Ford wants to use walking robots to help self-driving cars deliver packages. LAS VEGAS – The arrival of the machines is inevitable. And Sin City knows it. MGM Resorts International this year plans to install at Las Vegas resorts an unknown number of automated beverage systems programmed to mix hundreds of different drinks – all at the touch of a button. It's a job that for many years depended on the hands of living, breathing service workers.

We are in the foothills of an AI journey. On April 8 the EU issued in-depth guidelines on developing and implementing trustworthy Artificial Intelligence. The guidelines identify fundamental requirements for AI in Europe and set a global standard for efforts to advance AI that is Ethical and Responsible. "It's like putting the foundations in before you build a house. Now is the time to do it," said Liam Benham, Vice President, Government and Regulatory Affairs, in Europe.

About Singularity University: Singularity University is a benefit corporation headquartered at NASA's research campus in Silicon Valley. We provide educational programs, innovative partnerships and a startup accelerator to help individuals, businesses, institutions, investors, NGOs and governments understand cutting-edge technologies, and how to utilize these technologies to positively impact billions of people.

While AI is being leveraged in a wide number of areas, cybersecurity is one that has received special attention because of the rate at which threats are evolving and the volume of attacks. Organizations require a solution that can keep up. AI sometimes is championed as that solution – a silver bullet that will "solve" cybersecurity. While that isn't the case, AI is an exciting technology that provides some real-world benefits today, and promises to have even greater potential for the future.

A remarkable time of human promise has been ushered in by the convergence of the ever-expanding availability of big data, the soaring speed and stretch of cloud computing platforms, and the advancement of increasingly sophisticated machine learning algorithms. Innovations in AI are already leaving a mark on government by improving the provision of essential social goods and services from healthcare, education, and transportation to food supply, energy, and environmental management. These bounties are likely just the start. The prospect that progress in AI will help government to confront some of its most urgent challenges is exciting, but legitimate worries abound. As with any new and rapidly evolving technology, a steep learning curve means that mistakes and miscalculations will be made and that both unanticipated and harmful impacts will occur. This guide, written for department and delivery leads in the UK public sector and adopted by the British Government in its publication, 'Using AI in the Public Sector,' identifies the potential harms caused by AI systems and proposes concrete, operationalisable measures to counteract them. It stresses that public sector organisations can anticipate and prevent these potential harms by stewarding a culture of responsible innovation and by putting in place governance processes that support the design and implementation of ethical, fair, and safe AI systems. It also highlights the need for algorithmically supported outcomes to be interpretable by their users and made understandable to decision subjects in clear, non-technical, and accessible ways. Finally, it builds out a vision of human-centred and context-sensitive implementation that gives a central role to communication, evidence-based reasoning, situational awareness, and moral justifiability.

Recent advances in language modeling have resulted in significant breakthroughs on a wide variety of benchmarks in natural language processing Dai et al. [2018], Gong et al. [2018], Takase et al. [2018]. Capturing long-term dependencies has especially been a major focus, with approaches ranging from explicit memory-based neural networks Grave et al. [2016], Ke et al. [2018] to optimization improvements aimed at stabilizing training Le et al. [2015], Trinh et al. [2018]. In this paper, we address a basic question: how do the long-term dependencies in a language model's generations compare to those of the underlying language? Furthermore, if there are measurable discrepancies, this leads to the question of whether and how we can use them to improve these models. Starting from Shannon's seminal work that essentially introduced statistical language modeling Shannon [1951], the most classical and widely studied long-term property of a language model is its entropy rate -- the average amount of information contained per word, conditioned on the preceding words. A learned model provides an upper bound for the entropy rate of a language, via its cross-entropy loss. The exponential of the entropy rate can be interpreted as the effective support size of the distribution of the next word (intuitively, the average number of "plausible" word choices to continue a document), and the perplexity score of a model (the exponential of the cross entropy loss) is an upper bound for this quantity.

Artificial Intelligence (AI) applications are being used to predict and assess behaviour in multiple domains, such as criminal justice and consumer finance, which directly affect human well-being. However, if AI is to improve people's lives, then people must be able to trust AI, which means being able to understand what the system is doing and why. Even though transparency is often seen as the requirement in this case, realistically it might not always be possible or desirable, whereas the need to ensure that the system operates within set moral bounds remains. In this paper, we present an approach to evaluate the moral bounds of an AI system based on the monitoring of its inputs and outputs. We place a "glass box" around the system by mapping moral values into explicit verifiable norms that constrain inputs and outputs, in such a way that if these remain within the box we can guarantee that the system adheres to the value. The focus on inputs and outputs allows for the verification and comparison of vastly different intelligent systems; from deep neural networks to agent-based systems. The explicit transformation of abstract moral values into concrete norms brings great benefits in terms of explainability; stakeholders know exactly how the system is interpreting and employing relevant abstract moral human values and calibrate their trust accordingly. Moreover, by operating at a higher level we can check the compliance of the system with different interpretations of the same value. These advantages will have an impact on the well-being of AI systems users at large, building their trust and providing them with concrete knowledge on how systems adhere to moral values.

A defence company has invented a new futuristic'rifle' that stops rogue drones by hacking into them - and forcing them to fly back to their pilots. DroneShield has developed a software similar to'Google Maps' for drones that instantly locates any drones - and sends them back to their pilots. The firm has previously worked with the British Army and provided assistance to the 2018 Korean Winter Olympics, and their tech is in use at airports. CEO Oleg Vornik remains tight-lipped on the exact cost of the system, but confirmed it ranges from five to seven figures. Mr Vornik also says the system could be used to protect airports from drone incursions - such as the one that brought chaos to Gatwick Airport, bringing it to a standstill for 33 hours before Christmas.

The Gartner Security & Risk Management Summit is just a few days away, and I'm delighted to have the opportunity to chat with attendees about how anomaly detection and machine learning can help give your organization a more proactive security posture. You don't need to have been in the cybersecurity space for long to be bewildered by and unsure about vendor claims around artificial intelligence, machine learning, and analytics. At Interset (acquired by Micro Focus in February of this year), we have regular conversations with security professionals who struggle to understand which techniques and tools are effective in boosting breach defense in the real world. Ultimately, these conversations lead to an important question for us: How can you implement user and entity behavioral analytics (UEBA) in a way that will enable an efficient security operations center (SOC)? There are multiple factors that go into an effective UEBA implementation, but it's helpful to start with ensuring that the math and machine learning powering the solution are suitable for your security objectives.

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