In the 2013 film Her, Spike Jonze offered his take on a society in which our closest relationships are with technology, not humans. The movie tells the story of a lonely man who purchases a voice-controlled robot that evolves to meet his needs. Samantha, as she's known, eventually becomes so in sync with him that they fall in love. Oddly enough, the technology in Her is not as far away as you might think. At her core, Samantha is an interface capable of interpreting needs through conversation and contextual cues.

The London-based big data startup Flock is building a platform for performing real-time quantified risk analysis of drone flights, including by applying AI (artificial intelligence) to tracking data sourced from urban environments. According to TechCrunch, the company is licensing data pertaining to the position of buildings, people and cars in urban environments, as well as weather conditions, and feeding that into its risk assessment platform. Idea being for its software to analyze a planned drone flight in real-time and perform a "cost/benefit" analysis -- to help insurers set premiums or drone operators decide whether or not to undertake a given flight. In the future the company intneds to apply machine learning algorithms to the urban data it's getting in order to generate "real-time risk reduction" for drone operators via predictive assessments for drone flight scheduling -- which it reckons could be used to power fully autonomous drone flights zipping along risk-minimized routes. "The idea is to have a robust trend analysis built in to the system so we can analyze historic data, and then build up a really good understanding of how cities move generally, how cities breathe, and how populations and traffic conditions change over time," says Ed Leon Klinger, CEO.

Stuart Russell argues for a fundamental reorientation of the field artificial intelligence. Click here to watch the full keynote http://oreil.ly/1YSpwEh Follow O'Reilly on Twitter: http://twitter.com/oreillymedia

The government has decided to collaborate with more than 20 firms and research institutions to promote development of artificial intelligence technology to be applied in the medical, manufacturing and other sectors. The public-private initiative involving entities such as the government-backed Riken research institute, Toyota Motor Corp and NEC Corp is aimed at finding technological solutions to labor shortage and aging society issues that Japan is facing. In the medical field, AI technology is expected to be used in diagnosing a patient's symptoms and giving doctors advice on optimal treatment by analyzing the patient's electronic medical record and a huge amount of past similar cases. The government sees the AI sector as one of the pillars of its growth strategy and set up a research and development center for innovative intelligence technologies at Riken in April.

This is a guest post by the Mycroft team as part of "the startup stories", a series of blog posts about how and why innovative companies are using Ubuntu technology. If you work in technology you've probably had this dream. You wake up and your whole house is a computer, a starship in cyberspace that listens to you, understands you, and performs tasks effortlessly. Mycroft A.I. is achieving that dream using Snappy Ubuntu Core. Mycroft is an open source / open hardware project that is working to bring natural language recognition and Internet of Things (IoT) integration to homes and offices everywhere.

Faculty Opening Young and research-intensive, Nanyang Technological University (NTU Singapore) is ranked 13th globally. It is also placed 1st amongst the world's best young universities and ranked 6th globally for Engineering and Technology. With an engineering research output ranked amongst the top seven universities in the world, the College of Engineering (CoE) is a recognised leader in innovation and technology. CoE is the largest College in NTU and comprises six internationally known engineering schools with more than 14,000 students, 550 faculty and 1,400 staff. At the College of Engineering, faculty are encouraged to seek new knowledge, discover and create innovative solutions.

Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes' probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this first part of a series, we will take a look at the theory of naive Bayes classifiers and introduce the basic concepts of text classification. In following articles, we will implement those concepts to train a naive Bayes spam filter and apply naive Bayes to song classification based on lyrics. Starting more than half a century ago, scientists became very serious about addressing the question: "Can we build a model that learns from available data and automatically makes the right decisions and predictions?" Looking back, this sounds almost like a rhetoric question, and the answer can be found in numerous applications that are emerging from the fields of pattern classification, machine learning, and artificial intelligence. Data from various sensoring devices combined with powerful learning algorithms and domain knowledge led to many great inventions that we now take for granted in our everyday life: Internet queries via search engines like Google, text recognition at the post office, barcode scanners at the supermarket, the diagnosis of diseases, speech recognition by Siri or Google Now on our mobile phone, just to name a few.

Most machine learning algorithms have been developed and statistically validated for linearly separable data. Popular examples are linear classifiers like Support Vector Machines (SVMs) or the (standard) Principal Component Analysis (PCA) for dimensionality reduction. However, most real world data requires nonlinear methods in order to perform tasks that involve the analysis and discovery of patterns successfully. The focus of this article is to briefly introduce the idea of kernel methods and to implement a Gaussian radius basis function (RBF) kernel that is used to perform nonlinear dimensionality reduction via BF kernel principal component analysis (kPCA). The main purpose of principal component analysis (PCA) is the analysis of data to identify patterns that represent the data "well." The principal components can be understood as new axes of the dataset that maximize the variance along those axes (the eigenvectors of the covariance matrix).

In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern classification and machine learning tasks, and various other data analyses. In this section want to recommend a way for installing the required Python-packages packages if you have not done so, yet. Otherwise you can skip this part. Although they can be installed step-by-step "manually", but I highly recommend you to take a look at the Anaconda Python distribution for scientific computing. Anaconda is distributed by Continuum Analytics, but it is completely free and includes more than 195 packages for science and data analysis as of today.

A Support Vector Machine (SVM) is a classification method that samples hyperplanes which separate between two or multiple classes. Eventually, the hyperplane with the highest margin is retained, where "margin" is defined as the minimum distance from sample points to the hyperplane. The sample point(s) that form margin are called support vectors and establish the final SVM model. Bayes classifiers are based on a statistical model (i.e., Bayes theorem: calculating posterior probabilities based on the prior probability and the so-called likelihood). A Naive Bayes classifier assumes that all attributes are conditionally independent, thereby, computing the likelihood is simplified to the product of the conditional probabilities of observing individual attributes given a particular class label. Artificial Neural Networks (ANN) are graph-like classifiers that mimic the structure of a human or animal "brain" where the interconnected nodes represent the neurons. Decision tree classifiers are tree like graphs, where nodes in the graph test certain conditions on a particular set of features, and branches split the decision towards the leaf nodes. Leaves represent lowest level in the graph and determine the class labels. Optimal tree are trained by minimizing Gini impurity, or maximizing information gain.