Tag Archives: harvard
Researchers at Harvard’s Wyss Institute have been testing a flexible, lightweight exosuit that can improve your metabolic efficiency by 4 to 10 percent while walking and running. This is very important because, according to a press release from Harvard, the suit can help you be faster and more efficient, whether you’re “walking at a leisurely pace,” or “running for your life.” Great!
Making humans better at running for their lives is something that we don’t put nearly enough research effort into, I think. The problem may not come up very often, but when it does, it’s super important (because, bears). So, sign me up for anything that we can do to make our desperate flights faster or more efficient—especially if it’s a lightweight, wearable exosuit that’s soft, flexible, and comfortable to wear.
This is the same sort of exosuit that was part of a DARPA program that we wrote about a few years ago, which was designed to make it easier for soldiers to carry heavy loads for long distances.
Photos: Wyss Institute at Harvard University
The system uses two waist-mounted electrical motors connected with cables to thigh straps that run down around your butt. The motors pull on the cables at the same time that your muscles actuate, helping them out and reducing the amount of work that your muscles put in without decreasing the amount of force they exert on your legs. The entire suit (batteries included) weighs 5 kilograms (11 pounds).
In order for the cables to actuate at the right time, the suit tracks your gait with two inertial measurement units (IMUs) on the thighs and one on the waist, and then adjusts its actuation profile accordingly. It works well, too, with measurable increases in performance:
We show that a portable exosuit that assists hip extension can reduce the metabolic rate of treadmill walking at 1.5 meters per second by 9.3 percent and that of running at 2.5 meters per second by 4.0 percent compared with locomotion without the exosuit. These reduction magnitudes are comparable to the effects of taking off 7.4 and 5.7 kilograms during walking and running, respectively, and are in a range that has shown meaningful athletic performance changes.
By increasing your efficiency, you can think of the suit as being able to make you walk or run faster, or farther, or carry a heavier load, all while spending the same amount of energy (or less), which could be just enough to outrun the bear that’s chasing you. Plus, it doesn’t appear to be uncomfortable to wear, and doesn’t require the user to do anything differently, which means that (unlike most robotics things) it’s maybe actually somewhat practical for real-world use—whether you’re indoors or outdoors, or walking or running, or being chased by a bear or not.
Sadly, I have no idea when you might be able to buy one of these things. But the researchers are looking for ways to make the suit even easier to use, while also reducing the weight and making the efficiency increase more pronounced. Harvard’s Conor Walsh says they’re “excited to continue to apply it to a range of applications, including assisting those with gait impairments, industry workers at risk of injury performing physically strenuous tasks, or recreational weekend warriors.” As a weekend warrior who is not entirely sure whether he can outrun a bear, I’m excited for this.
Reducing the metabolic rate of walking and running with a versatile, portable exosuit, by Jinsoo Kim, Giuk Lee, Roman Heimgartner, Dheepak Arumukhom Revi, Nikos Karavas, Danielle Nathanson, Ignacio Galiana, Asa Eckert-Erdheim, Patrick Murphy, David Perry, Nicolas Menard, Dabin Kim Choe, Philippe Malcolm, and Conor J. Walsh from the Wyss Institute for Biologically Inspired Engineering at Harvard University, appears in the current issue of Science. Continue reading
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