So far, the adoption rate of methods of artificial intelligence and machine learning (AI/ML) has been quite uneven across the economics profession. The uptake of these methods has been heavily concentrated in microeconomics where an explosion of data collection, particularly at the level of individual consumers (think of a firm like Amazon) has made the benefits of AI/ML especially clear and possible given that these models require massive amounts of data to be useful. Yet what are the prospects for applying the tools of AI to macroeconomics – that branch of economics that looks at the performance of big things like whole regions, countries, or even the globe? What are the differences between traditional statistical tools that macroeconomists use and AI/ML-based approaches? This thought piece is the first of three that will walk readers (whether economists or not) through my guesses at answering some of these questions.

The character of conflict between nations has fundamentally changed. Governments and militaries now fight on our behalf in the "gray zone," where the boundaries between peace and war are blurred. They must navigate a complex web of ambiguous and deeply interconnected challenges, ranging from political destabilization and disinformation campaigns to cyberattacks, assassinations, proxy operations, election meddling, or perhaps even human-made pandemics. Add to this list the existential threat of climate change (and its geopolitical ramifications) and it is clear that the description of what now constitutes a national security issue has broadened, each crisis straining or degrading the fabric of national resilience. Traditional analysis tools are poorly equipped to predict and respond to these blurred and intertwined threats.

The Pentagon for the first time publicly released drone footage of a botched strike in Kabul that killed 10 members of a family, including seven children, amid the chaotic US withdrawal from the country. The footage was initially obtained through a Freedom of Information Act lawsuit by The New York Times and was subsequently released by US Central Command on Thursday. It appears to underscore how, by the Pentagon's own account, limited intelligence, a heightened state of alert, and rushed decision-making led to the killing of civilians. The fuzzy footage, which officials told the newspaper was recorded by two MQ-9 Reaper drones, shows the moments before the fatal drone strike on a car in a courtyard in Kabul on August 29. One segment of footage appears to show a shorter, blurry figure in white next to a taller figure in black in the courtyard as the targeted car backs in to park, according to the analysis by the Times.

Artificial intelligence (AI) technology has become a critical disruptor in almost every industry and banking is no exception. The introduction of AI in banking apps and services has made the sector more customer-centric and technologically relevant. AI-based systems can help banks reduce costs by increasing productivity and making decisions based on information unfathomable to a human agent. Also, intelligent algorithms are able to spot anomalies and fraudulent information in a matter of seconds. A report by Business Insider suggests that nearly 80% of banks are aware of the potential benefits that AI presents to their sector.

Did you miss a session from the Future of Work Summit? Cyberattacks are happening faster, targeting multiple threat surfaces simultaneously using a broad range of techniques to evade detection and access valuable data. A favorite attack strategy of bad actors is to use various social engineering, phishing, ransomware, and malware techniques to gain privileged access credentials to bypass Identity Access Management (IAM) and Privileged Access Management (PAM) systems. Once in a corporate network, bad actors move laterally across an organization, searching for the most valuable data to exfiltrate, sell, or use to impersonate senior executives. IBM found that it takes an average of 287 days to identify and contain a data breach, at an average cost of $3.61M in a hybrid cloud environment.

Advances in Federated Learning and an abundance of user data have enabled rich collaborative learning between multiple clients, without sharing user data. This is done via a central server that aggregates learning in the form of weight updates. However, this comes at the cost of repeated expensive communication between the clients and the server, and concerns about compromised user privacy. The inversion of gradients into the data that generated them is termed data leakage. Encryption techniques can be used to counter this leakage, but at added expense. To address these challenges of communication efficiency and privacy, we propose TOFU, a novel algorithm which generates proxy data that encodes the weight updates for each client in its gradients. Instead of weight updates, this proxy data is now shared. Since input data is far lower in dimensional complexity than weights, this encoding allows us to send much lesser data per communication round. Additionally, the proxy data resembles noise, and even perfect reconstruction from data leakage attacks would invert the decoded gradients into unrecognizable noise, enhancing privacy. We show that TOFU enables learning with less than 1% and 7% accuracy drops on MNIST and on CIFAR-10 datasets, respectively. This drop can be recovered via a few rounds of expensive encrypted gradient exchange. This enables us to learn to near-full accuracy in a federated setup, while being 4x and 6.6x more communication efficient than the standard Federated Averaging algorithm on MNIST and CIFAR-10, respectively.

Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing the availability, maintenance and costs of complex systems. Reinforcement learning (RL) algorithms are particularly suitable for this type of problems given their learning capabilities. A special case of a prescriptive operation is the power allocation task, which can be considered as a sequential allocation problem, where the action space is bounded by a simplex constraint. A general continuous action-space solution of such sequential allocation problems has still remained an open research question for RL algorithms. In continuous action-space, the standard Gaussian policy applied in reinforcement learning does not support simplex constraints, while the Gaussian-softmax policy introduces a bias during training. In this work, we propose the Dirichlet policy for continuous allocation tasks and analyze the bias and variance of its policy gradients. We demonstrate that the Dirichlet policy is bias-free and provides significantly faster convergence, better performance and better hyperparameters robustness over the Gaussian-softmax policy. Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems. The experimental results show the potential to prescribe optimal operation, improve the efficiency and sustainability of multi-power source systems.

The COVID-19 pandemic has been a scourge upon humanity, claiming the lives of more than 5.1 million people worldwide; the global economy contracted by 3.5% in 2020. This paper presents a COVID-19 calculator, synthesizing existing published calculators and data points, to measure the positive U.S. socio-economic impact of a COVID-19 AI/ML pre-screening solution (algorithm & application).

Artificial intelligence (AI), especially deep learning, requires vast amounts of data for training, testing, and validation. Collecting these data and the corresponding annotations requires the implementation of imaging biobanks that provide access to these data in a standardized way. This requires careful design and implementation based on the current standards and guidelines and complying with the current legal restrictions. However, the realization of proper imaging data collections is not sufficient to train, validate and deploy AI as resource demands are high and require a careful hybrid implementation of AI pipelines both on-premise and in the cloud. This chapter aims to help the reader when technical considerations have to be made about the AI environment by providing a technical background of different concepts and implementation aspects involved in data storage, cloud usage, and AI pipelines.

The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems so that they are capable of learning (and improving) continuously, leveraging data on one task to improve performance on another, and doing so in a computationally sustainable way. Performers on this program developed systems capable of performing a diverse range of functions, including autonomous driving, real-time strategy, and drone simulation. These systems featured a diverse range of characteristics (e.g., task structure, lifetime duration), and an immediate challenge faced by the program's testing and evaluation team was measuring system performance across these different settings. This document, developed in close collaboration with DARPA and the program performers, outlines a formalism for constructing and characterizing the performance of agents performing lifelong learning scenarios. In Section 2, we introduce the general form of a lifelong learning scenario.