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#437466 How Future AI Could Recognize a Kangaroo ...

AI is continuously taking on new challenges, from detecting deepfakes (which, incidentally, are also made using AI) to winning at poker to giving synthetic biology experiments a boost. These impressive feats result partly from the huge datasets the systems are trained on. That training is costly and time-consuming, and it yields AIs that can really only do one thing well.

For example, to train an AI to differentiate between a picture of a dog and one of a cat, it’s fed thousands—if not millions—of labeled images of dogs and cats. A child, on the other hand, can see a dog or cat just once or twice and remember which is which. How can we make AIs learn more like children do?

A team at the University of Waterloo in Ontario has an answer: change the way AIs are trained.

Here’s the thing about the datasets normally used to train AI—besides being huge, they’re highly specific. A picture of a dog can only be a picture of a dog, right? But what about a really small dog with a long-ish tail? That sort of dog, while still being a dog, looks more like a cat than, say, a fully-grown Golden Retriever.

It’s this concept that the Waterloo team’s methodology is based on. They described their work in a paper published on the pre-print (or non-peer-reviewed) server arXiv last month. Teaching an AI system to identify a new class of objects using just one example is what they call “one-shot learning.” But they take it a step further, focusing on “less than one shot learning,” or LO-shot learning for short.

LO-shot learning consists of a system learning to classify various categories based on a number of examples that’s smaller than the number of categories. That’s not the most straightforward concept to wrap your head around, so let’s go back to the dogs and cats example. Say you want to teach an AI to identify dogs, cats, and kangaroos. How could that possibly be done without several clear examples of each animal?

The key, the Waterloo team says, is in what they call soft labels. Unlike hard labels, which label a data point as belonging to one specific class, soft labels tease out the relationship or degree of similarity between that data point and multiple classes. In the case of an AI trained on only dogs and cats, a third class of objects, say, kangaroos, might be described as 60 percent like a dog and 40 percent like a cat (I know—kangaroos probably aren’t the best animal to have thrown in as a third category).

“Soft labels can be used to represent training sets using fewer prototypes than there are classes, achieving large increases in sample efficiency over regular (hard-label) prototypes,” the paper says. Translation? Tell an AI a kangaroo is some fraction cat and some fraction dog—both of which it’s seen and knows well—and it’ll be able to identify a kangaroo without ever having seen one.

If the soft labels are nuanced enough, you could theoretically teach an AI to identify a large number of categories based on a much smaller number of training examples.

The paper’s authors use a simple machine learning algorithm called k-nearest neighbors (kNN) to explore this idea more in depth. The algorithm operates under the assumption that similar things are most likely to exist near each other; if you go to a dog park, there will be lots of dogs but no cats or kangaroos. Go to the Australian grasslands and there’ll be kangaroos but no cats or dogs. And so on.

To train a kNN algorithm to differentiate between categories, you choose specific features to represent each category (i.e. for animals you could use weight or size as a feature). With one feature on the x-axis and the other on the y-axis, the algorithm creates a graph where data points that are similar to each other are clustered near each other. A line down the center divides the categories, and it’s pretty straightforward for the algorithm to discern which side of the line new data points should fall on.

The Waterloo team kept it simple and used plots of color on a 2D graph. Using the colors and their locations on the graphs, the team created synthetic data sets and accompanying soft labels. One of the more simplistic graphs is pictured below, along with soft labels in the form of pie charts.

Image Credit: Ilia Sucholutsky & Matthias Schonlau
When the team had the algorithm plot the boundary lines of the different colors based on these soft labels, it was able to split the plot up into more colors than the number of data points it was given in the soft labels.

While the results are encouraging, the team acknowledges that they’re just the first step, and there’s much more exploration of this concept yet to be done. The kNN algorithm is one of the least complex models out there; what might happen when LO-shot learning is applied to a far more complex algorithm? Also, to apply it, you still need to distill a larger dataset down into soft labels.

One idea the team is already working on is having other algorithms generate the soft labels for the algorithm that’s going to be trained using LO-shot; manually designing soft labels won’t always be as easy as splitting up some pie charts into different colors.

LO-shot’s potential for reducing the amount of training data needed to yield working AI systems is promising. Besides reducing the cost and the time required to train new models, the method could also make AI more accessible to industries, companies, or individuals who don’t have access to large datasets—an important step for democratization of AI.

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#437460 This Week’s Awesome Tech Stories From ...

ARTIFICIAL INTELLIGENCE
A Radical New Technique Lets AI Learn With Practically No Data
Karen Hao | MIT Technology Review
“Shown photos of a horse and a rhino, and told a unicorn is something in between, [children] can recognize the mythical creature in a picture book the first time they see it. …Now a new paper from the University of Waterloo in Ontario suggests that AI models should also be able to do this—a process the researchers call ‘less than one’-shot, or LO-shot, learning.”

FUTURE
Artificial General Intelligence: Are We Close, and Does It Even Make Sense to Try?
Will Douglas Heaven | MIT Technology Review
“A machine that could think like a person has been the guiding vision of AI research since the earliest days—and remains its most divisive idea. …So why is AGI controversial? Why does it matter? And is it a reckless, misleading dream—or the ultimate goal?”

HEALTH
The Race for a Super-Antibody Against the Coronavirus
Apoorva Mandavilli | The New York Times
“Dozens of companies and academic groups are racing to develop antibody therapies. …But some scientists are betting on a dark horse: Prometheus, a ragtag group of scientists who are months behind in the competition—and yet may ultimately deliver the most powerful antibody.”

SPACE
How to Build a Spacecraft to Save the World
Daniel Oberhaus | Wired
“The goal of the Double Asteroid Redirection Test, or DART, is to slam the [spacecraft] into a small asteroid orbiting a larger asteroid 7 million miles from Earth. …It should be able to change the asteroid’s orbit just enough to be detectable from Earth, demonstrating that this kind of strike could nudge an oncoming threat out of Earth’s way. Beyond that, everything is just an educated guess, which is exactly why NASA needs to punch an asteroid with a robot.”

TRANSPORTATION
Inside Gravity’s Daring Mission to Make Jetpacks a Reality
Oliver Franklin-Wallis | Wired
“The first time someone flies a jetpack, a curious thing happens: just as their body leaves the ground, their legs start to flail. …It’s as if the vestibular system can’t quite believe what’s happening. This isn’t natural. Then suddenly, thrust exceeds weight, and—they’re aloft. …It’s that moment, lift-off, that has given jetpacks an enduring appeal for over a century.”

FUTURE OF FOOD
Inside Singapore’s Huge Bet on Vertical Farming
Megan Tatum | MIT Technology Review
“…to cram all [of Singapore’s] gleaming towers and nearly 6 million people into a land mass half the size of Los Angeles, it has sacrificed many things, including food production. Farms make up no more than 1% of its total land (in the United States it’s 40%), forcing the small city-state to shell out around $10 billion each year importing 90% of its food. Here was an example of technology that could change all that.”

COMPUTING
The Effort to Build the Mathematical Library of the Future
Kevin Hartnett | Quanta
“Digitizing mathematics is a longtime dream. The expected benefits range from the mundane—computers grading students’ homework—to the transcendent: using artificial intelligence to discover new mathematics and find new solutions to old problems.”

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#437407 Nvidia’s Arm Acquisition Brings the ...

Artificial intelligence and mobile computing have been two of the most disruptive technologies of this century. The unification of the two companies that made them possible could have wide-ranging consequences for the future of computing.

California-based Nvidia’s graphics processing units (GPUs) have powered the deep learning revolution ever since Google researchers discovered in 2011 that they could run neural networks far more efficiently than conventional CPUs. UK company Arm’s energy-efficient chip designs have dominated the mobile and embedded computing markets for even longer.

Now the two will join forces after the American company announced a $40 billion deal to buy Arm from its Japanese owner, Softbank. In a press release announcing the deal, Nvidia touted its potential to rapidly expand the reach of AI into all areas of our lives.

“In the years ahead, trillions of computers running AI will create a new internet-of-things that is thousands of times larger than today’s internet-of-people,” said Nvidia founder and CEO Jensen Huang. “Uniting NVIDIA’s AI computing capabilities with the vast ecosystem of Arm’s CPU, we can advance computing from the cloud, smartphones, PCs, self-driving cars and robotics, to edge IoT, and expand AI computing to every corner of the globe.”

There are good reasons to believe the hype. The two companies are absolutely dominant in their respective fields—Nvidia’s GPUs support more than 97 percent of AI computing infrastructure offered by big cloud service providers, and Arm’s chips power more than 90 percent of smartphones. And there’s little overlap in their competencies, which means the relationship could be a truly symbiotic one.

“I think the deal “fits like a glove” in that Arm plays in areas that Nvidia does not or isn’t that successful, while NVIDIA plays in many places Arm doesn’t or isn’t that successful,” analyst Patrick Moorhead wrote in Forbes.

One of the most obvious directions would be to expand Nvidia’s AI capabilities to the kind of low-power edge devices that Arm excels in. There’s growing demand for AI in devices like smartphones, wearables, cars, and drones, where transmitting data to the cloud for processing is undesirable either for reasons of privacy or speed.

But there might also be fruitful exchanges in the other direction. Huang told Moorhead a major focus would be bringing Arm’s expertise in energy efficiency to the data center. That’s a big concern for technology companies whose electricity bills and green credentials are taking a battering thanks to the huge amounts of energy required to run millions of computer chips around the clock.

The deal may not be plain sailing, though, most notably due to the two companies’ differing business models. While Nvidia sells ready-made processors, Arm simply creates chip designs and then licenses them to other companies who can then customize them to their particular hardware needs. It operates on an open-licence basis whereby any company with the necessary cash can access its designs.

As a result, its designs are found in products built by hundreds of companies that license its innovations, including Apple, Samsung, Huawei, Qualcomm, and even Nvidia. Some, including two of the company’s co-founders, have raised concerns that the purchase by Nvidia, which competes with many of these other companies, could harm the neutrality that has been central to its success.

It’s possible this could push more companies towards RISC-V, an open-source technology developed by researchers at the University of California at Berkeley that rivals Arm’s and is not owned by any one company. However, there are plenty of reasons why most companies still prefer arm over the less feature-rich open-source option, and it might take a considerable push to convince Arm’s customers to jump ship.

The deal will also have to navigate some thorny political issues. Unions, politicians, and business leaders in the UK have voiced concerns that it could lead to the loss of high-tech jobs, and government sources have suggested conditions could be placed on the deal.

Regulators in other countries could also put a spanner in the works. China is concerned that if Arm becomes US-owned, many of the Chinese companies that rely on its technology could become victims of export restrictions as the China-US trade war drags on. South Korea is also wary that the deal could create a new technology juggernaut that could dent Samsung’s growth in similar areas.

Nvidia has made commitments to keep Arm’s headquarters in the UK, which it says should lessen concerns around jobs and export restrictions. It’s also pledged to open a new world-class technology center in Cambridge and build a state-of-the-art AI supercomputer powered by Arm’s chips there. Whether the deal goes through still hangs in the balance, but of it does it could spur a whole new wave of AI innovation.

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#437395 Microsoft Had a Crazy Idea to Put ...

A little over two years ago, a shipping container-sized cylinder bearing Microsoft’s name and logo was lowered onto the ocean floor off the northern coast of Scotland. Inside were 864 servers, and their submersion was part of the second phase of the software giant’s Project Natick. Launched in 2015, the project’s purpose is to determine the feasibility of underwater data centers powered by offshore renewable energy.

A couple months ago, the deep-sea servers were brought back up to the surface so engineers could inspect them and evaluate how they’d performed while under water.

But wait—why were they there in the first place?

As bizarre as it seems to sink hundreds of servers into the ocean, there are actually several very good reasons to do so. According to the UN, about 40 percent of the world’s population lives within 60 miles of an ocean. As internet connectivity expands to cover most of the globe in the next few years, millions more people will come online, and a lot more servers will be needed to manage the increased demand and data they’ll generate.

In densely-populated cities real estate is expensive and can be hard to find. But know where there’s lots of cheap, empty space? At the bottom of the ocean. This locale also carries the added benefit of being really cold (depending where we’re talking, that is; if you’re looking off the coast of, say, Mumbai or Abu Dhabi, the waters are warmer).

Servers generate a lot of heat, and datacenters use most of their electricity for cooling. Keeping not just the temperature but also the humidity level constant is important for optimal functioning of the servers; neither of these vary much 100 feet under water.

Finally, installing data centers on the ocean floor is, surprisingly, much faster than building them on land. Microsoft claims its server-holding cylinders will take less than 90 days to go from factory ship to operation, as compared to the average two years it takes to get a terrestrial data center up and running.

Microsoft’s Special Projects team operated the underwater data center for two years, and it took a full day to dredge it up and bring it to the surface. One of the first things researchers did was to insert test tubes into the container to take samples of the air inside; they’ll use it to try to determine how gases released from the equipment may have impacted the servers’ operating environment.

The container was filled with dry nitrogen upon deployment, which seems to have made for a much better environment than the oxygen that land-bound servers are normally surrounded by; the failure rate of the servers in the water was just one-eighth that of Microsoft’s typical rate for its servers on land. The team thinks the nitrogen atmosphere was helpful because it’s less corrosive than oxygen. The fact that no humans entered the container for the entirety of its operations helped, too (no moving around of components or having to turn on lights or adjust the temperature).

Ben Cutler, a project manager in Microsoft’s Special Projects research group who leads Project Natick, believes the results of this phase of the project are sufficient to show that underwater data centers are worth pursuing. “We are now at the point of trying to harness what we have done as opposed to feeling the need to go and prove out some more,” he said.

Cutler envisions putting underwater datacenters near offshore wind farms to power them sustainably. The data centers of the future will require less human involvement, instead being managed and run primarily by technologies like robotics and AI. In this kind of “lights-out” datacenter, the servers would be swapped out about once every five years, with any that fail before then being taken offline.

The final step in this phase of Project Natick is to recycle all the components used for the underwater data center, including the steel pressure vessel, heat exchangers, and the servers themselves—and restoring the sea bed where the cylinder rested back to its original condition.

If Cutler’s optimism is a portent of things to come, it may not be long before the ocean floor is dotted with sustainable datacenters to feed our ever-increasing reliance on our phones and the internet.

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#437337 6G Will Be 100 Times Faster Than ...

Though 5G—a next-generation speed upgrade to wireless networks—is scarcely up and running (and still nonexistent in many places) researchers are already working on what comes next. It lacks an official name, but they’re calling it 6G for the sake of simplicity (and hey, it’s tradition). 6G promises to be up to 100 times faster than 5G—fast enough to download 142 hours of Netflix in a second—but researchers are still trying to figure out exactly how to make such ultra-speedy connections happen.

A new chip, described in a paper in Nature Photonics by a team from Osaka University and Nanyang Technological University in Singapore, may give us a glimpse of our 6G future. The team was able to transmit data at a rate of 11 gigabits per second, topping 5G’s theoretical maximum speed of 10 gigabits per second and fast enough to stream 4K high-def video in real time. They believe the technology has room to grow, and with more development, might hit those blistering 6G speeds.

NTU final year PhD student Abhishek Kumar, Assoc Prof Ranjan Singh and postdoc Dr Yihao Yang. Dr Singh is holding the photonic topological insulator chip made from silicon, which can transmit terahertz waves at ultrahigh speeds. Credit: NTU Singapore
But first, some details about 5G and its predecessors so we can differentiate them from 6G.

Electromagnetic waves are characterized by a wavelength and a frequency; the wavelength is the distance a cycle of the wave covers (peak to peak or trough to trough, for example), and the frequency is the number of waves that pass a given point in one second. Cellphones use miniature radios to pick up electromagnetic signals and convert those signals into the sights and sounds on your phone.

4G wireless networks run on millimeter waves on the low- and mid-band spectrum, defined as a frequency of a little less (low-band) and a little more (mid-band) than one gigahertz (or one billion cycles per second). 5G kicked that up several notches by adding even higher frequency millimeter waves of up to 300 gigahertz, or 300 billion cycles per second. Data transmitted at those higher frequencies tends to be information-dense—like video—because they’re much faster.

The 6G chip kicks 5G up several more notches. It can transmit waves at more than three times the frequency of 5G: one terahertz, or a trillion cycles per second. The team says this yields a data rate of 11 gigabits per second. While that’s faster than the fastest 5G will get, it’s only the beginning for 6G. One wireless communications expert even estimates 6G networks could handle rates up to 8,000 gigabits per second; they’ll also have much lower latency and higher bandwidth than 5G.

Terahertz waves fall between infrared waves and microwaves on the electromagnetic spectrum. Generating and transmitting them is difficult and expensive, requiring special lasers, and even then the frequency range is limited. The team used a new material to transmit terahertz waves, called photonic topological insulators (PTIs). PTIs can conduct light waves on their surface and edges rather than having them run through the material, and allow light to be redirected around corners without disturbing its flow.

The chip is made completely of silicon and has rows of triangular holes. The team’s research showed the chip was able to transmit terahertz waves error-free.

Nanyang Technological University associate professor Ranjan Singh, who led the project, said, “Terahertz technology […] can potentially boost intra-chip and inter-chip communication to support artificial intelligence and cloud-based technologies, such as interconnected self-driving cars, which will need to transmit data quickly to other nearby cars and infrastructure to navigate better and also to avoid accidents.”

Besides being used for AI and self-driving cars (and, of course, downloading hundreds of hours of video in seconds), 6G would also make a big difference for data centers, IoT devices, and long-range communications, among other applications.

Given that 5G networks are still in the process of being set up, though, 6G won’t be coming on the scene anytime soon; a recent whitepaper on 6G from Japanese company NTTDoCoMo estimates we’ll see it in 2030, pointing out that wireless connection tech generations have thus far been spaced about 10 years apart; we got 3G in the early 2000s, 4G in 2010, and 5G in 2020.

In the meantime, as 6G continues to develop, we’re still looking forward to the widespread adoption of 5G.

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