Prior probability models are a central component of many image processing problems, but density estimation is notoriously difficult for high-dimensional signals such as photographic images. Deep neural networks have provided state-of-the-art solutions for problems such as denoising, which implicitly rely on a prior probability model of natural images. Here, we develop a robust and general methodology for making use of this implicit prior. We rely on a little-known statistical result due to Miyasawa (1961), who showed that the least-squares solution for removing additive Gaussian noise can be written directly in terms of the gradient of the log of the noisy signal density. We use this fact to develop a stochastic coarse-to-fine gradient ascent procedure for drawing high-probability samples from the implicit prior embedded within a CNN trained to perform blind (i.e., unknown noise level) least-squares denoising. A generalization of this algorithm to constrained sampling provides a method for using the implicit prior to solve any linear inverse problem, with no additional training. We demonstrate this general form of transfer learning in multiple applications, using the same algorithm to produce high-quality solutions for deblurring, super-resolution, inpainting, and compressive sensing.

This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Very often, accurate simulations correspond to high computational efforts whereas coarse simulations can be obtained at a smaller cost. In this setting, simulation results obtained at several levels of fidelity can be combined in order to estimate quantities of interest (the optimal value of the output, the probability that the output exceeds a given threshold...) in an efficient manner. To do so, we propose a new Bayesian sequential strategy called Maximal Rate of Stepwise Uncertainty Reduction (MR-SUR), that selects additional simulations to be performed by maximizing the ratio between the expected reduction of uncertainty and the cost of simulation. This generic strategy unifies several existing methods, and provides a principled approach to develop new ones. We assess its performance on several examples, including a computationally intensive problem of fire safety analysis where the quantity of interest is the probability of exceeding a tenability threshold during a building fire.

Social media platforms now serve billions of users by providing convenient means of communication, content sharing and even payment between different users. Due to such convenient and anarchic nature, they have also been used rampantly to promote and conduct business activities between unregistered market participants without paying taxes. Tax authorities worldwide face difficulties in regulating these hidden economy activities by traditional regulatory means. This paper presents a machine learning based Regtech tool for international tax authorities to detect transaction-based tax evasion activities on social media platforms. To build such a tool, we collected a dataset of 58,660 Instagram posts and manually labelled 2,081 sampled posts with multiple properties related to transaction-based tax evasion activities. Based on the dataset, we developed a multi-modal deep neural network to automatically detect suspicious posts. The proposed model combines comments, hashtags and image modalities to produce the final output. As shown by our experiments, the combined model achieved an AUC of 0.808 and F1 score of 0.762, outperforming any single modality models. This tool could help tax authorities to identify audit targets in an efficient and effective manner, and combat social e-commerce tax evasion in scale.

Graph signal processing (GSP) is an important methodology for studying data residing on irregular structures. As acquired data is increasingly taking the form of multi-way tensors, new signal processing tools are needed to maximally utilize the multi-way structure within the data. In this paper, we review modern signal processing frameworks generalizing GSP to multi-way data, starting from graph signals coupled to familiar regular axes such as time in sensor networks, and then extending to general graphs across all tensor modes. This widely applicable paradigm motivates reformulating and improving upon classical problems and approaches to creatively address the challenges in tensor-based data. We synthesize common themes arising from current efforts to combine GSP with tensor analysis and highlight future directions in extending GSP to the multi-way paradigm. Over the past decade, graph signal processing (GSP) [1] has laid the foundation for generalizing classical Fourier theory as defined on a regular grid, such as time, to handle signals on irregular structures, such as networks. GSP, however, is currently limited to single-way analysis: graph signals are processed independently of one another, thus ignoring the geometry between multiple graph signals. In the coming decade, generalizing GSP to handle multi-way data, represented by multidimensional arrays or tensors, with graphs underlying each axis of the data will be essential for modern signal processing. To introduce the concept of way, consider a network of N sensors each measuring a signal sampled at T time points. On the one hand, classic signal processing treats these signals as a collection of N independent 1D timeseries ignoring the relation structure of the graph. T. Both are single-way perspectives that ignore the underlying geometry of the other way (also referred to as mode).

The report published on the global Healthcare Artificial Intelligence Software market is a comprehensive analysis of a variety of factors that are prevalent in the Healthcare Artificial Intelligence Software market. An industrial overview of the global market is provided along with the market growth hoped to be achieved with the products that are sold. Major companies who occupy a large market share and the different products sold by them in the global market are identified and are mentioned in the report. The current market share occupied by the global Healthcare Artificial Intelligence Software market from the year 2020 to the year 2030 has been presented. Global Healthcare Artificial Intelligence Software Market size is estimated to be USD 4.68 billion in 2019 and is predicted to reach USD 354.47 billion by 2030 with a CAGR of 48.2% from 2020-2030.

About 80,000 years ago, the so-called Out of Africa occurred, when part of the human population, which already consisted of modern humans, abandoned the African continent and migrated to other continents, giving rise to all the current populations,

Data is turning out to be more valuable than we thought. Google and Facebook's ad revenues exceeded $200 billion last year. They can hope to have a bigger source of income soon, thanks to the income generated by the Artificial Intelligence (AI) business built using the data of billions of individuals. No wonder, getting hold of data by paying top dollars is the new game in the digital world. This may explain the sudden investments of Google, Facebook, Intel, and many others in India, one of the largest data generators of the world.

The economic opportunities arising from the introduction of artificial intelligence are enormous, and there are a number of opportunities that developing countries can currently exploit. Access to the Internet, especially to mobile devices, has greatly increased in recent years, and this is one of the key factors that gives the impression that developing worlds riding on the technological advances of the introduction of artificial intelligence are great. Artificial intelligence creates huge opportunities for developing countries in terms of economic growth and job creation. This is an opportunity for developing countries to capitalise on this progress and use the latest technology and user-friendly solutions to improve people's quality of life and create new economic opportunities. By discussing and exchanging these ideas, this symposium aims to stimulate the development of a more established, broader field of research and development in the field of artificial intelligence.

OpenAI's paper describes many incredible tasks that GPT-3 can accomplish. For instance, given some text input, it can predict what should come next alarmingly well. In one study, test subjects were asked to differentiate between 500 word long articles written by humans or articles written by GTP-3. When GPT-3 is used with 175 billion parameters, they were only able to correctly identify text from other humans at a rate of 52%. That implies GPT-3 at maximum capacity can nearly replicate human written articles!

What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.