HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and systems, published over the last two decades. From this review, we identified seven themes associated with the evolution of a project: from establishing community partnerships to sustaining results. Our findings suggest that several tensions characterize these projects, many of which relate to the power and position of researchers, and the computing research environment, relative to community partners. We discuss the implications of our findings and offer methodological proposals to guide HCI, and computing research more broadly, towards practices that center communities.

We present a data-driven approach to learning surrogate models for amplitude equations, and illustrate its application to interfacial dynamics of phase field systems. In particular, we demonstrate learning effective partial differential equations describing the evolution of phase field interfaces from full phase field data. We illustrate this on a model phase field system, where analytical approximate equations for the dynamics of the phase field interface (a higher order eikonal equation and its approximation, the Kardar-Parisi-Zhang (KPZ) equation) are known. For this system, we discuss data-driven approaches for the identification of equations that accurately describe the front interface dynamics. When the analytical approximate models mentioned above become inaccurate, as we move beyond the region of validity of the underlying assumptions, the data-driven equations outperform them. In these regimes, going beyond black-box identification, we explore different approaches to learn data-driven corrections to the analytically approximate models, leading to effective gray box partial differential equations.

Reinforcement Learning (RL) has become an increasingly important research area as the success of machine learning algorithms and methods grows. To combat the safety concerns surrounding the freedom given to RL agents while training, there has been an increase in work concerning Safe Reinforcement Learning (SRL). However, these new and safe methods have been held to less scrutiny than their unsafe counterparts. For instance, comparisons among safe methods often lack fair evaluation across similar initial condition bounds and hyperparameter settings, use poor evaluation metrics, and cherry-pick the best training runs rather than averaging over multiple random seeds. In this work, we conduct an ablation study using evaluation best practices to investigate the impact of run time assurance (RTA), which monitors the system state and intervenes to assure safety, on effective learning. By studying multiple RTA approaches in both on-policy and off-policy RL algorithms, we seek to understand which RTA methods are most effective, whether the agents become dependent on the RTA, and the importance of reward shaping versus safe exploration in RL agent training. Our conclusions shed light on the most promising directions of SRL, and our evaluation methodology lays the groundwork for creating better comparisons in future SRL work.

The ability of deep neural networks to perform complex tasks more accurately than manually-crafted solutions has created a substantial demand for more complex models processing larger amounts of data. However, the traditional computing architecture has reached a bottleneck in processing performance due to data movement from memory to computing. Considerable efforts have been made towards custom hardware acceleration, among which are optical neural networks (ONNs). These excel at energy efficient linear operations but struggle with scalability and the integration of linear and nonlinear functions. Here, we introduce our multiplicative analog frequency transform optical neural network (MAFT-ONN) that encodes the data in the frequency domain to compute matrix-vector products in a single-shot using a single photoelectric multiplication, and then implements the nonlinear activation for all neurons using a single electro-optic modulator. We experimentally demonstrate a 3-layer DNN with our architecture using a simple hardware setup assembled with commercial components. Additionally, this is the first DNN hardware accelerator suitable for analog inference of temporal waveforms like voice or radio signals, achieving bandwidth-limited throughput and speed-of-light limited latency. Our results demonstrate a highly scalable ONN with a straightforward path to surpassing the current computing bottleneck, in addition to enabling new possibilities for high-performance analog deep learning of temporal waveforms.

Data science and AI has become a requirement for business growth. The technology has advanced enough to predict customer s choices and satisfy their needs. The volume of data generated per day is predicted to reach 463 exabytes by 2025. On the Internet, the world spends about $1 million per minute on goods. This huge amount of data, known as big data, has increased the need for qualified data science workers.

The tension between technology and work is at least as old as the economics profession itself. A question some people are asking now is: if computers run by artificial intelligence can do the job of humans, will work disappear someday? Two economists are proposing a couple different scenarios in a new paper that is part science fiction and part mathematical models. In one scenario, lower-paid workers who are not highly valued by society – say, McDonald's hamburger flippers – are more readily replaced by computers than a scientist searching for a cure for Alzheimer's disease. This will drive down wages for a larger and larger segment of the lower-paid labor force.

Irish officials may be questioned over the country's plans for facial recognition technology for surveillance during a session with the United Nations Human Rights Committee in Geneva this week. The Irish Council for Civil Liberties (ICCL) has submitted a Shadow Report on what they determine as gaps between the International Covenant on Civil and Political Rights and the reality in Ireland, plus recommendations to rectify them. The group also blames Irish authorities for failing to uphold GDPR, thus allowing surveillance to remain business as usual for digital companies worldwide. The ICCL report, an alternative to the report submitted by the Irish state, is endorsed by 37 organizations and has identified gaps across areas such as the right to a fair trial and freedom from torture, as well as three breaches involving police surveillance and six across data protection. The UN Human Rights Committee meets every four years and countries are invited in turn to defend their human rights provision.

Drought, extreme temperatures and thousands of lightning bolts each day led to the ignition of wildfires across Alaska's interior. More than 2.4 million acres have burned this year by wildfires, which is double the acreage that is typically scorched at this point in the state's wildfire season. The Alaska wildfire season typically begins in late May and ends in late July, and the National Park Services states that, on average, one million acres burn statewide each year. The blazes are being ignited by lightning strikes plaguing the state - nearly 25,000 bolts were detected between June 28 and July 4 and more than 10,000 have hit since then. There are only about 1,000 firefighters in the interior of the state who are tirelessly working around the clock to put out more than 225 fires, which are forcing hundreds of residents from their homes.

Each political group of the European Parliament has submitted several hundred amendments, bringing the total to several thousand. The deluge has come equally from the left and the right – and will now have to be reconciled in a summer of negotiations. One of the most controversial topics is on definitions. Left-of-center parliamentarians are pushing for a broad general definition of artificial intelligence (AI) rather than accepting a narrow list of AI techniques. Their goal is to make the regulation future-proof.

The U.S. Army is continuing to consider ways artificial intelligence, or AI, can augment and improve current operations for sustainment enterprises, including medical logistics. C.J. Lovelace, U.S. Army Medical Logistics Command, reports. Trezia Davenport and Alan Gonzalez, assigned to the 563rd Medical Logistics Company, assemble a tactical combat medical care resupply set in support of U.S. Forces Korea during an exercise at the Army's prepositioned stocks site in South Korea. Lt. Col. Marcus D. Perkins, immediate past commander of the U.S. Army Medical Materiel Center-Korea (USAMMC-K), took part as a panelist and medical logistics subject-matter expert during the virtual 2022 DOD Digital and AI Symposium in June. USAMMC-K is a direct reporting unit to U.S. Army Medical Logistics Command.