Artificial intelligence robots systematically destroy wall in the name of art

Generally speaking, we use robots to help us build or create things. An artwork on display at the UK's Foundation for Art and Creative Technology (FACT), however, does quite the opposite. Accomplice comprises a number of robots that are systematically destroying a gallery wall over the course of an exhibition.

The artwork is designed by Petra Gemeinboeck and Rob Saunders and was first developed in 2012 during a residency at the Ars Electronica Futurelab in Linz, Austria. It explores the concept of machine autonomy and aims to remind the audience that society not only depends on technology, but is shaped by it too.

Building on an earlier work by Gemeinboeck and Saunders called Zwischenrume (In-between Spaces), Accomplice uses a system that allows the robots to share the wall space whilst using the same mechanism for moving around. They are able to communicate with each other through rhythmic knocking signals.

Gemeinboeck explains to Gizmag that the robots use a computational model of curiosity, based on a combination of reinforcement and unsupervised learning. The robots are rewarded for discovering novelty, such as surprising or unexpected consequences of their actions. They then use use these experiences to create a model of how the world reacts to them. As such, they are constantly experimenting and learning.

"One of the consequences of the robots actions is to open up holes in the wall, through which they can observe the outside world," says Gemeinboeck. "This is when a more passive form of machine learning, unsupervised learning, takes over. In this case the robots are motivated to learn about new experiences but have little control over the creation of those experiences through action. The robots are sensitive to color and motion, and so people wearing colors they haven't seen recently or who are moving in different ways to recent visitors may reward the robot by providing it with something new to learn. From the robots perspective, the audience performs for them."

Gemeinboeck explains that the basic idea was to couple the wall with a dynamic machine mechanism. "We use an advanced linear motion systems from Igus for the vertical motion and a belt drive for the horizontal motion," she says. "For the control system, we use off-the-shelf low-power computing systems a Beagleboard XM handles the central control, a Raspberry Pi mainly takes care of the machine vision and an Arduino with a RAMPS board shield takes care of the motor control. Weve developed our own custom control software that builds on Robs research in Computational Creativity. The electromechanical punch is custom-built as well, driven by a solenoid."

The artists worked with a kinetic sculpture to create the electromechanical punch and, although many of the components used were off-the-shelf, others had to be created, a process that Gemeinboeck says required a great deal of prototyping over several months. The work as a whole was programmed mostly in Python, with some C and Java.

Accomplice has garnered a variety of reactions from audience members in the places that it has been exhibited. According to Gemeinboeck, some people have been "disconcerted" by the apparently destructive machinery that appears to have no human control, whilst others are more intrigued or amused.

"We have often observed that audiences respond more like visitors in a zoo; they spend a surprising amount of time to observe whats going on and begin to speculate about the machines' intent," she says. "In some instances we have also seen the audiences perspective to shift in interesting ways they have felt uneasy at first and saw what was going on as a violent act, but after a while began to look at the wall from 'the other side', from the machines' perspective, which opened up more playful possibilities."

Accomplice is on display at FACT until 22nd June as part of the Science Fiction: New Death exhibition.

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Artificial intelligence robots systematically destroy wall in the name of art

Vicarious founder Dileep George gives advice for those starting out in artificial intelligence

Dileep George's firm Vicarious, in which Facebook CEO Mark Zuckerberg and Hollywood filmstar Ashton Kutcher put in money, uses visual perception system to interpret the contents of photographs and videos in a manner similar to the human brain. The system claims to reliably solve modern Captchas, including Google's reCaptchas, the world's most widely used test of a machine's ability to act human. George shares advice for those setting out in the field in an interview with Krishna Bahirwani.

How much neuroscience is essential for a computer science/information technology student to pursue Artificial Intelligence? Can you make any recommendations? What is important is to get a system level understanding of neuroscience rather than the minutiae of neuron function. There are many papers that attempt to do this. The one about 'Hierarchical Bayesian Inference in the Visual Cortex' by Lee and Mumford is a good overview paper. One could follow the references from their to build a more detailed picture. There are many popular science books that attempt to build a framework for understanding neuroscience data. 'On Intelligence' by Jeff Hawkins and 'How to build a Mind' by Ray Kurzweil. I would also recommend my PhD thesis and papers and references therein to get some of the relevant neuroscience knowledge.

How important according to you is the study of Mathematics for Machine Intelligence? Very important. Mathematics is important to read and understand the papers in the area and to formulate problems and solutions.

Is it possible for a layman to understand the mathematical nature of Artificial Intelligence by simply observing it? Well, a layman would be able to appreciate the mathematical structure, but to understand it and to operate with it they would need to study the underlying mathematics principles. The underlying mathematics is not very complex, but it requires you to build a large body of knowledge so that you understand what the experts in the field are talking about.

If you were given a student with some programming and no mathematical background to mentor where would you advise him to start his studies in AI from? The student will have to first learn some basic mathematics concepts -- linear algebra, probability theory, optimisation, information theory. Once they have those concepts, one early book to master is Judea Pearls 'Probabilistic Reasoning in Intelligent Systems'. Another book is Kevin Murphy's 'Machine Learning, a probabilistic perspective'. Bishop's 'Neural Networks' is a classic that everyone should read.

You have been mentored by the likes of Jeff Hawkins and Nils Nilsson. Was it easy to get such pioneers in the field to mentor you? Not exactly. I joined Stanford with the aim of understanding the brain, but I had to explore several paths and deal with a few unexpected setbacks before I met Jeff Hawkins. Working with Jeff required me to step out of the comfort zone of doing a "normal" PhD at Stanford. Being in Silicon Valley helps -- all around you are examples of people trying to do the impossible in unconventional ways.

There is a lot of information available on the internet about artificial intelligence and similar subject is there anything that can help students differentiate from what's real and what's not? This is hard -- learn about all of them and then come to own conclusions.

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Vicarious founder Dileep George gives advice for those starting out in artificial intelligence

Artificial Intelligence Is The Next Frontier For Social Networks

BI Intelligence

Now, though, Facebook, Twitter, LinkedIn, and others are beginning to use artificial intelligence techniques to build out their "deep learning" capacities. They're starting to process all the activity occurring over their networks, from conversations, to photo facial recognition, to gaming activity.

In arecent reportfromBI Intelligence, we show how advancesin cutting-edge artificial intelligence research, which program machines to perform high-level thought and abstractions,are helping social networks and their advertisers glean insights from this vast ocean of unstructured consumer data. Thanks to deep learning, social media has the potential to become far more personalized. New marketing fields are quickly emerging, too:audience clustering, predictive marketing, and sophisticated brand sentiment analysis.

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Here are some of the major acquisitions and hires from the AI field that occurred in recent months:

In full, the report:

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Johnny Depp says being famous is like being a 'fugitive'

NEW YORK, April 4 (UPI) -- Johnny Depp still hasn't gotten used to fame, and says being a celebrity is akin to "living like a fugitive."

The notoriously private star appeared in an interview Friday on Today to discuss his new film, Transcendence. The 50-year-old actor also confirmed his engagement to 27-year-old model and actress Amber Heard, after rumors of the pair's commitment were left unanswered for months.

"Everything has to be some sort of a strategy," Depp says of life in the spotlight. "[It's] a little bit like liking like a fugitive... To get you into the hotel, to get you out of the hotel, to get you into the restaurant, to get you out of the restaurant."

The actor takes care to acknowledge his fans, and says he is "honored" by their support and loyalty. He affirms he will always be there for them, "because those people that buy the tickets are the people that I consider my boss."

Depp portrays artificial intelligence researcher Dr. Will Caster in his new movie, Transcendence. The film marks longtime cinematographer Wally Pfister's directorial debut, and is produced by Christopher Nolan and Emma Thomas.

Caster is a researcher whose mind is uploaded to his project's computer after he suffers an injury. The machine possesses sentience and collective intelligence abilities, and extremists opposed to technological advancement attempt to stop Caster and the computer.

The actor admits that his role in the sci-fi thriller had little effect on his own technological skills.

"I'm a complete and utter oaf," Depp says of his attempts to text message. "A grown man...attempting to send an important message with these thumbs? You know, it's truly given meaning to the expression, 'I'm all thumbs.'"

Transcendence debuts in theaters on April 17.

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Its Comedian vs. Computer in a Battle for Humor Supremacy

Photo: Sam Gustin/WIRED

Over the years, weve become accustomed to computers besting humans in tests of raw intelligence. Deep Blue out-maneuvered world chess champion Garry Kasparov in 1997, and in 2011 Watson trounced Jeopardy winners Brad Rutter and Ken Jennings. Sure, computers can play the Sicilian Defense better and have a firmer grasp of obscure geographybut facts and analysis arent everything. We wanted to establish a different litmus test for computer supremacy, so we devised a new matchup between man and machine to establish once and for all whos funnier. Your contestants in the bout: stand-up comic Myq Kaplan versus Manatee the joke-telling computer.

Its perfect timing for such a duel. Computers in the comedy business are becoming an increasingly big deal. Last fall, the Association for the Advancement of Artificial Intelligence held its first-ever symposium on artificial intelligence and humor. Comedic robots are making a splash at SXSW Interactive and garnering GQ profiles. And a few years ago, Manatee caused a political stir when pundits learned its designers at Northwestern University had scored more than $700,000 in federal stimulus funds to help develop it.

Its no surprise, really. The name of the game in the tech industry is making interactions between people and their gizmos ever more humanand human often equals humorous. That means everything from computer programs that make fun of themselves when they make a mistake to a GPS device that sarcastically acknolwedges when you ignore its directions. Just the facts, maam, isnt going to play as machines get more and more involved in our lives, says Northwestern University professor Kristian Hammond, who helped design Manatee and now develops news-writing computer programs for the company Narrative Science. Its all about making the communication between people and the machine a smooth, compelling interaction.

Theres another reason computer scientists are eager to tackle comedy: jokes are some of the toughest tests of their programs. If artificial intelligence programs are truly going to model human intelligence, they have to be able to grasp all the clever ways people make things funny.

In fact, scientists have been hard at work for decades designing robo-jokesters. Among the efforts are JAPE, the Joke Analysis and Production Engine; STANDUP, the System To Augment Non-speakers Dialogue Using Puns; LIBJOB, the light bulb joke generator; SASI, a sarcasm-detecting program; and DEviaNT, the Double Entendre via Noun Transfer program, which finds the perfect spots in natural language to insert Thats what she said. Plus, for computer programmers looking for just the right witty acronym for the next big comedy computer, theres the HAHAcronym Generator.

What have most of these attempts discovered? That up until now, computers have been able to tell jokes, but only really dumb ones. Consider the following computer-generated zingers:

What kind of animal rides a catamaran?

A cat.

What is the difference between leaves and a car?

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Why Nvidia thinks it can power the AI revolution

6 hours ago Mar. 31, 2014 - 12:07 PM PDT

Smarter robots and devices are coming to a home near you, and chipmaker Nvidia wants to help make it happen. It wont develop the algorithms that dictate their behavior or build the sensors that let them take in our world, but its graphics-processing units, or GPUs, might be a great way to handle the heavy computing necessary to make many forms of artificial intelligence a reality.

Most applications dont use GPUs exclusively, but rather offload the most computationally intensive tasks onto them from standard microprocessors. Called GPU acceleration, the practice is very common in supercomputing workloads and its becoming ubiquitous in the area of computer vision and object recognition, too. In 2013, more than 80 percent of the teams participating in the ImageNet image-recognition competition utilized GPUs, said Sumit Gupta, general manager of the the Advanced Computing Group at Nvidia.

In March 2013, Google acquired DNNresearch, a deep learning startup co-created by University of Toronto professor Geoff Hinton. Part of the rationale behind that acquisition was teams performance of Hintons team in the 2012 ImageNet competition, where the groups GPU-powered deep learning models easily bested previous approaches.

Source: Nvidia

It turns out that the deep neural network problem is just a slam dunk for the GPU, Gupta said. Thats because deep learning algorithms often require a lot of computing power to process their data (e.g., images or text) and extract the defining features of the things included in that data. Especially during the training phase, when the models and algorithms are being tuned for accuracy, they need to process a lot of data.

Numerous customers are using Nvidias Tesla GPUs forimage and speech recognition, including Adobe and Chinese search giant Baidu. Nvidia is working on other aspects of machine learning as well, Gupta noted. Netflix uses them (in the Amazon Web Services cloud) to power its recommendation engine, Russian search engine Yandex uses GPUs to power its search engine, and IBM uses them to run clustering algorithms inHadoop.

Nvidia might be so excited about machine learning because it has been pushing GPUs as a general-purpose computing platform not just a graphics and gaming chip for years with mixed results. The company has tried to do this by simplify programming its processors via the CUDA language it has developed, but Gupta acknowledged theres still an overall lack of knowledge about how to use GPUs effectively. Thats why so much real innovation still remains with these large users that have the parallel-programming skills necessary to take advantage of 2,500 or more cores at a time (and even more in multi-GPU systems).

Source: Nvidia

However, Nvidia is looking beyond servers and into robotics to fuel some of its machine learning ambitions over the next decade. Last week, the company announced its Jetson TK1 development kit, which Gupta called a supercomputing version of Raspberry Pi. At $192, the kit is programmable using CUDA and includes all the ports one might expect to see, as well as a Tegra K1 system-on-a-chip (the latest version of Nvidias mobile processor) thats comprised of a 192-core Kepler GPU, an ARM Cortex A15 CPU and 300 gigaflops of performance.

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Latest XPrize challenges teams to deliver a TED Talk using artificial intelligence

The XPrize Foundation wants to find out if an artificial intelligence can compose and deliver a good TED Talk

TED Talks are known for being delivered in a captivating, compelling fashion that's why the events' organizers are fairly picky when it comes to selecting speakers. With that in mind, XPrize has teamed up with TED for its latest competition, in which an artificial intelligence (AI) must deliver a TED Talk with no human assistance.

For those who don't know, TED is a non-profit group that regularly presents conferences featuring lectures on "ideas worth spreading." The topics can include just about anything that will enlighten the listeners. The XPrize Foundation, on the other hand, stages competitions in which teams compete to achieve a goal that drives innovation.

The just-announced AI XPrize is described as "a modern-day Turing test to be awarded to the first AI to walk or roll out on stage and present a TED Talk so compelling that it commands a standing ovation from you, the audience."

Although the rules and format of the contest have yet to be finalized, at this point the competing AIs could be contained within a robot, they could simply be disembodied voices, or they could take some other form. A suggested scenario involves them being presented with a topic for a 3-minute talk, 30 minutes before having to take the stage. They would then have to autonomously compose a lecture, deliver it to a live audience in an engaging fashion, and then answer two questions on the topic, as posed by the host of the conference.

The winner would be decided based on the audience's applause.

As mentioned, though, that's just a suggestion. XPrize is open to hearing other peoples' ideas, which can be submitted via the link below.

Source: XPrize

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Latest XPrize challenges teams to deliver a TED Talk using artificial intelligence