Clausematch, a software-as-a-service provider for smart document management, has teamed up with Aberdeen Standard Investments (ASI) in Singapore to develop a buy-side solution for the asset management industry under a Digital Accelerator Program run by the Investment Management Association of Singapore (IMAS). The AI and machine-learning-enabled platform developed by Clausematch enabled ASI to test a faster process to confirm ownership and responsibility for policy changes, improved centralization of governance, and oversight of policies and procedures. "Working closely with the team at ASI for the IMAS DAP has been a rewarding experience for all involved. This was the first time we've collaborated with an investment management firm, and we quickly saw how the platform could help ASI solve some of its compliance and AML challenges," says Arathi Arul, Business Development VP for APAC at ClauseMatch. "The project was a real validation of ClauseMatch's flexibility: we were able to quickly adapt to the needs of the buy-side and see huge potential in helping other firms in the sector overcome similar obstacles."

The Defense Innovation Board, a panel of 16 prominent technologists advising the Pentagon, today voted to approve AI ethics principles for the Department of Defense. The report includes 12 recommendations for how the U.S. military can apply ethics in the future for both combat and non-combat AI systems. The principles are broken into five main principles: responsible, equitable, traceable, reliable, and governable. The principles state that humans should remain responsible for "developments, deployments, use and outcomes," and AI systems used by the military should be free of bias that can lead to unintended human harm. AI deployed by the DoD should also be reliable, governable, and use "transparent and auditable methodologies, data sources, and design procedure and documentation."

This article was submitted in response to the call for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It addresses the first question (parts a. and b.), which asks how artificial intelligence will affect the character and/or the nature of war, and what might happen if the United States fails to develop robust AI capabilities that address national security issues. In the 1983 film WarGames, Professor Falken bursts into the war room at NORAD to warn, "What you see on these screens up here is a fantasy -- a computer-enhanced hallucination. Those blips are not real missiles, they're phantoms!" The Soviet nuclear attack onscreen, he explained, was instead a simulation created by "WOPR," an artificial intelligence of Falken's own invention.

Deep generative models for graphs have shown great promise in the area of drug design, but have so far found little application beyond generating graph-structured molecules. In this work, we demonstrate a proof of concept for the challenging task of road network extraction from image data introducing the Generative Graph Transformer (GGT): a deep autoregressive model based on state-of-the-art attention mechanisms. In road network extraction, the goal is to learn to reconstruct graphs representing the road networks pictured in satellite images. A PyTorch implementation of GGT is available here. The proposed GGT model is designed for the recurrent generation of graphs, conditioned on other data such as an image, by means of the encoder-decoder architecture outlined in Figure 1.

Robotics and Artificial Intelligence (AI) are suffering the same fate as Gemini's hoping to foresee their future by watching Carl Sagan's "Cosmos." While Robotics and AI are often associated with one another, these two fields originate from entirely different disciplines. Read on to learn how companies like Tomahawk Robotics are leveraging these two different, yet complementary, technologies to solve important problems. Often used interchangeably, it is important for industry professionals to properly differentiate between these two technologies. The world of Computer Science claims AI as it relates to the computation, reasoning, and instruction within a computer operation.

Artificial Intelligence (AI) has a rapidly growing presence in today's world. Applications are present in industries ranging from manufacturing to education. It has become clear that AI has the potential to revolutionize how the everyday world works. Join us to learn more about how AI can support your business and personal life. Researchers in the Faculty of Business and IT will provide real-life examples and present how AI changes our everyday life, with topics ranging from digital security, gaming, healthcare, online social connections and more!

As artificial intelligence (AI) and autonomous machines become more common in agriculture, the industry is going through enormous changes. Ofir Schlam, CEO and co-founder of Taranis, a leading precision agriculture intelligence platform, recently shared more information about these changes in an interview. Taranis is an AI-powered agriculture intelligence platform that was selected to be part of John Deere's startup collaborator. It uses sophisticated computer vision, data science and deep learning algorithms to enable farmers to make informed decisions. The platform is capable of monitoring fields and finding early symptoms of uneven emergence, weeds, nutrient deficiencies, disease or insect infestations, water damage and equipment issues.

The technology industry, regulators and privacy advocates continue to debate and push forward the idea that AI development needs to be'responsible and ethical'. However, what that actually looks like - considering so much AI and ML activity is veiled in secrecy - continues to be up for debate. Sure, controls can be put in place, organisations can have strong governance structures, but we are far from an internationally recognised'standard' around how AI should be created and used. This was the topic of debate during a panel at this week's IoT Solutions World Congress in Barcelona, where experts and delegates from industry debated the challenges and pitfalls of developing AI applications and tools ethically. The conversation was one of the more honest ones I've listened to in recent years on the topic. Some conclusions that were drawn included the suggestion that the tech industry should hand over their'black boxes' and trade secrets, and that maybe as a society we should make the decision to just not use some technology.

Applications are invited for a PhD studentship, to be undertaken at Imperial College London (Electrical and Electronic Engineering Department). This studentship will form part of a newly established International Centre for Spatial Computational Learning http://spatialml.net, and a supervisory team will be allocated based on the student's interest from the Imperial College supervisors participating in the Centre. This is an exciting cutting-edge project involving close collaboration between Imperial College (UK), the University of California Los Angeles (USA), the University of Toronto (Canada), and the University of Southampton (UK). The successful candidate will be based at Imperial but will have the opportunity to travel frequently to America to attend research meetings and for a placement period at either UCLA or Toronto. Traditional deep learning has been based on the idea of large-scale linear arithmetic units, effectively computing matrix-matrix multiplication, combined with nonlinear activation functions.

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