Tag Archives: mimic
#431593 Toyota’s Humanoid T-HR3
Meet Toyota’s 3rd-generation T-HR3 robot, that mimic human movements via virtual reality!
Posted in Human Robots
#431592 Reactive Content Will Get to Know You ...
The best storytellers react to their audience. They look for smiles, signs of awe, or boredom; they simultaneously and skillfully read both the story and their sitters. Kevin Brooks, a seasoned storyteller working for Motorola’s Human Interface Labs, explains, “As the storyteller begins, they must tune in to… the audience’s energy. Based on this energy, the storyteller will adjust their timing, their posture, their characterizations, and sometimes even the events of the story. There is a dialog between audience and storyteller.”
Shortly after I read the script to Melita, the latest virtual reality experience from Madrid-based immersive storytelling company Future Lighthouse, CEO Nicolas Alcalá explained to me that the piece is an example of “reactive content,” a concept he’s been working on since his days at Singularity University.
For the first time in history, we have access to technology that can merge the reactive and affective elements of oral storytelling with the affordances of digital media, weaving stunning visuals, rich soundtracks, and complex meta-narratives in a story arena that has the capability to know you more intimately than any conventional storyteller could.
It’s no understatement to say that the storytelling potential here is phenomenal.
In short, we can refer to content as reactive if it reads and reacts to users based on their body rhythms, emotions, preferences, and data points. Artificial intelligence is used to analyze users’ behavior or preferences to sculpt unique storylines and narratives, essentially allowing for a story that changes in real time based on who you are and how you feel.
The development of reactive content will allow those working in the industry to go one step further than simply translating the essence of oral storytelling into VR. Rather than having a narrative experience with a digital storyteller who can read you, reactive content has the potential to create an experience with a storyteller who knows you.
This means being able to subtly insert minor personal details that have a specific meaning to the viewer. When we talk to our friends we often use experiences we’ve shared in the past or knowledge of our audience to give our story as much resonance as possible. Targeting personal memories and aspects of our lives is a highly effective way to elicit emotions and aid in visualizing narratives. When you can do this with the addition of visuals, music, and characters—all lifted from someone’s past—you have the potential for overwhelmingly engaging and emotionally-charged content.
Future Lighthouse inform me that for now, reactive content will rely primarily on biometric feedback technology such as breathing, heartbeat, and eye tracking sensors. A simple example would be a story in which parts of the environment or soundscape change in sync with the user’s heartbeat and breathing, or characters who call you out for not paying attention.
The next step would be characters and situations that react to the user’s emotions, wherein algorithms analyze biometric information to make inferences about states of emotional arousal (“why are you so nervous?” etc.). Another example would be implementing the use of “arousal parameters,” where the audience can choose what level of “fear” they want from a VR horror story before algorithms modulate the experience using information from biometric feedback devices.
The company’s long-term goal is to gather research on storytelling conventions and produce a catalogue of story “wireframes.” This entails distilling the basic formula to different genres so they can then be fleshed out with visuals, character traits, and soundtracks that are tailored for individual users based on their deep data, preferences, and biometric information.
The development of reactive content will go hand in hand with a renewed exploration of diverging, dynamic storylines, and multi-narratives, a concept that hasn’t had much impact in the movie world thus far. In theory, the idea of having a story that changes and mutates is captivating largely because of our love affair with serendipity and unpredictability, a cultural condition theorist Arthur Kroker refers to as the “hypertextual imagination.” This feeling of stepping into the unknown with the possibility of deviation from the habitual translates as a comforting reminder that our own lives can take exciting and unexpected turns at any moment.
The inception of the concept into mainstream culture dates to the classic Choose Your Own Adventure book series that launched in the late 70s, which in its literary form had great success. However, filmic takes on the theme have made somewhat less of an impression. DVDs like I’m Your Man (1998) and Switching (2003) both use scene selection tools to determine the direction of the storyline.
A more recent example comes from Kino Industries, who claim to have developed the technology to allow filmmakers to produce interactive films in which viewers can use smartphones to quickly vote on which direction the narrative takes at numerous decision points throughout the film.
The main problem with diverging narrative films has been the stop-start nature of the interactive element: when I’m immersed in a story I don’t want to have to pick up a controller or remote to select what’s going to happen next. Every time the audience is given the option to take a new path (“press this button”, “vote on X, Y, Z”) the narrative— and immersion within that narrative—is temporarily halted, and it takes the mind a while to get back into this state of immersion.
Reactive content has the potential to resolve these issues by enabling passive interactivity—that is, input and output without having to pause and actively make decisions or engage with the hardware. This will result in diverging, dynamic narratives that will unfold seamlessly while being dependent on and unique to the specific user and their emotions. Passive interactivity will also remove the game feel that can often be a symptom of interactive experiences and put a viewer somewhere in the middle: still firmly ensconced in an interactive dynamic narrative, but in a much subtler way.
While reading the Melita script I was particularly struck by a scene in which the characters start to engage with the user and there’s a synchronicity between the user’s heartbeat and objects in the virtual world. As the narrative unwinds and the words of Melita’s character get more profound, parts of the landscape, which seemed to be flashing and pulsating at random, come together and start to mimic the user’s heartbeat.
In 2013, Jane Aspell of Anglia Ruskin University (UK) and Lukas Heydrich of the Swiss Federal Institute of Technology proved that a user’s sense of presence and identification with a virtual avatar could be dramatically increased by syncing the on-screen character with the heartbeat of the user. The relationship between bio-digital synchronicity, immersion, and emotional engagement is something that will surely have revolutionary narrative and storytelling potential.
Image Credit: Tithi Luadthong / Shutterstock.com Continue reading
Posted in Human Robots
#431165 Intel Jumps Into Brain-Like Computing ...
The brain has long inspired the design of computers and their software. Now Intel has become the latest tech company to decide that mimicking the brain’s hardware could be the next stage in the evolution of computing.
On Monday the company unveiled an experimental “neuromorphic” chip called Loihi. Neuromorphic chips are microprocessors whose architecture is configured to mimic the biological brain’s network of neurons and the connections between them called synapses.
While neural networks—the in vogue approach to artificial intelligence and machine learning—are also inspired by the brain and use layers of virtual neurons, they are still implemented on conventional silicon hardware such as CPUs and GPUs.
The main benefit of mimicking the architecture of the brain on a physical chip, say neuromorphic computing’s proponents, is energy efficiency—the human brain runs on roughly 20 watts. The “neurons” in neuromorphic chips carry out the role of both processor and memory which removes the need to shuttle data back and forth between separate units, which is how traditional chips work. Each neuron also only needs to be powered while it’s firing.
At present, most machine learning is done in data centers due to the massive energy and computing requirements. Creating chips that capture some of nature’s efficiency could allow AI to be run directly on devices like smartphones, cars, and robots.
This is exactly the kind of application Michael Mayberry, managing director of Intel’s research arm, touts in a blog post announcing Loihi. He talks about CCTV cameras that can run image recognition to identify missing persons or traffic lights that can track traffic flow to optimize timing and keep vehicles moving.
There’s still a long way to go before that happens though. According to Wired, so far Intel has only been working with prototypes, and the first full-size version of the chip won’t be built until November.
Once complete, it will feature 130,000 neurons and 130 million synaptic connections split between 128 computing cores. The device will be 1,000 times more energy-efficient than standard approaches, according to Mayberry, but more impressive are claims the chip will be capable of continuous learning.
Intel’s newly launched self-learning neuromorphic chip.
Normally deep learning works by training a neural network on giant datasets to create a model that can then be applied to new data. The Loihi chip will combine training and inference on the same chip, which will allow it to learn on the fly, constantly updating its models and adapting to changing circumstances without having to be deliberately re-trained.
A select group of universities and research institutions will be the first to get their hands on the new chip in the first half of 2018, but Mayberry said it could be years before it’s commercially available. Whether commercialization happens at all may largely depend on whether early adopters can get the hardware to solve any practically useful problems.
So far neuromorphic computing has struggled to gain traction outside the research community. IBM released a neuromorphic chip called TrueNorth in 2014, but the device has yet to showcase any commercially useful applications.
Lee Gomes summarizes the hurdles facing neuromorphic computing excellently in IEEE Spectrum. One is that deep learning can run on very simple, low-precision hardware that can be optimized to use very little power, which suggests complicated new architectures may struggle to find purchase.
It’s also not easy to transfer deep learning approaches developed on conventional chips over to neuromorphic hardware, and even Intel Labs chief scientist Narayan Srinivasa admitted to Forbes Loihi wouldn’t work well with some deep learning models.
Finally, there’s considerable competition in the quest to develop new computer architectures specialized for machine learning. GPU vendors Nvidia and AMD have pivoted to take advantage of this newfound market and companies like Google and Microsoft are developing their own in-house solutions.
Intel, for its part, isn’t putting all its eggs in one basket. Last year it bought two companies building chips for specialized machine learning—Movidius and Nervana—and this was followed up with the $15 billion purchase of self-driving car chip- and camera-maker Mobileye.
And while the jury is still out on neuromorphic computing, it makes sense for a company eager to position itself as the AI chipmaker of the future to have its fingers in as many pies as possible. There are a growing number of voices suggesting that despite its undoubted power, deep learning alone will not allow us to imbue machines with the kind of adaptable, general intelligence humans possess.
What new approaches will get us there are hard to predict, but it’s entirely possible they will only work on hardware that closely mimics the one device we already know is capable of supporting this kind of intelligence—the human brain.
Image Credit: Intel Continue reading
Posted in Human Robots
#431110 Soft robotics: self-contained soft ...
Researchers at Columbia Engineering have solved a long-standing issue in the creation of untethered soft robots whose actions and movements can help mimic natural biological systems. A group in the Creative Machines lab led by Hod Lipson, professor of mechanical engineering, has developed a 3D-printable synthetic soft muscle, a one-of-a-kind artificial active tissue with intrinsic expansion ability that does not require an external compressor or high voltage equipment as previous muscles required. The new material has a strain density (expansion per gram) that is 15 times larger than natural muscle, and can lift 1000 times its own weight. Continue reading
Posted in Human Robots