On your screens, in your pockets and one day may even be walking to a home near you. The headlines tend to group together this vast and diverse field into one subject. Robots emerging from the labs, algorithms playing ancient games and winning, AI and its promises are becoming a part of our everyday lives. While all of these instances have some relationship to AI, this is not a monolithic field, but one that has many separate and distinct disciplines. A lot of the times we use the term Artificial intelligence as an all-encompassing umbrella term that covers everything.

A framework is introduced for actively and adaptively solving a sequence of machine learning problems, which are changing in bounded manner from one time step to the next. An algorithm is developed that actively queries the labels of the most informative samples from an unlabeled data pool, and that adapts to the change by utilizing the information acquired in the previous steps. Our analysis shows that the proposed active learning algorithm based on stochastic gradient descent achieves a near-optimal excess risk performance for maximum likelihood estimation. Furthermore, an estimator of the change in the learning problems using the active learning samples is constructed, which provides an adaptive sample size selection rule that guarantees the excess risk is bounded for sufficiently large number of time steps. Experiments with synthetic and real data are presented to validate our algorithm and theoretical results.

Dating and romantic relationships not only play a huge role in our personal lives but also collectively influence and shape society. Today, many romantic partnerships originate from the Internet, signifying the importance of technology and the web in modern dating. In this paper, we present a text-based computational approach for estimating the relationship compatibility of two users on social media. Unlike many previous works that propose reciprocal recommender systems for online dating websites, we devise a distant supervision heuristic to obtain real world couples from social platforms such as Twitter. Our approach, the CoupleNet is an end-to-end deep learning based estimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users. Intuitively, our approach performs both user profiling and match-making within a unified end-to-end framework. CoupleNet utilizes hierarchical recurrent neural models for learning representations of user profiles and subsequently coupled attention mechanisms to fuse information aggregated from two users. To the best of our knowledge, our approach is the first data-driven deep learning approach for our novel relationship recommendation problem. We benchmark our CoupleNet against several machine learning and deep learning baselines. Experimental results show that our approach outperforms all approaches significantly in terms of precision. Qualitative analysis shows that our model is capable of also producing explainable results to users.

Hi Amazon Alexa Fans, We're looking forward to another great meetup at Amazon HQ! Please join us for pizza, drinks and two great presentations. We're also excited to have Peter Nann present'Voice Design - Top 10 Tips to make your VUI sing!' (not literally). In this presentation Peter will go through practical tips that most developers can use to quickly (and often easily) improve the quality of the voice experience for users. Developers, designers and organisations often fall into the trap of thinking that voice design is easy - "We all have decades of experience conversing, right?" and/or "Amazon has made this easy!" The truth is, natural conversation is subtle and hard to codify - even with the best tools - and it's easy to create a voice interface that seems workable (especially to an engineer), but which is down-right unnatural, robotic, even painful and confusing.

I completed my PhD in astrophysics at the University of Sheffield, UK, focusing on studying galaxy formation and evolution. After my PhD I did a number of post doctoral position until I jumped to the private sector early in 2014. Currently, I head the data science team at Simply Business, and insurance company. Previously I have worked in the fashion and retail industries developing recommendation algorithms. In addition I am also an advisor at jaggu.com, a start-up focused on data enrichment and recommendation systems in the retail space.

Although AI hype today exceeds adoption and usage, best-in-class companies are piloting cloud computing AI projects to discover the most relevant use cases which provide the most beneficial financial and business outcomes. Bots are micro-services or apps that can operate on other bots, applications or services in response to event triggers or user requests in many cloud computing applications Often emulating a human, they automate tasks based on predefined rules or via more sophisticated algorithms which may involve machine learning. Robotic process automation (RPA) bots automate mundane yet complex human tasks primarily related to form-driven workflows such as data collection, sorting, filtering, searching and categorizing. Chatbots and virtual assistants (bots with simple natural language query capability) assist with high-volume, low-value interactions with customers, employees, suppliers, partners and other roles. Chatbots and VAs (virtual customer assistants [VCAs]) are predominantly used for customer service and support.

Some people believe that Alexa is listening all the time. Some people believe that Alexa records every word for posterity. Some people believe that having an Amazon Echo increases their risk of being hacked. For a solid overview of Alexa Voice Services (AVS), which is the underlying technology for Amazon's Echo products, please read my essay: Just How Dangerous Is Alexa? It explains how voice-enabled smart speakers work and should help you frame your own thesis about Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) and Natural Language Processing (NLP).

Voice control gadgets – such as Amazon's Alexa, Google's Home or Apple's Homepod – are becoming increasingly popular, but people should pause for thought about advances in machine learning that could lead to applications understanding different emotions in speech. The CEO of Google, Sundar Pichai, recently said that 20% of the company's searches are initiated by voice via mobile phones. And, at the end of 2017, analysis of the US market suggested that a total of 44m Amazon Alexa and Google Home devices had been sold. The technology has increasingly impressive abilities to recognize words, but – as an expert on acoustics – it is clear to me that verbal communication is far more complex. How things are said can be just as important as the words themselves.

The future of audio isn't wired--and Apple knows it. The iPhone's headphone jack, a beloved former hardware accessory, was eliminated with the iPhone 7. "It's clear to me that Apple has forceful, but considered opinions about how the next generation of phones should fit into our lives," the Verge's Nilay Patel wrote in his review of the phone in 2016. "But it's also clear that the iPhone 7 is a transitional step to that vision of the future, not a complete expression of it." We learned more about what that future would encompass when Apple also introduced AirPods, its wireless, Siri-imbued earbuds. However, since the launch of the iPhone 7, Apple has babied those of us not ready commit to the wireless audio way of life by including Lightning adapter–based white earbuds and a Lightning-to-3.5 mm jack dongle in the box with new iPhones.

Artificial Intelligence (AI) is basically intelligence demonstrated outside the human mind, essentially by machines. Machine Learning (ML) is a way of achieving AI and can be defined as the ability of computers to learn using statistical techniques without being specially programmed. Both the terms are symbiotic but also mutually exclusive in their own right with different definitions. We are not unfamiliar with the concept of AI, which has been time and again explored and exploited by popular media. Movies have gone as far as to show us a world dominated by AI-enabled machines and robots, and these movies, more often than not, have ended up portraying negative repercussions of an AI-enabled society. This has more or less shaped up the general feeling revolving around AI in the society.