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The upcoming US presidential election seems set to be something of a mess—to put it lightly. Covid-19 will likely deter millions from voting in person, and mail-in voting isn’t shaping up to be much more promising. This all comes at a time when political tensions are running higher than they have in decades, issues that shouldn’t be political (like mask-wearing) have become highly politicized, and Americans are dramatically divided along party lines.
So the last thing we need right now is yet another wrench in the spokes of democracy, in the form of disinformation; we all saw how that played out in 2016, and it wasn’t pretty. For the record, disinformation purposely misleads people, while misinformation is simply inaccurate, but without malicious intent. While there’s not a ton tech can do to make people feel safe at crowded polling stations or up the Postal Service’s budget, tech can help with disinformation, and Microsoft is trying to do so.
On Tuesday the company released two new tools designed to combat disinformation, described in a blog post by VP of Customer Security and Trust Tom Burt and Chief Scientific Officer Eric Horvitz.
The first is Microsoft Video Authenticator, which is made to detect deepfakes. In case you’re not familiar with this wicked byproduct of AI progress, “deepfakes” refers to audio or visual files made using artificial intelligence that can manipulate peoples’ voices or likenesses to make it look like they said things they didn’t. Editing a video to string together words and form a sentence someone didn’t say doesn’t count as a deepfake; though there’s manipulation involved, you don’t need a neural network and you’re not generating any original content or footage.
The Authenticator analyzes videos or images and tells users the percentage chance that they’ve been artificially manipulated. For videos, the tool can even analyze individual frames in real time.
Deepfake videos are made by feeding hundreds of hours of video of someone into a neural network, “teaching” the network the minutiae of the person’s voice, pronunciation, mannerisms, gestures, etc. It’s like when you do an imitation of your annoying coworker from accounting, complete with mimicking the way he makes every sentence sound like a question and his eyes widen when he talks about complex spreadsheets. You’ve spent hours—no, months—in his presence and have his personality quirks down pat. An AI algorithm that produces deepfakes needs to learn those same quirks, and more, about whoever the creator’s target is.
Given enough real information and examples, the algorithm can then generate its own fake footage, with deepfake creators using computer graphics and manually tweaking the output to make it as realistic as possible.
The scariest part? To make a deepfake, you don’t need a fancy computer or even a ton of knowledge about software. There are open-source programs people can access for free online, and as far as finding video footage of famous people—well, we’ve got YouTube to thank for how easy that is.
Microsoft’s Video Authenticator can detect the blending boundary of a deepfake and subtle fading or greyscale elements that the human eye may not be able to see.
In the blog post, Burt and Horvitz point out that as time goes by, deepfakes are only going to get better and become harder to detect; after all, they’re generated by neural networks that are continuously learning from and improving themselves.
Microsoft’s counter-tactic is to come in from the opposite angle, that is, being able to confirm beyond doubt that a video, image, or piece of news is real (I mean, can McDonald’s fries cure baldness? Did a seal slap a kayaker in the face with an octopus? Never has it been so imperative that the world know the truth).
A tool built into Microsoft Azure, the company’s cloud computing service, lets content producers add digital hashes and certificates to their content, and a reader (which can be used as a browser extension) checks the certificates and matches the hashes to indicate the content is authentic.
Finally, Microsoft also launched an interactive “Spot the Deepfake” quiz it developed in collaboration with the University of Washington’s Center for an Informed Public, deepfake detection company Sensity, and USA Today. The quiz is intended to help people “learn about synthetic media, develop critical media literacy skills, and gain awareness of the impact of synthetic media on democracy.”
The impact Microsoft’s new tools will have remains to be seen—but hey, we’re glad they’re trying. And they’re not alone; Facebook, Twitter, and YouTube have all taken steps to ban and remove deepfakes from their sites. The AI Foundation’s Reality Defender uses synthetic media detection algorithms to identify fake content. There’s even a coalition of big tech companies teaming up to try to fight election interference.
One thing is for sure: between a global pandemic, widespread protests and riots, mass unemployment, a hobbled economy, and the disinformation that’s remained rife through it all, we’re going to need all the help we can get to make it through not just the election, but the rest of the conga-line-of-catastrophes year that is 2020.
Image Credit: Darius Bashar on Unsplash Continue reading
Human brain maps are a dime a dozen these days. Maps that detail neurons in a certain region. Maps that draw out functional connections between those cells. Maps that dive deeper into gene expression. Or even meta-maps that combine all of the above.
But have you ever wondered: how well do those maps represent my brain? After all, no two brains are alike. And if we’re ever going to reverse-engineer the brain as a computer simulation—as Europe’s Human Brain Project is trying to do—shouldn’t we ask whose brain they’re hoping to simulate?
Enter a new kind of map: the Julich-Brain, a probabilistic map of human brains that accounts for individual differences using a computational framework. Rather than generating a static PDF of a brain map, the Julich-Brain atlas is also dynamic, in that it continuously changes to incorporate more recent brain mapping results. So far, the map has data from over 24,000 thinly sliced sections from 23 postmortem brains covering most years of adulthood at the cellular level. But the atlas can also continuously adapt to progress in mapping technologies to aid brain modeling and simulation, and link to other atlases and alternatives.
In other words, rather than “just another” human brain map, the Julich-Brain atlas is its own neuromapping API—one that could unite previous brain-mapping efforts with more modern methods.
“It is exciting to see how far the combination of brain research and digital technologies has progressed,” said Dr. Katrin Amunts of the Institute of Neuroscience and Medicine at Research Centre Jülich in Germany, who spearheaded the study.
The Old Dogma
The Julich-Brain atlas embraces traditional brain-mapping while also yanking the field into the 21st century.
First, the new atlas includes the brain’s cytoarchitecture, or how brain cells are organized. As brain maps go, these kinds of maps are the oldest and most fundamental. Rather than exploring how neurons talk to each other functionally—which is all the rage these days with connectome maps—cytoarchitecture maps draw out the physical arrangement of neurons.
Like a census, these maps literally capture how neurons are distributed in the brain, what they look like, and how they layer within and between different brain regions.
Because neurons aren’t packed together the same way between different brain regions, this provides a way to parse the brain into areas that can be further studied. When we say the brain’s “memory center,” the hippocampus, or the emotion center, the “amygdala,” these distinctions are based on cytoarchitectural maps.
Some may call this type of mapping “boring.” But cytoarchitecture maps form the very basis of any sort of neuroscience understanding. Like hand-drawn maps from early explorers sailing to the western hemisphere, these maps provide the brain’s geographical patterns from which we try to decipher functional connections. If brain regions are cities, then cytoarchitecture maps attempt to show trading or other “functional” activities that occur in the interlinking highways.
You might’ve heard of the most common cytoarchitecture map used today: the Brodmann map from 1909 (yup, that old), which divided the brain into classical regions based on the cells’ morphology and location. The map, while impactful, wasn’t able to account for brain differences between people. More recent brain-mapping technologies have allowed us to dig deeper into neuronal differences and divide the brain into more regions—180 areas in the cortex alone, compared with 43 in the original Brodmann map.
The new study took inspiration from that age-old map and transformed it into a digital ecosystem.
A Living Atlas
Work began on the Julich-Brain atlas in the mid-1990s, with a little help from the crowd.
The preparation of human tissue and its microstructural mapping, analysis, and data processing is incredibly labor-intensive, the authors lamented, making it impossible to do for the whole brain at high resolution in just one lab. To build their “Google Earth” for the brain, the team hooked up with EBRAINS, a shared computing platform set up by the Human Brain Project to promote collaboration between neuroscience labs in the EU.
First, the team acquired MRI scans of 23 postmortem brains, sliced the brains into wafer-thin sections, and scanned and digitized them. They corrected distortions from the chopping using data from the MRI scans and then lined up neurons in consecutive sections—picture putting together a 3D puzzle—to reconstruct the whole brain. Overall, the team had to analyze 24,000 brain sections, which prompted them to build a computational management system for individual brain sections—a win, because they could now track individual donor brains too.
Their method was quite clever. They first mapped their results to a brain template from a single person, called the MNI-Colin27 template. Because the reference brain was extremely detailed, this allowed the team to better figure out the location of brain cells and regions in a particular anatomical space.
However, MNI-Colin27’s brain isn’t your or my brain—or any of the brains the team analyzed. To dilute any of Colin’s potential brain quirks, the team also mapped their dataset onto an “average brain,” dubbed the ICBM2009c (catchy, I know).
This step allowed the team to “standardize” their results with everything else from the Human Connectome Project and the UK Biobank, kind of like adding their Google Maps layer to the existing map. To highlight individual brain differences, the team overlaid their dataset on existing ones, and looked for differences in the cytoarchitecture.
The microscopic architecture of neurons change between two areas (dotted line), forming the basis of different identifiable brain regions. To account for individual differences, the team also calculated a probability map (right hemisphere). Image credit: Forschungszentrum Juelich / Katrin Amunts
Based on structure alone, the brains were both remarkably different and shockingly similar at the same time. For example, the cortexes—the outermost layer of the brain—were physically different across donor brains of different age and sex. The region especially divergent between people was Broca’s region, which is traditionally linked to speech production. In contrast, parts of the visual cortex were almost identical between the brains.
The Brain-Mapping Future
Rather than relying on the brain’s visible “landmarks,” which can still differ between people, the probabilistic map is far more precise, the authors said.
What’s more, the map could also pool yet unmapped regions in the cortex—about 30 percent or so—into “gap maps,” providing neuroscientists with a better idea of what still needs to be understood.
“New maps are continuously replacing gap maps with progress in mapping while the process is captured and documented … Consequently, the atlas is not static but rather represents a ‘living map,’” the authors said.
Thanks to its structurally-sound architecture down to individual cells, the atlas can contribute to brain modeling and simulation down the line—especially for personalized brain models for neurological disorders such as seizures. Researchers can also use the framework for other species, and they can even incorporate new data-crunching processors into the workflow, such as mapping brain regions using artificial intelligence.
Fundamentally, the goal is to build shared resources to better understand the brain. “[These atlases] help us—and more and more researchers worldwide—to better understand the complex organization of the brain and to jointly uncover how things are connected,” the authors said.
Image credit: Richard Watts, PhD, University of Vermont and Fair Neuroimaging Lab, Oregon Health and Science University Continue reading
The scale of goods moving around the planet at any moment is staggering. Raw materials are dug up in one country, spun into parts and pieces in another, and assembled into products in a third. Crossing oceans and continents, they find their way to a local store or direct to your door.
Magically, a roll of toilet paper, power tool, or tube of toothpaste is there just when you need it.
Even more staggering is that this whole system, the global supply chain, works so well that it’s effectively invisible most of the time. Until now, that is. The pandemic has thrown a floodlight on the inner workings of this modern wonder—and it’s exposed massive vulnerabilities.
The e-commerce supply chain is an instructive example. As the world went into lockdown, and everything non-essential went online, demand for digital fulfillment skyrocketed.
Even under “normal” conditions, most e-commerce warehouses were struggling to meet demand. But Covid-19 has further strained the ability to cope with shifting supply, an unprecedented tidal wave of orders, and labor shortages. Local stores are running out of key products. Online grocers and e-commerce platforms are suspending some home deliveries, restricting online purchases of certain items, and limiting new customers. The whole system is being severely tested.
Why? Despite an abundance of 21st century technology, we’re stuck in the 20th century.
Today’s supply chain consists of fleets of ships, trucks, warehouses, and importantly, people scattered around the world. While there are some notable instances of advanced automation, the overwhelming majority of work is still manual, resembling a sort of human-powered bucket brigade, with people wandering around warehouses or standing alongside conveyor belts. Each package of diapers or bottle of detergent ordered by an online customer might be touched dozens of times by warehouse workers before finding its way into a box delivered to a home.
The pandemic has proven the critical need for innovation due to increased demand, concerns about the health and safety of workers, and traceability and safety of products and services.
At the 2020 World Economic Forum, there was much discussion about the ongoing societal transformation in which humans and machines work in tandem, automating and augmenting the way we get things done. At the time, pre-pandemic, debate trended toward skepticism and fear of job losses, with some even questioning the ethics and need for these technologies.
Now, we see things differently. To make the global supply chain more resilient to shocks like Covid-19, we must look to technology.
Perfecting the Global Supply Chain: The Massive ‘Matter Router’
Technology has faced and overcome similar challenges in the past.
World War II, for example, drove innovation in techniques for rapid production of many products on a large scale, including penicillin. We went from the availability of one dose of the drug in 1941, to four million sterile packages of the drug every month four years later.
Similarly, today’s companies, big and small, are looking to automation, robotics, and AI to meet the pandemic head on. These technologies are crucial to scaling the infrastructure that will fulfill most of the world’s e-commerce and food distribution needs.
You can think of this new infrastructure as a rapidly evolving “matter router” that will employ increasingly complex robotic systems to move products more freely and efficiently.
Robots powered by specialized AI software, for example, are already learning to adapt to changes in the environment, using the most recent advances in industrial robotics and machine learning. When customers suddenly need to order dramatically new items, these robots don’t need to stop or be reprogrammed. They can perform new tasks by learning from experience using low-cost camera systems and deep learning for visual and image recognition.
These more flexible robots can work around the clock, helping make facilities less sensitive to sudden changes in workforce and customer demand and strengthening the supply chain.
Today, e-commerce is roughly 12% of retail sales in the US and is expected to rise well beyond 25% within the decade, fueled by changes in buying habits. However, analysts have begun to consider whether the current crisis might cause permanent jumps in those numbers, as it has in the past (for instance with the SARS epidemic in China in 2003). Whatever happens, the larger supply chain will benefit from greater, more flexible automation, especially during global crises.
We must create what Hamza Mudassire of the University of Cambridge calls a “resilient ecosystem that links multiple buyers with multiple vendors, across a mesh of supply chains.” This ecosystem must be backed by robust, efficient, and scalable automation that uses robotics, autonomous vehicles, and the Internet of Things to help track the flow of goods through the supply chain.
The good news? We can accomplish this with technologies we have today.
Image credit: Guillaume Bolduc / Unsplash Continue reading