Basic labour rights could be extended to robots. The European Parliament's legal affairs committee is considering plans to declare them'electronic persons'. MEPs, led by a socialist from Luxembourg, have suggested that member states should think about a world driven by automation - one solution discussed is a guaranteed universal basic income. Basic labour rights could be extended to robots. The European Parliament's legal affairs committee is considering plans to declare them'electronic persons' Robotics is expected to produce machines that are no longer just'tools' but agents in their own right, able to teach themselves, roam around and make decisions, the committee's report states.

"No Dr., or How I Learned to Stop Debugging and Love the Robot" In this talk, Guy will dicuss what developers must know to explore the power of machine learning services in the cloud. Using data to build machine learning models is a powerful alternative for heuristic or handwritten rules. This power is not limited to people with Ph.D. or MSc. in machine learning, statistics or computer science, but can be used successfully by competent developers. You will learn how to get started and how to think in machine learning terms when developing your next smart application. To gain background on machine learning in the cloud before the meetup, consider reading Guy's blog posts on machine learning.

Large-scale brain-like machines with human-like abilities to solve problems could become a reality, now that researchers have invented microscopic gadgets that mimic the connections between neurons in the human brain better than any previous devices. The new research could lead to better robots, self-driving cars, data mining, medical diagnosis, stock-trading analysis and "other smart human-interactive systems and machines in the future," said Tae-Woo Lee, a materials scientistat the Pohang University of Science and Technology in Korea and senior author of the study. The human brain's enormous computing power stems from its connections. Previous research suggested that the brain has approximately 100 billion neurons and roughly 1 quadrillion (1 million billion) connections wiring these cells together. At each of these connections, or synapses, a neuron typically fires about 10 times per second.

Specialized electronic circuits called graphic processing units, or GPUs, are at the heart of modern mobile phones, personal computers and gaming consoles. By combining multiple GPUs in concert, researchers can solve previously elusive image processing problems. For example, Google and Facebook have both developed extremely accurate facial recognition software using these new techniques. GPUs are also crucial to radiologists, because they can rapidly process large medical imaging datasets from CT, MRI, ultrasound and even conventional x-rays. Now some radiology groups and technology companies are combining multiple GPUs with artificial intelligence (AI) algorithms to help improve radiology care.

The global cognitive systems market will generate revenues worth 50 billion between 2016 and 2020, aided by development of self-learning IT systems, new research from Technavio reports. The report, Global Cognitive Systems Market 2016-2020, shows that cognitive applications are being increasingly integrated with machine learning, which helps real-time communications between people and IT systems. The applications also help draw independent conclusions as well as data processing capabilities. These are developed to mimic the human thinking process or learning, understanding and evaluating different situations. Predictive analytics are driving the growth, as well as the potential for modelling techniques and the potential data from the Internet of Things (IoT), says Amit Sharma, lead analyst, ICT at Technavio Research.

Twitter Inc. agreed to acquire a London-based artificial intelligence startup to make tweeted live videos look more professional. In a blog post Monday, Twitter Chief Executive Officer Jack Dorsey said he was buying Magic Pony Technology "so Twitter can continue to be the best place to see what's happening and why it matters, first." Seeking to shore up slowing growth, the social media company has in recent months begun emphasizing video on its site. Magic Pony uses machine learning, a way of teaching software to perform tasks without explicit programming instructions based on pattern recognition, a technology that's "increasingly at the core of everything we build at Twitter," Dorsey said. Twitter paid about 150 million for Magic Pony, according to a person familiar with the matter.

Pedro Domingos speaks on the future of the Information Age. Machine learning not only affects computers, but it will also change our lives. Pedro asks "what will the ultimate learning algorithm look like?" and discusses how future technology will change how we model many parts of our lives. Pedro Domingos is a professor of computer science at the University of Washington and the author of "The Master Algorithm". He is a winner of the SIGKDD Innovation Award, the highest honor in data science.

The analysis and diagnosis of Alzheimer's disease (AD) can be based on genetic variations, e.g., single nucleotide polymorphisms (SNPs) and phenotypic traits, e.g., Magnetic Resonance Imaging (MRI) features. We consider two important and related tasks: i) to select genetic and phenotypical markers for AD diagnosis and ii) to identify associations between genetic and phenotypical data. While previous studies treat these two tasks separately, they are tightly coupled because underlying associations between genetic variations and phenotypical features contain the biological basis for a disease. Here we present a new sparse Bayesian approach for joint association study and disease diagnosis. In this approach, common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels; in return, the disease status can guide the discovery of relationships between data sources. The sparse projection matrices not only reveal interactions between data sources but also select groups of biomarkers related to the disease. Moreover, to take advantage of the linkage disequilibrium (LD) measuring the non-random association of alleles, we incorporate a graph Laplacian type of prior in the model. To learn the model from data, we develop an efficient variational inference algorithm. Analysis on an imaging genetics dataset for the study of Alzheimer's Disease (AD) indicates that our model identifies biologically meaningful associations between genetic variations and MRI features, and achieves significantly higher accuracy for predicting ordinal AD stages than the competing methods.

Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that contribute to a better understanding of the process. Our application area is mining data from smart homes for elderly, where the ultimate goal is to signal deviations from usual behavior and provide timely recommendations in order to extend the period of independent living. Extracting individual process models showing user behavior is an important instrument in achieving this goal. However, the interpretation of sensor data at an appropriate abstraction level is not straightforward. For example, a motion sensor in a bedroom can be triggered by tossing and turning in bed or by getting up. We try to derive the actual activity depending on the context (time, previous events, etc.). In this paper we introduce the notion of label refinements, which links more abstract event descriptions with their more refined counterparts. We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective. Based on data from smart homes, we show how our statistical evaluation method for label refinements can be used in practice. Our method was able to select two label refinements out of a set of candidate label refinements that both had a positive effect on model precision.

Consider a number of workers running SGD independently on the same pool of data and averaging the models every once in a while -- a common but not well understood practice. We study model averaging as a variance-reducing mechanism and describe two ways in which the frequency of averaging affects convergence. For convex objectives, we show the benefit of frequent averaging depends on the gradient variance envelope. For non-convex objectives, we illustrate that this benefit depends on the presence of multiple globally optimal points. We complement our findings with multicore experiments on both synthetic and real data.