Chatbots can help secure purchases but also ship packages. Just in time for the holidays, UPS has launched a beta version of its chatbot that will mimic human conversations to help users find shipping locations, learn shipping rates and track packages. Available through Facebook Messenger, Skype and Amazon, UPS' release said it is different from the UPS website or mobile app in that users can use brief phrases like "shipping rates" and receive a voice response. "We see chatbots becoming an important communication channel for our customers over the next few years, and we're setting the stage for the incorporation of artificial intelligence throughout our customer-facing technologies," Stuart Marcus, UPS vice president of customer technology marketing, said in a release. Investing in a chatbot is likely useful for UPS' functionality, especially at times of high volume, such as the holiday rush.

Tuesday, 22nd of November, 2016, Baltimore, MD - Insilico Medicine, Inc and YMK Photonics, Inc announced today a research collaboration and business cooperation to develop photonics quantum computing and accelerated deep learning techniques for drug discovery, biomarker development and aging research. On the 15th of November, 2016 in the presence of over 800 YMK employees, customers, partners and investors, the Chairman of YMK Holdings, Mr. Kim Young Mo, the CEO of Insilico Medicine, Alex Zhavoronkov, PhD and Head of Insilico Korea, Professor Youngsook Park signed a memorandum of understanding to pursue mutual benefit in deep learning and cognitive photonics computing. "YMK is pursuing a very big vision. Extending healthy human longevity is not only the most altruistic cause, but a pressing socio-economic necessity. Insilico Medicine made substantial advances in applying deep learning techniques to drug development and aging research, but to accelerate the process and simulate entire human bodies and populations or generate optimal molecular structures, they could really benefit from our expertise in quantum computing. This collaboration is the first step towards cognitive quantum photonic computation for drug discovery and longevity research", said Mr. Kim Young Mo, the Chairman of YMK Holdings.

As artificial intelligence advances, the goal for modern tech companies is to build AI software that thinks for itself without human intervention. Towards that end, Amazon Web Services just picked MXNet, as its favored deep-learning framework to facilitate that work, according to a blog post Tuesday by Amazon chief technology officer Werner Vogels. Deep learning, as detailed in Fortune earlier this year, is a subset of AI that involves the use of software known as neural networks. Within this realm, software learns by churning through vast reams of data with the help of algorithms--not human programmers--to sort it out. Vogels said AWS will provide software code, documentation, and invest in the development of MXnet and the ecosystem of companies supporting it.

The most valuable part of machine learning is predictive modeling. This is the development of models that are trained on historical data and make predictions on new data. This cheat sheet contains my best advice distilled from years of my own application and studying top machine learning practitioners and competition winners. With this guide, you will not only get unstuck and lift performance, you might even achieve world-class results on your prediction problems. Note, the structure of this guide is based on an early guide that you might fine useful on improving performance for deep learning titled: How To Improve Deep Learning Performance.

In many respects, it is. For sure, the recent successes of deep learning have been amazing: we went from being really terrible at supervised learning on perceptual problems (image classification, speech recognition) to being really good at it. Deep learning has been transformative for many subfields of machine learning. But here's the thing: lots of people, most of them not directly involved with deep learning research, tend to extrapolate too much from these recent successes. For instance, when we started achieving below 4% top-5 error on the ImageNet classification task, people started claiming that we had "solved" computer vision.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. It is short-sighted to conclude all of that will be vacated because a change of leadership is coming to 1600 Pennsylvania Avenue. That does not mean there will not be challenges.

Today, typical machine learning processes are labor and compute intensive. We are helping compress the innovation cycle, with a range of purpose-built solutions to drive AI innovation. A flexible portfolio of technologies are enabling data scientists to build more advanced AI solutions and stimulate new idea exploration. Naveen Rao, Intel VP of Machine Learning, shares how his team uses Intel technology for machine learning that takes a cue from the human brain.

Upfront, he let the reporters and editors in the room know he thought their reporting has been unfair to him. During the wide-ranging conversation, Trump denounced Nazi celebrations in Washington, D.C., offered Jared Kushner as a peace-broker between Israel and Palestine, promised to stay open-minded about the Paris climate-change accord, and mused that prosecuting the Clintons would be a nationally divisive move. He also stood by his appointment of Steve Bannon, saying that had he thought Bannon were racist he wouldn't have hired him. The new feature is an expansion of its "popular times" product. There are currently 32.9 million millionaires.

Many important problems can be represented and studied using graphs -- social networks, interacting bacterias, brain network modules, hierarchical image clustering and many more. If we accept graphs as a basic means of structuring and analyzing data about the world, we shouldn't be surprised to see them being widely used in Machine Learning as a powerful tool that can enable intuitive properties and power a lot of useful features. Graph-based machine learning is destined to become a resilient piece of logic, transcending a lot of other techniques. This post explores the tendencies of nodes in a graph to spontaneously form clusters of internally dense linkage (hereby termed "community"); a remarkable and almost universal property of biological networks. This is particularly interesting knowing that a lot of information can be extrapolated from a node's neighbor (e.g. So how can we extract this kind of information?

Ritika Gunnar is vice president of offering management, data and analytics at IBM. She has also served as a software engineer and as vice president for information integration and governance in IBM's platform analytics group. In this exclusive interview with SearchCloudApplications, she discusses the evolution of the data science industry and the skills that developers must possess to flourish in a data-driven world. Bringing development and IT ops together can help you address many app deployment challenges. Our expert guide highlights the benefits of a DevOps approach. Explore how you can successfully integrate your teams to improve collaboration, streamline testing, and more.