Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures. However, the growing connectivity of the Internet of Vehicles (IoV) also increases the vulnerabilities to network attacks. To protect IoV systems against cyber threats, Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed using Machine Learning (ML) approaches. To accurately detect various types of attacks in IoV networks, we propose a novel ensemble IDS framework named Leader Class and Confidence Decision Ensemble (LCCDE). It is constructed by determining the best-performing ML model among three advanced ML algorithms (XGBoost, LightGBM, and CatBoost) for every class or type of attack. The class leader models with their prediction confidence values are then utilized to make accurate decisions regarding the detection of various types of cyber-attacks. Experiments on two public IoV security datasets (Car-Hacking and CICIDS2017 datasets) demonstrate the effectiveness of the proposed LCCDE for intrusion detection on both intra-vehicle and external networks.

In recent years, the number of machine learning (ML) technologies gaining regulatory approval for healthcare has increased significantly allowing them to be placed on the market. However, the regulatory frameworks applied to them were originally devised for traditional software, which has largely rule-based behaviour, compared to the data-driven and learnt behaviour of ML. As the frameworks are in the process of reformation, there is a need to proactively assure the safety of ML to prevent patient safety being compromised. The Assurance of Machine Learning for use in Autonomous Systems (AMLAS) methodology was developed by the Assuring Autonomy International Programme based on well-established concepts in system safety. This review has appraised the methodology by consulting ML manufacturers to understand if it converges or diverges from their current safety assurance practices, whether there are gaps and limitations in its structure and if it is fit for purpose when applied to the healthcare domain. Through this work we offer the view that there is clear utility for AMLAS as a safety assurance methodology when applied to healthcare machine learning technologies, although development of healthcare specific supplementary guidance would benefit those implementing the methodology.

A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines.

Ariel Koren, a Google employee who became a face of worker protests against the company's contract with the Israeli government, announced her resignation yesterday. The Jewish marketing manager says she faced retaliation from management and some colleagues for expressing pro-Palestinian views within the company. In October she joined other Google and Amazon employees in public opposition to Project Nimbus, a $1.2 billion contract for Google and Amazon to provide cloud computing to Israel, including its defense ministry. She says that Google later gave her an ultimatum: Agree to move to Brazil within 17 days or lose her job. Training documents leaked to the Intercept show Project Nimbus providing Israel with access to Google's cloud AI services, including face and expression detection, video analysis, and sentiment analysis.

Since 2016, Tesla has been marketing an expensive option called Full Self-Driving. A reasonable person might infer from the name that the software package enables a car to drive itself, fully. No car available for consumers to buy is capable of full self-driving. The California Department of Motor Vehicles has rules on its books that ban the advertisement of cars as "self-driving" when they are not. But it has never enforced those rules.

Since 2016, Tesla has been marketing an expensive option called Full Self-Driving. A reasonable person might infer from the name that the software package enables a car to drive itself, fully. No car available for consumers to buy is capable of full self-driving. The California Department of Motor Vehicles has rules on its books that ban the advertisement of cars as "self-driving" when they are not. But it has never enforced those rules.

Tehran, Iran – Iran has delivered a so-called "peace initiative" for ending the Ukraine war, proposed by a European leader, to Russia. Standing next to Russian Foreign Minister Sergey Lavrov during a news conference in Moscow on Wednesday, Iran's Foreign Minister Hossein Amirabdollahian confirmed that he had handed over the European proposal that he said was given to Iran's President Ebrahim Raisi. "There are ideas to help establish peace and stop the fighting in Ukraine, and I shared these ideas with Mr Lavrov," Amirabdollahian said. He did not name the European leader behind the initiative or reveal any further details but said the proposal includes points on the Zaporizhzhia nuclear plant and prisoners of war. Recent shelling around the plant in southeastern Ukraine has spurred fears of a possible nuclear disaster and prompted the International Atomic Energy Agency to deploy experts to visit the site.

Artificial Intelligence (AI) is transforming our way of life from new forms of social organization and scientific discovery to defense and intelligence. This explosive progress is especially apparent in the subfield of machine learning (ML), where AI systems learn autonomously by identifying patterns in large volumes of data.[1] Indeed, over the last five years, the fields of AI and ML have witnessed stunning advancements in computer vision (e.g., object recognition), speech recognition, and scientific discovery.[2], Experts are increasingly voicing concerns over AI risk from misuse by state and non-state actors, principally in the areas of cybersecurity and disinformation propagation. However, issues of control – for example, how advanced AI decision-making aligns with human goals – are not as prominent in the discussion of risk and could ultimately be equally or more dangerous than threats from nefarious actors.

The French government is successfully using AI to discover taxable swimming pools. IT firm Capgemini worked with Google to create the AI which analyses aerial photos taken by France's National Institute of Geographic and Forest Information, identifies pools, and then cross-references them with national tax and property registries. So far, the project has spotted 20,356 undeclared swimming pools. The government has used the data to collect almost €10 million in additional taxes. Despite the project's success, it's only been used for nine out of France's 96 metro areas.

Paula Harder, and co-authors Qidong Yang, Venkatesh Ramesh, Alex Hernandez-Garcia, Prasanna Sattigeri, Campbell D. Watson, Daniela Szwarcman and David Rolnick, recently wrote a paper on Generating physically-consistent high-resolution climate data with hard-constrained neural networks. In this interview, Paula tells us more about how they developed a method for super-resolution climate data where conservation laws are enforced. Our paper looks at super-resolution for climate data, which is called downscaling. Deep learning has been applied a lot recently in that area, but the neural networks employed tend to violate physical laws, such as mass conservation. In this work, we look at how to change neural super-resolution architectures such that given constraints like conservation laws are enforced.