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.

In the near future, you'll be able to launch and navigate Android and iOS apps using Alexa voice commands. Today, Amazon released a bunch of new developer tools. The most interesting might be Alexa for Apps, which allows developers to add Alexa functions to their Android and iOS apps. Amazon has tested the tool with companies like TikTok, Uber, Yellow Pages and Sonic. So already, you can ask Alexa to start your TikTok recording or open the Sonic app so you can check the menu.

Salesforce has quietly shuttered Einstein Voice Assistant and Einstein Voice Skills as it shifts focus towards its newly released Salesforce Anywhere app. The Einstein Voice Assistant first launched in beta last year. It's an extension of the company's Einstein Voice platform and allowed users to interact with the Salesforce platform via a mobile app or smart speaker device. Salesforce claimed the AI helper was more advanced than other digital assistants on the market, such as Alexa and Cortana, as it could be taught to recognise a company's specific jargon and acronyms. Einstein Voice Skills, which debuted in beta last November, enabled developers and admins can build custom voice-powered apps for employees to replace any type of manual data entry or manual Salesforce navigation.

The potential for the Internet of Things to lead to distortion in market competition is troubling European Union lawmakers who have today kicked off a sectoral inquiry. They're aiming to gather data from hundreds of companies operating in the smart home and connected device space -- via some 400 questionnaires, sent to companies big and small across Europe, Asia and the US -- using the intel gleaned to feed a public consultation slated for early next year when the Commission will also publish a preliminary report. In a statement on the launch of the sectoral inquiry today, the European Union's competition commissioner, Margrethe Vestager, said the risks to competition and open markets linked to the data collection capabilities of connected devices and voice assistants are clear. The aim of the exercise is therefore to get ahead of any data-fuelled competition risks in the space before they lead to irreversible market distortion. "One of the key issues here is data. Voice assistants and smart devices can collect a vast amount of data about our habits. And there's a risk that big companies could misuse the data collected through such devices, to cement their position in the market against the challenges of competition. They might even use their knowledge of how we access other services to enter the market for those services and take it over," said Vestager.

AI isn't the end-all-be-all when it comes to conversational experiences like Amazon's Alexa. In fact, manual solutions to problems are sometimes superior to automated, AI-driven fixes. That's according to Amazon Alexa AI director of research science Janet Slifka, who spoke during a session today at VentureBeat's Transform 2020 conference. "If someone calls customer service and says something to the effect, 'Alexa doesn't understand me,' in many cases, we can be faster to the customer with a manual fix," Slifka said, describing her team's work in triage. "In some cases, it's almost required when you need words to enter the lexicon. You're probably not going to wait until you rebuilt and deploy a statistical model when a new word gains prominence, like'Brexit.'"