Many learning tasks in machine learning can be viewed as taking a gradient step towards minimizing the average loss of a batch of examples in each training iteration. When noise is prevalent in the data, this uniform treatment of examples can lead to overfitting to noisy examples with larger loss values and result in poor generalization. Inspired by the expert setting in on-line learning, we present a flexible approach to learning from noisy examples. Specifically, we treat each training example as an expert and maintain a distribution over all examples. We alternate between updating the parameters of the model using gradient descent and updating the example weights using the exponentiated gradient update. Unlike other related methods, our approach handles a general class of loss functions and can be applied to a wide range of noise types and applications. We show the efficacy of our approach for multiple learning settings, namely noisy principal component analysis and a variety of noisy classification problems.

In a world abundant with diverse data arising from complex acquisition techniques, there is a growing need for new data analysis methods. In this paper we focus on high-dimensional data that are organized into several hierarchical datasets. We assume that each dataset consists of complex samples, and every sample has a distinct irregular structure modeled by a graph. The main novelty in this work lies in the combination of two complementing powerful data-analytic approaches: topological data analysis (TDA) and geometric manifold learning. Geometry primarily contains local information, while topology inherently provides global descriptors. Based on this combination, we present a method for building an informative representation of hierarchical datasets. At the finer (sample) level, we devise a new metric between samples based on manifold learning that facilitates quantitative structural analysis. At the coarser (dataset) level, we employ TDA to extract qualitative structural information from the datasets. We showcase the applicability and advantages of our method on simulated data and on a corpus of hyper-spectral images. We show that an ensemble of hyper-spectral images exhibits a hierarchical structure that fits well the considered setting. In addition, we show that our new method gives rise to superior classification results compared to state-of-the-art methods.

Once upon a time, there were real classrooms with teachers strictly evaluating you during your pen and paper examinations. This might be a story that our future kids will listen to if the'new normal' is planning to stay here. The Covid-19 pandemic brought rapid digital transformation and AI-driven automation into all industries. According to IDC, the worldwide revenues for the artificial intelligence market are forecast to grow 16.4% in 2021 to USD 327.5 billion. The growing significance of AI is also visible in the education sector.

IBM, an AI champion, offers this definition: "Artificial intelligence enables computers and machines to mimic the perception, learning, problem-solving …

Programming languages are the building blocks for communicating instructions to machines, without them the technology driven world we live in today wouldn't exist. Programming can be fun as well as challenging. Java is a general purpose high-level, object-oriented programming language. Java is one of the most commonly used languages for developing and delivering content on the web. An estimated nine million Java developers use it and more than three billion mobile phones run it.

VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture. The 2020 LinkedIn U.S. Emerging Jobs Report identified the top 15 jobs over the previous five years and emphasized that "artificial intelligence and data science roles continue to proliferate across nearly every industry." Artificial intelligence specialist (No. 1) showed 74% annual growth, and data scientist (No. 3) and data engineer (No. 8) followed with 37% and 33% annual growth. But the problem is: we don't have enough skilled talent to fill these jobs, and it's a national imperative that we change the way we imagine, educate, recruit and upskill our technical workforce. We must abandon the flawed idea that AI jobs are only for people with master's degrees or PhDs with decades of experience.

Since 1847, Harvard College founded the Lawrence University School as the first school of architecture and the sciences. This developed into the new John Paulson School of Engineering and Sciences in 2007. This school has 76 tenured staff members and over 1300 students. In its current state, Computer Science is accessible to Ph.D. students. Students in the program are allowed to participate in the cross-disciplinary study and work with other colleges.

McGill study aims to make social media contributions more useful to crisis managers Can combining deep learning (DL)— a subfield of artificial intelligence— with social network analysis (SNA), make social media contributions about extreme weather events a useful tool for crisis managers, first responders and government scientists? An interdisciplinary team of McGill researchers has brought these tools to the forefront in an effort to understand and manage extreme weather events. The researchers found that by using a noise reduction mechanism, valuable information could be filtered from social media to better assess trouble spots and assess users’ reactions vis-à-vis extreme weather events. The results of the study are published in the Journal of Contingencies and Crisis Management. Diving into a sea of information “We reduced the noise by finding out who was being listened to, and which were authoritative sources,” explains Renee Sieber, Associate Professor in McGill’s Department of Geography and lead author of this study. “This ability is important because it is quite difficult to assess the validity of the information shared by Twitter users.” The team based their study on Twitter data from the March 2019 Nebraska floods in the United States, which caused over $1 billion in damage and widespread evacuations of residents. In total, over 1,200 tweets were analyzed and classified. “Social network analysis can identify where ​people get their information during an extreme weather event. Deep learning allows us to better understand the content ​ of this information by classifying thousands of tweets into fixed categories, for example, ‘infrastructure and utilities damage’ or ‘sympathy and emotional support’,” says Sieber. The researchers then introduced a two-tiered DL classification model – a first in terms of integrating these methods in a way that could be useful to crisis managers. The study highlighted some issues regarding the use of social media analysis for this purpose, notably its failure to note that events are far more contextual than expected by labelled datasets, such as the CrisisNLP, and the lack of a universal language to categorize terms related to crisis management. The preliminary exploration performed by the researchers also found that a celebrity call out was featured prominently – this was indeed the case for the 2019 Nebraska floods, where a tweet from pop singer Justin Timberlake was shared by a large number of users, though it did not prove to be of use for crisis managers. “Our findings tell us that information content varies between different types of events, contrary to the belief that there is a universal language to categorize crisis management; this limits the use of labelled datasets on just a few types of events, as search terms may change from one event to another.” “The vast amount of social media data the public contributes about weather suggests it can provide critical information in crises, such as snowstorms, floods, and ice storms. We are currently exploring transferring this model to different types of weather crises and addressing the shortcomings of existing supervised approaches by combining these with other methods,” says Sieber. About this study “Using deep learning and social network analysis to understand and manage extreme flooding” by Renee Sieber and al. was published in the Journal of Contingencies and Crisis Management. This study was funded by Environment Canada. About McGill University Founded in Montreal, Quebec, in 1821, McGill University is Canada’s top ranked medical doctoral university. McGill is consistently ranked as one of the top universities, both nationally and internationally. It is a world-renowned institution of higher learning with research activities spanning two campuses, 11 faculties, 13 professional schools, 300 programs of study and over 40,000 students, including more than 10,200 graduate students. McGill attracts students from over 150 countries around the world, its 12,800 international students making up 31% per cent of the student body. Over half of McGill students claim a first language other than English, including approximately 19% of our students who say French is their mother tongue.

Speaking to established patent attorney Nick Transier, we explore why there has been a boom in AI and the special considerations behind AI patents.

In this paper, we investigate when system identification is statistically easy or hard, in the finite sample regime. Statistically easy to learn linear system classes have sample complexity that is polynomial with the system dimension. Most prior research in the finite sample regime falls in this category, focusing on systems that are directly excited by process noise. Statistically hard to learn linear system classes have worst-case sample complexity that is at least exponential with the system dimension, regardless of the identification algorithm. Using tools from minimax theory, we show that classes of linear systems can be hard to learn. Such classes include, for example, under-actuated or under-excited systems with weak coupling among the states. Having classified some systems as easy or hard to learn, a natural question arises as to what system properties fundamentally affect the hardness of system identifiability. Towards this direction, we characterize how the controllability index of linear systems affects the sample complexity of identification. More specifically, we show that the sample complexity of robustly controllable linear systems is upper bounded by an exponential function of the controllability index. This implies that identification is easy for classes of linear systems with small controllability index and potentially hard if the controllability index is large. Our analysis is based on recent statistical tools for finite sample analysis of system identification as well as a novel lower bound that relates controllability index with the least singular value of the controllability Gramian.