In their work Resource Democratization: Is Compute the Binding Constraint on AI Research?, presented at AAAI 2024, Rebecca Gelles, Veronica Kinoshita, Micah Musser and James Dunham investigate researchers' access to compute and the impact this has on their work. In this interview, Rebecca and Ronnie tell us about what inspired their study, their methodology and some of their main findings. We wanted to engage directly with AI researchers to understand their resource constraints when deciding what types of research to dedicate their time to. We also wanted to dig deeper and try to understand how resource constraints play out across the AI research community – particularly looking at divides between industry and academia, a popular topic of discussions about AI resource constraints. The reason we were interested in this question is that there's a lot of discourse, particularly from the policy community, about the democratization of AI, addressing inequities in the field, and the fear that progress in AI may be held back by resource constraints.

On the eve of New Hampshire's primary election, a flood of robocalls exhorted Democratic voters to sit out a write-in campaign supporting President Joe Biden during the state's presidential primary. An AI-generated voice on the line matched the uncanny cadence and signature catchphrase-- ("malarkey!")--characteristic to Biden. From that call to fake creations envisioning a cascade of calamities under Biden's watch to AI deepfakes of a Slovakian candidate for country leader pondering vote rigging and raising beer prices, AI is making its mark on elections worldwide. Against this backdrop, governments and several tech companies are taking some steps to mitigate risks--European lawmakers just approved a watershed law, and as recently as February tech companies signed a pledge at the Munich Security Conference. But much more needs to be done to protect American democracy.

Many US election deniers have spent the past three years inundating local election officials with paperwork and filing thousands of Freedom of Information Act requests in order to surface supposed instances of fraud. "I've had election officials telling me that in an office where there's one or two workers, they literally were satisfying public records requests from 9 to 5 every day, and then it's 5 o'clock and they would shift to their normal election duties," says Tammy Patrick, CEO of the National Association of Election Officials. In Washington state, elections officials were receiving so many FOIA requests following the 2020 presidential elections about the state's voter registration database that the legislature had to change the law, rerouting these requests to the Secretary of State's office to relieve the burden on local elections workers. "Our county auditors came in and testified as to how much time having to respond to public records requests was taking," says democratic state senator Patty Kederer, who cosponsored the legislation. "It can cost a lot of money to process those requests. And some of these smaller counties do not have the manpower to handle them. You could easily overwhelm some of our smaller counties."

So I was inspired after talking to Angela Huyue Zhang, a law professor in Hong Kong who's coming to teach at the University of Southern California this fall, about her new book on interpreting the logic and patterns behind China's tech regulations. We talked about how the Chinese government almost always swings back and forth between regulating tech too much and not enough, how local governments have gone to great lengths to protect local tech companies, and why AI companies in China are receiving more government goodwill than other sectors today. To learn more about Zhang's fascinating interpretation of the tech regulations in China, read my story published today. In this newsletter, I want to show you a particularly interesting part of the conversation we had, where Zhang expanded on how market overreactions to Chinese tech policies have become an integral part of the tech regulator's toolbox today. The capital markets, perpetually betting on whether tech companies are going to fare better or worse, are always looking for policy signals on whether China is going to start a new crackdown on certain technologies. As a result, they often overreact to every move by the Chinese government.

"It's very easy to see when you've got the memory right, because there is a very visceral reaction," says Pau Garcia, founder of Domestic Data Streamers. Dozens of people have now had their memories turned into images in this way via Synthetic Memories, a project run by Domestic Data Streamers. The studio uses generative image models, such as OpenAI's DALL-E, to bring people's memories to life. Since 2022, the studio, which has received funding from the UN and Google, has been working with immigrant and refugee communities around the world to create images of scenes that have never been photographed, or to re-create photos that were lost when families left their previous homes. Now Domestic Data Streamers is taking over a building next to the Barcelona Design Museum to record people's memories of the city using synthetic images.

Microsoft will invest 2.9 billion over the next two years to boost its hyperscale cloud computing and artificial intelligence infrastructure in Japan, marking its biggest investment in the country. The announcement was made on Tuesday in Washington after Microsoft President Brad Smith met Prime Minister Fumio Kishida, who is in the United States for the first official visit by a Japanese leader in nine years. The Nikkei newspaper had reported the new investment earlier. Microsoft will also expand its digital training programs to provide AI skills to more than 3 million people over the next three years, the company said in a statement. It plans to open a lab in Japan focused on AI and robotics, while deepening its cybersecurity collaboration with the Japanese government.

Frank Kendall, the secretary of the Air Force, told the U.S. Senate Committee on Appropriations he will get to fly in an AI-flown plane later this year. Air Force Secretary Frank Kendall told members of the U.S. Senate on Tuesday that he plans to ride in the cockpit of an aircraft operated by artificial intelligence to experience the technology of the military branch's future fleet. Kendall spoke before the U.S. Senate Appropriations Committee's defense panel on Tuesday, where he spoke about the future of air warfare being dependent on autonomously operated drones. In fact, the Air Force secretary is pushing to get over 1,000 of the AI-operated drones and plans to let one of them take him into the air later this spring. The aircraft he plans to board will be an F-16 which was converted for drone flight.

Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user, viz.\ as a tool for personalized explainability. An important class of concept-based explainability methods is constructed with empirically defined concepts, indirectly defined through a set of positive and negative examples, as in the TCAV approach (Kim et al., 2018). While it is appealing to the user to avoid formal definitions of concepts and their operationalization, it can be challenging to establish relevant concept datasets. Here, we address this challenge using general knowledge graphs (such as, e.g., Wikidata or WordNet) for comprehensive concept definition and present a workflow for user-driven data collection in both text and image domains. The concepts derived from knowledge graphs are defined interactively, providing an opportunity for personalization and ensuring that the concepts reflect the user's intentions. We test the retrieved concept datasets on two concept-based explainability methods, namely concept activation vectors (CAVs) and concept activation regions (CARs) (Crabbe and van der Schaar, 2022). We show that CAVs and CARs based on these empirical concept datasets provide robust and accurate explanations. Importantly, we also find good alignment between the models' representations of concepts and the structure of knowledge graphs, i.e., human representations. This supports our conclusion that knowledge graph-based concepts are relevant for XAI.

Autonomous systems are becoming increasingly prevalent in new vehicles. Due to their environmental friendliness and their remarkable capability to significantly enhance road safety, these vehicles have gained widespread recognition and acceptance in recent years. Automated Driving Systems (ADS) are intricate systems that incorporate a multitude of sensors and actuators to interact with the environment autonomously, pervasively, and interactively. Consequently, numerous studies are currently underway to keep abreast of these rapid developments. This paper aims to provide a comprehensive overview of recent advancements in ADS technologies. It provides in-depth insights into the detailed information about how data and information flow in the distributed system, including autonomous vehicles and other various supporting services and entities. Data validation and system requirements are emphasised, such as security, privacy, scalability, and data ownership, in accordance with regulatory standards. Finally, several current research directions in the AVs field will be discussed.

AI-empowered technologies' impact on the world is undeniable, reshaping industries, revolutionizing how humans interact with technology, transforming educational paradigms, and redefining social codes. However, this rapid growth is accompanied by two notable challenges: a lack of diversity within the AI field and a widening AI divide. In this context, This paper examines the intersection of AI and identity as a pathway to understand biases, inequalities, and ethical considerations in AI development and deployment. We present a multifaceted definition of AI identity, which encompasses its creators, applications, and their broader impacts. Understanding AI's identity involves understanding the associations between the individuals involved in AI's development, the technologies produced, and the social, ethical, and psychological implications. After exploring the AI identity ecosystem and its societal dynamics, We propose a framework that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity. This paper proposes the need for a comprehensive approach to fostering a more inclusive and responsible AI ecosystem through the lens of identity.