User behavior has been validated to be effective in revealing personalized preferences for commercial recommendations. However, few user-item interactions can be collected for new users, which results in a null space for their interests, i.e., the cold-start dilemma. In this paper, a two-tower framework, namely, the model-agnostic interest learning (MAIL) framework, is proposed to address the cold-start recommendation (CSR) problem for recommender systems. In MAIL, one unique tower is constructed to tackle the CSR from a zero-shot view, and the other tower focuses on the general ranking task. Specifically, the zero-shot tower first performs cross-modal reconstruction with dual auto-encoders to obtain virtual behavior data from highly aligned hidden features for new users; and the ranking tower can then output recommendations for users based on the completed data by the zero-shot tower. Practically, the ranking tower in MAIL is model-agnostic and can be implemented with any embedding-based deep models. Based on the co-training of the two towers, the MAIL presents an end-to-end method for recommender systems that shows an incremental performance improvement. The proposed method has been successfully deployed on the live recommendation system of NetEase Cloud Music to achieve a click-through rate improvement of 13% to 15% for millions of users. Offline experiments on real-world datasets also show its superior performance in CSR. Our code is available.

Retrieval with dense, fully-learned representations has the potential to address some fundamental challenges in sparse retrieval. Dense retrieval systems conduct first-stage retrieval using embedded For example, vocabulary mismatch can be solved if the embeddings representations and simple similarity metrics to match a query accurately capture the information need behind a query and to documents. Its effectiveness depends on encoded embeddings maps it to relevant documents. However, decades of IR research to capture the semantics of queries and documents, a challenging demonstrates that inferring a user's search intent from a concise task due to the shortness and ambiguity of search queries. This and often ambiguous search query is challenging [7]. Even with paper proposes ANCE-PRF, a new query encoder that uses pseudo powerful pre-trained language models, it is unrealistic to expect an relevance feedback (PRF) to improve query representations for encoder to perfectly embed the underlying information need from dense retrieval. ANCE-PRF uses a BERT encoder that consumes a few query terms.

We describe ACE0, a lightweight platform for evaluating the suitability and viability of AI methods for behaviour discovery in multiagent simulations. Specifically, ACE0 was designed to explore AI methods for multi-agent simulations used in operations research studies related to new technologies such as autonomous aircraft. Simulation environments used in production are often high-fidelity, complex, require significant domain knowledge and as a result have high R&D costs. Minimal and lightweight simulation environments can help researchers and engineers evaluate the viability of new AI technologies for behaviour discovery in a more agile and potentially cost effective manner. In this paper we describe the motivation for the development of ACE0. We provide a technical overview of the system architecture, describe a case study of behaviour discovery in the aerospace domain, and provide a qualitative evaluation of the system. The evaluation includes a brief description of collaborative research projects with academic partners, exploring different AI behaviour discovery methods.

Description logics are a powerful tool for describing ontological knowledge bases. That is, they give a factual account of the world in terms of individuals, concepts and relations. In the presence of uncertainty, such factual accounts are not feasible, and a subjective or epistemic approach is required. Aleatoric description logic models uncertainty in the world as aleatoric events, by the roll of the dice, where an agent has subjective beliefs about the bias of these dice. This provides a subjective Bayesian description logic, where propositions and relations are assigned probabilities according to what a rational agent would bet, given a configuration of possible individuals and dice. Aleatoric description logic is shown to generalise the description logic ALC, and can be seen to describe a probability space of interpretations of a restriction of ALC where all roles are functions. Several computational problems are considered and model-checking and consistency checking algorithms are presented. Finally, aleatoric description logic is shown to be able to model learning, where agents are able to condition their beliefs on the bias of dice according to observations.

Embedding approaches have become one of the most pervasive techniques for multi-label classification. However, the training process of embedding methods usually involves a complex quadratic or semidefinite programming problem, or the model may even involve an NP-hard problem. Thus, such methods are prohibitive on large-scale applications. More importantly, much of the literature has already shown that the binary relevance (BR) method is usually good enough for some applications. Unfortunately, BR runs slowly due to its linear dependence on the size of the input data. The goal of this paper is to provide a simple method, yet with provable guarantees, which can achieve competitive performance without a complex training process. To achieve our goal, we provide a simple stochastic sketch strategy for multi-label classification and present theoretical results from both algorithmic and statistical learning perspectives. Our comprehensive empirical studies corroborate our theoretical findings and demonstrate the superiority of the proposed methods.

Scientists at James Cook University have developed artificial intelligence systems that can forecast drought conditions. A trial conducted by Dr Bithin Datta and a team of students has looked at five years' worth of data for the Ross River catchment near Townsville, using AI tools to examine patterns signalling an impending drought. "I was really surprised at some of the results we got; it was quite successful," Dr Datta told AAP. Their AI system was able to accurately predict some of the key indicators of drought, such as soil moisture, salinity, groundwater levels and dam levels, between three and six months ahead of time. Dr Datta said the technology's potential uses could be a game-changer for farmers and for urban water management, with enormous economic benefits.

In this fourth release of our series dedicated to IEEE/RSJ IROS 2020 (International Conference on Intelligent Robots and Systems) original series Real Roboticist, we bring you Peter Corke. He is a distinguished professor of robotic vision at Queensland University of Technology, Director of the QUT Centre for Robotics, and Director of the ARC Centre of Excellence for Robotic Vision. If you've ever studied a robotics or computer vision course, you might have read a classic book: Peter Corke's Robotics, Vision and Control. Moreover, Peter has also released several open-source robotics resources and free courses, all available at his website. If you'd like to hear more about his career in robotics and education, his main challenges and what he learnt from them, and what's his advice for current robotics students, check out his video below.

The "Artificial Intelligence Global Market Report 2021: COVID-19 Growth and Change to 2030" report has been added to ResearchAndMarkets.com's offering. This report provides strategists, marketers and senior management with the critical information they need to assess the global artificial intelligence market. This report focuses on the artificial intelligence market which is experiencing strong growth. The report gives a guide to the artificial intelligence market which will be shaping and changing our lives over the next ten years and beyond, including the markets response to the challenge of the global pandemic. The global artificial intelligence market is expected to grow from $40.17 billion in 2020 to $51.56 billion in 2021 at a compound annual growth rate (CAGR) of 28.4%.

Lamb also said the modular method just makes more sense when scaling. Smart carts can cost retailers between $5,000 and $10,000 per unit and require a lot of maintenance compared to simple shopping carts, which tend to cost retailers less than $100 and will get beat up for years before being replaced. Amazon's walk-out tech is expected to cost retailers upwards of $1 million for installation and hardware, and that doesn't include maintenance over time. Currently, the full system that it's piloting is about $75,000 and includes 10 carts, an imaging station, a server station to run the system, full integration into a customer-facing store and Imagr support over the duration of the pilot. Imagr didn't share how much its current model of trolleys cost versus its modular system, but says it'll be a cheaper endeavor.

Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter thus, a number of approaches have been designed aiming to achieve a healthier and safer online news and media consumption. Automatic systems are able to support humans in detecting such content; yet, a major impediment to their broad adoption is that besides being accurate, the decisions of such systems need also to be interpretable in order to be trusted and widely adopted by users. Since misleading and propagandistic content influences readers through the use of a number of deception techniques, we propose to detect and to show the use of such techniques as a way to offer interpretability. In particular, we define qualitatively descriptive features and we analyze their suitability for detecting deception techniques. We further show that our interpretable features can be easily combined with pre-trained language models, yielding state-of-the-art results.