Well, that didn't happen, obviously. I sat down with MIT professor Max Tegmark, the founder and president of FLI, to take stock of what has happened since. Here are highlights of our conversation. On shifting the Overton window on AI risk: Tegmark told me that in conversations with AI researchers and tech CEOs, it had become clear that there was a huge amount of anxiety about the existential risk AI poses, but nobody felt they could speak about it openly "for fear of being ridiculed as Luddite scaremongerers." "The key goal of the letter was to mainstream the conversation, to move the Overton window so that people felt safe expressing these concerns," he says.

Nearly 1,500 US police departments operate drones but only about a dozen routinely dispatch them in response to 911 calls, according to ACLU research. Drone maker Skydio aims to see that change, with a new model launched last week called the X10. The goal, cofounder and CEO Adam Bry said during a launch event last week in San Francisco, is to "get drones everywhere they can be useful in public safety." The new drone is capable of flying at speeds of 45 miles per hour and is small enough to fit into the trunk of a police car. It has infrared sensors that can be used to track people and fly autonomously in the dark.

A strange thing happened last week when you searched for "tank man" on Google. Tap on image results and instead of the usual photos of Tiananmen Square in Beijing, and the iconic image of a brave protester staring down a convoy of tanks that was captured in 1989, the first result was the same historic moment – but from a different point of view. For a time last week, the first result on Google Images for "tank man" was instead an AI-generated image of the same protester, taking a selfie in front of the tank. The image was created by Midjourney, and was at least six months old. First reported by 404 Media, a new tech journalism startup set up by former Vice News staff, the emergence of the tank man selfie – which Google subsequently removed from search results for "tank man" – highlighted one of the main fears that Eddie Perez, Twitter's former head of election integrity, highlighted to me in a recent podcast interview: it's now possible, with the use of AI imagery, to create alternative history. And that has huge ramifications not only on our lives, but also our elections.

Allen explained that he "input numerous revisions and text prompts at least 624 times to arrive at the initial version of the image," which created an initial version of the image that Allen edited with Photoshop.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. Artificial intelligence (AI) is shaping up to be yet another public policy issue where elites are out of step with the public. In Silicon Valley, Congress and the Biden administration, leaders are buzzing about AI. For those in the Valley, killer robots with lasers for eyes dominate the conversation. The Beltway, in contrast, is spending a lot of political capital to deal with bias in algorithms. And yet, the general public isn't primarily worried about either machines gaining control or about algorithms being biased.

Bahrain's military command has accused Yemen's Houthi fighters of killing two Bahraini soldiers in a drone attack on Saudi Arabia's southern border with Yemen. A number of Bahraini soldiers were also wounded in the attack, Bahrain's military said in a statement, which was carried by the official Bahrain News Agency on Monday. The exact number of soldiers wounded was not released. "This terrorist attack was carried out by the Houthis, who sent aircraft targeting the position of the Bahraini guards on the southern border of the kingdom of Saudi Arabia despite the halt of military operations between the warring sides in Yemen," the Bahraini military statement said. The island nation of Bahrain is a close ally of Saudi Arabia, which has been at war with Iran-aligned Houthi fighters in Yemen for several years.

The popular robotic simulator, Gazebo, lacks the feature of simulating the effects of control latency that would make it a fully-fledged cyber-physical system (CPS) simulator. The CPS that we address to measure is a robotic arm (UR5) controlled remotely with velocity commands. The main goal is to measure Quality of Control (QoC) related KPIs during various network conditions in a simulated environment. We propose a Gazebo plugin to make the above measurement feasible by making Gazebo capable to delay internal control and status messages and also to interface with external network simulators to derive even more advanced network effects. Our preliminary evaluation shows that there is certainly an effect on the behavior of the robotic arm with the introduced network latency in line with our expectations, but a more detailed further study is needed.

The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a crucial role in understanding the organization and functioning of complex systems. We begin by introducing the concept of community structure, which refers to the arrangement of vertices into clusters, with strong internal connections and weaker connections between clusters. Then, we provide a thorough exposition of various community detection methods, including a new method designed by us. Additionally, we explore real-world applications of community detection in diverse networks. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs. It serves as a valuable resource for researchers and practitioners in multiple disciplines, offering insights into the challenges, methodologies, and applications of community detection in complex networks.

In particular, periodic orbits play a significant role in chaos theory. In [6], chaotic systems are defined as systems that are sensitive to initial conditions, are topologically transitive (meaning that any region of the phase space can be reached from any other region), and have dense periodic orbits. Notably, chaotic systems are constituted of infinitely many Unstable Periodic Orbits (UPOs) which essentially form a structured framework, or a "skeleton", for chaotic attractors. A periodic orbit is (orbitally) unstable if trajectories that start near the orbit do not remain close to it. Finding and stabilizing UPOs is an interesting and relevant research field with numerous applications such as the design of lasers [18], the control of seizure activities [22] or the design of control systems for satellites [30]. An important tool when studying the stability of periodic orbits of a given system is the Poincaré or return map which allows one to study the dynamics of this system in a lower dimensional subspace.

With the emergence of Artificial Intelligent chatbot tools such as ChatGPT and code writing AI tools such as GitHub Copilot, educators need to question what and how we should teach our courses and curricula in the future. In reality, automated tools may result in certain academic fields being deeply reduced in the number of employable people. In this work, we make a case study of cybersecurity undergrad education by using the lens of ``Understanding by Design'' (UbD). First, we provide a broad understanding of learning objectives (LOs) in cybersecurity from a computer science perspective. Next, we dig a little deeper into a curriculum with an undergraduate emphasis on cybersecurity and examine the major courses and their LOs for our cybersecurity program at Miami University. With these details, we perform a thought experiment on how attainable the LOs are with the above-described tools, asking the key question ``what needs to be enduring concepts?'' learned in this process. If an LO becomes something that the existence of automation tools might be able to do, we then ask ``what level is attainable for the LO that is not a simple query to the tools?''. With this exercise, we hope to establish an example of how to prompt ChatGPT to accelerate students in their achievements of LOs given the existence of these new AI tools, and our goal is to push all of us to leverage and teach these tools as powerful allies in our quest to improve human existence and knowledge.