Artificial intelligence's newest sensation -- the gabby chatbot-on-steroids ChatGPT -- is sending European rulemakers back to the drawing board on how to regulate AI. The chatbot dazzled the internet in past months with its rapid-fire production of human-like prose. It declared its love for a New York Times journalist. It wrote a haiku about monkeys breaking free from a laboratory. It even got to the floor of the European Parliament, where two German members gave speeches drafted by ChatGPT to highlight the need to rein in AI technology.

There is too much internet and our attempts to keep up with the breakneck pace of, well, everything these days -- it is breaking our brains. Parsing through the deluge of inundating information hoisted up by algorithmic systems built to maximize engagement has trained us as slavering Pavlovian dogs to rely on snap judgements and gut feelings in our decision making and opinion formation rather than deliberation and introspection. Which is fine when you're deciding between Italian and Indian for dinner or are waffling on a new paint color for the hallway, but not when we're out here basing existential life choices on friggin' vibes. In his latest book, I, HUMAN: AI, Automation, and the Quest to Reclaim What Makes Us Unique, professor of business psychology and Chief Innovation Officer at ManpowerGroup, Tomas Chamorro-Premuzic explores the myriad ways that AI systems now govern our daily lives and interactions. From finding love to finding gainful employment to finding out the score of yesterday's game, AI has streamlined the information gathering process.

From Devvrat Pandey: A new one report from an Australian think tank shows that India has made significant advances in critical technologies vital to the country's growth and development in the rapidly evolving global economy. However, China is far ahead of the West when it comes to the crucial technology sector. The Australian Strategic Policy Institute (ASPI) is an independent, nonpartisan think tank focused on defense and strategic policy issues. ASPI's Critical Technology Tracker is a new tool that analyzes global competition for critical technologies vital to national security and economic competitiveness. The report expresses concern that Western democracies have lost global technological competition, "including the race for breakthroughs in science and research and the ability to retain global talent – critical ingredients that underpin the development and control of the world's most important technologies, including that doesn't exist yet."

Representative Ted Lieu, Democrat of California, wrote in a guest essay in The New York Times in January that he was "freaked out" by the ability of the ChatGPT chatbot to mimic human writers. Another Democrat, Representative Jake Auchincloss of Massachusetts, gave a one-minute speech -- written by a chatbot -- calling for regulation of A.I. But even as lawmakers put a spotlight on the technology, few are taking action on it. No bill has been proposed to protect individuals or thwart the development of A.I.'s potentially dangerous aspects. And legislation introduced in recent years to curb A.I. applications like facial recognition have withered in Congress.

"By failing to establish such guardrails, policymakers are creating the conditions for a race to the bottom in irresponsible A.I.," she said. In the regulatory vacuum, the European Union has taken a leadership role. In 2021, E.U. policymakers proposed a law focused on regulating the A.I. technologies that might create the most harm, such as facial recognition and applications linked to critical public infrastructure like the water supply. The measure, which is expected to be passed as soon as this year, would require makers of A.I. to conduct risk assessments of how their applications could affect health, safety and individual rights, like freedom of expression. Companies that violated the law could be fined up to 6 percent of their global revenue, which could total billions of dollars for the world's largest tech platforms.

Adversarial attacks on a convolutional neural network (CNN) -- injecting human-imperceptible perturbations into an input image -- could fool a high-performance CNN into making incorrect predictions. The success of adversarial attacks raises serious concerns about the robustness of CNNs, and prevents them from being used in safety-critical applications, such as medical diagnosis and autonomous driving. Our work introduces a visual analytics approach to understanding adversarial attacks by answering two questions: (1) which neurons are more vulnerable to attacks and (2) which image features do these vulnerable neurons capture during the prediction? For the first question, we introduce multiple perturbation-based measures to break down the attacking magnitude into individual CNN neurons and rank the neurons by their vulnerability levels. For the second, we identify image features (e.g., cat ears) that highly stimulate a user-selected neuron to augment and validate the neuron's responsibility. Furthermore, we support an interactive exploration of a large number of neurons by aiding with hierarchical clustering based on the neurons' roles in the prediction. To this end, a visual analytics system is designed to incorporate visual reasoning for interpreting adversarial attacks. We validate the effectiveness of our system through multiple case studies as well as feedback from domain experts.

Everting, soft growing vine robots benefit from reduced friction with their environment, which allows them to navigate challenging terrain. Vine robots can use air pouches attached to their sides for lateral steering. However, when all pouches are serially connected, the whole robot can only perform one constant curvature in free space. It must contact the environment to navigate through obstacles along paths with multiple turns. This work presents a multi-segment vine robot that can navigate complex paths without interacting with its environment. This is achieved by a new steering method that selectively actuates each single pouch at the tip, providing high degrees of freedom with few control inputs. A small magnetic valve connects each pouch to a pressure supply line. A motorized tip mount uses an interlocking mechanism and motorized rollers on the outer material of the vine robot. As each valve passes through the tip mount, a permanent magnet inside the tip mount opens the valve so the corresponding pouch is connected to the pressure supply line at the same moment. Novel cylindrical pneumatic artificial muscles (cPAMs) are integrated into the vine robot and inflate to a cylindrical shape for improved bending characteristics compared to other state-of-the-art vine robots. The motorized tip mount controls a continuous eversion speed and enables controlled retraction. A final prototype was able to repeatably grow into different shapes and hold these shapes. We predict the path using a model that assumes a piecewise constant curvature along the outside of the multi-segment vine robot. The proposed multi-segment steering method can be extended to other soft continuum robot designs.

Recent works have shown that deep learning (DL) models can effectively learn city-wide crowd-flow patterns, which can be used for more effective urban planning and smart city management. However, DL models have been known to perform poorly on inconspicuous adversarial perturbations. Although many works have studied these adversarial perturbations in general, the adversarial vulnerabilities of deep crowd-flow prediction models in particular have remained largely unexplored. In this paper, we perform a rigorous analysis of the adversarial vulnerabilities of DL-based crowd-flow prediction models under multiple threat settings, making three-fold contributions. (1) We propose CaV-detect by formally identifying two novel properties - Consistency and Validity - of the crowd-flow prediction inputs that enable the detection of standard adversarial inputs with 0% false acceptance rate (FAR). (2) We leverage universal adversarial perturbations and an adaptive adversarial loss to present adaptive adversarial attacks to evade CaV-detect defense. (3) We propose CVPR, a Consistent, Valid and Physically-Realizable adversarial attack, that explicitly inducts the consistency and validity priors in the perturbation generation mechanism. We find out that although the crowd-flow models are vulnerable to adversarial perturbations, it is extremely challenging to simulate these perturbations in physical settings, notably when CaV-detect is in place. We also show that CVPR attack considerably outperforms the adaptively modified standard attacks in FAR and adversarial loss metrics. We conclude with useful insights emerging from our work and highlight promising future research directions.

Is it possible for machines to think like humans? And if it is, how should we go about teaching them to do so? As early as 1950, Alan Turing stated that we ought to teach machines in the way of teaching a child. Reinforcement learning with human feedback (RLHF) has emerged as a strong candidate toward allowing agents to learn from human feedback in a naturalistic manner. RLHF is distinct from traditional reinforcement learning as it provides feedback from a human teacher in addition to a reward signal. It has been catapulted into public view by multiple high-profile AI applications, including OpenAI's ChatGPT, DeepMind's Sparrow, and Anthropic's Claude. These highly capable chatbots are already overturning our understanding of how AI interacts with humanity. The wide applicability and burgeoning success of RLHF strongly motivate the need to evaluate its social impacts. In light of recent developments, this paper considers an important question: can RLHF be developed and used without negatively affecting human societies? Our objectives are threefold: to provide a systematic study of the social effects of RLHF; to identify key social and ethical issues of RLHF; and to discuss social impacts for stakeholders. Although text-based applications of RLHF have received much attention, it is crucial to consider when evaluating its social implications the diverse range of areas to which it may be deployed. We describe seven primary ways in which RLHF-based technologies will affect society by positively transforming human experiences with AI. This paper ultimately proposes that RLHF has potential to net positively impact areas of misinformation, AI value-alignment, bias, AI access, cross-cultural dialogue, industry, and workforce. As RLHF raises concerns that echo those of existing AI technologies, it will be important for all to be aware and intentional in the adoption of RLHF.

Federated learning (FL) is a promising technique for addressing the rising privacy and security issues. Its main ingredient is to cooperatively learn the model among the distributed clients without uploading any sensitive data. In this paper, we conducted a thorough review of the related works, following the development context and deeply mining the key technologies behind FL from both theoretical and practical perspectives. Specifically, we first classify the existing works in FL architecture based on the network topology of FL systems with detailed analysis and summarization. Next, we abstract the current application problems, summarize the general techniques and frame the application problems into the general paradigm of FL base models. Moreover, we provide our proposed solutions for model training via FL. We have summarized and analyzed the existing FedOpt algorithms, and deeply revealed the algorithmic development principles of many first-order algorithms in depth, proposing a more generalized algorithm design framework. Based on these frameworks, we have instantiated FedOpt algorithms. As privacy and security is the fundamental requirement in FL, we provide the existing attack scenarios and the defense methods. To the best of our knowledge, we are among the first tier to review the theoretical methodology and propose our strategies since there are very few works surveying the theoretical approaches. Our survey targets motivating the development of high-performance, privacy-preserving, and secure methods to integrate FL into real-world applications.