Tag Archives: species
#435260 How Tech Can Help Curb Emissions by ...
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.
Tree-Bombing Drones
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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#435046 The Challenge of Abundance: Boredom, ...
As technology continues to progress, the possibility of an abundant future seems more likely. Artificial intelligence is expected to drive down the cost of labor, infrastructure, and transport. Alternative energy systems are reducing the cost of a wide variety of goods. Poverty rates are falling around the world as more people are able to make a living, and resources that were once inaccessible to millions are becoming widely available.
But such a life presents fuel for the most common complaint against abundance: if robots take all the jobs, basic income provides us livable welfare for doing nothing, and healthcare is a guarantee free of charge, then what is the point of our lives? What would motivate us to work and excel if there are no real risks or rewards? If everything is simply given to us, how would we feel like we’ve ever earned anything?
Time has proven that humans inherently yearn to overcome challenges—in fact, this very desire likely exists as the root of most technological innovation. And the idea that struggling makes us stronger isn’t just anecdotal, it’s scientifically validated.
For instance, kids who use anti-bacterial soaps and sanitizers too often tend to develop weak immune systems, causing them to get sick more frequently and more severely. People who work out purposely suffer through torn muscles so that after a few days of healing their muscles are stronger. And when patients visit a psychologist to handle a fear that is derailing their lives, one of the most common treatments is exposure therapy: a slow increase of exposure to the suffering so that the patient gets stronger and braver each time, able to take on an incrementally more potent manifestation of their fears.
Different Kinds of Struggle
It’s not hard to understand why people might fear an abundant future as a terribly mundane one. But there is one crucial mistake made in this assumption, and it was well summarized by Indian mystic and author Sadhguru, who said during a recent talk at Google:
Stomach empty, only one problem. Stomach full—one hundred problems; because what we refer to as human really begins only after survival is taken care of.
This idea is backed up by Maslow’s hierarchy of needs, which was first presented in his 1943 paper “A Theory of Human Motivation.” Maslow shows the steps required to build to higher and higher levels of the human experience. Not surprisingly, the first two levels deal with physiological needs and the need for safety—in other words, with the body. You need to have food, water, and sleep, or you die. After that, you need to be protected from threats, from the elements, from dangerous people, and from disease and pain.
Maslow’s Hierarchy of Needs. Photo by Wikimedia User:Factoryjoe / CC BY-SA 3.0
The beauty of these first two levels is that they’re clear-cut problems with clear-cut solutions: if you’re hungry, then you eat; if you’re thirsty, then you drink; if you’re tired, then you sleep.
But what about the next tiers of the hierarchy? What of love and belonging, of self-esteem and self-actualization? If we’re lonely, can we just summon up an authentic friend or lover? If we feel neglected by society, can we demand it validate us? If we feel discouraged and disappointed in ourselves, can we simply dial up some confidence and self-esteem?
Of course not, and that’s because these psychological needs are nebulous; they don’t contain clear problems with clear solutions. They involve the external world and other people, and are complicated by the infinite flavors of nuance and compromise that are required to navigate human relationships and personal meaning.
These psychological difficulties are where we grow our personalities, outlooks, and beliefs. The truly defining characteristics of a person are dictated not by the physical situations they were forced into—like birth, socioeconomic class, or physical ailment—but instead by the things they choose. So a future of abundance helps to free us from the physical limitations so that we can truly commit to a life of purpose and meaning, rather than just feel like survival is our purpose.
The Greatest Challenge
And that’s the plot twist. This challenge to come to grips with our own individuality and freedom could actually be the greatest challenge our species has ever faced. Can you imagine waking up every day with infinite possibility? Every choice you make says no to the rest of reality, and so every decision carries with it truly life-defining purpose and meaning. That sounds overwhelming. And that’s probably because in our current socio-economic systems, it is.
Studies have shown that people in wealthier nations tend to experience more anxiety and depression. Ron Kessler, professor of health care policy at Harvard and World Health Organization (WHO) researcher, summarized his findings of global mental health by saying, “When you’re literally trying to survive, who has time for depression? Americans, on the other hand, many of whom lead relatively comfortable lives, blow other nations away in the depression factor, leading some to suggest that depression is a ‘luxury disorder.’”
This might explain why America scores in the top rankings for the most depressed and anxious country on the planet. We surpassed our survival needs, and instead became depressed because our jobs and relationships don’t fulfill our expectations for the next three levels of Maslow’s hierarchy (belonging, esteem, and self-actualization).
But a future of abundance would mean we’d have to deal with these levels. This is the challenge for the future; this is what keeps things from being mundane.
As a society, we would be forced to come to grips with our emotional intelligence, to reckon with philosophy rather than simply contemplate it. Nearly every person you meet will be passionately on their own customized life journey, not following a routine simply because of financial limitations. Such a world seems far more vibrant and interesting than one where most wander sleep-deprived and numb while attempting to survive the rat race.
We can already see the forceful hand of this paradigm shift as self-driving cars become ubiquitous. For example, consider the famous psychological and philosophical “trolley problem.” In this thought experiment, a person sees a trolley car heading towards five people on the train tracks; they see a lever that will allow them to switch the trolley car to a track that instead only has one person on it. Do you switch the lever and have a hand in killing one person, or do you let fate continue and kill five people instead?
For the longest time, this was just an interesting quandary to consider. But now, massive corporations have to have an answer, so they can program their self-driving cars with the ability to choose between hitting a kid who runs into the road or swerving into an oncoming car carrying a family of five. When companies need philosophers to make business decisions, it’s a good sign of what’s to come.
Luckily, it’s possible this forceful reckoning with philosophy and our own consciousness may be exactly what humanity needs. Perhaps our great failure as a species has been a result of advanced cognition still trapped in the first two levels of Maslow’s hierarchy due to a long history of scarcity.
As suggested in the opening scenes in 2001: A Space Odyssey, our ape-like proclivity for violence has long stayed the same while the technology we fight with and live amongst has progressed. So while well-off Americans may have comfortable lives, they still know they live in a system where there is no safety net, where a single tragic failure could still mean hunger and homelessness. And because of this, that evolutionarily hard-wired neurotic part of our brain that fears for our survival has never been able to fully relax, and so that anxiety and depression that come with too much freedom but not enough security stays ever present.
Not only might this shift in consciousness help liberate humanity, but it may be vital if we’re to survive our future creations as well. Whatever values we hold dear as a species are the ones we will imbue into the sentient robots we create. If machine learning is going to take its guidance from humanity, we need to level up humanity’s emotional maturity.
While the physical struggles of the future may indeed fall to the wayside amongst abundance, it’s unlikely to become a mundane world; instead, it will become a vibrant culture where each individual is striving against the most important struggle that affects all of us: the challenge to find inner peace, to find fulfillment, to build meaningful relationships, and ultimately, the challenge to find ourselves.
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#434786 AI Performed Like a Human on a Gestalt ...
Dr. Been Kim wants to rip open the black box of deep learning.
A senior researcher at Google Brain, Kim specializes in a sort of AI psychology. Like cognitive psychologists before her, she develops various ways to probe the alien minds of artificial neural networks (ANNs), digging into their gory details to better understand the models and their responses to inputs.
The more interpretable ANNs are, the reasoning goes, the easier it is to reveal potential flaws in their reasoning. And if we understand when or why our systems choke, we’ll know when not to use them—a foundation for building responsible AI.
There are already several ways to tap into ANN reasoning, but Kim’s inspiration for unraveling the AI black box came from an entirely different field: cognitive psychology. The field aims to discover fundamental rules of how the human mind—essentially also a tantalizing black box—operates, Kim wrote with her colleagues.
In a new paper uploaded to the pre-publication server arXiv, the team described a way to essentially perform a human cognitive test on ANNs. The test probes how we automatically complete gaps in what we see, so that they form entire objects—for example, perceiving a circle from a bunch of loose dots arranged along a clock face. Psychologist dub this the “law of completion,” a highly influential idea that led to explanations of how our minds generalize data into concepts.
Because deep neural networks in machine vision loosely mimic the structure and connections of the visual cortex, the authors naturally asked: do ANNs also exhibit the law of completion? And what does that tell us about how an AI thinks?
Enter the Germans
The law of completion is part of a series of ideas from Gestalt psychology. Back in the 1920s, long before the advent of modern neuroscience, a group of German experimental psychologists asked: in this chaotic, flashy, unpredictable world, how do we piece together input in a way that leads to meaningful perceptions?
The result is a group of principles known together as the Gestalt effect: that the mind self-organizes to form a global whole. In the more famous words of Gestalt psychologist Kurt Koffka, our perception forms a whole that’s “something else than the sum of its parts.” Not greater than; just different.
Although the theory has its critics, subsequent studies in humans and animals suggest that the law of completion happens on both the cognitive and neuroanatomical level.
Take a look at the drawing below. You immediately “see” a shape that’s actually the negative: a triangle or a square (A and B). Or you further perceive a 3D ball (C), or a snake-like squiggle (D). Your mind fills in blank spots, so that the final perception is more than just the black shapes you’re explicitly given.
Image Credit: Wikimedia Commons contributors, the free media repository.
Neuroscientists now think that the effect comes from how our visual system processes information. Arranged in multiple layers and columns, lower-level neurons—those first to wrangle the data—tend to extract simpler features such as lines or angles. In Gestalt speak, they “see” the parts.
Then, layer by layer, perception becomes more abstract, until higher levels of the visual system directly interpret faces or objects—or things that don’t really exist. That is, the “whole” emerges.
The Experiment Setup
Inspired by these classical experiments, Kim and team developed a protocol to test the Gestalt effect on feed-forward ANNs: one simple, the other, dubbed the “Inception V3,” far more complex and widely used in the machine vision community.
The main idea is similar to the triangle drawings above. First, the team generated three datasets: one set shows complete, ordinary triangles. The second—the “Illusory” set, shows triangles with the edges removed but the corners intact. Thanks to the Gestalt effect, to us humans these generally still look like triangles. The third set also only shows incomplete triangle corners. But here, the corners are randomly rotated so that we can no longer imagine a line connecting them—hence, no more triangle.
To generate a dataset large enough to tease out small effects, the authors changed the background color, image rotation, and other aspects of the dataset. In all, they produced nearly 1,000 images to test their ANNs on.
“At a high level, we compare an ANN’s activation similarities between the three sets of stimuli,” the authors explained. The process is two steps: first, train the AI on complete triangles. Second, test them on the datasets. If the response is more similar between the illusory set and the complete triangle—rather than the randomly rotated set—it should suggest a sort of Gestalt closure effect in the network.
Machine Gestalt
Right off the bat, the team got their answer: yes, ANNs do seem to exhibit the law of closure.
When trained on natural images, the networks better classified the illusory set as triangles than those with randomized connection weights or networks trained on white noise.
When the team dug into the “why,” things got more interesting. The ability to complete an image correlated with the network’s ability to generalize.
Humans subconsciously do this constantly: anything with a handle made out of ceramic, regardless of shape, could easily be a mug. ANNs still struggle to grasp common features—clues that immediately tells us “hey, that’s a mug!” But when they do, it sometimes allows the networks to better generalize.
“What we observe here is that a network that is able to generalize exhibits…more of the closure effect [emphasis theirs], hinting that the closure effect reflects something beyond simply learning features,” the team wrote.
What’s more, remarkably similar to the visual cortex, “higher” levels of the ANNs showed more of the closure effect than lower layers, and—perhaps unsurprisingly—the more layers a network had, the more it exhibited the closure effect.
As the networks learned, their ability to map out objects from fragments also improved. When the team messed around with the brightness and contrast of the images, the AI still learned to see the forest from the trees.
“Our findings suggest that neural networks trained with natural images do exhibit closure,” the team concluded.
AI Psychology
That’s not to say that ANNs recapitulate the human brain. As Google’s Deep Dream, an effort to coax AIs into spilling what they’re perceiving, clearly demonstrates, machine vision sees some truly weird stuff.
In contrast, because they’re modeled after the human visual cortex, perhaps it’s not all that surprising that these networks also exhibit higher-level properties inherent to how we process information.
But to Kim and her colleagues, that’s exactly the point.
“The field of psychology has developed useful tools and insights to study human brains– tools that we may be able to borrow to analyze artificial neural networks,” they wrote.
By tweaking these tools to better analyze machine minds, the authors were able to gain insight on how similarly or differently they see the world from us. And that’s the crux: the point isn’t to say that ANNs perceive the world sort of, kind of, maybe similar to humans. It’s to tap into a wealth of cognitive psychology tools, established over decades using human minds, to probe that of ANNs.
“The work here is just one step along a much longer path,” the authors conclude.
“Understanding where humans and neural networks differ will be helpful for research on interpretability by enlightening the fundamental differences between the two interesting species.”
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