Tag Archives: parts
Microsoft Invests $1 Billion in OpenAI to Pursue Holy Grail of Artificial Intelligence
James Vincent | The Verge
“i‘The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity,’ said [OpenAI cofounder] Sam Altman. ‘Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI.’i”
UPS Wants to Go Full-Scale With Its Drone Deliveries
Eric Adams | Wired
“If UPS gets its way, it’ll be known for vehicles other than its famous brown vans. The delivery giant is working to become the first commercial entity authorized by the Federal Aviation Administration to use autonomous delivery drones without any of the current restrictions that have governed the aerial testing it has done to date.”
Scientists Can Finally Build Feedback Circuits in Cells
Megan Molteni | Wired
“Network a few LOCKR-bound molecules together, and you’ve got a circuit that can control a cell’s functions the same way a PID computer program automatically adjusts the pitch of a plane. With the right key, you can make cells glow or blow themselves apart. You can send things to the cell’s trash heap or zoom them to another cellular zip code.”
Carbon Nanotubes Could Increase Solar Efficiency to 80 Percent
David Grossman | Popular Mechanics
“Obviously, that sort of efficiency rating is unheard of in the world of solar panels. But even though a proof of concept is a long way from being used in the real world, any further developments in the nanotubes could bolster solar panels in ways we haven’t seen yet.”
What Technology Is Most Likely to Become Obsolete During Your Lifetime?
Daniel Kolitz | Gizmodo
“Old technology seldom just goes away. Whiteboards and LED screens join chalk blackboards, but don’t eliminate them. Landline phones get scarce, but not phones. …And the technologies that seem to be the most outclassed may come back as a the cult objects of aficionados—the vinyl record, for example. All this is to say that no one can tell us what will be obsolete in fifty years, but probably a lot less will be obsolete than we think.”
The Human Brain Project Hasn’t Lived Up to Its Promise
Ed Yong | The Atlantic
“The HBP, then, is in a very odd position, criticized for being simultaneously too grandiose and too narrow. None of the skeptics I spoke with was dismissing the idea of simulating parts of the brain, but all of them felt that such efforts should be driven by actual research questions. …Countless such projects could have been funded with the money channeled into the HBP, which explains much of the furor around the project.”
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Trees are a low-tech, high-efficiency way to offset much of humankind’s negative impact on the climate. What’s even better, we have plenty of room for a lot more of them.
A new study conducted by researchers at Switzerland’s ETH-Zürich, published in Science, details how Earth could support almost an additional billion hectares of trees without the new forests pushing into existing urban or agricultural areas. Once the trees grow to maturity, they could store more than 200 billion metric tons of carbon.
Great news indeed, but it still leaves us with some huge unanswered questions. Where and how are we going to plant all the new trees? What kind of trees should we plant? How can we ensure that the new forests become a boon for people in those areas?
Answers to all of the above likely involve technology.
Math + Trees = Challenges
The ETH-Zürich research team combined Google Earth mapping software with a database of nearly 80,000 existing forests to create a predictive model for optimal planting locations. In total, 0.9 billion hectares of new, continuous forest could be planted. Once mature, the 500 billion new trees in these forests would be capable of storing about two-thirds of the carbon we have emitted since the industrial revolution.
Other researchers have noted that the study may overestimate how efficient trees are at storing carbon, as well as underestimate how much carbon humans have emitted over time. However, all seem to agree that new forests would offset much of our cumulative carbon emissions—still an impressive feat as the target of keeping global warming this century at under 1.5 degrees Celsius becomes harder and harder to reach.
Recently, there was a story about a Brazilian couple who replanted trees in the valley where they live. The couple planted about 2.7 million trees in two decades. Back-of-the-napkin math shows that they on average planted 370 trees a day, meaning planting 500 billion trees would take about 3.7 million years. While an over-simplification, the point is that planting trees by hand is not realistic. Even with a million people going at a rate of 370 trees a day, it would take 83 years. Current technologies are also not likely to be able to meet the challenge, especially in remote locations.
Technology can speed up the planting process, including a new generation of drones that take tree planting to the skies. Drone planting generally involves dropping biodegradable seed pods at a designated area. The pods dissolve over time, and the tree seeds grow in the earth below. DroneSeed is one example; its 55-pound drones can plant up to 800 seeds an hour. Another startup, Biocarbon Engineering, has used various techniques, including drones, to plant 38 different species of trees across three continents.
Drone planting has distinct advantages when it comes to planting in hard-to-access areas—one example is mangrove forests, which are disappearing rapidly, increasing the risk of floods and storm surges.
Challenges include increasing the range and speed of drone planting, and perhaps most importantly, the success rate, as automatic planting from a height is still likely to be less accurate when it comes to what depth the tree saplings are planted. However, drones are already showing impressive numbers for sapling survival rates.
AI, Sensors, and Eye-In-the-Sky
Planting the trees is the first step in a long road toward an actual forest. Companies are leveraging artificial intelligence and satellite imagery in a multitude of ways to increase protection and understanding of forested areas.
20tree.ai, a Portugal-based startup, uses AI to analyze satellite imagery and monitor the state of entire forests at a fraction of the cost of manual monitoring. The approach can lead to faster identification of threats like pest infestation and a better understanding of the state of forests.
AI can also play a pivotal role in protecting existing forest areas by predicting where deforestation is likely to occur.
Closer to the ground—and sometimes in it—new networks of sensors can provide detailed information about the state and needs of trees. One such project is Trace, where individual trees are equipped with a TreeTalker, an internet of things-based device that can provide real-time monitoring of the tree’s functions and well-being. The information can be used to, among other things, optimize the use of available resources, such as providing the exact amount of water a tree needs.
Budding Technologies Are Controversial
Trees are in many ways fauna’s marathon runners—slow-growing and sturdy, but still susceptible to sickness and pests. Many deforested areas are likely not as rich in nutrients as they once were, which could slow down reforestation. Much of the positive impact that said trees could have on carbon levels in the atmosphere is likely decades away.
Bioengineering, for example through CRISPR, could provide solutions, making trees more resistant and faster-growing. Such technologies are being explored in relation to Ghana’s at-risk cocoa trees. Other exponential technologies could also hold much future potential—for instance micro-robots to assist the dwindling number of bees with pollination.
These technologies remain mired in controversy, and perhaps rightfully so. Bioengineering’s massive potential is for many offset by the inherent risks of engineered plants out-competing existing fauna or growing beyond our control. Micro-robots for pollination may solve a problem, but don’t do much to address the root cause: that we seem to be disrupting and destroying integral parts of natural cycles.
Tech Not The Whole Answer
So, is it realistic to plant 500 billion new trees? The short answer would be that yes, it’s possible—with the help of technology.
However, there are many unanswered challenges. For example, many of areas identified by the ETH-Zürich research team are not readily available for reforestation. Some are currently reserved for grazing, others owned by private entities, and others again are located in remote areas or areas prone to political instability, beyond the reach of most replanting efforts.
If we do wish to plant 500 billion trees to offset some of the negative impacts we have had on the planet, we might well want to combine the best of exponential technology with reforestation as well as a move to other forms of agriculture.
Such an approach might also help address a major issue: that few of the proposed new forests will likely succeed without ensuring that people living in and around the areas where reforestation takes place become involved, and can reap rewards from turning arable land into forests.
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A few years back, DeepMind’s Demis Hassabis famously prophesized that AI and neuroscience will positively feed into each other in a “virtuous circle.” If realized, this would fundamentally expand our insight into intelligence, both machine and human.
We’ve already seen some proofs of concept, at least in the brain-to-AI direction. For example, memory replay, a biological mechanism that fortifies our memories during sleep, also boosted AI learning when abstractly appropriated into deep learning models. Reinforcement learning, loosely based on our motivation circuits, is now behind some of AI’s most powerful tools.
Hassabis is about to be proven right again.
Last week, two studies independently tapped into the power of ANNs to solve a 70-year-old neuroscience mystery: how does our visual system perceive reality?
The first, published in Cell, used generative networks to evolve DeepDream-like images that hyper-activate complex visual neurons in monkeys. These machine artworks are pure nightmare fuel to the human eye; but together, they revealed a fundamental “visual hieroglyph” that may form a basic rule for how we piece together visual stimuli to process sight into perception.
In the second study, a team used a deep ANN model—one thought to mimic biological vision—to synthesize new patterns tailored to control certain networks of visual neurons in the monkey brain. When directly shown to monkeys, the team found that the machine-generated artworks could reliably activate predicted populations of neurons. Future improved ANN models could allow even better control, giving neuroscientists a powerful noninvasive tool to study the brain. The work was published in Science.
The individual results, though fascinating, aren’t necessarily the point. Rather, they illustrate how scientists are now striving to complete the virtuous circle: tapping AI to probe natural intelligence. Vision is only the beginning—the tools can potentially be expanded into other sensory domains. And the more we understand about natural brains, the better we can engineer artificial ones.
It’s a “great example of leveraging artificial intelligence to study organic intelligence,” commented Dr. Roman Sandler at Kernel.co on Twitter.
ANNs and biological vision have quite the history.
In the late 1950s, the legendary neuroscientist duo David Hubel and Torsten Wiesel became some of the first to use mathematical equations to understand how neurons in the brain work together.
In a series of experiments—many using cats—the team carefully dissected the structure and function of the visual cortex. Using myriads of images, they revealed that vision is processed in a hierarchy: neurons in “earlier” brain regions, those closer to the eyes, tend to activate when they “see” simple patterns such as lines. As we move deeper into the brain, from the early V1 to a nub located slightly behind our ears, the IT cortex, neurons increasingly respond to more complex or abstract patterns, including faces, animals, and objects. The discovery led some scientists to call certain IT neurons “Jennifer Aniston cells,” which fire in response to pictures of the actress regardless of lighting, angle, or haircut. That is, IT neurons somehow extract visual information into the “gist” of things.
That’s not trivial. The complex neural connections that lead to increasing abstraction of what we see into what we think we see—what we perceive—is a central question in machine vision: how can we teach machines to transform numbers encoding stimuli into dots, lines, and angles that eventually form “perceptions” and “gists”? The answer could transform self-driving cars, facial recognition, and other computer vision applications as they learn to better generalize.
Hubel and Wiesel’s Nobel-prize-winning studies heavily influenced the birth of ANNs and deep learning. Much of earlier ANN “feed-forward” model structures are based on our visual system; even today, the idea of increasing layers of abstraction—for perception or reasoning—guide computer scientists to build AI that can better generalize. The early romance between vision and deep learning is perhaps the bond that kicked off our current AI revolution.
It only seems fair that AI would feed back into vision neuroscience.
Hieroglyphs and Controllers
In the Cell study, a team led by Dr. Margaret Livingstone at Harvard Medical School tapped into generative networks to unravel IT neurons’ complex visual alphabet.
Scientists have long known that neurons in earlier visual regions (V1) tend to fire in response to “grating patches” oriented in certain ways. Using a limited set of these patches like letters, V1 neurons can “express a visual sentence” and represent any image, said Dr. Arash Afraz at the National Institute of Health, who was not involved in the study.
But how IT neurons operate remained a mystery. Here, the team used a combination of genetic algorithms and deep generative networks to “evolve” computer art for every studied neuron. In seven monkeys, the team implanted electrodes into various parts of the visual IT region so that they could monitor the activity of a single neuron.
The team showed each monkey an initial set of 40 images. They then picked the top 10 images that stimulated the highest neural activity, and married them to 30 new images to “evolve” the next generation of images. After 250 generations, the technique, XDREAM, generated a slew of images that mashed up contorted face-like shapes with lines, gratings, and abstract shapes.
This image shows the evolution of an optimum image for stimulating a visual neuron in a monkey. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“The evolved images look quite counter-intuitive,” explained Afraz. Some clearly show detailed structures that resemble natural images, while others show complex structures that can’t be characterized by our puny human brains.
This figure shows natural images (right) and images evolved by neurons in the inferotemporal cortex of a monkey (left). Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“What started to emerge during each experiment were pictures that were reminiscent of shapes in the world but were not actual objects in the world,” said study author Carlos Ponce. “We were seeing something that was more like the language cells use with each other.”
This image was evolved by a neuron in the inferotemporal cortex of a monkey using AI. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
Although IT neurons don’t seem to use a simple letter alphabet, it does rely on a vast array of characters like hieroglyphs or Chinese characters, “each loaded with more information,” said Afraz.
The adaptive nature of XDREAM turns it into a powerful tool to probe the inner workings of our brains—particularly for revealing discrepancies between biology and models.
The Science study, led by Dr. James DiCarlo at MIT, takes a similar approach. Using ANNs to generate new patterns and images, the team was able to selectively predict and independently control neuron populations in a high-level visual region called V4.
“So far, what has been done with these models is predicting what the neural responses would be to other stimuli that they have not seen before,” said study author Dr. Pouya Bashivan. “The main difference here is that we are going one step further and using the models to drive the neurons into desired states.”
It suggests that our current ANN models for visual computation “implicitly capture a great deal of visual knowledge” which we can’t really describe, but which the brain uses to turn vision information into perception, the authors said. By testing AI-generated images on biological vision, however, the team concluded that today’s ANNs have a degree of understanding and generalization. The results could potentially help engineer even more accurate ANN models of biological vision, which in turn could feed back into machine vision.
“One thing is clear already: Improved ANN models … have led to control of a high-level neural population that was previously out of reach,” the authors said. “The results presented here have likely only scratched the surface of what is possible with such implemented characterizations of the brain’s neural networks.”
To Afraz, the power of AI here is to find cracks in human perception—both our computational models of sensory processes, as well as our evolved biological software itself. AI can be used “as a perfect adversarial tool to discover design cracks” of IT, said Afraz, such as finding computer art that “fools” a neuron into thinking the object is something else.
“As artificial intelligence researchers develop models that work as well as the brain does—or even better—we will still need to understand which networks are more likely to behave safely and further human goals,” said Ponce. “More efficient AI can be grounded by knowledge of how the brain works.”
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