As interest in religion wanes around the world, and secularism surges, religious leaders have begun searching for innovative ways to spread their message and connect with potential adherents. In the United States, some churches have relaxed dress codes, revamped worship spaces and churned out sermons that place less emphasis on sin, all while embracing Instagram and refashioning faith as a lifestyle brand. In Japan -- where low fertility rates and an aging population are reducing religious affiliation -- Buddhist priests have been warning for more than a decade that their ancient tradition risks extinction. To reverse course, a Kyoto temple has settled on a new plan for connecting with the masses, one that channels ancient wisdom through the technology of the future. Its name is "Mindar," a mostly aluminum androgynous robotic priest who gives plain-spoken sermons that are designed to stimulate interest in Buddhist teachings, according to the machine's designers. Although the robot preaches, it is not programmed to converse with worshipers, though its sermons are translated to English and Chinese on a nearby wall, creators say.

Here at Reviewed, we make a pretty big fuss about media streaming devices: which ones are the best for which people, and why? While we think the other streamers are great (and in some cases better choices), our readers maintain a lot of loyalty to the Amazon Fire TV Stick. Available at a very affordable $40 price point, the Fire TV Stick (and Fire TV Stick 4K) maintain best-seller status month after month, especially when they receive big discounts during events like Prime Day. If you've been thinking about picking up a media streaming device and you've been wondering which streaming device is the best, you might want to consider the Amazon Fire TV Stick. One of the best reasons to spring for either the Amazon Fire TV Stick or Amazon Fire TV Stick 4K is to use Alexa, Amazon's voice assistant, which is built right into the remote on both of the most recent models. If you're already familiar with Alexa, this is a great way to integrate your TV with that ecosystem to a degree.

Abstract: It is well known that Convolutional Neural Networks (CNNs) have significant redundancy in their filter weights. Various methods have been proposed in the literature to compress trained CNNs. These include techniques like pruning weights, filter quantization and representing filters in terms of a basis functions. Our approach falls in this latter class of strategies, but is distinct in that that we show both compressed learning and representation can be achieved without significant modifications of popular CNN architectures. Specifically, any convolution layer of the CNN is easily replaced by two successive convolution layers: the first is a set of fixed filters (that represent the knowledge space of the entire layer and do not change), which is followed by a layer of one-dimensional filters (that represent the learned knowledge in this space).

I've implemented SVRG (Stochastic Variance Reduction Gradient Descent) optimizer for Keras. The goal is to make this optimizer available in Keras as well, which may be beneficial in the case of RL as some papers claimed it is advantageous over Adam.

The goal of the Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2019 is to discuss and disseminate relevant findings and parallels between the computational neuro/cognitive science and machine learning/artificial intelligence communities. In the past few years, machine learning tools -- especially deep neural networks -- have permeated the vision/cognitive/neuro science communities to become the leading computational models that describe many cognitive tasks. Huge strides are also being made on the machine learning/artificial intelligence community with biologically inspired algorithms providing large efficiency gains in both computational and learning capabilities. However, many mysteries remain with regards to the alignment of human and machine perception, and there are cases where we see divergent rather than convergent representations. To resolve such questions, this workshop aims to bring fruitful discussions between scientists and engineers with multi-disciplinary backgrounds to review the recent progress in shared visual representations in both humans and machines, and in doing so identifying road-blocks and areas of interest to further accelerate the growth of both fields.

OpenAI, the AI company that Elon Musk founded and then quit, has just released a more powerful version of its AI text-writing software. The company still won't release their full software - that can be used to write fake news and messages en masse - due to fears it might be misused. OpenAI says its text-writing system is so advanced it can write news stories and even fiction that passes as human. A user can feed the system text - anything from a few sentences to pages of it - and the system will then continue that same text in an uncannily well-written, contextually relevant, human style. However, after releasing its original system, GPT-2, in February, the company said the full software was too dangerous to release to the public - a weaker version was made available. Now, the company has announced it has released a version of GPT-2 that is six times more powerful.

The global Artificial Intelligence in Manufacturing Market size was $513.6 million in 2018, and is projected to reach $15,273.7 million in 2025, growing …

In February, an artificial intelligence lab cofounded by Elon Musk informed the world that its latest breakthrough was too risky to release to the public.

Artificial intelligence "AI," is one of the most talked about topics in the media today. In fact, according to data from TechNews, since January, there have been an average of 4,000 articles per month that mention AI. From the role AI is playing in helping autonomous vehicles make decisions in milliseconds to how it is providing recommendations to healthcare providers about potentially life saving treatment options to all of the "boring" AI uses in between, we read about and engage with AI on a daily basis. However, with all of the buzz that AI is creating, there are some areas of concern including the ethical debate surrounding AI and whether or not AI will take jobs away. Additionally, it is becoming increasingly difficult for organizations to cut through the noise the topic of AI is creating.