Helsinki, Finland - An audience of international peace brokers have gathered inside a room in the historic House of Estates. They have come from South Sudan, the Central African Republic, Ukraine, Colombia and elsewhere to hear a scientist speak. That scientist is Timo Honkela, and his keynote speech on the second day of April's National Dialogues conference is titled Peace from a Different Perspective - a Dialogue of a Million People. But 54-year-old Honkela is working on a machine that he hopes will facilitate world peace. "World peace would be a good goal to work for in my remaining days," he says, smiling over a cup of coffee during a break in the conference.

Because all of the low-hanging scientific and technological fruit has supposedly been plucked. You can invent broad technologies like electrification, the light bulb, plumbing and sanitation, the telephone, refrigeration, the internal combustion engine, and the digital computer only once. Therefore most new technologies will consist of slight improvements on the old ones and that will not propel future economic growth. But have all broad technologies really been invented already? Below are three core technologies whose elaborations during the next decade will conjure into existence a world with far less transactional friction, amazing cures, and much smarter machines.

IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their "Introduction to Artificial Intelligence" course available free online. By the time the course began in October, 160,000 people in 190 countries had signed up for it. At the same time Andrew Ng, also a Stanford professor, made one of his courses, on machine learning, available free online, for which 100,000 people enrolled. Both courses ran for ten weeks. Such online courses, with short video lectures, discussion boards for students and systems to grade their coursework automatically, became known as Massive Open Online Courses (MOOCs).

Knoema is the latest data provider to add a conversational interface. The McLean, Virginia-based company has launched an artificial intelligence (AI)-powered chatbot called Yodatai, which it describes as the first digital assistant for public and corporate data. The name, CEO Vladimir Bougay told me, is a shortening of "your data AI." His company provides access to industry, governmental and market data from thousands of providers, including the US Census, the US Department of Energy and other sources around the world. Additionally, Yodatai has been integrated with product analytics platform Amplitude and can access other databases via API.

Inequality goes back to the Stone Age. Thirty thousand years ago, bands of hunter-gatherers in Russia buried some members in sumptuous graves replete with thousands of ivory beads, bracelets, jewels and art objects, while other members had to settle for a bare hole in the ground. Nevertheless, ancient hunter-gatherer groups were still more egalitarian than any subsequent human society, because they had very little property. Property is a pre-requisite for long-term inequality. Following the agricultural revolution, property multiplied and with it inequality.

Jane Wang started out as an applied physicist modeling the complex network dynamics of memory systems in the brain before moving into experimental cognitive neuroscience as a postdoc at Northwestern. Since joining DeepMind two years ago, her non-machine learning background has equipped her with a unique set of tools and perspectives for tackling the hardest AI problems. "It's exhilarating to formulate theories of human brain function as powerful deep reinforcement learning models that can solve similarly complex tasks," she shares. Though Wang has been successful without a formal AI background, she's concerned the steep learning curve and hypercompetitive atmosphere of AI research can discourage diverse participation. "Although competitiveness drives the field forward, it also discourages those who wish to work in more inclusive, cooperative environments," she warns.

Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a ``causal design,'' i.e., without anticipating indirect influences appropriately in time, we argue that such networks return spurious results. By identifying activator random variables, we propose activator dynamic Bayesian networks (ADBNs) which are able to rapidly adapt to contexts under a causal use of time, anticipating indirect influences on a solid mathematical basis using familiar Bayesian network semantics. ADBNs are well-defined dynamic probabilistic graphical models allowing one to model cyclic dependencies from local and causal perspectives while preserving a classical, familiar calculus and classically known algorithms, without introducing any overhead in modeling or inference.

A stunning new animation reveals the moment the moon crosses in front of the sun during a partial solar eclipse. The phenomenon was captured on May 25 by NASA's Solar Dynamics Observatory (SDO), during a lunar transit that lasted nearly an hour. During this time, scientists say the moon covered roughly 89 percent of the sun, revealing a'crisp' view of the lunar horizon. The phenomenon was captured on May 25 by NASA's Solar Dynamics Observatory (SDO), during a lunar transit that lasted nearly an hour. The animation reveals the moon's path as it crossed in front of the sun NASA has revealed a plan to send a robot to the sun in 2018 to help understand space weather. This will bring it seven times closer to the sun's surface than any spacecraft before it.

In the wake of the recent Manchester terrorist attacks, in which 22 people--mostly parents, teenagers, and children as young as eight years old--were murdered by a suicide bomber, the question that lingered on so many people's minds is, "how?" How could he, the bomber, do it? It's a question that many people have found themselves asking too often, not just after highly publicized attacks in the U.S., but also in countries like Kenya and Nigeria where terror attacks by militant groups such as Boko Haram and al-Shabaab attract less global attention but are no less deadly, or heart-wrenchingly awful. And yet, despite behaviors that many would label immoral, terrorists often couch their activities in moral terms--invoking concepts such as "social cleansing" and "moral purification," attacking people and symbols that they believe are representative of moral failings. But how can people allegedly motivated by morality engage in behaviors that, from the outside, appear to be so clearly immoral? Agustín Ibáñez, a cognitive science researcher at Argentina's Ineco Foundation at Favaloro University, and Adolfo Garcia, a researcher at the National Scientific and Technical Research Council (CONICET), took an interesting tact towards understanding how the mind of a terrorist differs from the mind of, well, people who don't commit acts of terror.

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