Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence. We trained and tested the state-of-the-art neural machine translation (NMT) models on a silver standard training dataset and a gold standard testing dataset, respectively. The experimental results showed that the Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed Bidirectional Encoder Representations from Transformers (BERT)-based NMT models. We also verified the effectiveness of NMT models in translating health illiterate languages by comparing the ratio of health illiterate language in the sentence. The proposed NMT models were able to identify the correct complicated words and simplify into layman language while at the same time the models suffer from sentence completeness, fluency, readability, and have difficulty in translating certain medical terms.

Noisy labels in large E-commerce product data (i.e., product items are placed into incorrect categories) are a critical issue for product categorization task because they are unavoidable, non-trivial to remove and degrade prediction performance significantly. Training a product title classification model which is robust to noisy labels in the data is very important to make product classification applications more practical. In this paper, we study the impact of instance-dependent noise to performance of product title classification by comparing our data denoising algorithm and different noise-resistance training algorithms which were designed to prevent a classifier model from over-fitting to noise. We develop a simple yet effective Deep Neural Network for product title classification to use as a base classifier. Along with recent methods of stimulating instance-dependent noise, we propose a novel noise stimulation algorithm based on product title similarity. Our experiments cover multiple datasets, various noise methods and different training solutions. Results uncover the limit of classification task when noise rate is not negligible and data distribution is highly skewed.

Because of this non-linearity, their resolution requires more complex models often called "black boxes" because of their low explicability. In our research project, we aim to design a method to predict mobility information such as users' transport mode in real time from heterogeneous data (e.g., mobile phone data, smartphone sensors, etc.). This method must adapt quickly in a dynamic system where new transport modes and perturbations (e.g., changes in speed limits, COVID-19, etc.) may appear. Bringing up ever larger data streams requires the adoption of online learning techniques in which the model is updated with each new labeled point. Machine learning on dynamic systems (i.e., in which the behavior of individuals, the available sensors and the classes can evolve continuously) is one of the main motivations behind the design of Multi-Agent Systems (MAS). Recent approaches propose to transform a machine learning problem into a problem of cooperation between agents in order to reduce its complexity and to allow the system to adapt to the evolutions of the individuals (Capera et al., 2003). In this paper, we propose to use this collaborative approach to design an algorithm capable of solving supervised classification problems, some of which are non-linear, using linear classification models embedded in a multi-agent structure.

Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for causal inference. Bayesian Causal Forest (BCF) is a recent method that has been documented to perform well on data generating processes with strong confounding of the sort that is plausible in many applications. This paper develops a novel algorithm for fitting the BCF model, which is more efficient than the previously available Gibbs sampler. The new algorithm can be used to initialize independent chains of the existing Gibbs sampler leading to better posterior exploration and coverage of the associated interval estimates in simulation studies. The new algorithm is compared to related approaches via simulation studies as well as an empirical analysis.

In the law, sometimes there is a need to craft a somewhat fictional aspect for purposes of allowing the wheels of justice to spin freely and not get unduly gummed up. That's where legal fiction can handily come to play. Per the definition of the Cornell Law School's Legal Information Institute (LII), a legal fiction is formally denoted as "an assumption and acceptance of something as fact by a court, although it might not be, so as to allow a rule to operate or be applied in a manner that differs from its original purpose while leaving the letter of the law unchanged." This is done ostensibly in the pursuit of justice, but for which can also be more modestly employed in the interests of convenience or for other jurisprudential benefits. I am reminding you about the nature of legal fiction to provide a bit of a potential surprise or some might say a mind-bending bombshell about a loosely proposed legal fiction regarding AI. Some experts suggest that we might need to concoct a legal fiction associated with ascribing a form of legal personhood to AI systems.

Organizations are increasingly adopting AI as a way to enable data-driven decision making, and as a great source of automated predictions that will potentially generate interesting savings or new sources of revenue. Even our personal devices such as smartphones or voice assistants are already leveraging AI technologies. However, the level of AI maturity within the companies varies a lot, as well as the needs for AI-savvy professionals. Reality is that not everyone needs to be an AI expert or a data scientist. Companies need other kinds of profiles for which at least AI knowledge is required, such as product managers or top executives managing innovation initiatives. This course is designed to give you an introduction to the amazing world of Artificial Intelligence.

If you are ready to join a leader in the Retail and Technology space, please apply and Randstad will be more than happy to assist in helping you land your next role.

The graph represents a network of 1,430 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 01 February 2022 at 22:57 UTC. The requested start date was Tuesday, 01 February 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 3-day, 6-hour, 55-minute period from Friday, 28 January 2022 at 18:04 UTC to Tuesday, 01 February 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Luckily, there are several ways to manage school finances effectively. Educational institutions can adopt methods that are most suited to their requirements. Aspects like demographics, geographical location, etc., play a role in determining which of the below ways are more rewarding and useful to schools and colleges. It might seem counter-intuitive initially as you have to invest in new technology. However, choosing the right software and tools will reduce the need for working capital and minimize the use of resources over time.

HAI Fellowship Programs offer opportunities to explore topics, conduct research, and collaborate across disciplines related to AI technologies, applications, or impact. The Institute for Human-Centered Artificial Intelligence (HAI) offers a 2-quarter program for Stanford Graduate Students. The goal of this program is to encourage interdisciplinary research conversations, facilitate new collaborations, and grow the HAI community of graduate scholars who are working in the area of AI, broadly defined. HAI is seeking graduate students to participate in this program. We would like to ensure the cohort is well-rounded across disciplines.