At 4ft-nothing, with orb-like eyes, SoftBank's humanoid robot, Pepper, is designed to look friendly. But imagine if Pepper -- a powerful machine crammed with cameras, sensors and motors -- hurtled towards you at top speed? Or stood in your home, secretly recording your life? In 2017, Lucas Apa and Cesar Cerrudo, security researchers with the consultancy IOActive, showed that the version 2.5.5 of Pepper could be hacked through its software because of vulnerabilities that were discovered when it was connected to a network. They demonstrated that the robot could be controlled remotely, its limbs manipulated and its cameras used to spy on users. Yet more than a year later, SoftBank has not patched the software, according to an analysis of its change logs by Mr Apa.

Recruiting cyber security experts is becoming a challenge for companies, as skilled staff are in short supply -- but artificial intelligence is coming to the rescue. If there are enough experts around to make the AI work, that is. While AI is already taking the pressure off overstretched cyber security teams, the skills required from professionals are changing rapidly as the technology evolves. This is forcing companies to choose between retraining existing staff or hiring fresh talent. Machine learning -- which enables computers to identify and predict anomalies from previously observed patterns in large amounts of information -- is ideally suited to monitoring growing volumes of data for potential security breaches and cutting cyber security teams' workloads.

Setting the right price for a good or service is an old problem in economic theory. There are a vast amount of pricing strategies that depend on the objective sought. One company may seek to maximize profitability on each unit sold or on the overall market share, while another company needs to access a new market or to protect an existing one. In this blog post, we'll present the problem of price optimization for retail – which has its own particularities – and how retailers can take advantage of the tremendous power of Machine Learning (ML) technology to build effective solutions. Given that in these days it is very easy for a customer to compare prices thanks to online catalogs, specialized search tools or collaborative platforms, retailers must pay close attention to several parameters when setting prices.

Our eyes wander as we read text, and not just in the figurative sense -- between a series of rapid motions called saccades, eyes remain still for just 200-300 milliseconds on average. Those movements are rich with subtext -- they're driven by cognitive processes involving vision, attention, language, and motor control -- and according to new research from the University of Potsdam, Weizenbaum Institute for the Networked Society, and Leibniz Institute for Agricultural Engineering and Bioeconomy, they're enough to identify a person pretty accurately. A paper published on the preprint server Arxiv.org "Identification based on eye movements during reading may offer several advantages in many application areas," the researchers wrote. "Users can be identified unobtrusively while having access to a document they would read anyway, which saves time and attention."

ARTIFICIAL Intelligence (AI) is developing faster than we imagined. If we look closely, our lives are being enhanced by AI or at least with technologies and applications using algorithms that monitor and learn from our behaviour. Applications such as Google, Facebook, Spotify, Uber and Netflix use machine learning to extract street names from photos, recognise and tag friends, find favourite songs, estimate arrival times and pick-up locations, and recommend movies. As AI is developed to learn and think more like humans and to replace lower-skilled roles, its impact on future employment and data privacy has become a point of contention among technology experts, organisations, governments and the general public. In response, the University of Southampton Malaysia (UoSM) will be hosting Prof Dame Wendy Hall in a public lecture on The Artificial Intelligence Revolution on Sept 30 at The Majestic Hotel Kuala Lumpur.

Mario Klingemann (artist, neurographer, coder, data collector - Munich): That depends on how you define creativity. If commodified creativity like applying Instagram filters, the curation of Pinterest boards or drawing in adult painting books are the bar, then machines can already accomplish that. But if you set the bar higher and define creativity as the creation of something original, that is at the same time relevant to a human audience, then we still have a long way to go. Ultimately, I believe that machines will be able to accomplish that, but just as it is with the definition what "intelligent" means this will remain a moving target and once machines reach a certain milestone, like generate a song, make a movie or write a book we will probably find some explanation why that act was not "truly" creative.

If you are a senior data scientist or pro in predictive analytics, you would probably be using both R & Python, and maybe other tools like SAS, SQL etc. But, what if you are a beginner or just thinking about to start a career in data science, machine learning, and business analytics? Which one should you learn – R or Python? It has always been a topic of great debate among data scientists, researchers and analytics professionals. In this article, we will discuss R vs Python – usability, popularity index, advantages & limitations, job opportunities, and salaries. R is a statistical and visualization language which is deep and huge and mathematical.

Today's artificial intelligence systems, including the artificial neural networks broadly inspired by the neurons and connections of the nervous system, perform wonderfully at tasks with known constraints. They also tend to require a lot of computational power and vast quantities of training data. That all serves to make them great at playing chess or Go, at detecting if there's a car in an image, at differentiating between depictions of cats and dogs. "But they are rather pathetic at composing music or writing short stories," said Konrad Kording, a computational neuroscientist at the University of Pennsylvania. "They have great trouble reasoning meaningfully in the world." Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

We consider a general regularised interpolation problem for learning a parameter vector from data. The well known representer theorem says that under certain conditions on the regulariser there exists a solution in the linear span of the data points. This is the core of kernel methods in machine learning as it makes the problem computationally tractable. Most literature deals only with sufficient conditions for representer theorems in Hilbert spaces. We prove necessary and sufficient conditions for the existence of representer theorems in reflexive Banach spaces and illustrate why in a sense reflexivity is the minimal requirement on the function space. We further show that if the learning relies on the linear representer theorem the solution is independent of the regulariser and in fact determined by the function space alone. This in particular shows the value of generalising Hilbert space learning theory to Banach spaces.

Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce. While writing a program to automatically generate realistic grammatical errors would be difficult, one could learn the distribution of naturallyoccurring errors and attempt to introduce them into other datasets. Initial work on inducing errors in this way using statistical machine translation has shown promise; we investigate cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure. Our approach yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection, and a previously introduced model to gain further improvements of over 5% $F_{0.5}$ score. When attempting to determine if a given sentence is synthetic, a human annotator at best achieves 39.39 $F_1$ score, indicating that our model generates mostly human-like instances.