Technology is always evolving and the latest innovation, 'Voice Search' is gathering momentum, with the big players being Amazon's Alexa, Microsoft's Cortana, Ok Google and Apple's Siri at the forefront. Ever asked Siri or Alexa what the weather is like? Instead of typing and searching for a keyword or phrase, you can simply ask out loud where the nearest bar or coffee shop is. Utilising natural language processing, a computer science concerned with artificial intelligence (AI) and machine learning, these artificial assistants can listen and respond to search queries almost like a real human. Used by many consumers already, Voice Search is set to be one of the biggest SEO trends for 2017 and thus comes with many opportunities, as well as challenges to overcome. Google CEO Sundar Pichai announced during his Google I/O keynote that 1/5 searches made with Google Android App is a Voice Search.

By eliminating the friction of the traditional translation process, devices like Translate One2One will not only remove one of the biggest challenges for professionals when meeting and collaborating between cultures, but also offers enormous potential for communities around the world,

Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing — Query Planning — is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries. We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including dbpedia, yago, and freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.

Listen to The Modern Customer Podcast with Rob High here. Artificial intelligence seems to be popping up everywhere, and it has the potential to change nearly everything we know about data and the customer experience. However, it also brings up new issues regarding ethics and privacy. One of the keys to keeping AI ethical is for it to be transparent, says Rob High, vice president and chief technology officer of IBM Watson. When customers interact with a chatbot, for example, they need to know they are communicating with a machine and not an actual human.

An Australian startup revealed its flagship product, an earpiece that can interpret 8 different languages in real-time, at a United Nations event in Switzerland on Friday. Lingmo International, a startup based in West Gosford north of Sydney, launched its TranslateOne2One earpiece at the UN's Artificial Intelligence for Good Summit in Geneva, revealing that IBM Watson machine learning technology had been used for its algorithms. Traditionally, converting one language to another orally in real-time is called "interpreting" whereas the term "translation" is reserved for processing text across languages with some delay. Lingmo founder Danny May, however, describes his product as performing "translation in real-time". And what I mean by independent is that it doesn't require any connectivity to your phone by Bluetooth or wi-fi.

In two previous posts, I covered the emerging industry of cloud-based machine learning solutions. First, I covered Microsoft's Azure Machine Learning and IBM's Watson Analytics. Microsoft's Azure ML provides a graphical drag-and-drop interface for connecting preprogrammed components of a data science pipeline together. The service is similar to KNIME and seemed targeted for users who knew just enough to know what to do, but not so much that they would want to code up fresh algorithms. One value added for Microsoft's product is a smooth integration for companies which already have their data stored in Microsoft's Azure compute cloud.

The adoption and application of Artificial Intelligence (AI) continues to accelerate at an exponential rate in modern businesses. As referenced in the 2017 Tech Trend Report, AI is nearing completion of the next layer in technological advancement, integrated into everything individuals and organizations do. This trajectory is predicted to drive cumulative worldwide spending of $40.6 billion on AI projects by 2024 – according to Raconteur. This is expected to create mass opportunity for the pioneering businesses currently investing in AI development. Moving beyond the hype in existing media coverage, this post will uncover the reality behind what AI means for businesses today, in the near future, and beyond 2017.

IBM Watson had been fully programmed to decode fashion images and the key aspects associated with them. It was intelligent enough to detect the face in the image, the pose of the model as well as body, colour, cut and silhouette of an outfit. It could also tell if two outfits were similar and also determine the dominant colours in an image. It then processed this unimaginably huge amount of raw data to come up with useful insights, which were then shared with the designer duo. Unbelievable as it may seem, Watson had managed to condense years and years of Bollywood fashion into carefully analysed and accurate information. From popular colours through Bollywood's timeline to prints and silhouettes that dominated a particular season, Falguni and Shane Peacock now had everything they needed to kickstart their ideation process.

With the Salesforce-IBM global strategic partnership, IBM Watson, the leading AI platform for business, and Salesforce Einstein, AI that powers the world's #1 CRM, seamlessly connect to enable an entirely new level of intelligent customer engagement. Join the experts behind this partnership as they discuss how AI can transform your business--now. This is a private breakfast event and your RSVP is required.

Amazon's digital brain Alexa is very skillful, now at more than 12,000 third-party capabilities, but it can sometimes be difficult to wade through them all and discover new skills, and the platform lacks a central voice search to help users find the right skill for a particular task. Paul Allen's Allen Institute for Artificial Intelligence wanted to fill that void, and it built a voice-activated search for Alexa skills. But one problem: Amazon said no. Here is the answer the Allen Institute team got: "Thank you for the recent submission of your skill, 'Skill Search'. Unfortunately, your skill has not been published on Amazon Alexa. We don't allow skills that recommend skills to customers at this time. We will contact you if this feature becomes available."