This is our AI Daily Roundup today. We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities in artificial intelligence (AI), Machine Learning, Robotic Process Automation, Fintech, and human-system interactions. We cover the role of AI Daily Roundup and its application in various industries and daily lives. Businesses are immensely benefiting from AI adoption.

AI and automated decision making systems have become more prominent and ubiquitous in our lives and without the necessary governance, transparency or accountability structures. These systems are already used by government agencies and private companies for risk scoring on credit/loans, recruitment, housing, immigration, law enforcement, school admissions, health decisions, welfare / benefit eligibility - just to name a few. AIethicist.org is one of the first global repositories of reference & research material for anyone interested in the current research on AI ethics, responsible governance - and impact of AI on individuals and society. This site is updated on a regular basis with curated material.

SafeAI, a global leader in autonomous solutions for heavy equipment, in partnership with Obayashi Corporation hosted nearly 150 visitors to view its retrofitted autonomous Caterpillar 725, operating on a construction site in Japan for the first time ever. The demos, which took place over a two week period, brought together delegates from nearly 50 organizations across the construction industry ecosystem, including leading OEM, general contractors, technology companies, universities and research groups and Japanese government entities responsible for regulation. In addition to winning buy-in from key stakeholders, the demos mark a critical first step toward securing approval from the government and industry organizations to bring autonomous construction to Japan. Prior to the public demo, SafeAI and Obayashi spent 6 weeks extensively testing in a Japanese environment to confirm all the features previously developed in the US-based proof of concept (POC), which was completed in California in November 2021. Japanese regulations were not originally designed to support the use of autonomous vehicles for off-road construction.

Robotic microsurgery company Medical Microinstruments announced today that it raised $75 million in a Series B financing round. Pisa, Italy–based Medical Microinstruments plans to use proceeds from the financing round, along with its planned U.S. presence, to move into its next stage of growth through expanded indications and ongoing commercialization efforts for its Symani microsurgery system. The company designed Symani to address the challenges of microsurgery with the NanoWrist instruments for accessing and suturing small, delicate anatomy, such as veins, arteries, nerves and lymphatic vessels as small as 0.3mm in diameter. It provides motion scaling and tremor reduction to allow precise micro-movements. Symani received CE mark in 2019, and the company intends to accelerate commercialization in the U.S. and Asia-Pacific, as well as advance clinical research through an FDA investigational device exemption (IDE) pivotal study.

The U.S. Department of Transportation (DOT) announced this week that it's establishing a two year federal advisory committee that will make recommendations on how to best innovate the transportation industry. The Transforming Transportation Advisory Committee (TTAC) will be made up of 25 members appointed by the DOT secretary for up to two year terms. The TTAC will make recommendations on how the DOT can best handle emerging technologies. Members of the TTAC will include safety advocates, academic experts, representatives of organized labor, technical experts in automation, data, privacy and cybersecurity and industry representatives. According to the DOT, the committee's membership should be as balanced as possible.

DroneShield has announced it has signed a collaboration agreement with The Australian Missile Corporation (AMC), as the $1bn Guided Weapons and Explosive Ordnance (GWEO) enterprise enters the next phase. The AMC was one of the Australian-based GWEO enterprise panel partners invited by the Commonwealth Government in April to work with global missile manufacturing giants Lockheed Martin and Raytheon in establishing a local industry. Considered areas of cooperation between AMC and DroneShield include counterdrone security, prevalent in current battlefield as seen with the Ukraine war, as well as Electronic Warfare and associated Artificial Intelligence work. AMC's CEO, commented "We are pleased to cooperate with DroneShield, with its Australian sovereign capability, as we progress our GWEO program. Its world-leading technologies combined with its expertise in engineering and physics would be critical to the development of guided weapons in Australia."

The UK government has regularly stated its ambition to turn the UK into a hub for AI products and services, as have other jurisdictions. And regulation has emerged as a crucial element in ensuring a thriving AI industry given that the technology's advancement has led to an increased concern about how it is used. Indeed the ethical debate over AI use and ensuring that algorithms are explainable in some way has even involved the Pope. The UK's AI rulebook takes a principles-based approach that will enable regulators in different industries to apply the rules as they see fit. According to digital minister Damian Collins, this will enable a "flexible approach [that will] help us shape the future of AI".

The UK, particularly London, is a global hub for money laundering, a significant portion of which uses domestic property. However, understanding the distribution and characteristics of offshore domestic property in the UK is challenging due to data availability. This paper attempts to remedy that situation by enhancing a publicly available dataset of UK property owned by offshore companies. We create a data processing pipeline which draws on several datasets and machine learning techniques to create a parsed set of addresses classified into six use classes. The enhanced dataset contains 138,000 properties 44,000 more than the original dataset. The majority are domestic (95k), with a disproportionate amount of those in London (42k). The average offshore domestic property in London is worth 1.33 million GBP collectively this amounts to approximately 56 Billion GBP. We perform an in-depth analysis of the offshore domestic property in London, comparing the price, distribution and entropy/concentration with Airbnb property, low-use/empty property and conventional domestic property. We estimate that the total amount of offshore, low-use and airbnb property in London is between 144,000 and 164,000 and that they are collectively worth between 145-174 billion GBP. Furthermore, offshore domestic property is more expensive and has higher entropy/concentration than all other property types. In addition, we identify two different types of offshore property, nested and individual, which have different price and distribution characteristics. Finally, we release the enhanced offshore property dataset, the complete low-use London dataset and the pipeline for creating the enhanced dataset to reduce the barriers to studying this topic.

Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and data driven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization and then explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multiobjective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a small step forward towards understanding fairness in the context of optimization and promote research interest in fairness-aware multi-objective optimization.

Gathering cyber threat intelligence from open sources is becoming increasingly important for maintaining and achieving a high level of security as systems become larger and more complex. However, these open sources are often subject to information overload. It is therefore useful to apply machine learning models that condense the amount of information to what is necessary. Yet, previous studies and applications have shown that existing classifiers are not able to extract specific information about emerging cybersecurity events due to their low generalization ability. Therefore, we propose a system to overcome this problem by training a new classifier for each new incident. Since this requires a lot of labelled data using standard training methods, we combine three different low-data regime techniques - transfer learning, data augmentation, and few-shot learning - to train a high-quality classifier from very few labelled instances. We evaluated our approach using a novel dataset derived from the Microsoft Exchange Server data breach of 2021 which was labelled by three experts. Our findings reveal an increase in F1 score of more than 21 points compared to standard training methods and more than 18 points compared to a state-of-the-art method in few-shot learning. Furthermore, the classifier trained with this method and 32 instances is only less than 5 F1 score points worse than a classifier trained with 1800 instances.