Algorithms are a new tool in the fight against the trade of black market ivory tusks, pangolin scales and tiger skins. A group of researchers at the University of Southern California is working on technology to help rangers stay a step ahead of poachers. The Teamcore lab at USC's Center for Artificial Intelligence in Society is working on an AI-driven application called PAWS, short for Protection Assistant for Wildlife Security, which aims to equip wildlife defenders with optimized patrol routes. The illegal wildlife trade is considered the fourth most profitable criminal enterprise in the world, after drugs, weapons and human trafficking, U.K. Foreign Secretary Jeremy Hunt announced at the 2018 Wildlife Illegal Trade Conference in mid-October. Poaching continues to threaten the survival of species around the globe.

Google wants NASA to help it prove quantum supremacy within a matter of months, according to a Space Act Agreement obtained by MIT Technology Review. Quantum supremacy is the idea, so far undemonstrated, that a sufficiently powerful quantum computer will be able to complete certain mathematical calculations that classical supercomputers cannot. Proving it would be a big deal because it could kick-start a market for devices that might one day crack previously unbreakable codes, boost AI, improve weather forecasts, or model molecular interactions and financial systems in exquisite detail. The agreement, signed in July, calls on NASA to "analyze results from quantum circuits run on Google quantum processors, and ... provide comparisons with classical simulation to both support Google in validating its hardware and establish a baseline for quantum supremacy." Google confirmed to MIT Technology Review that the agreement covered its latest 72-qubit quantum chip, called Bristlecone.

Those who still think China has a never-ending army of cheap labor should think again. Chinese wages are rising, the population is aging - and China's government is promoting a robot revolution in manufacturing. Over each of the next three years, the growth of the use of robots in China is estimated to exceed 20 percent. Rising wages - driven by decades of growth - are eating into profits, pushing companies to shift manufacturing to Southeast Asia. Shanghai's minimum monthly wage is two-and-a-half times what it was a decade ago.

Welcome to the exciting world of audio analytics which can help to bridge this gap by learning the characteristics of human speech and the environment of communication. In space, the mechanical equipment screaming cannot be heard by anyone, unless, it is a pressurized environment with a microphone array availability nearby. Bosh and American space logistics company Astrobotic Technology Inc. will soon send roving robots into space and plan to test experimental sensor technology powered to assess how mechanical systems are running just by listening. This research will take place on board the International Space Station (ISS) and could commence as early as May 2019. The joint Bosch and Astrobotic research adds a bit of scientific repeatability to that idea, Bosch's SoundSee technology deploys an array of machine learning and microphones to study information contained in noises emitted from machinery aboard the ISS.

The European Space Agency's Mars rover is being constructed in Stevenage with the aim of exploring the Martian surface in 2021. It has a drill to probe up to two metres under the surface of Mars and has instruments on board that can examine the samples it finds. The 300kg robot vehicle is capable of travelling 2cm a second for at least 90 days, but scientists hope it will remain active for as long as the latest American rover Curiosity, which landed in August 2012 and is still exploring. Liz Seward, senior strategist at Airbus Defence and Space in Stevenage, said it was a first for the European space project. "It's part of a big plan called the global expedition road map," she said.

History has time and again taught us that, science fiction is only a fantasy until science makes it a reality. In the 1940s Isaac Asimov, a prolific science fiction writer wrote about a future where robots are a part of the human world. Similarly, in a sci-fi film, Robocop made more than 30 years ago, a robot is built-up in order to solve an unprecedented crime problem in dystopian crime-ridden Detroit. Today science fiction has become a reality. Police in different parts of the world are using robots for law enforcement and first, ever robotic police officers have become deployed across China, Dubai and Hyderabad in India.

Artificial Intelligence (AI) is at the frontier of a new techno-tsunami that is transforming the way we live and work. "Historically, an AV researcher might see 10,000 viruses in a career. Today there are over 700,000 per day," says Ryan Permeh, chief scientist of Cylance. Could AI be the solution to solving the big data problem, and bridging the widening workforce gap in the cybersecurity industry? Intelligent machines now have the power to make observations, understand requests, reason, draw data correlations, and derive conclusions.

Imagine a country with an army of techies, a government that supports AI and blockchain by setting a mandate and investing billions, large scale tech companies that are rapidly experimenting and implementing at scale, and an abundance of data to feed the application of these technologies. This just about covers the AI and blockchain playground that is China. The gloves are off, and over the coming years some of the greatest advancements will emanate from the east. An ambitious AI strategic plan was laid by the China's State Council in July 2017, aiming to create a domestic 1 trillion yuan ($150 billion) AI industry by 2030. Following this, Chinese president Xi Jinping called upon his country to take the lead in developing new technologies like artificial intelligence, the internet of things, and blockchain.

As statistical classifiers become integrated into real-world applications, it is important to consider not only their accuracy but also their robustness to changes in the data distribution. Although identifying and controlling for confounding variables Z - correlated with both the input X of a classifier and its output Y - has been assiduously studied in empirical social science, it is often neglected in text classification. This can be understood by the fact that, if we assume that the impact of confounding variables does not change between the time we fit a model and the time we use it, then prediction accuracy should only be slightly affected. We show in this paper that this assumption often does not hold and that when the influence of a confounding variable changes from training time to prediction time (i.e. under confounding shift), the classifier accuracy can degrade rapidly. We use Pearl's back-door adjustment as a predictive framework to develop a model robust to confounding shift under the condition that Z is observed at training time. Our approach does not make any causal conclusions but by experimenting on 6 datasets, we show that our approach is able to outperform baselines 1) in controlled cases where confounding shift is manually injected between fitting time and prediction time 2) in natural experiments where confounding shift appears either abruptly or gradually 3) in cases where there is one or multiple confounders. Finally, we discuss multiple issues we encountered during this research such as the effect of noise in the observation of Z and the importance of only controlling for confounding variables.

Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters. We introduce Mesh-TensorFlow, a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the "batch" dimension, in Mesh-TensorFlow, the user can specify any tensor-dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A Mesh-TensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce. We use Mesh-TensorFlow to implement an efficient data-parallel, model-parallel version of the Transformer sequence-to-sequence model. Using TPU meshes of up to 512 cores, we train Transformer models with up to 5 billion parameters, surpassing state of the art results on WMT'14 English-to-French translation task and the one-billion-word language modeling benchmark. Mesh-Tensorflow is available at https://github.com/tensorflow/mesh .