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#437150 AI Is Getting More Creative. But Who ...

Creativity is a trait that makes humans unique from other species. We alone have the ability to make music and art that speak to our experiences or illuminate truths about our world. But suddenly, humans’ artistic abilities have some competition—and from a decidedly non-human source.

Over the last couple years there have been some remarkable examples of art produced by deep learning algorithms. They have challenged the notion of an elusive definition of creativity and put into perspective how professionals can use artificial intelligence to enhance their abilities and produce beyond the known boundaries.

But when creativity is the result of code written by a programmer, using a format given by a software engineer, featuring private and public datasets, how do we assign ownership of AI-generated content, and particularly that of artwork? McKinsey estimates AI will annually generate value of $3.5 to $5.8 trillion across various sectors.

In 2018, a portrait that was christened Edmond de Belamy was made in a French art collective called Obvious. It used a database with 15,000 portraits from the 1300s to the 1900s to train a deep learning algorithm to produce a unique portrait. The painting sold for $432,500 in a New York auction. Similarly, a program called Aiva, trained on thousands of classical compositions, has released albums whose pieces are being used by ad agencies and movies.

The datasets used by these algorithms were different, but behind both there was a programmer who changed the brush strokes or musical notes into lines of code and a data scientist or engineer who fitted and “curated” the datasets to use for the model. There could also have been user-based input, and the output may be biased towards certain styles or unintentionally infringe on similar pieces of art. This shows that there are many collaborators with distinct roles in producing AI-generated content, and it’s important to discuss how they can protect their proprietary interests.

A perspective article published in Nature Machine Intelligence by Jason K. Eshraghian in March looks into how AI artists and the collaborators involved should assess their ownership, laying out some guiding principles that are “only applicable for as long as AI does not have legal parenthood, the way humans and corporations are accorded.”

Before looking at how collaborators can protect their interests, it’s useful to understand the basic requirements of copyright law. The artwork in question must be an “original work of authorship fixed in a tangible medium.” Given this principle, the author asked whether it’s possible for AI to exercise creativity, skill, or any other indicator of originality. The answer is still straightforward—no—or at least not yet. Currently, AI’s range of creativity doesn’t exceed the standard used by the US Copyright Office, which states that copyright law protects the “fruits of intellectual labor founded in the creative powers of the mind.”

Due to the current limitations of narrow AI, it must have some form of initial input that helps develop its ability to create. At the moment AI is a tool that can be used to produce creative work in the same way that a video camera is a tool used to film creative content. Video producers don’t need to comprehend the inner workings of their cameras; as long as their content shows creativity and originality, they have a proprietary claim over their creations.

The same concept applies to programmers developing a neural network. As long as the dataset they use as input yields an original and creative result, it will be protected by copyright law; they don’t need to understand the high-level mathematics, which in this case are often black box algorithms whose output it’s impossible to analyze.

Will robots and algorithms eventually be treated as creative sources able to own copyrights? The author pointed to the recent patent case of Warner-Lambert Co Ltd versus Generics where Lord Briggs, Justice of the Supreme Court of the UK, determined that “the court is well versed in identifying the governing mind of a corporation and, when the need arises, will no doubt be able to do the same for robots.”

In the meantime, Dr. Eshraghian suggests four guiding principles to allow artists who collaborate with AI to protect themselves.

First, programmers need to document their process through online code repositories like GitHub or BitBucket.

Second, data engineers should also document and catalog their datasets and the process they used to curate their models, indicating selectivity in their criteria as much as possible to demonstrate their involvement and creativity.

Third, in cases where user data is utilized, the engineer should “catalog all runs of the program” to distinguish the data selection process. This could be interpreted as a way of determining whether user-based input has a right to claim the copyright too.

Finally, the output should avoid infringing on others’ content through methods like reverse image searches and version control, as mentioned above.

AI-generated artwork is still a very new concept, and the ambiguous copyright laws around it give a lot of flexibility to AI artists and programmers worldwide. The guiding principles Eshraghian lays out will hopefully shed some light on the legislation we’ll eventually need for this kind of art, and start an important conversation between all the stakeholders involved.

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#436911 Scientists Linked Artificial and ...

Scientists have linked up two silicon-based artificial neurons with a biological one across multiple countries into a fully-functional network. Using standard internet protocols, they established a chain of communication whereby an artificial neuron controls a living, biological one, and passes on the info to another artificial one.

Whoa.

We’ve talked plenty about brain-computer interfaces and novel computer chips that resemble the brain. We’ve covered how those “neuromorphic” chips could link up into tremendously powerful computing entities, using engineered communication nodes called artificial synapses.

As Moore’s law is dying, we even said that neuromorphic computing is one path towards the future of extremely powerful, low energy consumption artificial neural network-based computing—in hardware—that could in theory better link up with the brain. Because the chips “speak” the brain’s language, in theory they could become neuroprosthesis hubs far more advanced and “natural” than anything currently possible.

This month, an international team put all of those ingredients together, turning theory into reality.

The three labs, scattered across Padova, Italy, Zurich, Switzerland, and Southampton, England, collaborated to create a fully self-controlled, hybrid artificial-biological neural network that communicated using biological principles, but over the internet.

The three-neuron network, linked through artificial synapses that emulate the real thing, was able to reproduce a classic neuroscience experiment that’s considered the basis of learning and memory in the brain. In other words, artificial neuron and synapse “chips” have progressed to the point where they can actually use a biological neuron intermediary to form a circuit that, at least partially, behaves like the real thing.

That’s not to say cyborg brains are coming soon. The simulation only recreated a small network that supports excitatory transmission in the hippocampus—a critical region that supports memory—and most brain functions require enormous cross-talk between numerous neurons and circuits. Nevertheless, the study is a jaw-dropping demonstration of how far we’ve come in recreating biological neurons and synapses in artificial hardware.

And perhaps one day, the currently “experimental” neuromorphic hardware will be integrated into broken biological neural circuits as bridges to restore movement, memory, personality, and even a sense of self.

The Artificial Brain Boom
One important thing: this study relies heavily on a decade of research into neuromorphic computing, or the implementation of brain functions inside computer chips.

The best-known example is perhaps IBM’s TrueNorth, which leveraged the brain’s computational principles to build a completely different computer than what we have today. Today’s computers run on a von Neumann architecture, in which memory and processing modules are physically separate. In contrast, the brain’s computing and memory are simultaneously achieved at synapses, small “hubs” on individual neurons that talk to adjacent ones.

Because memory and processing occur on the same site, biological neurons don’t have to shuttle data back and forth between processing and storage compartments, massively reducing processing time and energy use. What’s more, a neuron’s history will also influence how it behaves in the future, increasing flexibility and adaptability compared to computers. With the rise of deep learning, which loosely mimics neural processing as the prima donna of AI, the need to reduce power while boosting speed and flexible learning is becoming ever more tantamount in the AI community.

Neuromorphic computing was partially born out of this need. Most chips utilize special ingredients that change their resistance (or other physical characteristics) to mimic how a neuron might adapt to stimulation. Some chips emulate a whole neuron, that is, how it responds to a history of stimulation—does it get easier or harder to fire? Others imitate synapses themselves, that is, how easily they will pass on the information to another neuron.

Although single neuromorphic chips have proven to be far more efficient and powerful than current computer chips running machine learning algorithms in toy problems, so far few people have tried putting the artificial components together with biological ones in the ultimate test.

That’s what this study did.

A Hybrid Network
Still with me? Let’s talk network.

It’s gonna sound complicated, but remember: learning is the formation of neural networks, and neurons that fire together wire together. To rephrase: when learning, neurons will spontaneously organize into networks so that future instances will re-trigger the entire network. To “wire” together, downstream neurons will become more responsive to their upstream neural partners, so that even a whisper will cause them to activate. In contrast, some types of stimulation will cause the downstream neuron to “chill out” so that only an upstream “shout” will trigger downstream activation.

Both these properties—easier or harder to activate downstream neurons—are essentially how the brain forms connections. The “amping up,” in neuroscience jargon, is long-term potentiation (LTP), whereas the down-tuning is LTD (long-term depression). These two phenomena were first discovered in the rodent hippocampus more than half a century ago, and ever since have been considered as the biological basis of how the brain learns and remembers, and implicated in neurological problems such as addition (seriously, you can’t pass Neuro 101 without learning about LTP and LTD!).

So it’s perhaps especially salient that one of the first artificial-brain hybrid networks recapitulated this classic result.

To visualize: the three-neuron network began in Switzerland, with an artificial neuron with the badass name of “silicon spiking neuron.” That neuron is linked to an artificial synapse, a “memristor” located in the UK, which is then linked to a biological rat neuron cultured in Italy. The rat neuron has a “smart” microelectrode, controlled by the artificial synapse, to stimulate it. This is the artificial-to-biological pathway.

Meanwhile, the rat neuron in Italy also has electrodes that listen in on its electrical signaling. This signaling is passed back to another artificial synapse in the UK, which is then used to control a second artificial neuron back in Switzerland. This is the biological-to-artificial pathway back. As a testimony in how far we’ve come in digitizing neural signaling, all of the biological neural responses are digitized and sent over the internet to control its far-out artificial partner.

Here’s the crux: to demonstrate a functional neural network, just having the biological neuron passively “pass on” electrical stimulation isn’t enough. It has to show the capacity to learn, that is, to be able to mimic the amping up and down-tuning that are LTP and LTD, respectively.

You’ve probably guessed the results: certain stimulation patterns to the first artificial neuron in Switzerland changed how the artificial synapse in the UK operated. This, in turn, changed the stimulation to the biological neuron, so that it either amped up or toned down depending on the input.

Similarly, the response of the biological neuron altered the second artificial synapse, which then controlled the output of the second artificial neuron. Altogether, the biological and artificial components seamlessly linked up, over thousands of miles, into a functional neural circuit.

Cyborg Mind-Meld
So…I’m still picking my jaw up off the floor.

It’s utterly insane seeing a classic neuroscience learning experiment repeated with an integrated network with artificial components. That said, a three-neuron network is far from the thousands of synapses (if not more) needed to truly re-establish a broken neural circuit in the hippocampus, which DARPA has been aiming to do. And LTP/LTD has come under fire recently as the de facto brain mechanism for learning, though so far they remain cemented as neuroscience dogma.

However, this is one of the few studies where you see fields coming together. As Richard Feynman famously said, “What I cannot recreate, I cannot understand.” Even though neuromorphic chips were built on a high-level rather than molecular-level understanding of how neurons work, the study shows that artificial versions can still synapse with their biological counterparts. We’re not just on the right path towards understanding the brain, we’re recreating it, in hardware—if just a little.

While the study doesn’t have immediate use cases, practically it does boost both the neuromorphic computing and neuroprosthetic fields.

“We are very excited with this new development,” said study author Dr. Themis Prodromakis at the University of Southampton. “On one side it sets the basis for a novel scenario that was never encountered during natural evolution, where biological and artificial neurons are linked together and communicate across global networks; laying the foundations for the Internet of Neuro-electronics. On the other hand, it brings new prospects to neuroprosthetic technologies, paving the way towards research into replacing dysfunctional parts of the brain with AI chips.”

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#435642 Drone X Challenge 2020

Krypto Labs opens applications for Drone X Challenge 2020 Phase II, a US$1.5+ Million Global Challenge (US$1 Million Final Prize and US$500,000+ in R&D Grants)

In its most rewarding initiative to date, Krypto Labs, the global innovation hub with a unique ecosystem for funding ground-breaking startups, has announced the opening of Phase II of Drone X Challenge (DXC) 2020, the global multimillion-dollar challenge that is pushing the frontiers of innovation in drone technologies focusing on high payload capacity and high flight endurance.

Drone X Challenge 2020 is open to entrepreneurs, start-ups, researchers, university students and established companies. Teams that want to apply for Drone X Challenge 2020 Phase II will have to develop a drone system capable of achieving the minimum endurance and payload as per the category they are applying to.

Categories:

Fixed-wing drones battery powered
Fixed-wing drones hybrid/hydrocarbon powered
Multi-rotor drones battery powered
Multi-rotor drones hybrid/hydrocarbon powered

Drone X Challenge 2020 is divided in 3 phases and a final event, providing US$1 Million Final Prize. The outstanding applications that meet the requirements of Phase II will collectively receive US$300,000 in R&D grants.

The shortlisted teams of Phase I received US$320,000 in R&D grants, which required applicants to provide a technical proposal detailing the design of a drone capable of meeting the minimum requirements of payload and endurance.

The shortlisted teams of Drone X Challenge 2020 Phase I are:

RigiTech from Switzerland
Forward Robotics from Canada
Industrial Technology Research Institute (ITRI) from Taiwan
KopterKraft from Germany
DV8 Tech from USA
Richen Power from China
Industrial Technology Research Institute (ITRI) from Taiwan
Vulcan UAV Ltd from UK

Dr. Saleh Al Hashemi, Managing Director of Krypto Labs said: “This competition aligns with our efforts in contributing to the development of drone technology globally. We aim to redefine the way drone technologies are impacting our lives, and Krypto Labs is proud to be leading the way in the region by supporting startups, established companies, and industries involved in the field of drone development. By catalyzing and supporting these cutting-edge solutions, we aim to continue leveraging disruptive technologies that can create value and make an impact.”

For more information about Drone X Challenge 2020, please visit https://dronexchallenge2020.com. Continue reading

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#431134 Anthouse Pet Companion Robot Kickstarter

Press Release by: Anthouse.pet
New Ultimate Pet Companion Robot will Turn Heads and Make Your Dog Absolutely Love You.
Man’s Best Friend will soon have a new companion to play with this Fall. Introducing The Anthouse Pet Companion Robot, from the creators at Anthouse Technology Co., Ltd. The Anthouse Robot is the best pet robot for dogs that the market has ever seen. The product includes a range of smart functions all controlled via a smart phone app that pet owners can control to interact with and attend to their loving pets. Features include a camera that’s capable of recording video and taking photos of your pet, with a one-touch social media share button enabled; a walki-talki megaphone to speak to your pet directly; a dog food treat dispenser that can dispense treat servings depending a measure you select; self-directed automated charging (the robot will find it’s charging station whenever its batteries is nearly depleted); automated obstacle avoidance, and our very favorite, a mini-tennis ball launcher for non-stop fun and exercise for your pet. Never again will you have to wonder what your pet is doing. It’s the perfect user-friendly tech product for pet owners and their faithful friends to keep close despite the physical distance between. The Anthouse Pet Companion Robot is set to launch on Kickstarter on August 15th, 9AM PST with an early-bird pice offering of $349. For media review details, and to get an invitation to the official press kit and pre-launch Kickstarter video viewing, please contact Sarah Miller of the Anthouse team for details.
Photo By: Anthouse.pet

Contact Information:
Name: Sarah Miller
Email: hello@anthouse.pet
Phone: 1 (512) 333-2950
Facebook: @anthousepetrobot
Website:
www.anthouse.pet
On Kickstarter: August 15th, 9AM PST
General Press Kit: http://bit.ly/AnthousePressKit

Photo By: Anthouse.pet

Robotic Magazine’s Note: The press release above was provided by anthouse.pet to us. Robotic Magazine do not necessarily endorse any kickstarter campaigns. We publish relevant kickstarter campaigns at the request of the project owners, for free, to support development of robotics.

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#428626 Cimcorp to fully automate Turkish Tire ...

Cimcorp Selected to Supply Turnkey Automated Handling System to Large Turkish Tire Manufacturer, Petlas
The leading tire handling specialist’s system will handle tires in the tire-finishing and palletizing areas in Turkish manufacturer’s expanded facility
Ulvila, Finland – November 9, 2016 – Cimcorp, leading global supplier of turnkey automation for intralogistics and tire-handling solutions, announces it has been selected to implement a fully automated handling system in Petlas Tire Corporation’s (Petlas) factory in Kirsehir, Turkey. Based on Cimcorp’s Dream Factory solution, the automation will take care of the handling of passenger car radial (PCR) finished tires in the tire-finishing and palletizing areas. Work on the order is already underway and the’ turnkey material handling system will become fully operational in fall 2017.
The order, Cimcorp’s first project for Petlas, is part of a huge investment program to expand the Kirsehir plant in order to increase Petlas’ PCR production capacity and meet growing demand.
Turkey achieved record car production and export levels in 2015, with production up by 16 percent and exports up 12 percent over the preceding year. This growth rate is higher than in any other European country and, with its automotive plants rolling out 1.36 million vehicles in 2015, Turkey is now the seventh largest automotive producer in Europe.
With the production equipment – the tire-building machines, presses and testing machines – already installed, Petlas is commencing the automation of the plant’s material handling. This comprises Cimcorp’s robotic buffer stores, tire conveyors and control software – Cimcorp WCS (Warehouse Control Software) – to take care of all material flows. Using linear robots operating on overhead gantries, the system will automate the handling and transfer of finished tires from the trimming stations, through visual inspection and uniformity testing, to palletizing.
Yahya Ertem, general manager, Petlas Tire Corporation, said, “We think highly of Cimcorp’s software, which integrates the machines into one entity and keeps the flow of material and data under complete control. Cimcorp’s Dream Factory solution fits with our vision to achieve ‘excellence in business’ and will help us to achieve our strategic goals.”
Tero Peltomäki, vice president of sales and projects, Cimcorp, said, “It has been fantastic to work with the Petlas team, honing our design into the best possible solution for the Kirsehir plant. The automation will help Petlas to enhance its market position as a leading tire manufacturer and distributor and we look forward to working on future automation projects with the company.”
To receive high-resolution images, please send requests to Heidi Scott via email at: lasendio@dprgroup.com

About Cimcorp
Cimcorp Group – part of Murata Machinery, Ltd. (Muratec) – is a leading global supplier of turnkey automation for intralogistics, using advanced robotics and software technologies. As well as being a manufacturer and integrator of pioneering material handling systems for the tire industry, Cimcorp has developed unique robotic solutions for order fulfillment and storage that are being used in the food & beverage, retail, e-commerce, FMCG and postal services sectors. With locations in Finland, Canada and the United States, the group has around 300 employees and has delivered over 2,000 logistics automation solutions. Designed to reduce operating costs, ensure traceability and improve efficiency, these systems are used within manufacturing and distribution centers in 40 countries across five continents. For more information, visit www.cimcorp.com.
About Petlas Tire Corporation (Petlas)
Founded in 1976, Petlas Tire Corporation has operations in 98 countries worldwide and employs 2,150 people. The company’s plant in Kirsehir currently has the capacity to produce 8 million PCR (passenger car radial) tires, 2 million agricultural tires, 500,000 TBR (truck & bus radial) tires and 300,000 OTR (off-the-road) tires per year. For more information, visit www.petlas.com.

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