Measuring a document's complexity level is an open challenge, particularly when one is working on a diverse corpus of documents rather than comparing several documents on a similar topic or working on a language other than English. In this paper, we define a methodology to measure the complexity of French documents, using a new general and diversified corpus of texts, the "French Canadian complexity level corpus", and a wide range of metrics. We compare different learning algorithms to this task and contrast their performances and their observations on which characteristics of the texts are more significant to their complexity. Our results show that our methodology gives a general-purpose measurement of text complexity in French.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Thousands of artificial intelligence experts and machine learning researchers probably thought they were going to have a restful weekend. Then came Google engineer Blake Lemoine, who told the Washington Post on Saturday that he believed LaMDA, Google's conversational AI for generating chatbots based on large language models (LLM), was sentient. Lemoine, who worked for Google's Responsible AI organization until he was placed on paid leave last Monday, and who "became ordained as a mystic Christian priest, and served in the Army before studying the occult," had begun testing LaMDA to see if it used discriminatory or hate speech.

Retrace, a leader in dental artificial intelligence and provider of digital infrastructure for U.S. healthcare, announces the publication, "A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level" in the August 2022 Edition of the Journal of Dentistry. This groundbreaking study for the first time demonstrates how the use of a novel Generative Adversarial Network (GAN), (U.S. Patent Numbers: US 11,217,350 B2; US 11,276,151 B2; US 11,398,013 B2), often referred to as a "Deep Fake", improves the diagnostic accuracy of AI algorithms in identifying periodontal disease. Medical and dental AI imaging algorithms are often trained on limited data sets from a limited number of providers, patients and imaging sources. As a result, when these algorithms are used in a general production environment, the algorithms struggle to achieve the same level of accuracy as the environment they were trained in. "Over the past few years, we have seen a sharp rise in dental and medical imaging AI companies; some who have even received FDA Clearance," said Dr. Ali Sadat, Founder and CEO of Retrace.

New South Wales digital minister Victor Dominello has asked the state's information and privacy watchdogs to undertake a "scan of the AI and privacy landscape" in the wake of the robodebt royal commission launch. The move is a possible precursor to Australia's first Artificial Intelligence commissioner, a role the outgoing minister backed earlier this week. Mr Dominello told reporters on Monday a dedicated AI watchdog would be "good to see" as the next layer of independent oversight of government technologies and digital services. He said it likely won't arrive before he retires from politics early next year, but an AI commissioner is being explored and will be necessary to maintain public trust in government technology. "I want to make sure that there is oversight [and] there's checks and balances all the way because there will be failures," Mr Dominello said.

Following dozens of drone sightings -- and a handful of reports of a "guy in a jetpack" -- near Los Angeles International Airport, the Transportation Security Administration will test new technology to spot, track and identify drones in restricted airspace. The federally funded program would make LAX only the second airport using the "state-of-the-art technology," which is also being tested at Miami International Airport, with plans to expand across the country, according to the TSA's announcement Thursday. There have been 38 drone sightings this year at LAX, including one within 700 feet of an aircraft, according to the TSA. Since last year, there have been 90 visual sightings and 5,200 technical detections of drones within three miles of LAX. "While there are many beneficial uses for drones in our society, it is becoming far too common that drones are sighted near airports, which presents significant security risks and unnecessary disruptions to the traveling public," Rep. Lucille Roybal-Allard (D-Downey) said in a statement.

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on GitHub.

Dwarf galaxies are small, dark matter-dominated galaxies, some of which are embedded within the Milky Way. Their lack of baryonic matter (e.g., stars and gas) makes them perfect test beds for probing the properties of dark matter -- understanding the spatial dark matter distribution in these systems can be used to constrain microphysical dark matter interactions that influence the formation and evolution of structures in our Universe. We introduce a new method that leverages simulation-based inference and graph-based machine learning in order to infer the dark matter density profiles of dwarf galaxies from observable kinematics of stars gravitationally bound to these systems. Our approach aims to address some of the limitations of established methods based on dynamical Jeans modeling. We show that this novel method can place stronger constraints on dark matter profiles and, consequently, has the potential to weigh in on some of the ongoing puzzles associated with the small-scale structure of dark matter halos, such as the core-cusp discrepancy.

Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monitoring of phenological and climatic characteristics over extended time periods to model the complex relations involved in crop development. Remote sensing satellite images provided by various satellites circumnavigating the world are a cheap and reliable way to obtain data for yield prediction. The field of yield prediction is currently dominated by Deep Learning approaches. While the accuracies reached with those approaches are promising, the needed amounts of data and the ``black-box'' nature can restrict the application of Deep Learning methods. The limitations can be overcome by proposing a pipeline to process remote sensing images into feature-based representations that allow the employment of Extreme Gradient Boosting (XGBoost) for yield prediction. A comparative evaluation of soybean yield prediction within the United States shows promising prediction accuracies compared to state-of-the-art yield prediction systems based on Deep Learning. Feature importances expose the near-infrared spectrum of light as an important feature within our models. The reported results hint at the capabilities of XGBoost for yield prediction and encourage future experiments with XGBoost for yield prediction on other crops in regions all around the world.

Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures. Although researchers have proposed many techniques to detect HTs, several limitations exist, including: (i) a low success rate, (ii) high algorithmic complexity, and (iii) a large number of test patterns. Furthermore, the most pertinent drawback of prior detection techniques stems from an incorrect evaluation methodology, i.e., they assume that an adversary inserts HTs randomly. Such inappropriate adversarial assumptions enable detection techniques to claim high HT detection accuracy, leading to a "false sense of security." Unfortunately, to the best of our knowledge, despite more than a decade of research on detecting HTs inserted during fabrication, there have been no concerted efforts to perform a systematic evaluation of HT detection techniques. In this paper, we play the role of a realistic adversary and question the efficacy of HT detection techniques by developing an automated, scalable, and practical attack framework, ATTRITION, using reinforcement learning (RL). ATTRITION evades eight detection techniques across two HT detection categories, showcasing its agnostic behavior. ATTRITION achieves average attack success rates of $47\times$ and $211\times$ compared to randomly inserted HTs against state-of-the-art HT detection techniques. We demonstrate ATTRITION's ability to evade detection techniques by evaluating designs ranging from the widely-used academic suites to larger designs such as the open-source MIPS and mor1kx processors to AES and a GPS module. Additionally, we showcase the impact of ATTRITION-generated HTs through two case studies (privilege escalation and kill switch) on the mor1kx processor. We envision that our work, along with our released HT benchmarks and models, fosters the development of better HT detection techniques.

This report presents Waymo's proposal for a systematic fatigue risk management framework that addresses prevention, monitoring, and mitigation of fatigue-induced risks during on-road testing of ADS technology. The proposed framework remains flexible to incorporate continuous improvements, and was informed by state of the art practices, research, learnings, and experience (both internal and external to Waymo). Fatigue is a recognized contributory factor in a substantial fraction of on-road crashes involving human drivers, and mitigation of fatigue-induced risks is still an open concern researched world-wide. While the proposed framework was specifically designed in relation to on-road testing of SAE Level 4 ADS technology, it has implications and applicability to lower levels of automation as well.