Artificial intelligence (AI) is gaining traction in almost all industries, including the creative world. The emergence of AI-generated artwork has caught on as people seek to express their creativity through artwork that is unique, personalized, and reflects their identity. Tools like Google's Imagen, OpenAI DALLE-2, Midjourney, and Stable Diffusion are raising in popularity for generating artwork in seconds. While the process of creating art using AI is still largely accessible to the average person, there are challenges due to limited interaction and output that the average person has with tools like Imagen & DALLE-2 as well as concerns about how this technology may change the way we engage with art in the future. Stable Diffusion, on the other hand, is open-sourced and aims to make AI artwork and photo generation even more accessible and versatile compared to some of its counterparts.

White Shark Media, a leading digital marketing agency specializing in pay-per-click advertising, announced the launch of AdClicks, a new reporting software for marketing agencies and freelancers. Designed for marketing experts, by marketing experts, this new tool provides professionals with a single dashboard that allows them to better serve their clients with a one-stop-shop for all of their reporting needs. AdClicks helps marketing agencies and freelancers achieve their business goals with accurate data, performance recommendations and engaging client reporting from a single dashboard. "Innovation is at the heart of our work at White Shark Media, and we are always looking for creative solutions to some of marketing's biggest challenges," said Alexander Nygart, CEO of White Shark Media. "We are so excited to launch this new brand under White Shark Media and help marketers instantly, accurately and securely track their client's marketing data."

As part of a major sustainability effort, New York State Gov. Kathy Hochul has issued an executive order mandating that NY state agencies use the NY Power Authority's NY Energy Manager application, a system developed and deployed with the leading enterprise AI software application company. "This is great validation in the work C3 AI has done with our longtime customer, the NY Power Authority, and we look forward to helping Governor Hochul achieve her goal of making New York's public sector operations more sustainable." "We are pleased to receive such broad recognition and confidence in our enterprise AI energy management solution," said Ed Abbo, President and CTO of C3 AI. "This is great validation in the work C3 AI has done with our longtime customer, the NY Power Authority, and we look forward to helping Governor Hochul achieve her goal of making New York's public sector operations more sustainable." Announces Leadership Promotions to Drive Next Stage of Company Growth Among the many other goals spelled out in Executive Order 22, enabled by C3 AI, is a mandate for state operations to run on 100% clean electricity by 2030. The NY Energy Manager application, built on C3 AI Energy Management, has already been deployed to about 1,000 customers, including communities, businesses, municipalities, and electricity providers in New York. It will now serve as the system of record for all energy data from all state agencies.

The use of deepfakes to evade security controls and compromise organisations is on the rise among cybercriminals, with researchers seeing a 13% increase in the use of deepfakes compared with last year, said a new report. Deepfakes use deep learning artificial intelligence (AI) to replace the likeness of one person with another in video and other digital media. The findings from US-based cloud computing and virtualisation firm VMware's eighth annual'Global Incident Response Threat Report', which surveyed 125 cybersecurity professionals from around the world, also revealed an uptick in the overall cybersecurity attacks since Russia's invasion of Ukraine, as stated by two-thirds (65%) of those professionals. "Cybercriminals are now incorporating deepfakes into their attack methods to evade security controls," said Rick McElroy, principal cybersecurity strategist at VMware. "Two out of three respondents in our report saw malicious deepfakes used as part of an attack, a 13% increase from last year, with email as the top delivery method. Cybercriminals have evolved beyond using synthetic video and audio simply for influence operations or disinformation campaigns. Their new goal is to use deepfake technology to compromise organisations and gain access to their environment," added McElroy.

In our prior series of papers for Brookings, we explored the rise of national artificial intelligence (AI) strategy documents and sought to make sense of what each country was trying to do and how effectively they were doing it. In our concluding paper, we focused on where the U.S. was lagging behind and proposed options to remedy the lagging. In particular, we recommended three options: 1) apply lessons from the U.S. space race to invigorate talent development, (2) adopt a multi-national consortium approach (similar to NATO) and (3) create a robust partnership with one other country. Following the guidance of "Deep Throat" of Watergate fame, in this new series of articles, we follow the federal trail of money to understand the federal market for AI work, the hardware, software, and services being purchased. We also track the key players who allocate the money (legislators), spend the money (program managers), and receive the money (vendors). Taken together, this series provides a comprehensive look at federal IT spending, its direction, and its key players.

The future of defence aviation is autonomous. Recently Sheild AI joined the race and now Razor's Edge, based in Reston, Virginia, and defence- and security-focused VC firm announced the closing of its third startup investment fund at under $340M. The firm investment indicates that national security technology is a safe bet even in difficult economic times. According to the firm, it has surpassed its initial target of $250M and will target companies developing autonomous systems, space technologies, cybersecurity, AI and machine learning, digital signal processing, and other aerospace and defence technologies. With the new funding, the firm's total assets under management now exceed $600M.

In September 2022, the United Nations System Chief Executives Board for Coordination endorsed the Principles for the Ethical Use of Artificial Intelligence in the United Nations System, developed through the High-level Committee on Programmes (HLCP) which approved the Principles at an intersessional meeting in July 2022. These Principles were developed by a workstream co-led by United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Office of Information and Communications Technology of the United Nations Secretariat (OICT), in the HLCP Inter-Agency Working Group on Artificial Intelligence. The Principles are based on the Recommendation on the Ethics of Artificial Intelligence adopted by UNESCO's General Conference at its 41st session in November 2021. This set of ten principles, grounded in ethics and human rights, aims to guide the use of artificial intelligence (AI) across all stages of an AI system lifecycle across United Nations system entities. It is intended to be read with other related policies and international law, and includes the following principles: do no harm; defined purpose, necessity and proportionality; safety and security; fairness and non-discrimination; sustainability; right to privacy, data protection and data governance; human autonomy and oversight; transparency and explainability; responsibility and accountability; and inclusion and participation.

Applied mathematics and machine computations have raised a lot of hope since the recent success of supervised learning. Many practitioners in industries have been trying to switch from their old paradigms to machine learning. Interestingly, those data scientists spend more time scrapping, annotating and cleaning data than fine-tuning models. This thesis is motivated by the following question: can we derive a more generic framework than the one of supervised learning in order to learn from clutter data? This question is approached through the lens of weakly supervised learning, assuming that the bottleneck of data collection lies in annotation. We model weak supervision as giving, rather than a unique target, a set of target candidates. We argue that one should look for an ``optimistic'' function that matches most of the observations. This allows us to derive a principle to disambiguate partial labels. We also discuss the advantage to incorporate unsupervised learning techniques into our framework, in particular manifold regularization approached through diffusion techniques, for which we derived a new algorithm that scales better with input dimension then the baseline method. Finally, we switch from passive to active weakly supervised learning, introducing the ``active labeling'' framework, in which a practitioner can query weak information about chosen data. Among others, we leverage the fact that one does not need full information to access stochastic gradients and perform stochastic gradient descent.

Islamic and capitalist economies have several differences, the most fundamental being that the Islamic economy is characterized by the prohibition of interest (riba) and speculation (gharar) and the enforcement of Shariah-compliant profit-loss sharing (mudaraba, murabaha, salam, etc.) and wealth redistribution (waqf, sadaqah, and zakat). In this study, I apply new econophysics models of wealth exchange and redistribution to quantitatively compare these characteristics to those of capitalism and evaluate wealth distribution and disparity using a simulation. Specifically, regarding exchange, I propose a loan interest model representing finance capitalism and riba and a joint venture model representing shareholder capitalism and mudaraba; regarding redistribution, I create a transfer model representing inheritance tax and waqf. As exchanges are repeated from an initial uniform distribution of wealth, wealth distribution approaches a power-law distribution more quickly for the loan interest than the joint venture model; and the Gini index, representing disparity, rapidly increases. The joint venture model's Gini index increases more slowly, but eventually, the wealth distribution in both models becomes a delta distribution, and the Gini index gradually approaches 1. Next, when both models are combined with the transfer model to redistribute wealth in every given period, the loan interest model has a larger Gini index than the joint venture model, but both converge to a Gini index of less than 1. These results quantitatively reveal that in the Islamic economy, disparity is restrained by prohibiting riba and promoting reciprocal exchange in mudaraba and redistribution through waqf. Comparing Islamic and capitalist economies provides insights into the benefits of economically embracing the ethical practice of mutual aid and suggests guidelines for an alternative to capitalism.

Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences. We discuss an approach using the differentiable physics paradigm that combines known physics with machine learning to develop closure models for Burgers' turbulence. We consider the 1D Burgers system as a prototypical test problem for modeling the unresolved terms in advection-dominated turbulence problems. We train a series of models that incorporate varying degrees of physical assumptions on an a posteriori loss function to test the efficacy of models across a range of system parameters, including viscosity, time, and grid resolution. We find that constraining models with inductive biases in the form of partial differential equations that contain known physics or existing closure approaches produces highly data-efficient, accurate, and generalizable models, outperforming state-of-the-art baselines. Addition of structure in the form of physics information also brings a level of interpretability to the models, potentially offering a stepping stone to the future of closure modeling.