Tag Archives: computing
#433901 The SpiNNaker Supercomputer, Modeled ...
We’ve long used the brain as inspiration for computers, but the SpiNNaker supercomputer, switched on this month, is probably the closest we’ve come to recreating it in silicon. Now scientists hope to use the supercomputer to model the very thing that inspired its design.
The brain is the most complex machine in the known universe, but that complexity comes primarily from its architecture rather than the individual components that make it up. Its highly interconnected structure means that relatively simple messages exchanged between billions of individual neurons add up to carry out highly complex computations.
That’s the paradigm that has inspired the ‘Spiking Neural Network Architecture” (SpiNNaker) supercomputer at the University of Manchester in the UK. The project is the brainchild of Steve Furber, the designer of the original ARM processor. After a decade of development, a million-core version of the machine that will eventually be able to simulate up to a billion neurons was switched on earlier this month.
The idea of splitting computation into very small chunks and spreading them over many processors is already the leading approach to supercomputing. But even the most parallel systems require a lot of communication, and messages may have to pack in a lot of information, such as the task that needs to be completed or the data that needs to be processed.
In contrast, messages in the brain consist of simple electrochemical impulses, or spikes, passed between neurons, with information encoded primarily in the timing or rate of those spikes (which is more important is a topic of debate among neuroscientists). Each neuron is connected to thousands of others via synapses, and complex computation relies on how spikes cascade through these highly-connected networks.
The SpiNNaker machine attempts to replicate this using a model called Address Event Representation. Each of the million cores can simulate roughly a million synapses, so depending on the model, 1,000 neurons with 1,000 connections or 100 neurons with 10,000 connections. Information is encoded in the timing of spikes and the identity of the neuron sending them. When a neuron is activated it broadcasts a tiny packet of data that contains its address, and spike timing is implicitly conveyed.
By modeling their machine on the architecture of the brain, the researchers hope to be able to simulate more biological neurons in real time than any other machine on the planet. The project is funded by the European Human Brain Project, a ten-year science mega-project aimed at bringing together neuroscientists and computer scientists to understand the brain, and researchers will be able to apply for time on the machine to run their simulations.
Importantly, it’s possible to implement various different neuronal models on the machine. The operation of neurons involves a variety of complex biological processes, and it’s still unclear whether this complexity is an artefact of evolution or central to the brain’s ability to process information. The ability to simulate up to a billion simple neurons or millions of more complex ones on the same machine should help to slowly tease out the answer.
Even at a billion neurons, that still only represents about one percent of the human brain, so it’s still going to be limited to investigating isolated networks of neurons. But the previous 500,000-core machine has already been used to do useful simulations of the Basal Ganglia—an area affected in Parkinson’s disease—and an outer layer of the brain that processes sensory information.
The full-scale supercomputer will make it possible to study even larger networks previously out of reach, which could lead to breakthroughs in our understanding of both the healthy and unhealthy functioning of the brain.
And while neurological simulation is the main goal for the machine, it could also provide a useful research tool for roboticists. Previous research has already shown a small board of SpiNNaker chips can be used to control a simple wheeled robot, but Furber thinks the SpiNNaker supercomputer could also be used to run large-scale networks that can process sensory input and generate motor output in real time and at low power.
That low power operation is of particular promise for robotics. The brain is dramatically more power-efficient than conventional supercomputers, and by borrowing from its principles SpiNNaker has managed to capture some of that efficiency. That could be important for running mobile robotic platforms that need to carry their own juice around.
This ability to run complex neural networks at low power has been one of the main commercial drivers for so-called neuromorphic computing devices that are physically modeled on the brain, such as IBM’s TrueNorth chip and Intel’s Loihi. The hope is that complex artificial intelligence applications normally run in massive data centers could be run on edge devices like smartphones, cars, and robots.
But these devices, including SpiNNaker, operate very differently from the leading AI approaches, and its not clear how easy it would be to transfer between the two. The need to adopt an entirely new programming paradigm is likely to limit widespread adoption, and the lack of commercial traction for the aforementioned devices seems to back that up.
At the same time, though, this new paradigm could potentially lead to dramatic breakthroughs in massively parallel computing. SpiNNaker overturns many of the foundational principles of how supercomputers work that make it much more flexible and error-tolerant.
For now, the machine is likely to be firmly focused on accelerating our understanding of how the brain works. But its designers also hope those findings could in turn point the way to more efficient and powerful approaches to computing.
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#433892 The Spatial Web Will Map Our 3D ...
The boundaries between digital and physical space are disappearing at a breakneck pace. What was once static and boring is becoming dynamic and magical.
For all of human history, looking at the world through our eyes was the same experience for everyone. Beyond the bounds of an over-active imagination, what you see is the same as what I see.
But all of this is about to change. Over the next two to five years, the world around us is about to light up with layer upon layer of rich, fun, meaningful, engaging, and dynamic data. Data you can see and interact with.
This magical future ahead is called the Spatial Web and will transform every aspect of our lives, from retail and advertising, to work and education, to entertainment and social interaction.
Massive change is underway as a result of a series of converging technologies, from 5G global networks and ubiquitous artificial intelligence, to 30+ billion connected devices (known as the IoT), each of which will generate scores of real-world data every second, everywhere.
The current AI explosion will make everything smart, autonomous, and self-programming. Blockchain and cloud-enabled services will support a secure data layer, putting data back in the hands of users and allowing us to build complex rule-based infrastructure in tomorrow’s virtual worlds.
And with the rise of online-merge-offline (OMO) environments, two-dimensional screens will no longer serve as our exclusive portal to the web. Instead, virtual and augmented reality eyewear will allow us to interface with a digitally-mapped world, richly layered with visual data.
Welcome to the Spatial Web. Over the next few months, I’ll be doing a deep dive into the Spatial Web (a.k.a. Web 3.0), covering what it is, how it works, and its vast implications across industries, from real estate and healthcare to entertainment and the future of work. In this blog, I’ll discuss the what, how, and why of Web 3.0—humanity’s first major foray into our virtual-physical hybrid selves (BTW, this year at Abundance360, we’ll be doing a deep dive into the Spatial Web with the leaders of HTC, Magic Leap, and High-Fidelity).
Let’s dive in.
What is the Spatial Web?
While we humans exist in three dimensions, our web today is flat.
The web was designed for shared information, absorbed through a flat screen. But as proliferating sensors, ubiquitous AI, and interconnected networks blur the lines between our physical and online worlds, we need a spatial web to help us digitally map a three-dimensional world.
To put Web 3.0 in context, let’s take a trip down memory lane. In the late 1980s, the newly-birthed world wide web consisted of static web pages and one-way information—a monumental system of publishing and linking information unlike any unified data system before it. To connect, we had to dial up through unstable modems and struggle through insufferably slow connection speeds.
But emerging from this revolutionary (albeit non-interactive) infodump, Web 2.0 has connected the planet more in one decade than empires did in millennia.
Granting democratized participation through newly interactive sites and applications, today’s web era has turbocharged information-sharing and created ripple effects of scientific discovery, economic growth, and technological progress on an unprecedented scale.
We’ve seen the explosion of social networking sites, wikis, and online collaboration platforms. Consumers have become creators; physically isolated users have been handed a global microphone; and entrepreneurs can now access billions of potential customers.
But if Web 2.0 took the world by storm, the Spatial Web emerging today will leave it in the dust.
While there’s no clear consensus about its definition, the Spatial Web refers to a computing environment that exists in three-dimensional space—a twinning of real and virtual realities—enabled via billions of connected devices and accessed through the interfaces of virtual and augmented reality.
In this way, the Spatial Web will enable us to both build a twin of our physical reality in the virtual realm and bring the digital into our real environments.
It’s the next era of web-like technologies:
Spatial computing technologies, like augmented and virtual reality;
Physical computing technologies, like IoT and robotic sensors;
And decentralized computing: both blockchain—which enables greater security and data authentication—and edge computing, which pushes computing power to where it’s most needed, speeding everything up.
Geared with natural language search, data mining, machine learning, and AI recommendation agents, the Spatial Web is a growing expanse of services and information, navigable with the use of ever-more-sophisticated AI assistants and revolutionary new interfaces.
Where Web 1.0 consisted of static documents and read-only data, Web 2.0 introduced multimedia content, interactive web applications, and social media on two-dimensional screens. But converging technologies are quickly transcending the laptop, and will even disrupt the smartphone in the next decade.
With the rise of wearables, smart glasses, AR / VR interfaces, and the IoT, the Spatial Web will integrate seamlessly into our physical environment, overlaying every conversation, every road, every object, conference room, and classroom with intuitively-presented data and AI-aided interaction.
Think: the Oasis in Ready Player One, where anyone can create digital personas, build and invest in smart assets, do business, complete effortless peer-to-peer transactions, and collect real estate in a virtual world.
Or imagine a virtual replica or “digital twin” of your office, each conference room authenticated on the blockchain, requiring a cryptographic key for entry.
As I’ve discussed with my good friend and “VR guru” Philip Rosedale, I’m absolutely clear that in the not-too-distant future, every physical element of every building in the world is going to be fully digitized, existing as a virtual incarnation or even as N number of these. “Meet me at the top of the Empire State Building?” “Sure, which one?”
This digitization of life means that suddenly every piece of information can become spatial, every environment can be smarter by virtue of AI, and every data point about me and my assets—both virtual and physical—can be reliably stored, secured, enhanced, and monetized.
In essence, the Spatial Web lets us interface with digitally-enhanced versions of our physical environment and build out entirely fictional virtual worlds—capable of running simulations, supporting entire economies, and even birthing new political systems.
But while I’ll get into the weeds of different use cases next week, let’s first concretize.
How Does It Work?
Let’s start with the stack. In the PC days, we had a database accompanied by a program that could ingest that data and present it to us as digestible information on a screen.
Then, in the early days of the web, data migrated to servers. Information was fed through a website, with which you would interface via a browser—whether Mosaic or Mozilla.
And then came the cloud.
Resident at either the edge of the cloud or on your phone, today’s rapidly proliferating apps now allow us to interact with previously read-only data, interfacing through a smartphone. But as Siri and Alexa have brought us verbal interfaces, AI-geared phone cameras can now determine your identity, and sensors are beginning to read our gestures.
And now we’re not only looking at our screens but through them, as the convergence of AI and AR begins to digitally populate our physical worlds.
While Pokémon Go sent millions of mobile game-players on virtual treasure hunts, IKEA is just one of the many companies letting you map virtual furniture within your physical home—simulating everything from cabinets to entire kitchens. No longer the one-sided recipients, we’re beginning to see through sensors, creatively inserting digital content in our everyday environments.
Let’s take a look at how the latest incarnation might work. In this new Web 3.0 stack, my personal AI would act as an intermediary, accessing public or privately-authorized data through the blockchain on my behalf, and then feed it through an interface layer composed of everything from my VR headset, to numerous wearables, to my smart environment (IoT-connected devices or even in-home robots).
But as we attempt to build a smart world with smart infrastructure, smart supply chains and smart everything else, we need a set of basic standards with addresses for people, places, and things. Just like our web today relies on the Internet Protocol (TCP/IP) and other infrastructure, by which your computer is addressed and data packets are transferred, we need infrastructure for the Spatial Web.
And a select group of players is already stepping in to fill this void. Proposing new structural designs for Web 3.0, some are attempting to evolve today’s web model from text-based web pages in 2D to three-dimensional AR and VR web experiences located in both digitally-mapped physical worlds and newly-created virtual ones.
With a spatial programming language analogous to HTML, imagine building a linkable address for any physical or virtual space, granting it a format that then makes it interchangeable and interoperable with all other spaces.
But it doesn’t stop there.
As soon as we populate a virtual room with content, we then need to encode who sees it, who can buy it, who can move it…
And the Spatial Web’s eventual governing system (for posting content on a centralized grid) would allow us to address everything from the room you’re sitting in, to the chair on the other side of the table, to the building across the street.
Just as we have a DNS for the web and the purchasing of web domains, once we give addresses to spaces (akin to granting URLs), we then have the ability to identify and visit addressable locations, physical objects, individuals, or pieces of digital content in cyberspace.
And these not only apply to virtual worlds, but to the real world itself. As new mapping technologies emerge, we can now map rooms, objects, and large-scale environments into virtual space with increasing accuracy.
We might then dictate who gets to move your coffee mug in a virtual conference room, or when a team gets to use the room itself. Rules and permissions would be set in the grid, decentralized governance systems, or in the application layer.
Taken one step further, imagine then monetizing smart spaces and smart assets. If you have booked the virtual conference room, perhaps you’ll let me pay you 0.25 BTC to let me use it instead?
But given the Spatial Web’s enormous technological complexity, what’s allowing it to emerge now?
Why Is It Happening Now?
While countless entrepreneurs have already started harnessing blockchain technologies to build decentralized apps (or dApps), two major developments are allowing today’s birth of Web 3.0:
High-resolution wireless VR/AR headsets are finally catapulting virtual and augmented reality out of a prolonged winter.
The International Data Corporation (IDC) predicts the VR and AR headset market will reach 65.9 million units by 2022. Already in the next 18 months, 2 billion devices will be enabled with AR. And tech giants across the board have long begun investing heavy sums.
In early 2019, HTC is releasing the VIVE Focus, a wireless self-contained VR headset. At the same time, Facebook is charging ahead with its Project Santa Cruz—the Oculus division’s next-generation standalone, wireless VR headset. And Magic Leap has finally rolled out its long-awaited Magic Leap One mixed reality headset.
Mass deployment of 5G will drive 10 to 100-gigabit connection speeds in the next 6 years, matching hardware progress with the needed speed to create virtual worlds.
We’ve already seen tremendous leaps in display technology. But as connectivity speeds converge with accelerating GPUs, we’ll start to experience seamless VR and AR interfaces with ever-expanding virtual worlds.
And with such democratizing speeds, every user will be able to develop in VR.
But accompanying these two catalysts is also an important shift towards the decentralized web and a demand for user-controlled data.
Converging technologies, from immutable ledgers and blockchain to machine learning, are now enabling the more direct, decentralized use of web applications and creation of user content. With no central point of control, middlemen are removed from the equation and anyone can create an address, independently interacting with the network.
Enabled by a permission-less blockchain, any user—regardless of birthplace, gender, ethnicity, wealth, or citizenship—would thus be able to establish digital assets and transfer them seamlessly, granting us a more democratized Internet.
And with data stored on distributed nodes, this also means no single point of failure. One could have multiple backups, accessible only with digital authorization, leaving users immune to any single server failure.
Implications Abound–What’s Next…
With a newly-built stack and an interface built from numerous converging technologies, the Spatial Web will transform every facet of our everyday lives—from the way we organize and access our data, to our social and business interactions, to the way we train employees and educate our children.
We’re about to start spending more time in the virtual world than ever before. Beyond entertainment or gameplay, our livelihoods, work, and even personal decisions are already becoming mediated by a web electrified with AI and newly-emerging interfaces.
In our next blog on the Spatial Web, I’ll do a deep dive into the myriad industry implications of Web 3.0, offering tangible use cases across sectors.
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#433884 Designer Babies, and Their Babies: How ...
As if stand-alone technologies weren’t advancing fast enough, we’re in age where we must study the intersection points of these technologies. How is what’s happening in robotics influenced by what’s happening in 3D printing? What could be made possible by applying the latest advances in quantum computing to nanotechnology?
Along these lines, one crucial tech intersection is that of artificial intelligence and genomics. Each field is seeing constant progress, but Jamie Metzl believes it’s their convergence that will really push us into uncharted territory, beyond even what we’ve imagined in science fiction. “There’s going to be this push and pull, this competition between the reality of our biology with its built-in limitations and the scope of our aspirations,” he said.
Metzl is a senior fellow at the Atlantic Council and author of the upcoming book Hacking Darwin: Genetic Engineering and the Future of Humanity. At Singularity University’s Exponential Medicine conference last week, he shared his insights on genomics and AI, and where their convergence could take us.
Life As We Know It
Metzl explained how genomics as a field evolved slowly—and then quickly. In 1953, James Watson and Francis Crick identified the double helix structure of DNA, and realized that the order of the base pairs held a treasure trove of genetic information. There was such a thing as a book of life, and we’d found it.
In 2003, when the Human Genome Project was completed (after 13 years and $2.7 billion), we learned the order of the genome’s 3 billion base pairs, and the location of specific genes on our chromosomes. Not only did a book of life exist, we figured out how to read it.
Jamie Metzl at Exponential Medicine
Fifteen years after that, it’s 2018 and precision gene editing in plants, animals, and humans is changing everything, and quickly pushing us into an entirely new frontier. Forget reading the book of life—we’re now learning how to write it.
“Readable, writable, and hackable, what’s clear is that human beings are recognizing that we are another form of information technology, and just like our IT has entered this exponential curve of discovery, we will have that with ourselves,” Metzl said. “And it’s intersecting with the AI revolution.”
Learning About Life Meets Machine Learning
In 2016, DeepMind’s AlphaGo program outsmarted the world’s top Go player. In 2017 AlphaGo Zero was created: unlike AlphaGo, AlphaGo Zero wasn’t trained using previous human games of Go, but was simply given the rules of Go—and in four days it defeated the AlphaGo program.
Our own biology is, of course, vastly more complex than the game of Go, and that, Metzl said, is our starting point. “The system of our own biology that we are trying to understand is massively, but very importantly not infinitely, complex,” he added.
Getting a standardized set of rules for our biology—and, eventually, maybe even outsmarting our biology—will require genomic data. Lots of it.
Multiple countries already starting to produce this data. The UK’s National Health Service recently announced a plan to sequence the genomes of five million Britons over the next five years. In the US the All of Us Research Program will sequence a million Americans. China is the most aggressive in sequencing its population, with a goal of sequencing half of all newborns by 2020.
“We’re going to get these massive pools of sequenced genomic data,” Metzl said. “The real gold will come from comparing people’s sequenced genomes to their electronic health records, and ultimately their life records.” Getting people comfortable with allowing open access to their data will be another matter; Metzl mentioned that Luna DNA and others have strategies to help people get comfortable with giving consent to their private information. But this is where China’s lack of privacy protection could end up being a significant advantage.
To compare genotypes and phenotypes at scale—first millions, then hundreds of millions, then eventually billions, Metzl said—we’re going to need AI and big data analytic tools, and algorithms far beyond what we have now. These tools will let us move from precision medicine to predictive medicine, knowing precisely when and where different diseases are going to occur and shutting them down before they start.
But, Metzl said, “As we unlock the genetics of ourselves, it’s not going to be about just healthcare. It’s ultimately going to be about who and what we are as humans. It’s going to be about identity.”
Designer Babies, and Their Babies
In Metzl’s mind, the most serious application of our genomic knowledge will be in embryo selection.
Currently, in-vitro fertilization (IVF) procedures can extract around 15 eggs, fertilize them, then do pre-implantation genetic testing; right now what’s knowable is single-gene mutation diseases and simple traits like hair color and eye color. As we get to the millions and then billions of people with sequences, we’ll have information about how these genetics work, and we’re going to be able to make much more informed choices,” Metzl said.
Imagine going to a fertility clinic in 2023. You give a skin graft or a blood sample, and using in-vitro gametogenesis (IVG)—infertility be damned—your skin or blood cells are induced to become eggs or sperm, which are then combined to create embryos. The dozens or hundreds of embryos created from artificial gametes each have a few cells extracted from them, and these cells are sequenced. The sequences will tell you the likelihood of specific traits and disease states were that embryo to be implanted and taken to full term. “With really anything that has a genetic foundation, we’ll be able to predict with increasing levels of accuracy how that potential child will be realized as a human being,” Metzl said.
This, he added, could lead to some wild and frightening possibilities: if you have 1,000 eggs and you pick one based on its optimal genetic sequence, you could then mate your embryo with somebody else who has done the same thing in a different genetic line. “Your five-day-old embryo and their five-day-old embryo could have a child using the same IVG process,” Metzl said. “Then that child could have a child with another five-day-old embryo from another genetic line, and you could go on and on down the line.”
Sounds insane, right? But wait, there’s more: as Jason Pontin reported earlier this year in Wired, “Gene-editing technologies such as Crispr-Cas9 would make it relatively easy to repair, add, or remove genes during the IVG process, eliminating diseases or conferring advantages that would ripple through a child’s genome. This all may sound like science fiction, but to those following the research, the combination of IVG and gene editing appears highly likely, if not inevitable.”
From Crazy to Commonplace?
It’s a slippery slope from gene editing and embryo-mating to a dystopian race to build the most perfect humans possible. If somebody’s investing so much time and energy in selecting their embryo, Metzl asked, how will they think about the mating choices of their children? IVG could quickly leave the realm of healthcare and enter that of evolution.
“We all need to be part of an inclusive, integrated, global dialogue on the future of our species,” Metzl said. “Healthcare professionals are essential nodes in this.” Not least among this dialogue should be the question of access to tech like IVG; are there steps we can take to keep it from becoming a tool for a wealthy minority, and thereby perpetuating inequality and further polarizing societies?
As Pontin points out, at its inception 40 years ago IVF also sparked fear, confusion, and resistance—and now it’s as normal and common as could be, with millions of healthy babies conceived using the technology.
The disruption that genomics, AI, and IVG will bring to reproduction could follow a similar story cycle—if we’re smart about it. As Metzl put it, “This must be regulated, because it is life.”
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#433785 DeepMind’s Eerie Reimagination of the ...
If a recent project using Google’s DeepMind were a recipe, you would take a pair of AI systems, images of animals, and a whole lot of computing power. Mix it all together, and you’d get a series of imagined animals dreamed up by one of the AIs. A look through the research paper about the project—or this open Google Folder of images it produced—will likely lead you to agree that the results are a mix of impressive and downright eerie.
But the eerie factor doesn’t mean the project shouldn’t be considered a success and a step forward for future uses of AI.
From GAN To BigGAN
The team behind the project consists of Andrew Brock, a PhD student at Edinburgh Center for Robotics, and DeepMind intern and researcher Jeff Donahue and Karen Simonyan.
They used a so-called Generative Adversarial Network (GAN) to generate the images. In a GAN, two AI systems collaborate in a game-like manner. One AI produces images of an object or creature. The human equivalent would be drawing pictures of, for example, a dog—without necessarily knowing what a dog exactly looks like. Those images are then shown to the second AI, which has already been fed images of dogs. The second AI then tells the first one how far off its efforts were. The first one uses this information to improve its images. The two go back and forth in an iterative process, and the goal is for the first AI to become so good at creating images of dogs that the second can’t tell the difference between its creations and actual pictures of dogs.
The team was able to draw on Google’s vast vaults of computational power to create images of a quality and life-like nature that were beyond almost anything seen before. In part, this was achieved by feeding the GAN with more images than is usually the case. According to IFLScience, the standard is to feed about 64 images per subject into the GAN. In this case, the research team fed about 2,000 images per subject into the system, leading to it being nicknamed BigGAN.
Their results showed that feeding the system with more images and using masses of raw computer power markedly increased the GAN’s precision and ability to create life-like renditions of the subjects it was trained to reproduce.
“The main thing these models need is not algorithmic improvements, but computational ones. […] When you increase model capacity and you increase the number of images you show at every step, you get this twofold combined effect,” Andrew Brock told Fast Company.
The Power Drain
The team used 512 of Google’s AI-focused Tensor Processing Units (TPU) to generate 512-pixel images. Each experiment took between 24 and 48 hours to run.
That kind of computing power needs a lot of electricity. As artist and Innovator-In-Residence at the Library of Congress Jer Thorp tongue-in-cheek put it on Twitter: “The good news is that AI can now give you a more believable image of a plate of spaghetti. The bad news is that it used roughly enough energy to power Cleveland for the afternoon.”
Thorp added that a back-of-the-envelope calculation showed that the computations to produce the images would require about 27,000 square feet of solar panels to have adequate power.
BigGAN’s images have been hailed by researchers, with Oriol Vinyals, research scientist at DeepMind, rhetorically asking if these were the ‘Best GAN samples yet?’
However, they are still not perfect. The number of legs on a given creature is one example of where the BigGAN seemed to struggle. The system was good at recognizing that something like a spider has a lot of legs, but seemed unable to settle on how many ‘a lot’ was supposed to be. The same applied to dogs, especially if the images were supposed to show said dogs in motion.
Those eerie images are contrasted by other renditions that show such lifelike qualities that a human mind has a hard time identifying them as fake. Spaniels with lolling tongues, ocean scenery, and butterflies were all rendered with what looks like perfection. The same goes for an image of a hamburger that was good enough to make me stop writing because I suddenly needed lunch.
The Future Use Cases
GAN networks were first introduced in 2014, and given their relative youth, researchers and companies are still busy trying out possible use cases.
One possible use is image correction—making pixillated images clearer. Not only does this help your future holiday snaps, but it could be applied in industries such as space exploration. A team from the University of Michigan and the Max Planck Institute have developed a method for GAN networks to create images from text descriptions. At Berkeley, a research group has used GAN to create an interface that lets users change the shape, size, and design of objects, including a handbag.
For anyone who has seen a film like Wag the Dog or read 1984, the possibilities are also starkly alarming. GANs could, in other words, make fake news look more real than ever before.
For now, it seems that while not all GANs require the computational and electrical power of the BigGAN, there is still some way to reach these potential use cases. However, if there’s one lesson from Moore’s Law and exponential technology, it is that today’s technical roadblock quickly becomes tomorrow’s minor issue as technology progresses.
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#433764 AI on a MEMS Device Brings Neuromorphic ...
For first time artificial intelligence has been integrated into a MEMS device Continue reading
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