Grimes Confirms Tesla’s Elon Musk Is Father Of Her Child

In an interview with Rolling Stone, a pregnant Grimes officially confirmed that Tesla Inc. (NASDAQ: TSLA) CEO Elon Musk is the father of her child.

In January, the 31-year-old Canadian singer, whose real name is Claire Boucher, posted a photoshopped image of a fetus onto a nude photo of herself.

In response to a comment, she told a fan she’s “knocked up.”

Grimes has been in a relationship with Musk since May 2018.

In the interview, Grimes also talks about the possibilities of artificial intelligence taking over the creation of art — not to mention the rest of human society.

From a previous marriage to Justine Wilson, Musk has five sons: Nevada Alexander Musk, Griffin Musk, Kai Musk, Xavier Musk, Saxon Musk and Damian Musk. 

This content was originally published here.

Keigo – Human interaction assisted by AI | Product Hunt

Keigo – Mutual Understanding Assisted by AI
Keigo is here to help you to be the best version of yourself. In case you have an important interview, a first date or a social event. Imagine what your life will be like when you connect just a little better with the people you meet?
Keigo brings applied psychological insights for everyone. It combines social styles, personal advice, other peoples´ perceptions and artificial intelligence – and provides an easy user experience.

This content was originally published here.

Marvelstone silent on progress of its plans; web page blank; Lattice 80 UK dissolved, Garage – THE BUSINESS TIMES

: THE BUSINESS TIMES Garage – MARVELSTONE Group has previously said that it makes fintech investments, and builds hubs in artificial intelligence and cryptocurrencies, with founder Joe Cho Seunghyun and his wife Gina Heng making appearances at international tech conferences as representatives of Marvelstone. . Read more at The Business Times.

This content was originally published here.

The Impact of Artificial Intelligence on HVACR | 2019-02-25 | ACHR News

When thinking about artificial intelligence (AI), usually what comes to mind are movies that depict computers or robots becoming self-aware and proceeding to torment humans. Think of HAL in “2001: A Space Odyssey” or Skynet in the “Terminator” movies. Thankfully, the reality is much different. In fact, as far as the HVACR industry is concerned, AI is already providing opportunities to improve maintenance, comfort, and energy savings.

This content was originally published here.

Top 3 Artificial Intelligence Research Papers – June 2020

If you worked in the software development industry, sooner or later you will face projects where you need to transfer part of functionality from one programming language to another. Sometimes the whole projects are translated from one programming language to another. These are expensive endeavors. There is a famous example of how Bank of Australia spent around $750 million and 5 years of work to convert its platform from COBOL to Java. Basically, translating functionality from one language to another is not easy. For big projects, you need to be experienced in both languages. Of course, there are a number of tools that can help you with this, in fact, some of these tools are integrated as a part of some programming languages.

For example, Typescript uses such a tool to convert its code into JavaScript. This way you can use an object-oriented approach and type checking, and still use built software in the majority of browsers. These tools are called transcompiler, transpiler, or source-to-source compiler. Their purpose is to convert code from one programming language to another, given that languages work on the same level of abstraction. Authors of this paper use unsupervised learning to do so. Note that they focused on use cases of translation an existing codebase written in an obsolete or deprecated language to a newer one.

In a nutshell, a Cross-lingual Language Model (XLM) is pretrained with a masked language modeling objective on monolingual source code datasets and as a result, pieces of code that express the same instructions are mapped to the same representation, regardless of the programming language. XLM is used to initialize the TransCoder model. However, this on its own is not enough, because the decoder part of the transformer architecture requires additional attention parameters which are initialized randomly. That is how the first part of unsupervised training (initialization is done). The second part of this training, i.e. language modeling is done by training the model to encode and decode sequences with a Denoising Auto-Encoding (DAE) objective. This means that model is trained to predict a sequence of tokens given a corrupted version of that sequence. Corruption of the sequences is done by randomly masking, removing and shuffling input tokens.

In the end, the authors used Back-translation. The previous two steps of the training would be enough for this model, but in order to increase the quality of generated code authors added this step. In general, this process two models are trained: source-to-target and target-to-source. The purpose of target-to-source model is to generate noisy translations of the source language from the target language. These generated sequences of noisy codes are then used to train the source-to-target model. The two models are trained in parallel until convergence.

By now you have probably seen the results of this paper somewhere on the web. It is truly amazing how the solution proposed in it transforms sketches into face images. This is a very attractive field since it’s applications are many from character design to criminal investigations. In general, it would be cool to have such drawing assistance at your disposal. Thus far similar solutions used sketches as hard constraints, which didn’t always give good results. This is why the authors of this paper suggest the solution that is utilizing recent advances in image-to-image translation and with that use sketches as soft constraints to guide image synthesis. Basically, they form loose points from the sketch and then use deep learning to “fill” missing parts.

The solution relies heavily on some recent advances in deep learning, especially from conditional face generation. To be more precise the authors relied on Condition GANs and pix2pix principles for the image synthesis parts of the architecture. Apart from that, data preparation for this architecture is a bit specific, but it also provides aimed flexibility. The authors couldn’t use datasets with sketches, like CUHK face sketch database, because these contain shading effects, which authors wanted to avoid. They have built dataset form face image data of CelebAMask-HQ, which contains high-resolution facial images with semantic masks of facial attributes and processed with holistically-nested edge detection, APDrawingGAN and Photoshop’s Photocopy filter.

This paper is exploring the understanding of 3D structure on the images which is a challenging, but an integral part of many computer vision applications. In fact, the authors consider this problem under two challenging conditions. The first one is that there are no 2D or 3D ground truth about images, hence the unsupervised learning term in the headline and the second one is that model should not require multiple views of the same instance. Essentially, their main goal is to create a deep learning model that can output 3D shape of any instance given a single image of it, while keeping the unsupervised spirit.

To do so authors created an autoencoder based structure that splits the image into albedo, depth, illumination and viewpoint component. However, as expected, this is not enough and the model has to has some assumptions about the image. This is done in a really cool manner, meaning the model creates these assumptions on its own. One of the most important assumptions is the symmetry of the image. It does so by creating a dense map that contains the probability that a given pixel has a symmetric one. Note that here we talk about Bilateral symmetry, meaning that opposite sides of the image are similar but not identical. The whole thing is done by modeling asymmetric illumination and creating a confidence score that explains the probability of the pixel having a symmetric counterpart in the image for each pixel of the image.

This content was originally published here.

Mobile 360 – Intelligent Connectivity in Latin America – Livestream – Mobile World Live

Watch Live in Spanish
Mobile World Live will stream all keynote sessions on this page across the two days (please note all times are local time, Mexico CDT, BST-6). Bookmark this page for free access.


Opening Keynote: Intelligently Connecting Latin America

9:00 am – 10:45 am

Intelligent connectivity enables transformational new capabilities that will positively impact industries, societies and economies. Creating an intelligently connected world requires a combination of Artificial Intelligence and IoT with high-speed, reliable and cost-effective networks.

The 2019 opening keynote will feature mobile operators, regulators and ecosystem stakeholders who will share their approach intelligent connectivity, guidance on how it will impact industries in the region and a timeline for realizing its full potential.

9:00 am

The Power of Data and the Impact of Automation

9:00 am – 10:30 am

The world around us is becoming a web of connected devices and services. This vast amount of data generated is already a vital part of the decision to personalise content, products and experiences. Having said that, the amount of available data far exceeds the ability to process it for most enterprises, so an opportunity to better understand the customer base and make important decisions often goes untapped. How can companies become data-driven whilst maintaining their core business at heart? How much data is too much data?

Confirmed Speakers:

Get Our Daily Newsletter

This content was originally published here.

Google Celebrates Chinese New Year with Shadow Play Artificial Intelligence Game – TechEBlog

Google Chinese New Year Shadow Play
Google officially announced Monday that it would welcome the Year of the Pig with an artificial intelligence experiment using shadow art. This shadow play game of sorts utilizes front-facing cameras and users are taught how to create images of their Chinese zodiac sign using shadow puppetry. The Mountain View, California-based internet giant is betting on AI and machine learning-driven services in hopes of making such technologies ubiquitous, helpful and intuitive. Read more to see it in-action and for a link to try it out yourself.

“Last year, Google created an interactive art installation that leverages TensorFlow and TPUs to recognize a person’s hand gestures and transform the shadow figures into digital animations of the 12 Chinese zodiac animals. For the 2019 Lunar Year, Google has ported that experience into a browser-based game that teaches users how to contort hands into shadow puppets. Shadow Art uses TensorFlow.js to run the machine learning models directly in the browser on any phone or laptop that features a front-facing camera,” reports 9to5 Google. Try it out here.

Related Posts

UC Berkley researchers, lead by Richard Zhang, have developed an…

Say goodbye to Mitsubishi’s famed EVO sports compact line, and…

Self-driving cars are one thing, and Toyota’s Concept-i takes the…

This content was originally published here.

Lexus launches ad scripted entirely using artificial intelligence

Spot is directed by Oscar-winning Kevin Macdonald.

This content was originally published here.

Hyper-Converged Networks and Artificial Intelligence: Fighting at Machine Speed

By Travis Howard Lieutenant Stacey Alto sits in the Joint Intelligence Center aboard the Wasp-class Amphibious Assault ship USS ESSEX (LHD 2). As the Force

This content was originally published here.

Shanghai judicial courts start to replace clerks with AI assistants | South China Morning Post

A six-month programme in Shanghai will see 10 judicial courts use artificial intelligence technology to automate the job of law clerks in case hearings. Photo: Bloomberg
A six-month programme in Shanghai will see 10 judicial courts use artificial intelligence technology to automate the job of law clerks in case hearings. Photo: Bloomberg

This content was originally published here.