Tag Archives: neural networks

Artificial intelligence (AI) with ability to look inward performs better

An August 31, 2022 news item on ScienceDaily highlights the power of an introspective AI,

An artificial intelligence with the ability to look inward and fine tune its own neural network performs better when it chooses diversity over lack of diversity, a new study finds. The resulting diverse neural networks were particularly effective at solving complex tasks.

“We created a test system with a non-human intelligence, an artificial intelligence (AI), to see if the AI would choose diversity over the lack of diversity and if its choice would improve the performance of the AI,” says William Ditto, professor of physics at North Carolina State University, director of NC State’s Nonlinear Artificial Intelligence Laboratory (NAIL) and co-corresponding author of the work. “The key was giving the AI the ability to look inward and learn how it learns.”

An August 31, 2023 North Carolina State University (NCSU) news release (also on EurekAlert), describes how an AI can become ‘introspective’ and employ neural ‘diversity’, Note: A link has been removed,

Neural networks are an advanced type of AI loosely based on the way that our brains work. Our natural neurons exchange electrical impulses according to the strengths of their connections. Artificial neural networks create similarly strong connections by adjusting numerical weights and biases during training sessions. For example, a neural network can be trained to identify photos of dogs by sifting through a large number of photos, making a guess about whether the photo is of a dog, seeing how far off it is and then adjusting its weights and biases until they are closer to reality.

Conventional AI uses neural networks to solve problems, but these networks are typically composed of large numbers of identical artificial neurons. The number and strength of connections between those identical neurons may change as it learns, but once the network is optimized, those static neurons are the network.

Ditto’s team, on the other hand, gave its AI the ability to choose the number, shape and connection strength between neurons in its neural network, creating sub-networks of different neuron types and connection strengths within the network as it learns.

“Our real brains have more than one type of neuron,” Ditto says. “So we gave our AI the ability to look inward and decide whether it needed to modify the composition of its neural network. Essentially, we gave it the control knob for its own brain. So it can solve the problem, look at the result, and change the type and mixture of artificial neurons until it finds the most advantageous one. It’s meta-learning for AI.

“Our AI could also decide between diverse or homogenous neurons,” Ditto says. “And we found that in every instance the AI chose diversity as a way to strengthen its performance.”

The team tested the AI’s accuracy by asking it to perform a standard numerical classifying exercise, and saw that its accuracy increased as the number of neurons and neuronal diversity increased. A standard, homogenous AI could identify the numbers with 57% accuracy, while the meta-learning, diverse AI was able to reach 70% accuracy.

According to Ditto, the diversity-based AI is up to 10 times more accurate than conventional AI in solving more complicated problems, such as predicting a pendulum’s swing or the motion of galaxies.

“We have shown that if you give an AI the ability to look inward and learn how it learns it will change its internal structure – the structure of its artificial neurons – to embrace diversity and improve its ability to learn and solve problems efficiently and more accurately,” Ditto says. “Indeed, we also observed that as the problems become more complex and chaotic the performance improves even more dramatically over an AI that does not embrace diversity.”

The research appears in Scientific Reports, and was supported by the Office of Naval Research (under grant N00014-16-1-3066) and by United Therapeutics. Former post-doctoral researcher Anshul Choudhary is first author. John Lindner, visiting professor and emeritus professor of physics at the College of Wooster, NC State graduate student Anil Radhakrishnan and Sudeshna Sinha, professor of physics at the Indian Institute of Science Education and Research Mohali, also contributed to the work.

Here’s a link to and a citation for the paper,

Neuronal diversity can improve machine learning for physics and beyond by Anshul Choudhary, Anil Radhakrishnan, John F. Lindner, Sudeshna Sinha & William L. Ditto. Scientific Reports volume 13, Article number: 13962 (2023) DOI: https://doi.org/10.1038/s41598-023-40766-6 Published: 26 August 2023

This paper is open access.

Trying to understand how neural networks work

It seems no one (not even the experts) really understands how ‘artificial intelligence that learns’ actually works. This is the second time I’ve stumbled onto a similar statement made by experts. Last time (see my July 28, 2022 posting [part 1 of 2] and scroll down to the “A deep dive into AI?” subhead for a quote from a recent Council of Canadian Academies report on AI for Science and Engineering) they were unable to gave a stable definition for artificial intelligence.

Researchers at Los Alamos are looking at new ways to compare neural networks. This image was created with an artificial intelligence software called Stable Diffusion, using the prompt “Peeking into the black box of neural networks.” Courtesy: Los Alamos National Laboratorory

A September 13, 2022 Los Alamos National Laboratory (LANL) news release (also on EurekAlert) provides detail about how researchers are addressing the problem,

A team at Los Alamos National Laboratory has developed a novel approach for comparing neural networks that looks within the “black box” of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving cars.

“The artificial intelligence research community doesn’t necessarily have a complete understanding of what neural networks are doing; they give us good results, but we don’t know how or why,” said Haydn Jones, a researcher in the Advanced Research in Cyber Systems group at Los Alamos. “Our new method does a better job of comparing neural networks, which is a crucial step toward better understanding the mathematics behind AI.”

Jones is the lead author of the paper “If You’ve Trained One You’ve Trained Them All: Inter-Architecture Similarity Increases With Robustness,” which was presented recently at the Conference on Uncertainty in Artificial Intelligence. In addition to studying network similarity, the paper is a crucial step toward characterizing the behavior of robust neural networks.

Neural networks are high performance, but fragile. For example, self-driving cars use neural networks to detect signs. When conditions are ideal, they do this quite well. However, the smallest aberration — such as a sticker on a stop sign — can cause the neural network to misidentify the sign and never stop.

To improve neural networks, researchers are looking at ways to improve network robustness. One state-of-the-art approach involves “attacking” networks during their training process. Researchers intentionally introduce aberrations and train the AI to ignore them. This process is called adversarial training and essentially makes it harder to fool the networks.

Jones, Los Alamos collaborators Jacob Springer and Garrett Kenyon, and Jones’ mentor Juston Moore, applied their new metric of network similarity to adversarially trained neural networks, and found, surprisingly, that adversarial training causes neural networks in the computer vision domain to converge to very similar data representations, regardless of network architecture, as the magnitude of the attack increases.

“We found that when we train neural networks to be robust against adversarial attacks, they begin to do the same things,” Jones said.

There has been extensive effort in industry and in the academic community searching for the “right architecture” for neural networks, but the Los Alamos team’s findings indicate that the introduction of adversarial training narrows this search space substantially. As a result, the AI research community may not need to spend as much time exploring new architectures, knowing that adversarial training causes diverse architectures to converge to similar solutions.

“By finding that robust neural networks are similar to each other, we’re making it easier to understand how robust AI might really work. We might even be uncovering hints as to how perception occurs in humans and other animals,” Jones said.

Should you be curious about future events, the Association for Uncertainty in Artificial Intelligence (AUAI), a non-profit, organizes an annual conference.

Energy-efficient artificial synapse

This is the second neuromorphic computing chip story from MIT this summer in what has turned out to be a bumper crop of research announcements in this field. The first MIT synapse story was featured in a June 16, 2020 posting. Now, there’s a second and completely different team announcing results for their artificial brain synapse work in a June 19, 2020 news item on Nanowerk (Note: A link has been removed),

Teams around the world are building ever more sophisticated artificial intelligence systems of a type called neural networks, designed in some ways to mimic the wiring of the brain, for carrying out tasks such as computer vision and natural language processing.

Using state-of-the-art semiconductor circuits to simulate neural networks requires large amounts of memory and high power consumption. Now, an MIT [Massachusetts Institute of Technology] team has made strides toward an alternative system, which uses physical, analog devices that can much more efficiently mimic brain processes.

The findings are described in the journal Nature Communications (“Protonic solid-state electrochemical synapse for physical neural networks”), in a paper by MIT professors Bilge Yildiz, Ju Li, and Jesús del Alamo, and nine others at MIT and Brookhaven National Laboratory. The first author of the paper is Xiahui Yao, a former MIT postdoc now working on energy storage at GRU Energy Lab.

That description of the work is one pretty much every team working on developing memristive (neuromorphic) chips could use.

On other fronts, the team has produced a very attractive illustration accompanying this research (aside: Is it my imagination or has there been a serious investment in the colour pink and other pastels for science illustrations?),

A new system developed at MIT and Brookhaven National Lab could provide a faster, more reliable and much more energy efficient approach to physical neural networks, by using analog ionic-electronic devices to mimic synapses.. Courtesy of the researchers

A June 19, 2020 MIT news release, which originated the news item, provides more insight into this specific piece of research (hint: it’s about energy use and repeatability),

Neural networks attempt to simulate the way learning takes place in the brain, which is based on the gradual strengthening or weakening of the connections between neurons, known as synapses. The core component of this physical neural network is the resistive switch, whose electronic conductance can be controlled electrically. This control, or modulation, emulates the strengthening and weakening of synapses in the brain.

In neural networks using conventional silicon microchip technology, the simulation of these synapses is a very energy-intensive process. To improve efficiency and enable more ambitious neural network goals, researchers in recent years have been exploring a number of physical devices that could more directly mimic the way synapses gradually strengthen and weaken during learning and forgetting.

Most candidate analog resistive devices so far for such simulated synapses have either been very inefficient, in terms of energy use, or performed inconsistently from one device to another or one cycle to the next. The new system, the researchers say, overcomes both of these challenges. “We’re addressing not only the energy challenge, but also the repeatability-related challenge that is pervasive in some of the existing concepts out there,” says Yildiz, who is a professor of nuclear science and engineering and of materials science and engineering.

“I think the bottleneck today for building [neural network] applications is energy efficiency. It just takes too much energy to train these systems, particularly for applications on the edge, like autonomous cars,” says del Alamo, who is the Donner Professor in the Department of Electrical Engineering and Computer Science. Many such demanding applications are simply not feasible with today’s technology, he adds.

The resistive switch in this work is an electrochemical device, which is made of tungsten trioxide (WO3) and works in a way similar to the charging and discharging of batteries. Ions, in this case protons, can migrate into or out of the crystalline lattice of the material,  explains Yildiz, depending on the polarity and strength of an applied voltage. These changes remain in place until altered by a reverse applied voltage — just as the strengthening or weakening of synapses does.

The mechanism is similar to the doping of semiconductors,” says Li, who is also a professor of nuclear science and engineering and of materials science and engineering. In that process, the conductivity of silicon can be changed by many orders of magnitude by introducing foreign ions into the silicon lattice. “Traditionally those ions were implanted at the factory,” he says, but with the new device, the ions are pumped in and out of the lattice in a dynamic, ongoing process. The researchers can control how much of the “dopant” ions go in or out by controlling the voltage, and “we’ve demonstrated a very good repeatability and energy efficiency,” he says.

Yildiz adds that this process is “very similar to how the synapses of the biological brain work. There, we’re not working with protons, but with other ions such as calcium, potassium, magnesium, etc., and by moving those ions you actually change the resistance of the synapses, and that is an element of learning.” The process taking place in the tungsten trioxide in their device is similar to the resistance modulation taking place in biological synapses, she says.

“What we have demonstrated here,” Yildiz says, “even though it’s not an optimized device, gets to the order of energy consumption per unit area per unit change in conductance that’s close to that in the brain.” Trying to accomplish the same task with conventional CMOS type semiconductors would take a million times more energy, she says.

The materials used in the demonstration of the new device were chosen for their compatibility with present semiconductor manufacturing systems, according to Li. But they include a polymer material that limits the device’s tolerance for heat, so the team is still searching for other variations of the device’s proton-conducting membrane and better ways of encapsulating its hydrogen source for long-term operations.

“There’s a lot of fundamental research to be done at the materials level for this device,” Yildiz says. Ongoing research will include “work on how to integrate these devices with existing CMOS transistors” adds del Alamo. “All that takes time,” he says, “and it presents tremendous opportunities for innovation, great opportunities for our students to launch their careers.”

Coincidentally or not a University of Massachusetts at Amherst team announced memristor voltage use comparable to human brain voltage use (see my June 15, 2020 posting), plus, there’s a team at Stanford University touting their low-energy biohybrid synapse in a XXX posting. (June 2020 has been a particularly busy month here for ‘artificial brain’ or ‘memristor’ stories.)

Getting back to this latest MIT research, here’s a link to and a citation for the paper,

Protonic solid-state electrochemical synapse for physical neural networks by Xiahui Yao, Konstantin Klyukin, Wenjie Lu, Murat Onen, Seungchan Ryu, Dongha Kim, Nicolas Emond, Iradwikanari Waluyo, Adrian Hunt, Jesús A. del Alamo, Ju Li & Bilge Yildiz. Nature Communications volume 11, Article number: 3134 (2020) DOI: https://doi.org/10.1038/s41467-020-16866-6 Published: 19 June 2020

This paper is open access.

Artificial synapse courtesy of nanowires

It looks like a popsicle to me,

Caption: Image captured by an electron microscope of a single nanowire memristor (highlighted in colour to distinguish it from other nanowires in the background image). Blue: silver electrode, orange: nanowire, yellow: platinum electrode. Blue bubbles are dispersed over the nanowire. They are made up of silver ions and form a bridge between the electrodes which increases the resistance. Credit: Forschungszentrum Jülich

Not a popsicle but a representation of a device (memristor) scientists claim mimics a biological nerve cell according to a December 5, 2018 news item on ScienceDaily,

Scientists from Jülich [Germany] together with colleagues from Aachen [Germany] and Turin [Italy] have produced a memristive element made from nanowires that functions in much the same way as a biological nerve cell. The component is able to both save and process information, as well as receive numerous signals in parallel. The resistive switching cell made from oxide crystal nanowires is thus proving to be the ideal candidate for use in building bioinspired “neuromorphic” processors, able to take over the diverse functions of biological synapses and neurons.

A Dec. 5, 2018 Forschungszentrum Jülich press release (also on EurekAlert), which originated the news item, provides more details,

Computers have learned a lot in recent years. Thanks to rapid progress in artificial intelligence they are now able to drive cars, translate texts, defeat world champions at chess, and much more besides. In doing so, one of the greatest challenges lies in the attempt to artificially reproduce the signal processing in the human brain. In neural networks, data are stored and processed to a high degree in parallel. Traditional computers on the other hand rapidly work through tasks in succession and clearly distinguish between the storing and processing of information. As a rule, neural networks can only be simulated in a very cumbersome and inefficient way using conventional hardware.

Systems with neuromorphic chips that imitate the way the human brain works offer significant advantages. Experts in the field describe this type of bioinspired computer as being able to work in a decentralised way, having at its disposal a multitude of processors, which, like neurons in the brain, are connected to each other by networks. If a processor breaks down, another can take over its function. What is more, just like in the brain, where practice leads to improved signal transfer, a bioinspired processor should have the capacity to learn.

“With today’s semiconductor technology, these functions are to some extent already achievable. These systems are however suitable for particular applications and require a lot of space and energy,” says Dr. Ilia Valov from Forschungszentrum Jülich. “Our nanowire devices made from zinc oxide crystals can inherently process and even store information, as well as being extremely small and energy efficient,” explains the researcher from Jülich’s Peter Grünberg Institute.

For years memristive cells have been ascribed the best chances of being capable of taking over the function of neurons and synapses in bioinspired computers. They alter their electrical resistance depending on the intensity and direction of the electric current flowing through them. In contrast to conventional transistors, their last resistance value remains intact even when the electric current is switched off. Memristors are thus fundamentally capable of learning.

In order to create these properties, scientists at Forschungszentrum Jülich and RWTH Aachen University used a single zinc oxide nanowire, produced by their colleagues from the polytechnic university in Turin. Measuring approximately one ten-thousandth of a millimeter in size, this type of nanowire is over a thousand times thinner than a human hair. The resulting memristive component not only takes up a tiny amount of space, but also is able to switch much faster than flash memory.

Nanowires offer promising novel physical properties compared to other solids and are used among other things in the development of new types of solar cells, sensors, batteries and computer chips. Their manufacture is comparatively simple. Nanowires result from the evaporation deposition of specified materials onto a suitable substrate, where they practically grow of their own accord.

In order to create a functioning cell, both ends of the nanowire must be attached to suitable metals, in this case platinum and silver. The metals function as electrodes, and in addition, release ions triggered by an appropriate electric current. The metal ions are able to spread over the surface of the wire and build a bridge to alter its conductivity.

Components made from single nanowires are, however, still too isolated to be of practical use in chips. Consequently, the next step being planned by the Jülich and Turin researchers is to produce and study a memristive element, composed of a larger, relatively easy to generate group of several hundred nanowires offering more exciting functionalities.

The Italians have also written about the work in a December 4, 2018 news item for the Polytecnico di Torino’s inhouse magazine, PoliFlash’. I like the image they’ve used better as it offers a bit more detail and looks less like a popsicle. First, the image,

Courtesy: Polytecnico di Torino

Now, the news item, which includes some historical information about the memristor (Note: There is some repetition and links have been removed),

Emulating and understanding the human brain is one of the most important challenges for modern technology: on the one hand, the ability to artificially reproduce the processing of brain signals is one of the cornerstones for the development of artificial intelligence, while on the other the understanding of the cognitive processes at the base of the human mind is still far away.

And the research published in the prestigious journal Nature Communications by Gianluca Milano and Carlo Ricciardi, PhD student and professor, respectively, of the Applied Science and Technology Department of the Politecnico di Torino, represents a step forward in these directions. In fact, the study entitled “Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities” shows how it is possible to artificially emulate the activity of synapses, i.e. the connections between neurons that regulate the learning processes in our brain, in a single “nanowire” with a diameter thousands of times smaller than that of a hair.

It is a crystalline nanowire that takes the “memristor”, the electronic device able to artificially reproduce the functions of biological synapses, to a more performing level. Thanks to the use of nanotechnologies, which allow the manipulation of matter at the atomic level, it was for the first time possible to combine into one single device the synaptic functions that were individually emulated through specific devices. For this reason, the nanowire allows an extreme miniaturisation of the “memristor”, significantly reducing the complexity and energy consumption of the electronic circuits necessary for the implementation of learning algorithms.

Starting from the theorisation of the “memristor” in 1971 by Prof. Leon Chua – now visiting professor at the Politecnico di Torino, who was conferred an honorary degree by the University in 2015 – this new technology will not only allow smaller and more performing devices to be created for the implementation of increasingly “intelligent” computers, but is also a significant step forward for the emulation and understanding of the functioning of the brain.

“The nanowire memristor – said Carlo Ricciardirepresents a model system for the study of physical and electrochemical phenomena that govern biological synapses at the nanoscale. The work is the result of the collaboration between our research team and the RWTH University of Aachen in Germany, supported by INRiM, the National Institute of Metrological Research, and IIT, the Italian Institute of Technology.”

h.t for the Italian info. to Nanowerk’s Dec. 10, 2018 news item.

Here’s a link to and a citation for the paper,

Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities by Gianluca Milano, Michael Luebben, Zheng Ma, Rafal Dunin-Borkowski, Luca Boarino, Candido F. Pirri, Rainer Waser, Carlo Ricciardi, & Ilia Valov. Nature Communicationsvolume 9, Article number: 5151 (2018) DOI: https://doi.org/10.1038/s41467-018-07330-7 Published: 04 December 2018

This paper is open access.

Just use the search term “memristor” in the blog search engine if you’re curious about the multitudinous number of postings on the topic here.

Injectable bandages for internal bleeding and hydrogel for the brain

This injectable bandage could be a gamechanger (as they say) if it can be taken beyond the ‘in vitro’ (i.e., petri dish) testing stage. A May 22, 2018 news item on Nanowerk makes the announcement (Note: A link has been removed),

While several products are available to quickly seal surface wounds, rapidly stopping fatal internal bleeding has proven more difficult. Now researchers from the Department of Biomedical Engineering at Texas A&M University are developing an injectable hydrogel bandage that could save lives in emergencies such as penetrating shrapnel wounds on the battlefield (Acta Biomaterialia, “Nanoengineered injectable hydrogels for wound healing application”).

A May 22, 2018 US National Institute of Biomedical Engineering and Bioengiineering news release, which originated the news item, provides more detail (Note: Links have been removed),

The researchers combined a hydrogel base (a water-swollen polymer) and nanoparticles that interact with the body’s natural blood-clotting mechanism. “The hydrogel expands to rapidly fill puncture wounds and stop blood loss,” explained Akhilesh Gaharwar, Ph.D., assistant professor and senior investigator on the work. “The surface of the nanoparticles attracts blood platelets that become activated and start the natural clotting cascade of the body.”

Enhanced clotting when the nanoparticles were added to the hydrogel was confirmed by standard laboratory blood clotting tests. Clotting time was reduced from eight minutes to six minutes when the hydrogel was introduced into the mixture. When nanoparticles were added, clotting time was significantly reduced, to less than three minutes.

In addition to the rapid clotting mechanism of the hydrogel composite, the engineers took advantage of special properties of the nanoparticle component. They found they could use the electric charge of the nanoparticles to add growth factors that efficiently adhered to the particles. “Stopping fatal bleeding rapidly was the goal of our work,” said Gaharwar. “However, we found that we could attach growth factors to the nanoparticles. This was an added bonus because the growth factors act to begin the body’s natural wound healing process—the next step needed after bleeding has stopped.”

The researchers were able to attach vascular endothelial growth factor (VEGF) to the nanoparticles. They tested the hydrogel/nanoparticle/VEGF combination in a cell culture test that mimics the wound healing process. The test uses a petri dish with a layer of endothelial cells on the surface that create a solid skin-like sheet. The sheet is then scratched down the center creating a rip or hole in the sheet that resembles a wound.

When the hydrogel containing VEGF bound to the nanoparticles was added to the damaged endothelial cell wound, the cells were induced to grow back and fill-in the scratched region—essentially mimicking the healing of a wound.

“Our laboratory experiments have verified the effectiveness of the hydrogel for initiating both blood clotting and wound healing,” said Gaharwar. “We are anxious to begin tests in animals with the hope of testing and eventual use in humans where we believe our formulation has great potential to have a significant impact on saving lives in critical situations.”

The work was funded by grant EB023454 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and the National Science Foundation. The results were reported in the February issue of the journal Acta Biomaterialia.

The paper was published back in April 2018 and there was an April 2, 2018 Texas A&M University news release on EurekAlert making the announcement (and providing a few unique details),

A penetrating injury from shrapnel is a serious obstacle in overcoming battlefield wounds that can ultimately lead to death.Given the high mortality rates due to hemorrhaging, there is an unmet need to quickly self-administer materials that prevent fatality due to excessive blood loss.

With a gelling agent commonly used in preparing pastries, researchers from the Inspired Nanomaterials and Tissue Engineering Laboratory have successfully fabricated an injectable bandage to stop bleeding and promote wound healing.

In a recent article “Nanoengineered Injectable Hydrogels for Wound Healing Application” published in Acta Biomaterialia, Dr. Akhilesh K. Gaharwar, assistant professor in the Department of Biomedical Engineering at Texas A&M University, uses kappa-carrageenan and nanosilicates to form injectable hydrogels to promote hemostasis (the process to stop bleeding) and facilitate wound healing via a controlled release of therapeutics.

“Injectable hydrogels are promising materials for achieving hemostasis in case of internal injuries and bleeding, as these biomaterials can be introduced into a wound site using minimally invasive approaches,” said Gaharwar. “An ideal injectable bandage should solidify after injection in the wound area and promote a natural clotting cascade. In addition, the injectable bandage should initiate wound healing response after achieving hemostasis.”

The study uses a commonly used thickening agent known as kappa-carrageenan, obtained from seaweed, to design injectable hydrogels. Hydrogels are a 3-D water swollen polymer network, similar to Jell-O, simulating the structure of human tissues.

When kappa-carrageenan is mixed with clay-based nanoparticles, injectable gelatin is obtained. The charged characteristics of clay-based nanoparticles provide hemostatic ability to the hydrogels. Specifically, plasma protein and platelets form blood adsorption on the gel surface and trigger a blood clotting cascade.

“Interestingly, we also found that these injectable bandages can show a prolonged release of therapeutics that can be used to heal the wound” said Giriraj Lokhande, a graduate student in Gaharwar’s lab and first author of the paper. “The negative surface charge of nanoparticles enabled electrostatic interactions with therapeutics thus resulting in the slow release of therapeutics.”

Nanoparticles that promote blood clotting and wound healing (red discs), attached to the wound-filling hydrogel component (black) form a nanocomposite hydrogel. The gel is designed to be self-administered to stop bleeding and begin wound-healing in emergency situations. Credit: Lokhande, et al. 1

Here’s a link to and a citation for the paper,

Nanoengineered injectable hydrogels for wound healing application by Giriraj Lokhande, James K. Carrow, Teena Thakur, Janet R. Xavier, Madasamy Parani, Kayla J. Bayless, Akhilesh K. Gaharwar. Acta Biomaterialia Volume 70, 1 April 2018, Pages 35-47
https://doi.org/10.1016/j.actbio.2018.01.045

This paper is behind a paywall.

Hydrogel and the brain

It’s been an interesting week for hydrogels. On May 21, 2018 there was a news item on ScienceDaily about a bioengineered hydrogel which stimulated brain tissue growth after a stroke (mouse model),

In a first-of-its-kind finding, a new stroke-healing gel helped regrow neurons and blood vessels in mice with stroke-damaged brains, UCLA researchers report in the May 21 issue of Nature Materials.

“We tested this in laboratory mice to determine if it would repair the brain in a model of stroke, and lead to recovery,” said Dr. S. Thomas Carmichael, Professor and Chair of neurology at UCLA. “This study indicated that new brain tissue can be regenerated in what was previously just an inactive brain scar after stroke.”

The brain has a limited capacity for recovery after stroke and other diseases. Unlike some other organs in the body, such as the liver or skin, the brain does not regenerate new connections, blood vessels or new tissue structures. Tissue that dies in the brain from stroke is absorbed, leaving a cavity, devoid of blood vessels, neurons or axons, the thin nerve fibers that project from neurons.

After 16 weeks, stroke cavities in mice contained regenerated brain tissue, including new neural networks — a result that had not been seen before. The mice with new neurons showed improved motor behavior, though the exact mechanism wasn’t clear.

Remarkable stuff.

Machine learning, neural networks, and knitting

In a recent (Tuesday, March 6, 2018) live stream ‘conversation’ (‘Science in Canada; Investing in Canadian Innovation’ now published on YouTube) between Canadian Prime Minister, Justin Trudeau, and US science communicator, Bill Nye, at the University of Ottawa, they discussed, amongst many other topics, what AI (artificial intelligence) can and can’t do. They seemed to agree that AI can’t be creative, i.e., write poetry, create works of art, make jokes, etc. A conclusion which is both (in my opinion) true and not true.

There are times when I think the joke may be on us (humans). Take for example this March 6, 2018 story by Alexis Madrigal for The Atlantic magazine (Note: Links have been removed),

SkyKnit: How an AI Took Over an Adult Knitting Community

Ribald knitters teamed up with a neural-network creator to generate new types of tentacled, cozy shapes.

Janelle Shane is a humorist [Note: She describes herself as a “Research Scientist in optics. Plays with neural networks. …” in her Twitter bio.] who creates and mines her material from neural networks, the form of machine learning that has come to dominate the field of artificial intelligence over the last half-decade.

Perhaps you’ve seen the candy-heart slogans she generated for Valentine’s Day: DEAR ME, MY MY, LOVE BOT, CUTE KISS, MY BEAR, and LOVE BUN.

Or her new paint-color names: Parp Green, Shy Bather, Farty Red, and Bull Cream.

Or her neural-net-generated Halloween costumes: Punk Tree, Disco Monster, Spartan Gandalf, Starfleet Shark, and A Masked Box.

Her latest project, still ongoing, pushes the joke into a new, physical realm. Prodded by a knitter on the knitting forum Ravelry, Shane trained a type of neural network on a series of over 500 sets of knitting instructions. Then, she generated new instructions, which members of the Ravelry community have actually attempted to knit.

“The knitting project has been a particularly fun one so far just because it ended up being a dialogue between this computer program and these knitters that went over my head in a lot of ways,” Shane told me. “The computer would spit out a whole bunch of instructions that I couldn’t read and the knitters would say, this is the funniest thing I’ve ever read.”

It appears that the project evolved,

The human-machine collaboration created configurations of yarn that you probably wouldn’t give to your in-laws for Christmas, but they were interesting. The user citikas was the first to post a try at one of the earliest patterns, “reverss shawl.” It was strange, but it did have some charisma.

Shane nicknamed the whole effort “Project Hilarious Disaster.” The community called it SkyKnit.

I’m not sure what’s meant by “community” as mentioned in the previous excerpt. Are we talking about humans only, AI only, or both humans and AI?

Here’s some of what underlies Skyknit (Note: Links have been removed),

The different networks all attempt to model the data they’ve been fed by tuning a vast, funky flowchart. After you’ve created a statistical model that describes your real data, you can also roll the dice and generate new, never-before-seen data of the same kind.

How this works—like, the math behind it—is very hard to visualize because values inside the model can have hundreds of dimensions and we are humble three-dimensional creatures moving through time. But as the neural-network enthusiast Robin Sloan puts it, “So what? It turns out imaginary spaces are useful even if you can’t, in fact, imagine them.”

Out of that ferment, a new kind of art has emerged. Its practitioners use neural networks not to attain practical results, but to see what’s lurking in the these vast, opaque systems. What did the machines learn about the world as they attempted to understand the data they’d been fed? Famously, Google released DeepDream, which produced trippy visualizations that also demonstrated how that type of neural network processed the textures and objects in its source imagery.

Madrigal’s article is well worth reading if you have the time. You can also supplement Madrigal’s piece with an August 9, 2017 article about Janelle Shane’s algorithmic experiments by Jacob Brogan for slate.com.

I found some SkyKnit examples on Ravelry including this one from the Dollybird Workshop,

© Chatelaine

SkyKnit fancy addite rifopshent
by SkyKnit
Published in
Dollybird Workshop
SkyKnit
Craft
Knitting
Category
Stitch pattern
Published
February 2018
Suggested yarn
Yarn weight
Fingering (14 wpi) ?
Gauge
24 stitches and 30 rows = 4 inches
in stockinette stitch
Needle size
US 4 – 3.5 mm

written-pattern

This pattern is available as a free Ravelry download

SkyKnit is a type of machine learning algorithm called an artificial neural network. Its creator, Janelle Shane of AIweirdness.com, gave it 88,000 lines of knitting instructions from Stitch-Maps.com and Ravelry, and it taught itself how to make new patterns. Join the discussion!

SkyKnit seems to have created something that has paralell columns, and is reversible. Perhaps a scarf?

Test-knitting & image courtesy of Chatelaine

Patterns may include notes from testknitters; yarn, needles, and gauge are totally at your discretion.

About the designer
SkyKnit’s favorites include lace, tentacles, and totally not the elimination of the human race.
For more information, see: http://aiweirdness.com/

Shane’s website, aiweirdness.com, is where she posts musings such as this (from a March 2, [?] 2018 posting), Note: A link has been removed,

If you’ve been on the internet today, you’ve probably interacted with a neural network. They’re a type of machine learning algorithm that’s used for everything from language translation to finance modeling. One of their specialties is image recognition. Several companies – including Google, Microsoft, IBM, and Facebook – have their own algorithms for labeling photos. But image recognition algorithms can make really bizarre mistakes.

image

Microsoft Azure’s computer vision API [application programming interface] added the above caption and tags. But there are no sheep in the image of above. None. I zoomed all the way in and inspected every speck.

….

I have become quite interested in Shane’s self descriptions such as this one from the aiweirdness.com website,

Portrait/Logo

About

I train neural networks, a type of machine learning algorithm, to write unintentional humor as they struggle to imitate human datasets. Well, I intend the humor. The neural networks are just doing their best to understand what’s going on. Currently located on the occupied land of the Arapahoe Nation.
https://wandering.shop/@janellecshane

As for the joke being on us, I can’t help remembering the Facebook bots that developed their own language (Facebotlish), and were featured in my June 30, 2017 posting, There’s a certain eerieness to it all, which seems an appropriate response in a year celebrating the 200th anniversary of Mary Shelley’s 1818 book, Frankenstein; or, the Modern Prometheus. I’m closing with a video clip from the 1931 movie,

Happy Weekend!

Paving the way for hardware neural networks?

I’m glad the Imperial College of London (ICL; UK) translated this research into something I can, more or less, understand because the research team’s title for their paper would have left me ‘confuzzled’ .Thank you for this November 20, 2017 ICL press release (also on EurekAlert) by Hayley Dunning,

Researchers have shown how to write any magnetic pattern desired onto nanowires, which could help computers mimic how the brain processes information.

Much current computer hardware, such as hard drives, use magnetic memory devices. These rely on magnetic states – the direction microscopic magnets are pointing – to encode and read information.

Exotic magnetic states – such as a point where three south poles meet – represent complex systems. These may act in a similar way to many complex systems found in nature, such as the way our brains process information.

Computing systems that are designed to process information in similar ways to our brains are known as ‘neural networks’. There are already powerful software-based neural networks – for example one recently beat the human champion at the game ‘Go’ – but their efficiency is limited as they run on conventional computer hardware.

Now, researchers from Imperial College London have devised a method for writing magnetic information in any pattern desired, using a very small magnetic probe called a magnetic force microscope.

With this new writing method, arrays of magnetic nanowires may be able to function as hardware neural networks – potentially more powerful and efficient than software-based approaches.

The team, from the Departments of Physics and Materials at Imperial, demonstrated their system by writing patterns that have never been seen before. They published their results today [November 20, 2017] in Nature Nanotechnology.

Interlocking hexagon patterns with complex magnetisation

‘Hexagonal artificial spin ice ground state’ – a pattern never demonstrated before. Coloured arrows show north or south polarisation

Dr Jack Gartside, first author from the Department of Physics, said: “With this new writing method, we open up research into ‘training’ these magnetic nanowires to solve useful problems. If successful, this will bring hardware neural networks a step closer to reality.”

As well as applications in computing, the method could be used to study fundamental aspects of complex systems, by creating magnetic states that are far from optimal (such as three south poles together) and seeing how the system responds.

Here’s a link to and a citation for the paper,

Realization of ground state in artificial kagome spin ice via topological defect-driven magnetic writing by Jack C. Gartside, Daan M. Arroo, David M. Burn, Victoria L. Bemmer, Andy Moskalenko, Lesley F. Cohen & Will R. Branford. Nature Nanotechnology (2017) doi:10.1038/s41565-017-0002-1 Published online: 20 November 2017

This paper is behind a paywall.

*Odd spacing eliminated and a properly embedded video added on February 6, 2018 at 18:16 hours PT.

Brain stuff: quantum entanglement and a multi-dimensional universe

I have two brain news bits, one about neural networks and quantum entanglement and another about how the brain operates in* more than three dimensions.

Quantum entanglement and neural networks

A June 13, 2017 news item on phys.org describes how machine learning can be used to solve problems in physics (Note: Links have been removed),

Machine learning, the field that’s driving a revolution in artificial intelligence, has cemented its role in modern technology. Its tools and techniques have led to rapid improvements in everything from self-driving cars and speech recognition to the digital mastery of an ancient board game.

Now, physicists are beginning to use machine learning tools to tackle a different kind of problem, one at the heart of quantum physics. In a paper published recently in Physical Review X, researchers from JQI [Joint Quantum Institute] and the Condensed Matter Theory Center (CMTC) at the University of Maryland showed that certain neural networks—abstract webs that pass information from node to node like neurons in the brain—can succinctly describe wide swathes of quantum systems.

An artist’s rendering of a neural network with two layers. At the top is a real quantum system, like atoms in an optical lattice. Below is a network of hidden neurons that capture their interactions (Credit: E. Edwards/JQI)

A June 12, 2017 JQI news release by Chris Cesare, which originated the news item, describes how neural networks can represent quantum entanglement,

Dongling Deng, a JQI Postdoctoral Fellow who is a member of CMTC and the paper’s first author, says that researchers who use computers to study quantum systems might benefit from the simple descriptions that neural networks provide. “If we want to numerically tackle some quantum problem,” Deng says, “we first need to find an efficient representation.”

On paper and, more importantly, on computers, physicists have many ways of representing quantum systems. Typically these representations comprise lists of numbers describing the likelihood that a system will be found in different quantum states. But it becomes difficult to extract properties or predictions from a digital description as the number of quantum particles grows, and the prevailing wisdom has been that entanglement—an exotic quantum connection between particles—plays a key role in thwarting simple representations.

The neural networks used by Deng and his collaborators—CMTC Director and JQI Fellow Sankar Das Sarma and Fudan University physicist and former JQI Postdoctoral Fellow Xiaopeng Li—can efficiently represent quantum systems that harbor lots of entanglement, a surprising improvement over prior methods.

What’s more, the new results go beyond mere representation. “This research is unique in that it does not just provide an efficient representation of highly entangled quantum states,” Das Sarma says. “It is a new way of solving intractable, interacting quantum many-body problems that uses machine learning tools to find exact solutions.”

Neural networks and their accompanying learning techniques powered AlphaGo, the computer program that beat some of the world’s best Go players last year (link is external) (and the top player this year (link is external)). The news excited Deng, an avid fan of the board game. Last year, around the same time as AlphaGo’s triumphs, a paper appeared that introduced the idea of using neural networks to represent quantum states (link is external), although it gave no indication of exactly how wide the tool’s reach might be. “We immediately recognized that this should be a very important paper,” Deng says, “so we put all our energy and time into studying the problem more.”

The result was a more complete account of the capabilities of certain neural networks to represent quantum states. In particular, the team studied neural networks that use two distinct groups of neurons. The first group, called the visible neurons, represents real quantum particles, like atoms in an optical lattice or ions in a chain. To account for interactions between particles, the researchers employed a second group of neurons—the hidden neurons—which link up with visible neurons. These links capture the physical interactions between real particles, and as long as the number of connections stays relatively small, the neural network description remains simple.

Specifying a number for each connection and mathematically forgetting the hidden neurons can produce a compact representation of many interesting quantum states, including states with topological characteristics and some with surprising amounts of entanglement.

Beyond its potential as a tool in numerical simulations, the new framework allowed Deng and collaborators to prove some mathematical facts about the families of quantum states represented by neural networks. For instance, neural networks with only short-range interactions—those in which each hidden neuron is only connected to a small cluster of visible neurons—have a strict limit on their total entanglement. This technical result, known as an area law, is a research pursuit of many condensed matter physicists.

These neural networks can’t capture everything, though. “They are a very restricted regime,” Deng says, adding that they don’t offer an efficient universal representation. If they did, they could be used to simulate a quantum computer with an ordinary computer, something physicists and computer scientists think is very unlikely. Still, the collection of states that they do represent efficiently, and the overlap of that collection with other representation methods, is an open problem that Deng says is ripe for further exploration.

Here’s a link to and a citation for the paper,

Quantum Entanglement in Neural Network States by Dong-Ling Deng, Xiaopeng Li, and S. Das Sarma. Phys. Rev. X 7, 021021 – Published 11 May 2017

This paper is open access.

Blue Brain and the multidimensional universe

Blue Brain is a Swiss government brain research initiative which officially came to life in 2006 although the initial agreement between the École Politechnique Fédérale de Lausanne (EPFL) and IBM was signed in 2005 (according to the project’s Timeline page). Moving on, the project’s latest research reveals something astounding (from a June 12, 2017 Frontiers Publishing press release on EurekAlert),

For most people, it is a stretch of the imagination to understand the world in four dimensions but a new study has discovered structures in the brain with up to eleven dimensions – ground-breaking work that is beginning to reveal the brain’s deepest architectural secrets.

Using algebraic topology in a way that it has never been used before in neuroscience, a team from the Blue Brain Project has uncovered a universe of multi-dimensional geometrical structures and spaces within the networks of the brain.

The research, published today in Frontiers in Computational Neuroscience, shows that these structures arise when a group of neurons forms a clique: each neuron connects to every other neuron in the group in a very specific way that generates a precise geometric object. The more neurons there are in a clique, the higher the dimension of the geometric object.

“We found a world that we had never imagined,” says neuroscientist Henry Markram, director of Blue Brain Project and professor at the EPFL in Lausanne, Switzerland, “there are tens of millions of these objects even in a small speck of the brain, up through seven dimensions. In some networks, we even found structures with up to eleven dimensions.”

Markram suggests this may explain why it has been so hard to understand the brain. “The mathematics usually applied to study networks cannot detect the high-dimensional structures and spaces that we now see clearly.”

If 4D worlds stretch our imagination, worlds with 5, 6 or more dimensions are too complex for most of us to comprehend. This is where algebraic topology comes in: a branch of mathematics that can describe systems with any number of dimensions. The mathematicians who brought algebraic topology to the study of brain networks in the Blue Brain Project were Kathryn Hess from EPFL and Ran Levi from Aberdeen University.

“Algebraic topology is like a telescope and microscope at the same time. It can zoom into networks to find hidden structures – the trees in the forest – and see the empty spaces – the clearings – all at the same time,” explains Hess.

In 2015, Blue Brain published the first digital copy of a piece of the neocortex – the most evolved part of the brain and the seat of our sensations, actions, and consciousness. In this latest research, using algebraic topology, multiple tests were performed on the virtual brain tissue to show that the multi-dimensional brain structures discovered could never be produced by chance. Experiments were then performed on real brain tissue in the Blue Brain’s wet lab in Lausanne confirming that the earlier discoveries in the virtual tissue are biologically relevant and also suggesting that the brain constantly rewires during development to build a network with as many high-dimensional structures as possible.

When the researchers presented the virtual brain tissue with a stimulus, cliques of progressively higher dimensions assembled momentarily to enclose high-dimensional holes, that the researchers refer to as cavities. “The appearance of high-dimensional cavities when the brain is processing information means that the neurons in the network react to stimuli in an extremely organized manner,” says Levi. “It is as if the brain reacts to a stimulus by building then razing a tower of multi-dimensional blocks, starting with rods (1D), then planks (2D), then cubes (3D), and then more complex geometries with 4D, 5D, etc. The progression of activity through the brain resembles a multi-dimensional sandcastle that materializes out of the sand and then disintegrates.”

The big question these researchers are asking now is whether the intricacy of tasks we can perform depends on the complexity of the multi-dimensional “sandcastles” the brain can build. Neuroscience has also been struggling to find where the brain stores its memories. “They may be ‘hiding’ in high-dimensional cavities,” Markram speculates.

###

About Blue Brain

The aim of the Blue Brain Project, a Swiss brain initiative founded and directed by Professor Henry Markram, is to build accurate, biologically detailed digital reconstructions and simulations of the rodent brain, and ultimately, the human brain. The supercomputer-based reconstructions and simulations built by Blue Brain offer a radically new approach for understanding the multilevel structure and function of the brain. http://bluebrain.epfl.ch

About Frontiers

Frontiers is a leading community-driven open-access publisher. By taking publishing entirely online, we drive innovation with new technologies to make peer review more efficient and transparent. We provide impact metrics for articles and researchers, and merge open access publishing with a research network platform – Loop – to catalyse research dissemination, and popularize research to the public, including children. Our goal is to increase the reach and impact of research articles and their authors. Frontiers has received the ALPSP Gold Award for Innovation in Publishing in 2014. http://www.frontiersin.org.

Here’s a link to and a citation for the paper,

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function by Michael W. Reimann, Max Nolte, Martina Scolamiero, Katharine Turner, Rodrigo Perin, Giuseppe Chindemi, Paweł Dłotko, Ran Levi, Kathryn Hess, and Henry Markram. Front. Comput. Neurosci., 12 June 2017 | https://doi.org/10.3389/fncom.2017.00048

This paper is open access.

*Feb. 3, 2021: ‘on’ changed to ‘in’

Dr. Wei Lu and bio-inspired ‘memristor’ chips

It’s been a while since I’ve featured Dr. Wei Lu’s work here. This April  15, 2010 posting features Lu’s most relevant previous work.) Here’s his latest ‘memristor’ work , from a May 22, 2017 news item on Nanowerk (Note: A link has been removed),

Inspired by how mammals see, a new “memristor” computer circuit prototype at the University of Michigan has the potential to process complex data, such as images and video orders of magnitude, faster and with much less power than today’s most advanced systems.

Faster image processing could have big implications for autonomous systems such as self-driving cars, says Wei Lu, U-M professor of electrical engineering and computer science. Lu is lead author of a paper on the work published in the current issue of Nature Nanotechnology (“Sparse coding with memristor networks”).

Lu’s next-generation computer components use pattern recognition to shortcut the energy-intensive process conventional systems use to dissect images. In this new work, he and his colleagues demonstrate an algorithm that relies on a technique called “sparse coding” to coax their 32-by-32 array of memristors to efficiently analyze and recreate several photos.

A May 22, 2017 University of Michigan news release (also on EurekAlert), which originated the news item, provides more information about memristors and about the research,

Memristors are electrical resistors with memory—advanced electronic devices that regulate current based on the history of the voltages applied to them. They can store and process data simultaneously, which makes them a lot more efficient than traditional systems. In a conventional computer, logic and memory functions are located at different parts of the circuit.

“The tasks we ask of today’s computers have grown in complexity,” Lu said. “In this ‘big data’ era, computers require costly, constant and slow communications between their processor and memory to retrieve large amounts data. This makes them large, expensive and power-hungry.”

But like neural networks in a biological brain, networks of memristors can perform many operations at the same time, without having to move data around. As a result, they could enable new platforms that process a vast number of signals in parallel and are capable of advanced machine learning. Memristors are good candidates for deep neural networks, a branch of machine learning, which trains computers to execute processes without being explicitly programmed to do so.

“We need our next-generation electronics to be able to quickly process complex data in a dynamic environment. You can’t just write a program to do that. Sometimes you don’t even have a pre-defined task,” Lu said. “To make our systems smarter, we need to find ways for them to process a lot of data more efficiently. Our approach to accomplish that is inspired by neuroscience.”

A mammal’s brain is able to generate sweeping, split-second impressions of what the eyes take in. One reason is because they can quickly recognize different arrangements of shapes. Humans do this using only a limited number of neurons that become active, Lu says. Both neuroscientists and computer scientists call the process “sparse coding.”

“When we take a look at a chair we will recognize it because its characteristics correspond to our stored mental picture of a chair,” Lu said. “Although not all chairs are the same and some may differ from a mental prototype that serves as a standard, each chair retains some of the key characteristics necessary for easy recognition. Basically, the object is correctly recognized the moment it is properly classified—when ‘stored’ in the appropriate category in our heads.”

Image of a memristor chip Image of a memristor chip Similarly, Lu’s electronic system is designed to detect the patterns very efficiently—and to use as few features as possible to describe the original input.

In our brains, different neurons recognize different patterns, Lu says.

“When we see an image, the neurons that recognize it will become more active,” he said. “The neurons will also compete with each other to naturally create an efficient representation. We’re implementing this approach in our electronic system.”

The researchers trained their system to learn a “dictionary” of images. Trained on a set of grayscale image patterns, their memristor network was able to reconstruct images of famous paintings and photos and other test patterns.

If their system can be scaled up, they expect to be able to process and analyze video in real time in a compact system that can be directly integrated with sensors or cameras.

The project is titled “Sparse Adaptive Local Learning for Sensing and Analytics.” Other collaborators are Zhengya Zhang and Michael Flynn of the U-M Department of Electrical Engineering and Computer Science, Garrett Kenyon of the Los Alamos National Lab and Christof Teuscher of Portland State University.

The work is part of a $6.9 million Unconventional Processing of Signals for Intelligent Data Exploitation project that aims to build a computer chip based on self-organizing, adaptive neural networks. It is funded by the [US] Defense Advanced Research Projects Agency [DARPA].

Here’s a link to and a citation for the paper,

Sparse coding with memristor networks by Patrick M. Sheridan, Fuxi Cai, Chao Du, Wen Ma, Zhengya Zhang, & Wei D. Lu. Nature Nanotechnology (2017) doi:10.1038/nnano.2017.83 Published online 22 May 2017

This paper is behind a paywall.

For the interested, there are a number of postings featuring memristors here (just use ‘memristor’ as your search term in the blog search engine). You might also want to check out ‘neuromorphic engineeering’ and ‘neuromorphic computing’ and ‘artificial brain’.

Emerging technology and the law

I have three news bits about legal issues that are arising as a consequence of emerging technologies.

Deep neural networks, art, and copyright

Caption: The rise of automated art opens new creative avenues, coupled with new problems for copyright protection. Credit: Provided by: Alexander Mordvintsev, Christopher Olah and Mike Tyka

Presumably this artwork is a demonstration of automated art although they never really do explain how in the news item/news release. An April 26, 2017 news item on ScienceDaily announces research into copyright and the latest in using neural networks to create art,

In 1968, sociologist Jean Baudrillard wrote on automatism that “contained within it is the dream of a dominated world […] that serves an inert and dreamy humanity.”

With the growing popularity of Deep Neural Networks (DNN’s), this dream is fast becoming a reality.

Dr. Jean-Marc Deltorn, researcher at the Centre d’études internationales de la propriété intellectuelle in Strasbourg, argues that we must remain a responsive and responsible force in this process of automation — not inert dominators. As he demonstrates in a recent Frontiers in Digital Humanities paper, the dream of automation demands a careful study of the legal problems linked to copyright.

An April 26, 2017 Frontiers (publishing) news release on EurekAlert, which originated the news item, describes the research in more detail,

For more than half a century, artists have looked to computational processes as a way of expanding their vision. DNN’s are the culmination of this cross-pollination: by learning to identify a complex number of patterns, they can generate new creations.

These systems are made up of complex algorithms modeled on the transmission of signals between neurons in the brain.

DNN creations rely in equal measure on human inputs and the non-human algorithmic networks that process them.

Inputs are fed into the system, which is layered. Each layer provides an opportunity for a more refined knowledge of the inputs (shape, color, lines). Neural networks compare actual outputs to expected ones, and correct the predictive error through repetition and optimization. They train their own pattern recognition, thereby optimizing their learning curve and producing increasingly accurate outputs.

The deeper the layers are, the higher the level of abstraction. The highest layers are able to identify the contents of a given input with reasonable accuracy, after extended periods of training.

Creation thus becomes increasingly automated through what Deltorn calls “the arcane traceries of deep architecture”. The results are sufficiently abstracted from their sources to produce original creations that have been exhibited in galleries, sold at auction and performed at concerts.

The originality of DNN’s is a combined product of technological automation on one hand, human inputs and decisions on the other.

DNN’s are gaining popularity. Various platforms (such as DeepDream) now allow internet users to generate their very own new creations . This popularization of the automation process calls for a comprehensive legal framework that ensures a creator’s economic and moral rights with regards to his work – copyright protection.

Form, originality and attribution are the three requirements for copyright. And while DNN creations satisfy the first of these three, the claim to originality and attribution will depend largely on a given country legislation and on the traceability of the human creator.

Legislation usually sets a low threshold to originality. As DNN creations could in theory be able to create an endless number of riffs on source materials, the uncurbed creation of original works could inflate the existing number of copyright protections.

Additionally, a small number of national copyright laws confers attribution to what UK legislation defines loosely as “the person by whom the arrangements necessary for the creation of the work are undertaken.” In the case of DNN’s, this could mean anybody from the programmer to the user of a DNN interface.

Combined with an overly supple take on originality, this view on attribution would further increase the number of copyrightable works.

The risk, in both cases, is that artists will be less willing to publish their own works, for fear of infringement of DNN copyright protections.

In order to promote creativity – one seminal aim of copyright protection – the issue must be limited to creations that manifest a personal voice “and not just the electric glint of a computational engine,” to quote Deltorn. A delicate act of discernment.

DNN’s promise new avenues of creative expression for artists – with potential caveats. Copyright protection – a “catalyst to creativity” – must be contained. Many of us gently bask in the glow of an increasingly automated form of technology. But if we want to safeguard the ineffable quality that defines much art, it might be a good idea to hone in more closely on the differences between the electric and the creative spark.

This research is and be will part of a broader Frontiers Research Topic collection of articles on Deep Learning and Digital Humanities.

Here’s a link to and a citation for the paper,

Deep Creations: Intellectual Property and the Automata by Jean-Marc Deltorn. Front. Digit. Humanit., 01 February 2017 | https://doi.org/10.3389/fdigh.2017.00003

This paper is open access.

Conference on governance of emerging technologies

I received an April 17, 2017 notice via email about this upcoming conference. Here’s more from the Fifth Annual Conference on Governance of Emerging Technologies: Law, Policy and Ethics webpage,

The Fifth Annual Conference on Governance of Emerging Technologies:

Law, Policy and Ethics held at the new

Beus Center for Law & Society in Phoenix, AZ

May 17-19, 2017!

Call for Abstracts – Now Closed

The conference will consist of plenary and session presentations and discussions on regulatory, governance, legal, policy, social and ethical aspects of emerging technologies, including (but not limited to) nanotechnology, synthetic biology, gene editing, biotechnology, genomics, personalized medicine, human enhancement technologies, telecommunications, information technologies, surveillance technologies, geoengineering, neuroscience, artificial intelligence, and robotics. The conference is premised on the belief that there is much to be learned and shared from and across the governance experience and proposals for these various emerging technologies.

Keynote Speakers:

Gillian HadfieldRichard L. and Antoinette Schamoi Kirtland Professor of Law and Professor of Economics USC [University of Southern California] Gould School of Law

Shobita Parthasarathy, Associate Professor of Public Policy and Women’s Studies, Director, Science, Technology, and Public Policy Program University of Michigan

Stuart Russell, Professor at [University of California] Berkeley, is a computer scientist known for his contributions to artificial intelligence

Craig Shank, Vice President for Corporate Standards Group in Microsoft’s Corporate, External and Legal Affairs (CELA)

Plenary Panels:

Innovation – Responsible and/or Permissionless

Ellen-Marie Forsberg, Senior Researcher/Research Manager at Oslo and Akershus University College of Applied Sciences

Adam Thierer, Senior Research Fellow with the Technology Policy Program at the Mercatus Center at George Mason University

Wendell Wallach, Consultant, ethicist, and scholar at Yale University’s Interdisciplinary Center for Bioethics

 Gene Drives, Trade and International Regulations

Greg Kaebnick, Director, Editorial Department; Editor, Hastings Center Report; Research Scholar, Hastings Center

Jennifer Kuzma, Goodnight-North Carolina GlaxoSmithKline Foundation Distinguished Professor in Social Sciences in the School of Public and International Affairs (SPIA) and co-director of the Genetic Engineering and Society (GES) Center at North Carolina State University

Andrew Maynard, Senior Sustainability Scholar, Julie Ann Wrigley Global Institute of Sustainability Director, Risk Innovation Lab, School for the Future of Innovation in Society Professor, School for the Future of Innovation in Society, Arizona State University

Gary Marchant, Regents’ Professor of Law, Professor of Law Faculty Director and Faculty Fellow, Center for Law, Science & Innovation, Arizona State University

Marc Saner, Inaugural Director of the Institute for Science, Society and Policy, and Associate Professor, University of Ottawa Department of Geography

Big Data

Anupam Chander, Martin Luther King, Jr. Professor of Law and Director, California International Law Center, UC Davis School of Law

Pilar Ossorio, Professor of Law and Bioethics, University of Wisconsin, School of Law and School of Medicine and Public Health; Morgridge Institute for Research, Ethics Scholar-in-Residence

George Poste, Chief Scientist, Complex Adaptive Systems Initiative (CASI) (http://www.casi.asu.edu/), Regents’ Professor and Del E. Webb Chair in Health Innovation, Arizona State University

Emily Shuckburgh, climate scientist and deputy head of the Polar Oceans Team at the British Antarctic Survey, University of Cambridge

 Responsible Development of AI

Spring Berman, Ira A. Fulton Schools of Engineering, Arizona State University

John Havens, The IEEE [Institute of Electrical and Electronics Engineers] Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems

Subbarao Kambhampati, Senior Sustainability Scientist, Julie Ann Wrigley Global Institute of Sustainability, Professor, School of Computing, Informatics and Decision Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University

Wendell Wallach, Consultant, Ethicist, and Scholar at Yale University’s Interdisciplinary Center for Bioethics

Existential and Catastrophic Ricks [sic]

Tony Barrett, Co-Founder and Director of Research of the Global Catastrophic Risk Institute

Haydn Belfield,  Academic Project Administrator, Centre for the Study of Existential Risk at the University of Cambridge

Margaret E. Kosal Associate Director, Sam Nunn School of International Affairs, Georgia Institute of Technology

Catherine Rhodes,  Academic Project Manager, Centre for the Study of Existential Risk at CSER, University of Cambridge

These were the panels that are of interest to me; there are others on the homepage.

Here’s some information from the Conference registration webpage,

Early Bird Registration – $50 off until May 1! Enter discount code: earlybirdGETs50

New: Group Discount – Register 2+ attendees together and receive an additional 20% off for all group members!

Click Here to Register!

Conference registration fees are as follows:

  • General (non-CLE) Registration: $150.00
  • CLE Registration: $350.00
  • *Current Student / ASU Law Alumni Registration: $50.00
  • ^Cybsersecurity sessions only (May 19): $100 CLE / $50 General / Free for students (registration info coming soon)

There you have it.

Neuro-techno future laws

I’m pretty sure this isn’t the first exploration of potential legal issues arising from research into neuroscience although it’s the first one I’ve stumbled across. From an April 25, 2017 news item on phys.org,

New human rights laws to prepare for advances in neurotechnology that put the ‘freedom of the mind’ at risk have been proposed today in the open access journal Life Sciences, Society and Policy.

The authors of the study suggest four new human rights laws could emerge in the near future to protect against exploitation and loss of privacy. The four laws are: the right to cognitive liberty, the right to mental privacy, the right to mental integrity and the right to psychological continuity.

An April 25, 2017 Biomed Central news release on EurekAlert, which originated the news item, describes the work in more detail,

Marcello Ienca, lead author and PhD student at the Institute for Biomedical Ethics at the University of Basel, said: “The mind is considered to be the last refuge of personal freedom and self-determination, but advances in neural engineering, brain imaging and neurotechnology put the freedom of the mind at risk. Our proposed laws would give people the right to refuse coercive and invasive neurotechnology, protect the privacy of data collected by neurotechnology, and protect the physical and psychological aspects of the mind from damage by the misuse of neurotechnology.”

Advances in neurotechnology, such as sophisticated brain imaging and the development of brain-computer interfaces, have led to these technologies moving away from a clinical setting and into the consumer domain. While these advances may be beneficial for individuals and society, there is a risk that the technology could be misused and create unprecedented threats to personal freedom.

Professor Roberto Andorno, co-author of the research, explained: “Brain imaging technology has already reached a point where there is discussion over its legitimacy in criminal court, for example as a tool for assessing criminal responsibility or even the risk of reoffending. Consumer companies are using brain imaging for ‘neuromarketing’, to understand consumer behaviour and elicit desired responses from customers. There are also tools such as ‘brain decoders’ which can turn brain imaging data into images, text or sound. All of these could pose a threat to personal freedom which we sought to address with the development of four new human rights laws.”

The authors explain that as neurotechnology improves and becomes commonplace, there is a risk that the technology could be hacked, allowing a third-party to ‘eavesdrop’ on someone’s mind. In the future, a brain-computer interface used to control consumer technology could put the user at risk of physical and psychological damage caused by a third-party attack on the technology. There are also ethical and legal concerns over the protection of data generated by these devices that need to be considered.

International human rights laws make no specific mention to neuroscience, although advances in biomedicine have become intertwined with laws, such as those concerning human genetic data. Similar to the historical trajectory of the genetic revolution, the authors state that the on-going neurorevolution will force a reconceptualization of human rights laws and even the creation of new ones.

Marcello Ienca added: “Science-fiction can teach us a lot about the potential threat of technology. Neurotechnology featured in famous stories has in some cases already become a reality, while others are inching ever closer, or exist as military and commercial prototypes. We need to be prepared to deal with the impact these technologies will have on our personal freedom.”

Here’s a link to and a citation for the paper,

Towards new human rights in the age of neuroscience and neurotechnology by Marcello Ienca and Roberto Andorno. Life Sciences, Society and Policy201713:5 DOI: 10.1186/s40504-017-0050-1 Published: 26 April 2017

©  The Author(s). 2017

This paper is open access.