Safety on a construction site is the most important concern of any construction effort. The dangers and risks associated are very real, with workplace injuries being proportionally higher than in any other private industry. With this unconscionable number in mind, decision-makers in the construction industry must explore new tools and strategies for mitigating risks. Fortunately, the rapid development of artificial intelligence (AI) tools is allowing for safer sites. Here, we'll explore these advancements in AI and how they are revolutionizing the safety and security of construction sites to create a more desirable working environment for all employees.

Ten years ago, I co-wrote and sold a comedy film script to 20th Century Fox. Called "The Lose," the elevator pitch was "The Fugitive meets Harold & Kumar set in Southeast Asia." Fox ended up shelving the project, but I always cherished the experience. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Businesses across industries are increasingly using artificial intelligence in a variety of ways to improve their internal operations, from handling repetitive tasks to interpreting data to streamlining overall processes (and more). For many companies, however, the primary benefit of AI is its potential to help them better interact with customers. Many consumers may think companies use AI simply for their own ends--for example, most of us are aware that AI plays a role in the ads we see online. However, many businesses leverage AI's learning abilities to try to make interactions and transactions faster and easier for the customers they serve, thereby improving the overall customer experience. Here, 12 members of Forbes Technology Council share ways AI is transforming and improving businesses' interactions with their customers.

This paper introduces a class of objects called decision rules that map infinite sequences of alternatives to a decision space. These objects can be used to model situations where a decision maker encounters alternatives in a sequence such as receiving recommendations. Within the class of decision rules, we study natural subclasses: stopping and uniform stopping rules. Our main result establishes the equivalence of these two subclasses of decision rules. Next, we introduce the notion of computability of decision rules using Turing machines and show that computable rules can be implemented using a simpler computational device: a finite automaton. We further show that computability of choice rules -- an important subclass of decision rules -- is implied by their continuity with respect to a natural topology. Finally, we introduce some natural heuristics in this framework and provide their behavioral characterization.

Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue. It formulates coarse-grained and fine-grained intent taxonomies based on the data collected from the TV series Superstore. The dataset consists of 2,224 high-quality samples with text, video, and audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes of speakers in each video segment and achieve an automatic process for speaker annotation. MIntRec is helpful for researchers to mine relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality and model cross-modal interactions by adapting three powerful multimodal fusion methods to build baselines. Extensive experiments show that employing the non-verbal modalities achieves substantial improvements compared with the text-only modality, demonstrating the effectiveness of using multimodal information for intent recognition. The gap between the best-performing methods and humans indicates the challenge and importance of this task for the community. The full dataset and codes are available for use at https://github.com/thuiar/MIntRec.

This article focuses on overlapped speech and gender detection in order to study interactions between women and men in French audiovisual media (Gender Equality Monitoring project). In this application context, we need to automatically segment the speech signal according to speakers gender, and to identify when at least two speakers speak at the same time. We propose to use WavLM model which has the advantage of being pre-trained on a huge amount of speech data, to build an overlapped speech detection (OSD) and a gender detection (GD) systems. In this study, we use two different corpora. The DIHARD III corpus which is well adapted for the OSD task but lack gender information. The ALLIES corpus fits with the project application context. Our best OSD system is a Temporal Convolutional Network (TCN) with WavLM pre-trained features as input, which reaches a new state-of-the-art F1-score performance on DIHARD. A neural GD is trained with WavLM inputs on a gender balanced subset of the French broadcast news ALLIES data, and obtains an accuracy of 97.9%. This work opens new perspectives for human science researchers regarding the differences of representation between women and men in French media.

Artificial Intelligence specialists from PwC and Infosys share their AI Strategy Playbook advice for global business leaders to leverage tech …

It's guiding our internet search results, removing the guesswork from our online shopping experience, assisting us in selecting the next Netflix show and even in the ads we see on Facebook. Algorithms are nothing more than math and code. However, they are created by humans and rely on our data. Since humans are susceptible to error and prejudice, the algorithms they create may have errors too. Depending on who designs them, how they are built, and how they are actually used, these systems may be biased.

His areas of expertise are deep reinforcement learning and information systems design.

The study utilized the proprietary Molecular Stethoscope cf-mRNA RNA-Seq, computational biology and machine learning technology to comprehensively …