Today, most of the large-scale conversational AI agents such as Alexa, Siri, or Google Assistant are built using manually annotated data to train the different components of the system including automatic speech recognition (ASR), natural language understanding (NLU), and entity resolution (ER). Typically, the accuracy of the machine learning models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time con-suming. In this paper, we propose a system that leverages customer/system interaction feedback signals to automate learning without any manual annotation. Users of these systems tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results.

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks.

Conversational Recommendation Systems (CRSs) have recently started to leverage pretrained language models (LM) such as BERT for their ability to semantically interpret a wide range of preference statement variations. However, pretrained LMs are well-known to be prone to intrinsic biases in their training data, which may be exacerbated by biases embedded in domain-specific language data(e.g., user reviews) used to fine-tune LMs for CRSs. We study a recently introduced LM-driven recommendation backbone (termed LMRec) of a CRS to investigate how unintended bias i.e., language variations such as name references or indirect indicators of sexual orientation or location that should not affect recommendations manifests in significantly shifted price and category distributions of restaurant recommendations. The alarming results we observe strongly indicate that LMRec has learned to reinforce harmful stereotypes through its recommendations. For example, offhand mention of names associated with the black community significantly lowers the price distribution of recommended restaurants, while offhand mentions of common male-associated names lead to an increase in recommended alcohol-serving establishments. These and many related results presented in this work raise a red flag that advances in the language handling capability of LM-drivenCRSs do not come without significant challenges related to mitigating unintended bias in future deployed CRS assistants with a potential reach of hundreds of millions of end-users.

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.