Computer vision engineers have used machine learning techniques for decades to detect objects of interest in images and to classify or identify categories of objects. They extract features representing points, regions, or objects of interest and then use those features to train a model to classify or learn patterns in the image data. In traditional machine learning, feature selection is a time-consuming manual process. Feature extraction usually involves processing each image with one or more image processing operations, such as calculating gradient to extract the discriminative information from each image. Deep learning algorithms can learn features, representations, and tasks directly from images, text, and sound, eliminating the need for manual feature selection.

"Smart money" VCs have an enviable investment history, with a pattern of successful exited companies and high returns. And these firms' portfolios can provide important indicators of where tech and innovation is going. We define smart money venture capital firms (VCs) as those firms with the best combination of portfolio valuations and investment outcomes. Our methodology led us to identify 24 venture capital firms that stand above the rest in terms of financial success. So where is smart money going?

Advancement in computers and technology have been incredibly positive for music creators around the globe. You can learn how to produce music on YouTube, purchase the same sample libraries as your favorite composers and create epic scores from your laptop, anywhere in the world. As a result, there are more music producers than ever. Computers helping humans create original music has become today's standard, but there are several companies looking to turn the tables on this process. Computer-generated songs are a fast-growing sector of the industry, looking to disrupt creation, licensing and access for musicians and non-musicians alike.

And after all that, you reach a customer service agent who can't give you the help you need. No wonder we hate customer service. A San Francisco startup founded by particle physics researchers is working to ease that agony, with help from AI and GPUs. The company, Deepgram, created technology that businesses can use to quickly assess customer calls to improve service. "You really don't want to be calling customer service, and you don't want your time wasted," said Scott Stephenson, Deepgram co-founder and CEO.

Several weeks after winning U.S. patent protection for its data preparation platform, Tamr Inc. announced a deal with Hewlett Packard Enterprise to resell its data unification software. The reseller deal certifies that Tamr's technology has been validated for interoperability with HPE systems as part of its third-party software and hardware offerings. It also raises the visibility of the startup's platform given the breadth of HPE's sales channel. Hewlett Packard Pathfinder was an early investor in Tamr, which was spun out of the Massachusetts Institute of Technologies' Computer Science and Artificial Intelligence Laboratory in 2014. The startup, based in Cambridge, Mass., raised more than $25 million in a Series B funding round in June 2015.

The Washington fight over the future of Obamacare will have enormous repercussions for our health care system, which now accounts for nearly 18 percent of the U.S. economy. First there was "open washing," the marketing strategy for dressing up proprietary software as open source. Next came "cloud washing," whereby … Advances in artificial intelligence, machine learning, and deep learning are impacting businesses. But, the terms are often used interchangeably. As consumer banking becomes increasingly virtual, banks are setting the bar high for their non-human ambassadors.

The Internet of Things is a powerful technological force. So many devices and activities in our personal and professional lives are connected to it--from smartphones and activity trackers to huge manufacturing centers and medical devices, more and more things are being connected to the IoT every day and transforming how we live and work. At the same time, artificial intelligence technology is also developing rapidly, with 80% of the world's largest companies having incorporated some kind of cognitive technology into their products. AI trains computers to take on some human forms and tasks and expands the world of technological possibilities to combine machine learning and human creativity. With two large technological movements making waves around the world, it makes sense to combine them and add an AI layer to the IoT.

Naver and LINE Corp. unveiled their artificial intelligence (AI)-based assistant platform Clova at the Mobile World Congress (MWC) 2017 in Barcelona, the companies reported Thursday. According to Naver and its mobile service subsidiary, Clova, which stands for "cloud virtual assistant," aims at an AI platform that works based on the five human senses. "Clova is a platform that incorporates various AI technologies including vocal, visual and conversational engines," LINE Corp. CEO Idezawa Takeshi said in his keynote at the MWC 2017, Wednesday. The two companies are jointly developing Clova by improving Naver Labs' voice-recognizing assistant service AMICA. They said it will expand the system to have more cognitive capabilities to make it more humanlike.

That's what keeps me going.

Automation is reaching into a vast range of professions, and my own is certainly no different. Through this blog and various other means, I try to locate interesting research and practices from around the world, and bring them together into some kind of narrative. With so much going on around the world, it stands to reason that a computer could be trained to do a similar task, and that's certainly the aim of Semantic Scholar, a new tool launched by The Allen Institute for Artificial Intelligence. The tool offers users a means of hunting for papers in specific fields, and then filter your search by date, publication and so on. The developers believe that Semantic Scholar stands above the likes of Google Scholar due to the smarter way in which it hunts down relevant papers.