Optimizing a given metric is a central aspect of most current AI approaches, yet overemphasizing metrics leads to manipulation, gaming, a myopic focus on short-term goals, and other unexpected negative consequences. This poses a fundamental contradiction for AI development. Through a series of real-world case studies, we look at various aspects of where metrics go wrong in practice and aspects of how our online environment and current business practices are exacerbating these failures. Finally, we propose a framework towards mitigating the harms caused by overemphasis of metrics within AI by: (1) using a slate of metrics to get a fuller and more nuanced picture, (2) combining metrics with qualitative accounts, and (3) involving a range of stakeholders, including those who will be most impacted.

As part of art-meets-tech collective Telemagic, Roos Groothuizen created the project'Concert in A.I.', a series of concerts where self-learning musical algorithm'AlgoRhythmics' is the host, conductor, instrument and musician. Arran Lyon was involved in the project as one of the coders. With the concerts, they search for new insights in music, creation, space and visual design. How do they look at the end result? Andreas Refsgaard is an artist and creative coder based in Copenhagen who uses algorithms and machine learning to make unconventional connections between inputs and outputs.

"Automated machine learning improves the productivity of data scientists by handling much of the repetitive tasks associated with model development," said Susan Kahler, Global Product Marketing Manager for AI at SAS. "This frees up data scientists to focus on creating solutions to best address the problem at hand." SAS Visual Data Mining and Machine Learning, which runs on SAS Viya, supports the end-to-end data mining and machine learning process with a comprehensive visual and programming interface. SAS empowers analytics team members of all skill levels with a simple, powerful and automated way to handle all tasks in the analytics life cycle.

As artificial intelligence continues to mature, we are seeing a corresponding growth in sophistication for humanoid robots and the applications for digital human beings in many aspects of modern-day life. To help you see the possibilities, we have pulled together some of the best examples of humanoid robots and where you might see digital humans in your everyday life today. Even though the earliest form of humanoid was created by Leonardo Da Vinci in 1495 (a mechanical armored suit that could sit, stand and walk), today's humanoid robots are powered by artificial intelligence and can listen, talk, move and respond. They use sensors and actuators (motors that control movement) and have features that are modeled after human parts. Whether they are structurally similar to a male (called an Android) or a female (Gynoid), it's a challenge to create realistic robots that replicate human capabilities.

Deep learning is on the rise in the recycling industry, according to the new e-book, Harnessing the Potential of AI, from Tomra Sorting Recycling, a sensor-based sorting technology company with headquarters in Norway. Dispelling a common misconception about AI, this latest e-book chronicles the 30-year history and contributions AI has already made to the recycling industry, as well the bright future that lies ahead, according to a Tomra news release. "In the months and years to come, those in the recycling industry will hear much more about deep learning, a powerful component of artificial intelligence," says Daniel Bender, technical manager, deep learning for Tomra Sorting. "Deep learning shows the promise of providing solutions for the industry's most complex sorting challenges. Recycling operations at the forefront of using AI to sort material stand to gain a significant advantage over companies who do not."

The recent advances in Natural Language Processing have been a boon for well-represented languages in terms of available curated data and research resources. One of the challenges for low-resourced languages is clear guidelines on the collection, curation and preparation of datasets for different use-cases. In this work, we take on the task of creation of two datasets that are focused on news headlines (i.e short text) for Setswana and Sepedi and creation of a news topic classification task. We document our work and also present baselines for classification. We investigate an approach on data augmentation, better suited to low resource languages, to improve the performance of the classifiers

Effective detection of fake news has recently attracted significant attention. Current studies have made significant contributions to predicting fake news with less focus on exploiting the relationship (similarity) between the textual and visual information in news articles. Attaching importance to such similarity helps identify fake news stories that, for example, attempt to use irrelevant images to attract readers' attention. In this work, we propose a $\mathsf{S}$imilarity-$\mathsf{A}$ware $\mathsf{F}$ak$\mathsf{E}$ news detection method ($\mathsf{SAFE}$) which investigates multi-modal (textual and visual) information of news articles. First, neural networks are adopted to separately extract textual and visual features for news representation. We further investigate the relationship between the extracted features across modalities. Such representations of news textual and visual information along with their relationship are jointly learned and used to predict fake news. The proposed method facilitates recognizing the falsity of news articles based on their text, images, or their "mismatches." We conduct extensive experiments on large-scale real-world data, which demonstrate the effectiveness of the proposed method.

An important problem in multiview representation learning is finding the optimal combination of views with respect to the specific task at hand. To this end, we introduce NAM: a Neural Attentive Multiview machine that learns multiview item representations and similarity by employing a novel attention mechanism. NAM harnesses multiple information sources and automatically quantifies their relevancy with respect to a supervised task. Finally, a very practical advantage of NAM is its robustness to the case of dataset with missing views. We demonstrate the effectiveness of NAM for the task of movies and app recommendations. Our evaluations indicate that NAM outperforms single view models as well as alternative multiview methods on item recommendations tasks, including cold-start scenarios.

Since its inception, Facebook has become an integral part of the online social community. People rely on Facebook to make connections with others and build communities. As a result, it is paramount to protect the integrity of such a rapidly growing network in a fast and scalable manner. In this paper, we present our efforts to protect various social media entities at Facebook from people who try to abuse our platform. We present a novel Temporal Interaction EmbeddingS (TIES) model that is designed to capture rogue social interactions and flag them for further suitable actions. TIES is a supervised, deep learning, production ready model at Facebook-scale networks. Prior works on integrity problems are mostly focused on capturing either only static or certain dynamic features of social entities. In contrast, TIES can capture both these variant behaviors in a unified model owing to the recent strides made in the domains of graph embedding and deep sequential pattern learning. To show the real-world impact of TIES, we present a few applications especially for preventing spread of misinformation, fake account detection, and reducing ads payment risks in order to enhance the platform's integrity.

Some machine learning systems, however, require even more power. For those, Arm also introduced the industry's very first microNPU, the Ethos-U55.