The interwebs, as of late, have been filled with images created by artificial intelligence rendering bots such as DALL-E and Midjourney -- and the humans (I think they're humans) using them as tools. Brooklyn-based artist Zach Katz has used it to reimagine the urban design of cities. A reporter at SFGATE has undertaken a similar project, asking DALL-E 2 to retool some of the city's architecture and infrastructure. In July, the Guardian rounded up four artists to come up with unlikely prompts -- such as "biotech harpy in field at sunset" -- for DALL-E Mini (the free, public version of DALL-E). Naturally, the advent of bots that can create an image out of a simple text command is drawing the scrutiny of illustrators.

The use of data would take a central role in a Labour government's industrial strategy, according to shadow business secretary Jonathan Reynolds A council would also be set up on a statutory footing to oversee the strategy and ensure it isn't abandoned at a later date. The opposition party said if it were to win the next general election, it would make "harnessing data for the public good" a key mission in its industrial strategy. Reynolds announced Labour's industrial strategy at the Labour Party conference in Liverpool. "It's a real industrial strategy – with ambition and the means to achieve it," he said. A key part of this, according to Reynolds, is using data.

Debris from a drone strike in northern Ethiopia's Tigray region has damaged a truck carrying humanitarian aid and belonging to the World Food Programme (WFP) and injured the truck's driver, the United Nations agency said on Monday. The WFP said the drone strike on Sunday hit near an area called Zana Woreda in northwestern Tigray, as two trucks were delivering relief supplies to families displaced by the nearly two-year long conflict. "Flying debris from the strike injured a driver contracted by WFP and caused minor damage to a WFP fleet truck," the spokesperson said, adding it was not possible to say yet whether further distributions would be suspended in the area. "WFP calls on all parties to respect and adhere to international humanitarian laws and to commit to safeguarding humanitarian workers, premises and assets." The WFP truck was delivering food to internally displaced people as hundreds of thousands have been uprooted by renewed fighting since August 24 after a five-month ceasefire broke down.

In recent years, many in the business world have seen a rise in the use of artificial intelligence (AI), from chatbots to image recognition and financial fraud detection. Gartner has predicted that AI software will reach $62 billion in 2022 alone, an increase of 21.3% from 2021. And there are huge potentials for AI in the UK, with predictions that it could deliver a 22% boost to the UK economy by 2030. With such an increase in the use of AI, it was only a matter of time before the regulation was tightened (and rightly so) to ensure businesses and consumers remained protected. Recently, the UK Government shared a new rulebook for AI innovation to boost public trust in the technology.

Artificial Intelligence (AI) has an increasing say in the range of opportunities we are offered in life. Artificial neural networks might be used in deciding whether you will get a loan, an apartment, or your next job based on datasets collected from around the globe. Generative adversarial networks (GANs) are used to produce real-looking but fake content online that can affect our political opinion-formation and election freedom. In some cases, our only contact for a service provider is an AI system, which is used to collect and analyze the content of customer input and to provide solutions with natural language processing. In the context of Western democracies, threats and issues related to these tools are frequently viewed as problematic. On the one hand, AI technologies are shown to help include more people in collective decision-making and potentially decrease the cognitive bias occurring when humans make decisions, leading to fairer outcomes.On the other hand, studies indicate that certain AI technologies can lead to biased decisions and decrease the level of human autonomy in a way that threatens our fundamental human rights. While recognizing individual cases where rights and freedoms are being violated, we can easily neglect rapid and in some cases alarming changes occurring in the big picture: People seem to have ever less control over their own lives and decisions that affect them. This has been brought forward by several authors and academics, such as James Muldoon in Platform Socialism, Shoshana Zuboff in Surveillance Capitalism and Mark Coeckelbergh in The Political Philosophy of AI. Control over one's life and collective decision-making are both essential building blocks of the fundamental structure of most Western societies: democracy. Whereas some attempts have already been made to better understand the relationship between AI and democracy (see, e.g., Nemitz 2018, Manheim & Kaplan 2019, and Mark Coeckelberg's above-mentioned book), the discussion remains limited.

For young Ukrainian geeks, making drones -- for reconnaissance or destruction -- in a house basement near the Donbas frontline is "new generation" guerrilla warfare. In dim light, the 20-somethings busily piece together electronic components spread out on tables, with the help of laptops and documents, while artillery fire thuds in the background. Next the repair room next door -- a laundry room before the war -- drones are patched up using spare parts taken from aircraft damaged "in battle" against the Russians. In the garden shed meanwhile, a 19-year-old, whose nom de guerre is Varnak, transforms grenades designed for grenade launchers into bombs to be dropped from drones. You just add fins to them and change the detonation system, he tells AFP, smiling.

Tilottama Goswami has received a BE degree with Honors in Computer Science and Engineering from the National Institute of Technology, Durgapur; and an MS degree in Computer Science (High Distinction) from Rivier University, Nashua, New Hampshire, United States. She was awarded a PhD in Computer Science from the University of Hyderabad. Presently, Dr. Goswami is Professor in the Department of Information Technology, Vasavi College of Engineering, Hyderabad, India. She has, overall, 23 years of experience in academia, research, and the IT industry. Her research interests are computer vision, machine learning, and image processing.

Classical machine learning (ML) provides a potentially powerful approach to solving challenging quantum many-body problems in physics and chemistry. However, the advantages of ML over more traditional methods have not been firmly established. In this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonians in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter. In contrast, under widely accepted complexity theory assumptions, classical algorithms that do not learn from data cannot achieve the same guarantee. We also prove that classical ML algorithms can efficiently classify a wide range of quantum phases of matter. Our arguments are based on the concept of a classical shadow, a succinct classical description of a many-body quantum state that can be constructed in feasible quantum experiments and be used to predict many properties of the state. Extensive numerical experiments corroborate our theoretical results in a variety of scenarios, including Rydberg atom systems, 2D random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases.

Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development. These connected cities have the potential to create a symbiosis of technology and society and revolutionize the cost of living, safety, ecological sustainability, and quality of life of societies on a world-wide scale. Some key components of the smart city construct are connected smart grids, self-driving cars, federated learning systems, smart utilities, large-scale public transit, and proactive surveillance systems. While exciting in prospect, these technologies and their subsequent integration cannot be attempted without addressing the potential societal impacts of such a high degree of automation and data sharing. Additionally, the feasibility of coordinating so many disparate tasks will require a fast, extensible, unifying framework. To that end, we propose FaRO2, a completely reimagined successor to FaRO1, built from the ground up. FaRO2 affords all of the same functionality as its predecessor, serving as a unified biometric API harness that allows for seamless evaluation, deployment, and simple pipeline creation for heterogeneous biometric software. FaRO2 additionally provides a fully declarative capability for defining and coordinating custom machine learning and sensor pipelines, allowing the distribution of processes across otherwise incompatible hardware and networks. FaRO2 ultimately provides a way to quickly configure, hot-swap, and expand large coordinated or federated systems online without interruptions for maintenance. Because much of the data collected in a smart city contains Personally Identifying Information (PII), FaRO2 also provides built-in tools and layers to ensure secure and encrypted streaming, storage, and access of PII data across distributed systems.

Although Deep Neural Networks (DNNs) have great generalization and prediction capabilities, their functioning does not allow a detailed explanation of their behavior. Opaque deep learning models are increasingly used to make important predictions in critical environments, and the danger is that they make and use predictions that cannot be justified or legitimized. Several eXplainable Artificial Intelligence (XAI) methods that separate explanations from machine learning models have emerged, but have shortcomings in faithfulness to the model actual functioning and robustness. As a result, there is a widespread agreement on the importance of endowing Deep Learning models with explanatory capabilities so that they can themselves provide an answer to why a particular prediction was made. First, we address the problem of the lack of universal criteria for XAI by formalizing what an explanation is. We also introduced a set of axioms and definitions to clarify XAI from a mathematical perspective. Finally, we present the Greybox XAI, a framework that composes a DNN and a transparent model thanks to the use of a symbolic Knowledge Base (KB). We extract a KB from the dataset and use it to train a transparent model (i.e., a logistic regression). An encoder-decoder architecture is trained on RGB images to produce an output similar to the KB used by the transparent model. Once the two models are trained independently, they are used compositionally to form an explainable predictive model. We show how this new architecture is accurate and explainable in several datasets.