The three-body problem, one of the most notoriously complex calculations in physics, may have met its match in artificial intelligence: a new neural network promises to find solutions up to 100 million times faster than existing techniques. First formulated by Sir Isaac Newton, the three-body problem involves calculating the movement of three gravitationally interacting bodies – such as the Earth, the Moon, and the Sun, for example – given their initial positions and velocities. It might sound simple at first, but the ensuing chaotic movement has stumped mathematicians and physicists for hundreds of years, to the extent that all but the most dedicated humans have tried to avoid thinking about it as much as possible. That's why chronometer time-keepers became more popular for calculating positions at sea rather than using the Moon and the stars – it was just less of a head-scratcher. Today the three-body problem is an important part of figuring out how black hole binaries might interact with single black holes, and from there how some of the most fundamental objects of the Universe interact with each other.

Adobe and Evergage have topped the latest Forrester Wave rating digital intelligence platform providers based on product offering, strategy and market presence. The Q4 Digital Intelligence Platform report placed both Adobe and Evergage in the leaders' quadrant out a total field of nine vendors. In its commentary, the analyst firm highlighted Adobe's best-of-breed capabilities and scale as key reasons for its leadership position, noting the core components of Adobe Analytics, Experience Platform and Adobe Target as providing a wide array of services for marketing and customer engagement. Specifically, these address data, analytics and experience optimisation and are also well integrated into a wider platform play, Forrester stated. Second in the pack was smaller player, Evergage, which Forrester praised for its all-in-one, tightly integrated offering.

Our proposal has three components: The first is a public data pool that would make data accessible to registered users. Local, state and federal governments have sizable data resources that would seed this digital commons. Users would be verified to block foreign governments, hackers and others with ill motives from access, and users would be prevented from using the data to engage in racial or other forms of discrimination and for microtargeted advertising. Some of the data may be very sensitive, and access to those resources would be highly regulated. We can imagine a variety of ways that regulation and technology together could protect privacy and still foster innovation: Data could be anonymized at the source; the commons could have an interface that allowed users to derive insight from the data set, while leaving the underlying information inaccessible; less sensitive data, like weather information, could be made available in a format optimized for training A.I. What's more, methods for safely sharing A.I. models without disclosing the underlying data are being developed today and could enable users of the data commons to collaborate on public-interest A.I. services.

"Awkward giggles," confusion, and nervousness: on October 21, Fujitsu Labs announced their software would be able to detect all of these emotional expressions and feelings. In May, journalists reported Amazon was working on a wearable device that will be able to detect its owner's feelings. It will "sync with a smartphone app," and supposedly be capable of detecting "joy, anger, sadness, fear, disgust, boredom…" Meanwhile, Affectiva is promoting the idea that its software might be able to tell you when you've hurt people's feelings on social media, while the Dutch company Braingineers touts its alleged ability to discern how "users really feel." These proposed inventions are based on shaky understandings of human psychology which presume that emotions are concrete, detectable entities that can be reliably recognized and identified across time and space. Such a view comes from Darwin; it was resuscitated in the 1960s by psychologist Paul Ekman, and still circulates widely today (especially in Silicon Valley, as Rich Firth-Godbehere has incisively noted).

Artificial Intelligence (AI) has been put forward as a potential solution for many of the gravest problems facing society, from the opioid crisis to poverty and famine. But although technology clearly has the potential to do a great deal of good, there's a sound business reason that tech companies often pour large amounts of resources into social projects that don't seem to align with their core business of selling software and services. This is down to the fact that tackling social issues often involves developing solutions to problems very similar to those faced by businesses. Additionally, working with governments or NGOs on building these solutions can often mean access to new datasets. Learning derived from these datasets can later be developed into products and services to offer to clients (even if the data itself isn't).

The report, jointly prepared by the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP) and Google, showcases successful models of leveraging artificial intelligence (AI) for improved public services in the Asia-Pacific region and beyond. Although AI is a widely discussed topic today, case studies on how AI is concretely applied in the public sector are rare. The report aims to fill the gap and features insights as well as context-specific recommendations from deployments of AI in a variety of sectors: health, justice, agriculture, environment, insurance and social welfare. Public-private partnerships will become increasingly important to complement government initiatives with industry knowledge and expertise. Amid the rapid pace of technological development, the report recommends that governments develop frameworks to regulate these partnerships and encourage more public information on AI projects to foster a landscape conducive to informed decision-making on AI partnerships.

Through this partnership, the companies will accelerate breakthroughs in AI and power OpenAI's efforts to create artificial general intelligence (AGI). The resulting enhancements to the Azure platform will also help developers build the next generation of AI applications. The companies will focus on building a computational platform in Azure of unprecedented scale, which will train and run increasingly advanced AI models, include hardware technologies that build on Microsoft's supercomputing technology, and adhere to the two companies' shared principles on ethics and trust. This will create the foundation for advancements in AI to be implemented in a safe, secure and trustworthy way and is a critical reason the companies chose to partner together. Over the past decade, innovative applications of deep neural networks coupled with increasing computational power have led to continuous AI breakthroughs in areas such as vision, speech, language processing, translation, robotic control and even gaming.

A lot of nuances of writing are lost on the internet -- things such as irony. That's why satirical material such as the writing of Andy Borowitz on the website of The New Yorker magazine has to be labeled as satire, to make sure we know. Scientists in recent years have become concerned: What about writing that isn't properly understood, such as satire mistaken for the truth, or, conversely, deliberate disinformation campaigns that are disguised as innocent satire? And so began a quest to divine some form of machine learning technology that could automatically identify satire as such and distinguish it from deliberate lies. In truth, a machine can't understand much of anything, really, and it certainly can't understand satire.

A lot of nuances of writing are lost on the internet -- things such as irony. That's why satirical material such as the writing of Andy Borowitz on the website of The New Yorker magazine has to be labeled as satire, to make sure we know. Scientists in recent years have become concerned: What about writing that isn't properly understood, such as satire mistaken for the truth, or, conversely, deliberate disinformation campaigns that are disguised as innocent satire? And so began a quest to divine some form of machine learning technology that could automatically identify satire as such and distinguish it from deliberate lies. In truth, a machine can't understand much of anything, really, and it certainly can't understand satire.

There are many opinions about how Artificial Intelligence (AI) is going to change the world with expectations about its capabilities for now and in the future. AI simply refers to intelligence displayed by machines in contrast to that displayed by humans. Although humans are intelligent, they cannot be programmed to exceed their current capabilities in the same way a machine can. This has led to the creation of smart machines that handle tasks otherwise difficult for humans to handle efficiently. Artificial intelligence is gradually becoming a constant presence in many technological applications.