The Agency

Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using Stable Diffusion, (2) filtering out low quality samples using defined quality metrics, (3) forcing the existence of specific patterns in generated images using accurate prompting and ControlNet. In order to show how CIA can be used to search for an optimal augmentation pipeline of training data, we study human object detection in a data constrained scenario, using YOLOv8n on COCO and Flickr30k datasets. We have recorded significant improvement using CIA-generated images, approaching the performances obtained when doubling the amount of real images in the dataset. Our findings suggest that our modular framework can significantly enhance object detection systems, and make it possible for future research to be done on data-constrained scenarios. The framework is available at: github.com/multitel-ai/CIA.

Uncertainty quantification is essential in decision-making, especially when joint distributions of random variables are involved. While conformal prediction provides distribution-free prediction sets with valid coverage guarantees, it traditionally focuses on single predictions. This paper introduces novel conformal prediction methods for estimating the sum or average of unknown labels over specific index sets. We develop conformal prediction intervals for single target to the prediction interval for sum of multiple targets. Under permutation invariant assumptions, we prove the validity of our proposed method. We also apply our algorithms on class average estimation and path cost prediction tasks, and we show that our method outperforms existing conformalized approaches as well as non-conformal approaches.

Learning robotic skills from raw human videos remains a non-trivial challenge. Previous works tackled this problem by leveraging behavior cloning or learning reward functions from videos. Despite their remarkable performances, they may introduce several issues, such as the necessity for robot actions, requirements for consistent viewpoints and similar layouts between human and robot videos, as well as low sample efficiency. To this end, our key insight is to learn task priors by contrasting videos and to learn action priors through imitating trajectories from videos, and to utilize the task priors to guide trajectories to adapt to novel scenarios. We propose a three-stage skill learning framework denoted as Contrast-Imitate-Adapt (CIA). An interaction-aware alignment transformer is proposed to learn task priors by temporally aligning video pairs. Then a trajectory generation model is used to learn action priors. To adapt to novel scenarios different from human videos, the Inversion-Interaction method is designed to initialize coarse trajectories and refine them by limited interaction. In addition, CIA introduces an optimization method based on semantic directions of trajectories for interaction security and sample efficiency. The alignment distances computed by IAAformer are used as the rewards. We evaluate CIA in six real-world everyday tasks, and empirically demonstrate that CIA significantly outperforms previous state-of-the-art works in terms of task success rate and generalization to diverse novel scenarios layouts and object instances.

Elon Musk's Neuralink has been accused of allowing and enabling animal cruelty in its labs for years, but the company is not the first to sacrifice heaps of animals on the altar of brain-computer interface science. In fact, the US government may have a worse body count over many decades, even though Neuralink has reportedly killed more than 1,500 animals along the way, including monkeys, pigs, and sheep while developing its brain chip. In some of the worst cases, a cat was operated on repeatedly to turn it into a secret listening device, and a shark was subjected to open-brain surgery to implant electrodes in an effort to control its behavior - all while the animals were still alive. A pig in the Neuralink facility, shown with its handler. Musk revealed the first recieptiant of his Neuralink brain chip this week.

The Chinese spies wanted more. In meetings during the pandemic with Chinese technology contractors, they complained that surveillance cameras tracking foreign diplomats, military officers and intelligence operatives in Beijing's embassy district fell short of their needs. The spies asked for an artificial intelligence program that would create instant dossiers on every person of interest in the area and analyze their behavior patterns. They proposed feeding the AI program information from databases and scores of cameras that would include car license plates, cellphone data, contacts and more. The AI-generated profiles would allow the Chinese spies to select targets and pinpoint their networks and vulnerabilities, according to internal meeting memos obtained by The New York Times.

The AAAI Fall Symposium Series took place in Arlington, USA, and comprised seven different symposia. One of these, the tenth Artificial Intelligence for Human-Robot Interaction (AI-HRI) symposium was run as a hybrid in-person/online event, and we tuned in to the opening keynote, which was given by Patrícia Alves-Oliveira. As a psychology student, Patrícia's dream was to become a therapist. However, an internship, where she encountered a robot for the first time, inspired her to change her plans, and she decided to go into the field of human-robot interaction. Following a PhD in the field, she worked as a postdoc, before heading to industry as a designer in the Amazon Astro robot team.

The AAAI Fall Symposium Series is taking place in Arlington, USA, and comprises seven different symposia. One of these, the tenth Artificial Intelligence for Human-Robot Interaction (AI-HRI) symposium is being run as a hybrid in-person/online event, and we tuned in to the opening keynote, which was given by Patrícia Alves-Oliveira. As a psychology student, Patrícia's dream was to become a therapist. However, an internship, where she encountered a robot for the first time, inspired her to change her plans, and she decided to go into the field of human-robot interaction. Following a PhD in the field, she worked as a postdoc, before heading to industry as a designer in the Amazon Astro robot team.

The CIA is set to launch its own ChatGPT-style AI tool to help sift through mountains of data for clues in ongoing investigations. Intended to mirror the famed OpenAI tech, the Central Intelligence Agency's latest initiative will use artificial intelligence to help analysts better access open-source intelligence, agency officials said. The CIA's Open Source Enterprise division developed the tech, which is also intended to be rolled out across the US government's 18 intelligence agencies in an effort to rival China's growing intelligence capabilities. 'We've gone from newspapers and radio, to newspapers and television, to newspapers and cable television, to basic internet, to big data, and it just keeps going,' said Randy Nixon, director of the CIA's AI division. Nixon noted that analyzing the level of data across the web is a significant challenge that the AI program would help handle, adding: 'We have to find the needles in the needle field.'

The CIA and other US intelligence agencies will soon have an AI chatbot similar to ChatGPT. The program, revealed on Tuesday by Bloomberg, will train on publicly available data and provide sources alongside its answers so agents can confirm their validity. The aim is for US spies to more easily sift through ever-growing troves of information, although the exact nature of what constitutes "public data" could spark some thorny privacy issues. "We've gone from newspapers and radio, to newspapers and television, to newspapers and cable television, to basic internet, to big data, and it just keeps going," Randy Nixon, the CIA's director of Open Source Enterprise, said in an interview with Bloomberg. "We have to find the needles in the needle field."