Collaborating Authors


Towards Risk Modeling for Collaborative AI Artificial Intelligence

Collaborative AI systems aim at working together with humans in a shared space to achieve a common goal. This setting imposes potentially hazardous circumstances due to contacts that could harm human beings. Thus, building such systems with strong assurances of compliance with requirements domain specific standards and regulations is of greatest importance. Challenges associated with the achievement of this goal become even more severe when such systems rely on machine learning components rather than such as top-down rule-based AI. In this paper, we introduce a risk modeling approach tailored to Collaborative AI systems. The risk model includes goals, risk events and domain specific indicators that potentially expose humans to hazards. The risk model is then leveraged to drive assurance methods that feed in turn the risk model through insights extracted from run-time evidence. Our envisioned approach is described by means of a running example in the domain of Industry 4.0, where a robotic arm endowed with a visual perception component, implemented with machine learning, collaborates with a human operator for a production-relevant task.

FDA leader talks evolving strategy for AI and machine learning validation


At a virtual meeting of the U.S. Food and Drug Administration's Center for Devices and Radiological Health and Patient Engagement Advisory Committee on Thursday, regulators offered updates and new discussion around medical devices and decision support powered by artificial intelligence. One of the topics on the agenda was how to strike a balance between safety and innovation with algorithms getting smarter and better trained by the day. In his discussion of AI and machine learning validation, Bakul Patel, director of the FDA's recently-launched Digital Health Center of Excellence, said he sees huge breakthroughs on the horizon. "This new technology is going to help us get to a different place and a better place," said Patel. You're seeing automated image diagnostics.

Assured Autonomy: Path Toward Living With Autonomous Systems We Can Trust Artificial Intelligence

The challenge of establishing assurance in autonomy is rapidly attracting increasing interest in the industry, government, and academia. Autonomy is a broad and expansive capability that enables systems to behave without direct control by a human operator. To that end, it is expected to be present in a wide variety of systems and applications. A vast range of industrial sectors, including (but by no means limited to) defense, mobility, health care, manufacturing, and civilian infrastructure, are embracing the opportunities in autonomy yet face the similar barriers toward establishing the necessary level of assurance sooner or later. Numerous government agencies are poised to tackle the challenges in assured autonomy. Given the already immense interest and investment in autonomy, a series of workshops on Assured Autonomy was convened to facilitate dialogs and increase awareness among the stakeholders in the academia, industry, and government. This series of three workshops aimed to help create a unified understanding of the goals for assured autonomy, the research trends and needs, and a strategy that will facilitate sustained progress in autonomy. The first workshop, held in October 2019, focused on current and anticipated challenges and problems in assuring autonomous systems within and across applications and sectors. The second workshop held in February 2020, focused on existing capabilities, current research, and research trends that could address the challenges and problems identified in workshop. The third event was dedicated to a discussion of a draft of the major findings from the previous two workshops and the recommendations.



The widespread use and increasing complexity of mission-critical and safety-critical systems at NASA and in the aerospace industry require advanced techniques that address these systems' specification, design, verification, validation, and certification requirements. The NASA Formal Methods Symposium (NFM) is a forum to foster collaboration between theoreticians and practitioners from NASA, academia, and industry. NFM's goals are to identify challenges and to provide solutions for achieving assurance for such critical systems. New developments and emerging applications like autonomous software for Unmanned Aerial Systems (UAS), UAS Traffic Management (UTM), advanced separation assurance algorithms for aircraft, and the need for system-wide fault detection, diagnosis, and prognostics provide new challenges for system specification, development, and verification approaches. Similar challenges need to be addressed during development and deployment of on-board software for both spacecraft and ground systems.

Singapore startup develops COVID-19 breath detection test


A Singapore startup has developed a breath test it says can detect COVID-19 in under 60 seconds. Based on clinical trials involving 180 patients, the system has clocked an accuracy rate of more than 90%. Developed by Breathonix, the breath test is a notable move away from the current screening standard involving a swab test. The latter may be uncomfortable and identifies COVID-19 through polymerase chain reaction (PCR) tests, which can take a few hours. Swift detection was key in effective contact tracing and stemming the spread of the coronavirus, and Breathonix's breath analysis technology offered a fast and convenient way to identify infections, the startup said in a statement Tuesday. A spinoff from the National University of Singapore (NUS), the company's two founders are graduates from the local university and is supported under the university's Graduate Research Innovation Programme.

Can Amazon convince you to welcome a security drone into your home?


The past few years of Alexa-related product launches have seen rise to some of the most unusual devices launched by a major tech company. There's been the Alexa ring, the Alexa glasses, the Alexa wall clock, and the Alexa microwave. This year, though, as Amazon released the biggest upgrade to Alexa since the agent first showed up in its cylindrical house called Echo, its developer brought forth a smaller range of Alexa devices. That may be in part because the company has been doing such a good job of getting third parties to spread the cyan-accompanied conversationalist far and wide as well as the company's commitment to sustainability, which not only favors fewer, more durable devices, but those using sustainable materials that may not be so easily leveraged in niche forays. In contrast to the Echo proliferation slowdown, Amazon's Ring product line continued to expand well beyond its signature video doorbell with a new premium service offering and a move into vehicles with a car alarm and camera connection service that showed more thoughtfulness than the dashboard screen invasions of Apple CarPlay and Android Auto. The division also showed off a small mailbox sensor that can alert you of new postal mail and address mail theft.

Digital transformation for the hospitality industry - Babin Business Consulting


Digital transformation is all about how companies decide to embrace new technology and change to optimize their business. Digital progress needs to be used for the best of companies, employees and customers. In the US alone, out of 10 companies 8 have started a digital transformation program. In this post, let's have a look at how the hospitality industry could be impacted. From the moment you start thinking about a hotel or a restaurant, AI powered algorithm can choose for you which one will be more suited for you.

CEO of tech start-up talks AI in finance


Shamus Rae, CEO of tech start-up Engine B, and Kirstin Gillon, Technical Manager in ICAEW's IT Faculty, consider the progress made by AI within finance. Shamus Rae (SR): I look at AI hitting the profession in three stages. Stage one is the different focus on play. The second stage is about process efficiency, and the third one is driving business model change and the service delivery of a specific service like audit. Different firms are at different stages of using AI.

NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels Artificial Intelligence

Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, small city, education, etc. In practice, people refer to crowdsourcing to get annotated labels. However, due to issues like data privacy, budget limitation, shortage of domain-specific annotators, the number of crowdsourced labels are still very limited. Moreover, because of annotators' diverse expertises, crowdsourced labels are often inconsistent. Thus, directly applying existing representation learning algorithms may easily get the overfitting problem and yield suboptimal solutions. In this paper, we propose \emph{NeuCrowd}, a unified framework for representation learning from crowdsourced labels. The proposed framework (1) creates a sufficient number of high-quality \emph{n}-tuplet training samples by utilizing safety-aware sampling and robust anchor generation; and (2) automatically learns a neural sampling network that adaptively learns to select effective samples for representation learning network. The proposed framework is evaluated on both synthetic and real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC\footnote{To encourage the reproducible results, we make our code public on a github repository, i.e., \url{}}.

Why Clearview AI is a threat to us all


Clearview AI was founded in 2017 by Richard Schwartz and now-CEO Hoan Ton-That. The company counts Peter Thiel and AngelList founder Naval Ravikant among its investors. Clearview's technology is actually quite simple: Its facial recognition algorithm compares the image of a person's face from security camera footage to an existing database of potential matches. Marketed primarily to law enforcement agencies, the Clearview app allows users to take and upload a picture of a person then view all of the public images of that person as well as links to where those photos were published. Basically, if you're caught on camera anywhere in public, local law enforcement can use that image to mine your entire online presence for information about you, effectively ending any semblance of personal privacy.