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Microsoft: Here's how we're adding news, weather and traffic to your Windows 10 taskbar

ZDNet

Microsoft has released a new preview of a future Windows 10 taskbar that shows how it plans to display news and weather in the task bar. Microsoft is kicking off 2021 with more user interface changes as it gears up for the big Sun Valley UI update in for the 21H2 Windows 10 release. The widget panel features news and other tidbits that can be personalized or turned off, and are accessible from the taskbar. A point to keep in mind is that the feature isn't guaranteed to turn up in a future version of Windows 10, but if things go well, it probably will turn up in Windows 10 21H2. That the software giant is bothering to show off improvements to the feature bodes well for it.


Top 50+ Artificial Intelligence Companies

#artificialintelligence

Artificial Intelligence is quickly becoming one of the quintessential industries today and reshaping the world as we know it. It has proven its worth in different areas of work with an undeniable impact on the market. Siri to Alexa and self-driving cars to manufacturing-robots are just a few examples of what AI companies have achieved. Tech giants like Amazon, Google, Microsoft and Apple are putting their resources in Artificial Intelligence and are running the race of becoming the biggest artificial intelligence companies in the world. Organisations like NASA are now using artificial intelligence to make themselves even more efficient, a report says. Technical edge is the key to most of the businesses today. As big players are putting everything they have got to get that technical edge, small players may find it overwhelming, if not unfair, to compete with them. Apart from these giants, there are several AI companies which have shown the potential of changing the world and solve the possible disparity some companies may experience due to their technical prowess.



The case against investing in machine learning: Seven reasons not to and what to do instead

#artificialintelligence

The word on the street is if you don't invest in ML as a company or become an ML specialist, the industry will leave you behind. The hype has caught on at all levels, catching everyone from undergrads to VCs. Words like "revolutionary," "innovative," "disruptive," and "lucrative" are frequently used to describe ML. Allow me to share some perspective from my experiences that will hopefully temper this enthusiasm, at least a tiny bit. This essay materialized from having the same conversation several times over with interlocutors who hope ML can unlock a bright future for them. I'm here to convince you that investing in an ML department or ML specialists might not be in your best interest. That is not always true, of course, so read this with a critical eye. The names invoke a sense of extraordinary success, and for a good reason. Yet, these companies dominated their industries before Andrew Ng's launched his first ML lectures on Coursera. The difference between "good enough" and "state-of-the-art" machine learning is significant in academic publications but not in the real world. About once or twice a year, something pops into my newsfeed, informing me that someone improved the top 1 ImageNet accuracy from 86 to 87 or so. Our community enshrines state-of-the-art with almost religious significance, so this score's systematic improvement creates an impression that our field is racing towards unlocking the singularity. No-one outside of academia cares if you can distinguish between a guitar and a ukulele 1% better. Sit back and think for a minute.


Salesforce-backed AI project SharkEye aims to protect beachgoers

#artificialintelligence

Salesforce is backing an AI project called SharkEye which aims to save the lives of beachgoers from one of the sea's deadliest predators. Shark attacks are, fortunately, quite rare. However, they do happen and most cases are either fatal or cause life-changing injuries. Just last week, a fatal shark attack in Australia marked the eighth of the year--an almost 100-year record for the highest annual death toll. Once rare sightings in Southern California beaches are now becoming increasingly common as sharks are preferring the warmer waters close to shore.


Artificial Intelligence Stocks To Buy And Watch Amid Rising AI Competition

#artificialintelligence

Artificial intelligence stocks are rarer than you might think. Many companies tout AI technology initiatives and machine learning. But there really are few -- if any -- public, pure-play artificial intelligence stocks. The "AI" stock ticker, though, has been claimed. Startup C3.ai, which sells AI software for the enterprise market, filed on Nov. 13 for an initial public offering. Thomas Siebel, who started Siebel Systems and sold it to Oracle for nearly $6 billion in 2006, founded Redwood City, Calif.-based C3.ai.


Gong Raises $200 Million to Surface Sales Insights with Artificial Intelligence – IAM Network

#artificialintelligence

Gong is a San Francisco based tech platform and among the leading SaaS business in the fast-developing category of Conversation Intelligence. It helps companies by offering sales representatives a new way to increase sales. It also enables sales teams to gain insights regarding the things happening to their employees working remotely. In August this year, as we already covered, Gong raised $200 million at a $2.2 billion valuation and indicated that its profits tripled because sales teams are now working from home during this global pandemic. This latest funding round was led by Coatue and later joined by Thrive Capital, Salesforce, and Index ventures. With other investors' help, including Sequoia Capital, NextWorld Capital, and Battery Ventures, it brings Gong's total funding to $334 million.


Response Selection for Multi-Party Conversations with Dynamic Topic Tracking

arXiv.org Artificial Intelligence

While participants in a multi-party multi-turn conversation simultaneously engage in multiple conversation topics, existing response selection methods are developed mainly focusing on a two-party single-conversation scenario. Hence, the prolongation and transition of conversation topics are ignored by current methods. In this work, we frame response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context. With this new formulation, we propose a novel multi-task learning framework that supports efficient encoding through large pretrained models with only two utterances at once to perform dynamic topic disentanglement and response selection. We also propose Topic-BERT an essential pretraining step to embed topic information into BERT with self-supervised learning. Experimental results on the DSTC-8 Ubuntu IRC dataset show state-of-the-art results in response selection and topic disentanglement tasks outperforming existing methods by a good margin.


New Data Processing Module Makes Deep Neural Networks Smarter

#artificialintelligence

Artificial intelligence researchers at North Carolina State University have improved the performance of deep neural networks by combining feature normalization and feature attention modules into a single module that they call attentive normalization (AN). The hybrid module improves the accuracy of the system significantly, while using negligible extra computational power. "Feature normalization is a crucial element of training deep neural networks, and feature attention is equally important for helping networks highlight which features learned from raw data are most important for accomplishing a given task," says Tianfu Wu, corresponding author of a paper on the work and an assistant professor of electrical and computer engineering at NC State. "But they have mostly been treated separately. We found that combining them made them more efficient and effective."


Artificial Intelligence in the Creative Industries: A Review

arXiv.org Artificial Intelligence

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity.