Every year, we are confronted with new facts and scenarios emerging out of climate change and global warming, each more terrifying and apocalyptic than the last. In fact, several well-known scientists and tech moguls have made predictions regarding them. Prominent linguist and public intellectual Noam Chomsky said last year that the COP22 Marrakech climate summit in Morocco, which began on Nov. 7, "basically ceased" to function on Nov.9 after the delegates were confronted with the news that Donald Trump was elected as the next U.S. president. Speaking to over 2,000 people at Riverside Church on Dec. 5, Chomsky also made several related climate change predictions. "The question that was left was whether it would be possible to carry forward this global effort to deal with the highly critical problem of environmental catastrophe, if the leader of the free world, the richest and most powerful country in history, would pull out completely, as appeared to be the case," Chomsky said.

Chichewa (also Chewa and Nyanja) is a Bantu language of about 12 Million speakers, spoken mostly in Malawi and around. Lines 216-221 have the code commented out that enable it to be run from command line instead of via a web form. The parser implements the description of Chichewa affixes outline in: Mchombo, Sam, (2004). Cambridge Syntax Guides Page references in the comments of the code are references to pages in this text. The parser was used as part of my PhD, so you can use this citation if you want to reference the code in a publication: Munro, Robert (2012). It was built to compare a hand-crafted parser with morphological parsers that learned using superivsed and unsupervised machine learning.

Monte Carlo Tree Search (MCTS) is a family of directed search algorithms that has gained widespread attention in recent years. Despite the vast amount of research into MCTS, the effect of modifications on the algorithm, as well as the manner in which it performs in various domains, is still not yet fully known. In particular, the effect of using knowledge-heavy rollouts in MCTS still remains poorly understood, with surprising results demonstrating that better-informed rollouts often result in worse-performing agents. We present experimental evidence suggesting that, under certain smoothness conditions, uniformly random simulation policies preserve the ordering over action preferences. This explains the success of MCTS despite its common use of these rollouts to evaluate states. We further analyse non-uniformly random rollout policies and describe conditions under which they offer improved performance.

Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dimensional feature spaces. We derive a variable kernel bandwidth estimator by minimizing the leave-one-out entropy objective function and show that this estimator is capable of performing estimation in high-dimensional feature spaces with great success. We compare the performance of this estimator to state-of-the art maximum-likelihood estimators on a number of representative high-dimensional machine learning tasks and show that the newly introduced minimum leave-one-out entropy estimator performs optimally on a number of high-dimensional datasets considered.

Inference from an observed or hypothesized condition to a plausible cause or explanation for this condition is known as abduction. For many tasks, the acquisition of the necessary knowledge by machine learning has been widely found to be highly effective. However, the semantics of learned knowledge are weaker than the usual classical semantics, and this necessitates new formulations of many tasks. We focus on a recently introduced formulation of the abductive inference task that is thus adapted to the semantics of machine learning. A key problem is that we cannot expect that our causes or explanations will be perfect, and they must tolerate some error due to the world being more complicated than our formalization allows. This is a version of the qualification problem, and in machine learning, this is known as agnostic learning. In the work by Juba that introduced the task of learning to make abductive inferences, an algorithm is given for producing k-DNF explanations that tolerates such exceptions: if the best possible k-DNF explanation fails to justify the condition with probability ε, then the algorithm is promised to find a k-DNF explanation that fails to justify the condition with probability at most O(nkε), where n is the number of propositional attributes used to describe the domain. Here, we present an improved algorithm for this task. When the best k- DNF fails with probability ε, our algorithm finds a k-DNF that fails with probability at most O ̃(nk/2ε) (i.e., suppressing logarithmic factors in n and 1/ε). We also examine the empirical advantage of this new algorithm over the previous algorithm in two test domains, one of explaining conditions generated by a “noisy” k-DNF rule, and another of explaining conditions that are actually generated by a linear threshold rule.

Science council moves to safeguard South Africa's robotics prowess Since Czech playwright Karel Capek popularised and, indeed, named the concept of the robot in his 1920 science-fiction play, RUR (Rossum's Universal Robots) – the word is derived from the Czech word'robota', which means labour – it has exerted a fascination on both the popular and the scientific, and on engineering and technological minds. The robot quickly became a mainstay of science fiction, sometimes benign (as with Robby the Robot in the film Forbidden Planet or R2D2 and C3PO from the Star Wars series), sometimes hostile. The first real working robot, however, bore no resemblance to the humanoid robots beloved of science fiction. This was Unimate, developed by the Unimation (Universal Automation) company in the US, which was specifically founded to manufacture robots for industry. Unimate, which entered service on a General Motors assembly line in 1961, was the forerunner of all today's industrial robots and, being in the form of a large mechanical arm, also set the format most such robots still follow.

Antipoaching patrols like this team at the Lewa Wildlife Conservancy in Kenya may soon use AI technology to stay one step ahead of criminals. Poachers kill an estimated 96 African elephants every day, causing conservationists to warn that the iconic animals could disappear in our lifetime if the tide doesn't turn. But now scientists hope a new artificial intelligence (AI) tool could help wildlife officials get a leg up against poachers. PAWS, which stands for Protection Assistant for Wildlife Security, is a newly developed AI that takes data about previous poaching activities and outputs routes for patrols based on where poaching is likely to occur. These routes are also randomized to keep poachers from learning patrol patterns.

They have been responsible for innumerable deaths in the Middle East during the last decade and, if Amazon has its way, will deliver millions of toasters, gift sets and novels in the future. But recently drones have begun to fulfil a less utilitarian kind of role: competition in the nascent world of futuristic motorsports. A confluence of technological advances has made drone racing possible. A minuscule camera, mounted on the drone's nose, allows the pilot, as competitors are luxuriously titled, to control the vehicle through virtual reality-style goggles, as if perched in its tiny cockpit. With powerful lithium batteries, the size of which dictates the speed class of the drone, these machines, which are typically the size of a box of tissues, can reach speeds in excess of 120mph.

Artificial Intelligence (AI) has captured the attention of C-suite executives across all industries and is poised to transform businesses in ways we've never seen since the impact of computer technology in the late 20th century. We are already seeing venture capitalists funding AI start-ups at a rapid pace. Technology companies are also moving swiftly to create and capture value in this emerging area. High-profile acquisitions by Google, Apple and Facebook are piquing interest in Artificial Intelligence technologies such as robotics, expert systems, computer vision, speech, gesture and facial recognition. Companies are creating new research labs devoted to innovating with these technologies. In Africa, AI has a strong role to play.

The American workforce is going to change dramatically as robots and automation continue to take the place of humans. To combat that, eBay founder Pierre Omidyar has pledged nearly half a million dollars to test a universal basic income program. The cash will come from the Omidyar Network, the tech mogul's philanthropic investment firm and be doled out to people living in Kenya through a program called GiveDirectly. The money will be used to run a 12-year pilot program to test the feasibility of universal basic income. Through the program, a group of 6,000 Kenyan citizens will be granted regular stipends for the duration of the experiment. About 20,000 more people will also receive some form of cash to supplement their income.