As far as party games go, the Imitation Game is a pretty clever and fairly entertaining one. A man and a women answer questions as if they were the other, trying to fool party guests into guessing the wrong gender when identifying the answers. The idea of impersonation is too intriguing to pass up in a fun context.

This game, with its basic premise and mystery, is the basis of the infamous Turing Test. British mathematician Alan Turing used the game to describe and develop a test that measures a machine’s ability to imitate a human. The question long asked by early robotics pioneers was “Can machines think?” But Turing reframed the question to instead ask, “Can machines show they can think like a human?”

His test revolutionized how machine intelligence was viewed in relation to human thinking and functionality. The philosophy behind his question and test has guided the progress made in advancing modern artificial intelligence (AI).

While the Turing Test has historical significance and contributed a great deal to the study and growth of technology, it shouldn’t be the sole standard or measure of artificial intelligence. As the goals of artificial intelligence have made gains, the Turing Test is becoming a relic of the past and an irrelevant measure of the effectiveness of AI with a few caveats.

Complete human behavior

The Turing Test was developed to measure if a machine could act like a human, meaning it would imitate both intelligent and unintelligent behavior. Turing’s test was not a way to perfect a machine to solve a complicated problem, but to emulate the human brain—imperfections and all. In the context of the test, a machine would imitate human typing errors to try and prove it was human and not machine.

It’s important to remember that the Imitation Game—both the original party favorite and the technical version—is a game of deception not accuracy. The machine is trying to be identified as the human and the woman is trying to be identified as the man. With honest answers and the element of imitation removed, it becomes a question and answer game with very little point.

To be the ultimate player of the Turing game, machines are not programmed to produce the best answers, but the most human ones. When just answering Turing’s reframed question, “Can machines behave like humans?” the answer can most certainly be yes.

The Loebner competition, founded in 1991 as a glorified show of the Turing Test, proved that. Declared “artificial stupidity,” the Loebner competition is reviled by AI experts because instead of focusing on improving AI to do what it is intended, people are competing to prove a bot can replicate human fallibility.

Updated standards

Now, as scientists and engineers try to craft artificial intelligence for practical purposes and daily use, the importance of the Turing Test is shrinking, but not disappearing. The goal of AI now is to, in fact, solve complicated problems and equations that humans would normally struggle with. Machine learning and the advancement of how technology understands and fits into the world is becoming the standard. Machines are here to help, not simply create more human-like behavior.

On the other hand, including human-like elements in robotics and AI keeps the technology grounded in human reality. As Professor Sethu Vijayakumar FRSE, who holds a Chair in Robotics within the School of Informatics at the University of Edinburgh told Sputnik International, human forms and human behavior in robots, like the one Vijayakumar is working on to man a mission to Mars, help the people using it understand its functions in a relatable context.

His point is that we should use artificial intelligence as a way to enhance the human experience. Using technology to accomplish tasks risky to human life and using the technology to refine discoveries is an ideal application of AI and robots.

Developing AI should still involve the Turing Test to ensure there is a human element included in its design. It shouldn’t be the end all be all of AI’s effectiveness or ability to succeed in the world, but it should be a small part of determining how well it will fit into a society’s reality.

Amazon Picking Challenge 2016 Pick Task Finals – Team Delft. Source: RoboValley/YouTube

Amazon’s robot champion; Bipedal robot’s testing terrain; Robots thinking like predators; Major flaw in Turing Test; Google car is cyclist-aware and more. Find out what’s happening in our robotics universe this week.

Amazon’s latest robot champion uses deep learning to stock shelves

(The Verge)

The winners of Amazon’s robot picking challenge –a joint team from the TU Delft Robotics Institute in the Netherlands and the company Delft Robotics– managed to pick items from a mock Amazon warehouse shelf at a speed of around 100 an hour with a failure rate of 16.7 percent. (Humans can pick at a rate of around 400 per hour in a similar set-up.)

Although the picking challenge is essentially a try-out for Amazon’s own warehouses, the company insists that its intention is not to make its human workers obsolete. “Robotics enhance the job for employees but [do] not replace them,” a spokesperson for the company told TechRepublic. “In fact, we continue to hire. Many of those roles are being created in buildings where employees are working alongside Amazon robotic drive units.” The spokesperson added that in the company’s robot-filled warehouses the end result was “a symphony of humans and technology.”

The Delft teams used deep learning to achieve their winning performance:

“The answer was artificial intelligence. The researchers used deep learning techniques — which can churn through vast amounts of data to look for recurring patterns — to analyze 3D scans of the objects their robot had to pick and replace. A custom gripper-suction arm attached to an off-the-shelf Yaskawa robotic arm was used to do the actual mechanical work, while the AI-powered software gave the robot an edge over the competition.”

Marlo takes a stroll across the wave field for the first time

(Popular Science)

MARLO, a bipedal robot developed by researchers at the University of Michigan, is being tested with some success on the university’s “Wave Field” an art installation that has become a robot testing due to its challenging, uneven terrain.

“The robot has no feeling in her tiny feet, but she senses the angles of her joints — for instance, her knee angles, hip angles and the rotation angle of her torso,” said project lead and University of Michigan professor Jessy Grizzle. “It’s like walking blindfolded and on stilts.”

An adapted version of the algorithms MARLO uses have already been successfully deployed in a prosthetic leg, by Robert Gregg, an assistant professor of mechanical engineering and bioengineering at the University of Texas-Dallas.

Scientists taught a robot to hunt prey

(Motherboard)

Allowing robots to act like predators could lead to more capable robots, according to a team of scientists at the Institute of Neuroinformatics at the University of Zurich in Switzerland.

“The researchers aim to design software that would allow a robot to assess its environment and find a target in real time and space. If robots are ever going to make it out of the lab and into our daily lives, this is a skill they’re going to need.

The predator robot’s hardware, like its behaviour, is modeled after animals. The key to its success as a hunter is a “silicon retina”—co-invented by Delbruck as part of the European Union-funded VISUALISE project—that mimics the human eye to speedily process visual data.”

The researcher’s aim is not to create a breed of killer robot designed to take over the world, however. Robots with the ability to teach themselves how to follow a target effectively will make for excellent automated shopping carts and luggage bots, could improve companion robot effectiveness, and could our ability to create safe, autonomous robot swarms.

Google’s robot cars recognize cyclists’ hand signals — better than most cyclists (recode)

Good news for cyclists this week: Google’s robot cars are proving quite adept at recognizing various types of cyclist hand signals.

“Cyclists often make hand signals far in advance of a turn, and our software is designed to remember previous signals from a rider so it can better anticipate a rider’s turn down the road,” said Google.

“Google’s robot car will even surrender the entire lane to the cyclist if they’re showing signs of moving closer. It might strike some drivers as overly polite, but for a robot-driven car, erring on the cautious side while navigating around humans is probably the best bet.”

Study exposes major flaw in classic artificial intelligence test

(Alphagalileo)

There is a serious flaw in the Turing Test, according to a study published in the Journal of Experimental and Theoretical Artificial Intelligence this week:

“If a machine were to ‘take the Fifth Amendment’ – that is, exercise the right to remain silent throughout the test – it could, potentially, pass the test and thus be regarded as a thinking entity, authors Kevin Warwick and Huma Shah of Coventry University argue. However, if this is the case, any silent entity could pass the test, even if it were clearly incapable of thought.”

This isn’t mere speculation on the authors’ parts. They studied transcripts of a number of conversations from actual Turing tests in which the hidden machine remained silent and found that In each case, the human judge was unable to say for certain whether they were interacting with a person or a machine. All of which means, that a machine could pass the Turing Test by remaining silent.

As the old saying goes: “Better to remain silent and be thought a fool than to speak and to remove all doubt.”

The role of telepresence robotics in workforce inclusion

(TechCrunch)

Amid all the media stories about robots causing widespread unemployment (and worse, up to and including the end of the world!) it was great to read this piece by Peter Hirst, who highlights some of the social and employment benefits that telepresence robots can bring, from improving diversity among executive talent to removing some of the workplace barriers that people with disabilities must face.

“What if people with mobility challenges had easy access to telepresence robots to attend school or go to the office? Why can’t these robots work the same way as bike rentals in many cities around the world where people can check them out by the hour for sightseeing or for commuting? Add a strong citywide wireless network and presto! — a whole new swath of population has access to employment, education and entertainment.”

And Finally…

https://www.youtube.com/watch?v=yDWflGb51IM

https://www.youtube.com/watch?v=FE6UFLodZLg

https://www.youtube.com/watch?v=JHb0M6dlDkg

Terrorists want to create a Robot Army (Daily Mail)

Do robots dream of electric scene partners? (American Theatre)

Automatica’s exhibitors were trumpeting Industry 4.0. Is this the turning point for robotics? (SPARC)

Toyota robot to be used as standard at next year’s RoboCup (NIKKEI Asian Review)

Delivery robots to replace takeaway drivers in London trial (The Telegraph)

Robot lawyers could make time-consuming, expensive court conflict thing of the past (ABC News)

Alex Garland’s first feature film as a director, Ex Machina, had its US debut at SxSW on March 14. This stylish idea film explores the Turing Test in a very Pinteresque fashion as a young coder falls in love with an advanced AI. Ex Machina is beautifully framed, but Garland’s stark script succeeds on the strength of the acting from Domhnall Gleeson, Alicia Vikander and Oscar Isaac.

Garland’s writing career launched in 1997 with the best selling novel “The Beach”, which the Times called the Gen X answer to Lord of the Flies. After a string of cult successes like 28 Days Later, Sunshine, Dredd and drafts of Halo and Logans Run, Garland became fascinated with the emerging promise and perils of AI. In the Q&A following the SxSW screening, Garland talked about feeling a zeitgeist, a technological and cultural turning point, compelling him and other film makers and writers to address robots and artificial intelligence.

Although Garland says that he’s on the side of the robots, it’s an uneasy truce. Garland describes his film as the story of ‘two brains torturing each other’. That’s true. In Ex Machina, Tony Stark meets John Searle in a gripping drawing room theater, when a billionaire tech genius recruits a young coder to administer the Turing Test to his secret advanced embodied AI.

And it’s a stark film, there are only 4 characters; 2 men and 2 women. 2 AIs and 2 humans. And which two are the brains? That is supposed to be uncertain, but anyone who has used the Bechdel Test to analyze films or popular culture for gender issues knows exactly where the ‘brains’ are.

The Bechdel Test started as a gender litmus and has become a remarkably useful indicator of power imbalance. The test is named after Amy Bechdel, a cartoonist who outlined the rules in a 1985 cartoon. To pass the Bechdel Test, a film has to have two women in it, who talk to each other, about something other than a man .

Sometimes the proviso is added that the women have to have names, because some films can have many women characters, but if the characters are all “girl at checkout” and “girl with gun” then they are just devices to add color or move the action forward. And of course, possession of a name is an important indicator of personhood, or identity awareness, so it’s always one of the first steps to separate the beings from the machines.

Many films seem at first glance to have badass female characters, but when put to the Bechdel Test, it becomes clear that they never talk to anyone but the main man, or if they talk to each other, it’s about the main male characters. So really, they have no interiority, no self awareness and are probably going to fail a Turing Test. That’s where I think it would be very interesting if the Turing Test were to meet the Bechdel Test more often.

Garland is also playing games with gender and the alienness of AI in Ex Machina. There is a beautiful scene where Ava, the AI, performs a reverse strip tease, putting on her human body.

But I’m afraid that Ex Machina falls at the final fence, as does just about every other science fiction film I’ve ever seen, aside from Alien. The Bechdel Test is useful for more than examining gender representation. It can be our Turing Test for creating believable alien or artificial life forms. If you look at our filmic or cultural representations of the other or alien, then you have to be struck by the singular nature of them all. From Frankenstein to Big Hero 6, do they have any reality without the central human characters?

No – they are alone. Even Alien is alone. At least in Frankenstein, it is the utter aloneness of the new form that is the whole story. Films that have pushed the envelope are few. And doing a quick mental check, I was left feeling empathy for the ‘others’ in only a couple, like Westworld, BladeRunner and Planet of the Apes, and the books of writers like Brin and Cherryh.

How believable are our ‘other’ AIs and robots? Brad Templeton said that an autonomous vehicle isn’t autonomous until we tell it to go to the office and it decides to go to the beach instead. A life outside of our anthropomorphic story is what’s missing from our AIs, aliens and others. We don’t really care about them or their lives outside of their impact on our own. And this makes us poorer.

The final shot is a haunting homage to Plato’s Cave’ – although Garland credits his Director of Photography entirely for it. In The Republic, Plato posed the question: what if humans were born chained to face a cave wall seeing the world only as the shadows passing in front of a fire behind them in the mouth of the cave? Imagine the difference when you see the world, unchained from the cave.

I can’t say more. Go see Ex Machina. And use the Bechdel Test on everything.

https://youtu.be/XYGzRB4Pnq8

In this first part of the ShanghAI Lecture series, Rolf Pfeifer gives an overview of the content and scope of the project, discusses the meaning of “Intelligence”, the Turing Test, and IQ.

The ShanghAI Lectures are a videoconference-based lecture series on Embodied Intelligence run by Rolf Pfeifer and organized by me and partners around the world. The name goes back to the first lectures of the series in 2009, which were held from Shanghai Jiao Tong University in China.

The lectures roughly follow Rolf Pfeifer’s book “How the Body Shapes the Way We Think” (co-authored with Josh Bongard). Additional information, including information on publications and the series’ sponsors can be found at the ShanghAI Lectures website, which also provided a support framework to bring together students and researchers in an interactive setting during the semester.

Starting with this post, recordings of these lectures and guest presentations given as part of the ShanghAI Lectures are now made available on Robohub.

Related links:

• Slides (17.4 MB)
• ShanghAI Lectures website
• Book: How the Body Shapes the Way We Think