The new smartphone from Andy Rubin, which will be the debut product of his new company Essential, will indeed run Android for its operating system. It looked that way from the tiny peek at the corner we got from Rubin’s tweet earlier this week, but now Google’s Eric Schmidt has confirmed it’ll be one of a few “phenomenal new choices for Android users coming very soon.”
First of all:
But furthermore, this sounds like things between Essential and Google are very friendly, which could mean we see a new type of close collaboration evocative of the Nexus era. Google has clearly staked out its own, new kind of territory with phones it builds itself, opting for a strategy competitive with what Apple and Samsung have done with their top-tier premium devices.
Also, Schmidt’s teaser comes on Samsung Galaxy S8 day, which seems like no accidental omen. Android’s leading ranks have typically included Samsung and basically no one else, so it’ll be very interesting indeed if Essential is one among a few new options headed to the table.
Matrix Labs just completed a successful crowdfunding campaign for what amounts to a AI voice recognition system for the Raspberry Pi which allows you, a mere mortal, to make your own Alexa in your basement. Created by Rodolfo Saccoman and Brian Sanchez their first board, the Creator, fits right on top of a standard RaspPi and gives you an 8 microphone array, a temperature sensor, ultraviolet sensor, pressure sensor, and 3D positioning sensors. The Voice is a bit simpler and offers “an open-source VOICE RECOGNITION platform consisting of a 3.14-inches in diameter dev board with a radial array of 7 MEMS microphones connected to a Xilinx Spartan6 FPGA & 64 Mbit SDRAM with 18 RGBW LED’s & 64 GPIO pins.” This means you can offer a light show with your voice recognition
The $99 Voice is shipping soon and you can grab the Creator now.
The company raised $5.8M in funding since inception from Azoic Ventures and Rokk3r Labs. Their latest product was priced at $99 and should be on sale online soon. Basically Matrix wants you to build robots with their stuff.
“Our goal is to create hardware as an enabler to launch apps, businesses, and ideas,” said Saccoman. “Furthermore, we want to remove the barriers to entry for developers of all levels to easily incorporate machine learning, computer vision, and artificial intelligence into their creations.”
As you can see the Voice and the Creator fit right on top of your Raspberry Pi and adds some real impressive functionality. The entire system is obviously for DIY fans but because it’s completely open source you can build out based on the base offering. It’s a surprisingly cool piece of kit and I, for one, welcome our voice activated Raspberry Pi-powered robotic overlords.
A billion and a half photos find their way onto Facebook every single day and the company is racing to understand them and their moving counterparts with the hope of increasing engagement. And while machine learning is undoubtedly the map to the treasure, Facebook and it’s competitors are still trying to work out how to deal with the spoils once they find them. Facebook AI Similarity Search (FAISS), released as an open source library last month, began as an internal research project to address bottlenecks slowing the process of identifying similar content once a user’s preferences are understood. Under the leadership of Yann LeCun, Facebook’s AI Research (FAIR) lab is making it possible for everyone to more quickly relate needles within a haystack.
On its own, training a machine learning model is already an incredibly intensive computational process. But a funny thing happens when machine learning models comb over videos, pictures and text — new information gets created! FAISS is able to efficiently search across billions of dimensions of data to identify similar content.
In an interview with TechCrunch, Jeff Johnson, one of the three FAIR researchers working on the project, emphasized that FAISS isn’t so much a fundamental AI advancement as a fundamental AI enabling technique.
Imagine you wanted to perform object recognition on a public video that a user shared to understand its contents so you could serve up a relevant ad. First you’d have to train and run that algorithm on the video, coming up with a bunch of new data.
From that, let’s say you discover that your target user is a big fan of trucks, the outdoors and adventure. This is helpful, but it’s still hard to say what advertisement you should display — A rugged tent? An ATV? A Ford F-150?
To figure this out, you would want to create a vector representation of the video you analyzed and compare it to your corpus of advertisements with the intent of finding the most similar video. This process would require a similarity search, whereby vectors are compared in multi-dimensional space.
In real life, the property of being an adventurous outdoorsy fan of trucks could constitute hundreds or even thousands of dimensions of information. Multiply this by the number of different videos you’re searching across and you can see why the library you implement for similarity search is important.
“At Facebook we have massive amounts of computing power and data and the question is how we can best take advantage of that by combining old and new techniques,” posited Johnson.
Facebook reports that implementing k-nearest neighbor across GPUs resulted in an 8.5x improvement in processing time. Within the previously explained vector space, nearest neighbor algorithms let us identify the most closely related vectors.
More efficient similarity search opens up possibilities for recommendation engines and personal assistants alike. Facebook M, its own intelligent assistant, relies on having humans in the loop to assist users. Facebook considers “M” to be a test bed to experiment with the relationship between humans and AI. LeCun noted that there are a number of domains within M where FAISS could be useful.
“An intelligent virtual assistant looking for an answer would need to look through a very long list,” LeCun explained to me. “Finding nearest neighbors is a very important functionality.”
Improved similarity search could support memory networks to help keep track of context and basic factual knowledge, LeCun continued. Short term memory contrasts with learned skills like finding the optimal solution to a puzzle. In the future, a machine might be able to watch a video or read a story and then answer critical follow up questions about it.
More broadly, FAISS could support more dynamic content on the platform. LeCun noted that news and memes change every day and better methods of searching content could drive better user experiences.
A billion and a half new photos a day presents Facebook with a billion and a half opportunities to better understand its users. Each and every fleeting chance at boosting engagement is dependent on being able to quickly and accurately sift through content and that means more than just tethering GPUs.
Featured Image: Bryce Durbin
Welcome back to another week of . On this week’s podcast, the Mashable tech team breaks down the new electronics restrictions that’ll affect passengers flying to the U.S. and UK from select Middle Eastern and North African countries.
This week’s MashTalk is hosted by Senior Tech Correspondent Raymond Wong, with commentary from Chief Correspondent Lance Ulanoff and Tech Reporter Brett Williams.
Real Time News Reporter Colin Daileda joins to give us the full skinny on the new electronics restrictions (2:07). In a nutshell, electronics that are larger than a smartphone (i.e. tablets, laptops and game devices) can’t be carried on board flights from listed airlines and must be placed in checked baggage. It’s a huge hassle for everyone involved, and most warranties (like Apple Care) probably won’t cover any accidental damage to your devices if you’re forced to check them (9:51).
Next, we nerd out over the newly announced Product (RED) iPhones (11:53). Apple’s partnered with (RED) for 10 years and this is the first time its most profitable device goes red, with a portion of all proceeds going toward The Global Fund to help fight HIV/AIDs. At the end of the day, though, it’s just a new color for the iPhone (15:03). We also quickly discuss the new $329 iPad (19:04) that replaces the iPad Air 2. It’s a minor refresh that makes the iPad more affordable than ever before, but it mostly feels meh.
Finally, Business Reporter Emma Hinchcliffe swings by to talk about Facebook. It’s always Facebook these days, isn’t it? More specifically, Facebook posted a code of conduct for its F8 developer conference happening next month (24:36). Is it ridiculous or admirable that Facebook needs to tell people to, essentially, not be assholes? Brett and Emma dive in (27:07) to give us their perspectives.