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Not many people can answer that question.

This paper, using science fiction as a heuristic support for exploring technical potentialities, is based on part of the works of Iain M. Banks, the novels of the "Culture series", in order to examine the role of artificial intelligences and the effects they could have on the life of a community from a political point of view. This series of science fiction novels portrays a galactic civilization based on anarchistic principles in which intelligent machines are largely responsible for managing the tasks linked to the handling of community affairs, thus freeing up the population to pursue more spiritual or fun activities. The first part of this paper shows that beyond the elements included in the stories, the Culture novels can be a way to address political questions that are raised by the widespread presence of highly evolved machines in the organization of a society. The second part, which takes into consideration the supposed founding principles of this civilization, examines the anarchist thought in order not only to display the correspondences between this thought and the vision of Iain M. Banks, but also to show that the various anarchistic currents are in a way outdistanced by the emerging challenges posed by these novels. The third part, written again from a political standpoint, attempts to establish more concrete connections, based on discernable evolutions in computerization or automation of technological systems, which seem to be working their way into a growing number of social processes and their regulation.

In manufacturing, advanced robotic technology has opened up the possibility of integrating highly autonomous mobile robots into human teams. However, with this capability comes the issue of how to maximize both team efficiency and the desire of human team members to work with these robotic counterparts. To address this concern, we conducted a set of experiments studying the effects of shared decision-making authority in human–robot and human-only teams. We found that an autonomous robot can outperform a human worker in the execution of part or all of the process of task allocation (\(p 0.001\) for both), and that people preferred to cede their control authority to the robot \((p 0.001)\). We also established that people value human teammates more than robotic teammates; however, providing robots authority over team coordination more strongly improved the perceived value of these agents than giving similar authority to another human teammate \((p 0.001)\).

In-demand skills for the IoT include machine learning, data integration, and network connectivity. Placing career bets in IT is something that we do as an ongoing effort. My goal is always to lead, not follow. While I hit some out of the park with that philosophy, there were times when I looked behind me and nobody had followed. Fortunately, I've racked up more wins than losses.

Work on a new project by Deakin University and Melbourne-based software company Ytek has begun, aimed to develop skills of those training in the emergency response, defence, and aerospace sectors using machine learning algorithms. Dr James Zhang, a researcher from Deakin University's Institute for Intelligent Systems Research and Innovation, is working with Ytek to develop simulation solutions used to train surgeons, emergency workers, soldiers, and pilots. As part of the project, Zhang and Ytek will research how machine learning algorithms can help monitor and evaluate a trainee's conduct in mission-critical simulations by using sensors on training tools, such as manikins, to evaluate how trainers can assess students in practical training. Ytek CEO Richard Yanieri said the desired outcome of the project is to improve the practical training for students. "We've been working with Deakin's School of Medicine to understand their needs so that we can tailor a solution that works for this industry," he said.

Machine learning has become a core component of companies' analytic initiatives and is no longer the "fine china" only brought out for special occasions, according to a manager with Hewlett-Packard Enterprise, which today announced that its Vertica analytics database now runs popular classes of machine learning algorithms. While previous versions of Vertica could run R algorithms -- as opposed to shipping them off to run on a Hadoop cluster or another adjacent system -- Vertica 8.0 will be the first version of the flagship columnar database that formally supports a broad collection of popular machine learning algorithms, according to Jeff Veis, vice president of marketing for Big Data Platforms at HPE (NYSE: HPE). "It used to be niche, or maybe like fine china for special occasions, to use machine learning, and now it's showing up as a must-have for almost all our customers," Veis tells Datanami. "It's becoming very important to do that form of advanced analytics. We brought that in-database so you can run it across your whole data set."

It's clear that Messenger is one of the main places where consumers want to communicate and this provides an incredible opportunity to deliver unique experiences to a captive audience and bots are a great way to accomplish this. While it's easy to find great use cases in most verticals, this post will focus on healthcare, specifically medication adherence. Here's a definition of medication adherence: Adherence to, or compliance with, a medication regimen is generally defined as the extent to which a person takes medications as prescribed by their healthcare providers. There's been a big investment in medication adherence and there's good reasons. According to a World Health Organization report (and countless other sources) available in print only "adherence to long-term therapy for chronic illnesses in developed countries averages 50%" and "removing barriers to adherence must become a central component of efforts to improve population health worldwide."

The first intelligent robots that humans interact with on a regular basis will likely be self-driving cars--not a humanoid working in the cubicle next door. Drive.ai, an autonomous vehicle tech startup founded by former graduate students working in Stanford University's Artificial Intelligence Lab, officially came out of stealth mode--a temporary quiet period to avoid alerting competitors--on Tuesday with some details about what it's building and a high-profile addition to its board. Steve Girsky, who sat on the General Motors board for seven years until June, has joined the Drive.ai The Mountain View-based startup, which has raised 12 million from an undisclosed venture capital firm and strategic investors, was forced out of stealth in April when it was awarded a license to test autonomous vehicles in California. But until now, little was known about what Drive.ai was working on. The startup is focused on developing deep learning software--a sophisticated form of artificial intelligence--and applying it to everything the self-driving car does from recognizing objects to making decisions.

What can software designers and ICT specialists learn from maggots? Quite a lot, it would appear. Through understanding how complex learning processes in simple organisms work, EU-funded scientists hope to usher in an era of self-learning robots and predictive computing. Even with limited brain power, an organism can choose the right thing to do in response to external stimuli, which is something that current computational learning theory cannot fully account for. Learning from maggots The EU-funded MINIMAL project, launched in 2014, has focused on the learning processes in a relatively simple animal, the fruit fly larva (maggots).