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The narrative that often accompanies most stories about artificial intelligence these days is how machines will disrupt any number of industries, from healthcare to transportation. It makes sense. After all, technology already drives many of the innovations in these sectors of the economy.
But sneakers and the red carpet? The definitively low-tech fashion industry would seem to be one of the last to turn over its creative direction to data scientists and machine learning algorithms.
However, big brands, e-commerce giants, and numerous startups are betting that AI can ingest data and spit out Chanel. Maybe it’s not surprising, given that fashion is partly about buzz and trends—and there’s nothing more buzzy and trendy in the world of tech today than AI.
In its annual survey of the $3 trillion fashion industry, consulting firm McKinsey predicted that while AI didn’t hit a “critical mass” in 2018, it would increasingly influence the business of everything from design to manufacturing.
“Fashion as an industry really has been so slow to understand its potential roles interwoven with technology. And, to be perfectly honest, the technology doesn’t take fashion seriously.” This comment comes from Zowie Broach, head of fashion at London’s Royal College of Arts, who as a self-described “old fashioned” designer has embraced the disruptive nature of technology—with some caveats.
Co-founder in the late 1990s of the avant-garde fashion label Boudicca, Broach has always seen tech as a tool for designers, even setting up a website for the company circa 1998, way before an online presence became, well, fashionable.
Broach told Singularity Hub that while she is generally optimistic about the future of technology in fashion—the designer has avidly been consuming old sci-fi novels over the last few years—there are still a lot of difficult questions to answer about the interface of algorithms, art, and apparel.
For instance, can AI do what the great designers of the past have done? Fashion was “about designing, it was about a narrative, it was about meaning, it was about expression,” according to Broach.
AI that designs products based on data gleaned from human behavior can potentially tap into the Pavlovian response in consumers in order to make money, Broach noted. But is that channeling creativity, or just digitally dabbling in basic human brain chemistry?
She is concerned about people retaining control of the process, whether we’re talking about their data or their designs. But being empowered with the insights machines could provide into, for example, the geographical nuances of fashion between Dubai, Moscow, and Toronto is thrilling.
“What is it that we want the future to be from a fashion, an identity, and design perspective?” she asked.
Off on the Right Foot
Silicon Valley and some of the biggest brands in the industry offer a few answers about where AI and fashion are headed (though not at the sort of depths that address Broach’s broader questions of aesthetics and ethics).
Take what is arguably the biggest brand in fashion, at least by market cap but probably not by the measure of appearances on Oscar night: Nike. The $100 billion shoe company just gobbled up an AI startup called Celect to bolster its data analytics and optimize its inventory. In other words, Nike hopes it will be able to figure out what’s hot and what’s not in a particular location to stock its stores more efficiently.
The company is going even further with Nike Fit, a foot-scanning platform using a smartphone camera that applies AI techniques from fields like computer vision and machine learning to find the best fit for each person’s foot. The algorithms then identify and recommend the appropriately sized and shaped shoe in different styles.
No doubt the next step will be to 3D print personalized and on-demand sneakers at any store.
San Francisco-based startup ThirdLove is trying to bring a similar approach to bra sizes. Its 20-member data team, Fortune reported, has developed the Fit Finder quiz that uses machine learning algorithms to help pick just the right garment for every body type.
Data scientists are also a big part of the team at Stitch Fix, a former San Francisco startup that went public in 2017 and today sports a market cap of more than $2 billion. The online “personal styling” company uses hundreds of algorithms to not only make recommendations to customers, but to help design new styles and even manage the subscription-based supply chain.
Future of Fashion
E-commerce giant Amazon has thrown its own considerable resources into developing AI applications for retail fashion—with mixed results.
One notable attempt involved a “styling assistant” that came with the company’s Echo Look camera that helped people catalog and manage their wardrobes, evening helping pick out each day’s attire. The company more recently revisited the direct consumer side of AI with an app called StyleSnap, which matches clothes and accessories uploaded to the site with the retailer’s vast inventory and recommends similar styles.
Behind the curtains, Amazon is going even further. A team of researchers in Israel have developed algorithms that can deduce whether a particular look is stylish based on a few labeled images. Another group at the company’s San Francisco research center was working on tech that could generate new designs of items based on images of a particular style the algorithms trained on.
“I will say that the accumulation of many new technologies across the industry could manifest in a highly specialized style assistant, far better than the examples we’ve seen today. However, the most likely thing is that the least sexy of the machine learning work will become the most impactful, and the public may never hear about it.”
That prediction is from an online interview with Leanne Luce, a fashion technology blogger and product manager at Google who recently wrote a book called, succinctly enough, Artificial Intelligence and Fashion.
Data Meets Design
Academics are also sticking their beakers into AI and fashion. Researchers at the University of California, San Diego, and Adobe Research have previously demonstrated that neural networks, a type of AI designed to mimic some aspects of the human brain, can be trained to generate (i.e., design) new product images to match a buyer’s preference, much like the team at Amazon.
Meanwhile, scientists at Hong Kong Polytechnic University are working with China’s answer to Amazon, Alibaba, on developing a FashionAI Dataset to help machines better understand fashion. The effort will focus on how algorithms approach certain building blocks of design, what are called “key points” such as neckline and waistline, and “fashion attributes” like collar types and skirt styles.
The man largely behind the university’s research team is Calvin Wong, a professor and associate head of Hong Kong Polytechnic University’s Institute of Textiles and Clothing. His group has also developed an “intelligent fabric defect detection system” called WiseEye for quality control, reducing the chance of producing substandard fabric by 90 percent.
Wong and company also recently inked an agreement with RCA to establish an AI-powered design laboratory, though the details of that venture have yet to be worked out, according to Broach.
One hope is that such collaborations will not just get at the technological challenges of using machines in creative endeavors like fashion, but will also address the more personal relationships humans have with their machines.
“I think who we are, and how we use AI in fashion, as our identity, is not a superficial skin. It’s very, very important for how we define our future,” Broach said.
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Quadrupedal robots are making significant advances lately, and just in the past few months we’ve seen Boston Dynamics’ Spot hauling a truck, IIT’s HyQReal pulling a plane, MIT’s MiniCheetah doing backflips, Unitree Robotics’ Laikago towing a van, and Ghost Robotics’ Vision 60 exploring a mine. Robot makers are betting that their four-legged machines will prove useful in a variety of applications in construction, security, delivery, and even at home.
ANYbotics has been working on such applications for years, testing out their ANYmal robot in places where humans typically don’t want to go (like offshore platforms) as well as places where humans really don’t want to go (like sewers), and they have a better idea than most companies what can make quadruped robots successful.
This week, ANYbotics is announcing a completely new quadruped platform, ANYmal C, a major upgrade from the really quite research-y ANYmal B. The new quadruped has been optimized for ruggedness and reliability in industrial environments, with a streamlined body painted a color that lets you know it means business.
ANYmal C’s physical specs are pretty impressive for a production quadruped. It can move at 1 meter per second, manage 20-degree slopes and 45-degree stairs, cross 25-centimeter gaps, and squeeze through passages just 60 centimeters wide. It’s packed with cameras and 3D sensors, including a lidar for 3D mapping and simultaneous localization and mapping (SLAM). All these sensors (along with the vast volume of gait research that’s been done with ANYmal) make this one of the most reliably autonomous quadrupeds out there, with real-time motion planning and obstacle avoidance.
ANYmal can autonomously attach itself to a cone-shaped docking station to recharge.
ANYmal C is also one of the ruggedest legged robots in existence. The 50-kilogram robot is IP67 rated, meaning that it’s completely impervious to dust and can withstand being submerged in a meter of water for an hour. If it’s submerged for longer than that, you’re absolutely doing something wrong. The robot will run for over 2 hours on battery power, and if that’s not enough endurance, don’t worry, because ANYmal can autonomously impale itself on a weird cone-shaped docking station to recharge.
ANYmal C’s sensor payload includes cameras and a lidar for 3D mapping and SLAM.
As far as what ANYmal C is designed to actually do, it’s mostly remote inspection tasks where you need to move around through a relatively complex environment, but where for whatever reason you’d be better off not sending a human. ANYmal C has a sensor payload that gives it lots of visual options, like thermal imaging, and with the ability to handle a 10-kilogram payload, the robot can be adapted to many different environments.
Over the next few months, we’re hoping to see more examples of ANYmal C being deployed to do useful stuff in real-world environments, but for now, we do have a bit more detail from ANYbotics CTO Christian Gehring.
IEEE Spectrum: Can you tell us about the development process for ANYmal C?
Christian Gehring: We tested the previous generation of ANYmal (B) in a broad range of environments over the last few years and gained a lot of insights. Based on our learnings, it became clear that we would have to re-design the robot to meet the requirements of industrial customers in terms of safety, quality, reliability, and lifetime. There were different prototype stages both for the new drives and for single robot assemblies. Apart from electrical tests, we thoroughly tested the thermal control and ingress protection of various subsystems like the depth cameras and actuators.
What can ANYmal C do that the previous version of ANYmal can’t?
ANYmal C was redesigned with a focus on performance increase regarding actuation (new drives), computational power (new hexacore Intel i7 PCs), locomotion and navigation skills, and autonomy (new depth cameras). The new robot additionally features a docking system for autonomous recharging and an inspection payload as an option. The design of ANYmal C is far more integrated than its predecessor, which increases both performance and reliability.
How much of ANYmal C’s development and design was driven by your experience with commercial or industry customers?
Tests (such as the offshore installation with TenneT) and discussions with industry customers were important to get the necessary design input in terms of performance, safety, quality, reliability, and lifetime. Most customers ask for very similar inspection tasks that can be performed with our standard inspection payload and the required software packages. Some are looking for a robot that can also solve some simple manipulation tasks like pushing a button. Overall, most use cases customers have in mind are realistic and achievable, but some are really tough for the robot, like climbing 50° stairs in hot environments of 50°C.
Can you describe how much autonomy you expect ANYmal C to have in industrial or commercial operations?
ANYmal C is primarily developed to perform autonomous routine inspections in industrial environments. This autonomy especially adds value for operations that are difficult to access, as human operation is extremely costly. The robot can naturally also be operated via a remote control and we are working on long-distance remote operation as well.
Do you expect that researchers will be interested in ANYmal C? What research applications could it be useful for?
ANYmal C has been designed to also address the needs of the research community. The robot comes with two powerful hexacore Intel i7 computers and can additionally be equipped with an NVIDIA Jetson Xavier graphics card for learning-based applications. Payload interfaces enable users to easily install and test new sensors. By joining our established ANYmal Research community, researchers get access to simulation tools and software APIs, which boosts their research in various areas like control, machine learning, and navigation.
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When every other tech startup claims to use artificial intelligence, it can be tough to figure out if an AI service or product works as advertised. In the midst of the AI “gold rush,” how can you separate the nuggets from the fool’s gold?
There’s no shortage of cautionary tales involving overhyped AI claims. And applying AI technologies to health care, education, and law enforcement mean that getting it wrong can have real consequences for society—not just for investors who bet on the wrong unicorn.
So IEEE Spectrum asked experts to share their tips for how to identify AI hype in press releases, news articles, research papers, and IPO filings.
“It can be tricky, because I think the people who are out there selling the AI hype—selling this AI snake oil—are getting more sophisticated over time,” says Tim Hwang, director of the Harvard-MIT Ethics and Governance of AI Initiative.
The term “AI” is perhaps most frequently used to describe machine learning algorithms (and deep learning algorithms, which require even less human guidance) that analyze huge amounts of data and make predictions based on patterns that humans might miss. These popular forms of AI are mostly suited to specialized tasks, such as automatically recognizing certain objects within photos. For that reason, they are sometimes described as “weak” or “narrow” AI.
Some researchers and thought leaders like to talk about the idea of “artificial general intelligence” or “strong AI” that has human-level capacity and flexibility to handle many diverse intellectual tasks. But for now, this type of AI remains firmly in the realm of science fiction and is far from being realized in the real world.
“AI has no well-defined meaning and many so-called AI companies are simply trying to take advantage of the buzz around that term,” says Arvind Narayanan, a computer scientist at Princeton University. “Companies have even been caught claiming to use AI when, in fact, the task is done by human workers.”
Here are three ways to recognize AI hype.
Look for Buzzwords
One red flag is what Hwang calls the “hype salad.” This means stringing together the term “AI” with many other tech buzzwords such as “blockchain” or “Internet of Things.” That doesn’t automatically disqualify the technology, but spotting a high volume of buzzwords in a post, pitch, or presentation should raise questions about what exactly the company or individual has developed.
Other experts agree that strings of buzzwords can be a red flag. That’s especially true if the buzzwords are never really explained in technical detail, and are simply tossed around as vague, poorly-defined terms, says Marzyeh Ghassemi, a computer scientist and biomedical engineer at the University of Toronto in Canada.
“I think that if it looks like a Google search—picture ‘interpretable blockchain AI deep learning medicine’—it's probably not high-quality work,” Ghassemi says.
Hwang also suggests mentally replacing all mentions of “AI” in an article with the term “magical fairy dust.” It’s a way of seeing whether an individual or organization is treating the technology like magic. If so—that’s another good reason to ask more questions about what exactly the AI technology involves.
And even the visual imagery used to illustrate AI claims can indicate that an individual or organization is overselling the technology.
“I think that a lot of the people who work on machine learning on a day-to-day basis are pretty humble about the technology, because they’re largely confronted with how frequently it just breaks and doesn't work,” Hwang says. “And so I think that if you see a company or someone representing AI as a Terminator head, or a big glowing HAL eye or something like that, I think it’s also worth asking some questions.”
Interrogate the Data
It can be hard to evaluate AI claims without any relevant expertise, says Ghassemi at the University of Toronto. Even experts need to know the technical details of the AI algorithm in question and have some access to the training data that shaped the AI model’s predictions. Still, savvy readers with some basic knowledge of applied statistics can search for red flags.
To start, readers can look for possible bias in training data based on small sample sizes or a skewed population that fails to reflect the broader population, Ghassemi says. After all, an AI model trained only on health data from white men would not necessarily achieve similar results for other populations of patients.
“For me, a red flag is not demonstrating deep knowledge of how your labels are defined.”
—Marzyeh Ghassemi, University of Toronto
How machine learning and deep learning models perform also depends on how well humans labeled the sample datasets use to train these programs. This task can be straightforward when labeling photos of cats versus dogs, but gets more complicated when assigning disease diagnoses to certain patient cases.
Medical experts frequently disagree with each other on diagnoses—which is why many patients seek a second opinion. Not surprisingly, this ambiguity can also affect the diagnostic labels that experts assign in training datasets. “For me, a red flag is not demonstrating deep knowledge of how your labels are defined,” Ghassemi says.
Such training data can also reflect the cultural stereotypes and biases of the humans who labeled the data, says Narayanan at Princeton University. Like Ghassemi, he recommends taking a hard look at exactly what the AI has learned: “A good way to start critically evaluating AI claims is by asking questions about the training data.”
Another red flag is presenting an AI system’s performance through a single accuracy figure without much explanation, Narayanan says. Claiming that an AI model achieves “99 percent” accuracy doesn’t mean much without knowing the baseline for comparison—such as whether other systems have already achieved 99 percent accuracy—or how well that accuracy holds up in situations beyond the training dataset.
Narayanan also emphasized the need to ask questions about an AI model’s false positive rate—the rate of making wrong predictions about the presence of a given condition. Even if the false positive rate of a hypothetical AI service is just one percent, that could have major consequences if that service ends up screening millions of people for cancer.
Readers can also consider whether using AI in a given situation offers any meaningful improvement compared to traditional statistical methods, says Clayton Aldern, a data scientist and journalist who serves as managing director for Caldern LLC. He gave the hypothetical example of a “super-duper-fancy deep learning model” that achieves a prediction accuracy of 89 percent, compared to a “little polynomial regression model” that achieves 86 percent on the same dataset.
“We're talking about a three-percentage-point increase on something that you learned about in Algebra 1,” Aldern says. “So is it worth the hype?”
Don’t Ignore the Drawbacks
The hype surrounding AI isn’t just about the technical merits of services and products driven by machine learning. Overblown claims about the beneficial impacts of AI technology—or vague promises to address ethical issues related to deploying it—should also raise red flags.
“If a company promises to use its tech ethically, it is important to question if its business model aligns with that promise,” Narayanan says. “Even if employees have noble intentions, it is unrealistic to expect the company as a whole to resist financial imperatives.”
One example might be a company with a business model that depends on leveraging customers’ personal data. Such companies “tend to make empty promises when it comes to privacy,” Narayanan says. And, if companies hire workers to produce training data, it’s also worth asking whether the companies treat those workers ethically.
The transparency—or lack thereof—about any AI claim can also be telling. A company or research group can minimize concerns by publishing technical claims in peer-reviewed journals or allowing credible third parties to evaluate their AI without giving away big intellectual property secrets, Narayanan says. Excessive secrecy is a big red flag.
With these strategies, you don’t need to be a computer engineer or data scientist to start thinking critically about AI claims. And, Narayanan says, the world needs many people from different backgrounds for societies to fully consider the real-world implications of AI.
Editor’s Note: The original version of this story misspelled Clayton Aldern’s last name as Alderton. Continue reading
Life is pretty different now than it was 20 years ago, or even 10 years ago. It’s sort of exciting, and sort of scary. And hold onto your hat, because it’s going to keep changing—even faster than it already has been.
The good news is, maybe there won’t be too many big surprises, because the future will be shaped by trends that have already been set in motion. According to Singularity University co-founder and XPRIZE founder Peter Diamandis, a lot of these trends are unstoppable—but they’re also pretty predictable.
At SU’s Global Summit, taking place this week in San Francisco, Diamandis outlined some of the meta-trends he believes are key to how we’ll live our lives and do business in the (not too distant) future.
Increasing Global Abundance
Resources are becoming more abundant all over the world, and fewer people are seeing their lives limited by scarcity. “It’s hard for us to realize this as we see crisis news, but what people have access to is more abundant than ever before,” Diamandis said. Products and services are becoming cheaper and thus available to more people, and having more resources then enables people to create more, thus producing even more resources—and so on.
Need evidence? The proportion of the world’s population living in extreme poverty is currently lower than it’s ever been. The average human life expectancy is longer than it’s ever been. The costs of day-to-day needs like food, energy, transportation, and communications are on a downward trend.
Take energy. In most of the world, though its costs are decreasing, it’s still a fairly precious commodity; we turn off our lights and our air conditioners when we don’t need them (ideally, both to save money and to avoid wastefulness). But the cost of solar energy has plummeted, and the storage capacity of batteries is improving, and solar technology is steadily getting more efficient. Bids for new solar power plants in the past few years have broken each other’s records for lowest cost per kilowatt hour.
“We’re not far from a penny per kilowatt hour for energy from the sun,” Diamandis said. “And if you’ve got energy, you’ve got water.” Desalination, for one, will be much more widely feasible once the cost of the energy needed for it drops.
Knowledge is perhaps the most crucial resource that’s going from scarce to abundant. All the world’s knowledge is now at the fingertips of anyone who has a mobile phone and an internet connection—and the number of people connected is only going to grow. “Everyone is being connected at gigabit connection speeds, and this will be transformative,” Diamandis said. “We’re heading towards a world where anyone can know anything at any time.”
Increasing Capital Abundance
It’s not just goods, services, and knowledge that are becoming more plentiful. Money is, too—particularly money for business. “There’s more and more capital available to invest in companies,” Diamandis said. As a result, more people are getting the chance to bring their world-changing ideas to life.
Venture capital investments reached a new record of $130 billion in 2018, up from $84 billion in 2017—and that’s just in the US. Globally, VC funding grew 21 percent from 2017 to a total of $207 billion in 2018.
Through crowdfunding, any person in any part of the world can present their idea and ask for funding. That funding can come in the form of a loan, an equity investment, a reward, or an advanced purchase of the proposed product or service. “Crowdfunding means it doesn’t matter where you live, if you have a great idea you can get it funded by people from all over the world,” Diamandis said.
All this is making a difference; the number of unicorns—privately-held startups valued at over $1 billion—currently stands at an astounding 360.
One of the reasons why the world is getting better, Diamandis believes, is because entrepreneurs are trying more crazy ideas—not ideas that are reasonable or predictable or linear, but ideas that seem absurd at first, then eventually end up changing the world.
Everyone and Everything, Connected
As already noted, knowledge is becoming abundant thanks to the proliferation of mobile phones and wireless internet; everyone’s getting connected. In the next decade or sooner, connectivity will reach every person in the world. 5G is being tested and offered for the first time this year, and companies like Google, SpaceX, OneWeb, and Amazon are racing to develop global satellite internet constellations, whether by launching 12,000 satellites, as SpaceX’s Starlink is doing, or by floating giant balloons into the stratosphere like Google’s Project Loon.
“We’re about to reach a period of time in the next four to six years where we’re going from half the world’s people being connected to the whole world being connected,” Diamandis said. “What happens when 4.2 billion new minds come online? They’re all going to want to create, discover, consume, and invent.”
And it doesn’t stop at connecting people. Things are becoming more connected too. “By 2020 there will be over 20 billion connected devices and more than one trillion sensors,” Diamandis said. By 2030, those projections go up to 500 billion and 100 trillion. Think about it: there’s home devices like refrigerators, TVs, dishwashers, digital assistants, and even toasters. There’s city infrastructure, from stoplights to cameras to public transportation like buses or bike sharing. It’s all getting smart and connected.
Soon we’ll be adding autonomous cars to the mix, and an unimaginable glut of data to go with them. Every turn, every stop, every acceleration will be a data point. Some cars already collect over 25 gigabytes of data per hour, Diamandis said, and car data is projected to generate $750 billion of revenue by 2030.
“You’re going to start asking questions that were never askable before, because the data is now there to be mined,” he said.
Increasing Human Intelligence
Indeed, we’ll have data on everything we could possibly want data on. We’ll also soon have what Diamandis calls just-in-time education, where 5G combined with artificial intelligence and augmented reality will allow you to learn something in the moment you need it. “It’s not going and studying, it’s where your AR glasses show you how to do an emergency surgery, or fix something, or program something,” he said.
We’re also at the beginning of massive investments in research working towards connecting our brains to the cloud. “Right now, everything we think, feel, hear, or learn is confined in our synaptic connections,” Diamandis said. What will it look like when that’s no longer the case? Companies like Kernel, Neuralink, Open Water, Facebook, Google, and IBM are all investing billions of dollars into brain-machine interface research.
Increasing Human Longevity
One of the most important problems we’ll use our newfound intelligence to solve is that of our own health and mortality, making 100 years old the new 60—then eventually, 120 or 150.
“Our bodies were never evolved to live past age 30,” Diamandis said. “You’d go into puberty at age 13 and have a baby, and by the time you were 26 your baby was having a baby.”
Seeing how drastically our lifespans have changed over time makes you wonder what aging even is; is it natural, or is it a disease? Many companies are treating it as one, and using technologies like senolytics, CRISPR, and stem cell therapy to try to cure it. Scaffolds of human organs can now be 3D printed then populated with the recipient’s own stem cells so that their bodies won’t reject the transplant. Companies are testing small-molecule pharmaceuticals that can stop various forms of cancer.
“We don’t truly know what’s going on inside our bodies—but we can,” Diamandis said. “We’re going to be able to track our bodies and find disease at stage zero.”
The world is far from perfect—that’s not hard to see. What’s less obvious but just as true is that we’re living in an amazing time. More people are coming together, and they have more access to information, and that information moves faster, than ever before.
“I don’t think any of us understand how fast the world is changing,” Diamandis said. “Most people are fearful about the future. But we should be excited about the tools we now have to solve the world’s problems.”
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