When you think of artificial intelligence (AI) your mind is naturally drawn to Skynet or Blade Runner. Evolved, sentient beings, often with a desire to rise up against humanity for some reason. While we're not quite there (yet) AI technology is certainly on the rise. Especially when it comes to AI marketing. AI has substantial benefits and applications in marketing in fact -- so it's time e-commerce companies got on board to leverage this transformative technology.

The US Department of Energy (DOE) has long stood out as one of the most science, technology, and innovation-focused US federal agencies. It should come as little surprise then that the DOE continues to invest in transformative technology such as artificial intelligence and machine learning. The DOE established the Artificial Intelligence and Technology (AITO) office to help transform the DOE into a world leading Artificial Intelligence (AI) enterprise by accelerating the research, development, delivery, and adoption of AI. Pamela Isom, the new Director of the AITO, will be presenting at the February 2022 AI in Government event to share how they are maximizing the impacts of AI through strategic coordination, planning, and customer service excellence. In this interview article Ms. Isom goes into greater detail about how the DOE is leveraging data, and transformative technologies to help advance the agency's core missions.

Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty.

In reaction to growing concerns about the potential harms of artificial intelligence (AI), societies have begun to demand more transparency about how AI models and systems are created and used. To address these concerns, several efforts have proposed documentation templates containing questions to be answered by model developers. These templates provide a useful starting point, but no single template can cover the needs of diverse documentation consumers. It is possible in principle, however, to create a repeatable methodology to generate truly useful documentation. Richards et al. [25] proposed such a methodology for identifying specific documentation needs and creating templates to address those needs. Although this is a promising proposal, it has not been evaluated. This paper presents the first evaluation of this user-centered methodology in practice, reporting on the experiences of a team in the domain of AI for healthcare that adopted it to increase transparency for several AI models. The methodology was found to be usable by developers not trained in user-centered techniques, guiding them to creating a documentation template that addressed the specific needs of their consumers while still being reusable across different models and use cases. Analysis of the benefits and costs of this methodology are reviewed and suggestions for further improvement in both the methodology and supporting tools are summarized.

We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve relevant relations for a given context to improve text generation. Experiments on WikiText-103, WMT19, and enwik8 English datasets demonstrate that our approach produces a better language model in terms of perplexity and bits per character. We also show that relational memory improves coherence, is complementary to token-based memory, and enables causal interventions. Our model provides a simple yet effective way to combine an autoregressive language model with a knowledge graph for a more coherent and logical generation.

Data science is a hot topic with an extensive scope, both in terms of theory and applications. Simultaneously, Data Science applications provide important challenges that can often be addressed only with innovative Machine Learning algorithms and methodologies. This special issue will highlight the latest development of the Machine Learning foundations of data science and on the synergy of data science and machine learning. Following the great success of the 2021 MLJ special issue with DSAA'2021, this 2022 special issue will further capture the state-of-the-art machine learning advances for data science. Accepted papers will be published in MLJ and presented at a journal track of the 2022 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2022) in Shenzhen, October 2022.

This paper offers a comprehensive survey of recent developments in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We include basic concepts, focus our survey on a broad spectrum of different approaches arising in the last ten years and propose a systematization which offers a practical framework to compare those approaches on the methods level.

In the realm of image processing and computer vision (CV), machine learning (ML) architectures are widely applied. Convolutional neural networks (CNNs) solve a wide range of image processing issues and can solve image compression problem. Compression of images is necessary due to bandwidth and memory constraints. Helpful, redundant, and irrelevant information are three different forms of information found in images. This paper aims to survey recent techniques utilizing mostly lossy image compression using ML architectures including different auto-encoders (AEs) such as convolutional auto-encoders (CAEs), variational auto-encoders (VAEs), and AEs with hyper-prior models, recurrent neural networks (RNNs), CNNs, generative adversarial networks (GANs), principal component analysis (PCA) and fuzzy means clustering. We divide all of the algorithms into several groups based on architecture. We cover still image compression in this survey. Various discoveries for the researchers are emphasized and possible future directions for researchers. The open research problems such as out of memory (OOM), striped region distortion (SRD), aliasing, and compatibility of the frameworks with central processing unit (CPU) and graphics processing unit (GPU) simultaneously are explained. The majority of the publications in the compression domain surveyed are from the previous five years and use a variety of approaches.

This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral, probabilistic, and deep metric learning. We first start with the definition of distance metric, Mahalanobis distance, and generalized Mahalanobis distance. In spectral methods, we start with methods using scatters of data, including the first spectral metric learning, relevant methods to Fisher discriminant analysis, Relevant Component Analysis (RCA), Discriminant Component Analysis (DCA), and the Fisher-HSIC method. Then, large-margin metric learning, imbalanced metric learning, locally linear metric adaptation, and adversarial metric learning are covered. We also explain several kernel spectral methods for metric learning in the feature space. We also introduce geometric metric learning methods on the Riemannian manifolds. In probabilistic methods, we start with collapsing classes in both input and feature spaces and then explain the neighborhood component analysis methods, Bayesian metric learning, information theoretic methods, and empirical risk minimization in metric learning. In deep learning methods, we first introduce reconstruction autoencoders and supervised loss functions for metric learning. Then, Siamese networks and its various loss functions, triplet mining, and triplet sampling are explained. Deep discriminant analysis methods, based on Fisher discriminant analysis, are also reviewed. Finally, we introduce multi-modal deep metric learning, geometric metric learning by neural networks, and few-shot metric learning.

Autodesk's Will Harris, Foundry's Mathieu Mazerolle and Unity Technologies' Brian Gaffney will discuss how their companies are incorporating machine learning into software tools to make higher quality and more realistic visual effects and boost production speed. Visual Effects Supervisor Ryan Laney will describe the novel way artificial intelligence and machine learning were used to mask the identities of interview subjects in the award-winning HBO documentary Welcome to Chechnya. "Machine learning is poised to transform visual effects production, accelerating workflows and paving the way for a new generation of astonishingly real visual effects," says Barry Goch, who will moderate the discussion. "Will Harris, Mathieu Mazerolle and Brian Gaffney will demonstrate game-changing technologies. Ryan Laney will share his experience in applying machine learning to a real-world production."