Advances in voice-controlled assistants paved the way into the consumer market. For professional or industrial use, the capabilities of such assistants are too limited or too time-consuming to implement due to the higher complexity of data, possible AI-based operations, and requests. In the light of these deficits, this paper presents Insightful Assistant---a pipeline concept based on a novel operation graph representation resulting from the intents detected. Using a predefined set of semantically annotated (executable) functions, each node of the operation graph is assigned to a function for execution. Besides basic operations, such functions can contain artificial intelligence (AI) based operations (e.g., anomaly detection). The result is then visualized to the user according to type and extracted user preferences in an automated way. We further collected a unique crowd-sourced set of 869 requests, each with four different variants expected visualization, for an industrial dataset. The evaluation of our proof-of-concept prototype on this dataset shows its feasibility: it achieves an accuracy of up to 95.0% (74.5%) for simple (complex) request detection with different variants and a top3-accuracy up to 95.4% for data-/user-adaptive visualization.

The market for artificial intelligence has exploded over the last decade. While platforms such as Amazon's Alexa, Apple's Siri, Google's Assistant, and Microsoft's Cortana have dominated the market, these companies have not been the source of that growth. Instead, stiff competition between the biggest corporations has led to aggressive acquisitions of AI startups. According to data compiled by CB Insights, there have been a total of 635 artificial intelligence acquisitions between 2010 and September 2019. Those purchases also increased six times over from 2013 to 2018, with 166 acquisitions in 2018 alone--a 38 percent increase year over year.

The market for artificial intelligence has exploded over the last decade. While platforms such as Amazon's Alexa, Apple's Siri, Google's Assistant, and Microsoft's Cortana have dominated the market, these companies have not been the source of that growth. Instead, stiff competition between the biggest corporations has led to aggressive acquisitions of AI startups. According to data compiled by CB Insights, there have been a total of 635 artificial intelligence acquisitions between 2010 and September 2019. Those purchases also increased six times over from 2013 to 2018, with 166 acquisitions in 2018 alone--a 38 percent increase year over year.

This speaker pumps out a lot of sound for its size, and it manages to balance portability and durability surprisingly well. The swivel handle on top makes it very easy to carry, and with its water-resistant design, the Portable Smart Speaker is at home at the beach or poolside. The Portable Smart Speaker is water-resistant. Here are the Portable Smart Speaker's Specs: The Portable Smart Speaker's round design allows for a 360-degree sound field, which is perfect for great sound in pretty much any space. Despite its compact size, this speaker puts out a surprising amount of volume, but it does begin to lose a little clarity as it approaches its max.

Although the revealing of Google's Recommendation AI has already been done during the company's Cloud Next event in 2019, Google is now launching its beta version for its customers. A fully managed service -- Google's Recommendation AI -- targeting retail businesses, has been designed to help in delivering personalised recommendation of products to customers at scale. According to the blog post written by the product manager, Pallav Mehta, the move has been taken in sync with the ongoing shift of retail companies towards data-driven strategies and the increasing customer demand. To keep up their relevance in this competitive scenario, the retail companies now require to provide an ultimate personalised experience to customers. And one such way of enhancing the experience is by recommending them products matching their interest, preferences and need.

Kevin Gray: What are recommender systems? Anna Farzindar: When you search for a product on Amazon, the algorithm suggests other items with the note "Recommended for you, Kevin" or "Customers who bought this item also bought…" Recommender systems predict the preference of the user for these items, which could be in form of a rating or response. When more data becomes available for a customer profile, the recommendations become more accurate. There are a variety of applications for recommendations including movies (e.g. Could you give us a brief history of how they came about?

From smart speakers to e-readers and voice-controlled devices, Amazon has dozens of models that are dominating the main-stream market. If you're trying to decide which smart device works for you and your home, then we have highlighted some of the best Amazon devices currently on sale on the mega-site now that are worth taking advantage of. This week, you can take advantage of top-tech savings including 54 per cent off the Echo Plus, the Kindle Paperwhite for under £100 and the hugely popular Echo Dot for only £29.99. Whether you're looking to raise your home's IQ with the help of Alexa or simply looking for a good way to surf the web and watch your favourite shows, then there is plenty of choice out there. Here are five of the best device deals on Amazon.

It is never easy to predict what society and technology will look like in the coming decades, but one futurist used the imaginations of children to come up with ideas. Futurist Brian David Johnson spoke to kids aged 8-13 as part of a study into their vision of life in the 2050s for the Institution of Engineering and Technology (IET). 'The current generation of young minds is nothing like we've seen before', Johnson explained, saying they were born and grew up constantly connected. Every child he spoke to was optimistic about the future, with many showing'jump-out-of-their-seat' levels of excitement about'what is to come' as they reach adulthood. He used the conversations he had with the children and their parents to formulate predictions about the future of smart homes, food and personal virtual assistants. Futurist Brian David Johnson spoke to children aged 8-13 as part of a study into their vision of life in the 2050s for the Institution of Engineering and Technology (IET).

Session-based recommendation has received growing attention recently due to the increasing privacy concern. Despite the recent success of neural session-based recommenders, they are typically developed in an offline manner using a static dataset. However, recommendation requires continual adaptation to take into account new and obsolete items and users, and requires "continual learning" in real-life applications. In this case, the recommender is updated continually and periodically with new data that arrives in each update cycle, and the updated model needs to provide recommendations for user activities before the next model update. A major challenge for continual learning with neural models is catastrophic forgetting, in which a continually trained model forgets user preference patterns it has learned before. To deal with this challenge, we propose a method called Adaptively Distilled Exemplar Replay (ADER) by periodically replaying previous training samples (i.e., exemplars) to the current model with an adaptive distillation loss. Experiments are conducted based on the state-of-the-art SASRec model using two widely used datasets to benchmark ADER with several well-known continual learning techniques. We empirically demonstrate that ADER consistently outperforms other baselines, and it even outperforms the method using all historical data at every update cycle. This result reveals that ADER is a promising solution to mitigate the catastrophic forgetting issue towards building more realistic and scalable session-based recommenders.

Natural Language Processing is among the hottest topic in the field of data science. Companies are putting tons of money into research in this field. Everyone is trying to understand Natural Language Processing and its applications to make a career around it. Every business out there wants to integrate it into their business somehow. Because just in a few years' time span, natural language processing has evolved into something so powerful and impactful, which no one could have imagined.