With every new technological advancement, there's always someone poking at it from behind the computer screen, trying to find a vulnerability. This isn't new (Antheus biometric data breach, Robinhood data security incident, anyone?), but it is something that the average consumer doesn't think about enough. One of the biggest blind spots most of us have is surrounding the privacy of our data with voice assistants like Siri or Alexa. Technologies like speech-to-text software and conversational artificial intelligence (AI) are steadily increasing in popularity, and this means we need to start considering the privacy implications more seriously. Text-to-speech tools and chatbots are quickly becoming everyday technologies for businesses.

The growing socialisation with female virtual assistants in AI is rapidly diminishing the significance of women to'a gendered female who responds on-demand'. The world's skewed gender bias is no longer gasping news. It is rigidly handcuffed into the social fabric through stereotypes and traditionally upheld norms. Such systems create divisive spaces, both socially and digitally. While these voids are glaringly visible in the socio-political vantage, they remain latent in the emerging futuristic technologies, primarily machine learning with Big data.

The conversational agents is one of the most interested topics in computer science field in the recent decade. Which can be composite from more than one subject in this field, which you need to apply Natural Language Processing Concepts and some Artificial Intelligence Techniques such as Deep Learning methods to make decision about how should be the response. This paper is dedicated to discuss the system architecture for the conversational agent and explain each component in details.

One of the most surprising and fascinating applications of Artificial Intelligence is for sure recommender systems. In a nutshell, a recommender system is a tool that suggests you the next content given what you have already seen and liked. Companies like Spotify, Netflix or Youtube use recommender systems to suggest you the next video or song to watch given what you have already seen or listened to. The idea of build recommender system has surely not been developed yesterday. In 2006 Netflix announced a 1 million dollar reward to the research team able to build the best recommender system possible given some test data.

Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective location recommender systems. In this paper, we present Axolotl (Automated cross Location-network Transfer Learning), a novel method aimed at transferring location preference models learned in a data-rich region to significantly boost the quality of recommendations in a data-scarce region. Axolotl predominantly deploys two channels for information transfer, (1) a meta-learning based procedure learned using location recommendation as well as social predictions, and (2) a lightweight unsupervised cluster-based transfer across users and locations with similar preferences. Both of these work together synergistically to achieve improved accuracy of recommendations in data-scarce regions without any prerequisite of overlapping users and with minimal fine-tuning. We build Axolotl on top of a twin graph-attention neural network model used for capturing the user- and location-conditioned influences in a user-mobility graph for each region. We conduct extensive experiments on 12 user mobility datasets across the U.S., Japan, and Germany, using 3 as source regions and 9 of them (that have much sparsely recorded mobility data) as target regions. Empirically, we show that Axolotl achieves up to 18% better recommendation performance than the existing state-of-the-art methods across all metrics.

Inside my iPhone is a cornucopia of Irish men. "It's currently clear and 25 degrees," Colin Farrell replies when I ask him the weather. "A 7.45am alarm is now off," says Michael Fassbender when I beg him for some extra sleep. "Here's what I found on Google," Domnhall Gleeson cheerily answers when I screech: "I have spilt coffee all over my stovetop – how to clean white shirt and kitchen bench?" I feel like he is negging me – or playing hard to get, perhaps.

Apple is once again honoring regulations requiring it to allow alternative payment options in the App Store, although this one is highly specific. As Reuters reports, Apple confirmed it would comply with orders from the Netherlands' Authority for Consumers and Markets forcing it to allow third-party payment systems in Tinder and other locally-offered dating apps. The regulator determined on December 24th that Apple had abused its market power by requiring its in-app purchasing platform, and gave Apple until January 15th to make the change if it wanted to avoid fines. It contended that allowing third-party options would "compromise the user experience" while posing new privacy and security threats, and reminded developers they (or their payment partners) would be responsible for handling refunds and similar issues. Apple is appealing the ACM's decision.

Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

Artificial intelligence (AI) is starting to be utilized in almost every aspect of the business world, and this article showcases 21 interesting examples and use cases of this. The article is divided into two categories. The first category discusses 10 examples and use cases related to business processes that will be changed by artificial intelligence, and the second covers 10 examples and use cases related to industries being changed by AI. As you read, think about whether there are any examples of how artificial intelligence is being used in business today that you would suggest I add to this list. If you think of any, please feel free to post them in the comments section. Practically every business process will be changed and taken over by artificial intelligence, since AI can be trained to do almost everything that involves a process, and do it better than a human.

AIBrain is a well-known artificial intelligence start-up in Palo Alto for its vision of augmenting human intelligence with AI models. It leverages artificial intelligence strategies to enhance the functionalities of multiple industries, especially sports. AI strategies are spot-on to meet customer satisfaction across the world with their own in-house AI models. Let's explore what AIBrain is focused on providing the world with cutting-edge technologies such as artificial intelligence in Industry 4.0. AIBrain is focused on the sports industry with Sports AI known as Sports AI Virtual Assistant (SAIVA), especially Football AI. It is a collaboration with its sister company known as Turing AI Cultures GmbH, Berlin.