The Committee on Standards in Public Life today published its report and recommendations to the Prime Minister to ensure that high standards of conduct are upheld as technologically assisted decision making is adopted more widely across the public sector. The Committee also published new polling on public attitudes to AI. "Honesty, integrity, objectivity, openness, leadership, selflessness and accountability were first outlined by Lord Nolan as the standards expected of all those who act on the public's behalf. "Artificial intelligence – and in particular, machine learning – will transform the way public sector organisations make decisions and deliver public services. Demonstrating high standards will help realise the huge potential benefits of AI in public service delivery. However, it is clear that the public need greater reassurance about the use of AI in the public sector.

Caption Health, a leading medical AI company, announced that the US Food and Drug Administration (FDA) authorized marketing of Caption Guidance, software that assists medical professionals in the acquisition of cardiac ultrasound images. Caption Guidance uses artificial intelligence to provide real-time guidance and diagnostic quality assessment of images, empowering healthcare providers--even those without prior ultrasound experience--with the ability to capture diagnostic quality images. Empowering more clinicians with ultrasound image acquisition capability will bring the benefits of ultrasound to more patients, help standardize the quality of care, and help institutions realize valuable cost and time savings. Caption Guidance was authorized via the De Novo pathway, a regulatory pathway reserved for novel technologies. The granting of this De Novo is groundbreaking, as Caption Guidance is the first medical software authorized by the FDA that provides real-time AI guidance for medical imaging acquisition.

A team of artificial intelligence researchers at the University at Buffalo plans to study the brain waves and eye movements of around 25 people, Digital Trends reports, while they play a video game. They'll then use the information they glean from the gamers to build an advanced AI -- so that it can then coordinate the actions of entire fleets of autonomous military robots. The U.S. Defense Advanced Research Projects Agency -- better known as DARPA -- has awarded the UB team a $316,000 grant for the study, which researcher Souma Chowdhury told Digital Trends is moving at "a pretty aggressive pace." The team still needs to gather the gamer data, but that shouldn't take too long. The researchers have already built a real-time strategy game for the study, with a round of the game taking about five to 10 minutes to complete.

It can also reflect human flaws and inconsistencies, including 180 known types of bias. Biased AI is everywhere, and like humans, it can discriminate against gender, race, age, disability and ideology. AI bias has enormous potential to negatively affect women, minorities, the disabled, the elderly and other groups. Computer vision has more issues with false-positive facial identification for women and people of color, according to research by MIT and Stanford University. A recent ACLU experiment discovered that nearly 17 percent of professional athlete photos were falsely matched to mugshots in an arrest database.

Amid the cacophony of concern over artificial intelligence (AI) taking over jobs (and the world) and cheers for what it can do to increase productivity and profits, the potential for AI to do good can be overlooked. Technology leaders such as Microsoft, IBM, Huawei and Google have entire sections of their business focused on the topic and dedicate resources to build AI solutions for good and to support developers who do. In the fight to solve extraordinarily difficult challenges, humans can use all the help we can get. Here are 8 powerful examples of artificial intelligence for good as it is applied to some of the toughest challenges facing society today. There are more than 1 billion people living with a disability around the world.

Call takers of the Victorian triple zero (000) hotline will soon be making life-saving decisions using a revolutionary artificial intelligence (AI) technology that will fast-track urgent care to people suffering cardiac arrests. According to a recent press release, Minister for Health Jenny Mikakos has announced that Ambulance Victoria would receive AU$ 1.71 million from the Victorian Government's Safer Care Victoria Innovation Fund for two ground-breaking new projects. She explained that they are always looking for new, cutting edge ways to innovate and deliver the very best and safest care. All Victorians deserve access to world-class treatment when they need it most, no matter where they live. The first project is the Artificial Intelligence in Cardiac Arrest project, which will receive $1.36 million. It will help ESTA triple zero operators identify signs of cardiac arrest over the phone, which will save an additional 185 lives each year.

Mixed Cooperation and competition are the actual scenarios of deploying multi-robot systems, such as the multi-UAV/UGV teaming for tracking criminal vehicles and protecting important individuals. Types and the total number of robot are all important factors that influence mixed cooperation quality. In various real-world environments, such as open space, forest, and urban building clusters, robot deployments have been influenced largely, as different robots have different configurations to support different environments. For example, UGVs are good at moving on the urban roads and reach the forest area while UAVs are good at flying in open space and around the high building clusters. However, it is challenging to design the collective behaviors for robot cooperation according to the dynamic changes in robot capabilities, working status, and environmental constraints. To solve this question, we proposed a novel proficiency-aware mixed environment multi-agent deep reinforcement learning (Mix-DRL). In Mix-DRL, robot capability and environment factors are formalized into the model to update the policy to model the nonlinear relations between heterogeneous team deployment strategies and the real-world environmental conditions. Mix-DRL can largely exploit robot capability while staying aware of the environment limitations. With the validation of a heterogeneous team with 2 UAVs and 2 UGVs in tasks, such as social security for criminal vehicle tracking, the Mix-DRL's effectiveness has been evaluated with $14.20\%$ of cooperation improvement. Given the general setting of Mix-DRL, it can be used to guide the general cooperation of UAVs and UGVs for multi-target tracking.

We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics. Making the problem harder, we assume that the states we wish to predict are unobserved in the training data. Specifically, given a parametric model over the transition rates of a CTMC and some known transition rates, we wish to extrapolate its steady state distribution to states that are unobserved. A technical roadblock to learn a CTMC from its steady state has been that the chain rule to compute gradients will not work over the arbitrarily long sequences necessary to reach steady state ---from where the aggregate statistics are sampled. To overcome this optimization challenge, we propose $\infty$-SGD, a principled stochastic gradient descent method that uses randomly-stopped estimators to avoid infinite sums required by the steady state computation, while learning even when only a subset of the CTMC states can be observed. We apply $\infty$-SGD to a real-world testbed and synthetic experiments showcasing its accuracy, ability to extrapolate the steady state distribution to unobserved states under unobserved conditions (heavy loads, when training under light loads), and succeeding in difficult scenarios where even a tailor-made extension of existing methods fails.

Federated learning is gaining significant interests as it enables model training over a large volume of data that is distributedly stored over many users, while protecting the privacy of the individual users. However, a major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users. In fact, the overhead of state-of-the-art protocols for secure model aggregation grows quadratically with the number of users. We propose a new scheme, named Turbo-Aggregate, that in a network with $N$ users achieves a secure aggregation overhead of $O(N\log{N})$, as opposed to $O(N^2)$, while tolerating up to a user dropout rate of $50\%$. Turbo-Aggregate employs a multi-group circular strategy for efficient model aggregation, and leverages additive secret sharing and novel coding techniques for injecting aggregation redundancy in order to handle user dropouts while guaranteeing user privacy. We experimentally demonstrate that Turbo-Aggregate achieves a total running time that grows almost linear in the number of users, and provides up to $14\times$ speedup over the state-of-the-art schemes with upto $N=200$ users. We also experimentally evaluate the impact of several key network parameters (e.g., user dropout rate, bandwidth, and model size) on the performance of Turbo-Aggregate.

We consider the problem of prediction by a machine learning algorithm, called learner, within an adversarial learning setting. The learner's task is to correctly predict the class of data passed to it as a query. However, along with queries containing clean data, the learner could also receive malicious or adversarial queries from an adversary. The objective of the adversary is to evade the learner's prediction mechanism by sending adversarial queries that result in erroneous class prediction by the learner, while the learner's objective is to reduce the incorrect prediction of these adversarial queries without degrading the prediction quality of clean queries. We propose a game theory-based technique called a Repeated Bayesian Sequential Game where the learner interacts repeatedly with a model of the adversary using self play to determine the distribution of adversarial versus clean queries. It then strategically selects a classifier from a set of pre-trained classifiers that balances the likelihood of correct prediction for the query along with reducing the costs to use the classifier. We have evaluated our proposed technique using clean and adversarial text data with deep neural network-based classifiers and shown that the learner can select an appropriate classifier that is commensurate with the query type (clean or adversarial) while remaining aware of the cost to use the classifier.