Environment Animals Wildlife 14,000-year-old woolly rhinoceros DNA extracted from wolf's stomach The two-horned prehistoric mammal went extinct about 8,700 years ago. Breakthroughs, discoveries, and DIY tips sent six days a week. Towards the end of the last ice age, an ancient wolf feasted on a young woolly rhinoceros (). When the wolf died, it ended up buried in Siberian permafrost for about 14,000 years until it was uncovered by paleontologists in 2015. Luckily for scientists, some woolly rhinoceros tissue remained inside of the wolf's stomach.

Cave lions likely killed'Yuka' when she was around 8 years old. Breakthroughs, discoveries, and DIY tips sent every weekday. A 40,000-year-old juvenile woolly mammoth named Yuka is not only remarkable because she was uncovered nearly intact or her grisly cause of death. Her muscles provided paleogeneticists with the oldest known RNA sequences ever recovered. Detailed in a study published on November 14 in the journal, the samples contradict previous assumptions about the genetic material's resilience while furthering our understanding of the famous, extinct megafauna.

Imagine that you have been convicted of a crime and an algorithm is to help the judge by proposing the sentence. When computers are programmed, discrimination is built into the algorithms, so if you look a certain way you get a harder sentence. This example is not fiction. It comes from the USA where AI is being used in the legal system to propose sentencing for criminal offences, and it proposes harder sentences for black people. It might be chance, a prejudiced system developer or perhaps distorted data that the system had to practice on.

The intensive fires in the Amazon, the rapid melting of glaciers and ice sheets, and continued loss of biodiversity all illustrate that our planet is changing at a dangerous pace. At the same time, we are entering a period of unprecedented technological change. Artificial intelligence (AI) in combination with sensor technology and robotics are likely to change the way we all perceive and respond to social and environmental changes. How can we ensure that applications of artificial intelligence help us address these urgent challenges? On Oct. 15, Princeton University joined representatives from U.S. and Swedish academia, Swedish government, Google, Ericsson, USAID, U.N. Development Programme and U.N. Global Pulse, to launch a project that will explore how applications of artificial intelligence can help accelerate innovations in line with targets set by the U.N. Sustainable Development Goals.

The intensive fires in the Amazon, the rapid melting of glaciers and ice sheets, and continued loss of biodiversity all illustrate that our planet is changing at a dangerous pace. At the same time, we are entering a period of unprecedented technological change. Artificial intelligence (AI) in combination with sensor technology and robotics are likely to change the way we all perceive and respond to social and environmental changes. How can we ensure that applications of artificial intelligence help us address these urgent challenges? On Oct. 15, Princeton University joined representatives from U.S. and Swedish academia, Swedish government, Google, Ericsson, USAID, U.N. Development Programme, and U.N. Global Pulse, to launch a project that will explore how applications of artificial intelligence can help accelerate innovations in line with targets set by the U.N. Sustainable Development Goals.

An investigation, recently published in Nature, and carried out by scientists at Stockholm University has shown that liquid water is much more complex than meets the eye. With the use of the x-ray laser at the SLAC National Accelerator Laboratory in California the team of scientists have probed the finer movements of liquid water on incredibly short timescales. 'A schematic of the approach used to capture water dynamics on the ultrafast timescale. If one were able to photograph the molecules in real space with different exposure times, the image would become gradually blurry because of the motion of the molecules. This is done with x-ray scattering in the so-called reciprocal space, where the diffraction pattern is gradually smoother for longer pulse durations.' This experimental investigation is the first of its kind to'photograph' water molecules on timescales as short as millionths of a billionth of a second.

The equivalence problem for program schemes, or for programs, is reduced to the proving of a theorem in second-order logic. This work extends Manna's first-order logic reductions. Some examples of the technique are given together with a suggested method for obtaining proofs in special cases by firstorder methods. INTRODUCTION Several workers in recent years have considered using techniques and ideas of various mathematical theories of computation for proving interesting results about computer programs. This paper is concerned with two of these approaches.

The necessary logical apparatus can be kept remarkably simple. We use a formulation of predicate logic containing individual constants and function symbols. To simplify the description of the method, it is convenient to restrict the formulae F to which the method is applicable. Firstly, it is supposed that F is closed and in prenex normal form. Secondly, it is supposed that all existential quantifiers are eliminated. To see how this can The main part of this paper was also presented in lectures at the University of Stockholm and the Technische Hochschule of Hanover in the spring of 1967.

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques A. Natural Language Processing Ovnrview The most common way that human beings communicate Is by speaking or writing In one of the "natural" languages, like English, French, or Chinese. Computer programming languages, on the other hand, seem awkward to humans. These "artificial" languages are designed to have a rigid format, or syntax, so that a computer program reading and compiling code written In an artificial language can understand what the programmer means. In addition to being structurally simpler than natural languages, the artificial languages can express easily only those concepts that are important In programming: "Do this then do that," "See it such and such Is true," etc. The things that can be expressed In a language are referred to as the semantics of the language. The research on understanding natural language described in this section of the Handbook is concerned with programs that deal with the full range of meaning of languages like English.