KINGSTON, R.I. – Oct. 27, 2022 – Icebergs originating from Greenland not only impact the environment by affecting sea-level rise and local ecosystems, but they can also damage offshore equipment and disrupt marine transportation. Researchers Mingxi Zhou and Chris Roman from the University of Rhode Island, and Zhuoyuan Song from the University of Hawaiʻi at Mānoa, received a $1.5 million award from the National Science Foundation to develop multi-robot systems for iceberg studies. The research team will develop multiple robotic platforms, including an autonomous surface vehicle, an autonomous underwater vehicle, and several underwater profiling floats, to map the shape of icebergs and assess their surrounding water. They will record the shape, size and drifting speed of the icebergs, and the properties of the surrounding water, creating a unique dataset to better understand iceberg melting and drifting processes. Zhou is the lead researcher on the project.

Peak's inaugural Decision Intelligence (DI) Maturity Index found that while the U.S. was an early leader in artificial intelligence (AI), India is now the more mature market when it comes to readying their business to adopt AI. While the U.S. was an early leader in AI, with 28% of U.S. businesses adopting the technology over six years ago – compared to 25% in India and 20% in the U.K. – India is the more mature market when it comes to leveraging AI, scoring 64 (out of 100) on Peak's DI maturity scale, while the U.S. charted 52 and the U.K. just 44. What's setting Indian businesses apart is internal communication and education about AI to ensure broad support – 18% of U.S. workers weren't sure if their business used AI, compared to only 2% of Indian workers. Further, 78% of junior staff in India expect AI to have a positive impact on worker well-being over the next five years, compared to 47% of those in the U.S. The report also found that the way businesses structure data teams is crucial to successful AI adoption, with the majority of Indian businesses having data practitioners embedded in commercial teams to support analysis – by contrast most U.S. businesses have a central data team. Moreover, while California is historically seen as the mecca of tech innovation, New York is ahead in AI leadership as it scored an average of 61 out of 100, compared to California, which charted at 58. This is because New York is the top financial services center in the U.S. – an industry that is the second most-mature industry behind IT, computing and technology with a mean maturity score of 56 across all three markets (U.S., U.K. and India).

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it's used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data.

Abstract: We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge Distillation (West et al., 2022), where latent knowledge in pre-trained language models is distilled via explicit examples sampled from the teacher models, further purified with three types of filters: length, fidelity, and Information Bottleneck. Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration. Starting off from a relatively modest set of GPT3-generated summaries, we demonstrate how iterative knowledge distillation can lead to considerably smaller, but better summarizers with sharper controllability. A useful by-product of this iterative distillation process is a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios.

In this blog, we shall discuss about how to build a neural network to translate from English to German. This problem appeared as the Capstone project for the coursera course Tensorflow 2: Customising your model, a part of the specialization Tensorflow2 for Deep Learning, by the Imperial College, London. The problem statement / description / steps are taken from the course itself. We shall use the concepts from the course, including building more flexible model architectures, freezing layers, data processing pipeline and sequence modelling. Here we shall use a language dataset from http://www.manythings.org/anki/

This semester I started a new graduate course at Northeastern on Visualization for Machine Learning. I am particularly excited about this course because it strongly connects with our research over the last few years. As usual, teaching is an excellent way to understand our research work better. We are about six weeks into the semester, and I am ready to share some details and observations. The course targets graduate students, and for this reason, it is heavily based on paper reading.

Are you looking for the Best Time Series Courses Online? If yes, this article is for you. In this article, I listed the Best Time Series Courses Online. So, give a few minutes to this article and find the best time series course for you. A time series is a set of numerical measurements of the same entity taken at equally spaced intervals over time.

The Minor in Artificial Intelligence is designed for non-Computer Science majors to have significant exposure in fundamentals of computer science and the modern technical area of artificial intelligence, which can provide valuable knowledge and skill in the development of students' majors and for the job market.

Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance to examine the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical research examining the adaptive and temporal characteristics of CPS which might lead to an oversimplified representation of the real complexity of the CPS process. To further understand the nature of CPS in online interaction settings, this research collected multimodal process and performance data (i.e., verbal audios, computer screen recordings, concept map data) and proposed a three-layered analytical framework that integrated AI algorithms with learning analytics to analyze the regularity of groups collaboration patterns. The results detected three types of collaborative patterns in groups, namely the behaviour-oriented collaborative pattern (Type 1) associated with medium-level performance, the communication - behaviour - synergistic collaborative pattern (Type 2) associated with high-level performance, and the communication-oriented collaborative pattern (Type 3) associated with low-level performance. The research further highlighted the multimodal, dynamic, and synergistic characteristics of groups collaborative patterns to explain the emergence of an adaptive, self-organizing system during the CPS process.

Traditional (fickle) adversarial examples involve finding a small perturbation that does not change an input's true label but confuses the classifier into outputting a different prediction. Conversely, obstinate adversarial examples occur when an adversary finds a small perturbation that preserves the classifier's prediction but changes the true label of an input. Adversarial training and certified robust training have shown some effectiveness in improving the robustness of machine learnt models to fickle adversarial examples. We show that standard adversarial training methods focused on reducing vulnerability to fickle adversarial examples may make a model more vulnerable to obstinate adversarial examples, with experiments for both natural language inference and paraphrase identification tasks. To counter this phenomenon, we introduce Balanced Adversarial Training, which incorporates contrastive learning to increase robustness against both fickle and obstinate adversarial examples.