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#437293 These Scientists Just Completed a 3D ...
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
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#437269 DeepMind’s Newest AI Programs Itself ...
When Deep Blue defeated world chess champion Garry Kasparov in 1997, it may have seemed artificial intelligence had finally arrived. A computer had just taken down one of the top chess players of all time. But it wasn’t to be.
Though Deep Blue was meticulously programmed top-to-bottom to play chess, the approach was too labor-intensive, too dependent on clear rules and bounded possibilities to succeed at more complex games, let alone in the real world. The next revolution would take a decade and a half, when vastly more computing power and data revived machine learning, an old idea in artificial intelligence just waiting for the world to catch up.
Today, machine learning dominates, mostly by way of a family of algorithms called deep learning, while symbolic AI, the dominant approach in Deep Blue’s day, has faded into the background.
Key to deep learning’s success is the fact the algorithms basically write themselves. Given some high-level programming and a dataset, they learn from experience. No engineer anticipates every possibility in code. The algorithms just figure it.
Now, Alphabet’s DeepMind is taking this automation further by developing deep learning algorithms that can handle programming tasks which have been, to date, the sole domain of the world’s top computer scientists (and take them years to write).
In a paper recently published on the pre-print server arXiv, a database for research papers that haven’t been peer reviewed yet, the DeepMind team described a new deep reinforcement learning algorithm that was able to discover its own value function—a critical programming rule in deep reinforcement learning—from scratch.
Surprisingly, the algorithm was also effective beyond the simple environments it trained in, going on to play Atari games—a different, more complicated task—at a level that was, at times, competitive with human-designed algorithms and achieving superhuman levels of play in 14 games.
DeepMind says the approach could accelerate the development of reinforcement learning algorithms and even lead to a shift in focus, where instead of spending years writing the algorithms themselves, researchers work to perfect the environments in which they train.
Pavlov’s Digital Dog
First, a little background.
Three main deep learning approaches are supervised, unsupervised, and reinforcement learning.
The first two consume huge amounts of data (like images or articles), look for patterns in the data, and use those patterns to inform actions (like identifying an image of a cat). To us, this is a pretty alien way to learn about the world. Not only would it be mind-numbingly dull to review millions of cat images, it’d take us years or more to do what these programs do in hours or days. And of course, we can learn what a cat looks like from just a few examples. So why bother?
While supervised and unsupervised deep learning emphasize the machine in machine learning, reinforcement learning is a bit more biological. It actually is the way we learn. Confronted with several possible actions, we predict which will be most rewarding based on experience—weighing the pleasure of eating a chocolate chip cookie against avoiding a cavity and trip to the dentist.
In deep reinforcement learning, algorithms go through a similar process as they take action. In the Atari game Breakout, for instance, a player guides a paddle to bounce a ball at a ceiling of bricks, trying to break as many as possible. When playing Breakout, should an algorithm move the paddle left or right? To decide, it runs a projection—this is the value function—of which direction will maximize the total points, or rewards, it can earn.
Move by move, game by game, an algorithm combines experience and value function to learn which actions bring greater rewards and improves its play, until eventually, it becomes an uncanny Breakout player.
Learning to Learn (Very Meta)
So, a key to deep reinforcement learning is developing a good value function. And that’s difficult. According to the DeepMind team, it takes years of manual research to write the rules guiding algorithmic actions—which is why automating the process is so alluring. Their new Learned Policy Gradient (LPG) algorithm makes solid progress in that direction.
LPG trained in a number of toy environments. Most of these were “gridworlds”—literally two-dimensional grids with objects in some squares. The AI moves square to square and earns points or punishments as it encounters objects. The grids vary in size, and the distribution of objects is either set or random. The training environments offer opportunities to learn fundamental lessons for reinforcement learning algorithms.
Only in LPG’s case, it had no value function to guide that learning.
Instead, LPG has what DeepMind calls a “meta-learner.” You might think of this as an algorithm within an algorithm that, by interacting with its environment, discovers both “what to predict,” thereby forming its version of a value function, and “how to learn from it,” applying its newly discovered value function to each decision it makes in the future.
Prior work in the area has had some success, but according to DeepMind, LPG is the first algorithm to discover reinforcement learning rules from scratch and to generalize beyond training. The latter was particularly surprising because Atari games are so different from the simple worlds LPG trained in—that is, it had never seen anything like an Atari game.
Time to Hand Over the Reins? Not Just Yet
LPG is still behind advanced human-designed algorithms, the researchers said. But it outperformed a human-designed benchmark in training and even some Atari games, which suggests it isn’t strictly worse, just that it specializes in some environments.
This is where there’s room for improvement and more research.
The more environments LPG saw, the more it could successfully generalize. Intriguingly, the researchers speculate that with enough well-designed training environments, the approach might yield a general-purpose reinforcement learning algorithm.
At the least, though, they say further automation of algorithm discovery—that is, algorithms learning to learn—will accelerate the field. In the near term, it can help researchers more quickly develop hand-designed algorithms. Further out, as self-discovered algorithms like LPG improve, engineers may shift from manually developing the algorithms themselves to building the environments where they learn.
Deep learning long ago left Deep Blue in the dust at games. Perhaps algorithms learning to learn will be a winning strategy in the real world too.
Image credit: Mike Szczepanski / Unsplash Continue reading
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#437265 This Russian Firm’s Star Designer Is ...
Imagine discovering a new artist or designer—whether visual art, fashion, music, or even writing—and becoming a big fan of her work. You follow her on social media, eagerly anticipate new releases, and chat about her talent with your friends. It’s not long before you want to know more about this creative, inspiring person, so you start doing some research. It’s strange, but there doesn’t seem to be any information about the artist’s past online; you can’t find out where she went to school or who her mentors were.
After some more digging, you find out something totally unexpected: your beloved artist is actually not a person at all—she’s an AI.
Would you be amused? Annoyed? Baffled? Impressed? Probably some combination of all these. If you wanted to ask someone who’s had this experience, you could talk to clients of the biggest multidisciplinary design company in Russia, Art.Lebedev Studio (I know, the period confused me at first too). The studio passed off an AI designer as human for more than a year, and no one caught on.
They gave the AI a human-sounding name—Nikolay Ironov—and it participated in more than 20 different projects that included designing brand logos and building brand identities. According to the studio’s website, several of the logos the AI made attracted “considerable public interest, media attention, and discussion in online communities” due to their unique style.
So how did an AI learn to create such buzz-worthy designs? It was trained using hand-drawn vector images each associated with one or more themes. To start a new design, someone enters a few words describing the client, such as what kind of goods or services they offer. The AI uses those words to find associated images and generate various starter designs, which then go through another series of algorithms that “touch them up.” A human designer then selects the best options to present to the client.
“These systems combined together provide users with the experience of instantly converting a client’s text brief into a corporate identity design pack archive. Within seconds,” said Sergey Kulinkovich, the studio’s art director. He added that clients liked Nikolay Ironov’s work before finding out he was an AI (and liked the media attention their brands got after Ironov’s identity was revealed even more).
Ironov joins a growing group of AI “artists” that are starting to raise questions about the nature of art and creativity. Where do creative ideas come from? What makes a work of art truly great? And when more than one person is involved in making art, who should own the copyright?
Art.Lebedev is far from the first design studio to employ artificial intelligence; Mailchimp is using AI to let businesses design multi-channel marketing campaigns without human designers, and Adobe is marketing its new Sensei product as an AI design assistant.
While art made by algorithms can be unique and impressive, though, there’s one caveat that’s important to keep in mind when we worry about human creativity being rendered obsolete. Here’s the thing: AIs still depend on people to not only program them, but feed them a set of training data on which their intelligence and output are based. Depending on the size and nature of an AI’s input data, its output will look pretty different from that of a similar system, and a big part of the difference will be due to the people that created and trained the AIs.
Admittedly, Nikolay Ironov does outshine his human counterparts in a handful of ways; as the studio’s website points out, he can handle real commercial tasks effectively, he doesn’t sleep, get sick, or have “crippling creative blocks,” and he can complete tasks in a matter of seconds.
Given these superhuman capabilities, then, why even keep human designers on staff? As detailed above, it will be a while before creative firms really need to consider this question on a large scale; for now, it still takes a hard-working creative human to make a fast-producing creative AI.
Image Credit: Art.Lebedev Continue reading
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#437261 How AI Will Make Drug Discovery ...
If you had to guess how long it takes for a drug to go from an idea to your pharmacy, what would you guess? Three years? Five years? How about the cost? $30 million? $100 million?
Well, here’s the sobering truth: 90 percent of all drug possibilities fail. The few that do succeed take an average of 10 years to reach the market and cost anywhere from $2.5 billion to $12 billion to get there.
But what if we could generate novel molecules to target any disease, overnight, ready for clinical trials? Imagine leveraging machine learning to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.
Welcome to the future of AI and low-cost, ultra-fast, and personalized drug discovery. Let’s dive in.
GANs & Drugs
Around 2012, computer scientist-turned-biophysicist Alex Zhavoronkov started to notice that artificial intelligence was getting increasingly good at image, voice, and text recognition. He knew that all three tasks shared a critical commonality. In each, massive datasets were available, making it easy to train up an AI.
But similar datasets were present in pharmacology. So, back in 2014, Zhavoronkov started wondering if he could use these datasets and AI to significantly speed up the drug discovery process. He’d heard about a new technique in artificial intelligence known as generative adversarial networks (or GANs). By pitting two neural nets against one another (adversarial), the system can start with minimal instructions and produce novel outcomes (generative). At the time, researchers had been using GANs to do things like design new objects or create one-of-a-kind, fake human faces, but Zhavoronkov wanted to apply them to pharmacology.
He figured GANs would allow researchers to verbally describe drug attributes: “The compound should inhibit protein X at concentration Y with minimal side effects in humans,” and then the AI could construct the molecule from scratch. To turn his idea into reality, Zhavoronkov set up Insilico Medicine on the campus of Johns Hopkins University in Baltimore, Maryland, and rolled up his sleeves.
Instead of beginning their process in some exotic locale, Insilico’s “drug discovery engine” sifts millions of data samples to determine the signature biological characteristics of specific diseases. The engine then identifies the most promising treatment targets and—using GANs—generates molecules (that is, baby drugs) perfectly suited for them. “The result is an explosion in potential drug targets and a much more efficient testing process,” says Zhavoronkov. “AI allows us to do with fifty people what a typical drug company does with five thousand.”
The results have turned what was once a decade-long war into a month-long skirmish.
In late 2018, for example, Insilico was generating novel molecules in fewer than 46 days, and this included not just the initial discovery, but also the synthesis of the drug and its experimental validation in computer simulations.
Right now, they’re using the system to hunt down new drugs for cancer, aging, fibrosis, Parkinson’s, Alzheimer’s, ALS, diabetes, and many others. The first drug to result from this work, a treatment for hair loss, is slated to start Phase I trials by the end of 2020.
They’re also in the early stages of using AI to predict the outcomes of clinical trials in advance of the trial. If successful, this technique will enable researchers to strip a bundle of time and money out of the traditional testing process.
Protein Folding
Beyond inventing new drugs, AI is also being used by other scientists to identify new drug targets—that is, the place to which a drug binds in the body and another key part of the drug discovery process.
Between 1980 and 2006, despite an annual investment of $30 billion, researchers only managed to find about five new drug targets a year. The trouble is complexity. Most potential drug targets are proteins, and a protein’s structure—meaning the way a 2D sequence of amino acids folds into a 3D protein—determines its function.
But a protein with merely a hundred amino acids (a rather small protein) can produce a googol-cubed worth of potential shapes—that’s a one followed by three hundred zeroes. This is also why protein-folding has long been considered an intractably hard problem for even the most powerful of supercomputers.
Back in 1994, to monitor supercomputers’ progress in protein-folding, a biannual competition was created. Until 2018, success was fairly rare. But then the creators of DeepMind turned their neural networks loose on the problem. They created an AI that mines enormous datasets to determine the most likely distance between a protein’s base pairs and the angles of their chemical bonds—aka, the basics of protein-folding. They called it AlphaFold.
On its first foray into the competition, contestant AIs were given 43 protein-folding problems to solve. AlphaFold got 25 right. The second-place team managed a meager three. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.
Drug Delivery
Another theater of war for improved drugs is the realm of drug delivery. Even here, converging exponential technologies are paving the way for massive implications in both human health and industry shifts.
One key contender is CRISPR, the fast-advancing gene-editing technology that stands to revolutionize synthetic biology and treatment of genetically linked diseases. And researchers have now demonstrated how this tool can be applied to create materials that shape-shift on command. Think: materials that dissolve instantaneously when faced with a programmed stimulus, releasing a specified drug at a highly targeted location.
Yet another potential boon for targeted drug delivery is nanotechnology, whereby medical nanorobots have now been used to fight incidences of cancer. In a recent review of medical micro- and nanorobotics, lead authors (from the University of Texas at Austin and University of California, San Diego) found numerous successful tests of in vivo operation of medical micro- and nanorobots.
Drugs From the Future
Covid-19 is uniting the global scientific community with its urgency, prompting scientists to cast aside nation-specific territorialism, research secrecy, and academic publishing politics in favor of expedited therapeutic and vaccine development efforts. And in the wake of rapid acceleration across healthcare technologies, Big Pharma is an area worth watching right now, no matter your industry. Converging technologies will soon enable extraordinary strides in longevity and disease prevention, with companies like Insilico leading the charge.
Riding the convergence of massive datasets, skyrocketing computational power, quantum computing, cognitive surplus capabilities, and remarkable innovations in AI, we are not far from a world in which personalized drugs, delivered directly to specified targets, will graduate from science fiction to the standard of care.
Rejuvenational biotechnology will be commercially available sooner than you think. When I asked Alex for his own projection, he set the timeline at “maybe 20 years—that’s a reasonable horizon for tangible rejuvenational biotechnology.”
How might you use an extra 20 or more healthy years in your life? What impact would you be able to make?
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This article originally appeared on diamandis.com. Read the original article here.
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