The federal government is often accused of not being able to keep policy aligned as new technologies emerge. Over the past two years, the administration has issued a lot of guidance around robotic process automation, or RPA, and the broader technology field of artificial intelligence. Whether sparked by China's announcement of its intention to be the world's AI superpower or just good strategic leadership, the emphasis is welcomed. While the latest executive order from the White House was immediately panned as an "unfunded mandate," critics might consider the substantial leadership of several mid-level agencies before calling for funding in these fiscally challenging times. Executive branch leadership over the past few years has been encouraging agencies to begin their path to AI, and Congress and agencies have begun that process.

From surveillance capitalism to the future of the surveillance state, the future of AI and machine intelligence is the churning of data. If data is the new oil so many things are coming to pass including the possibility of that artificial intelligence regulation may be impossible. We live in an era of AI hype so unparalleled that it sounds almost like the crypto fraud. Come to think of it, 40% of AI startups might not even be using real AI at all. Humanity though likely will be faced by an automation crisis of the disruption of jobs.

Privacy and cybersecurity are converging. "It's not just a coincidence that privacy issues dominated 2018," writes Andrew Burt (chief privacy officer and legal engineer at Immuta) in his Harvard Business Review article Privacy and Cybersecurity Are Converging. "These events are symptoms of larger, profound shifts in the world of data privacy and security that have major implications for how organizations think about and manage both." Burt's concern is not new. Something else threatened by the power of AI and machine learning is online anonymity.

Technological advancements in the medical field are vital to improving the way patients receive care. In many cases, there is a need for more resources to be directed towards patient care. But the current reality for many patients, especially children with chronic illnesses, is that medical professionals and families are often forced to carry a heavy load in caring for them. To address this need within the healthcare sector, there has been an uptick in the size of Australia's medtech startup community, with the NSW government expecting the industry to create 28,000 jobs and add AU$18 billion in gross domestic product to Australia by 2025. Among the medtech startups in Australia is ikkiworks, which developed a companion robot that helps soothe and monitor the vital signs of children with chronic illness while they are away from the hospital.

Sanskrit is not only limited to spiritualism and philosophy, but can also be used for machine learning and artificial intelligence, said Indian President Ram Nath Kovind. Speaking at the 17th convocation of the Shri Lal Bahadur Shastri Rashtriya Sanskrit Vidyapeetha in New Delhi, Kovind emphasised that Sanskrit is the language of science and spirituality. "It is not that the works in Sanskrit are limited to spiritualism, philosophy, devotion, ritualism or literature. It is also the language of knowledge and science. Important works of scientists and mathematicians such as Aryabhatt, Varaah Mihir, Bhaskar, Charak and Sushrut were created in Sanskrit," he told the gathered audience.

Today's customers demand more options, more creative solutions, greater flexibility and faster responses from banks and other financial institutions. Survival and success for financial institutions in this new world requires that they operate with intelligence, agility and speed to keep up with evolving customer preferences and technologies. Consequently, more and more customer interactions and financial transactions are going digital as online and mobile payments, customer on-boarding and account opening are on the rise. Yet, while digital interfaces present an opening for innovative business services, they also yield new challenges, such as pressure on back office operations or increased regulatory scrutiny. Largely automated interactions generate more data to analyse, demand higher volumes of sample testing, and expand the compliance burden. To create a flawless customer experience, the back office has to keep up as well.

Artificial intelligence machines are still a long way from the agile, haughty humanoid machines popular in science fiction novels and movies, and an increasing number of TV commercials. But in some respects, they might not be too far off, to judge from work being done on the next generation of AI systems. If Defense Department researchers have their way, the next wave of AI systems will be able to think on their feet to a degree they can't now. And, when necessary, they will engage in a kind of Vulcan mind meld with their human counterparts. Those are the goals of two projects included in the Defense Advanced Research Projects Agency's AI Next campaign, a $2 billion effort to fast-track development of what DARPA calls the "third wave" of AI technologies.

In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We illustrate the use of this dataset with two example applications: forecasting future EVE irradiance from present EVE irradiance and translating HMI observations into AIA observations. For each application we provide metrics and baselines for future model comparison. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a direct result of the 2018 NASA Frontier Development Laboratory Program. Please see the appendix for access to the dataset.

Despite huge success, deep networks are unable to learn effectively in sequential multitask learning settings as they forget the past learned tasks after learning new tasks. Inspired from complementary learning systems theory, we address this challenge by learning a generative model that couples the current task to the past learned tasks through a discriminative embedding space. We learn an abstract level generative distribution in the embedding that allows the generation of data points to represent the experience. We sample from this distribution and utilize experience replay to avoid forgetting and simultaneously accumulate new knowledge to the abstract distribution in order to couple the current task with past experience. We demonstrate theoretically and empirically that our framework learns a distribution in the embedding that is shared across all task and as a result tackles catastrophic forgetting.

Machine learning models often excel in the accuracy of their predictions but are opaque due to their non-linear and non-parametric structure. This makes statistical inference challenging and disqualifies them from many applications where model interpretability is crucial. This paper proposes the Shapley regression framework as an approach for statistical inference on non-linear or non-parametric models. Inference is performed based on the Shapley value decomposition of a model, a pay-off concept from cooperative game theory. I show that universal approximators from machine learning are estimation consistent and introduce hypothesis tests for individual variable contributions, model bias and parametric functional forms. The inference properties of state-of-the-art machine learning models - like artificial neural networks, support vector machines and random forests - are investigated using numerical simulations and real-world data. The proposed framework is unique in the sense that it is identical to the conventional case of statistical inference on a linear model if the model is linear in parameters. This makes it a well-motivated extension to more general models and strengthens the case for the use of machine learning to inform decisions.