Microsoft's latest breakthrough could make DNA-based data centers possible

Much of the discussion of the fourth industrial revolution relates to the disruptive impact of artificial intelligence, robotics, biotech, and big data on the world of work and business. It could lead to huge gains in productivity, wealth creation and human happiness. Equally, it may kill millions of jobs, fuel social tensions, and widen inequality. Civil society's place in this massive societal shake-out, reckons Andy Haldane, is relatively unexplored – but it will be profound. Haldane, the Bank of England's chief economist, is regarded as a "maverick" thinker among central bankers on account not only of his views on banking and financial regulation, but society more widely: from poverty ("scarcity of money reshapes your brain and reshapes your decision-making") to the importance of trade unions.

BRUSSELS, BELGIUM – IBM today issued the following statement welcoming the Organization for Economic Cooperation and Development's (OECD) release of new Principles on Artificial Intelligence: "The OECD's Principles on AI provide sound policy guidance for governments and stakeholders around the world that are working to advance responsible, human-centred AI. IBM is proud to have contributed our deep AI expertise to their development, and we fully agree in our own guidance to governments with the OECD's view that AI must be fair, explainable and secure. We also support their emphasis on the need for greater investment in AI skills and research. "In the 1980s, OECD guidelines on data protection and privacy provided the essential, international foundation for privacy legislation enacted by many countries. The organization is well-positioned to provide a similar global basis for balanced and consistent approaches to AI policies that prioritize trust and maximize the benefits to society while mitigating risks.

SEOUL, South Korea – North Korea has labeled Joe Biden a "fool of low IQ" and an "imbecile bereft of elementary quality as a human being" after the Democratic presidential hopeful during a recent speech called North Korean leader Kim Jong Un a tyrant. Pyongyang's official Korean Central News Agency on Wednesday accused Biden of insulting the country's supreme leadership and committing an "intolerable and serious politically-motivated provocation" against the North. Biden during a campaign launch in Philadelphia on Saturday accused President Donald Trump of cozying up to "dictators and tyrants" like Kim and Russian President Vladimir Putin.

NASA's next trip to the moon will entail 37 separate launches over a decade and culminate in the construction of a moon base by 2028, according to leaked documents that detail the agency's'Artemis' plan. Information on the nascent mission come from documents obtained by Ars Technica, and, for the first time, show a detailed glimpse of America's first human-led mission to the moon since 1972. In a graphic, NASA breaks down a year-by-year guide of the construction of the'Gateway' a space station and waypoint on the way to the moon, human test flights, and a lunar landing slated for 2024. Russia and the United States are cooperating on a NASA-led project to build the first lunar space station, codenamed the Lunar Gateway. The agreement, signed in September 2017, is part of a long-term project to send humans to Mars.

We have all read market predictions describing billions of devices and the hundreds of billions dollars in profit that the Internet of Things (IoT) promises.a Security and the challenges it represents27 are often highlighted as major issues for IoT, alongside scalability and standardization. In 2017, FBI Director James Comey warned, during a senate hearing, of the threat represented by a botnet taking control of devices owned by unsuspecting users. Such a botnet can seize control of devices ranging from connected dishwashers,b to smart home cameras and connected toys, not only using them as a platform to launch cyber-attacks, but also potentially harvesting the data such devices collect. In addition to concerns about cyber-security, corporate usage of personal data has seen increased public scrutiny. A recent focus of concern has been connected home hubs (such as Amazon Alexa and Google Home).c

Columbia University is learning how to build and train self-aware neural networks, systems that can adapt and improve by using internal simulations and knowledge of their own structures. The University of California, Irvine, is studying the dual memory architecture of the hippocampus and cortex to replay relevant memories in the background, allowing the systems to become more adaptable and predictive while retaining previous learning. Tufts University is examining an intercellular regeneration mechanism observed in lower animals such as salamanders to create flexible robots capable of adapting to changes in their environment by altering their structures and functions on the fly. SRI International is developing methods to use environmental signals and their relevant context to represent goals in a fluid way rather than as discrete tasks, enabling AI agents to adapt their behavior on the go.

Systems that recommend products, services, or other attention-targets have become indispensable in the effective curation of information. Such personalization and recommendation techniques have become ubiquitous not only in product/content recommendation and ad placements but also in a wide range of applications like drug testing, spatial sampling, environmental monitoring, and rate adaptation in communication networks; see e.g., Villar et al. (2015); Combes et al. (2014); Srinivas et al. (2010). These are often modeled as sequential decision making or bandit problems, where an algorithm needs to choose among a set of decisions (or arms) sequentially to maximize a desired performance criterion. Recently, an important variant of the bandit problem was proposed by Locatelli et al. (2016) and Gotovos et al. (2013), where the goal is to rapidly identify all arms that are above (and below) a fixed threshold. This thresholding bandit framework, which may be thought of as a version of the combinatorial pure exploration problem (Chen et al., 2014), is useful in various applications like environmental monitoring, where one might want to identify the hypoxic (low-oxygen-content) regions in a lake; like crowd-sourcing, where one might want to keep all workers whose productivity trumps the cost to hire them; or like political polling, where one wants to identify which political candidate individual voting districts prefer.

Commitments capture how an agent relates to another agent, whereas goals describe states of the world that an agent is motivated to bring about. Commitments are elements of the social state of a set of agents whereas goals are elements of the private states of individual agents. It makes intuitive sense that goals and commitments are understood as being complementary to each other. More importantly, an agent's goals and commitments ought to be coherent, in the sense that an agent's goals would lead it to adopt or modify relevant commitments and an agent's commitments would lead it to adopt or modify relevant goals. However, despite the intuitive naturalness of the above connections, they have not been adequately studied in a formal framework. This article provides a combined operational semantics for goals and commitments by relating their respective life cycles as a basis for how these concepts (1) cohere for an individual agent and (2) engender cooperation among agents.

Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting.