Creating nanomaterials with flame spray pyrolysis is complex, but scientists at Argonne have discovered how applying artificial intelligence can lead to an easier process and better performance. During a tour of the Manufacturing and Engineering Research Facility at the U.S. Department of Energy's Argonne National Laboratory, Marius Stan, the Intelligent Materials Design lead in Argonne's Applied Materials Division (AMD), encountered a new experimental setup. As he watched the machine in the experiment, which relies on flame to produce nanomaterials, he had a thought: Could artificial intelligence be used to optimize this complex process? When asked to explain the process, Stan put it simply: "It's where scientists put chemicals in a flame and wait for a miracle--for particles to appear at the end of the process, particles that have important properties for a variety of applications." Flame spray pyrolysis is a technology that enables the manufacturing of nanomaterials in high volumes, which in turn is critical to producing a wide range of industrial materials, like chemical catalysts, battery electrolytes/cathodes and pigments.

These are tricky topics to navigate but ones which many journalists are increasingly grappling with as tech stories become more mainstream. There have been some teething issues though. The classic example in 2015 was when NPR mapped the most common job in every US state using data derived from the Bureau of Labor Statistics. Truck drivers dominated the map. The issue is in the nuance of what'truck driver' means; the category includes anything from delivery drivers to those driving 16-wheel lorries.

JEDDAH: Saudi Arabia will be a global artificial intelligence (AI) leader by 2030, a prominent Saudi expert has said. Dr. Abdullah bin Sharaf Al-Ghamdi, president of the Saudi Data and Artificial Intelligence Authority (SDAIA), made the comments during a media briefing on shaping new frontiers at the International Media Center in Riyadh ahead of the G20 Leaders' Summit. Last year, the authority developed the Estishraf Platform, an AI-based platform that utilizes data to create diversified insights and respond to the top priorities of decision-makers in Saudi Arabia. "Through this platform we were able to earn revenues amounting to SR43 billion ($11.5 billion), only in 2019," said Al-Ghamdi. "Undoubtedly, this is an excellent indicator for the great opportunities the national economy is waiting for after AI has become a knowledge-based economy."

During a press meet recently, a Facebook spokesperson said that the social media giant would be redoubling its efforts to counter'harmful content' on its platform using artificial intelligence. Reportedly, Ryan Barnes, the Facebook Product Manager of Community Integrity, said that the company would use AI to prioritise harmful content. This move is targeting at helping its over 15,000 human reviewers and moderators in dealing with reported contents. Barnes said during the press interaction, "We want to make sure we're getting to the worst of the worst, prioritising real-world imminent harm above all." With that being said, there have been numerous attempts in the past to bring AI into the content moderation process on Facebook's platforms. However, not all of them have met with success.

This week, the U.S. Environmental Protection Agency (EPA) celebrates America Recycles Week by hosting two days of free, virtual events that focus on creating a more robust and sustainable recycling system in the U.S. and abroad. Today, the America Recycles: Innovation Fair will feature more than 40 innovators from across the recycling system via virtual exhibit halls demonstrating their state-of-the-art products, services, outreach, and technologies. They are advancing the recycling system through strategies such as: deploying artificial intelligence robots to enhance operations at recycling facilities; using hard-to-recycle plastics in 3D printing materials; installing small system sorting units in stadiums and small communities; creating new construction materials from hard-to-recycle plastics; and using automated technology and recycled glass bottles to create new glassware. "EPA is proud to showcase top recycling innovators at the virtual Innovation Fair today," said EPA Administrator Andrew Wheeler. "Tomorrow's America Recycles Summit will include EPA's announcement of the first National Recycling Goal, which will prompt a whole new level of dialogue among stakeholders on how to improve our domestic recycling infrastructure."

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This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations. Non-Bayesian social learning theory provides a framework that solves this problem in an efficient manner by allowing the agents to sequentially communicate and update their beliefs for each hypothesis over the network. Most existing approaches assume that agents have access to exact statistical models for each hypothesis. However, in many practical applications, agents learn the likelihood models based on limited data, which induces uncertainty in the likelihood function parameters. In this work, we build upon the concept of uncertain models to incorporate the agents' uncertainty in the likelihoods by identifying a broad set of parametric distribution that allows the agents' beliefs to converge to the same result as a centralized approach. Furthermore, we empirically explore extensions to non-parametric models to provide a generalized framework of uncertain models in non-Bayesian social learning.

Artificial Intelligence (AI) governance regulates the exercise of authority and control over the management of AI. It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. While topics such as AI governance and AI ethics are thoroughly discussed on a theoretical, philosophical, societal and regulatory level, there is limited work on AI governance targeted to companies and corporations. This work views AI products as systems, where key functionality is delivered by machine learning (ML) models leveraging (training) data. We derive a conceptual framework by synthesizing literature on AI and related fields such as ML. Our framework decomposes AI governance into governance of data, (ML) models and (AI) systems along four dimensions. It relates to existing IT and data governance frameworks and practices. It can be adopted by practitioners and academics alike. For practitioners the synthesis of mainly research papers, but also practitioner publications and publications of regulatory bodies provides a valuable starting point to implement AI governance, while for academics the paper highlights a number of areas of AI governance that deserve more attention.

The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities. However, using various perturbations, multiple recent works have shown that good performance on a dataset might not indicate performance that correlates well with human's expectations from models that "understand" language. In this work we consider a top performing model on several Multiple Choice Question Answering (MCQA) datasets, and evaluate it against a set of expectations one might have from such a model, using a series of zero-information perturbations of the model's inputs. Our results show that the model clearly falls short of our expectations, and motivates a modified training approach that forces the model to better attend to the inputs. We show that the new training paradigm leads to a model that performs on par with the original model while better satisfying our expectations.

We present a novel variant of Domain Adversarial Networks with impactful improvements to the loss functions, training paradigm, and hyperparameter optimization. New loss functions are defined for both forks of the DANN network, the label predictor and domain classifier, in order to facilitate more rapid gradient descent, provide more seamless integration into modern neural networking frameworks, and allow previously unavailable inferences into network behavior. Using these loss functions, it is possible to extend the concept of 'domain' to include arbitrary user defined labels applicable to subsets of the training data, the test data, or both. As such, the network can be operated in either 'On the Fly' mode where features provided by the feature extractor indicative of differences between 'domain' labels in the training data are removed or in 'Test Collection Informed' mode where features indicative of difference between 'domain' labels in the combined training and test data are removed (without needing to know or provide test activity labels to the network). This work also draws heavily from previous works on Robust Training which draws training examples from a L_inf ball around the training data in order to remove fragile features induced by random fluctuations in the data. On these networks we explore the process of hyperparameter optimization for both the domain adversarial and robust hyperparameters. Finally, this network is applied to the construction of a binary classifier used to identify the presence of EM signal emitted by a turbopump. For this example, the effect of the robust and domain adversarial training is to remove features indicative of the difference in background between instances of operation of the device - providing highly discriminative features on which to construct the classifier.