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#435211 iRobot Acquires Root Robotics to Boost ...
Root promises to teach coding skills to children, starting as young as 4 years old Continue reading
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#435196 Avatar Love? New ‘Black Mirror’ ...
This week, the widely-anticipated fifth season of the dystopian series Black Mirror was released on Netflix. The storylines this season are less focused on far-out scenarios and increasingly aligned with current issues. With only three episodes, this season raises more questions than it answers, often leaving audiences bewildered.
The episode Smithereens explores our society’s crippling addiction to social media platforms and the monopoly they hold over our data. In Rachel, Jack and Ashley Too, we see the disruptive impact of technologies on the music and entertainment industry, and the price of fame for artists in the digital world. Like most Black Mirror episodes, these explore the sometimes disturbing implications of tech advancements on humanity.
But once again, in the midst of all the doom and gloom, the creators of the series leave us with a glimmer of hope. Aligned with Pride month, the episode Striking Vipers explores the impact of virtual reality on love, relationships, and sexual fluidity.
*The review contains a few spoilers.*
Striking Vipers
The first episode of the season, Striking Vipers may be one of the most thought-provoking episodes in Black Mirror history. Reminiscent of previous episodes San Junipero and Hang the DJ, the writers explore the potential for technology to transform human intimacy.
The episode tells the story of two old friends, Danny and Karl, whose friendship is reignited in an unconventional way. Karl unexpectedly appears at Danny’s 38th birthday and reintroduces him to the VR version of a game they used to play years before. In the game Striking Vipers X, each of the players is represented by an avatar of their choice in an uncanny digital reality. Following old tradition, Karl chooses to become the female fighter, Roxanne, and Danny takes on the role of the male fighter, Lance. The state-of-the-art VR headsets appear to use an advanced form of brain-machine interface to allow each player to be fully immersed in the virtual world, emulating all physical sensations.
To their surprise (and confusion), Danny and Karl find themselves transitioning from fist-fighting to kissing. Over the course of many games, they continue to explore a sexual and romantic relationship in the virtual world, leaving them confused and distant in the real world. The virtual and physical realities begin to blur, and so do the identities of the players with their avatars. Danny, who is married (in a heterosexual relationship) and is a father, begins to carry guilt and confusion in the real world. They both wonder if there would be any spark between them in real life.
The brain-machine interface (BMI) depicted in the episode is still science fiction, but that hasn’t stopped innovators from pushing the technology forward. Experts today are designing more intricate BMI systems while programming better algorithms to interpret the neural signals they capture. Scientists have already succeeded in enabling paralyzed patients to type with their minds, and are even allowing people to communicate with one another purely through brainwaves.
The convergence of BMIs with virtual reality and artificial intelligence could make the experience of such immersive digital realities possible. Virtual reality, too, is decreasing exponentially in cost and increasing in quality.
The narrative provides meaningful commentary on another tech area—gaming. It highlights video games not necessarily as addictive distractions, but rather as a platform for connecting with others in a deeper way. This is already very relevant. Video games like Final Fantasy are often a tool for meaningful digital connections for their players.
The Implications of Virtual Reality on Love and Relationships
The narrative of Striking Vipers raises many novel questions about the implications of immersive technologies on relationships: could the virtual world allow us a safe space to explore suppressed desires? Can virtual avatars make it easier for us to show affection to those we care about? Can a sexual or romantic encounter in the digital world be considered infidelity?
Above all, the episode explores the therapeutic possibilities of such technologies. While many fears about virtual reality had been raised in previous seasons of Black Mirror, this episode was focused on its potential. This includes the potential of immersive technology to be a source of liberation, meaningful connections, and self-exploration, as well as a tool for realizing our true identities and desires.
Once again, this is aligned with emerging trends in VR. We are seeing the rise of social VR applications and platforms that allow you to hang out with your friends and family as avatars in the virtual space. The technology is allowing for animation movies, such as Coco VR, to become an increasingly social and interactive experience. Considering that meaningful social interaction can alleviate depression and anxiety, such applications could contribute to well-being.
Techno-philosopher and National Geographic host Jason Silva points out that immersive media technologies can be “engines of empathy.” VR allows us to enter virtual spaces that mimic someone else’s state of mind, allowing us to empathize with the way they view the world. Silva said, “Imagine the intimacy that becomes possible when people meet and they say, ‘Hey, do you want to come visit my world? Do you want to see what it’s like to be inside my head?’”
What is most fascinating about Striking Vipers is that it explores how we may redefine love with virtual reality; we are introduced to love between virtual avatars. While this kind of love may seem confusing to audiences, it may be one of the complex implications of virtual reality on human relationships.
In many ways, the title Black Mirror couldn’t be more appropriate, as each episode serves as a mirror to the most disturbing aspects of our psyches as they get amplified through technology. However, what we see in uplifting and thought-provoking plots like Striking Vipers, San Junipero, and Hang The DJ is that technology could also amplify the most positive aspects of our humanity. This includes our powerful capacity to love.
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#435127 Teaching AI the Concept of ‘Similar, ...
As a human you instinctively know that a leopard is closer to a cat than a motorbike, but the way we train most AI makes them oblivious to these kinds of relations. Building the concept of similarity into our algorithms could make them far more capable, writes the author of a new paper in Science Robotics.
Convolutional neural networks have revolutionized the field of computer vision to the point that machines are now outperforming humans on some of the most challenging visual tasks. But the way we train them to analyze images is very different from the way humans learn, says Atsuto Maki, an associate professor at KTH Royal Institute of Technology.
“Imagine that you are two years old and being quizzed on what you see in a photo of a leopard,” he writes. “You might answer ‘a cat’ and your parents might say, ‘yeah, not quite but similar’.”
In contrast, the way we train neural networks rarely gives that kind of partial credit. They are typically trained to have very high confidence in the correct label and consider all incorrect labels, whether ”cat” or “motorbike,” equally wrong. That’s a mistake, says Maki, because ignoring the fact that something can be “less wrong” means you’re not exploiting all of the information in the training data.
Even when models are trained this way, there will be small differences in the probabilities assigned to incorrect labels that can tell you a lot about how well the model can generalize what it has learned to unseen data.
If you show a model a picture of a leopard and it gives “cat” a probability of five percent and “motorbike” one percent, that suggests it picked up on the fact that a cat is closer to a leopard than a motorbike. In contrast, if the figures are the other way around it means the model hasn’t learned the broad features that make cats and leopards similar, something that could potentially be helpful when analyzing new data.
If we could boost this ability to identify similarities between classes we should be able to create more flexible models better able to generalize, says Maki. And recent research has demonstrated how variations of an approach called regularization might help us achieve that goal.
Neural networks are prone to a problem called “overfitting,” which refers to a tendency to pay too much attention to tiny details and noise specific to their training set. When that happens, models will perform excellently on their training data but poorly when applied to unseen test data without these particular quirks.
Regularization is used to circumvent this problem, typically by reducing the network’s capacity to learn all this unnecessary information and therefore boost its ability to generalize to new data. Techniques are varied, but generally involve modifying the network’s structure or the strength of the weights between artificial neurons.
More recently, though, researchers have suggested new regularization approaches that work by encouraging a broader spread of probabilities across all classes. This essentially helps them capture more of the class similarities, says Maki, and therefore boosts their ability to generalize.
One such approach was devised in 2017 by Google Brain researchers, led by deep learning pioneer Geoffrey Hinton. They introduced a penalty to their training process that directly punished overconfident predictions in the model’s outputs, and a technique called label smoothing that prevents the largest probability becoming much larger than all others. This meant the probabilities were lower for correct labels and higher for incorrect ones, which was found to boost performance of models on varied tasks from image classification to speech recognition.
Another came from Maki himself in 2017 and achieves the same goal, but by suppressing high values in the model’s feature vector—the mathematical construct that describes all of an object’s important characteristics. This has a knock-on effect on the spread of output probabilities and also helped boost performance on various image classification tasks.
While it’s still early days for the approach, the fact that humans are able to exploit these kinds of similarities to learn more efficiently suggests that models that incorporate them hold promise. Maki points out that it could be particularly useful in applications such as robotic grasping, where distinguishing various similar objects is important.
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