We are at the cusp of a transition from "learning from data" to "learning what data to learn from" as a central focus of artificial intelligence (AI) research. While the first-order learning problem is not completely solved, large models under unified architectures, such as transformers, have shifted the learning bottleneck from how to effectively train our models to how to effectively acquire and use task-relevant data. This problem, which we frame as exploration, is a universal aspect of learning in open-ended domains, such as the real world. Although the study of exploration in AI is largely limited to the field of reinforcement learning, we argue that exploration is essential to all learning systems, including supervised learning. We propose the problem of generalized exploration to conceptually unify exploration-driven learning between supervised learning and reinforcement learning, allowing us to highlight key similarities across learning settings and open research challenges. Importantly, generalized exploration serves as a necessary objective for maintaining open-ended learning processes, which in continually learning to discover and solve new problems, provides a promising path to more general intelligence.

Modern decision-making systems, from robots to web recommendation engines, are expected to adapt: to user preferences, changing circumstances or even new tasks. Yet, it is still uncommon to deploy a dynamically learning agent (rather than a fixed policy) to a production system, as it's perceived as unsafe. Using historical data to reason about learning algorithms, similar to offline policy evaluation (OPE) applied to fixed policies, could help practitioners evaluate and ultimately deploy such adaptive agents to production. In this work, we formalize offline learner simulation (OLS) for reinforcement learning (RL) and propose a novel evaluation protocol that measures both fidelity and efficiency of the simulation. For environments with complex high-dimensional observations, we propose a semi-parametric approach that leverages recent advances in latent state discovery in order to achieve accurate and efficient offline simulations. In preliminary experiments, we show the advantage of our approach compared to fully non-parametric baselines.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Nvidia and Rescale today announced several enhancements designed to simplify artificial intelligence (AI) development and optimize high-performance computing (HPC) workflows. Nvidia is powering a new AI compute recommendation engine (CRE) to replace a more manually tuned approach. Both developments promise to make it easier to spin up new scientific workloads and operate them more efficiently. This will also apply equally to public cloud service and private cloud infrastructure.

"Hey Alexa, can you make some money?" As reported by The Wall Street Journal, Amazon is aiming to cut costs by slimming down some of its less profitable departments. The big one is Alexa, Amazon's voice assistant software. Despite Alexa's existence inside millions of Echo devices and other smart speakers around the world, the business of building, supporting, and licensing a voice assistant platform has apparently been less profitable than Amazon hoped. Amazon has a couple options here.

AI stocks may be excellent long-term investments. The global artificial intelligence industry is expected to grow from $59.7 billion in 2021 to $422.4 billion by 2028, according to Zion Market Research. Virtually every industry is being disrupted by AI, automation and robotics. Whether it be machine learning, smart applications and appliances, digital assistants or autonomous vehicles, companies that aren't investing in AI products and services risk becoming obsolete. Countless companies stand to benefit from AI, but a handful of stocks have AI and automation as a central part of their businesses.

Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door. Manually setting up these rules for smart devices or applications, however, is time-consuming and inefficient. Rule recommendation systems can automatically suggest rules for users by learning which rules are popular based on those previously deployed (e.g., in others' smart homes). Conventional recommendation formulations require a central server to record the rules used in many users' homes, which compromises their privacy and leaves them vulnerable to attacks on the central server's database of rules. Moreover, these solutions typically leverage generic user-item matrix methods that do not fully exploit the structure of the rule recommendation problem. In this paper, we propose a new rule recommendation system, dubbed as FedRule, to address these challenges. One graph is constructed per user upon the rules s/he is using, and the rule recommendation is formulated as a link prediction task in these graphs. This formulation enables us to design a federated training algorithm that is able to keep users' data private. Extensive experiments corroborate our claims by demonstrating that FedRule has comparable performance as the centralized setting and outperforms conventional solutions.

Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task is the existence of severe selection bias caused by the inherent self-selection behavior of users and the item selection process of systems. Currently, doubly robust (DR) learning approaches achieve the state-of-the-art performance for debiasing CVR prediction. However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice. Motivated by such analysis, we propose a generalized learning framework that not only unifies existing DR methods, but also provides a valuable opportunity to develop a series of new debiasing techniques to accommodate different application scenarios. Based on the framework, we propose two new DR methods, namely DR-BIAS and DR-MSE. DR-BIAS directly controls the bias of DR loss, while DR-MSE balances the bias and variance flexibly, which achieves better generalization performance. In addition, we propose a novel tri-level joint learning optimization method for DR-MSE in CVR prediction, and an efficient training algorithm correspondingly. We conduct extensive experiments on both real-world and semi-synthetic datasets, which validate the effectiveness of our proposed methods.

Artificial intelligence is rapidly transforming all sectors of our society. Whether we realize it or not, every time we do a Google search or ask Siri a question, we're using AI. For better or worse, the same is true about the very character of warfare. This is the reason why the Department of Defense – like its counterparts in China and Russia– is investing billions of dollars to develop and integrate AI into defense systems. It's also the reason why DoD is now embracing initiatives that envision future technologies, including the next phase of AI – artificial general intelligence.

Both social media and the internet as a whole are ever-changing digital landscapes--and that means the companies that advertise on them must advance alongside them. With new platforms, changing regulations and evolving user preferences, advertising is likely to experience a few changes in the coming new year--but to what extent? While no one is certain what changes will take place, nine members of Young Entrepreneur Council share their predictions for the future of advertising below, and explain why 2023 could be the year these new trends will start to take shape. Young Entrepreneur Council members predict how advertising could change in the new year. The future of advertising is AI-powered in 2023.

We use learning to rank, sequence-based recommendation models, content or behavior representation-based algorithms, and collaborative filtering algorithms too. After the customer browses around, searching for hotels in city A, B or C, a substantial portion search for a single hotel and look at the responses/price/content to review. For such "direct hotel search" scenarios, it is prudent to show a few alternatives as well, for customers to compare and contrast them. More important if alternative hotels can bring higher value preposition to the business or to the customer (for ex., better service/value for same price). In this scenario, one could recommend hotels similar to the pivot hotel user has searched.