Dr. Stephen Murphy had conducted countless hip replacement operations before, but this one was different. In this one, he and his team could see a 3D hologram overlaid on the patient -- a digital model of the patient's body that existed directly in his line of vision. The surgical team had a form of X-ray vision with augmented reality. "We had done a lot of testing on real human specimens, so we knew what it was going to look like, but to see it in a live patient for the first time was just unbelievable," Murphy said in an interview with Freethink. "It feels to the surgeon as if she has been transported inside of the patient."

The continuous progress of technology has led to different government organizations having to modify their structures, as well as the way in which they execute their processes. Nowadays, applying tools such as Artificial Intelligence (AI) in government is essential, since AI makes all operations more efficient, allows citizens to listen better, have greater sensitivity about what they are asking for, what they need, and know the general feeling you have. In other words, it can be said that Artificial Intelligence is an extraordinary content source for the public sector and, above all, it is a great value . Many developed and developing countries are already implementing AI in different activities within the Public Administration. An example of this is what the Government of Finland is doing, which is conducting tests with what is considered, so far, the most ambitious public assistant based on Artificial Intelligence in the world: AuroraAI .

Machine learning algorithms do a lot for us every day--send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to watch next. Now, we are increasingly using these same algorithms to make environmental predictions for us. A team of researchers from the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey recently published a new study on predicting flow and temperature in river networks in the 2021 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining (SDM21) proceedings. The study was funded by the National Science Foundation (NSF). The research demonstrates a new machine learning method where the algorithm is "taught" the rules of the physical world in order to make better predictions and steer the algorithm toward physically meaningful relationships between inputs and outputs.

This long-running series should provide you with a very basic understanding of what AI is, what it can do, and how it works. In addition to the article you're currently reading, the guide contains articles on (in order published) neural networks, computer vision, natural language processing, algorithms, artificial general intelligence, the difference between video game AI and real AI, the difference between human and machine intelligence, and ethics. In this edition of the guide, we'll take a glance at global AI policy. In the coming years it will be important for everyone to understand what those differences can mean for our safety and privacy. Artificial intelligence has traditionally been swept in with other technologies when it comes to policy and regulation.

We would never allow a drug to be sold in the market without having gone through rigorous testing -- not even in the context of a health crisis like the coronavirus pandemic. Then why do we allow algorithms that can be just as damaging as a potent drug to be let loose into the world without having undergone similarly rigorous testing? At the moment, anyone can design an algorithm and use it to make important decisions about people -- whether they get a loan, or a job, or an apartment, or a prison sentence -- without any oversight or any kind of evidence-based requirement. The general population is being used as guinea pigs. Artificial intelligence is a predictive technology.

U.S. troops in eastern Syria came under rocket attack Monday, with no reported casualties, one day after U.S. Air Force planes carried out airstrikes near the Iraq-Syria border against what the Pentagon said were facilities used by Iran-backed militia groups to support drone strikes inside Iraq. Iraq's military condemned the U.S. airstrikes, and the militia groups called for revenge against the United States. Pentagon Press Secretary John Kirby said the militias were using the facilities to launch unmanned aerial vehicle attacks against U.S. troops in Iraq. It was the second time the administration has taken military action in the region since Biden took over earlier this year. There was no indication that Sunday's attacks were meant as the start of a wider, sustained U.S. air campaign in the border region.

Standardized body region labelling of individual images provides data that can improve human and computer use of medical images. A CNN-based classifier was developed to identify body regions in CT and MRI. 17 CT (18 MRI) body regions covering the entire human body were defined for the classification task. Three retrospective databases were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region. The test databases originated from a different healthcare network. Accuracy, recall and precision of the classifier was evaluated for patient age, patient gender, institution, scanner manufacturer, contrast, slice thickness, MRI sequence, and CT kernel. The data included a retrospective cohort of 2,934 anonymized CT cases (training: 1,804 studies, validation: 602 studies, test: 528 studies) and 3,185 anonymized MRI cases (training: 1,911 studies, validation: 636 studies, test: 638 studies). 27 institutions from primary care hospitals, community hospitals and imaging centers contributed to the test datasets. The data included cases of all genders in equal proportions and subjects aged from a few months old to +90 years old. An image-level prediction accuracy of 91.9% (90.2 - 92.1) for CT, and 94.2% (92.0 - 95.6) for MRI was achieved. The classification results were robust across all body regions and confounding factors. Due to limited data, performance results for subjects under 10 years-old could not be reliably evaluated. We show that deep learning models can classify CT and MRI images by body region including lower and upper extremities with high accuracy.

Electronic health record (EHR) coding is the task of assigning ICD codes to each EHR. Most previous studies either only focus on the frequent ICD codes or treat rare and frequent ICD codes in the same way. These methods perform well on frequent ICD codes but due to the extremely unbalanced distribution of ICD codes, the performance on rare ones is far from satisfactory. We seek to improve the performance for both frequent and rare ICD codes by using a contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR coding as a few-shot learning task. First, we construct a heterogeneous EHR word-entity (HEWE) graph for each EHR, where the words and entities extracted from an EHR serve as nodes and the relations between them serve as edges. Then, CoGraph learns similarities and dissimilarities between HEWE graphs from different ICD codes so that information can be transferred among them. In a few-shot learning scenario, the model only has access to frequent ICD codes during training, which might force it to encode features that are useful for frequent ICD codes only. To mitigate this risk, CoGraph devises two graph contrastive learning schemes, GSCL and GECL, that exploit the HEWE graph structures so as to encode transferable features. GSCL utilizes the intra-correlation of different sub-graphs sampled from HEWE graphs while GECL exploits the inter-correlation among HEWE graphs at different clinical stages. Experiments on the MIMIC-III benchmark dataset show that CoGraph significantly outperforms state-of-the-art methods on EHR coding, not only on frequent ICD codes, but also on rare codes, in terms of several evaluation indicators. On frequent ICD codes, GSCL and GECL improve the classification accuracy and F1 by 1.31% and 0.61%, respectively, and on rare ICD codes CoGraph has more obvious improvements by 2.12% and 2.95%.

AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI (such as machine learning) to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this survey, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary's methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 33 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a user study spanning industry and academia, we rank the AI threats and provide insights on the adversaries.

Israel's Polaris Solutions, a survival product manufacturer, has unveiled a redesigned camouflage net that claims to make soldiers virtually'undetectable.' Developed in partnership with Israel's Ministry of Defense (MoD), the Kit 300 sheet is made of thermal visual concealment (TVC) material that combines microfibers, metals and polymers to make soldiers harder to see with the human eye and thermal cameras. The sheet weighs just 1.1 pounds, allowing soldiers to easily roll it up and carry it while trekking through dangerous war zones. Soldiers wrap it around themselves when on the move and join their sheets together to build a barrier that resembles rock when they set up a position. Israel's Polaris Solutions, a firm that creates technology for survivability solutions LESS JARGONY, has unveiled a redesigned camouflage net that claims to make soldiers virtually'undetectable' 'Someone staring at them with binoculars from afar will not see soldiers,' Gal Harari, the head of the detectors and imaging technology branch of the MoD's research and development unit, said in a statement. The Kit 300 aims to reinvent the traditional camouflage gear that has gone nearly unchanged.