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.

Late last week, Google research scientist Fei Xia sat in the center of a bright, open-plan kitchen and typed a command into a laptop connected to a one-armed, wheeled robot resembling a large floor lamp. The robot promptly zoomed over to a nearby countertop, gingerly picked up a bag of multigrain chips with a large plastic pincer, and wheeled over to Xia to offer up a snack. The most impressive thing about that demonstration, held in Google's robotics lab in Mountain View, California, was that no human coder had programmed the robot to understand what to do in response to Xia's command. Its control software had learned how to translate a spoken phrase into a sequence of physical actions using millions of pages of text scraped from the web. That means a person doesn't have to use specific preapproved wording to issue commands, as can be necessary with virtual assistants such as Alexa or Siri.

The idea that machines can replicate or even exceed human thinking has served as the inspiration for advanced computing frameworks – and is now seeing vast investment by countless companies. At the center of this concept are artificial intelligence (AI) and machine learning (ML). These terms are often used synonymously and interchangeably. In reality, AI and ML represent two different things--though they are related. Artificial intelligence can be defined as a computing system's ability to imitate or mimic human thinking and behavior. Machine learning, a subset of AI, refers to a system that learns without being explicitly programmed or directly managed by humans.

MOUNTAIN VIEW, Calif., Aug 16 (Reuters) - Alphabet Inc's (GOOGL.O) Google is combining the eyes and arms of physical robots with the knowledge and conversation skills of virtual chatbots to help its employees fetch soda and chips from breakrooms with ease. The mechanical waiters, shown in action to reporters last week, embody an artificial intelligence breakthrough that paves the way for multipurpose robots as easy to control as ones that perform single, structured tasks such as vacuuming or standing guard. Google robots are not ready for sale. They perform only a few dozen simple actions, and the company has not yet embedded them with the "OK, Google" summoning feature familiar to consumers. While Google says it is pursuing development responsibly, adoption could ultimately stall over concerns such as robots becoming surveillance machines, or being equipped with chat technology that can give offensive responses, as Meta Platforms Inc (META.O) and others have experienced in recent years.