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#431836 Do Our Brains Use Deep Learning to Make ...
The first time Dr. Blake Richards heard about deep learning, he was convinced that he wasn’t just looking at a technique that would revolutionize artificial intelligence. He also knew he was looking at something fundamental about the human brain.
That was the early 2000s, and Richards was taking a course with Dr. Geoff Hinton at the University of Toronto. Hinton, a pioneer architect of the algorithm that would later take the world by storm, was offering an introductory course on his learning method inspired by the human brain.
The key words here are “inspired by.” Despite Richards’ conviction, the odds were stacked against him. The human brain, as it happens, seems to lack a critical function that’s programmed into deep learning algorithms. On the surface, the algorithms were violating basic biological facts already proven by neuroscientists.
But what if, superficial differences aside, deep learning and the brain are actually compatible?
Now, in a new study published in eLife, Richards, working with DeepMind, proposed a new algorithm based on the biological structure of neurons in the neocortex. Also known as the cortex, this outermost region of the brain is home to higher cognitive functions such as reasoning, prediction, and flexible thought.
The team networked their artificial neurons together into a multi-layered network and challenged it with a classic computer vision task—identifying hand-written numbers.
The new algorithm performed well. But the kicker is that it analyzed the learning examples in a way that’s characteristic of deep learning algorithms, even though it was completely based on the brain’s fundamental biology.
“Deep learning is possible in a biological framework,” concludes the team.
Because the model is only a computer simulation at this point, Richards hopes to pass the baton to experimental neuroscientists, who could actively test whether the algorithm operates in an actual brain.
If so, the data could then be passed back to computer scientists to work out the next generation of massively parallel and low-energy algorithms to power our machines.
It’s a first step towards merging the two fields back into a “virtuous circle” of discovery and innovation.
The blame game
While you’ve probably heard of deep learning’s recent wins against humans in the game of Go, you might not know the nitty-gritty behind the algorithm’s operations.
In a nutshell, deep learning relies on an artificial neural network with virtual “neurons.” Like a towering skyscraper, the network is structured into hierarchies: lower-level neurons process aspects of an input—for example, a horizontal or vertical stroke that eventually forms the number four—whereas higher-level neurons extract more abstract aspects of the number four.
To teach the network, you give it examples of what you’re looking for. The signal propagates forward in the network (like climbing up a building), where each neuron works to fish out something fundamental about the number four.
Like children trying to learn a skill the first time, initially the network doesn’t do so well. It spits out what it thinks a universal number four should look like—think a Picasso-esque rendition.
But here’s where the learning occurs: the algorithm compares the output with the ideal output, and computes the difference between the two (dubbed “error”). This error is then “backpropagated” throughout the entire network, telling each neuron: hey, this is how far off you were, so try adjusting your computation closer to the ideal.
Millions of examples and tweakings later, the network inches closer to the desired output and becomes highly proficient at the trained task.
This error signal is crucial for learning. Without efficient “backprop,” the network doesn’t know which of its neurons are off kilter. By assigning blame, the AI can better itself.
The brain does this too. How? We have no clue.
Biological No-Go
What’s clear, though, is that the deep learning solution doesn’t work.
Backprop is a pretty needy function. It requires a very specific infrastructure for it to work as expected.
For one, each neuron in the network has to receive the error feedback. But in the brain, neurons are only connected to a few downstream partners (if that). For backprop to work in the brain, early-level neurons need to be able to receive information from billions of connections in their downstream circuits—a biological impossibility.
And while certain deep learning algorithms adapt a more local form of backprop— essentially between neurons—it requires their connection forwards and backwards to be symmetric. This hardly ever occurs in the brain’s synapses.
More recent algorithms adapt a slightly different strategy, in that they implement a separate feedback pathway that helps the neurons to figure out errors locally. While it’s more biologically plausible, the brain doesn’t have a separate computational network dedicated to the blame game.
What it does have are neurons with intricate structures, unlike the uniform “balls” that are currently applied in deep learning.
Branching Networks
The team took inspiration from pyramidal cells that populate the human cortex.
“Most of these neurons are shaped like trees, with ‘roots’ deep in the brain and ‘branches’ close to the surface,” says Richards. “What’s interesting is that these roots receive a different set of inputs than the branches that are way up at the top of the tree.”
This is an illustration of a multi-compartment neural network model for deep learning. Left: Reconstruction of pyramidal neurons from mouse primary visual cortex. Right: Illustration of simplified pyramidal neuron models. Image Credit: CIFAR
Curiously, the structure of neurons often turn out be “just right” for efficiently cracking a computational problem. Take the processing of sensations: the bottoms of pyramidal neurons are right smack where they need to be to receive sensory input, whereas the tops are conveniently placed to transmit feedback errors.
Could this intricate structure be evolution’s solution to channeling the error signal?
The team set up a multi-layered neural network based on previous algorithms. But rather than having uniform neurons, they gave those in middle layers—sandwiched between the input and output—compartments, just like real neurons.
When trained with hand-written digits, the algorithm performed much better than a single-layered network, despite lacking a way to perform classical backprop. The cell-like structure itself was sufficient to assign error: the error signals at one end of the neuron are naturally kept separate from input at the other end.
Then, at the right moment, the neuron brings both sources of information together to find the best solution.
There’s some biological evidence for this: neuroscientists have long known that the neuron’s input branches perform local computations, which can be integrated with signals that propagate backwards from the so-called output branch.
However, we don’t yet know if this is the brain’s way of dealing blame—a question that Richards urges neuroscientists to test out.
What’s more, the network parsed the problem in a way eerily similar to traditional deep learning algorithms: it took advantage of its multi-layered structure to extract progressively more abstract “ideas” about each number.
“[This is] the hallmark of deep learning,” the authors explain.
The Deep Learning Brain
Without doubt, there will be more twists and turns to the story as computer scientists incorporate more biological details into AI algorithms.
One aspect that Richards and team are already eyeing is a top-down predictive function, in which signals from higher levels directly influence how lower levels respond to input.
Feedback from upper levels doesn’t just provide error signals; it could also be nudging lower processing neurons towards a “better” activity pattern in real-time, says Richards.
The network doesn’t yet outperform other non-biologically derived (but “brain-inspired”) deep networks. But that’s not the point.
“Deep learning has had a huge impact on AI, but, to date, its impact on neuroscience has been limited,” the authors say.
Now neuroscientists have a lead they could experimentally test: that the structure of neurons underlie nature’s own deep learning algorithm.
“What we might see in the next decade or so is a real virtuous cycle of research between neuroscience and AI, where neuroscience discoveries help us to develop new AI and AI can help us interpret and understand our experimental data in neuroscience,” says Richards.
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#431828 This Self-Driving AI Is Learning to ...
I don’t have to open the doors of AImotive’s white 2015 Prius to see that it’s not your average car. This particular Prius has been christened El Capitan, the name written below the rear doors, and two small cameras are mounted on top of the car. Bundles of wire snake out from them, as well as from the two additional cameras on the car’s hood and trunk.
Inside is where things really get interesting, though. The trunk holds a computer the size of a microwave, and a large monitor covers the passenger glove compartment and dashboard. The center console has three switches labeled “Allowed,” “Error,” and “Active.”
Budapest-based AImotive is working to provide scalable self-driving technology alongside big players like Waymo and Uber in the autonomous vehicle world. On a highway test ride with CEO Laszlo Kishonti near the company’s office in Mountain View, California, I got a glimpse of just how complex that world is.
Camera-Based Feedback System
AImotive’s approach to autonomous driving is a little different from that of some of the best-known systems. For starters, they’re using cameras, not lidar, as primary sensors. “The traffic system is visual and the cost of cameras is low,” Kishonti said. “A lidar can recognize when there are people near the car, but a camera can differentiate between, say, an elderly person and a child. Lidar’s resolution isn’t high enough to recognize the subtle differences of urban driving.”
Image Credit: AImotive
The company’s aiDrive software uses data from the camera sensors to feed information to its algorithms for hierarchical decision-making, grouped under four concurrent activities: recognition, location, motion, and control.
Kishonti pointed out that lidar has already gotten more cost-efficient, and will only continue to do so.
“Ten years ago, lidar was best because there wasn’t enough processing power to do all the calculations by AI. But the cost of running AI is decreasing,” he said. “In our approach, computer vision and AI processing are key, and for safety, we’ll have fallback sensors like radar or lidar.”
aiDrive currently runs on Nvidia chips, which Kishonti noted were originally designed for graphics, and are not terribly efficient given how power-hungry they are. “We’re planning to substitute lower-cost, lower-energy chips in the next six months,” he said.
Testing in Virtual Reality
Waymo recently announced its fleet has now driven four million miles autonomously. That’s a lot of miles, and hard to compete with. But AImotive isn’t trying to compete, at least not by logging more real-life test miles. Instead, the company is doing 90 percent of its testing in virtual reality. “This is what truly differentiates us from competitors,” Kishonti said.
He outlined the three main benefits of VR testing: it can simulate scenarios too dangerous for the real world (such as hitting something), too costly (not every company has Waymo’s funds to run hundreds of cars on real roads), or too time-consuming (like waiting for rain, snow, or other weather conditions to occur naturally and repeatedly).
“Real-world traffic testing is very skewed towards the boring miles,” he said. “What we want to do is test all the cases that are hard to solve.”
On a screen that looked not unlike multiple games of Mario Kart, he showed me the simulator. Cartoon cars cruised down winding streets, outfitted with all the real-world surroundings: people, trees, signs, other cars. As I watched, a furry kangaroo suddenly hopped across one screen. “Volvo had an issue in Australia,” Kishonti explained. “A kangaroo’s movement is different than other animals since it hops instead of running.” Talk about cases that are hard to solve.
AImotive is currently testing around 1,000 simulated scenarios every night, with a steadily-rising curve of successful tests. These scenarios are broken down into features, and the car’s behavior around those features fed into a neural network. As the algorithms learn more features, the level of complexity the vehicles can handle goes up.
On the Road
After Kishonti and his colleagues filled me in on the details of their product, it was time to test it out. A safety driver sat in the driver’s seat, a computer operator in the passenger seat, and Kishonti and I in back. The driver maintained full control of the car until we merged onto the highway. Then he flicked the “Allowed” switch, his copilot pressed the “Active” switch, and he took his hands off the wheel.
What happened next, you ask?
A few things. El Capitan was going exactly the speed limit—65 miles per hour—which meant all the other cars were passing us. When a car merged in front of us or cut us off, El Cap braked accordingly (if a little abruptly). The monitor displayed the feed from each of the car’s cameras, plus multiple data fields and a simulation where a blue line marked the center of the lane, measured by the cameras tracking the lane markings on either side.
I noticed El Cap wobbling out of our lane a bit, but it wasn’t until two things happened in a row that I felt a little nervous: first we went under a bridge, then a truck pulled up next to us, both bridge and truck casting a complete shadow over our car. At that point El Cap lost it, and we swerved haphazardly to the right, narrowly missing the truck’s rear wheels. The safety driver grabbed the steering wheel and took back control of the car.
What happened, Kishonti explained, was that the shadows made it hard for the car’s cameras to see the lane markings. This was a new scenario the algorithm hadn’t previously encountered. If we’d only gone under a bridge or only been next to the truck for a second, El Cap may not have had so much trouble, but the two events happening in a row really threw the car for a loop—almost literally.
“This is a new scenario we’ll add to our testing,” Kishonti said. He added that another way for the algorithm to handle this type of scenario, rather than basing its speed and positioning on the lane markings, is to mimic nearby cars. “The human eye would see that other cars are still moving at the same speed, even if it can’t see details of the road,” he said.
After another brief—and thankfully uneventful—hands-off cruise down the highway, the safety driver took over, exited the highway, and drove us back to the office.
Driving into the Future
I climbed out of the car feeling amazed not only that self-driving cars are possible, but that driving is possible at all. I squint when driving into a tunnel, swerve to avoid hitting a stray squirrel, and brake gradually at stop signs—all without consciously thinking to do so. On top of learning to steer, brake, and accelerate, self-driving software has to incorporate our brains’ and bodies’ unconscious (but crucial) reactions, like our pupils dilating to let in more light so we can see in a tunnel.
Despite all the progress of machine learning, artificial intelligence, and computing power, I have a wholly renewed appreciation for the thing that’s been in charge of driving up till now: the human brain.
Kishonti seemed to feel similarly. “I don’t think autonomous vehicles in the near future will be better than the best drivers,” he said. “But they’ll be better than the average driver. What we want to achieve is safe, good-quality driving for everyone, with scalability.”
AImotive is currently working with American tech firms and with car and truck manufacturers in Europe, China, and Japan.
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#431669 Technologically enhanced humans—a look ...
What exactly do we mean by an "enhanced" human? When this possibility is brought up, what is generally being referred to is the addition of human and machine-based performances (expanding on the figure of the cyborg popularised by science fiction). But enhanced in relation to what? According to which reference values and criteria? How, for example, can happiness be measured? A good life? Sensations, like smells or touch which connect us to the world? How happy we feel when we are working? All these dimensions that make life worth living. We must be careful here not to give in to the magic of figures. A plus can hide a minus; something gained may conceal something lost. What is gained or lost, however, is difficult to identify as it is neither quantifiable nor measurable. Continue reading
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#431653 9 Robot Animals Built From Nature’s ...
Millions of years of evolution have allowed animals to develop some elegant and highly efficient solutions to problems like locomotion, flight, and dexterity. As Boston Dynamics unveils its latest mechanical animals, here’s a rundown of nine recent robots that borrow from nature and why.
SpotMini – Boston Dynamics
Starting with BigDog in 2005, the US company has built a whole stable of four-legged robots in recent years. Their first product was designed to be a robotic packhorse for soldiers that borrowed the quadrupedal locomotion of animals to travel over terrain too rough for conventional vehicles.
The US Army ultimately rejected the robot for being too noisy, according to the Guardian, but since then the company has scaled down its design, first to the Spot, then a first edition of the SpotMini that came out last year.
The latter came with a robotic arm where its head should be and was touted as a domestic helper, but a sleeker second edition without the arm was released earlier this month. There’s little detail on what the new robot is designed for, but the more polished design suggests a more consumer-focused purpose.
OctopusGripper – Festo
Festo has released a long line of animal-inspired machines over the years, from a mechanical kangaroo to robotic butterflies. Its latest creation isn’t a full animal—instead it’s a gripper based on an octopus tentacle that can be attached to the end of a robotic arm.
The pneumatically-powered device is made of soft silicone and features two rows of suction cups on its inner edge. By applying compressed air the tentacle can wrap around a wide variety of differently shaped objects, just like its natural counterpart, and a vacuum can be applied to the larger suction cups to grip the object securely. Because it’s soft, it holds promise for robots required to operate safely in collaboration with humans.
CRAM – University of California, Berkeley
Cockroaches are renowned for their hardiness and ability to disappear down cracks that seem far too small for them. Researchers at UC Berkeley decided these capabilities could be useful for search and rescue missions and so set about experimenting on the insects to find out their secrets.
They found the bugs can squeeze into gaps a fifth of their normal standing height by splaying their legs out to the side without significantly slowing themselves down. So they built a palm-sized robot with a jointed plastic shell that could do the same to squeeze into crevices half its normal height.
Snake Robot – Carnegie Mellon University
Search and rescue missions are a common theme for animal-inspired robots, but the snake robot built by CMU researchers is one of the first to be tested in a real disaster.
A team of roboticists from the university helped Mexican Red Cross workers search collapsed buildings for survivors after the 7.1-magnitude earthquake that struck Mexico City in September. The snake design provides a small diameter and the ability to move in almost any direction, which makes the robot ideal for accessing tight spaces, though the team was unable to locate any survivors.
The snake currently features a camera on the front, but researchers told IEEE Spectrum that the experience helped them realize they should also add a microphone to listen for people trapped under the rubble.
Bio-Hybrid Stingray – Harvard University
Taking more than just inspiration from the animal kingdom, a group from Harvard built a robotic stingray out of silicone and rat heart muscle cells.
The robot uses the same synchronized undulations along the edge of its fins to propel itself as a ray does. But while a ray has two sets of muscles to pull the fins up and down, the new device has only one that pulls them down, with a springy gold skeleton that pulls them back up again. The cells are also genetically modified to be activated by flashes of light.
The project’s leader eventually hopes to engineer a human heart, and both his stingray and an earlier jellyfish bio-robot are primarily aimed at better understanding how that organ works.
Bat Bot – Caltech
Most recent advances in drone technology have come from quadcopters, but Caltech engineers think rigid devices with rapidly spinning propellers are probably not ideal for use in close quarters with humans.
That’s why they turned to soft-winged bats for inspiration. That’s no easy feat, though, considering bats use more than 40 joints with each flap of their wings, so the team had to optimize down to nine joints to avoid it becoming too bulky. The simplified bat can’t ascend yet, but its onboard computer and sensors let it autonomously carry out glides, turns, and dives.
Salto – UC Berkeley
While even the most advanced robots tend to plod around, tree-dwelling animals have the ability to spring from branch to branch to clear obstacles and climb quickly. This could prove invaluable for search and rescue robots by allowing them to quickly traverse disordered rubble.
UC Berkeley engineers turned to the Senegal bush baby for inspiration after determining it scored highest in “vertical jumping agility”—a combination of how high and how frequently an animal can jump. They recreated its ability to get into a super-low crouch that stores energy in its tendons to create a robot that could carry out parkour-style double jumps off walls to quickly gain height.
Pleurobot – École Polytechnique Fédérale de Lausanne
Normally robots are masters of air, land, or sea, but the robotic salamander built by researchers at EPFL can both walk and swim.
Its designers used X-ray videos to carefully study how the amphibians move before using this to build a true-to-life robotic version using 3D printed bones, motorized joints, and a synthetic nervous system made up of electronic circuitry.
The robot’s low center of mass and segmented legs make it great at navigating rough terrain without losing balance, and the ability to swim gives added versatility. They also hope it will help paleontologists gain a better understanding of the movements of the first tetrapods to transition from water to land, which salamanders are the best living analog of.
Eelume – Eelume
A snakelike body isn’t only useful on land—eels are living proof it’s an efficient way to travel underwater, too. Norwegian robotics company Eelume has borrowed these principles to build a robot capable of sub-sea inspection, maintenance, and repair.
The modular design allows operators to put together their own favored configuration of joints and payloads such as sensors and tools. And while an early version of the robot used the same method of locomotion as an eel, the latest version undergoing sea trials has added a variety of thrusters for greater speeds and more maneuverability.
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#431599 8 Ways AI Will Transform Our Cities by ...
How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound.
The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, technical fellow and a managing director at Microsoft Research.
Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city. Here’s how they think it will affect eight key domains of city life in the next fifteen years.
1. Transportation
The speed of the transition to AI-guided transport may catch the public by surprise. Self-driving vehicles will be widely adopted by 2020, and it won’t just be cars — driverless delivery trucks, autonomous delivery drones, and personal robots will also be commonplace.
Uber-style “cars as a service” are likely to replace car ownership, which may displace public transport or see it transition towards similar on-demand approaches. Commutes will become a time to relax or work productively, encouraging people to live further from home, which could combine with reduced need for parking to drastically change the face of modern cities.
Mountains of data from increasing numbers of sensors will allow administrators to model individuals’ movements, preferences, and goals, which could have major impact on the design city infrastructure.
Humans won’t be out of the loop, though. Algorithms that allow machines to learn from human input and coordinate with them will be crucial to ensuring autonomous transport operates smoothly. Getting this right will be key as this will be the public’s first experience with physically embodied AI systems and will strongly influence public perception.
2. Home and Service Robots
Robots that do things like deliver packages and clean offices will become much more common in the next 15 years. Mobile chipmakers are already squeezing the power of last century’s supercomputers into systems-on-a-chip, drastically boosting robots’ on-board computing capacity.
Cloud-connected robots will be able to share data to accelerate learning. Low-cost 3D sensors like Microsoft’s Kinect will speed the development of perceptual technology, while advances in speech comprehension will enhance robots’ interactions with humans. Robot arms in research labs today are likely to evolve into consumer devices around 2025.
But the cost and complexity of reliable hardware and the difficulty of implementing perceptual algorithms in the real world mean general-purpose robots are still some way off. Robots are likely to remain constrained to narrow commercial applications for the foreseeable future.
3. Healthcare
AI’s impact on healthcare in the next 15 years will depend more on regulation than technology. The most transformative possibilities of AI in healthcare require access to data, but the FDA has failed to find solutions to the difficult problem of balancing privacy and access to data. Implementation of electronic health records has also been poor.
If these hurdles can be cleared, AI could automate the legwork of diagnostics by mining patient records and the scientific literature. This kind of digital assistant could allow doctors to focus on the human dimensions of care while using their intuition and experience to guide the process.
At the population level, data from patient records, wearables, mobile apps, and personal genome sequencing will make personalized medicine a reality. While fully automated radiology is unlikely, access to huge datasets of medical imaging will enable training of machine learning algorithms that can “triage” or check scans, reducing the workload of doctors.
Intelligent walkers, wheelchairs, and exoskeletons will help keep the elderly active while smart home technology will be able to support and monitor them to keep them independent. Robots may begin to enter hospitals carrying out simple tasks like delivering goods to the right room or doing sutures once the needle is correctly placed, but these tasks will only be semi-automated and will require collaboration between humans and robots.
4. Education
The line between the classroom and individual learning will be blurred by 2030. Massive open online courses (MOOCs) will interact with intelligent tutors and other AI technologies to allow personalized education at scale. Computer-based learning won’t replace the classroom, but online tools will help students learn at their own pace using techniques that work for them.
AI-enabled education systems will learn individuals’ preferences, but by aggregating this data they’ll also accelerate education research and the development of new tools. Online teaching will increasingly widen educational access, making learning lifelong, enabling people to retrain, and increasing access to top-quality education in developing countries.
Sophisticated virtual reality will allow students to immerse themselves in historical and fictional worlds or explore environments and scientific objects difficult to engage with in the real world. Digital reading devices will become much smarter too, linking to supplementary information and translating between languages.
5. Low-Resource Communities
In contrast to the dystopian visions of sci-fi, by 2030 AI will help improve life for the poorest members of society. Predictive analytics will let government agencies better allocate limited resources by helping them forecast environmental hazards or building code violations. AI planning could help distribute excess food from restaurants to food banks and shelters before it spoils.
Investment in these areas is under-funded though, so how quickly these capabilities will appear is uncertain. There are fears valueless machine learning could inadvertently discriminate by correlating things with race or gender, or surrogate factors like zip codes. But AI programs are easier to hold accountable than humans, so they’re more likely to help weed out discrimination.
6. Public Safety and Security
By 2030 cities are likely to rely heavily on AI technologies to detect and predict crime. Automatic processing of CCTV and drone footage will make it possible to rapidly spot anomalous behavior. This will not only allow law enforcement to react quickly but also forecast when and where crimes will be committed. Fears that bias and error could lead to people being unduly targeted are justified, but well-thought-out systems could actually counteract human bias and highlight police malpractice.
Techniques like speech and gait analysis could help interrogators and security guards detect suspicious behavior. Contrary to concerns about overly pervasive law enforcement, AI is likely to make policing more targeted and therefore less overbearing.
7. Employment and Workplace
The effects of AI will be felt most profoundly in the workplace. By 2030 AI will be encroaching on skilled professionals like lawyers, financial advisers, and radiologists. As it becomes capable of taking on more roles, organizations will be able to scale rapidly with relatively small workforces.
AI is more likely to replace tasks rather than jobs in the near term, and it will also create new jobs and markets, even if it’s hard to imagine what those will be right now. While it may reduce incomes and job prospects, increasing automation will also lower the cost of goods and services, effectively making everyone richer.
These structural shifts in the economy will require political rather than purely economic responses to ensure these riches are shared. In the short run, this may include resources being pumped into education and re-training, but longer term may require a far more comprehensive social safety net or radical approaches like a guaranteed basic income.
8. Entertainment
Entertainment in 2030 will be interactive, personalized, and immeasurably more engaging than today. Breakthroughs in sensors and hardware will see virtual reality, haptics and companion robots increasingly enter the home. Users will be able to interact with entertainment systems conversationally, and they will show emotion, empathy, and the ability to adapt to environmental cues like the time of day.
Social networks already allow personalized entertainment channels, but the reams of data being collected on usage patterns and preferences will allow media providers to personalize entertainment to unprecedented levels. There are concerns this could endow media conglomerates with unprecedented control over people’s online experiences and the ideas to which they are exposed.
But advances in AI will also make creating your own entertainment far easier and more engaging, whether by helping to compose music or choreograph dances using an avatar. Democratizing the production of high-quality entertainment makes it nearly impossible to predict how highly fluid human tastes for entertainment will develop.
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