Artificial intelligence (AI) is a potentially transformational technology that will impact how people work and socialize and how economies grow. AI will also have wide-ranging international implications, from national security to international trade. In this submission, we address the significance of international cooperation as a vehicle for realizing the ambitious goals in the key areas of AI innovation and regulation set out in the European Commission's white paper on AI. We focus particularly on the EU relationship with the U.S., which as both a major EU trading partner and a world leader in AI, is a logical partner for such cooperation. Specifically, the white paper observes that the "EU will continue to cooperate with like-minded countries, but also with global players, on AI, based on an approach based on EU rules and values."

At The Good AI, we strongly believe Artificial Intelligence (AI) should be inclusive and celebrate diversity. However, AI is also the reflector of its creators and this translates into the reproduction of certain biases into AI products related to race, gender or sexual orientation among others. The following article from the MIT Technology Review explains how. In the light of this, the tech industry has an important responsibility towards society, and the death of George Floyd at the hands of a city police officer in Minneapolis, USA on 25 May 2020, -one in a long series of racists attacks against African Americans -, should urge us to take action. We need to make sure we are not perpetuating and letting racism or any other kind of discrimination take roots in our AI systems.

Like any technology, AI holds the potential to be weaponized, and more of this type of activity is certainly on the horizon. Cybersecurity leaders have to beat bad actors to the chase by understanding how AI will be weaponized and being able to confront it head-on. To do this, it's important for senior leaders understand how AI can be used by bad actors, in order to ensure their organizations are two steps ahead. Cyber criminals are opportunistic – so it's not surprising that as AI grows in adoption and sophistication, cybercriminals are also looking to seize upon its potential. This isn't exactly new – back in 2018, the Electronic Frontier Foundation was warning about all of the potential malicious uses of AI.

First, ML will struggle most in adversarial situations, where other agents are incentivized to subvert the model. On the military side, this includes cyberdefense, intelligence collection, and any use of ML on the battlefield. On the civilian side, it applies to detecting fraud, human trafficking, poaching, or other illegal behaviors. In such settings, we must also assess the costs of failure and speed of turnaround. In a military setting, even temporary failure of an ML system can be catastrophic, while poachers who temporarily evade detection may still be eventually caught.

Our company has built a platform to produce high-quality cells and tissues for regenerative medicine. That pursuit involves multiple disciplines, which means everyone here is an expert in a different language. Some of us are fluent in stem-cell biology, others in optical engineering, others in machine learning. When we started the company it wasn't possible to do biology and engineering under the same roof. When we finally moved into a shared space we were able to learn each other's lexicons, and we became more strongly aligned.

With its White Paper on Artificial Intelligence (AI), the European Commission embraces the potential of AI in the European economy and labour market. They have the potential to serve applicants, clients and society by enabling better matches and a faster, more efficient process. These improvements will prove essential for the recovery of the European labour markets following the drastic impact of the Covid-19 pandemic. Still, the human touch will remain crucial in the recruitment industry. Leveraging technology in a smart way allows us to free-up time to focus more on those elements of our work that require human creativity and emotion – traits that technology cannot emulate. The future will be one combining smart tech and human touch.

The U.S. has started to catch up to China on the adoption of Artificial Intelligence technology, says AI expert Kai-Fu Lee. When Lee--the chairman and CEO of Sinovation Ventures--wrote his book AI Superpowers in 2018, he argued that China was faster in implementing and monetizing AI technology. But the U.S. has started to close the gap on adopting and using AI day-to-day Lee said at Wednesday's TIME100 Talks event. "China was way ahead in things like mobile payments, food delivery, robotics for delivery, things like that, but we also saw recently, in the U.S., very quickly peoples' habits were forming about ordering food from home, about use of robotics in various places, in using more mobile technologies, mobile payments," said Lee, who has been at the forefront of AI innovation for over three decades at Apple, Microsoft, Google and today as an investor in Chinese tech startups. The Chinese Communist Party has placed a huge focus in recent years on technological advancement to drive its economic growth.

Dependability assurance of systems embedding machine learning(ML) components---so called learning-enabled systems (LESs)---is a key step for their use in safety-critical applications. In emerging standardization and guidance efforts, there is a growing consensus in the value of using assurance cases for that purpose. This paper develops a quantitative notion of assurance that an LES is dependable, as a core component of its assurance case, also extending our prior work that applied to ML components. Specifically, we characterize LES assurance in the form of assurance measures: a probabilistic quantification of confidence that an LES possesses system-level properties associated with functional capabilities and dependability attributes. We illustrate the utility of assurance measures by application to a real world autonomous aviation system, also describing their role both in i) guiding high-level, runtime risk mitigation decisions and ii) as a core component of the associated dynamic assurance case.

We aim at solving the problem of predicting people's ideology, or political tendency. We estimate it by using Twitter data, and formalize it as a classification problem. Ideology-detection has long been a challenging yet important problem. Certain groups, such as the policy makers, rely on it to make wise decisions. Back in the old days when labor-intensive survey-studies were needed to collect public opinions, analyzing ordinary citizens' political tendencies was uneasy. The rise of social medias, such as Twitter, has enabled us to gather ordinary citizen's data easily. However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity. The data differ dramatically from many commonly-used datasets, thus brings unique challenges. In our work, first we built our own datasets from Twitter. Next, we proposed TIMME, a multi-task multi-relational embedding model, that works efficiently on sparsely-labeled heterogeneous real-world dataset. It could also handle the incompleteness of the input features. Experimental results showed that TIMME is overall better than the state-of-the-art models for ideology detection on Twitter. Our findings include: links can lead to good classification outcomes without text; conservative voice is under-represented on Twitter; follow is the most important relation to predict ideology; retweet and mention enhance a higher chance of like, etc. Last but not least, TIMME could be extended to other datasets and tasks in theory.

We study probabilistic safety for Bayesian Neural Networks (BNNs) under adversarial input perturbations. Given a compact set of input points, $T \subseteq \mathbb{R}^m$, we study the probability w.r.t. the BNN posterior that all the points in $T$ are mapped to the same region $S$ in the output space. In particular, this can be used to evaluate the probability that a network sampled from the BNN is vulnerable to adversarial attacks. We rely on relaxation techniques from non-convex optimization to develop a method for computing a lower bound on probabilistic safety for BNNs, deriving explicit procedures for the case of interval and linear function propagation techniques. We apply our methods to BNNs trained on a regression task, airborne collision avoidance, and MNIST, empirically showing that our approach allows one to certify probabilistic safety of BNNs with millions of parameters.