Tag Archives: neural networks

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 on 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.

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.

Vector Institute and Canada’s artificial intelligence sector

On the heels of the March 22, 2017 federal budget announcement of $125M for a Pan-Canadian Artificial Intelligence Strategy, the University of Toronto (U of T) has announced the inception of the Vector Institute for Artificial Intelligence in a March 28, 2017 news release by Jennifer Robinson (Note: Links have been removed),

A team of globally renowned researchers at the University of Toronto is driving the planning of a new institute staking Toronto’s and Canada’s claim as the global leader in AI.

Geoffrey Hinton, a University Professor Emeritus in computer science at U of T and vice-president engineering fellow at Google, will serve as the chief scientific adviser of the newly created Vector Institute based in downtown Toronto.

“The University of Toronto has long been considered a global leader in artificial intelligence research,” said U of T President Meric Gertler. “It’s wonderful to see that expertise act as an anchor to bring together researchers, government and private sector actors through the Vector Institute, enabling them to aim even higher in leading advancements in this fast-growing, critical field.”

As part of the Government of Canada’s Pan-Canadian Artificial Intelligence Strategy, Vector will share $125 million in federal funding with fellow institutes in Montreal and Edmonton. All three will conduct research and secure talent to cement Canada’s position as a world leader in AI.

In addition, Vector is expected to receive funding from the Province of Ontario and more than 30 top Canadian and global companies eager to tap this pool of talent to grow their businesses. The institute will also work closely with other Ontario universities with AI talent.

(See my March 24, 2017 posting; scroll down about 25% for the science part, including the Pan-Canadian Artificial Intelligence Strategy of the budget.)

Not obvious in last week’s coverage of the Pan-Canadian Artificial Intelligence Strategy is that the much lauded Hinton has been living in the US and working for Google. These latest announcements (Pan-Canadian AI Strategy and Vector Institute) mean that he’s moving back.

A March 28, 2017 article by Kate Allen for TorontoStar.com provides more details about the Vector Institute, Hinton, and the Canadian ‘brain drain’ as it applies to artificial intelligence, (Note:  A link has been removed)

Toronto will host a new institute devoted to artificial intelligence, a major gambit to bolster a field of research pioneered in Canada but consistently drained of talent by major U.S. technology companies like Google, Facebook and Microsoft.

The Vector Institute, an independent non-profit affiliated with the University of Toronto, will hire about 25 new faculty and research scientists. It will be backed by more than $150 million in public and corporate funding in an unusual hybridization of pure research and business-minded commercial goals.

The province will spend $50 million over five years, while the federal government, which announced a $125-million Pan-Canadian Artificial Intelligence Strategy in last week’s budget, is providing at least $40 million, backers say. More than two dozen companies have committed millions more over 10 years, including $5 million each from sponsors including Google, Air Canada, Loblaws, and Canada’s five biggest banks [Bank of Montreal (BMO). Canadian Imperial Bank of Commerce ({CIBC} President’s Choice Financial},  Royal Bank of Canada (RBC), Scotiabank (Tangerine), Toronto-Dominion Bank (TD Canada Trust)].

The mode of artificial intelligence that the Vector Institute will focus on, deep learning, has seen remarkable results in recent years, particularly in image and speech recognition. Geoffrey Hinton, considered the “godfather” of deep learning for the breakthroughs he made while a professor at U of T, has worked for Google since 2013 in California and Toronto.

Hinton will move back to Canada to lead a research team based at the tech giant’s Toronto offices and act as chief scientific adviser of the new institute.

Researchers trained in Canadian artificial intelligence labs fill the ranks of major technology companies, working on tools like instant language translation, facial recognition, and recommendation services. Academic institutions and startups in Toronto, Waterloo, Montreal and Edmonton boast leaders in the field, but other researchers have left for U.S. universities and corporate labs.

The goals of the Vector Institute are to retain, repatriate and attract AI talent, to create more trained experts, and to feed that expertise into existing Canadian companies and startups.

Hospitals are expected to be a major partner, since health care is an intriguing application for AI. Last month, researchers from Stanford University announced they had trained a deep learning algorithm to identify potentially cancerous skin lesions with accuracy comparable to human dermatologists. The Toronto company Deep Genomics is using deep learning to read genomes and identify mutations that may lead to disease, among other things.

Intelligent algorithms can also be applied to tasks that might seem less virtuous, like reading private data to better target advertising. Zemel [Richard Zemel, the institute’s research director and a professor of computer science at U of T] says the centre is creating an ethics working group [emphasis mine] and maintaining ties with organizations that promote fairness and transparency in machine learning. As for privacy concerns, “that’s something we are well aware of. We don’t have a well-formed policy yet but we will fairly soon.”

The institute’s annual funding pales in comparison to the revenues of the American tech giants, which are measured in tens of billions. The risk the institute’s backers are taking is simply creating an even more robust machine learning PhD mill for the U.S.

“They obviously won’t all stay in Canada, but Toronto industry is very keen to get them,” Hinton said. “I think Trump might help there.” Two researchers on Hinton’s new Toronto-based team are Iranian, one of the countries targeted by U.S. President Donald Trump’s travel bans.

Ethics do seem to be a bit of an afterthought. Presumably the Vector Institute’s ‘ethics working group’ won’t include any regular folks. Is there any thought to what the rest of us think about these developments? As there will also be some collaboration with other proposed AI institutes including ones at the University of Montreal (Université de Montréal) and the University of Alberta (Kate McGillivray’s article coming up shortly mentions them), might the ethics group be centered in either Edmonton or Montreal? Interestingly, two Canadians (Timothy Caulfield at the University of Alberta and Eric Racine at Université de Montréa) testified at the US Commission for the Study of Bioethical Issues Feb. 10 – 11, 2014 meeting, the Brain research, ethics, and nanotechnology. Still speculating here but I imagine Caulfield and/or Racine could be persuaded to extend their expertise in ethics and the human brain to AI and its neural networks.

Getting back to the topic at hand the ‘AI sceneCanada’, Allen’s article is worth reading in its entirety if you have the time.

Kate McGillivray’s March 29, 2017 article for the Canadian Broadcasting Corporation’s (CBC) news online provides more details about the Canadian AI situation and the new strategies,

With artificial intelligence set to transform our world, a new institute is putting Toronto to the front of the line to lead the charge.

The Vector Institute for Artificial Intelligence, made possible by funding from the federal government revealed in the 2017 budget, will move into new digs in the MaRS Discovery District by the end of the year.

Vector’s funding comes partially from a $125 million investment announced in last Wednesday’s federal budget to launch a pan-Canadian artificial intelligence strategy, with similar institutes being established in Montreal and Edmonton.

“[A.I.] cuts across pretty well every sector of the economy,” said Dr. Alan Bernstein, CEO and president of the Canadian Institute for Advanced Research, the organization tasked with administering the federal program.

“Silicon Valley and England and other places really jumped on it, so we kind of lost the lead a little bit. I think the Canadian federal government has now realized that,” he said.

Stopping up the brain drain

Critical to the strategy’s success is building a homegrown base of A.I. experts and innovators — a problem in the last decade, despite pioneering work on so-called “Deep Learning” by Canadian scholars such as Yoshua Bengio and Geoffrey Hinton, a former University of Toronto professor who will now serve as Vector’s chief scientific advisor.

With few university faculty positions in Canada and with many innovative companies headquartered elsewhere, it has been tough to keep the few graduates specializing in A.I. in town.

“We were paying to educate people and shipping them south,” explained Ed Clark, chair of the Vector Institute and business advisor to Ontario Premier Kathleen Wynne.

The existence of that “fantastic science” will lean heavily on how much buy-in Vector and Canada’s other two A.I. centres get.

Toronto’s portion of the $125 million is a “great start,” said Bernstein, but taken alone, “it’s not enough money.”

“My estimate of the right amount of money to make a difference is a half a billion or so, and I think we will get there,” he said.

Jessica Murphy’s March 29, 2017 article for the British Broadcasting Corporation’s (BBC) news online offers some intriguing detail about the Canadian AI scene,

Canadian researchers have been behind some recent major breakthroughs in artificial intelligence. Now, the country is betting on becoming a big player in one of the hottest fields in technology, with help from the likes of Google and RBC [Royal Bank of Canada].

In an unassuming building on the University of Toronto’s downtown campus, Geoff Hinton laboured for years on the “lunatic fringe” of academia and artificial intelligence, pursuing research in an area of AI called neural networks.

Also known as “deep learning”, neural networks are computer programs that learn in similar way to human brains. The field showed early promise in the 1980s, but the tech sector turned its attention to other AI methods after that promise seemed slow to develop.

“The approaches that I thought were silly were in the ascendancy and the approach that I thought was the right approach was regarded as silly,” says the British-born [emphasis mine] professor, who splits his time between the university and Google, where he is a vice-president of engineering fellow.

Neural networks are used by the likes of Netflix to recommend what you should binge watch and smartphones with voice assistance tools. Google DeepMind’s AlphaGo AI used them to win against a human in the ancient game of Go in 2016.

Foteini Agrafioti, who heads up the new RBC Research in Machine Learning lab at the University of Toronto, said those recent innovations made AI attractive to researchers and the tech industry.

“Anything that’s powering Google’s engines right now is powered by deep learning,” she says.

Developments in the field helped jumpstart innovation and paved the way for the technology’s commercialisation. They also captured the attention of Google, IBM and Microsoft, and kicked off a hiring race in the field.

The renewed focus on neural networks has boosted the careers of early Canadian AI machine learning pioneers like Hinton, the University of Montreal’s Yoshua Bengio, and University of Alberta’s Richard Sutton.

Money from big tech is coming north, along with investments by domestic corporations like banking multinational RBC and auto parts giant Magna, and millions of dollars in government funding.

Former banking executive Ed Clark will head the institute, and says the goal is to make Toronto, which has the largest concentration of AI-related industries in Canada, one of the top five places in the world for AI innovation and business.

The founders also want it to serve as a magnet and retention tool for top talent aggressively head-hunted by US firms.

Clark says they want to “wake up” Canadian industry to the possibilities of AI, which is expected to have a massive impact on fields like healthcare, banking, manufacturing and transportation.

Google invested C$4.5m (US$3.4m/£2.7m) last November [2016] in the University of Montreal’s Montreal Institute for Learning Algorithms.

Microsoft is funding a Montreal startup, Element AI. The Seattle-based company also announced it would acquire Montreal-based Maluuba and help fund AI research at the University of Montreal and McGill University.

Thomson Reuters and General Motors both recently moved AI labs to Toronto.

RBC is also investing in the future of AI in Canada, including opening a machine learning lab headed by Agrafioti, co-funding a program to bring global AI talent and entrepreneurs to Toronto, and collaborating with Sutton and the University of Alberta’s Machine Intelligence Institute.

Canadian tech also sees the travel uncertainty created by the Trump administration in the US as making Canada more attractive to foreign talent. (One of Clark’s the selling points is that Toronto as an “open and diverse” city).

This may reverse the ‘brain drain’ but it appears Canada’s role as a ‘branch plant economy’ for foreign (usually US) companies could become an important discussion once more. From the ‘Foreign ownership of companies of Canada’ Wikipedia entry (Note: Links have been removed),

Historically, foreign ownership was a political issue in Canada in the late 1960s and early 1970s, when it was believed by some that U.S. investment had reached new heights (though its levels had actually remained stable for decades), and then in the 1980s, during debates over the Free Trade Agreement.

But the situation has changed, since in the interim period Canada itself became a major investor and owner of foreign corporations. Since the 1980s, Canada’s levels of investment and ownership in foreign companies have been larger than foreign investment and ownership in Canada. In some smaller countries, such as Montenegro, Canadian investment is sizable enough to make up a major portion of the economy. In Northern Ireland, for example, Canada is the largest foreign investor. By becoming foreign owners themselves, Canadians have become far less politically concerned about investment within Canada.

Of note is that Canada’s largest companies by value, and largest employers, tend to be foreign-owned in a way that is more typical of a developing nation than a G8 member. The best example is the automotive sector, one of Canada’s most important industries. It is dominated by American, German, and Japanese giants. Although this situation is not unique to Canada in the global context, it is unique among G-8 nations, and many other relatively small nations also have national automotive companies.

It’s interesting to note that sometimes Canadian companies are the big investors but that doesn’t change our basic position. And, as I’ve noted in other postings (including the March 24, 2017 posting), these government investments in science and technology won’t necessarily lead to a move away from our ‘branch plant economy’ towards an innovative Canada.

You can find out more about the Vector Institute for Artificial Intelligence here.

BTW, I noted that reference to Hinton as ‘British-born’ in the BBC article. He was educated in the UK and subsidized by UK taxpayers (from his Wikipedia entry; Note: Links have been removed),

Hinton was educated at King’s College, Cambridge graduating in 1970, with a Bachelor of Arts in experimental psychology.[1] He continued his study at the University of Edinburgh where he was awarded a PhD in artificial intelligence in 1977 for research supervised by H. Christopher Longuet-Higgins.[3][12]

It seems Canadians are not the only ones to experience  ‘brain drains’.

Finally, I wrote at length about a recent initiative taking place between the University of British Columbia (Vancouver, Canada) and the University of Washington (Seattle, Washington), the Cascadia Urban Analytics Cooperative in a Feb. 28, 2017 posting noting that the initiative is being funded by Microsoft to the tune $1M and is part of a larger cooperative effort between the province of British Columbia and the state of Washington. Artificial intelligence is not the only area where US technology companies are hedging their bets (against Trump’s administration which seems determined to terrify people from crossing US borders) by investing in Canada.

For anyone interested in a little more information about AI in the US and China, there’s today’s (March 31, 2017)earlier posting: China, US, and the race for artificial intelligence research domination.

The Canadian science scene and the 2017 Canadian federal budget

There’s not much happening in the 2017-18 budget in terms of new spending according to Paul Wells’ March 22, 2017 article for TheStar.com,

This is the 22nd or 23rd federal budget I’ve covered. And I’ve never seen the like of the one Bill Morneau introduced on Wednesday [March 22, 2017].

Not even in the last days of the Harper Conservatives did a budget provide for so little new spending — $1.3 billion in the current budget year, total, in all fields of government. That’s a little less than half of one per cent of all federal program spending for this year.

But times are tight. The future is a place where we can dream. So the dollars flow more freely in later years. In 2021-22, the budget’s fifth planning year, new spending peaks at $8.2 billion. Which will be about 2.4 per cent of all program spending.

He’s not alone in this 2017 federal budget analysis; CBC (Canadian Broadcasting Corporation) pundits, Chantal Hébert, Andrew Coyne, and Jennifer Ditchburn said much the same during their ‘At Issue’ segment of the March 22, 2017 broadcast of The National (news).

Before I focus on the science and technology budget, here are some general highlights from the CBC’s March 22, 2017 article on the 2017-18 budget announcement (Note: Links have been removed,

Here are highlights from the 2017 federal budget:

  • Deficit: $28.5 billion, up from $25.4 billion projected in the fall.
  • Trend: Deficits gradually decline over next five years — but still at $18.8 billion in 2021-22.
  • Housing: $11.2 billion over 11 years, already budgeted, will go to a national housing strategy.
  • Child care: $7 billion over 10 years, already budgeted, for new spaces, starting 2018-19.
  • Indigenous: $3.4 billion in new money over five years for infrastructure, health and education.
  • Defence: $8.4 billion in capital spending for equipment pushed forward to 2035.
  • Care givers: New care-giving benefit up to 15 weeks, starting next year.
  • Skills: New agency to research and measure skills development, starting 2018-19.
  • Innovation: $950 million over five years to support business-led “superclusters.”
  • Startups: $400 million over three years for a new venture capital catalyst initiative.
  • AI: $125 million to launch a pan-Canadian Artificial Intelligence Strategy.
  • Coding kids: $50 million over two years for initiatives to teach children to code.
  • Families: Option to extend parental leave up to 18 months.
  • Uber tax: GST to be collected on ride-sharing services.
  • Sin taxes: One cent more on a bottle of wine, five cents on 24 case of beer.
  • Bye-bye: No more Canada Savings Bonds.
  • Transit credit killed: 15 per cent non-refundable public transit tax credit phased out this year.

You can find the entire 2017-18 budget here.

Science and the 2017-18 budget

For anyone interested in the science news, you’ll find most of that in the 2017 budget’s Chapter 1 — Skills, Innovation and Middle Class jobs. As well, Wayne Kondro has written up a précis in his March 22, 2017 article for Science (magazine),

Finance officials, who speak on condition of anonymity during the budget lock-up, indicated the budgets of the granting councils, the main source of operational grants for university researchers, will be “static” until the government can assess recommendations that emerge from an expert panel formed in 2015 and headed by former University of Toronto President David Naylor to review basic science in Canada [highlighted in my June 15, 2016 posting ; $2M has been allocated for the advisor and associated secretariat]. Until then, the officials said, funding for the Natural Sciences and Engineering Research Council of Canada (NSERC) will remain at roughly $848 million, whereas that for the Canadian Institutes of Health Research (CIHR) will remain at $773 million, and for the Social Sciences and Humanities Research Council [SSHRC] at $547 million.

NSERC, though, will receive $8.1 million over 5 years to administer a PromoScience Program that introduces youth, particularly unrepresented groups like Aboriginal people and women, to science, technology, engineering, and mathematics through measures like “space camps and conservation projects.” CIHR, meanwhile, could receive modest amounts from separate plans to identify climate change health risks and to reduce drug and substance abuse, the officials added.

… Canada’s Innovation and Skills Plan, would funnel $600 million over 5 years allocated in 2016, and $112.5 million slated for public transit and green infrastructure, to create Silicon Valley–like “super clusters,” which the budget defined as “dense areas of business activity that contain large and small companies, post-secondary institutions and specialized talent and infrastructure.” …

… The Canadian Institute for Advanced Research will receive $93.7 million [emphasis mine] to “launch a Pan-Canadian Artificial Intelligence Strategy … (to) position Canada as a world-leading destination for companies seeking to invest in artificial intelligence and innovation.”

… Among more specific measures are vows to: Use $87.7 million in previous allocations to the Canada Research Chairs program to create 25 “Canada 150 Research Chairs” honoring the nation’s 150th year of existence, provide $1.5 million per year to support the operations of the office of the as-yet-unappointed national science adviser [see my Dec. 7, 2016 post for information about the job posting, which is now closed]; provide $165.7 million [emphasis mine] over 5 years for the nonprofit organization Mitacs to create roughly 6300 more co-op positions for university students and grads, and provide $60.7 million over five years for new Canadian Space Agency projects, particularly for Canadian participation in the National Aeronautics and Space Administration’s next Mars Orbiter Mission.

Kondros was either reading an earlier version of the budget or made an error regarding Mitacs (from the budget in the “A New, Ambitious Approach to Work-Integrated Learning” subsection),

Mitacs has set an ambitious goal of providing 10,000 work-integrated learning placements for Canadian post-secondary students and graduates each year—up from the current level of around 3,750 placements. Budget 2017 proposes to provide $221 million [emphasis mine] over five years, starting in 2017–18, to achieve this goal and provide relevant work experience to Canadian students.

As well, the budget item for the Pan-Canadian Artificial Intelligence Strategy is $125M.

Moving from Kondros’ précis, the budget (in the “Positioning National Research Council Canada Within the Innovation and Skills Plan” subsection) announces support for these specific areas of science,

Stem Cell Research

The Stem Cell Network, established in 2001, is a national not-for-profit organization that helps translate stem cell research into clinical applications, commercial products and public policy. Its research holds great promise, offering the potential for new therapies and medical treatments for respiratory and heart diseases, cancer, diabetes, spinal cord injury, multiple sclerosis, Crohn’s disease, auto-immune disorders and Parkinson’s disease. To support this important work, Budget 2017 proposes to provide the Stem Cell Network with renewed funding of $6 million in 2018–19.

Space Exploration

Canada has a long and proud history as a space-faring nation. As our international partners prepare to chart new missions, Budget 2017 proposes investments that will underscore Canada’s commitment to innovation and leadership in space. Budget 2017 proposes to provide $80.9 million on a cash basis over five years, starting in 2017–18, for new projects through the Canadian Space Agency that will demonstrate and utilize Canadian innovations in space, including in the field of quantum technology as well as for Mars surface observation. The latter project will enable Canada to join the National Aeronautics and Space Administration’s (NASA’s) next Mars Orbiter Mission.

Quantum Information

The development of new quantum technologies has the potential to transform markets, create new industries and produce leading-edge jobs. The Institute for Quantum Computing is a world-leading Canadian research facility that furthers our understanding of these innovative technologies. Budget 2017 proposes to provide the Institute with renewed funding of $10 million over two years, starting in 2017–18.

Social Innovation

Through community-college partnerships, the Community and College Social Innovation Fund fosters positive social outcomes, such as the integration of vulnerable populations into Canadian communities. Following the success of this pilot program, Budget 2017 proposes to invest $10 million over two years, starting in 2017–18, to continue this work.

International Research Collaborations

The Canadian Institute for Advanced Research (CIFAR) connects Canadian researchers with collaborative research networks led by eminent Canadian and international researchers on topics that touch all humanity. Past collaborations facilitated by CIFAR are credited with fostering Canada’s leadership in artificial intelligence and deep learning. Budget 2017 proposes to provide renewed and enhanced funding of $35 million over five years, starting in 2017–18.

Earlier this week, I highlighted Canada’s strength in the field of regenerative medicine, specifically stem cells in a March 21, 2017 posting. The $6M in the current budget doesn’t look like increased funding but rather a one-year extension. I’m sure they’re happy to receive it  but I imagine it’s a little hard to plan major research projects when you’re not sure how long your funding will last.

As for Canadian leadership in artificial intelligence, that was news to me. Here’s more from the budget,

Canada a Pioneer in Deep Learning in Machines and Brains

CIFAR’s Learning in Machines & Brains program has shaken up the field of artificial intelligence by pioneering a technique called “deep learning,” a computer technique inspired by the human brain and neural networks, which is now routinely used by the likes of Google and Facebook. The program brings together computer scientists, biologists, neuroscientists, psychologists and others, and the result is rich collaborations that have propelled artificial intelligence research forward. The program is co-directed by one of Canada’s foremost experts in artificial intelligence, the Université de Montréal’s Yoshua Bengio, and for his many contributions to the program, the University of Toronto’s Geoffrey Hinton, another Canadian leader in this field, was awarded the title of Distinguished Fellow by CIFAR in 2014.

Meanwhile, from chapter 1 of the budget in the subsection titled “Preparing for the Digital Economy,” there is this provision for children,

Providing educational opportunities for digital skills development to Canadian girls and boys—from kindergarten to grade 12—will give them the head start they need to find and keep good, well-paying, in-demand jobs. To help provide coding and digital skills education to more young Canadians, the Government intends to launch a competitive process through which digital skills training organizations can apply for funding. Budget 2017 proposes to provide $50 million over two years, starting in 2017–18, to support these teaching initiatives.

I wonder if BC Premier Christy Clark is heaving a sigh of relief. At the 2016 #BCTECH Summit, she announced that students in BC would learn to code at school and in newly enhanced coding camp programmes (see my Jan. 19, 2016 posting). Interestingly, there was no mention of additional funding to support her initiative. I guess this money from the federal government comes at a good time as we will have a provincial election later this spring where she can announce the initiative again and, this time, mention there’s money for it.

Attracting brains from afar

Ivan Semeniuk in his March 23, 2017 article (for the Globe and Mail) reads between the lines to analyze the budget’s possible impact on Canadian science,

But a between-the-lines reading of the budget document suggests the government also has another audience in mind: uneasy scientists from the United States and Britain.

The federal government showed its hand at the 2017 #BCTECH Summit. From a March 16, 2017 article by Meera Bains for the CBC news online,

At the B.C. tech summit, Navdeep Bains, Canada’s minister of innovation, said the government will act quickly to fast track work permits to attract highly skilled talent from other countries.

“We’re taking the processing time, which takes months, and reducing it to two weeks for immigration processing for individuals [who] need to come here to help companies grow and scale up,” Bains said.

“So this is a big deal. It’s a game changer.”

That change will happen through the Global Talent Stream, a new program under the federal government’s temporary foreign worker program.  It’s scheduled to begin on June 12, 2017.

U.S. companies are taking notice and a Canadian firm, True North, is offering to help them set up shop.

“What we suggest is that they think about moving their operations, or at least a chunk of their operations, to Vancouver, set up a Canadian subsidiary,” said the company’s founder, Michael Tippett.

“And that subsidiary would be able to house and accommodate those employees.”

Industry experts says while the future is unclear for the tech sector in the U.S., it’s clear high tech in B.C. is gearing up to take advantage.

US business attempts to take advantage of Canada’s relative stability and openness to immigration would seem to be the motive for at least one cross border initiative, the Cascadia Urban Analytics Cooperative. From my Feb. 28, 2017 posting,

There was some big news about the smallest version of the Cascadia region on Thursday, Feb. 23, 2017 when the University of British Columbia (UBC) , the University of Washington (state; UW), and Microsoft announced the launch of the Cascadia Urban Analytics Cooperative. From the joint Feb. 23, 2017 news release (read on the UBC website or read on the UW website),

In an expansion of regional cooperation, the University of British Columbia and the University of Washington today announced the establishment of the Cascadia Urban Analytics Cooperative to use data to help cities and communities address challenges from traffic to homelessness. The largest industry-funded research partnership between UBC and the UW, the collaborative will bring faculty, students and community stakeholders together to solve problems, and is made possible thanks to a $1-million gift from Microsoft.

Today’s announcement follows last September’s [2016] Emerging Cascadia Innovation Corridor Conference in Vancouver, B.C. The forum brought together regional leaders for the first time to identify concrete opportunities for partnerships in education, transportation, university research, human capital and other areas.

A Boston Consulting Group study unveiled at the conference showed the region between Seattle and Vancouver has “high potential to cultivate an innovation corridor” that competes on an international scale, but only if regional leaders work together. The study says that could be possible through sustained collaboration aided by an educated and skilled workforce, a vibrant network of research universities and a dynamic policy environment.

It gets better, it seems Microsoft has been positioning itself for a while if Matt Day’s analysis is correct (from my Feb. 28, 2017 posting),

Matt Day in a Feb. 23, 2017 article for the The Seattle Times provides additional perspective (Note: Links have been removed),

Microsoft’s effort to nudge Seattle and Vancouver, B.C., a bit closer together got an endorsement Thursday [Feb. 23, 2017] from the leading university in each city.

The partnership has its roots in a September [2016] conference in Vancouver organized by Microsoft’s public affairs and lobbying unit [emphasis mine.] That gathering was aimed at tying business, government and educational institutions in Microsoft’s home region in the Seattle area closer to its Canadian neighbor.

Microsoft last year [2016] opened an expanded office in downtown Vancouver with space for 750 employees, an outpost partly designed to draw to the Northwest more engineers than the company can get through the U.S. guest worker system [emphasis mine].

This was all prior to President Trump’s legislative moves in the US, which have at least one Canadian observer a little more gleeful than I’m comfortable with. From a March 21, 2017 article by Susan Lum  for CBC News online,

U.S. President Donald Trump’s efforts to limit travel into his country while simultaneously cutting money from science-based programs provides an opportunity for Canada’s science sector, says a leading Canadian researcher.

“This is Canada’s moment. I think it’s a time we should be bold,” said Alan Bernstein, president of CIFAR [which on March 22, 2017 was awarded $125M to launch the Pan Canada Artificial Intelligence Strategy in the Canadian federal budget announcement], a global research network that funds hundreds of scientists in 16 countries.

Bernstein believes there are many reasons why Canada has become increasingly attractive to scientists around the world, including the political climate in the United States and the Trump administration’s travel bans.

Thankfully, Bernstein calms down a bit,

“It used to be if you were a bright young person anywhere in the world, you would want to go to Harvard or Berkeley or Stanford, or what have you. Now I think you should give pause to that,” he said. “We have pretty good universities here [emphasis mine]. We speak English. We’re a welcoming society for immigrants.”​

Bernstein cautions that Canada should not be seen to be poaching scientists from the United States — but there is an opportunity.

“It’s as if we’ve been in a choir of an opera in the back of the stage and all of a sudden the stars all left the stage. And the audience is expecting us to sing an aria. So we should sing,” Bernstein said.

Bernstein said the federal government, with this week’s so-called innovation budget, can help Canada hit the right notes.

“Innovation is built on fundamental science, so I’m looking to see if the government is willing to support, in a big way, fundamental science in the country.”

Pretty good universities, eh? Thank you, Dr. Bernstein, for keeping some of the boosterism in check. Let’s leave the chest thumping to President Trump and his cronies.

Ivan Semeniuk’s March 23, 2017 article (for the Globe and Mail) provides more details about the situation in the US and in Britain,

Last week, Donald Trump’s first budget request made clear the U.S. President would significantly reduce or entirely eliminate research funding in areas such as climate science and renewable energy if permitted by Congress. Even the National Institutes of Health, which spearheads medical research in the United States and is historically supported across party lines, was unexpectedly targeted for a $6-billion (U.S.) cut that the White House said could be achieved through “efficiencies.”

In Britain, a recent survey found that 42 per cent of academics were considering leaving the country over worries about a less welcoming environment and the loss of research money that a split with the European Union is expected to bring.

In contrast, Canada’s upbeat language about science in the budget makes a not-so-subtle pitch for diversity and talent from abroad, including $117.6-million to establish 25 research chairs with the aim of attracting “top-tier international scholars.”

For good measure, the budget also includes funding for science promotion and $2-million annually for Canada’s yet-to-be-hired Chief Science Advisor, whose duties will include ensuring that government researchers can speak freely about their work.

“What we’ve been hearing over the last few months is that Canada is seen as a beacon, for its openness and for its commitment to science,” said Ms. Duncan [Kirsty Duncan, Minister of Science], who did not refer directly to either the United States or Britain in her comments.

Providing a less optimistic note, Erica Alini in her March 22, 2017 online article for Global News mentions a perennial problem, the Canadian brain drain,

The budget includes a slew of proposed reforms and boosted funding for existing training programs, as well as new skills-development resources for unemployed and underemployed Canadians not covered under current EI-funded programs.

There are initiatives to help women and indigenous people get degrees or training in science, technology, engineering and mathematics (the so-called STEM subjects) and even to teach kids as young as kindergarten-age to code.

But there was no mention of how to make sure Canadians with the right skills remain in Canada, TD’s DePratto {Toronto Dominion Bank} Economics; TD is currently experiencing a scandal {March 13, 2017 Huffington Post news item}] told Global News.

Canada ranks in the middle of the pack compared to other advanced economies when it comes to its share of its graduates in STEM fields, but the U.S. doesn’t shine either, said DePratto [Brian DePratto, senior economist at TD .

The key difference between Canada and the U.S. is the ability to retain domestic talent and attract brains from all over the world, he noted.

To be blunt, there may be some opportunities for Canadian science but it does well to remember (a) US businesses have no particular loyalty to Canada and (b) all it takes is an election to change any perceived advantages to disadvantages.

Digital policy and intellectual property issues

Dubbed by some as the ‘innovation’ budget (official title:  Building a Strong Middle Class), there is an attempt to address a longstanding innovation issue (from a March 22, 2017 posting by Michael Geist on his eponymous blog (Note: Links have been removed),

The release of today’s [march 22, 2017] federal budget is expected to include a significant emphasis on innovation, with the government revealing how it plans to spend (or re-allocate) hundreds of millions of dollars that is intended to support innovation. Canada’s dismal innovation record needs attention, but spending our way to a more innovative economy is unlikely to yield the desired results. While Navdeep Bains, the Innovation, Science and Economic Development Minister, has talked for months about the importance of innovation, Toronto Star columnist Paul Wells today delivers a cutting but accurate assessment of those efforts:

“This government is the first with a minister for innovation! He’s Navdeep Bains. He frequently posts photos of his meetings on Twitter, with the hashtag “#innovation.” That’s how you know there is innovation going on. A year and a half after he became the minister for #innovation, it’s not clear what Bains’s plans are. It’s pretty clear that within the government he has less than complete control over #innovation. There’s an advisory council on economic growth, chaired by the McKinsey guru Dominic Barton, which periodically reports to the government urging more #innovation.

There’s a science advisory panel, chaired by former University of Toronto president David Naylor, that delivered a report to Science Minister Kirsty Duncan more than three months ago. That report has vanished. One presumes that’s because it offered some advice. Whatever Bains proposes, it will have company.”

Wells is right. Bains has been very visible with plenty of meetings and public photo shoots but no obvious innovation policy direction. This represents a missed opportunity since Bains has plenty of policy tools at his disposal that could advance Canada’s innovation framework without focusing on government spending.

For example, Canada’s communications system – wireless and broadband Internet access – falls directly within his portfolio and is crucial for both business and consumers. Yet Bains has been largely missing in action on the file. He gave approval for the Bell – MTS merger that virtually everyone concedes will increase prices in the province and make the communications market less competitive. There are potential policy measures that could bring new competitors into the market (MVNOs [mobile virtual network operators] and municipal broadband) and that could make it easier for consumers to switch providers (ban on unlocking devices). Some of this falls to the CRTC, but government direction and emphasis would make a difference.

Even more troubling has been his near total invisibility on issues relating to new fees or taxes on Internet access and digital services. Canadian Heritage Minister Mélanie Joly has taken control of the issue with the possibility that Canadians could face increased costs for their Internet access or digital services through mandatory fees to contribute to Canadian content.  Leaving aside the policy objections to such an approach (reducing affordable access and the fact that foreign sources now contribute more toward Canadian English language TV production than Canadian broadcasters and distributors), Internet access and e-commerce are supposed to be Bains’ issue and they have a direct connection to the innovation file. How is it possible for the Innovation, Science and Economic Development Minister to have remained silent for months on the issue?

Bains has been largely missing on trade related innovation issues as well. My Globe and Mail column today focuses on a digital-era NAFTA, pointing to likely U.S. demands on data localization, data transfers, e-commerce rules, and net neutrality.  These are all issues that fall under Bains’ portfolio and will impact investment in Canadian networks and digital services. There are innovation opportunities for Canada here, but Bains has been content to leave the policy issues to others, who will be willing to sacrifice potential gains in those areas.

Intellectual property policy is yet another area that falls directly under Bains’ mandate with an obvious link to innovation, but he has done little on the file. Canada won a huge NAFTA victory late last week involving the Canadian patent system, which was challenged by pharmaceutical giant Eli Lilly. Why has Bains not promoted the decision as an affirmation of how Canada’s intellectual property rules?

On the copyright front, the government is scheduled to conduct a review of the Copyright Act later this year, but it is not clear whether Bains will take the lead or again cede responsibility to Joly. The Copyright Act is statutorily under the Industry Minister and reform offers the chance to kickstart innovation. …

For anyone who’s not familiar with this area, innovation is often code for commercialization of science and technology research efforts. These days, digital service and access policies and intellectual property policies are all key to research and innovation efforts.

The country that’s most often (except in mainstream Canadian news media) held up as an example of leadership in innovation is Estonia. The Economist profiled the country in a July 31, 2013 article and a July 7, 2016 article on apolitical.co provides and update.

Conclusions

Science monies for the tri-council science funding agencies (NSERC, SSHRC, and CIHR) are more or less flat but there were a number of line items in the federal budget which qualify as science funding. The $221M over five years for Mitacs, the $125M for the Pan-Canadian Artificial Intelligence Strategy, additional funding for the Canada research chairs, and some of the digital funding could also be included as part of the overall haul. This is in line with the former government’s (Stephen Harper’s Conservatives) penchant for keeping the tri-council’s budgets under control while spreading largesse elsewhere (notably the Perimeter Institute, TRIUMF [Canada’s National Laboratory for Particle and Nuclear Physics], and, in the 2015 budget, $243.5-million towards the Thirty Metre Telescope (TMT) — a massive astronomical observatory to be constructed on the summit of Mauna Kea, Hawaii, a $1.5-billion project). This has lead to some hard feelings in the past with regard to ‘big science’ projects getting what some have felt is an undeserved boost in finances while the ‘small fish’ are left scrabbling for the ever-diminishing (due to budget cuts in years past and inflation) pittances available from the tri-council agencies.

Mitacs, which started life as a federally funded Network Centre for Excellence focused on mathematics, has since shifted focus to become an innovation ‘champion’. You can find Mitacs here and you can find the organization’s March 2016 budget submission to the House of Commons Standing Committee on Finance here. At the time, they did not request a specific amount of money; they just asked for more.

The amount Mitacs expects to receive this year is over $40M which represents more than double what they received from the federal government and almost of 1/2 of their total income in the 2015-16 fiscal year according to their 2015-16 annual report (see p. 327 for the Mitacs Statement of Operations to March 31, 2016). In fact, the federal government forked over $39,900,189. in the 2015-16 fiscal year to be their largest supporter while Mitacs’ total income (receipts) was $81,993,390.

It’s a strange thing but too much money, etc. can be as bad as too little. I wish the folks Mitacs nothing but good luck with their windfall.

I don’t see anything in the budget that encourages innovation and investment from the industrial sector in Canada.

Finallyl, innovation is a cultural issue as much as it is a financial issue and having worked with a number of developers and start-up companies, the most popular business model is to develop a successful business that will be acquired by a large enterprise thereby allowing the entrepreneurs to retire before the age of 30 (or 40 at the latest). I don’t see anything from the government acknowledging the problem let alone any attempts to tackle it.

All in all, it was a decent budget with nothing in it to seriously offend anyone.

Communicating science effectively—a December 2016 book from the US National Academy of Sciences

I stumbled across this Dec. 13, 2016  essay/book announcement by Dr. Andrew Maynard and Dr. Dietram A. Scheufele on The Conversation,

Many scientists and science communicators have grappled with disregard for, or inappropriate use of, scientific evidence for years – especially around contentious issues like the causes of global warming, or the benefits of vaccinating children. A long debunked study on links between vaccinations and autism, for instance, cost the researcher his medical license but continues to keep vaccination rates lower than they should be.

Only recently, however, have people begun to think systematically about what actually works to promote better public discourse and decision-making around what is sometimes controversial science. Of course scientists would like to rely on evidence, generated by research, to gain insights into how to most effectively convey to others what they know and do.

As it turns out, the science on how to best communicate science across different issues, social settings and audiences has not led to easy-to-follow, concrete recommendations.

About a year ago, the National Academies of Sciences, Engineering and Medicine brought together a diverse group of experts and practitioners to address this gap between research and practice. The goal was to apply scientific thinking to the process of how we go about communicating science effectively. Both of us were a part of this group (with Dietram as the vice chair).

The public draft of the group’s findings – “Communicating Science Effectively: A Research Agenda” – has just been published. In it, we take a hard look at what effective science communication means and why it’s important; what makes it so challenging – especially where the science is uncertain or contested; and how researchers and science communicators can increase our knowledge of what works, and under what conditions.

At some level, all science communication has embedded values. Information always comes wrapped in a complex skein of purpose and intent – even when presented as impartial scientific facts. Despite, or maybe because of, this complexity, there remains a need to develop a stronger empirical foundation for effective communication of and about science.

Addressing this, the National Academies draft report makes an extensive number of recommendations. A few in particular stand out:

  • Use a systems approach to guide science communication. In other words, recognize that science communication is part of a larger network of information and influences that affect what people and organizations think and do.
  • Assess the effectiveness of science communication. Yes, researchers try, but often we still engage in communication first and evaluate later. Better to design the best approach to communication based on empirical insights about both audiences and contexts. Very often, the technical risk that scientists think must be communicated have nothing to do with the hopes or concerns public audiences have.
  • Get better at meaningful engagement between scientists and others to enable that “honest, bidirectional dialogue” about the promises and pitfalls of science that our committee chair Alan Leshner and others have called for.
  • Consider social media’s impact – positive and negative.
  • Work toward better understanding when and how to communicate science around issues that are contentious, or potentially so.

The paper version of the book has a cost but you can get a free online version.  Unfortunately,  I cannot copy and paste the book’s table of contents here and was not able to find a book index although there is a handy list of reference texts.

I have taken a very quick look at the book. If you’re in the field, it’s definitely worth a look. It is, however, written for and by academics. If you look at the list of writers and reviewers, you will find over 90% are professors at one university or another. That said, I was happy to see references to Dan Kahan’s work at the Yale Law School’s Culture Cognition Project cited. As happens they weren’t able to cite his latest work [***see my xxx, 2017 curiosity post***], released about a month after “Communicating Science Effectively: A Research Agenda.”

I was unable to find any reference to science communication via popular culture. I’m a little dismayed as I feel that this is a seriously ignored source of information by science communication specialists and academicians but not by the folks at MIT (Massachusetts Institute of Technology) who announced a wireless app in the same week as it was featured in an episode of the US television comedy, The Big Bang Theory. Here’s more from MIT’s emotion detection wireless app in a Feb. 1, 2017 news release (also on EurekAlert),

It’s a fact of nature that a single conversation can be interpreted in very different ways. For people with anxiety or conditions such as Asperger’s, this can make social situations extremely stressful. But what if there was a more objective way to measure and understand our interactions?

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute of Medical Engineering and Science (IMES) say that they’ve gotten closer to a potential solution: an artificially intelligent, wearable system that can predict if a conversation is happy, sad, or neutral based on a person’s speech patterns and vitals.

“Imagine if, at the end of a conversation, you could rewind it and see the moments when the people around you felt the most anxious,” says graduate student Tuka Alhanai, who co-authored a related paper with PhD candidate Mohammad Ghassemi that they will present at next week’s Association for the Advancement of Artificial Intelligence (AAAI) conference in San Francisco. “Our work is a step in this direction, suggesting that we may not be that far away from a world where people can have an AI social coach right in their pocket.”

As a participant tells a story, the system can analyze audio, text transcriptions, and physiological signals to determine the overall tone of the story with 83 percent accuracy. Using deep-learning techniques, the system can also provide a “sentiment score” for specific five-second intervals within a conversation.

“As far as we know, this is the first experiment that collects both physical data and speech data in a passive but robust way, even while subjects are having natural, unstructured interactions,” says Ghassemi. “Our results show that it’s possible to classify the emotional tone of conversations in real-time.”

The researchers say that the system’s performance would be further improved by having multiple people in a conversation use it on their smartwatches, creating more data to be analyzed by their algorithms. The team is keen to point out that they developed the system with privacy strongly in mind: The algorithm runs locally on a user’s device as a way of protecting personal information. (Alhanai says that a consumer version would obviously need clear protocols for getting consent from the people involved in the conversations.)

How it works

Many emotion-detection studies show participants “happy” and “sad” videos, or ask them to artificially act out specific emotive states. But in an effort to elicit more organic emotions, the team instead asked subjects to tell a happy or sad story of their own choosing.

Subjects wore a Samsung Simband, a research device that captures high-resolution physiological waveforms to measure features such as movement, heart rate, blood pressure, blood flow, and skin temperature. The system also captured audio data and text transcripts to analyze the speaker’s tone, pitch, energy, and vocabulary.

“The team’s usage of consumer market devices for collecting physiological data and speech data shows how close we are to having such tools in everyday devices,” says Björn Schuller, professor and chair of Complex and Intelligent Systems at the University of Passau in Germany, who was not involved in the research. “Technology could soon feel much more emotionally intelligent, or even ‘emotional’ itself.”

After capturing 31 different conversations of several minutes each, the team trained two algorithms on the data: One classified the overall nature of a conversation as either happy or sad, while the second classified each five-second block of every conversation as positive, negative, or neutral.

Alhanai notes that, in traditional neural networks, all features about the data are provided to the algorithm at the base of the network. In contrast, her team found that they could improve performance by organizing different features at the various layers of the network.

“The system picks up on how, for example, the sentiment in the text transcription was more abstract than the raw accelerometer data,” says Alhanai. “It’s quite remarkable that a machine could approximate how we humans perceive these interactions, without significant input from us as researchers.”

Results

Indeed, the algorithm’s findings align well with what we humans might expect to observe. For instance, long pauses and monotonous vocal tones were associated with sadder stories, while more energetic, varied speech patterns were associated with happier ones. In terms of body language, sadder stories were also strongly associated with increased fidgeting and cardiovascular activity, as well as certain postures like putting one’s hands on one’s face.

On average, the model could classify the mood of each five-second interval with an accuracy that was approximately 18 percent above chance, and a full 7.5 percent better than existing approaches.

The algorithm is not yet reliable enough to be deployed for social coaching, but Alhanai says that they are actively working toward that goal. For future work the team plans to collect data on a much larger scale, potentially using commercial devices such as the Apple Watch that would allow them to more easily implement the system out in the world.

“Our next step is to improve the algorithm’s emotional granularity so that it is more accurate at calling out boring, tense, and excited moments, rather than just labeling interactions as ‘positive’ or ‘negative,’” says Alhanai. “Developing technology that can take the pulse of human emotions has the potential to dramatically improve how we communicate with each other.”

This research was made possible in part by the Samsung Strategy and Innovation Center.

Episode 14 of season 10 of The Big Bang Theory was titled “The Emotion Detection Automation”  (full episode can be found on this webpage) and broadcast on Feb. 2, 2017. There’s also a Feb. 2, 2017 recap (recapitulation) by Lincee Ray for EW.com (it seems Ray is unaware that there really is such a machine),

Who knew we would see the day when Sheldon and Raj figured out solutions for their social ineptitudes? Only The Big Bang Theory writers would think to tackle our favorite physicists’ lack of social skills with an emotion detector and an ex-girlfriend focus group. It’s been a while since I enjoyed both storylines as much as I did in this episode. That’s no bazinga.

When Raj tells the guys that he is back on the market, he wonders out loud what is wrong with his game. Why do women reject him? Sheldon receives the information like a scientist and runs through many possible answers. Raj shuts him down with a simple, “I’m fine.”

Sheldon is irritated when he learns that this obligatory remark is a mask for what Raj is really feeling. It turns out, Raj is not fine. Sheldon whines, wondering why no one just says exactly what’s on their mind. It’s quite annoying for those who struggle with recognizing emotional cues.

Lo and behold, Bernadette recently read about a gizmo that was created for people who have this exact same anxiety. MIT has a prototype, and because Howard is an alum, he can probably submit Sheldon’s name as a beta tester.

Of course this is a real thing. If anyone can build an emotion detector, it’s a bunch of awkward scientists with zero social skills.

This is the first time I’ve noticed an academic institution’s news release to be almost simultaneous with mention of its research in a popular culture television program, which suggests things have come a long way since I featured news about a webinar by the National Academies ‘ Science and Entertainment Exchange for film and television productions collaborating with scientists in an Aug. 28, 2012 post.

One last science/popular culture moment: Hidden Figures, a movie about African American women who were human computers supporting NASA (US National Aeronautics and Space Agency) efforts during the 1960s space race and getting a man on the moon was (shockingly) no. 1 in the US box office for a few weeks (there’s more about the movie here in my Sept. 2, 2016 post covering then upcoming movies featuring science).  After the movie was released, Mary Elizabeth Williams wrote up a Jan. 23, 2017 interview with the ‘Hidden Figures’ scriptwriter for Salon.com

I [Allison Schroeder] got on the phone with her [co-producer Renee Witt] and Donna  [co-producer Donna Gigliotti] and I said, “You have to hire me for this; I was born to write this.” Donna sort of rolled her eyes and was like, “God, these Hollywood types would say anything.” I said, “No, no, I grew up at Cape Canaveral. My grandmother was a computer programmer at NASA, my grandfather worked on the Mercury prototype, and I interned there all through high school and then the summer after my freshman year at Stanford I interned. I worked at a missile launch company.”

She was like, “OK that’s impressive.” And I said, “No, I literally grew up climbing on the Mercury capsule — hitting all the buttons, trying to launch myself into space.”

She said, “Well do you think you can handle the math?” I said that I had to study a certain amount of math at Stanford for economics degree. She said, “Oh, all right, that sounds pretty good.”

I pitched her a few scenes. I pitched her the end of the movie that you saw with Katherine running the numbers as John Glenn is trying to get up in space. I pitched her the idea of one of the women as a mechanic and to see her legs underneath the engine. You’re used to seeing a guy like that, but what would it be like to see heels and pantyhose and a skirt and she’s a mechanic and fixing something? Those are some of the scenes that I pitched them, and I got the job.

I love that the film begins with setting up their mechanical aptitude. You set up these are women; you set up these women of color. You set up exactly what that means in this moment in history. It’s like you just go from there.

I was on a really tight timeline because this started as an indie film. It was just Donna Gigliotti, Renee Witt, me and the author Margot Lee Shetterly for about a year working on it. I was only given four weeks for research and 12 weeks for writing the first draft. I’m not sure if I hadn’t known NASA and known the culture and just knew what the machines would look like, knew what the prototypes looked like, if I could have done it that quickly. I turned in that draft and Donna was like, “OK you’ve got the math and the science; it’s all here. Now go have fun.” Then I did a few more drafts and that was really enjoyable because I could let go of the fact I did it and make sure that the characters and the drive of the story and everything just fit what needed to happen.

For anyone interested in the science/popular culture connection, David Bruggeman of the Pasco Phronesis blog does a better job than I do of keeping up with the latest doings.

Getting back to ‘Communicating Science Effectively: A Research Agenda’, even with a mention of popular culture, it is a thoughtful book on the topic.

Maths gallery at the UK’s Science Museum takes flight

Mathematics: The Winton Gallery at the Science Museum, Zaha Hadid Architects’ only permanent public museum exhibition design. London. Photograph: Nicholas Guttridge/NIck Guttridge

This exhibition looks great in the picture, I wonder what the experience is like. Alex Bellos is certainly enthusiastic in his Dec. 7, 2016 posting on the Guardian’s website,

Mathematics underlies all science, so for a science museum to be worthy of the name, maths needs to included somewhere. Yet maths, which deals mainly in abstract objects, is [a] challenge for museums, which necessarily contain physical ones. The Science Museum’s approach in its new gallery is to tell historical stories about the influence of mathematics in the real world, rather than actually focussing directly on the mathematical ideas involved. The result is a stunning gallery, with fascinating objects beautifully laid out, yet which eschews explaining any maths. (If you want to learn simple mathematical ideas, you can always head to the museum’s new interactive gallery, Wonderlab).

Much of the attention on Mathematics: The Winton Gallery – the main funders are David Harding, founder and CEO of investment firm Winton, and his wife Claudia – has been on Zaha Hadid’s design. The gallery is the first UK project by Zaha Hadid Architects to open since her unexpected death in March [2016], and the only permanent public museum exhibition she designed. Her first degree was in maths, before she turned to architecture.

Hanging from the ceiling is an aeroplane – the Handley Page ‘Gugnunc’, built in 1929 for a competition to build safe aircraft – and surrounding it is a swirly ceiling sculpture that represents the mathematical equations that describe airflow. In fact, the entire gallery follows the contours of the flow, providing the positions of the cabinets below.

The Science Museum’s previous maths gallery, which had not been updated in decades, contained about 600 objects, including cabinets crammed with geometrical objects and many examples of the same thing, such as medieval slide rules or Victorian curve-drawing machines. The new gallery has less than a quarter of that number of objects in the same space.

Every object now is in its own cabinet, and the extra space means you can walk around them from all angles, as well as making the gallery feel more manageable. Rather than being bombarded with stuff, you are given a single object to contemplate that tells part of a wider story.

In a section on “form and beauty”, there is a modern replica of a 1920s chair based on French architect’s Le Corbusier’s Modulor system of proportions, and two J W Turner sketches from his Royal Academy lectures on perspective.

The section “trade and travel” has a 3-metre long replica of the 1973 Globtik Tokyo oil tanker, then the largest ship in the world. In its massive cabinet it looks as terrifying as a Damien Hirst shark. The maths link? Because British mathematician William Froode a century before had worked out that bulbous bows were better than sharp bows at the fronts of boats and ships.

The new maths gallery is a wonderfully attractive space, full of interesting and thought-provoking objects, and a very welcome addition [geddit?] to London’s museums. Go!

A Dec. 8 (?), 2016 [London, UK] Science Museum press release is the first example I’ve seen of the funders being highlighted quite so prominently, i.e., before the press release proper,

Mathematics: The Winton Gallery designed by Zaha Hadid Architects opens at the Science Museum

  • A stunning new permanent gallery that reveals the importance of mathematics in all our lives through remarkable historical artefacts, stories and design
  • Free to visit and open daily from 8 December 2016
  • The only permanent public museum exhibition designed by Zaha Hadid anywhere in the world

Principal Funder: David and Claudia Harding
Principal Sponsor: Samsung
Major Sponsor: MathWorks

On 8 December 2016 the Science Museum will open an inspirational new mathematics gallery, designed by Zaha Hadid Architects.

Mathematics: The Winton Gallery brings together remarkable stories, historical artefacts and design to highlight the central role of mathematical practice in all our lives, and explores how mathematicians, their tools and ideas have helped build the modern world over the past four centuries.

More than 100 treasures from the Science Museum’s world-class science, technology, engineering and mathematics collections have been selected to tell powerful stories about how mathematics has shaped, and been shaped by, some of our most fundamental human concerns – from trade and travel to war, peace, life, death, form and beauty.

Curator Dr David Rooney said, ‘At its heart this gallery reveals a rich cultural story of human endeavour that has helped transform the world over the last four hundred years. Mathematical practice underpins so many aspects of our lives and work, and we hope that bringing together these remarkable stories, people and exhibits will inspire visitors to think about the role of mathematics in a new light.’

Positioned at the centre of the gallery is the Handley Page ‘Gugnunc’ aeroplane, built in 1929 for a competition to construct safe aircraft. Ground-breaking aerodynamic research influenced the wing design of this experimental aeroplane, helping to shift public opinion about the safety of flying and to secure the future of the aviation industry. This aeroplane encapsulates the gallery’s overarching theme, illustrating how mathematical practice has helped solve real-world problems and in this instance paved the way for the safe passenger flights that we rely on today.

Mathematics also defines Zaha Hadid Architects’ enlightening design for the gallery. Inspired by the Handley Page aircraft, the design is driven by equations of airflow used in the aviation industry. The layout and lines of the gallery represent the air that would have flowed around this historic aircraft in flight, from the positioning of the showcases and benches to the three-dimensional curved surfaces of the central pod structure.

Mathematics: The Winton Gallery is the first permanent public museum exhibition designed by Zaha Hadid Architects anywhere in the world. The gallery is also the first of Zaha Hadid Architects’ projects to open in the UK since Dame Zaha Hadid’s sudden death in March 2016. The late Dame Zaha first became interested in geometry while studying mathematics at university. Mathematics and geometry have a strong connection with architecture and she continued to examine these relationships throughout each of her projects; with mathematics always central to her work. As Dame Zaha said, ‘When I was growing up in Iraq, math was an everyday part of life. We would play with math problems just as we would play with pens and paper to draw – math was like sketching.’

Ian Blatchford, Director of the Science Museum Group, said, ‘We were hugely impressed by the ideas and vision of the late Dame Zaha Hadid and Patrik Schumacher when they first presented their design for the new mathematics gallery over two years ago. It was a terrible shock for us all when Dame Zaha died suddenly in March this year, but I am sure that this gallery will be a lasting tribute to this world-changing architect and provide inspiration for our millions of visitors for many years to come.’

From a beautiful 17th century Islamic astrolabe that uses ancient mathematical techniques to map the night sky, to an early example of the famous Enigma machine, designed to resist even the most advanced mathematical techniques for code breaking during the Second World War, each historic object within the gallery has an important story to tell. Archive photography and film helps to capture these stories, and introduces the wide range of people who made, used or were impacted by each mathematical device or idea.

Some instruments and objects within the gallery clearly reference their mathematical origin. Others may surprise visitors and appear rooted in other disciplines, from classical architecture to furniture design. Visitors will see a box of glass eyes used by Francis Galton in his 1884 Anthropometric Laboratory to help measure the physical characteristics of the British public and develop statistics to support a wider social and political movement he termed ‘eugenics’. On the other side of the gallery is the pioneering Wisard pattern-recognition machine built in 1981 to attempt to re-create the ‘neural networks’ of the brain. This early Artificial Intelligence machine worked, until 1995, on a variety of projects, from banknote recognition to voice analysis, and from foetal growth monitoring in hospitals to covert surveillance for the Home Office.

A richly illustrated book has been published by Scala to accompany the new gallery. Mathematics: How it Shaped Our World, written by David Rooney, expands on the themes and stories that are celebrated in the gallery itself and includes a series of newly commissioned essays written by world-leading experts in the history and modern practice of mathematics.

David Harding, Principal Funder of the gallery and Founder and CEO of Winton said, ‘Mathematics, whilst difficult for many, is incredibly useful. To those with an aptitude for it, it is also beautiful. I’m delighted that this gallery will be both useful and beautiful.’

Mathematics: The Winton Gallery is free to visit and open daily from 8 December 2016. The gallery has been made possible through an unprecedented donation from long-standing supporters of science, David and Claudia Harding. It has also received generous support from Samsung as Principal Sponsor, MathWorks as Major Sponsor, with additional support from Adrian and Jacqui Beecroft, Iain and Jane Bratchie, the Keniston-Cooper Charitable Trust, Dr Martin Schoernig, Steve Mobbs and Pauline Thomas.

After the press release, there is the most extensive list of ‘Abouts’ I’ve seen yet (Note: This includes links to the Science Museum and other agencies),

About the Science Museum
The Science Museum’s world-class collection forms an enduring record of scientific, technological and medical achievements from across the globe. Welcoming over 3 million visitors a year, the Museum aims to make sense of the science that shapes our lives, inspiring visitors with iconic objects, award-winning exhibitions and incredible stories of scientific achievement. More information can be found at sciencemuseum.org.uk

About Curator David Rooney
Mathematics: The Winton Gallery has been curated by Dr David Rooney, who was responsible for the award-winning 2012 Science Museum exhibition Codebreaker: Alan Turing’s Life and Legacy as well as developing galleries on time and navigation at the National Maritime Museum, Greenwich. David writes and speaks widely on the history of technology and engineering. His critically acclaimed first book, Ruth Belville: The Greenwich Time Lady, was described by Jonathan Meades as ‘an engrossing and eccentric slice of London history’, and by the Daily Telegraph as ‘a gem of a book’. He has recently authored Mathematics: How It Shaped Our World, to accompany the new mathematics gallery, and is currently writing a political history of traffic.

About David and Claudia Harding
David and Claudia Harding are associated with Winton, one of the world’s leading quantitative investment management firms which David founded in 1997. Winton uses mathematical and scientific methods to devise, evaluate and execute investment ideas on behalf of clients all over the world. A British-based company, Winton and David and Claudia Harding have donated to numerous scientific and mathematical causes in the UK and internationally, including Cambridge University, the Crick Institute, the Max Planck Institute, and the Science Museum. The main themes of their philanthropy have been supporting basic scientific research and the communication of scientific ideas. David and Claudia reside in London.

About Samsung’s Citizenship Programmes
Samsung is committed to help close the digital divide and skills gap in the UK. Samsung Digital Classrooms in schools, charities/non-profit organisations and cultural partners provide access to the latest technology. Samsung is also providing the training and maintenance support necessary to help make the transition and integration of the new technology as smooth as possible. Samsung also offers qualifications and training in technology for young people and teachers through its Digital Academies. These initiatives will inspire young people, staff and teachers to learn and teach in new exciting ways and to help encourage young people into careers using technology. Find out more

About MathWorks
MathWorks is the leading developer of mathematical computing software. MATLAB, the language of technical computing, is a programming environment for algorithm development, data analysis, visualisation, and numeric computation. Simulink is a graphical environment for simulation and Model-Based Design for multidomain dynamic and embedded systems. Engineers and scientists worldwide rely on these product families to accelerate the pace of discovery, innovation, and development in automotive, aerospace, electronics, financial services, biotech-pharmaceutical, and other industries. MATLAB and Simulink are also fundamental teaching and research tools in the world’s universities and learning institutions. Founded in 1984, MathWorks employs more than 3000 people in 15 countries, with headquarters in Natick, Massachusetts, USA. For additional information, visit mathworks.com

About Zaha Hadid Architects
Zaha Hadid founded Zaha Hadid Architects (ZHA) in 1979. Each of ZHA’s projects builds on over thirty years of exploration and research in the interrelated fields of urbanism, architecture and design. Hadid’s pioneering vision redefined architecture for the 21st century and captured imaginations across the globe. Her legacy is embedded within the DNA of the design studio she created as ZHA’s projects combine the unwavering belief in the power of invention with concepts of connectivity and fluidity.

ZHA is currently working on a diversity of projects worldwide including the new Beijing Airport Terminal Building in Daxing, China, the Sleuk Rith Institute in Phnom Penh, Cambodia and 520 West 28th Street in New York City, USA. The practice’s portfolio includes cultural, academic, sporting, residential, and transportation projects across six continents.

About Discover South Kensington
Discover South Kensington brings together the Science Museum and other leading cultural and educational organisations to promote innovation and learning. South Kensington is the home of science, arts and inspiration. Discovery is at the core of what happens here and there is so much to explore every day. discoversouthken.com

About Zaha Hadid: Early Paintings and Drawings at the Serpentine Sackler Gallery
This week an exhibition of paintings and drawings by Zaha Hadid will open at the Serpentine Galleries that will reveal her as an artist with drawing at the very heart of her work. It will include calligraphic drawings and rarely seen private notebooks, showing her complex thoughts about architecture’s forms and relationship to the world we live in. Zaha Hadid: Early Paintings and Drawings at the Serpentine Sackler Gallery is free to visit and runs from 8th December 2016 – 12th February 2017.

I found the mentions of Zaha Hadid fascinating and so I looked her up on Wikipedia, where I found this (Note: Links have been removed),

Dame Zaha Mohammad Hadid, DBE (Arabic: زها حديد‎‎ Zahā Ḥadīd; 31 October 1950 – 31 March 2016) was an Iraqi-born British architect. She was the first woman to receive the Pritzker Architecture Prize, in 2004.[1] She received the UK’s most prestigious architectural award, the Stirling Prize, in 2010 and 2011. In 2012, she was made a Dame by Elizabeth II for services to architecture, and in 2015 she became the first woman to be awarded the Royal Gold Medal from the Royal Institute of British Architects.[2]

She was dubbed by The Guardian as the ‘Queen of the curve’.[3] She liberated architectural geometry[4] with the creation of highly expressive, sweeping fluid forms of multiple perspective points and fragmented geometry that evoke the chaos and flux of modern life.[5] A pioneer of parametricism, and an icon of neo-futurism, with a formidable personality, her acclaimed work and ground-breaking forms include the aquatic centre for the London 2012 Olympics, the Broad Art Museum in the US, and the Guangzhou Opera House in China.[6] At the time of her death in 2016, Zaha Hadid Architects in London was the fastest growing British architectural firm.[7] Many of her designs are to be released posthumously, ranging in variation from the 2017 Brit Awards statuette to a 2022 FIFA World Cup stadium.[8][9]

Dubbed ‘Queen of the curve’, Hadid has a reputation as the world’s top female architect,[3][62][63][64][65] although her reputation is not without criticism. She is considered an architect of unconventional thinking, whose buildings are organic, dynamic and sculptural.[66][67] Stanton and others also compliment her on her unique organic designs: “One of the main characteristics of her work is that however clearly recognizable, it can never be pigeonholed into a stylistic signature. Digital knowledge, technology-driven mutations, shapes inspired by the organic and biological world, as well as geometrical interpretation of the landscape are constant elements of her practice. Yet, the multiplicity and variety of the combination among these facets prevent the risk of self-referential solutions and repetitions.”[68] Allison Lee Palmer considers Hadid a leader of Deconstructivism in architecture, writing that, “Almost all of Hadid’s buildings appear to melt, bend, and curve into a new architectural language that defies description. Her completed buildings span the globe and include the Jockey Club Innovation Tower on the north side of the Hong Kong Polytechnic University in Hong Kong, completed in 2013, that provides Hong Kong an entry into the world stage of cutting-edge architecture by revealing a design that dissolved traditional architecture, the so called modernist “glass box,” into a shattering of windows and melting of walls to form organic structures with halls and stairways that flow through the building, pooling open into rooms and foyers.”[69]

Hadid’s architectural language has been described by some as “famously extravagant” with many of her projects sponsored by “dictator states”. [emphasis mine] [70] Rowan Moore described Hadid’s Heydar Aliyev Center as “not so different from the colossal cultural palaces long beloved of Soviet and similar regimes”. Architect Sean Griffiths characterised Hadid’s work as “an empty vessel that sucks in whatever ideology might be in proximity to it”.[71] Art historian Maike Aden criticises in particular the foreclosure of Zaha Hadid’s architecture of the MAXXI in Rome towards the public and the urban life that undermines even the most impressive program to open the museum.[72]

If you think about it, most of the world’s great monuments were built by dictators or omnipotent rulers of one country or another. Getting the money and commitment can present an ethical/moral issue for any artist or architect who has a ‘grand design’.