Artificial Intelligence (AI) is a developing force in the innovation business. Artificial intelligence is becoming the center point at meetings and showing potential across a wide variety of projects, including retail and manufacturing. New items are being integrated with virtual assistants, while chatbots address client inquiries on everything from your online office provider's website to your web hosting service provider's help page. But then, organizations, for example, Google, Microsoft, and Salesforce are integrating AI as an insight layer across their whole tech stack. Indeed, AI is having its moment.

Tokyo (Japan), September 20 (ANI): According to a new study from the University of Tokyo, robotic eyes on autonomous vehicles could improve pedestrian safety. Participants acted out scenarios in virtual reality (VR), deciding whether or not to cross a road in front of a moving vehicle. Participants were able to make safer or more efficient choices when that vehicle was outfitted with robotic eyes that either looked at the pedestrian (registering their presence) or away (not registering their presence). Self-driving vehicles seem to be just around the corner. Whether they'll be delivering packages, ploughing fields or busing kids to school, a lot of research is underway to turn a once futuristic idea into reality.

Anand is the CEO & Product Owner at Amoeboids. The overnight changes the pandemic brought to work life over two years ago would have been a lot more difficult if not for digital tools. Employees appear to agree; according to an Oracle and Workplace Intelligence report, 82% of employees surveyed believed AI can support their careers better than humans, and a whopping 85% wanted technology to help define their future. But what is HR's role in this? Using people analytics in HR functions can bring about positive change--but only if employers cultivate the right approach for their organization.

Artificial intelligence has become a significant force in technology and innovation, drastically altering various industries. Startups worldwide are utilizing AI to address issues and improve the quality of our daily lives. This article covers some of the most intriguing AI-based startups founded in South Korea. As a result of their belief that software stacks placed between their silicon and algorithms may provide adequate abstract layers, AI chip engineers frequently ignore the requirements and more advanced deep learning features. Rebellions Inc. is creating AI accelerators by bridging the gap between deep learning algorithms and underlying silicon architectures. They re-architect AI processors to incorporate complex deep learning features through silicon-dedicated DL kernels, pushing algorithm bounds to use silicon resources better.

This paper tackles recipe generation from unsegmented cooking videos, a task that requires agents to (1) extract key events in completing the dish and (2) generate sentences for the extracted events. Our task is similar to dense video captioning (DVC), which aims at detecting events thoroughly and generating sentences for them. However, unlike DVC, in recipe generation, recipe story awareness is crucial, and a model should output an appropriate number of key events in the correct order. We analyze the output of the DVC model and observe that although (1) several events are adoptable as a recipe story, (2) the generated sentences for such events are not grounded in the visual content. Based on this, we hypothesize that we can obtain correct recipes by selecting oracle events from the output events of the DVC model and re-generating sentences for them. To achieve this, we propose a novel transformer-based joint approach of training event selector and sentence generator for selecting oracle events from the outputs of the DVC model and generating grounded sentences for the events, respectively. In addition, we extend the model by including ingredients to generate more accurate recipes. The experimental results show that the proposed method outperforms state-of-the-art DVC models. We also confirm that, by modeling the recipe in a story-aware manner, the proposed model output the appropriate number of events in the correct order.

As both legged robots and embedded compute have become more capable, researchers have started to focus on field deployment of these robots. Robust autonomy in unstructured environments requires perception of the world around the robot in order to avoid hazards. However, incorporating perception online while maintaining agile motion is more challenging for legged robots than other mobile robots due to the complex planners and controllers required to handle the dynamics of locomotion. This report will compare three recent approaches for perceptive locomotion and discuss the different ways in which vision can be used to enable legged autonomy.

Deep learning is playing a vital role in every field which involves data. It has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using traditional machine learning techniques in the past. In this study we focused on classification of protein sequences with deep learning techniques. The study of amino acid sequence is vital in life sciences. We used different word embedding techniques from Natural Language processing to represent the amino acid sequence as vectors. Our main goal was to classify sequences to four group of classes, that are DNA, RNA, Protein and hybrid. After several tests we have achieved almost 99% of train and test accuracy. We have experimented on CNN, LSTM, Bidirectional LSTM, and GRU.

The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict households' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land-use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.

Data-free Knowledge Distillation (DFKD) has attracted attention recently thanks to its appealing capability of transferring knowledge from a teacher network to a student network without using training data. The main idea is to use a generator to synthesize data for training the student. As the generator gets updated, the distribution of synthetic data will change. Such distribution shift could be large if the generator and the student are trained adversarially, causing the student to forget the knowledge it acquired at previous steps. To alleviate this problem, we propose a simple yet effective method called Momentum Adversarial Distillation (MAD) which maintains an exponential moving average (EMA) copy of the generator and uses synthetic samples from both the generator and the EMA generator to train the student. Since the EMA generator can be considered as an ensemble of the generator's old versions and often undergoes a smaller change in updates compared to the generator, training on its synthetic samples can help the student recall the past knowledge and prevent the student from adapting too quickly to new updates of the generator. Our experiments on six benchmark datasets including big datasets like ImageNet and Places365 demonstrate the superior performance of MAD over competing methods for handling the large distribution shift problem. Our method also compares favorably to existing DFKD methods and even achieves state-of-the-art results in some cases.

As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites. In the past few decades, the rapid developments of recommender systems have significantly benefited human by creating economic value, saving time and effort, and promoting social good. However, recent studies have found that data-driven recommender systems can pose serious threats to users and society, such as spreading fake news to manipulate public opinion in social media sites, amplifying unfairness toward under-represented groups or individuals in job matching services, or inferring privacy information from recommendation results. Therefore, systems' trustworthiness has been attracting increasing attention from various aspects for mitigating negative impacts caused by recommender systems, so as to enhance the public's trust towards recommender systems techniques. In this survey, we provide a comprehensive overview of Trustworthy Recommender systems (TRec) with a specific focus on six of the most important aspects; namely, Safety & Robustness, Nondiscrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. For each aspect, we summarize the recent related technologies and discuss potential research directions to help achieve trustworthy recommender systems in the future.