smart display will cost $149, according to Android Authority. The Home Hub appears to have a seven-inch screen and apes the design of Google's other smart home products such as Google Home Max and Mini. The Google Assistant-powered device is said to weigh just 480 grams, which as the same as Google Home.

It's a simple question that any person with a watch can answer with minimal effort. But when you ask an Amazon Echo the same question, a vast system powered by natural resources and human labor is activated to drum up the answer. As many of us reckon with Silicon Valley's impact on the world and consider how it has upended life, work, and even democracy, we also must consider the infrastructure–and the tangible harm it can do–that usually remains hidden beneath these seemingly simple user experiences. It's an aspect of AI that is nearly impossible to comprehend, let alone visualize, but a new map created by the AI researcher Kate Crawford and data visualization specialist Vladan Joler attempts this dizzying task anyway. Called Anatomy of an AI, the map and the corresponding essay lay out the components of the Amazon Echo, from the human workers mining the rare earth materials that power its chips to the black box of Amazon Web Services to the submarine internet cables that pass information across oceans.

It's Tuesday, and we're testing out new Siri Shortcuts on iOS 12 (you've updated, right?), getting all the details on Audi's high-tech SUV and rejoicing that Twitter plans to revive the purely chronological timeline. If you have to ask how much it costs, you can't afford the trip. SpaceX reveals identity of the world's first lunar space tourist When the Big Falcon Rocket (BFR) launches in 2023, Japanese billionaire Yusaku Maezawa will go on a five-day expedition with six to eight artists -- from architects and musicians to fashion designers and visual-media creators -- selected from around the globe. The BFR is expected to be ready for ground testing at some point in 2019. Musk believes the first uncrewed BFR flight to Mars will take place as early as 2022 while crewed flights should start two years after that.

Google has launched a new venture capital program focused on artificial intelligence, Axios is reporting. Google declined to comment on the report, which states that the initiative will be led by longtime Google VP of engineering Anna Patterson and involve a rotating cast of engineers instead of the venture investors who work for Alphabet Inc.'s corporate venture unit, GV . It isn't completely surprising that Google might create an investing practice around AI, particularly given Google CEO Sundar Pichai's recent pronouncement that Google is becoming "AI first" rather than "mobile first." Indeed, AI was the running thread throughout the recent Google I/O developer conference, where the company introduced new tensor processing unit (TPU) chips that promise to more quickly train and run AI models for researchers and businesses; it also announced (among many other things) that its Google Assistant, the company's virtual personal assistant that's available on devices like the Google Home and Pixel phone, will soon grow more conversational. According to Axios, Patterson and company will reportedly be co-investing with GV when it makes sense to do so.

Recommendation systems can be defined as software applications that draw out and learn from data such as preferences, their actions (clicks, for example), browsing history, and generated recommendations, which are products that the system determines are appealing to the user in the immediate future. In this tutorial, we will learn to build a recommendation system with Scala and Apache Spark. This article is an excerpt taken from Modern Scala Projects written Ilango Gurusamy. In the preceding diagram, can be thought of as a recommendation ecosystem, where the recommendation system is at the heart of it. Implementation is documented in the following subsections.

Shortcuts is definitely one of the biggest -- not only will it use notifications to suggest actions you can take, such as returning a missed FaceTime call or turning on Do Not Disturb before a meeting on your calendar, but you can also use the brand new Shortcuts app to create your own customized Siri-based triggers for third-party apps. For built-in iOS apps, shortcuts are suggestions that try to make your life a little easier; third-party apps also can monitor your activity and suggest actions based on repeated behavior, but not all of them will. Did you miss a phone call? Siri will suggest calling that person back; a simple tap on the shortcut brings up the option to FaceTime or place a phone call. Do you have a meeting you need to call into on your calendar?

Amazon could be planning to seriously beef up its line of Alexa-enabled devices. The internet giant is mulling the launch of eight new devices that can be controlled just by using your voice and its digital assistant technology, Alexa. It could release a microwave, amplifier, receiver, subwoofer and an in-car gadget, CNBC reported, citing sources close to the situation. Smart speakers are becoming a fixture in our households. That's according to Adobe, which found in a poll of US consumers that nearly half could own one by the end of the year Amazon could reveal these new devices at an event by the end of this year, an internal document indicated, CNBC said.

Models that attempt to extract insight from this firehose of information must face the torrential covariate shift that is endemic to the Twitter platform. While regularly-retrained algorithms can maintain performance in the face of this shift, fixed model features that fail to represent new trends and tokens can quickly become stale, resulting in performance degradation. To mitigate this problem we employ learned features, or embedding models, that can efficiently represent the most relevant aspects of a data distribution. Sharing these embedding models across teams can also reduce redundancy and multiplicatively increase cross-team modeling productivity. In this paper, we detail the commoditized tools, algorithms and pipelines that we have developed and are developing at Twitter to regularly generate high quality, up-to-date embeddings and share them broadly across the company.