There is vast scope for agricultural productivity to improve. Traditional farming practices are still shockingly outdated in many parts of the world. AI is one tool that can help best achieve this, and a number of startups are already making progress. Switzerland-based agricultural tech firm Gamaya, for example, this year announced $3.2 million in funding for its AI project - drones equipped with hyperspectral cameras that capture changes in water and fertilizer use, crop yields, and pests, data from which is analyzed using AI algorithms to highlight potential issues to farmers. The technology can also be used for finding patterns that can predict outcomes of farmers' decisions, giving them a better idea of where to invest and apply appropriate resources.

Big data collected from customer behaviour from all strata of the Internet has added valuable information to improve sales force and customer services. With the analytical power of Machine Learning, the mass of data can be transformed into thin causalities. For example, the Artificial Intelligence algorithms are able to review the complexity of Big Data and simplify the client's information through accurate analysis of the buying journey. Since its first public use in the late 1990s, Machine Learning continues to be discussed. AlphaGo, a computer program developed by Google DeepMind in London to play the board game Go, represents one of the most notable examples of deep learning; that is to say a machine now is able to independently analyze the amounts of data with extremely high performance.

Episode 9: For the past five years, Russia's been building walls around its web and packing it with tech oligarchs, startup cities, face-finding algorithms, hacker hunters, and, of course, a few bears. The best mouse I've ever met lives in Akademgorodok, a Siberian city about an hour's drive from Novosibirsk. The mouse is not alive. It's a 2-foot statue that stands watch over a genetic engineering laboratory. Frozen in time, the mouse wears glasses and a lab coat, using a pair of knitting needles to stitch together strands of DNA.

Artificial intelligence is getting its teeth into lip reading. A project by Google's DeepMind and the University of Oxford applied deep learning to a huge data set of BBC programmes to create a lip-reading system that leaves professionals in the dust. The AI system was trained using some 5000 hours from six different TV programmes, including Newsnight, BBC Breakfast and Question Time. In total, the videos contained 118,000 sentences. First the University of Oxford and DeepMind researchers trained the AI on shows that aired between January 2010 and December 2015. Then they tested its performance on programmes broadcast between March and September 2016.

Furby and Clippy were early forms; driverless cars and Facebook's chatbots pick up the mantle today. But if AI is to continue its evolution, it'll have to get more convincingly human. Right now, its capacity for emotional depth is seriously lacking. At a cognitive architectures conference in New York last week, Alexei Samsonovich, a professor in the Cybernetics Department at the Moscow Engineering Physics Institute, proposed a multi-part test. It would involve a human and a machine interacting, but under the guise of avatars in a virtual world.

Artificial Intelligence has been doubted as "beneficiary" since humans realized it can someday turn into a real and serious threat to human race. Can smart machines, objects, humans turned into God-like cyborg transform into dangerous threat towards humans worldwide? Is it even thinkable that human race can come to an end because we ourselves were able to create machines smarter and much more powerful than us that can one day make us become extinct? "It's our responsibility to think about all of the consequences good and bad. We've had the same debate about atomic power and nanotechnology. With any powerful technology there's always the dialogue about how do you use it deliver the most benefit and how it can be used to deliver the most harm," Professor Hawking has recently said for BBC.

When you think about artificial intelligence (AI), robots, androids and other futuristic technologies may come to mind. And while some of the most successful tech companies like Facebook, Amazon and Google have built their success on industry-changing applications of artificial intelligence, the concept is still fairly new to content marketing teams. In the most basic sense "artificial intelligence" broadly refers to the processes and technologies that are created to teach machines to perform intelligent tasks. For content marketers, "intelligent tasks" typically refer to algorithms designed to process data. This goes far beyond automation, which is where the majority of marketing technology supports marketing teams today.

This paper describes a computational model, called the Dirichlet process Gaussian mixture model with latent joints (DPGMM-LJ), that can find latent tree structure embedded in data distribution in an unsupervised manner. By combining DPGMM-LJ and a pre-existing body map formation method, we propose a method that enables an agent having multi-link body structure to discover its kinematic structure, i.e., body schema, from tactile information alone. The DPGMM-LJ is a probabilistic model based on Bayesian nonparametrics and an extension of Dirichlet process Gaussian mixture model (DPGMM). In a simulation experiment, we used a simple fetus model that had five body parts and performed structured random movements in a womb-like environment. It was shown that the method could estimate the number of body parts and kinematic structures without any pre-existing knowledge in many cases. Another experiment showed that the degree of motor coordination in random movements affects the result of body schema formation strongly. It is confirmed that the accuracy rate for body schema estimation had the highest value 84.6% when the ratio of motor coordination was 0.9 in our setting. These results suggest that kinematic structure can be estimated from tactile information obtained by a fetus moving randomly in a womb without any visual information even though its accuracy was not so high. They also suggest that a certain degree of motor coordination in random movements and the sufficient dimension of state space that represents the body map are important to estimate body schema correctly.

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks by the average value and generalise Spearman's \rho to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's \rho, which measures correlation between a set of ranked lists. Multivariate Spearman's \rho is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best or worst elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's \rho. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008.

We aim to create a framework for transfer learning using latent factor models to learn the dependence structure between a larger source dataset and a target dataset. The methodology is motivated by our goal of building a risk-assessment model for surgery patients, using both institutional and national surgical outcomes data. The national surgical outcomes data is collected through NSQIP (National Surgery Quality Improvement Program), a database housing almost 4 million patients from over 700 different hospitals. We build a latent factor model with a hierarchical prior on the loadings matrix to appropriately account for the different covariance structure in our data. We extend this model to handle more complex relationships between the populations by deriving a scale mixture formulation using stick-breaking properties. Our model provides a transfer learning framework that utilizes all information from both the source and target data, while modeling the underlying inherent differences between them.