Looking at all of the very large convolutional neural networks such as ResNets, VGGs, and the like, it begs the question on how we can make all of these networks smaller with less parameters while still maintaining the same level of accuracy or even improving generalization of the model using a smaller amount of parameters. One approach is depthwise separable convolutions, also known by separable convolutions in TensorFlow and Pytorch (not to be confused with spatially separable convolutions which are also referred to as separable convolutions). Depthwise separable convolutions were introduced by Sifre in "Rigid-motion scattering for image classification" and has been adopted by popular model architectures such as MobileNet and a similar version in Xception. In this tutorial, we'll be looking at what depthwise separable convolutions are and how we can use them to speed up our convolutional neural network image models. Before diving into depthwise and depthwise separable convolutions, it might be helpful to have a quick recap on convolutions.

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Mentions of artificial intelligence within the filings of companies in the maritime industry rose 67% between the first and second quarters of 2022. In total, the frequency of sentences related to artificial intelligence between July 2021 and June 2022 was 295% higher than in 2016 when GlobalData, from whom our data for this article is taken, first began to track the key issues referred to in company filings. When companies in the maritime industry publish annual and quarterly reports, ESG reports and other filings, GlobalData analyses the text and identifies individual sentences that relate to disruptive forces facing companies in the coming years. Artificial intelligence is one of these topics - companies that excel and invest in these areas are thought to be better prepared for the future business landscape and better equipped to survive unforeseen challenges. To assess whether artificial intelligence is featuring more in the summaries and strategies of companies in the maritime industry, two measures were calculated.

As general chair of this year's ACM Conference on Knowledge Discovery and Data Mining (KDD), Huzefa Rangwala, a senior manager at the Amazon Machine Learning Solutions Lab, has a broad view of the topics under discussion there. Two of the most prominent, he says, are graph neural networks and fairness in AI. Graphs are data representations that can encode relationships between different data items, and graph neural networks are machine learning models that are useful for knowledge discovery because they can be used to infer graph structures. "Our world is connected in lots of ways, so you'll see graph neural networks find applications in lots of different domains, all the way from social networks and transportation networks to knowledge graphs and drug discovery," Rangwala says. The Amazon Machine Learning Solutions Lab brings the expertise of Amazon scientists and the resources of Amazon Web Services to bear on customers' machine learning problems.

New Delhi: Google has announced to expand content advisories to searches where its AI systems don't have high confidence in the overall quality of the results available for the search. Pandu Nayak, Google Fellow and Vice President, Search, said that this doesn't mean that no helpful information is available, or that a particular result is low-quality. "These notices provide context about the whole set of results on the page, and you can always see the results for your query, even when the advisory is present," he said in a blog post late on Thursday. "We have deeply invested in both information quality and information literacy on Google Search and News, and today we have a few new developments about this important work," said Nayak. Google also introduced latest AI model, called Multitask Unified Model (MUM), to improve search result quality in snippets' which are shown on top of the page for searches.

Since the beginning of the Covid-19 pandemic, the online fitness trend has exploded. That's led to a surge of research into interactive AI coaches to offer user's an interactive experience in the comfort of their homes. This article describes the first phase of developing an AI yoga instructor that can view the user's alignment and recommend adjustments to improve their postures and, therefore, prevent injuries. During the first phase of the project, the goal is to find a model that produces the best accuracy score for correctly identifying five yoga poses: Warrior 2, Downward Facing Dog, Goddess, Plank, and Tree. Consisting of 1,547 images split into 5 classes, or poses, this dataset is smaller and contains less poses than will be necessary for later phases of the project.

Andrew Watson is Vice President of AI and R & D at Healx. Prior to joining Healx he worked at the technology giant Dyson, where he was the founding member of the Machine Learning Research Department, leading the research and implementation of machine learning and artificial intelligence across a variety of global product categories. In his time as Director of Machine Learning at Dyson, Andrew also established a new research group, focused on the intersection between machine learning and cutting-edge biomedical research. Healx is an AI-powered, patient-inspired technology company, dedicated to helping rare disease patients around the world access life-improving therapies. There are 7,000 known rare diseases that affect 400 million people across the globe but only 5% of those conditions have approved treatments.

It's been a brutal year for Peloton and the company is enacting more major changes in a bid to get back on track. On the consumer front, Peloton is reversing price cuts to two of its core pieces of fitness equipment. The Bike is going back up from $1,995 to $2,495 in the US. The Tread, meanwhile, will be more expensive than before Peloton reduced prices in April to focus on subscription revenues. The Tread will go up by $800 in the US to $3,495.

SHANGHAI, Aug 11 (Reuters) - Xiaomi Corp (1810.HK) is currently running tests on 140 vehicles across China for autonomous driving, CEO Lei Jun said on Thursday. The news marks the latest benchmark for the smartphone maker, which announced last year it would enter the competitive EV sector and challenge established brands and younger tech companies. Speaking at a livestreamed company event, Lei said the company had 500 staff devoted solely to developing autonomous driving technology for its upcoming car and had put 3.3 billion yuan ($490 million) towards the initiative. At the event, Lei also unveiled a humanoid robot dubbed CyberOne, weighing 52kg and measuring 177cm tall, although it was not ready for mass production. Many other Chinese companies are investing in self-driving technology, including ridehailing giant Didi Global and search engine provider Baidu Inc .

Southwest Research Institute's automated, 14-passenger shuttle is deployed at the company's San Antonio campus. Southwest Research Institute (SwRI) has deployed an autonomous shuttle at its 1,500-acre campus in San Antonio. The shuttle can carry up to 14 passengers for campus tours, while also gathering important information to help researchers better understand autonomous driving. SwRI is a developer of software systems for autonomous vehicles and robotics. Its Ranger system is at work on the shuttle, helping it to autonomously drive unique routes on the campus.