Sitecore Symposium 2019, Orlando, Fla. – Nov. 5, 2019 -- Sitecore, the global leader in digital experience management software, today announced Sitecore AI, a new machine learning framework that empowers marketers with predictive insights to automate the delivery of personalized digital experiences. Automated Personalization -- an add-on for the Sitecore Experience PlatformTM (XP) 9.0 and above -- is the first of the next-generation services within Sitecore AI, providing one of the industry's first solutions powered by Microsoft Azure to automatically identify visitor trends, create customer segments, and modify page elements to deliver personalized experiences. Sitecore AI amplifies the power of Sitecore XP to deliver individualized, contextual experiences that guide visitors along their journey with content that is useful and actionable. It can analyze a customer's behavior to understand their interests and intent, enabling marketers to learn about where each person is in their journey -- without the guesswork -- and determines the best content for each customer while continuously optimizing their experience, nurturing them toward conversion. "Sitecore AI helps us solve the content crisis for marketers by giving them the intelligent insights they need to optimize content creation and personalized experience delivery at massive scale," said Desta Price, executive vice president of product, Sitecore.

In the recent update of GraphVite, we release a new large-scale knowledge graph dataset, along with new benchmarks of knowledge graph embedding methods. The dataset, Wikidata5m, contains 5 million entities and 21 million facts constructed from Wikidata and Wikipedia. Most of the entities come from the general domain or the scientific domain, such as celebrities, events, concepts and things. To facilitate the usage of knowledge graph representations in semantic tasks, we provide a bunch of pre-trained embeddings from popular models, including TransE, DistMult, ComplEx, SimplE and RotatE. You can directly access these embeddings by natural language index, such as "machine learning", "united states" or even abbreviations like "m.i.t.".

Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Last fall, Google unveiled a breakthrough artificial intelligence technology called BERT that changed the way scientists build systems that learn how people write and talk.

It's been 7 months since my last commentary on the field, and as it became regular appearance in this blog (and in fact many people apparently enjoy this form and keep asking for it), it is a time for another one. For those new to the blog, here we generally strip the AI news coverage out of fluff and try to get to the substance, often with a fair dose of sarcasm and cynicism. The more pompous and grandiose the PR statement, the more sarcasm and cynicism - just to provide some balance in nature. The field of AI never fails to deliver on pompous and grandiose fake news hence I predict there will be a material for this blog for many years to come. Now that the introductory stuff is behind and you've been warned, let us go straight to what happened in the field since May 2019.

Software engineers are the most sought after in the drone industry both by platform manufacturers and by software companies. This is partly because some companies like Thales Alenia Space or DELAIR are both hardware and software manufacturers. Another reason is that hardware companies like the recently acquired Aeryon Labs, Flyability and Matternet are hiring software engineers for their R&D divisions firstly in order to continue increase the level of automation within their products (e.g. Hardware engineers are exclusively sought after by hardware manufacturers and, thanks to the many features which drones can and must contain, they come from a variety of backgrounds: electrical engineering, mechanical engineering, and aeronautical engineering. Sales roles are the portion of drone jobs which are customer oriented and as drone companies grow bigger increasingly focus on specific regions (for example, companies seeking sales executives for specifically California, or the Eastern Cape).

A team of Japanese researchers from Yamagata University and IBM Research have discovered an incredible 143 stunning geoglyphs, etched into the desert in southern Peru around the enigmatic Nazca Lines. It is yet another example of how technology is assisting archaeology because a number of images were found using state-of-the-art AI technology developed by IBM, finds that were then confirmed with an on-site investigations. The geoglyphs include humans, birds, camels, cats and other animals and were found between 2016 and 2018. They were identified through fieldwork and analysing high-resolution 3D data and aerial photography. Incredibly, one geoglyph in particular was solely discovered with AI technology, without the aid of humans, making it the first geoglyph discovered by an AI.

How do you get machines to perform better? Tell them they could croak at any minute. In a new paper from the University of Southern California, scientists say that "in a dynamic and unpredictable world, an intelligent agent should hold its own meta-goal of self-preservation." Lead researcher Antonio Damasio is a luminary in the field of intelligence and the brain. In his profile at the Edge Foundation, they say Damasio "has made seminal contributions to the understanding of brain processes underlying emotions, feelings, decision-making and consciousness."

Conversational robots and virtual characters can enhance learning and expand entertainment options for children, a trio of studies by Disney Research shows, though exactly how these autonomous agents interact with children sometimes depends on a child's age. Pre-school children responding to an on-screen character, for instance, may be happiest if the character simply waits for their responses or repeats a question. Older children talking with a robot, on the other hand, appreciate it when the robot references their previous conversations, while younger children are just as happy if the robot treats each conversation as a new encounter. "Teasing out these nuances is necessary if we are to make the interactions between automated characters and children as engaging as possible," said Jill Fain Lehman, senior research scientist. Lehman and other staff members of Disney Research will present findings from the three studies at the Interaction Design and Children Conference in Palo Alto, Calif., June 27-30. "Though parent-child interaction remains the most important factor in child development, the prospect of automated characters that can interact with children offers exciting opportunities for further enhancing learning and play," said Markus Gross, vice president at Disney Research.

We are inviting recent life science Ph.D. graduates interested in learning computer-assisted drug discovery (CADD) to apply to our post-doctoral fellowship program. The position will be based out of the Atomwise office in San Francisco, CA, USA. As a member of the Atomwise CADD team, fellows will also contribute to pharmaceutical hit-to-lead programs with the goal of identifying clinic-ready drug candidates.

Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns, and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives. Although having a good understanding of programming languages, Machine Learning algorithms and following a data-driven approach is necessary to become a Data Scientist, Data Science isn't all about these fields. In this blog post, you will understand the importance of Math and Statistics for Data Science and how they can be used to build Machine Learning models.