Are people willing to take orders from a non-human, robotic boss? - Video

A study conducted by a team of researchers at Human Computer Interaction (MCI) Lab in Manitoba Canada, has revealed evidence that suggests that people can be prodded into doing something they don't want to do, by a robot.

They've posted a blog entry on their web site describing an experiment they carried out to learn more about how people might respond to a robot boss, versus a human one, and the results they found.

The experiment consisted of asking volunteers to complete different tasks, some fun (singing songs they liked), some tedious and boring (changing file name extensions for a very large number of files).

Some of the volunteers were asked to perform the tasks by a human being, others were asked to do the same tasks by a small friendly-looking Aldebaran Nao humanoid robot.

The volunteers and their taskmasters were set up in an office-type environment, with desks set apart from one another.

The participants were filmed as they carried out the experiment and the researchers analyzed the results afterwards.

All of the volunteers were told repeatedly before the experiment that they could stop any task they chose at any time, with no negative consequences.

In studying the video, the researchers found that 46 percent of the volunteers (both male and female) complied with a request to perform a task (which took 80 minutes) they didn't want to do, when asked to do so by the robot, compared to 86 percent compliance when asked by a human "boss."

The researchers note the lower percentage but also point out that nearly half of those who participated complied when asked to do something they didn't want to do, when asked by a robot.

The research is being carried out to learn more about how future humans might interact with future robots in real workplace environments.

The team's initial findings indicate that humans will not summarily dismiss a robot authority figure, and many will do as it asks.

The team plans to continue with its research, no doubt, looking to find the limits of such interactions.

Nobel Laureate Daniel Kahneman on de-biasing thinking in decision-making - Video

How do you increase the chances that the thinking behind a decision is valid? Daniel Kahneman discusses the Pre-Mortem, a simple technique for "de-biasing" the thinking that goes into a decision before it is locked in.

In just three minutes, Kahneman makes the case for shining a light on the thinking that has led to a decision before there is no turning back.

Prof. Daniel Kahneman: "Thinking, Fast and Slow" - Video

Public Lecture by Prof. Daniel Kahneman

Thinking, Fast and Slow

Tuesday, April 16, 2013

Aula, University of Zurich

DARPA MUSE: Deep program analysis, and big data analytics, create public database

DARPA's MUSE seeks to leverage deep program analysis and big data analytics to create a public database containing mined inferences about salient properties, behaviours and vulnerabilities of software drawn from the hundreds of billions of lines of open source code available today.

The program aims to make significant advances in the way software is built, debugged, verified, maintained and understood, and to enable the automated repair of existing programs and synthesis of new ones.

During the past decade information technologies have driven the productivity gains essential to U.S. economic competitiveness, and computing systems now control significant elements of critical national infrastructure.

As a result, tremendous resources are devoted to ensuring that programs are correct, especially at scale.

Unfortunately, in spite of developers' best efforts, software errors are at the root of most execution errors and security vulnerabilities.

To help improve this state, DARPA has created the Mining and Understanding Software Enclaves (MUSE) program.

MUSE seeks to make significant advances in the way software is built, debugged, verified, maintained and understood.

The collective knowledge gleaned from MUSE's efforts would facilitate new mechanisms for dramatically improving software correctness, and help develop radically different approaches for automatically constructing and repairing complex software.

Suresh Jagannathan

"Our goal is to apply the principles of big data analytics to identify and understand deep commonalities among the constantly evolving corpus of software drawn from the hundreds of billions of lines of open source code available today," said Suresh Jagannathan, DARPA program manager.

"We're aiming to treat programs—more precisely, facts about programs—as data, discovering new relationships (enclaves) among this 'big code' to build better, more robust software."

Central to MUSE's approach is the creation of a community infrastructure that would incorporate a continuously operational specification-mining engine.

This engine would leverage deep program analyses and foundational ideas underlying big data analytics to populate and refine a database containing inferences about salient properties, behaviours and vulnerabilities of the program components in the corpus.

If successful, MUSE could provide numerous capabilities that have so far remained elusive.

"Ideally, we could enable a paradigm shift in the way we think about software construction and maintenance, replacing the existing costly and laborious test/debug/validate cycle with 'always on' program analysis, mining, inspection and discovery," Jagannathan said.

"We could see scalable automated mechanisms to identify and repair program errors, as well as tools to efficiently create new, custom programs from existing components based only a description of desired properties."

Diversity at CERN: Great science needs great people

Great science needs great people, a look at diversity at CERN. 

A word from the DG: Strength in diversity 

The CERN Diversity Programme