In 2014, I got into a heated conversation with a colleague. He argued that, within just a few years, robots would replace writers in content marketing. I rolled my eyes so hard my brain ached. The idea of machines crafting sentences as elegant and nuanced as a human writer was both heretical and impossible, I thought. Turns out he was right, but the development isn't the sci-fi creative dystopia I imagined … in most cases.

Since the days of the Manhattan Project, the Energy Department has been a world leader in high performance computing. These powerful machines provide scientists and researchers the ability to quickly analyze and develop insights from massive datasets for answers to the world's most complex problems. AI transcends computational analyses of known challenges and has the capacity to learn from amounts of data beyond human comprehension to find answers to questions we didn't even know we had… in effect reverse-engineering knowledge itself. Insights from artifical intelligence has the potential to transform nearly every aspect of the world as we know it. Today, it is being applied to accelerate the pace of discovery in a wide variety of areas including energy, materials science, health care, national security, emergency response, transportation, and more.

A massive defense policy bill approved by the Senate on Thursday is loaded with provisions to advance the Pentagon's tech and protect the country against digital threats posed by Russia and China. The 2020 National Defense Authorization Act, which passed the upper chamber with a vote of 86-8, would direct some $750 billion to fund the Defense Department, intelligence community and national security programs at the Energy Department in 2020. The legislation would authorize hundreds of millions of dollars for artificial intelligence and cyber research, and support efforts to lock down the government supply chain, fight foreign misinformation and bolster the Pentagon's tech workforce. Thursday's vote comes as an early move in what's expected to be a heated partisan battle over next year's government funding. "This is an important bipartisan package that will strengthen our military, support our troops and enhance national security," Senate Armed Services Committee Ranking Member Jack Reed, D-R.I., said in a statement.

Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences. We propose a new orthogonal score method for treatment effect estimation and inference that results in asymptotically valid confidence intervals assuming only good estimation properties of the hazard outcome model and the conditional probability of treatment. This guarantee allows us to provide valid inference for the conditional treatment effect under the high-dimensional additive hazards model under considerably more generality than existing approaches. In addition, we develop a new Hazards Difference (HDi), estimator. We showcase that our approach has double-robustness properties in high dimensions: with cross-fitting, the HDi estimate is consistent under a wide variety of treatment assignment models; the HDi estimate is also consistent when the hazards model is misspecified and instead the true data generating mechanism follows a partially linear additive hazards model. We further develop a novel sparsity doubly robust result, where either the outcome or the treatment model can be a fully dense high-dimensional model. We apply our methods to study the treatment effect of radical prostatectomy versus conservative management for prostate cancer patients using the SEER-Medicare Linked Data.

Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly, but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.

A Los Angeles police officer wears an Axon body camera in 2017. On Thursday, the company announced it is holding off on facial recognition software, citing its unreliability. A Los Angeles police officer wears an Axon body camera in 2017. On Thursday, the company announced it is holding off on facial recognition software, citing its unreliability. The largest manufacturer of police body cameras is rejecting the possibility of selling facial recognition technology – at least, for now.

This month, about 50 feet (15 meters) under a Swiss glacier, you can experience what it might be like to live on the moon. Space agencies like NASA are looking to the moon as a waystation to venture beyond, to Mars and other cosmic destinations. One of the most viable spots for a lunar base could be inside a crater on the south pole of the moon, where scientists have confirmed the existence of water ice, a crucial resource for astronauts. To offer an idea at what such a habitat might look like, European researchers and students are conducting a mock moon habitat trial under a glacier near the famous Matterhorn in Switzerland's Alps. Called IGLUNA, the demonstration is organized by the Swiss Space Center and the European Space Agency.

WASHINGTON - For its next mission in our solar system, NASA plans to fly a drone copter to Saturn's largest moon, Titan, in search of the building blocks of life, the space agency said Thursday. The Dragonfly mission, which will launch in 2026 and land in 2034, will send a rotorcraft to fly to dozens of locations across the icy moon, which has a substantial atmosphere and is viewed by scientists as an equivalent of very early Earth. It is the only celestial body besides our planet known to have liquid rivers, lakes and seas on its surface, though these contain hydrocarbons like methane and ethane, not water. "Visiting this mysterious ocean world could revolutionize what we know about life in the universe, " said NASA administrator Jim Bridenstine. "This cutting-edge mission would have been unthinkable even just a few years ago, but we're now ready for Dragonfly's amazing flight."

It is commonly understood that AI is one of the most disruptive technologies being developed. It may affect almost every aspect of society – from knowledge sharing to economic interactions, from making art crafts to finding cures for our diseases – and of personal life – from making friends, to finding a partner, from dealing with the pain for the loss of beloved people, to helping us managing our households through smart objects. Understanding the relationship between AI and society is a complex endeavour, since its shape and its evolution are not an immutable technological law, but instead the consequence of specific choices, both private and public, that could very well change over time and may of course influence its sustainability. Some powerful politicians like Vladimir Putin have declared that who will lead the researches in the field of AI, will lead the world, and of course many funds are coming from the armies (Harari, 2015) and from governments that seem to be working for monitoring and controlling us (Greenwald, 2015; Zuboff, 2018). Many others come from the finance world and are meant to increase the incomes of a few rich persons, regardless the risks ran by the rest of the population (O'Neil, 2016).

While artificial intelligence has tremendous potential for revolutionizing healthcare delivery, there are many possible pitfalls and ill-intended uses of this powerful technology. "With the great promise of AI comes an even greater responsibility," Tourassi testified on Wednesday before a House committee hearing on AI's societal and ethical implications. "There are many ethical questions when applying AI in medicine." With respect to ethics, she observed that the massive volumes of health data being leveraged by AI must be carefully protected to preserve privacy. "The sheer volume, variability and sensitive nature of the personal data being collected require newer, extensive, secure and sustainable computational infrastructure and algorithms," according to Tourassi's testimony.