New Stanford research finds computers are better judges of personality than friends and family

New research shows that a computer's analysis of data can better judge a person's psychological traits than family and friends.

Computers can judge personality traits far more precisely than ever believed, according to newly published research.

In fact, they might do so better than one's friends and colleagues. The study, published Jan. 12 and conducted jointly by researchers at Stanford University and the University of Cambridge, compares the ability of computers and people to make accurate judgments about our personalities. People's judgments were based on their familiarity with the judged individual, while the computer used digital signals – Facebook "likes."

The researchers were Michal Kosinski, co-lead author and a postdoctoral fellow at Stanford's Department of Computer Science; Wu Youyou, co-lead author and a doctoral student at the University of Cambridge; and David Stillwell, a researcher at the University of Cambridge.

According to Kosinski, the findings reveal that by mining a person's Facebook "likes," a computer was able to predict a person's personality more accurately than most of their friends and family. Only a person's spouse came close to matching the computer's results.

The computer predictions were based on which articles, videos, artists and other items the person had liked on Facebook. The idea was to see how closely a computer prediction could match the subject's own scores on the five most basic personality dimensions: openness, conscientiousness, extraversion, agreeableness and neuroticism.

The researchers noted, "This is an emphatic demonstration of the ability of a person's psychological traits to be discovered by an analysis of data, not requiring any person-to-person interaction. It shows that machines can get to know us better than we'd previously thought, a crucial step in interactions between people and computers."

Kosinski, a computational social scientist, pointed out that "the findings also suggest that in the future, computers could be able to infer our psychological traits and react accordingly, leading to the emergence of emotionally intelligent and socially skilled machines."

"In this context," he added, "the human-computer interactions depicted in science fiction films such as Her seem not to be beyond our reach."

He said the research advances previous work from the University of Cambridge in 2013 that showed that a variety of psychological and demographic characteristics could be "predicted with startling accuracy" through Facebook likes.

The study's methodology

In the new study, researchers collected personality self-ratings of 86,220 volunteers using a standard, 100-item long personality questionnaire. Human judges, including Facebook friends and family members, expressed their judgment of a subject's personality using a 10-item questionnaire. Computer-based personality judgments, based on their Facebook likes, were obtained for the participants.

The results showed that a computer could more accurately predict the subject's personality than a work colleague by analyzing just 10 likes; more than a friend or a roommate with 70; a family member with 150; and a spouse with 300 likes.

"Given that an average Facebook user has about 227 likes (and this number is growing steadily), artificial intelligence has a potential to know us better than our closest companions do," wrote Kosinski and his colleagues.

Why are machines better in judging personality than human beings?

Kosinski said that computers have a couple of key advantages over human beings in the area of personality analysis. Above all, they can retain and access large quantities of information, and analyze all this data through algorithms.

This provides the accuracy that the human mind has a hard time achieving due to a human tendency to give too much weight to one or two examples or to lapse into non-rational ways of thinking, the researchers wrote.

Nevertheless, the authors concede that the detection of some personality traits might be best left to human beings, such as "those (traits) without digital footprints and those depending on subtle cognition."

'Digital footprints'

Wu, co-lead author of the study, explains that the plot behind a movie like Her (released in 2013) becomes increasingly realistic. The film involves a man who strikes up a relationship with an advanced computer operating system that promises to be an intuitive entity in its own right.

"The ability to accurately assess psychological traits and states, using digital footprints of behavior, occupies an important milestone on the path toward more social human-computer interactions," said Wu.

Such data-driven decisions could improve people's lives, the researchers said. For example, recruiters could better match candidates with jobs based on their personality, and companies could better match products and services with consumers' personalities.

"The ability to judge personality is an essential component of social living – from day-to-day decisions to long-term plans such as whom to marry, trust, hire or elect as president," said Stillwell.

Dystopia concerns

The researchers acknowledge that this type of research may conjure up privacy concerns about online data mining and tracking the activities of users.

"A future with our habits being an open book may seem dystopian to those who worry about privacy," they wrote.

Kosinski said, "We hope that consumers, technology developers and policymakers will tackle those challenges by supporting privacy-protecting laws and technologies, and giving the users full control over their digital footprints."

In July, Kosinski will begin a new appointment as an assistant professor at Stanford Graduate School of Business.

Source: Stanford university

Self-repairing software tackles malware

Eric Eide, University of Utah research assistant professor of computer science, stands in the computer science department's "Machine Room" where racks of web servers sit. It is on these computers that Eide, U computer science associate professor John Regehr, and their research team created and tested A3, a suite of computer applications that defeat malware and automatically repair the damage it causes. The project could help lead to better consumer software defenses.
Credit: Dan Hixson/University of Utah College of Engineering

University of Utah computer scientists have developed software that not only detects and eradicates never-before-seen viruses and other malware, but also automatically repairs damage caused by them. The software then prevents the invader from ever infecting the computer again.

A3 is a software suite that works with a virtual machine -- a virtual computer that emulates the operations of a computer without dedicated hardware. The A3 software is designed to watch over the virtual machine's operating system and applications, says Eric Eide, University of Utah research assistant professor of computer science leading the university's A3 team with U computer science associate professor John Regehr. A3 is designed to protect servers or similar business-grade computers that run on the Linux operating system. It also has been demonstrated to protect military applications.

The new software called A3, or Advanced Adaptive Applications, was co-developed by Massachusetts-based defense contractor, Raytheon BBN, and was funded by Clean-Slate Design of Resilient, Adaptive, Secure Hosts, a program of the Defense Advanced Research Projects Agency (DARPA). The four-year project was completed in late September.

There are no plans to adapt A3 for home computers or laptops, but Eide says this could be possible in the future.

"A3 technologies could find their way into consumer products someday, which would help consumer devices protect themselves against fast-spreading malware or internal corruption of software components. But we haven't tried those experiments yet," he says.

U computer scientists have created "stackable debuggers," multiple de-bugging applications that run on top of each other and look inside the virtual machine while it is running, constantly monitoring for any out-of-the-ordinary behavior in the computer.

Unlike a normal virus scanner on consumer PCs that compares a catalog of known viruses to something that has infected the computer, A3 can detect new, unknown viruses or malware automatically by sensing that something is occurring in the computer's operation that is not correct. It then can stop the virus, approximate a repair for the damaged software code, and then learn to never let that bug enter the machine again.

While the military has an interest in A3 to enhance cybersecurity for its mission-critical systems, A3 also potentially could be used in the consumer space, such as in web services like Amazon. If a virus or attack stops the service, A3 could repair it in minutes without having to take the servers down.

To test A3's effectiveness, the team from the U and Raytheon BBN used the infamous software bug called Shellshock for a demonstration to DARPA officials in Jacksonville, Florida, in September. A3 discovered the Shellshock attack on a Web server and repaired the damage in four minutes, Eide says. The team also tested A3 successfully on another half-dozen pieces of malware.

Shellshock was a software vulnerability in UNIX-based computers (which include many web servers and most Apple laptops and desktop computers) that would allow a hacker to take control of the computer. It was first discovered in late September. Within the first 24 hours of the disclosure of Shellshock, security researchers reported that more than 17,000 attacks 

by hackers had been made with the bug.

"It is a pretty big deal that a computer system could automatically, and in a short amount of time, find an acceptable fix to a widespread and important security vulnerability," Eide says. 

"It's pretty cool when you can pick the Bug of the Week and it works."

Now that the team's project into A3 is completed and proves their concept, Eide says the U team would like to build on the research and figure out a way to use A3 in cloud computing, a way of harnessing far-flung computer networks to deliver storage, software applications and servers to a local user via the Internet.

The A3 software is open source, meaning it is free for anyone to use, but Eide believes many of the A3 technologies could be incorporated into commercial products.

Other U members of the A3 team include research associate David M. Johnson, systems programmer Mike Hibler and former graduate student Prashanth Nayak.

Source: University of Utah

Can a stack of computer servers survive an earthquake?

The rack of servers shook, but did not fall, during a simulation that mimicked 80 percent of the force of 1994's Northridge earthquake. Credit: Cory Nealon, University at Buffalo

How do you prevent an earthquake from destroying expensive computer systems?

That's the question earthquake engineer Claudia Marin-Artieda, PhD, associate professor of civil engineering at Howard University, aims to answer through a series of experiments conducted at the University at Buffalo.

"The loss of functionality of essential equipment and components can have a disastrous impact. We can limit these sorts of equipment losses by improving their seismic performance," Marin-Artieda said.
In buildings such as data centers, power plants and hospitals, it could be catastrophic to have highly-sensitive equipment swinging, rocking, falling and generally bashing into things.

In high-seismic regions, new facilities often are engineered with passive protective systems that provide overall seismic protection. But often, existing facilities are conventional fixed-base buildings in which seismic demands on sensitive equipment located within are significantly amplified. In such buildings, sensitive equipment needs to be secured from these damaging earthquake effects, Marin-Artieda said.
The stiffer the building, the greater the magnification of seismic effects, she added.

"It is like when you are riding a rollercoaster," she said. "If your body is relaxed, you don't feel strong inertial effects. But if you hold your body rigid, you'll feel the inertial effects much more, and you'll get knocked about in the car."

The experiments were conducted this month at the University at Buffalo's Network for Earthquake Engineering Simulation (NEES), a shared network of laboratories based at Purdue University.
Marin-Artieda and her team used different devices for supporting 40 computer servers donated by Yahoo Labs. The researchers attached the servers to a frame in multiple configurations on seismically isolated platforms. They then subjected the frame to a variety of three-directional ground motions with the servers in partial operation to monitor how they react to an earthquake simulation.

Preliminary work confirmed, among other things, that globally and locally installed seismic isolation and damping systems can significantly reduce damage to computer systems and other electronic equipment.

Base isolation is a technique that sets objects atop an energy-absorbing base; damping employs energy-absorbing devices within the object to be protected from an earthquake's damaging effects.
Marin-Artieda plans to expand the research by developing a framework for analysis, design and implementation of the protective measures.

The research is funded by the National Science Foundation. In addition to Yahoo Labs, industry partners include Seismic Foundation Control Inc., The VMC Group, Minus K Technology Inc., Base Isolation of Alaska, and Roush Industries Inc. All provided in-kind materials for the experiments.

Video showing one of the tests, which mimics 80 percent of the force of 1994's Northridge earthquake: https://www.youtube.com/watch?v=hTkemnt8hR4

Source: University at Buffalo

The Data mining disaster: Computer technology can mine data from social media during disasters

Computer technology that can mine data from social media during times of natural or other disaster could provide invaluable insights for rescue workers and decision makers, according to an international team writing in the International Journal of Emergency Management.

Adam Zagorecki of the Centre for Simulation and Analytics, at Cranfield University, Shrivenham, UK and David Johnson of Missouri State University, Springfield, USA and Jozef Ristvej of the University of Zilina, Zilina, Slovakia, explain that when disaster strikes the situation can change rapidly. Whether that is during flooding, landslide, earthquake or terrorist attack, understanding the complexities of the situation can mean the difference between saving human and animal lives, reducing environmental impact and preventing major economic loss.

The team points out that advances in information technology have had a profound impact on disaster management. First, these advances make unprecedented volumes of data available to decision makers. This, however, brings with it the problem of managing and using that data. The team has surveyed the state of the art in data mining and machine learning in this context. They have found that whereas earlier applications were focused on specific problems, such as modeling the dispersion by wind of plumes -- whether from a chemical plant leak, fire or nuclear incident -- and monitoring rescue robots, there are much broader applications, such as constructing situational awareness and real-time threat assessment.

Data mining during a disaster can pull in information from unstructured data from news reports, incident activity reports, and announcements, as well as structured textual data from emergency services, situational reports and damage assessment forms. In addition, it can utilize remote sensing data, as well as more frequently now, mobile phone images and video, and satellite and aerial images.
Critically, the team also reveals that the advent of social media is playing an important role in generating a real-time data stream that grows quickly whenever disaster strikes and those involved have access to wireless communications and or internet connectivity. In particular, data mining of social media can assist with the response phase of disaster management. This information can quickly provide data points for models that are not available in conventional simulations.

"Disasters often undergo rapid substantial evolution; therefore, disaster management is a non-uniform process characterized by phases, although these phases are not distinct in nature," the team reports, they have now highlighted the challenges and hinted at future trends that might improve disaster response through the use of modern data mining technology.


Source: Inderscience