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As general chair of this year's ACM Conference on Knowledge Discovery and Data Mining (KDD), Huzefa Rangwala, a senior manager at the Amazon Machine Learning Solutions Lab, has a broad view of the topics under discussion there. Two of the most prominent, he says, are graph neural networks and fairness in AI. Graphs are data representations that can encode relationships between different data items, and graph neural networks are machine learning models that are useful for knowledge discovery because they can be used to infer graph structures. "Our world is connected in lots of ways, so you'll see graph neural networks find applications in lots of different domains, all the way from social networks and transportation networks to knowledge graphs and drug discovery," Rangwala says. The Amazon Machine Learning Solutions Lab brings the expertise of Amazon scientists and the resources of Amazon Web Services to bear on customers' machine learning problems.

New Delhi: Google has announced to expand content advisories to searches where its AI systems don't have high confidence in the overall quality of the results available for the search. Pandu Nayak, Google Fellow and Vice President, Search, said that this doesn't mean that no helpful information is available, or that a particular result is low-quality. "These notices provide context about the whole set of results on the page, and you can always see the results for your query, even when the advisory is present," he said in a blog post late on Thursday. "We have deeply invested in both information quality and information literacy on Google Search and News, and today we have a few new developments about this important work," said Nayak. Google also introduced latest AI model, called Multitask Unified Model (MUM), to improve search result quality in snippets' which are shown on top of the page for searches.

You've probably done your online searches on "Feature Selection", and you've probably found tons of articles describing the three umbrella terms that group selection methodologies, i.e., "Filter Methods", "Wrapper Methods" and "Embedded Methods". Under the "Filter Methods", we find statistical tests that select features based on their distributions. These methods are computationally very fast, but in practice they do not render good features for our models. In addition, when we have big datasets, p-values for statistical tests tend to be very small, highlighting as significant tiny differences in distributions, that may not be really important. The "Wrapper Methods" category includes greedy algorithms that will try every possible feature combination based on a step forward, step backward, or exhaustive search.

FundamentalVR secures Series B growth investments to disrupt the medical simulation market – expected to be worth $4.2 billion by 2025. BOSTON – August 11, 2022 – FundamentalVR has raised an additional $20 million to significantly accelerate medical skill-transfer and increase surgical proficiency through its world-leading medical simulation platform, Fundamental Surgery. The transaction was led by EQT Life Sciences investing from the LSP Health Economics Fund 2, and joined by prior investors Downing Ventures, Tern Plc and Sana Kliniken. The new investments follow a Series A round in October 2019 and bring the company's total funding to over $30 million. FundamentalVR is the world's first scalable medical simulation platform to combine virtual reality and haptics through data, artificial intelligence, and multimodal learning.

In the healthcare industry, data science and artificial intelligence (AI) play a pivotal role in bringing together innovation and patient care and they have the potential to transform how healthcare is delivered. Healthcare data open the road to many discoveries. For example, it provides the foundation to run medical evaluation and to produce more effective drugs more quickly. Data scientists are using powerful predictive analytical tools to detect chronic diseases at an early level and to identify successful interventions quickly. Analytical tools will make possible to identify more quickly the best drug for a certain treatment as well as the most efficient route to produce it.

Interpretive scholars generate knowledge from text corpora by manually sampling documents, applying codes, and refining and collating codes into categories until meaningful themes emerge. Given a large corpus, machine learning could help scale this data sampling and analysis, but prior research shows that experts are generally concerned about algorithms potentially disrupting or driving interpretive scholarship. We take a human-centered design approach to addressing concerns around machine-assisted interpretive research to build Scholastic, which incorporates a machine-in-the-loop clustering algorithm to scaffold interpretive text analysis. As a scholar applies codes to documents and refines them, the resulting coding schema serves as structured metadata which constrains hierarchical document and word clusters inferred from the corpus. Interactive visualizations of these clusters can help scholars strategically sample documents further toward insights. Scholastic demonstrates how human-centered algorithm design and visualizations employing familiar metaphors can support inductive and interpretive research methodologies through interactive topic modeling and document clustering.

We present RealityTalk, a system that augments real-time live presentations with speech-driven interactive virtual elements. Augmented presentations leverage embedded visuals and animation for engaging and expressive storytelling. However, existing tools for live presentations often lack interactivity and improvisation, while creating such effects in video editing tools require significant time and expertise. RealityTalk enables users to create live augmented presentations with real-time speech-driven interactions. The user can interactively prompt, move, and manipulate graphical elements through real-time speech and supporting modalities. Based on our analysis of 177 existing video-edited augmented presentations, we propose a novel set of interaction techniques and then incorporated them into RealityTalk. We evaluate our tool from a presenter's perspective to demonstrate the effectiveness of our system.

While several methods have been proposed to tackle this problem, there is limited work explaining why these methods work well. This paper has the goal of better explaining a popularly used technique for avoiding catastrophic forgetting: quadratic regularization. We show that quadratic regularizers prevent forgetting of past tasks by interpolating current and previous values of model parameters at every training iteration. Over multiple training iterations, this interpolation operation reduces the learning rates of more important model parameters, thereby minimizing their movement. Our analysis also reveals two drawbacks of quadratic regularization: (a) dependence of parameter interpolation on training hyperparameters, which often leads to training instability and (b) assignment of lower importance to deeper layers, which are generally the place forgetting occurs in DNNs. Via a simple modification to the order of operations, we show these drawbacks can be easily avoided, resulting in 6.2% higher average accuracy at 4.5% lower average forgetting. We confirm the robustness of our results by training over 2000 models in different settings. Learning algorithms are often designed under the assumption of independent and identical data distributions. However, this assumption is violated in several practical scenarios, such as continual learning and lifelong learning, where data distributions evolve constantly. In such settings, deep neural networks (DNNs) witness catastrophic forgetting and have difficulty adapting to new tasks without losing performance on previously learned ones. Several past works have tried to address this problem. While these works have shown promising results, a detailed understanding of their proposed methods is still to be developed. Understanding the reasons due to which existing methods for preventing catastrophic forgetting work or fail can open the possibility of developing better methods. With this motivation, in this work, we analyze quadratic regularization, a popular technique for preventing catastrophic forgetting in DNNs.

Future wireless networks (5G and beyond) are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have been dramatically growth with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated in applications throughout all layers of the network. However, the security concerns on network functions of NextG using AI-based models, i.e., model poising, have not been investigated deeply. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB's 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks, while making models more robust against any attacks through mitigation methods. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack against the channel estimation model. The results indicated that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.