AWS re:Invent is a learning conference hosted by Amazon Web Services for the global cloud computing community. We are super excited to join you at the 10th annual re:Invent to share the latest from AWS leaders and discover more ways to learn and build. Let's celebrate this milestone, which will be offered in person in Las Vegas (November 29-December 3) and in virtual (November 29–December 10) formats. The health and safety of our customers, and partners remains our top priority and you can learn more about it in health measures page. If you haven't already registered, don't forget to register and save your spot at your favorite sessions. The AWS DevOps and Developer Productivity track at re:Invent offers you with sessions that are combination of cultural philosophies, practices, and tools that increase an organization's ability to deliver applications and services at high velocity.

Make your Child a Star in this AI future! We are proud to bring our very successful Artificial Intelligence online Bootcamps once again during this School holidays. More than10000 students and 150 schools have successfully completed our Pre Bootcamps and Bootcamps... Our Bootcamps follow the Creative Computing curriculum recommended by Harvard Graduate School of Education. We focus on developing the Creativity, Imagination, and interest of the child while building Creative Computing Skills.

Data is what powers machine learning solutions. Quality datasets enable training models with the needed detection and classification accuracy, though sometimes the accumulation of sufficient and applicable training data that should be fed into the model is a complex challenge. For instance, to create data-intensive apps human annotators are required to label a huge number of samples, which results in complexity of management and high costs for businesses. In addition to that, there is the difficulty associated with data acquisition related to safety regulations, privacy, or ethical concerns. When we have a limited dataset including only a finite number of samples per class, few-shot learning may be useful.

On a May evening in 1959, C.P. Snow, a popular novelist and former research scientist, gave a lecture before a gathering of dons and students at the University of Cambridge, his alma mater. He called his talk "The Two Cultures and the Scientific Revolution." Snow declared that a gulf of mutual incomprehension divided literary intellectuals and scientists. "The non-scientists have a rooted impression that the scientists are shallowly optimistic, unaware of man's condition," Snow said. "On the other hand, the scientists believe that the literary intellectuals are totally lacking in foresight, peculiarly unconcerned with their brother men, in a deep sense anti-intellectual, anxious to restrict both art and thought to the existential moment." Snow didn't expect much of his talk.

This study examines the use of natural language processing (NLP) models to evaluate whether language patterns used by item writers in a medical licensure exam might contain evidence of biased or stereotypical language. This type of bias in item language choices can be particularly impactful for items in a medical licensure assessment, as it could pose a threat to content validity and defensibility of test score validity evidence. To the best of our knowledge, this is the first attempt using machine learning (ML) and NLP to explore language bias on a large item bank. Using a prediction algorithm trained on clusters of similar item stems, we demonstrate that our approach can be used to review large item banks for potential biased language or stereotypical patient characteristics in clinical science vignettes. The findings may guide the development of methods to address stereotypical language patterns found in test items and enable an efficient updating of those items, if needed, to reflect contemporary norms, thereby improving the evidence to support the validity of the test scores.

Algorithmic decisions made by machine learning models in high-stakes domains may have lasting impacts over time. Unfortunately, naive applications of standard fairness criterion in static settings over temporal domains may lead to delayed and adverse effects. To understand the dynamics of performance disparity, we study a fairness problem in Markov decision processes (MDPs). Specifically, we propose return parity, a fairness notion that requires MDPs from different demographic groups that share the same state and action spaces to achieve approximately the same expected time-discounted rewards. We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies. Motivated by our decomposition theorem, we propose algorithms to mitigate return disparity via learning a shared group policy with state visitation distributional alignment using integral probability metrics. We conduct experiments to corroborate our results, showing that the proposed algorithm can successfully close the disparity gap while maintaining the performance of policies on two real-world recommender system benchmark datasets.

Automated question quality rating (AQQR) aims to evaluate question quality through computational means, thereby addressing emerging challenges in online learnersourced question repositories. Existing methods for AQQR rely solely on explicitly-defined criteria such as readability and word count, while not fully utilising the power of state-of-the-art deep-learning techniques. We propose DeepQR, a novel neural-network model for AQQR that is trained using multiple-choice-question (MCQ) datasets collected from PeerWise, a widely-used learnersourcing platform. Along with designing DeepQR, we investigate models based on explicitly-defined features, or semantic features, or both. We also introduce a self-attention mechanism to capture semantic correlations between MCQ components, and a contrastive-learning approach to acquire question representations using quality ratings. Extensive experiments on datasets collected from eight university-level courses illustrate that DeepQR has superior performance over six comparative models.

Many researchers believe that Artificial Intelligence (AI) has more potential to change higher education than any other technology. The potential and impact of AI on teaching have prompted some colleges and universities to take a closer look at it, accelerating its adoption across campuses. For perspective, the global AI market is projected to reach almost $170 billion by 2025. By 2028, the AI market size is expected to gain momentum by reaching over $360 billion, registering a growth rate of 33.6 percent between 2021 and 2028, according to a research firm Fortune Business Insights' report. The market is mostly segmented into Machine Learning, Natural Language Processing (NLP), image processing, and speech recognition. Other research conducted by Klutka et al. back in 2018 listed the following goals for AI in higher education, all of which are now emerging as reality: AI technology and Machine Learning (ML) can increase the level of education bringing countless benefits to both students and faculty.

Pilot program's main goal to deter drivers from speeding past school buses when stop-arm is out The latest breaking updates, delivered straight to your email inbox. Pilot program's main goal to deter drivers from speeding past school buses when stop-arm is out A pilot program at the Sacramento City Unified School District is hoping to improve student safety through the use of artificial intelligence cameras on school buses. The District has partnered with company BusPatrol to put cameras on five school buses to deter drivers from speeding past buses when their stop-arm is out, which is a violation of law. "We use an AI engine named Ava to be able to automatically detect vehicles that illegally pass stopped school buses," BusPatrol CEO Jean Souliere said. Souliere said the cameras on the side of the buses take pictures of the license plates of cars that keep driving.

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.