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#437301 The Global Work Crisis: Automation, the ...
The alarm bell rings. You open your eyes, come to your senses, and slide from dream state to consciousness. You hit the snooze button, and eventually crawl out of bed to the start of yet another working day.
This daily narrative is experienced by billions of people all over the world. We work, we eat, we sleep, and we repeat. As our lives pass day by day, the beating drums of the weekly routine take over and years pass until we reach our goal of retirement.
A Crisis of Work
We repeat the routine so that we can pay our bills, set our kids up for success, and provide for our families. And after a while, we start to forget what we would do with our lives if we didn’t have to go back to work.
In the end, we look back at our careers and reflect on what we’ve achieved. It may have been the hundreds of human interactions we’ve had; the thousands of emails read and replied to; the millions of minutes of physical labor—all to keep the global economy ticking along.
According to Gallup’s World Poll, only 15 percent of people worldwide are actually engaged with their jobs. The current state of “work” is not working for most people. In fact, it seems we as a species are trapped by a global work crisis, which condemns people to cast away their time just to get by in their day-to-day lives.
Technologies like artificial intelligence and automation may help relieve the work burdens of millions of people—but to benefit from their impact, we need to start changing our social structures and the way we think about work now.
The Specter of Automation
Automation has been ongoing since the Industrial Revolution. In recent decades it has taken on a more elegant guise, first with physical robots in production plants, and more recently with software automation entering most offices.
The driving goal behind much of this automation has always been productivity and hence, profits: technology that can act as a multiplier on what a single human can achieve in a day is of huge value to any company. Powered by this strong financial incentive, the quest for automation is growing ever more pervasive.
But if automation accelerates or even continues at its current pace and there aren’t strong social safety nets in place to catch the people who are negatively impacted (such as by losing their jobs), there could be a host of knock-on effects, including more concentrated wealth among a shrinking elite, more strain on government social support, an increase in depression and drug dependence, and even violent social unrest.
It seems as though we are rushing headlong into a major crisis, driven by the engine of accelerating automation. But what if instead of automation challenging our fragile status quo, we view it as the solution that can free us from the shackles of the Work Crisis?
The Way Out
In order to undertake this paradigm shift, we need to consider what society could potentially look like, as well as the problems associated with making this change. In the context of these crises, our primary aim should be for a system where people are not obligated to work to generate the means to survive. This removal of work should not threaten access to food, water, shelter, education, healthcare, energy, or human value. In our current system, work is the gatekeeper to these essentials: one can only access these (and even then often in a limited form), if one has a “job” that affords them.
Changing this system is thus a monumental task. This comes with two primary challenges: providing people without jobs with financial security, and ensuring they maintain a sense of their human value and worth. There are several measures that could be implemented to help meet these challenges, each with important steps for society to consider.
Universal basic income (UBI)
UBI is rapidly gaining support, and it would allow people to become shareholders in the fruits of automation, which would then be distributed more broadly.
UBI trials have been conducted in various countries around the world, including Finland, Kenya, and Spain. The findings have generally been positive on the health and well-being of the participants, and showed no evidence that UBI disincentivizes work, a common concern among the idea’s critics. The most recent popular voice for UBI has been that of former US presidential candidate Andrew Yang, who now runs a non-profit called Humanity Forward.
UBI could also remove wasteful bureaucracy in administering welfare payments (since everyone receives the same amount, there’s no need to prevent false claims), and promote the pursuit of projects aligned with peoples’ skill sets and passions, as well as quantifying the value of tasks not recognized by economic measures like Gross Domestic Product (GDP). This includes looking after children and the elderly at home.
How a UBI can be initiated with political will and social backing and paid for by governments has been hotly debated by economists and UBI enthusiasts. Variables like how much the UBI payments should be, whether to implement taxes such as Yang’s proposed valued added tax (VAT), whether to replace existing welfare payments, the impact on inflation, and the impact on “jobs” from people who would otherwise look for work require additional discussion. However, some have predicted the inevitability of UBI as a result of automation.
Universal healthcare
Another major component of any society is the healthcare of its citizens. A move away from work would further require the implementation of a universal healthcare system to decouple healthcare from jobs. Currently in the US, and indeed many other economies, healthcare is tied to employment.
Universal healthcare such as Medicare in Australia is evidence for the adage “prevention is better than cure,” when comparing the cost of healthcare in the US with Australia on a per capita basis. This has already presented itself as an advancement in the way healthcare is considered. There are further benefits of a healthier population, including less time and money spent on “sick-care.” Healthy people are more likely and more able to achieve their full potential.
Reshape the economy away from work-based value
One of the greatest challenges in a departure from work is for people to find value elsewhere in life. Many people view their identities as being inextricably tied to their jobs, and life without a job is therefore a threat to one’s sense of existence. This presents a shift that must be made at both a societal and personal level.
A person can only seek alternate value in life when afforded the time to do so. To this end, we need to start reducing “work-for-a-living” hours towards zero, which is a trend we are already seeing in Europe. This should not come at the cost of reducing wages pro rata, but rather could be complemented by UBI or additional schemes where people receive dividends for work done by automation. This transition makes even more sense when coupled with the idea of deviating from using GDP as a measure of societal growth, and instead adopting a well-being index based on universal human values like health, community, happiness, and peace.
The crux of this issue is in transitioning away from the view that work gives life meaning and life is about using work to survive, towards a view of living a life that itself is fulfilling and meaningful. This speaks directly to notions from Maslow’s hierarchy of needs, where work largely addresses psychological and safety needs such as shelter, food, and financial well-being. More people should have a chance to grow beyond the most basic needs and engage in self-actualization and transcendence.
The question is largely around what would provide people with a sense of value, and the answers would differ as much as people do; self-mastery, building relationships and contributing to community growth, fostering creativity, and even engaging in the enjoyable aspects of existing jobs could all come into play.
Universal education
With a move towards a society that promotes the values of living a good life, the education system would have to evolve as well. Researchers have long argued for a more nimble education system, but universities and even most online courses currently exist for the dominant purpose of ensuring people are adequately skilled to contribute to the economy. These “job factories” only exacerbate the Work Crisis. In fact, the response often given by educational institutions to the challenge posed by automation is to find new ways of upskilling students, such as ensuring they are all able to code. As alluded to earlier, this is a limited and unimaginative solution to the problem we are facing.
Instead, education should be centered on helping people acknowledge the current crisis of work and automation, teach them how to derive value that is decoupled from work, and enable people to embrace progress as we transition to the new economy.
Disrupting the Status Quo
While we seldom stop to think about it, much of the suffering faced by humanity is brought about by the systemic foe that is the Work Crisis. The way we think about work has brought society far and enabled tremendous developments, but at the same time it has failed many people. Now the status quo is threatened by those very developments as we progress to an era where machines are likely to take over many job functions.
This impending paradigm shift could be a threat to the stability of our fragile system, but only if it is not fully anticipated. If we prepare for it appropriately, it could instead be the key not just to our survival, but to a better future for all.
Image Credit: mostafa meraji from Pixabay Continue reading
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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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#437290 Exploring the interactions between ...
Over the past few years, robotics researchers have designed tiny and untethered swimming robots, also known as microswimmers, with increasingly advanced sensing and locomotion capabilities. These microrobots could prove very useful in medical settings, particularly for the implementation of minimally invasive targeted therapies in parts of the body that are difficult to reach, such as the central nervous system or vascular system. 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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