Ng announced Tuesday that he raised money from venture capital firms New Enterprise Associates, Sequoia Capital and Greylock Partners as well as SoftBank Group Corp. Under Ng, Baidu released a voice-based operating system that users can talk to - much like Amazon's Alexa voice assistant or Apple's Siri - and also started working on self-driving cars and face recognition technology to open things like transit turnstiles when users approach. I think it's a more systematic, repeatable process than most people think," said Ng, who also taught artificial intelligence courses at Stanford University. The first company to receive money from the fund will be Landing.ai,
Google Pixel Tomorrow, Oct 4, is Google's day with the tech giant expected to launch products from new Pixel smartphones to showcase what Android can do ( and already leaked as below), to the much hyped about smart home hub, the Google Home speaker. This product, which will make use of its voice-enabled AI Google Assistant, is meant to be a direct rival to Amazon's Alexa. But as voice-control heats up, and consumers grow more comfortable running their homes by just saying what they want, tech companies are eager to be the app used to run people's lives. Images of Google Pixel phones were leaked late last night by a retailer Carphone Warehouse, before images were taken down. California has just given the greenlight to self-driving cars--without drivers.
At a surprise event in Seattle, Amazon announced updates to a few Echo-branded devices and unveiled what seems like a dozen entirely new Alexa products. From a new microwave to a wall clock (not joking), Amazon introduced basically everything but the kitchen sink during its event. Almost all these new devices come with built-in Alexa voice control, or they are designed to work with both existing and new Echo products. We've delved into all the details and have rounded up what matters most: Prices, features, and release dates. The updates include the voice-controlled Echo Dot, Echo Show, and Echo Plus, all three of which now come with sound improvements, though the new Echo Dot also comes with a redesign.
The complete part of the earthquake frequency-magnitude distribution (FMD), above completeness magnitude mc, is well described by the Gutenberg-Richter law. The parameter mc however varies in space due to the seismic network configuration, yielding a convoluted FMD shape below max(mc). This paper investigates the shape of the generalized FMD (GFMD), which may be described as a mixture of elemental FMDs (eFMDs) defined as asymmetric Laplace distributions of mode mc [Mignan, 2012, https://doi.org/10.1029/2012JB009347]. An asymmetric Laplace mixture model (GFMD- ALMM) is thus proposed with its parameters (detection parameter kappa, Gutenberg-Richter beta-value, mc distribution, as well as number K and weight w of eFMD components) estimated using a semi-supervised hard expectation maximization approach including BIC penalties for model complexity. The performance of the proposed method is analysed, with encouraging results obtained: kappa, beta, and the mc distribution range are retrieved for different GFMD shapes in simulations, as well as in regional catalogues (southern and northern California, Nevada, Taiwan, France), in a global catalogue, and in an aftershock sequence (Christchurch, New Zealand). We find max(mc) to be conservative compared to other methods, kappa = k/log(10) = 3 in most catalogues (compared to beta = b/log(10) = 1), but also that biases in kappa and beta may occur when rounding errors are present below completeness. The GFMD-ALMM, by modelling different FMD shapes in an autonomous manner, opens the door to new statistical analyses in the realm of incomplete seismicity data, which could in theory improve earthquake forecasting by considering c. ten times more events.
University of Toronto graduate student Avishek "Joey" Bose, under the supervision of associate professor Parham Aarabi in the school's department of electrical and computer engineering, has created an algorithm that dynamically disrupts facial recognition systems. The project has privacy-related and even safety-related implications for systems that use so-called machine learning -- and for all of us whose data may be used in ways we don't realize. Major companies such as Amazon, Google, Facebook and Netflix are today leveraging machine learning. Financial trading firms and health care companies are using it, too -- as are smart car manufacturers. What is machine learning, anyway?