Only Homo sapiens, of all the descendants of Homo erectus, survived on earth whereas other species such as homo soloensis, homo denisova, Homo neanderthalensis, Homo floresiensis faded away more than 40,000 years ago. What advantages did Homo sapiens possess that helped them to flourish while other species are extinct? Apparently a cognitive revolution (according to Prof. Yuval Harari in his famous book Sapiens) triggered by some kind of genetic mutation provided Homo Species with more cerebral power and thus they acquired an ability not possessed by any other species – ability to imagine things that did not exist. This ability helped them to invent things including powerful communicative languages, religion, tools and more. Does current cognitive revolution ushered under various nomenclatures such as artificial intelligence, cognitive computing etc provide more powers, this time to machines and bring in unprecedented progress to human life?

Narrow artificial intelligence, or non-sentient AI, is now part of of our everyday lives. Amazon (NASDAQ:AMZN) recommends additional products based on previous purchases, Google (NASDAQ:GOOG) fills in search queries, Siri responds to your voice. More often than not, we trust the information we receive from these algorithms. But are we prepared to do the same when it comes to our health? Artificial intelligence is revolutionizing technology, but also medicine.

In 1990 Tim Bernes-Lee began writing the World Wide Web, an innovation that changed the world as we knew it forever. But as a five-year-old, my mind was being blown by a dancing flower. My brothers, friends and dog were all mesemerised by this incredible new toy that came to life and "danced" (jiggled) when music was played – it genuinely felt like sci-fi magic. Almost thirty years later and our dancing flowers have evolved into AI powered devices like Google Home, Amazon Echo and the new Apple HomePod - able to turn on lights, order pizzas and even tell us jokes. We've replaced dancing flowers in pots with talking bots that look like plant pots.

Before voice assistants like Apple's Siri and Amazon's Alexa gained relevancy, Will Underwood and Jarrod Wolf were researching semantic search and realized a lot could be done with natural-language search by taking the words humans use on a daily basis and making them comprehensible to a machine. By analyzing reviews and ratings, AddStructure can better help users find what they're looking for based on what they're researching or requesting at any given moment, along with what they've asked about in the past. While many agencies are beginning to build voice skills for Amazon and Google devices, building custom products provides more autonomy than they have with Alexa's parent company, Amazon. In the third quarter of this year, Strike Social will start offering a new AI service to help clients buy digital media in a way that the company's co-founder, Patrick McKenna, says will make buying across YouTube and other platforms faster, cheaper and smarter.

Nikolas Badminton is a researcher and futurist based in Vancouver, Canada, and provides opinions about humanity, the future of work and life with technology. His opinions and thoughts on the future can be seen at nikolasbadminton.com. Money makes the world go round, or so they say. Payments, investments, insurance and billions of transactions are the beating heart of a fractal economy, which echoes the messy complexity of natural systems, such as the growth of living organisms and the bouncing of atoms. Financial systems are larger than the sum of their parts.

Your message has been sent. There was an error emailing this page. With their rainbow of hues and myriad party tricks, color-tunable LEDs get all the press in the world of smart lighting. It's fun stuff, but the reality is that most of us will rarely find much of a need to turn all the lights in the house blue or red--unless it's time to celebrate our team winning the World Series. Even then, you'll probably want to turn them all back to white after the celebration.

The announcement of Apple's HomePod smart speaker at last week's WWDC event marks the latest entrant to a growing market for voice assistants, currently led by tech giants Amazon and Google. Each new launch brings the promise of a slicker user experience and a more efficient use of our time. It might take several iterations of these voice assistants to integrate seamlessly into our daily routines, but the first stage for any company looking to take advantage of this new tech will be to ask: How do users feel about it all? We recently partnered with Mindshare Futures and J. Walter Thompson Innovation Group for their Speak Easy research project to answer that very question. Our portion of the study involved observing 102 smartphone users as they carried out a selection of tasks using Amazon's Alexa, Google Assistant, text-based search, and questions directed to a real person.

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques.

Artificial Intelligence (AI) may be out of the sci-fi closet but the adoption within MarTech industry is still very much in its nascent stage. With the advent of wireless and virtual technologies in last two decades, businesses have evolved at a breakneck pace. And now, the biggest force pushing that momentum is the refined combination of AI and Machine Learning (AI/ML). While the first half of the decade focused on leveraging Big Data, AI technology in business, especially in sales and marketing, has taken a major lead with its equitable adoption rate. The promise AI offers – intelligent software running on AI/ML algorithms would perform tasks better, faster and cheaper, is an optimistic opportunity within MarTech.

Companies and developers focused this week on showcasing their latest games and hardware for the upcoming year at E3 2017. However, developer Electronic Arts wants to use artificial intelligence and machine learning to plan out its longer-term future. At a press event Saturday, EA unveiled an internal group dubbed SEED, which is short for its Search For Extraordinary Experiences Division. While details on the new division's goals were initially scant, EA CEO Andrew Wilson highlighted the company's ambitious goals for the group in an interview with Glixel. Read: Apple Siri 2.0 Might Have Machine Learning, Improved AI, Facial Recognition And More Wilson said EA's recent push to make its Frostbite engine the core of its major franchises allowed the studio to start thinking more broadly about what it can do technologically with games.