Transformer benefits from the high parallelization of attention networks in fast training, but it still suffers from slow decoding partially due to the linear dependency O(m) of the decoder self-attention on previous target words at inference. In this paper, we propose a generalized average attention network (AAN+) aiming at speeding up decoding by reducing the dependency from O(m) to O(1). We find that the learned self-attention weights in the decoder follow some patterns which can be approximated via a dynamic structure. Based on this insight, we develop AAN+, extending our previously proposed average attention (Zhang et al., 2018a, AAN) to support more general position- and content-based attention patterns. AAN+ only requires to maintain a small constant number of hidden states during decoding, ensuring its O(1) dependency. We apply AAN+ as a drop-in replacement of the decoder selfattention and conduct experiments on machine translation (with diverse language pairs), table-to-text generation and document summarization. With masking tricks and dynamic programming, AAN+ enables Transformer to decode sentences around 20% faster without largely compromising in the training speed and the generation performance. Our results further reveal the importance of the localness (neighboring words) in AAN+ and its capability in modeling long-range dependency.

What we usually think of as Artificial Intelligence (AI) today, when we see human-like robots and holograms in our fiction, talking and acting like …

Reddit is a top-rated social media platform among Millenials and Gen Z. This is a result of its transparency and user-friendliness. It offers customers a smooth, ad-free experience while holding some of the best content available. Let's look at some of the top AI subreddits to find the best content on the latest advancements in Artificial Intelligence. With over 167k members, one can find the latest news, examples of AI in practice, and discussions and questions from those working on or studying it.

Artificial intelligence (AI) may be opening up new opportunities and markets for businesses of all sizes, but for a disparate group of hackers, this has provided the opportunity to deceive machine learning (ML) systems through a process called data poisoning. And these attacks are being carried out unnoticed every day, say experts, and this is not only losing potential income for businesses, but it is also infecting machine learning systems that go on to reinfect those ML models that rely on user input for ongoing training. McKinsey puts a US$10 trillion–US$15 trillion value on the potential global impact of AI-ML technologies and says early leaders in the field are already seeing 250% increase in five-year total shareholder returns. But when McKinsey asked more than 1,000 executives about their digital transformation work, 72% of organisations surveyed said they have not successfully scaled. Even hackers just starting out on their dark arts find data poisoning attacks relatively easy to perform because creating "polluted" data can often be done without any great knowledge of the system to be influenced.

No financial terms of the acquisition were disclosed. In an increasingly competitive ad tech environment, the acquisition will further advance Samba TV to measure a greater immersive viewing experience from both linear and streaming. For example, with the acquisition, Samba TV will be able to identify and analyze additional on-screen content including brands logos, products and people in real-time. The capabilities also include product placement, logos as well as integrated ads. The expectation is to gain a greater awareness for marketers into the value of its connected TV advertising investments in a non-intrusive manner.

Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entities. This research introduces an ask-to-generate approach that automatically generates NER datasets by asking questions in simple natural language to an open-domain question answering system (e.g., "Which disease?"). Despite using fewer in-domain resources, our models, solely trained on the generated datasets, largely outperform strong low-resource models by an average F1 score of 19.4 for six popular NER benchmarks. Furthermore, our models provide competitive performance with rich-resource models that additionally leverage in-domain dictionaries provided by domain experts. In few-shot NER, we outperform the previous best model by an F1 score of 5.2 on three benchmarks and achieve new state-of-the-art performance.

Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.

Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions. While fact-checking is a hard task in general, in many cases, false statements can be easily debunked based on analytics over tables with reliable information. Hence, table-based fact verification has recently emerged as an important and growing research area. Yet, progress has been limited due to the lack of datasets that can be used to pre-train language models (LMs) to be aware of common table operations, such as aggregating a column or comparing tuples. To bridge this gap, in this paper we introduce PASTA, a novel state-of-the-art framework for table-based fact verification via pre-training with synthesized sentence-table cloze questions. In particular, we design six types of common sentence-table cloze tasks, including Filter, Aggregation, Superlative, Comparative, Ordinal, and Unique, based on which we synthesize a large corpus consisting of 1.2 million sentence-table pairs from WikiTables. PASTA uses a recent pre-trained LM, DeBERTaV3, and further pretrains it on our corpus. Our experimental results show that PASTA achieves new state-of-the-art performance on two table-based fact verification benchmarks: TabFact and SEM-TAB-FACTS. In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4.7 points (85.6% vs. 80.9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1.5 points (90.6% vs. 92.1%).

In the ecommerce world, digital images are everything. A new platform is using artificial intelligence (AI) to give brands the ability to evaluate visual content "through the eyes" of their target audiences in real time. Vizit, a provider of image analytics software for global brands and retailers, uses the organic interactions millions of consumers have with online commercial imagery to generate new AI-powered models of their visual preferences, called Vizit Audience Lenses. The goal is to ensure the images a brand uses for representing its products online are effective at capturing the desired audience's attention and triggering sales conversions. Vizit is the latest entrant in the crowded digital shelf space, which refers to wherever products are made visible and available for online purchase.

A robot equipped with artificial intelligence is seen at the AI Xperience Centre at the Vrije Universiteit Brussel in Brussels.