The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias. Most applications to date have been in computer vision, although some work in healthcare has started to emerge. The use of deep learning (DL) in cardiac MR segmentation has led to impressive results in recent years, and such techniques are starting to be translated into clinical practice. However, no work has yet investigated the fairness of such models. In this work, we perform such an analysis for racial/gender groups, focusing on the problem of training data imbalance, using a nnU-Net model trained and evaluated on cine short axis cardiac MR data from the UK Biobank dataset, consisting of 5,903 subjects from 6 different racial groups. We find statistically significant differences in Dice performance between different racial groups. To reduce the racial bias, we investigated three strategies: (1) stratified batch sampling, in which batch sampling is stratified to ensure balance between racial groups; (2) fair meta-learning for segmentation, in which a DL classifier is trained to classify race and jointly optimized with the segmentation model; and (3) protected group models, in which a different segmentation model is trained for each racial group. We also compared the results to the scenario where we have a perfectly balanced database. To assess fairness we used the standard deviation (SD) and skewed error ratio (SER) of the average Dice values. Our results demonstrate that the racial bias results from the use of imbalanced training data, and that all proposed bias mitigation strategies improved fairness, with the best SD and SER resulting from the use of protected group models.

We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment with domain-specific signals and datasets for solving a variety of complex problems on their own. This versatility to solve diverse problems is achieved by transforming complex Spatio-temporal datasets to make them consumable by standard deep learning models, in this case, Convolutional Neural Networks (CNNs), and giving the ability to formulate disparate problems in a standard way, eg. semantic segmentation. With an intuitive user interface, a feature store that hosts derivatives of complex feature engineering, a deep learning kernel, and a scalable data processing mechanism, Trinity provides a powerful platform for domain experts to share the stage with scientists and engineers in solving business-critical problems. It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment. In this paper, we present our motivation behind Trinity and its design along with showcasing sample applications to motivate the idea of lowering the bar to using AI.

DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions, using a dataset of text–image pairs. It has a diverse set of capabilities, including creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images. In short it is phenomenal! Make sure to subscribe to the channel Deep Learning Explainer, please. OpenAI achieves insanely great results using GPT-3 in a system called DALL-E to generate very credible images from text descriptions alone.

EuroHPC – its BullSequana will be used in five EuroHPC supercomputing centres – Sofia Tech Park at Bulgaria ("Discover"), CINECA in Italy ("Da Vinci"), IZUM in Slovenia, ("Vega") LuxProvide in Luxembourg ("MeluXina" which is also #4 in the GREEN500) and in the Minho Advanced Computing Centre in Portugal, reinforcing Atos' position as a European leader in high-performance computing. The Linkoping University's'Berzelius' supercomputer for AI will use Atos' recently announced Atos ThinkAI solution to enable its researchers speed-up processing times on their complex data, empowering them to gain insights faster, using the power of deep learning and analytics. "Topaze" at the CCRT – a new supercomputer at the French CCRT based on the BullSequana XH2000 solution from Atos, is the result of joint R&D by Atos and the CEA's Military Applications Directorate (DAM). It will soon be open to the first users, to start the'Grand Challenges' phase, for very large-scale simulations. "Noctua2" at Paderborn University – the recently announced "Noctua2" supercomputer at the University of Paderborn, based on Atos" BullSequana XH2000, will give the University the modern, highly available and flexible supercomputer infrastructure that is needed for excellent science and research. EuroHPC – its BullSequana will be used in five EuroHPC supercomputing centres – Sofia Tech Park at Bulgaria ("Discover"), CINECA in Italy ("Da Vinci"), IZUM in Slovenia, ("Vega") LuxProvide in Luxembourg ("MeluXina" which is also #4 in the GREEN500) and in the Minho Advanced Computing Centre in Portugal, reinforcing Atos' position as a European leader in high-performance computing. The Linkoping University's'Berzelius' supercomputer for AI will use Atos' recently announced Atos ThinkAI solution to enable its researchers speed-up processing times on their complex data, empowering them to gain insights faster, using the power of deep learning and analytics. "Topaze" at the CCRT – a new supercomputer at the French CCRT based on the BullSequana XH2000 solution from Atos, is the result of joint R&D by Atos and the CEA's Military Applications Directorate (DAM). It will soon be open to the first users, to start the'Grand Challenges' phase, for very large-scale simulations. "Noctua2" at Paderborn University – the recently announced "Noctua2" supercomputer at the University of Paderborn, based on Atos" BullSequana XH2000, will give the University the modern, highly available and flexible supercomputer infrastructure that is needed for excellent science and research.

Artificial intelligence (AI) is an IT area that produces advanced technology capable of performing certain jobs and delivers varied outcomes. AI seems to have an attractive characteristic in the enterprise sector to assess and assist processes that offer the chance to achieve this same objective. There is, however, a break between what we observe in the real world of AI and the virtual world. Companies employ AI-based machines to carry out activities that needed intellect and involvement previously. Deep learning algorithms enable these independent learning methods by absorbing enormous amounts of unstructured data.

In 1958, the New York Times reported on a demonstration by the US Navy of Frank Rosenblatt's "perceptron" (a rudimentary precursor to today's deep neural networks): "The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself, and be conscious of its existence". This optimistic take was quickly followed by similar proclamations from AI pioneers, this time about the promise of logic-based "symbolic" AI. In 1960 Herbert Simon declared that, "Machines will be capable, within twenty years, of doing any work that a man can do". The following year, Claude Shannon echoed this prediction: "I confidently expect that within a matter of 10 or 15 years, something will emerge from the laboratory which is not too far from the robot of science fiction fame". And a few years later Marvin Minsky predicted that, "Within a generation...the problems of creating'artificial intelligence' will be substantially solved". John McCarthy promoted the term Artificial Intelligence with a wishful thinking that, 'Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve the kinds of problems now reserved for humans, and improve themselves.' AI was assumed to simulate human reasoning, giving the ability of a computer program to learn and think.

Powerful deep learning algorithms open an opportunity for solving non-image Machine Learning (ML) problems by transforming these problems to into the image recognition problems. The CPC-R algorithm presented in this chapter converts non-image data into images by visualizing non-image data. Then deep learning CNN algorithms solve the learning problems on these images. The design of the CPC-R algorithm allows preserving all high-dimensional information in 2-D images. The use of pair values mapping instead of single value mapping used in the alternative approaches allows encoding each n-D point with 2 times fewer visual elements.

The various levels, quests and characters in modern video games play a major role in these games' engagement and entertainment values. One way to keep things fresh is procedural content generation (PCG), the algorithmic generation of game content using a random process that can produce an unpredictable range of possible gameplay spaces, freeing human game designers from the laborious task of manual content generation. Recent improvements in machine learning (ML) have spurred interest in applying such techniques to PCG, but research on level generation in 3D games remains limited. In the popular 3D Minecraft game, for example, humans still play a central role in content generation -- structures have to be placed manually in a fixed world because the Minecraft World Generator can't generate new structures on its own. To fill the gap between the Minecraft World Generator's PCG and manually created custom structures, a research team from Leibniz University Hannover recently introduced World-GAN, a 3D generative adversarial network (GAN) that can learn and generate structures directly in the Minecraft 3D voxel space.

Deep learning-based techniques have been widely applied to the program analysis tasks, in fields such as type inference, fault localization, and code summarization. Hitherto deep learning-based software engineering systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label a prohibitively large amount of data. However, most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose cross-lingual adaptation of program analysis, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others. Specifically, we implemented a cross-lingual adaptation framework, PLATO, to transfer a deep learning-based type inference procedure across weakly typed languages, e.g., Python to JavaScript and vice versa. PLATO incorporates a novel joint graph kernelized attention based on abstract syntax tree and control flow graph, and applies anchor word augmentation across different languages. Besides, by leveraging data from strongly typed languages, PLATO improves the perplexity of the backbone cross-programming-language model and the performance of downstream cross-lingual transfer for type inference. Experimental results illustrate that our framework significantly improves the transferability over the baseline method by a large margin.

Employing a deep convolutional neural network (deep CNN) trained on spin configurations of the 2D Ising model and the temperatures, we examine whether the deep CNN can detect the phase transition of the 2D $q$-state Potts model. To this end, we generate binarized images of spin configurations of the $q$-state Potts model ($q\ge 3$) by replacing the spin variables $\{0,1,\dots,\lfloor q/2\rfloor-1\}$ and $\{\lfloor q/2\rfloor,\dots,q-1\}$ with $\{0\}$ and $\{1\}$, respectively. Then, we input these images to the trained CNN to output the predicted temperatures. The binarized images of the $q$-state Potts model are entirely different from Ising spin configurations, particularly at the transition temperature. Moreover, our CNN model is not trained on the information about whether phases are ordered/disordered but is naively trained by Ising spin configurations labeled with temperatures at which they are generated. Nevertheless, the deep CNN can detect the transition point with high accuracy, regardless of the type of transition. We also find that, in the high-temperature region, the CNN outputs the temperature based on the internal energy, whereas, in the low-temperature region, the output depends on the magnetization and possibly the internal energy as well. However, in the vicinity of the transition point, the CNN may use more general factors to detect the transition point.