Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can be modeled as a retrosynthesis planning problem with a catalyst allowed. This is a crucial step in efficient biomass conversion. The traditional computational chemistry approach to identifying possible reaction pathways involves computing the reaction energies of hundreds of intermediates, which is a critical bottleneck in silico reaction discovery. Deep reinforcement learning has shown in other domains that a well-trained agent with little or no prior human knowledge can surpass human performance. While some effort has been made to adapt machine learning techniques to the retrosynthesis planning problem, value-added chemical discovery presents unique challenges. Specifically, the reaction can occur in several different sites in a molecule, a subtle case that has never been treated in previous works. With a more versatile formulation of the problem as a Markov decision process, we address the problem using deep reinforcement learning techniques and present promising preliminary results.

This paper presents a general approach for open-domain question answering (QA) that models interactions between paragraphs using structural information from a knowledge base. We first describe how to construct a graph of passages from a large corpus, where the relations are either from the knowledge base or the internal structure of Wikipedia. We then introduce a reading comprehension model which takes this graph as an input, to better model relationships across pairs of paragraphs. This approach consistently outperforms competitive baselines in three open-domain QA datasets, WebQuestions, Natural Questions and TriviaQA, improving the pipeline-based state-of-the-art by 3--13%.

Autonomous vehicles (AVs) are inevitably entering our lives with potential benefits for improved traffic safety, mobility, and accessibility. However, AVs' benefits also introduce a serious potential challenge, in the form of complex interactions with human-driven vehicles (HVs). The emergence of AVs introduces uncertainty in the behavior of human actors and in the impact of the AV manufacturer on autonomous driving design. This paper thus aims to investigate how AVs affect road safety and to design socially optimal liability rules for AVs and human drivers. A unified game is developed, including a Nash game between human drivers, a Stackelberg game between the AV manufacturer and HVs, and a Stackelberg game between the law maker and other users. We also establish the existence and uniqueness of the equilibrium of the game. The game is then simulated with numerical examples to investigate the emergence of human drivers' moral hazard, the AV manufacturer's role in traffic safety, and the law maker's role in liability design. Our findings demonstrate that human drivers could develop moral hazard if they perceive their road environment has become safer and an optimal liability rule design is crucial to improve social welfare with advanced transportation technologies. More generally, the game-theoretic model developed in this paper provides an analytical tool to assist policy-makers in AV policymaking and hopefully mitigate uncertainty in the existing regulation landscape about AV technologies.

Moral standards of human interaction with artificial intelligence should be drawn up, Russian President Vladimir Putin said at the AI Journey conference in Moscow on Saturday. "Discussion is currently underway on social aspects and implications of the use of artificial intelligence. It is a very important issue," the Russian president said. "I suggest that the professional community and companies should contemplate drawing up a set of moral rules for interaction between humans and artificial intelligence," he said recalling that "human beings are the highest value." "Technology must not be invented for the sake of technology," he stressed. "Our main goal is sustainable and harmonious development, a higher life quality and new opportunities for citizens."

Scientists from Intel, Amazon, Facebook and Google joined some of the leading academic minds on artificial intelligence (AI) to discuss the future of machine learning during the inaugural Responsible Machine Learning Summit hosted by UC Santa Barbara. With more than 120 students, faculty, business leaders and invited guests on hand, every speaker and panelist agreed on the importance of establishing an ethical foundation for machine learning, in which a computer uses algorithms and data to make predictions or decisions on its own. "We need to better understand the mutual influence between society and machine learning," said William Wang, a professor of computer science and organizer of the event. "Personally, I'm interested in improving the quality of life by learning the important societal factors and impacts that should be considered when building algorithms, such as fairness, transparency, privacy and accountability." The summit served as the opening for Wang's Center for Responsible Machine Learning.

When Indian Defense Minister Rajnath Singh said that blockchain and artificial intelligence would "revolutionize war," as Cointelegraph reported on Nov. 4, did he take things a bit too far? Jet engines have revolutionized warfare, and so have missiles and nuclear weapons -- but shared digital ledgers? Former NATO Secretary General Anders Fogh Rasmussen, for one, told Cointelegraph that he agrees with India's minister when he was asked about blockchain's possible military uses: Digital technologies have been transforming warfare since the 1990's so emerging technologies such as blockchain have the potential to define the war industry over the coming decades. Data and data sharing will be critical for warfare in the future, particularly with the development of artificial intelligence." "Sharing data is fundamentally about transactions.

It's That Time of Year Again--Fall Is Here and Packs of Robot Dogs Are Frolicking in the Leaves James Vincent The Verge "There's nothing I like more on bright and cold autumnal days than heading down to the park and watching the robot dogs playing in piles of leaves. To hear the scuttle of their little metal legs! To imagine the joy in their tiny silicon brains! The World's First Gattaca Baby Tests Are Finally Here Antonio Regalado MIT Technology Review "Anxious couples are approaching fertility doctors in the US with requests for a hotly debated new genetic test being called "23andMe, but on embryos." Elon Musk Says Building the First Sustainable City on Mars Will Take 1,000 Starships and 20 Years Darrell Etherington Tech Crunch "Addressing a question about comments he made earlier this week at the US Air Force startup pitch day event in California, Musk said that his stated launch cost of only around $2 million per Starship flight are essentially required, should the final goal be to set up a'self-sustaining city on Mars.'i"

The robots are coming – for jobs. This is the plain, cold, hard fact we now face as we head towards the third decade of the 21st Century. The technology-driven world in which we now live is one filled with promise – cars that drive themselves, algorithms that respond to customer service inquiries, automated business intelligence on tap. Yet, this brave new world is also filled with challenges. For even as AI and automation increase productivity and improve our lives, their widespread adoption means that many work activities humans currently perform will soon be displaced – if they haven't been already. What this doesn't mean, however, is that there will be a shortage of jobs in the future.

Synchrotron light sources are powerful facilities that produce light in a variety of "colors," or wavelengths--from the infrared to X-rays--by accelerating electrons to emit light in controlled beams. Synchrotrons like the Advanced Light Source at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) allow scientists to explore samples in a variety of ways using this light, in fields ranging from materials science, biology, and chemistry to physics and environmental science. Researchers have found ways to upgrade these machines to produce more intense, focused, and consistent light beams that enable new, and more complex and detailed studies across a broad range of sample types. Many of these synchrotron facilities deliver different types of light for dozens of simultaneous experiments. And little tweaks to enhance light-beam properties at these individual beamlines can feed back into the overall light-beam performance across the entire facility.

Law360 (November 8, 2019, 5:46 PM EST) -- The Pentagon's increasing interest in using artificial intelligence has led to ethical conflicts with …