Sudoku is a puzzle in which players insert the numbers one to nine into a grid consisting of nine squares subdivided into a further nine smaller squares in such a way that every number appears once in each horizontal line, vertical line, and square. Using OpenCV, Deep Learning, and Backtracking Algorithm, We can solve the sudoku puzzle. First, build the Character Recognition model that can extract digits from a Sudoku grid image and then work on a backtracking approach to solve it. Deep Learning-based AI_Sudoku_Solver architecture uses OpenCV (opencv 4.2.0) and Python (python 3.7). The model Convolution Neural Network(CNN) uses Keras (keras 2.3.1) on Tensorflow for Digit Recognition.

Some of the most dazzling recent advances in artificial intelligence have come thanks to resources only available at big tech companies, where thousands of powerful computers and terabytes of data can be as copious as free granola bars and nap pods. A new project aims to show this needn't be the case, by cobbling together the code, data, and computer power needed to reproduce one of the most epic--and potentially useful--AI algorithms developed in recent years. Eleuther is an open source effort to match GPT-3, a powerful language algorithm released in 2020 by the company OpenAI that is sometimes capable of writing strikingly coherent articles in English when given a text prompt. Eleuther is still some way from matching the full capabilities of GPT-3, but last week the researchers released a new version of their model, called GPT-Neo, which is about as powerful as the least sophisticated version of GPT-3. Open sourcing big AI projects could make the technology more accessible and widespread at a time when it has become increasingly entrenched at big tech firms.

Back in the 1960s, Joseph Weizenbaum of the MIT Artificial Intelligence Laboratory created one of the first chatbots, which he named ELIZA. Weizenbaum modeled its conversational style after Carl Rogers, a psychotherapist who was known for parroting patients' responses back at them. His hypothesis was that while chatbots could emulate human conversation superficially, they could not fully capture the nuances of a genuine discussion between humans. To Weizenbaum's complete surprise, ELIZA did end up fooling many people into believing they were having a therapeutic breakthrough with a real live therapist. While modern customer service bots aren't likely to help customers dig deep into their psychological issues, the technology behind computers processing human language is getting more advanced day by day.

Businesses will prioritize building AI technologies that can interpret and respond to human emotions as they look to connect with consumers. Over the last decade, artificial intelligence has gone from buzzword to a must-have business competence. From retail to healthcare to financial services, AI is penetrating nearly every industry, with advances in deep learning, computer vision, and more paving the way. Download our full report to find out the top trends poised to reshape industries in 2021. AI, though, has largely been challenged when it comes to recognizing and reacting to human emotion. In fact, the AI Now Institute at New York University called for a ban on the use of emotion recognition tech "in important decisions that impact people's lives and access to opportunities" in its 2019 report.

Ishiguro's book is fiction, but his suggestion that a new type of literature may be on the horizon is not. In May 2020, the San Francisco–based start-up OpenAI first publicly described its new language-processing software, which writes remarkably well. Generative Pre-trained Transformer 3, or GPT-3, is one of many recent advances in AI demonstrating that machines can do many basic and not-so-basic forms of digital labor. In turn, AI's capacity for creativity--one of those supposedly sacrosanct human attributes--is becoming more and more of an existential sticking point as humans learn to live alongside intelligent machines. "Given any text prompt," the company's website says, the GPT-3 interface "will return a text completion, attempting to match the pattern you gave it." It can do this because it has been pretrained in semantic analysis by reading a huge portion of the internet.

Structural health monitoring (SHM) systems use the non-destructive testing principle for damage identification. As part of SHM, the propagation of ultrasonic guided waves (UGWs) is tracked and analyzed for the changes in the associated wave pattern. These changes help identify the location of a structural damage, if any. We advance existing research by accounting for uncertainty in the material and geometric properties of a structure. The physics model used in this study comprises of a monolithically coupled system of acoustic and elastic wave equations, known as the wave propagation in fluid-solid and their interface (WpFSI) problem. As the UGWs propagate in the solid, fluid, and their interface, the wave signal displacement measurements are contrasted against the benchmark pattern. For the numerical solution, we develop an efficient algorithm that successfully addresses the inherent complexity of solving the multiphysics problem under uncertainty. We present a procedure that uses Gaussian process regression and convolutional neural network for predicting the UGW propagation in a solid-fluid and their interface under uncertainty. First, a set of training images for different realizations of the uncertain parameters of the inclusion inside the structure is generated using a monolithically-coupled system of acoustic and elastic wave equations. Next, Gaussian processes trained with these images are used for predicting the propagated wave with convolutional neural networks for further enhancement to produce high-quality images of the wave patterns for new realizations of the uncertainty. The results indicate that the proposed approach provides an accurate prediction for the WpFSI problem in the presence of uncertainty.

The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density <+/-3%; dose <+/-20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.

Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experience, which are interleaved with new data during training. The amount of patterns stored in the buffer is a critical parameter which largely influences the final performance and the memory footprint of the approach. This work introduces Distilled Replay, a novel replay strategy for Continual Learning which is able to mitigate forgetting by keeping a very small buffer (up to $1$ pattern per class) of highly informative samples. Distilled Replay builds the buffer through a distillation process which compresses a large dataset into a tiny set of informative examples. We show the effectiveness of our Distilled Replay against naive replay, which randomly samples patterns from the dataset, on four popular Continual Learning benchmarks.

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named ``embedder" is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADG-Seq2Seq, yields significant improvements over existing state-of-the-art methods and maintains its performance as the length of the target code increases. Extensive ablation tests show that the proposed ADG embedding is effective and outperforms the baselines.

This paper proposes a new approach to detecting neural Trojans on Deep Neural Networks during inference. This approach is based on monitoring the inference of a machine learning model, computing the attribution of the model's decision on different features of the input, and then statistically analyzing these attributions to detect whether an input sample contains the Trojan trigger. The anomalous attributions, aka misattributions, are then accompanied by reverse-engineering of the trigger to evaluate whether the input sample is truly poisoned with a Trojan trigger. We evaluate our approach on several benchmarks, including models trained on MNIST, Fashion MNIST, and German Traffic Sign Recognition Benchmark, and demonstrate the state of the art detection accuracy.