-- Coordinated defensive escorts can aid a navigating payload by positioning themselves in order to maintain the safety of the payload from obstacles. In this paper, we present a novel, end-to-end solution for coordinating an escort team for protecting high-value payloads. Our solution employs deep reinforcement learning (RL) in order to train a team of escorts to maintain payload safety while navigating alongside the payload. This is done in a distributed fashion, relying only on limited range positional information of other escorts, the payload, and the obstacles. When compared to a state-of-art algorithm for obstacle avoidance, our solution with a single escort increases navigation success up to 31%. Additionally, escort teams increase success rate by up to 75% percent over escorts in static formations. We also show that this learned solution is general to several adaptations in the scenario including: a changing number of escorts in the team, changing obstacle density, and changes in payload conformation. Successful navigation in crowded scenarios often requires assuming a nonzero collision probability between the agent and stochastic obstacles [1]. This required assumption of risk is potentially frightening given the value of cargo that modern autonomous agents will be transporting, e.g., human life.

In exchange for large quantities of data and processing power, deep neural networks have yielded models that provide state of the art predication capabilities in many fields. However, a lack of strong guarantees on their behaviour have raised concerns over their use in safety-critical applications. A first step to understanding these networks is to develop alternate representations that allow for further analysis. It has been shown that neural networks with piecewise affine activation functions are themselves piecewise affine, with their domains consisting of a vast number of linear regions. So far, the research on this topic has focused on counting the number of linear regions, rather than obtaining explicit piecewise affine representations. This work presents a novel algorithm that can compute the piecewise affine form of any fully connected neural network with rectified linear unit activations.

This paper investigates how to efficiently transition and update policies, trained initially with demonstrations, using off-policy actor-critic reinforcement learning. It is well-known that techniques based on Learning from Demonstrations, for example behavior cloning, can lead to proficient policies given limited data. However, it is currently unclear how to efficiently update that policy using reinforcement learning as these approaches are inherently optimizing different objective functions. Previous works have used loss functions which combine behavioral cloning losses with reinforcement learning losses to enable this update, however, the components of these loss functions are often set anecdotally, and their individual contributions are not well understood. In this work we propose the Cycle-of-Learning (CoL) framework that uses an actor-critic architecture with a loss function that combines behavior cloning and 1-step Q-learning losses with an off-policy pre-training step from human demonstrations. This enables transition from behavior cloning to reinforcement learning without performance degradation and improves reinforcement learning in terms of overall performance and training time. Additionally, we carefully study the composition of these combined losses and their impact on overall policy learning. We show that our approach outperforms state-of-the-art techniques for combining behavior cloning and reinforcement learning for both dense and sparse reward scenarios. Our results also suggest that directly including the behavior cloning loss on demonstration data helps to ensure stable learning and ground future policy updates.

Evidence-based reasoning is at the core of ma ny problem - solving and decision-making tasks in a wide variety of domains. Generalizing from the research and development of cognitive agents in several such domains, this paper presents progress toward a computational theory for the development of instructable cognitive agents for evide nce-based reasoning tasks. The paper also illustrates the application of this theory to the development of four prototype cognitive agents in domains that are critical to the government and the public sector . Two agents function as cognitive assistants, one in intelligence analysis, and the other in science education . The other two agents operate autonomously, one in cybersecurity and the other in intelligence, surveillance, and reconnaissance. The paper concludes with the directions of future research on th e proposed computational theory.

Just over two years ago, China announced an audacious plan to overtake the US and lead the "world in AI [artificial intelligence] technology and applications by 2030". It is already widely regarded to have overtaken the EU in many aspects. But now its plans may be knocked off course by the US restricting certain Chinese companies from buying technologies developed or manufactured in the States. Washington's justification is that the organisations involved have made products used to commit human rights abuses against China's Muslim ethnic minorities. But it is notable that those on its blacklist include many of China's official "national AI champions", among them: Like the telecoms firm Huawei before them, they now face major disruption as a consequence of the Trump administration's intervention.

Launched in January this year, the Maha Agri Tech project seeks to use technology to address various cultivation risks ranging from poor rains to pest attacks, accurately predict crop-wise and area-wise yield and eventually to use this data to inform policy decisions including pricing, warehousing and crop insurance. When farmers in six districts of Maharashtra begin sowing for the coming rabi season, this project will enter its second phase where artificial intelligence and satellite imagery will be used to mitigate risks. Fields of the farmers that are part of the project will be monitored via satellite images at every stage right until the harvest. In its first phase the Maha Agri Tech project used satellite images and analysis from the Maharashtra Remote Sensing Application Centre (MRSAC) and the National Remote Sensing Centre (NRSC) in Hyderabad to assess the acreage and the conditions of select crops in select talukas. In its second phase, various data sets from diverse data providers will be combined to build yield modelling and a geospatial database of soil nutrients, rainfall, moisture stress and other parameters to facilitate location-specific advisories to farmers.

In the first decades of the 20th century, people around the world began succumbing to an entirely new cause of mortality. These new deaths, due to the dangers of the automobile, soon became accepted as a lamentable but normal part of modern life. A hundred years later, with 1.25 million people worldwide (about 30,000 in the U.S.) being killed every year in road crashes, there's now an effort to reject the perception that these deaths are normal or acceptable. As reported in the New York Times, a growing number of safety advocates, government officials, and journalists are moving away from the phrase "car accident" on the grounds that it presumes that the drivers involved are blameless--a presumption that is correct only 6 percent of the time, according to a report by the U.S. Department of Transportation. The vast majority of such incidents are caused by drivers who make mistakes, take risks, or drive while distracted or impaired.

On September 26, 2019, the US Food and Drug Administration (FDA) published six guidance documents clarifying its scope of authority and enforcement discretion policies in light of the 21st Century Cures Act (Cures Act). The long-awaited draft guidance on Clinical Decision Support (CDS) software sets forth FDA's proposed approach to regulating CDS, including software that incorporates machine learning (ML) technology. Companies developing ML software for life science applications should consider reviewing FDA's planned approach to inform their regulatory strategies. In a long-awaited move, FDA published a draft guidance on CDS software. With the rise of artificial intelligence (AI) and machine learning (ML), CDS presents a novel opportunity to analyze immensely large amounts of data for patterns or other information that may be relevant to a particular patient's diagnosis.

Deepfakes have been known to make politicians appear to do and say unusual things. While some deepfakes are silly and fun, others are misleading and even abusive. Two new California laws aim to put a stop to these more nefarious video forgeries. California Gov. Gavin Newsom on Thursday signed AB 730, which makes it illegal to distribute manipulated videos that aim to discredit a political candidate and deceive voters within 60 days of an election. He also signed AB 602, which gives Californians the right to sue someone who creates deepfakes that place them in pornographic material without consent.

The US Army's modernisation priorities will fuel a massive expansion in the data generated by soldiers, units and military platforms. It has become clear that artificial Intelligence (AI) will play a key role in processing this data – and exploiting the opportunities it provides. The army has identified six broad areas of focus for its future development: long-range precision fires; next-generation combat vehicles (NGCV); future vertical lift; army network; air and missile defence; and soldier lethality. As technology evolves across these priorities, there will be a significant increase in the amount of data available to individuals, units, and beyond, said Aneesh Kothari, Vice President for Marketing at Systel, Inc. Kothari pointed to NGCV, which spans several manned and unmanned vehicle platforms. As such vehicles are upgraded, they are increasingly outfitted with full HD, high-resolution sensor suites and cameras, such as EO/IR systems, generating full motion video (FMV) data.