If you're seeing something long and challenging, remember that having an alcoholic drink beforehand is asking for trouble. So be sure to do it. Decorate a room as if you're a set designer, letting your imagination run wild. As William Morris said, bin whatever isn't useful or beautiful. Study your favourite standup and learn their best joke off by heart. It's not just about making your friends laugh: comedy teaches confidence and communication. From Evan Hansen to Alexander Hamilton to Mary Poppins, find a character whose feelings mirror yours – then unleash that emotion. Improvisation isn't just some zany thing comedians do on telly. It's a philosophy, as Pippa Evans' recent book Improv Your Life shows. When you're thrown a curveball, deviate from your standard script.

Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive. Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM imaging tasks. Consequently, generalizability becomes a major concern. On the other hand, physical principles underlie EM phenomena and provide baselines for current imaging techniques. To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work. This article surveys various schemes to incorporate physics in learning-based EM imaging. We first introduce background on EM imaging and basic formulations of the inverse problem. We then focus on three types of strategies combining physics and ML for linear and nonlinear imaging and discuss their advantages and limitations. Finally, we conclude with open challenges and possible ways forward in this fast-developing field. Our aim is to facilitate the study of intelligent EM imaging methods that will be efficient, interpretable and controllable.

The 44th International Conference on Software Engineering(ICSE 2022) was held in person from May 22 to May 27, 2022 in Pittsburgh, PA, USA. Since ICSE was held as a solely virtual conference for the last two years, the opportunity to interact with other members of the software engineering community in person and to engage in insightful discussions in a physical room was greatly welcomed. Each day was organized into paper sessions, poster sessions, and Birds of a Feather(BoF) sessions, in addition to plenty of time for networking. Each paper session consisted of around 6-10 5 minute talks and time for questions for the authors. The Birds of a Feather sessions allowed for a broader discussion on a topic; the sessions varied in terms of topics and format. In this document, we summarize themes of research that we observed at the conference.

Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale. At the same time, various personality type categorization schemes such as the Myers-Briggs Personality Type indicator make it possible to reveal the dependencies between personality traits and user content preferences on a wider scale by categorizing audience behaviours in a unified and structured manner. This problem is yet to be studied in depth by the research community, while the level of impact of different personality traits on content recommendation accuracy has not been widely utilised and comprehensively evaluated so far. Specifically, in this work we investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system called Personality Content Marketing Recommender Engine, or PersiC. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations, which when deployed are able to improve digital advertising efficiency by over 420% as compared to the original human-guided approach.

Gunasekar et al. (2017) observed We study the implicit regularization effects of that for matrix factorization when there are no constraints on deep learning in tensor factorization. While implicit the rank, the solution of the optimization problem via gradient regularization in deep matrix and'shallow' descent turns out to be a low-rank matrix. Furthermore, tensor factorization via linear and certain type of they conjectured that, with small enough learning rate and non-linear neural networks promotes low-rank solutions initialization, gradient descent on full-dimensional matrix with at most quadratic growth, we show factorization converges to the solution with minimal nuclear that its effect in deep tensor factorization grows norm. Arora et al. (2019) and Razin & Cohen (2020) extended polynomially with the depth of the network. This the analysis to deep matrix factorization and showed provides a remarkably faithful description of the in this case that implicit regularization of gradient descent observed experimental behaviour. Using numerical cannot be formulated as a norm-minimization problem. By experiments, we demonstrate the benefits of studying the dynamics of gradient descent, they found theoretically this implicit regularization in yielding a more accurate and experimentally that it instead promotes sparsity estimation and better convergence properties. of the singular values of the learned matrix, indicating that implicit regularization in deep learning has to be studied from a dynamical point of view. Moreover, Razin et al. (2021) studied implicit regularization in'shallow' tensor

Artificial intelligence has seen a slew of investments throughout the Middle East, with many nations seeing the tech as playing a key role in creating a more sustainable future

Francesco Petruccione was born in 1961 in Genova (Italy). He studied Physics at the University of Freiburg i. Br. and received his PhD in 1988. He was conferred the "Habilitation" degree (Dr. In 2004 he was appointed Professor of Theoretical Physics at the University of KwaZulu-Natal (UKZN), in Durban (South Africa). In 2005 he was awarded an Innovation Fund grant to set up a Centre for Quantum Technology.

Disclaimer: this text expresses the opinions of a student, researcher, and engineer who studies and works in the field of Artificial Intelligence in the Netherlands. I think the contents are not as nuanced as they could be, but the text is informed -- in a way, it is just my opinion. Allow me then to begin by iterating Wittgensteins' de facto sentence with which he ends his first treaty in philosophy, Tractatus Logico-Philosophicus: "Whereof one cannot speak thereof one must remain silent"[7]. The problem with Ethics of AI, put succinctly, is the demand for morally-based changes to an empirical scientific field -- the field of AI or Computer Science. These changes have been easily justified in AI due to its engineering counterpart -- one of the fastest growing and most productive technological fields at the moment whose range of possible reforms threatens every social dimension. Most of these changes, for better and for worst, have been demanded by the political class and for the most part only in the West. The aim of this article is not to take any part in the political discussion, although this might be impossible by definition -- after all, everything is political. It is still important to attempt to disentangle the views expressed here-in from those barked in the political sphere. The very root of the problem is linked to the over-politicization, indeed, perhaps even radicalization of systems that are not political by nature, like Science. The problem, that a scientific field has been mixed-up with its applications in industry -- is a prominent one.

While artificial intelligence (AI) has emerged as the next major wave of innovation, its power dynamics are not evenly distributed. This is according to Mozilla's Internet Health Report 2022, which examines how humanity and the internet intersect, scrutinising the nature of an AI-driven world. AI in this case includes a wide range of automation and algorithmic processes, including machine learning, computer vision, natural language processing, and more, states the report. Mozilla found that the growing power disparity between who benefits from AI and who is harmed by AI is the top challenge facing the health of the internet. Solana Larsen, Mozilla's internet health report editor, explains: "The centralisation of influence and control over AI doesn't work to the advantage of the majority of people. We need to strengthen technology ecosystems beyond the realm of big tech and venture capital start-ups if we want to unlock the full potential of trustworthy AI." Research and advisory firm IDC forecasts that worldwide revenue for the AI market will grow by 19.6% year-over-year in 2022, to $432.8 billion, with the market expected to break the $500 billion mark in 2023.

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