Question answering over mixed sources, like text and tables, has been advanced by verbalizing all contents and encoding it with a language model. A prominent case of such heterogeneous data is personal information: user devices log vast amounts of data every day, such as calendar entries, workout statistics, shopping records, streaming history, and more. Information needs range from simple look-ups to queries of analytical nature. The challenge is to provide humans with convenient access with small footprint, so that all personal data stays on the user devices. We present ReQAP, a novel method that creates an executable operator tree for a given question, via recursive decomposition. Operators are designed to enable seamless integration of structured and unstructured sources, and the execution of the operator tree yields a traceable answer. We further release the PerQA benchmark, with persona-based data and questions, covering a diverse spectrum of realistic user needs.

The most exciting moment of any machine learning system is when you get to deploy your model, but deploying becomes hard due to statistical issues such as "when past model performance is no more guaranteed for future and model performance degrade over a period of time due to changes of data when the model is deployed in a cloud with frequent data changes" and system engine such as system demands monitoring the ML system often which is manual in nature and tedious which needs to be handled through automation as much as possible. Now, How to deal with the statistical issue or degrading performance of the model?. How to handle the data changes once the model is deployed? That is where Concept and Data drift comes into the picture. Concept Drift refers to if the desired mapping from x to y changes and it leads to inaccurate predictions due to huge data distribution changes in the productized model.

Customers love voice and chat assistants, the conversational interfaces that turn on the lights, help home chefs cook an egg to perfection, and make it easy for consumers to research and buy goods online. However, while customers are already building strong relationships with these conversational assistants, retailers are still learning how to best use conversational bots to drive engagement and strengthen their customer relationships. Nonetheless, these conversational assistants represent a fantastic opportunity for retailers to humanize their interactions with customers at scale, as long as it's done with proper understanding of what it takes to engage with customers and how to deploy voice and chat to drive growth and return in 2020. Conversational interfaces fall into two categories: voice and chat. Voice assistants are mediums that can be accessed through voice commands on a smart speaker or smartphone application. Examples include Google Home and Google Assistant, Amazon Alexa, Apple Siri, and Microsoft Cortana.

AI and machine learning are changing global consumption habits, and companies are playing catch-up. Good salespersonship is a species of street smarts. It's about quickly sizing up your customers and pitching your wares in terms that reverberate with their unspoken needs and desires. As AI and machine learning increasingly intersect with e-commerce, these priceless human skills are finding algorithmic analogues – not just at point of sale, but throughout the customer journey. The results will be familiar to online shoppers everywhere.

A common theme among some of the most notable advances and new devices was the integration of artificial intelligence in smart and innovative ways. Despite a handful of flubs, AI-powered technologies still helped make the world a little smarter, kinder, and more innovative this year. Here are some of the moments when AI really shone in 2017. Earlier this month, NASA announced it used machine learning to discover two new planets. Researchers used old data from the Kepler space telescope to locate the two new additions to our galaxy. This wasn't the first time researchers applied AI to sift through the massive amount of data NASA's telescopes collect, but it is a promising example of how neural networks can find even some of the weakest signs of distant worlds. Thanks to AI, we have now discovered a planetary system that ties our solar system in the number of planets it has, which brings us one step closer to discovering more of the mysteries the giant void around us contains.

The little Echo Dot sitting calmly next to my bed never ceases to surprise the futurist in me. I use it for mundane daily tasks like setting alarms and not so mundane tasks like ten minute meditations and listening to the sounds of a babbling brook while easing in to a deep sleep. Its a very interesting product and I wanted to dig deeper and analyze its strategic value to Amazon and explore how transformative it could be to our daily lives. In this essay I hope to present my analysis with some facts, assumptions and hypotheses. To understand why Alexa is such a big deal its important for us to dive deep in to Amazon's core revenue drivers and its overall business model.