Intent detection is a key part of any Natural Language Understanding (NLU) system of a conversational assistant. Detecting the correct intent is essential yet difficult for email conversations where multiple directives and intents are present. In such settings, conversation context can become a key disambiguating factor for detecting the user's request from the assistant. One prominent way of incorporating context is modeling past conversation history like task-oriented dialogue models. However, the nature of email conversations (long form) restricts direct usage of the latest advances in task-oriented dialogue models. So in this paper, we provide an effective transfer learning framework (EMToD) that allows the latest development in dialogue models to be adapted for long-form conversations. We show that the proposed EMToD framework improves intent detection performance over pre-trained language models by 45% and over pre-trained dialogue models by 30% for task-oriented email conversations. Additionally, the modular nature of the proposed framework allows plug-and-play for any future developments in both pre-trained language and task-oriented dialogue models.

In 2002, I waited more than ten minutes to download a single song using a 56k dial-up modem. Audio cassettes were still very much in vogue. Fast forward to 2022, and one can now instruct their phone or car to play their favorite tracks using their voice. We can sign into our favorite music streaming service automatically, and it shows us the music and artists that may fit depending on the mood, the time or the occasion. One can automate nearly all electrical systems in their house to operate on their schedule (remind them to get groceries, switch on lights when they enter, etc.).

In the PaLM-SayCan project, Google is combining current robotics technology with advances in large language models. Advances in large-scale AI language models have so far mainly arrived in our digital lives, such as text translation, text and image generation, or behind the scenes, when tech platforms use language AI to moderate the content. In the PaLM-SayCan project, various Google divisions are now combining the company's most advanced large-scale speech model to date with an everyday robot that could one day help in the home – an assistant for the real world. But that will take a while yet. Google unveiled the giant AI language model PaLM in early April, crediting the model with "breakthrough capabilities" in language understanding and, specifically, reasoning. PaLM stands for "Pathways Language Model" – making it a building block in Google's grand Pathways AI strategy for next-generation AI that can efficiently handle thousands or millions of tasks.

Google CEO Sundar Pichai has described the advent of artificial intelligence (AI) as more revolutionary than the discovery of fire or electricity. According to PwC, it has the potential to contribute $15 trillion to the global industry by 2030. I have worked with countless organizations on their AI strategies; however, companies wanting to capitalize on AI face significant barriers regarding the skills and resources needed to put it to use. To bridge this gap, many businesses turn to AI consultancies. These experts have the specialized knowledge and experience required to help companies deploy AI to create more intelligent products and services, improve their internal processes, and ultimately make better use of the data available to them. One of the most frequently asked questions I get from clients is: can you recommend an AI consulting firm to help us turn our AI ambitions into practice?

All beloved by the gay community way before they went mainstream. Similarly, no celebration of a decade of dating apps would be complete without acknowledging that the LGBTQ community ran to a different calendar there, too. The daddy of our contributions to now-ubiquitous swipe culture is the infamous Grindr, launched in 2009 and originally designed to coordinate hookups between likeminded gentlemen tired of chatting on glitchy websites or over discounted cocktails in samey bars. Grindr's runaway success wasn't just down to cutting out various dating-world middlemen, it also fulfilled a genuine need for the LGBTQ community. Marginalised people have always found sanctuary on the internet, scurrying to secluded corners to be better understood by those who shared their distinctive struggles, kinks or slightly nerdy hobbies; all things that might be mocked by the more conventionally attractive bantersauruses roaming our school corridors and haunting the chain pubs on our high streets.

Apple's Machine Learning and AI team transform every Apple product and because we fully integrate hardware and software, we can collaborate to deliver amazing experiences while protecting user data. The Machine Learning Platform and Technology Team is building and improving the on device inference stack. We are looking for a driven and dedicated ML Software Performance Engineer. In this role, you will work with a team to analyze the behavior of current models and products and also directly implement changes to the inference stack. The work that we do is a vital component of how users and developers experience ML on Apple's products. Join this group, and you'll have a direct impact on the performance of ML across Apple's products. C/C or similar languages with willingness to learn.

Automated recommendations can nowadays be found on many e-commerce platforms, and such recommendations can create substantial value for consumers and providers. Often, however, not all recommendable items have the same profit margin, and providers might thus be tempted to promote items that maximize their profit. In the short run, consumers might accept non-optimal recommendations, but they may lose their trust in the long run. Ultimately, this leads to the problem of designing balanced recommendation strategies, which consider both consumer and provider value and lead to sustained business success. This work proposes a simulation framework based on agent-based modeling designed to help providers explore longitudinal dynamics of different recommendation strategies. In our model, consumer agents receive recommendations from providers, and the perceived quality of the recommendations influences the consumers' trust over time. We design several recommendation strategies which either give more weight on provider profit or on consumer utility. Our simulations show that a hybrid strategy that puts more weight on consumer utility but without ignoring profitability considerations leads to the highest cumulative profit in the long run. This hybrid strategy results in a profit increase of about 20 % compared to pure consumer or profit oriented strategies. We also find that social media can reinforce the observed phenomena. In case when consumers heavily rely on social media, the cumulative profit of the best strategy further increases. To ensure reproducibility and foster future research, we publicly share our flexible simulation framework.

Growing attention has been paid in Conversational Recommendation System (CRS), which works as a conversation-based and recommendation task-oriented tool to provide items of interest and explore user preference. However, existing work in CRS fails to explicitly show the reasoning logic to users and the whole CRS still remains a black box. Therefore we propose a novel end-to-end framework named Explanation Generation for Conversational Recommendation (EGCR) based on generating explanations for conversational agents to explain why they make the action. EGCR incorporates user reviews to enhance the item representation and increase the informativeness of the whole conversation. To the best of our knowledge, this is the first framework for explainable conversational recommendation on real-world datasets. Moreover, we evaluate EGCR on one benchmark conversational recommendation datasets and achieve better performance on both recommendation accuracy and conversation quality than other state-of-the art models. Finally, extensive experiments demonstrate that generated explanations are not only having high quality and explainability, but also making CRS more trustworthy. We will make our code available to contribute to the CRS community

In an era of information explosion, recommendation systems play an important role in people's daily life by facilitating content exploration. It is known that user activeness, i.e., number of behaviors, tends to follow a long-tail distribution, where the majority of users are with low activeness. In practice, we observe that tail users suffer from significantly lower-quality recommendation than the head users after joint training. We further identify that a model trained on tail users separately still achieve inferior results due to limited data. Though long-tail distributions are ubiquitous in recommendation systems, improving the recommendation performance on the tail users still remains challenge in both research and industry. Directly applying related methods on long-tail distribution might be at risk of hurting the experience of head users, which is less affordable since a small portion of head users with high activeness contribute a considerate portion of platform revenue. In this paper, we propose a novel approach that significantly improves the recommendation performance of the tail users while achieving at least comparable performance for the head users over the base model. The essence of this approach is a novel Gradient Aggregation technique that learns common knowledge shared by all users into a backbone model, followed by separate plugin prediction networks for the head users and the tail users personalization. As for common knowledge learning, we leverage the backward adjustment from the causality theory for deconfounding the gradient estimation and thus shielding off the backbone training from the confounder, i.e., user activeness. We conduct extensive experiments on two public recommendation benchmark datasets and a large-scale industrial datasets collected from the Alipay platform. Empirical studies validate the rationality and effectiveness of our approach.

Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose ISCON, which implicitly contextualizes sessions. ISCON first generates implicit contexts for sessions by creating a session-item graph, learning graph embeddings, and clustering to assign sessions to contexts. ISCON then trains a session context predictor and uses the predicted contexts' embeddings to enhance the next-item prediction accuracy. Experiments on four datasets show that ISCON has superior next-item prediction accuracy than state-of-the-art models. A case study of ISCON on the Reddit dataset confirms that assigned session contexts are unique and meaningful.