For decades, thanks to popular sci-fi movies and books, the collective imagination has been frequently struck with the idea of intelligent computers outsmarting and replacing humans. Fortunately, that imaginary scenario hasn't been brought into reality yet. But something else has happened: the emergence of artificial intelligence (AI), particularly cognitive computing. AI has been turning into a significant part of our daily lives. The digital personal assistants, smartphones, self-driving cars, music and movie applications, online shopping sites, and every application that can learn and adapt to human preferences are powered by AI technology.

Recently, a report was released regarding the misuse of companies claiming to use artificial intelligence [29] [30] on their products and services. According to the Verge [29], 40% of European startups claiming to use AI don't use the technology. Last year, TechTalks, also stumbled upon such misuse by companies claiming to use machine learning and advanced artificial intelligence to gather and examine thousands of users' data to enhance user experience in their products and services [2] [33]. Unfortunately, there's still much confusion within the public and the media regarding what genuinely is artificial intelligence [44] and what exactly is machine learning [18]. Often the terms are being used as synonyms.

With machine learning, computer systems can take all the customer data and utilise it. It operates on what's been programmed while also adjusting to new conditions or changes. Algorithms adapt to data, developing behaviours that were not programmed in advance. Learning to read and recognise context means a digital assistant could scan emails and extract the essential information. Inherent in this learning is the ability to make predictions about future customer behaviours. This helps you understand your customers more intimately and not just be responsive, but proactive.

For a long time, technology has been creating opportunities for people with impairments, from automated scooters to cochlear implants. AI will begin to boost such efforts in the upcoming years with new powers and wider access. There is a huge and vast market to tap into -- with over a billion people living with impairments throughout the world. A designer's urge to support his blind friend write more comprehensibly inspired one of the first typewriters. Alexander Graham Bell's had a deaf parent, and it was through his work with the deaf community that the telephone was born.

Recently, Amazon introduced a feature that allowed Alexa to hear certain types of sounds, called Custom Sound Detection. Now, it's adding two new specific Alexa sound detectors for "water running" and "appliance beeping" that can be used to set up routines or reminders. It also rolled a number of other new features for things like prescription refills, ultrasound motion detection and more. It was already possible to have Alexa identify those two specific sounds, but the new update means you won't have to bother training it. With the new features, you can use the Alexa app to send a notification when the washer beeps to indicate your laundry is done.

The company that owns Tinder will pay $441 million to the popular dating app's founders to settle a dispute over the valuation of stock options, documents showed Wednesday. The suit filed in New York in 2018 contended that Tinder owner Match Group, and its then parent firm InterActiveCorp, schemed to dramatically drive down the value of stock options and then eliminate them altogether. Co-creators Sean Rad, Justin Mateen and Jonathan Badeen alleged Match and IAC relied on bogus figures to arrive at a valuation of $3 billion in 2017 -- when Tinder was actually worth more than four times that. Tinder's owner is paying the app's founders millions to settle a lawsuit Photo: AFP / Aamir QURESHI Created in 2012, Tinder now has more than 10 million paying users who can quickly scroll through possible romantic matches, and then swipe left or right to signal interest. With options on about 20 percent of Tinder's stock, the founders and their early employees felt they had been shortchanged by several billion dollars.

Many disabled people choose to employ their own personal assistants to help them with tasks such as getting out of bed in the morning and supporting them through the day so they can work. There are currently more than 100,0000 vacancies in the social care sector and with more competitive salaries being offered in other sectors this means that some disabled people are now struggling to get the support they need. The BBC's disability correspondent Nikki Fox met 16-year-old twins, Alex and Sam, who are on the search for a personal assistant.

The ubiquity of implicit feedback makes it indispensable for building recommender systems. However, it does not actually reflect the actual satisfaction of users. For example, in E-commerce, a large portion of clicks do not translate to purchases, and many purchases end up with negative reviews. As such, it is of importance to account for the inevitable noises in implicit feedback. However, little work on recommendation has taken the noisy nature of implicit feedback into consideration. In this work, we explore the central theme of denoising implicit feedback for recommender learning, including training and inference. By observing the process of normal recommender training, we find that noisy feedback typically has large loss values in the early stages. Inspired by this observation, we propose a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes the noisy interactions by two paradigms (i.e., Truncated Loss and Reweighted Loss). Furthermore, we consider extra feedback (e.g., rating) as auxiliary signal and propose three strategies to incorporate extra feedback into ADT: finetuning, warm-up training, and colliding inference. We instantiate the two paradigms on the widely used binary cross-entropy loss and test them on three representative recommender models. Extensive experiments on three benchmarks demonstrate that ADT significantly improves the quality of recommendation over normal training without using extra feedback. Besides, the proposed three strategies for using extra feedback largely enhance the denoising ability of ADT.

Recommendation systems are algorithms with an objective to suggest the most relevant information to users, whether that be similar products on Amazon, similar TV shows on Netflix, or similar songs on Spotify. There are two main types of recommendation systems: collaborative filtering and content-based filtering. Recommendation systems are one of the most widely used and most practical data science applications. Not only that, but it also has one of the highest ROIs when it comes to data products. It's estimated that Amazon increased its sales by 29% in 2019, specifically due to its recommendation system. As well, Netflix claimed that its recommendation system was worth a staggering $1 billion in 2016! But what makes it so profitable? As I alluded to earlier, it's about one thing: relevancy. By providing users with more relevant products, shows, or songs, you're ultimately increasing their likelihood to purchase more and/or stay engaged longer.

AI development is one of the fastest growing sectors in technology. Just like the personal assistant AI offered by Apple, Google, and Samsung (among others) for their smart products, there are AI systems aimed at easing some of the stresses that come with managing a development project. While the applications for AI are nearly endless, today we're going to dive into three ways AI will positively influence the construction industry by means of safety, generative design, and efficiency through robots. Active construction sites can be dangerous places for workers, so why not take every measure possible to prevent worksite injuries? While drones are already being used to monitor worksite progress, Skycatch is developing drones with AI "brains" that can identify every object on a worksite and determine if there are any potential safety risks that may not have been noticed on the ground.