Tag Archives: education
#435804 New AI Systems Are Here to Personalize ...
The narratives about automation and its impact on jobs go from urgent to hopeful and everything in between. Regardless where you land, it’s hard to argue against the idea that technologies like AI and robotics will change our economy and the nature of work in the coming years.
A recent World Economic Forum report noted that some estimates show automation could displace 75 million jobs by 2022, while at the same time creating 133 million new roles. While these estimates predict a net positive for the number of new jobs in the coming decade, displaced workers will need to learn new skills to adapt to the changes. If employees can’t be retrained quickly for jobs in the changing economy, society is likely to face some degree of turmoil.
According to Bryan Talebi, CEO and founder of AI education startup Ahura AI, the same technologies erasing and creating jobs can help workers bridge the gap between the two.
Ahura is developing a product to capture biometric data from adult learners who are using computers to complete online education programs. The goal is to feed this data to an AI system that can modify and adapt their program to optimize for the most effective teaching method.
While the prospect of a computer recording and scrutinizing a learner’s behavioral data will surely generate unease across a society growing more aware and uncomfortable with digital surveillance, some people may look past such discomfort if they experience improved learning outcomes. Users of the system would, in theory, have their own personalized instruction shaped specifically for their unique learning style.
And according to Talebi, their systems are showing some promise.
“Based on our early tests, our technology allows people to learn three to five times faster than traditional education,” Talebi told me.
Currently, Ahura’s system uses the video camera and microphone that come standard on the laptops, tablets, and mobile devices most students are using for their learning programs.
With the computer’s camera Ahura can capture facial movements and micro expressions, measure eye movements, and track fidget score (a measure of how much a student moves while learning). The microphone tracks voice sentiment, and the AI leverages natural language processing to review the learner’s word usage.
From this collection of data Ahura can, according to Talebi, identify the optimal way to deliver content to each individual.
For some users that might mean a video tutorial is the best style of learning, while others may benefit more from some form of experiential or text-based delivery.
“The goal is to alter the format of the content in real time to optimize for attention and retention of the information,” said Talebi. One of Ahura’s main goals is to reduce the frequency with which students switch from their learning program to distractions like social media.
“We can now predict with a 60 percent confidence interval ten seconds before someone switches over to Facebook or Instagram. There’s a lot of work to do to get that up to a 95 percent level, so I don’t want to overstate things, but that’s a promising indication that we can work to cut down on the amount of context-switching by our students,” Talebi said.
Talebi repeatedly mentioned his ambition to leverage the same design principles used by Facebook, Twitter, and others to increase the time users spend on those platforms, but instead use them to design more compelling and even addictive education programs that can compete for attention with social media.
But the notion that Ahura’s system could one day be used to create compelling or addictive education necessarily presses against a set of justified fears surrounding data privacy. Growing anxiety surrounding the potential to misuse user data for social manipulation is widespread.
“Of course there is a real danger, especially because we are collecting so much data about our users which is specifically connected to how they consume content. And because we are looking so closely at the ways people interact with content, it’s incredibly important that this technology never be used for propaganda or to sell things to people,” Talebi tried to assure me.
Unsurprisingly (and worrying), using this AI system to sell products to people is exactly where some investors’ ambitions immediately turn once they learn about the company’s capabilities, according to Talebi. During our discussion Talebi regularly cited the now infamous example of Cambridge Analytica, the political consulting firm hired by the Trump campaign to run a psychographically targeted persuasion campaign on the US population during the most recent presidential election.
“It’s important that we don’t use this technology in those ways. We’re aware that things can go sideways, so we’re hoping to put up guardrails to ensure our system is helping and not harming society,” Talebi said.
Talebi will surely need to take real action on such a claim, but says the company is in the process of identifying a structure for an ethics review board—one that carries significant influence with similar voting authority as the executive team and the regular board.
“Our goal is to build an ethics review board that has teeth, is diverse in both gender and background but also in thought and belief structures. The idea is to have our ethics review panel ensure we’re building things ethically,” he said.
Data privacy appears to be an important issue for Talebi, who occasionally referenced a major competitor in the space based in China. According to a recent article from MIT Tech Review outlining the astonishing growth of AI-powered education platforms in China, data privacy concerns may be less severe there than in the West.
Ahura is currently developing upgrades to an early alpha-stage prototype, but is already capturing data from students from at least one Ivy League school and a variety of other places. Their next step is to roll out a working beta version to over 200,000 users as part of a partnership with an unnamed corporate client who will be measuring the platform’s efficacy against a control group.
Going forward, Ahura hopes to add to its suite of biometric data capture by including things like pupil dilation and facial flushing, heart rate, sleep patterns, or whatever else may give their system an edge in improving learning outcomes.
As information technologies increasingly automate work, it’s likely we’ll also see rapid changes to our labor systems. It’s also looking increasingly likely that those same technologies will be used to improve our ability to give people the right skills when they need them. It may be one way to address the challenges automation is sure to bring.
Image Credit: Gerd Altmann / Pixabay Continue reading
Posted in Human Robots
#435687 Humanoid Robots Teach Coping Skills to ...
Photo: Rob Felt
IEEE Senior Member Ayanna Howard with one of the interactive androids that help children with autism improve their social and emotional engagement.
THE INSTITUTEChildren with autism spectrum disorder can have a difficult time expressing their emotions and can be highly sensitive to sound, sight, and touch. That sometimes restricts their participation in everyday activities, leaving them socially isolated. Occupational therapists can help them cope better, but the time they’re able to spend is limited and the sessions tend to be expensive.
Roboticist Ayanna Howard, an IEEE senior member, has been using interactive androids to guide children with autism on ways to socially and emotionally engage with others—as a supplement to therapy. Howard is chair of the School of Interactive Computing and director of the Human-Automation Systems Lab at Georgia Tech. She helped found Zyrobotics, a Georgia Tech VentureLab startup that is working on AI and robotics technologies to engage children with special needs. Last year Forbes named Howard, Zyrobotics’ chief technology officer, one of the Top 50 U.S. Women in Tech.
In a recent study, Howard and other researchers explored how robots might help children navigate sensory experiences. The experiment involved 18 participants between the ages of 4 and 12; five had autism, and the rest were meeting typical developmental milestones. Two humanoid robots were programmed to express boredom, excitement, nervousness, and 17 other emotional states. As children explored stations set up for hearing, seeing, smelling, tasting, and touching, the robots modeled what the socially acceptable responses should be.
“If a child’s expression is one of happiness or joy, the robot will have a corresponding response of encouragement,” Howard says. “If there are aspects of frustration or sadness, the robot will provide input to try again.” The study suggested that many children with autism exhibit stronger levels of engagement when the robots interact with them at such sensory stations.
It is one of many robotics projects Howard has tackled. She has designed robots for researching glaciers, and she is working on assistive robots for the home, as well as an exoskeleton that can help children who have motor disabilities.
Howard spoke about her work during the Ethics in AI: Impacts of (Anti?) Social Robotics panel session held in May at the IEEE Vision, Innovation, and Challenges Summit in San Diego. You can watch the session on IEEE.tv.
The next IEEE Vision, Innovation, and Challenges Summit and Honors Ceremony will be held on 15 May 2020 at the JW Marriott Parq Vancouver hotel, in Vancouver.
In this interview with The Institute, Howard talks about how she got involved with assistive technologies, the need for a more diverse workforce, and ways IEEE has benefited her career.
FOCUS ON ACCESSIBILITY
Howard was inspired to work on technology that can improve accessibility in 2008 while teaching high school students at a summer camp devoted to science, technology, engineering, and math.
“A young lady with a visual impairment attended camp. The robot programming tools being used at the camp weren’t accessible to her,” Howard says. “As an engineer, I want to fix problems when I see them, so we ended up designing tools to enable access to programming tools that could be used in STEM education.
“That was my starting motivation, and this theme of accessibility has expanded to become a main focus of my research. One of the things about this world of accessibility is that when you start interacting with kids and parents, you discover another world out there of assistive technologies and how robotics can be used for good in education as well as therapy.”
DIVERSITY OF THOUGHT
The Institute asked Howard why it’s important to have a more diverse STEM workforce and what could be done to increase the number of women and others from underrepresented groups.
“The makeup of the current engineering workforce isn’t necessarily representative of the world, which is composed of different races, cultures, ages, disabilities, and socio-economic backgrounds,” Howard says. “We’re creating products used by people around the globe, so we have to ensure they’re being designed for a diverse population. As IEEE members, we also need to engage with people who aren’t engineers, and we don’t do that enough.”
Educational institutions are doing a better job of increasing diversity in areas such as gender, she says, adding that more work is needed because the enrollment numbers still aren’t representative of the population and the gains don’t necessarily carry through after graduation.
“There has been an increase in the number of underrepresented minorities and females going into engineering and computer science,” she says, “but data has shown that their numbers are not sustained in the workforce.”
ROLE MODEL
Because there are more underrepresented groups on today’s college campuses that can form a community, the lack of engineering role models—although a concern on campuses—is more extreme for preuniversity students, Howard says.
“Depending on where you go to school, you may not know what an engineer does or even consider engineering as an option,” she says, “so there’s still a big disconnect there.”
Howard has been involved for many years in math- and science-mentoring programs for at-risk high school girls. She tells them to find what they’re passionate about and combine it with math and science to create something. She also advises them not to let anyone tell them that they can’t.
Howard’s father is an engineer. She says he never encouraged or discouraged her to become one, but when she broke something, he would show her how to fix it and talk her through the process. Along the way, he taught her a logical way of thinking she says all engineers have.
“When I would try to explain something, he would quiz me and tell me to ‘think more logically,’” she says.
Howard earned a bachelor’s degree in engineering from Brown University, in Providence, R.I., then she received both a master’s and doctorate degree in electrical engineering from the University of Southern California. Before joining the faculty of Georgia Tech in 2005, she worked at NASA’s Jet Propulsion Laboratory at the California Institute of Technology for more than a decade as a senior robotics researcher and deputy manager in the Office of the Chief Scientist.
ACTIVE VOLUNTEER
Howard’s father was also an IEEE member, but that’s not why she joined the organization. She says she signed up when she was a student because, “that was something that you just did. Plus, my student membership fee was subsidized.”
She kept the membership as a grad student because of the discounted rates members receive on conferences.
Those conferences have had an impact on her career. “They allow you to understand what the state of the art is,” she says. “Back then you received a printed conference proceeding and reading through it was brutal, but by attending it in person, you got a 15-minute snippet about the research.”
Howard is an active volunteer with the IEEE Robotics and Automation and the IEEE Systems, Man, and Cybernetics societies, holding many positions and serving on several committees. She is also featured in the IEEE Impact Creators campaign. These members were selected because they inspire others to innovate for a better tomorrow.
“I value IEEE for its community,” she says. “One of the nice things about IEEE is that it’s international.” Continue reading
Posted in Human Robots
#435674 MIT Future of Work Report: We ...
Robots aren’t going to take everyone’s jobs, but technology has already reshaped the world of work in ways that are creating clear winners and losers. And it will continue to do so without intervention, says the first report of MIT’s Task Force on the Work of the Future.
The supergroup of MIT academics was set up by MIT President Rafael Reif in early 2018 to investigate how emerging technologies will impact employment and devise strategies to steer developments in a positive direction. And the headline finding from their first publication is that it’s not the quantity of jobs we should be worried about, but the quality.
Widespread press reports of a looming “employment apocalypse” brought on by AI and automation are probably wide of the mark, according to the authors. Shrinking workforces as developed countries age and outstanding limitations in what machines can do mean we’re unlikely to have a shortage of jobs.
But while unemployment is historically low, recent decades have seen a polarization of the workforce as the number of both high- and low-skilled jobs have grown at the expense of the middle-skilled ones, driving growing income inequality and depriving the non-college-educated of viable careers.
This is at least partly attributable to the growth of digital technology and automation, the report notes, which are rendering obsolete many middle-skilled jobs based around routine work like assembly lines and administrative support.
That leaves workers to either pursue high-skilled jobs that require deep knowledge and creativity, or settle for low-paid jobs that rely on skills—like manual dexterity or interpersonal communication—that are still beyond machines, but generic to most humans and therefore not valued by employers. And the growth of emerging technology like AI and robotics is only likely to exacerbate the problem.
This isn’t the first report to note this trend. The World Bank’s 2016 World Development Report noted how technology is causing a “hollowing out” of labor markets. But the MIT report goes further in saying that the cause isn’t simply technology, but the institutions and policies we’ve built around it.
The motivation for introducing new technology is broadly assumed to be to increase productivity, but the authors note a rarely-acknowledged fact: “Not all innovations that raise productivity displace workers, and not all innovations that displace workers substantially raise productivity.”
Examples of the former include computer-aided design software that makes engineers and architects more productive, while examples of the latter include self-service checkouts and automated customer support that replace human workers, often at the expense of a worse customer experience.
While the report notes that companies have increasingly adopted the language of technology augmenting labor, in reality this has only really benefited high-skilled workers. For lower-skilled jobs the motivation is primarily labor cost savings, which highlights the other major force shaping technology’s impact on employment: shareholder capitalism.
The authors note that up until the 1980s, increasing productivity resulted in wage growth across the economic spectrum, but since then average wage growth has failed to keep pace and gains have dramatically skewed towards the top earners.
The report shies away from directly linking this trend to the birth of Reaganomics (something others have been happy to do), but it notes that American veneration of the shareholder as the primary stakeholder in a business and tax policies that incentivize investment in capital rather than labor have exacerbated the negative impacts technology can have on employment.
That means the current focus on re-skilling workers to thrive in the new economy is a necessary, but not sufficient, solution to the disruptive impact technology is having on work, the authors say.
Alongside significant investment in education, fiscal policies need to be re-balanced away from subsidizing investment in physical capital and towards boosting investment in human capital, the authors write, and workers need to have a greater say in corporate decision-making.
The authors point to other developed economies where productivity growth, income growth, and equality haven’t become so disconnected thanks to investments in worker skills, social safety nets, and incentives to invest in human capital. Whether such a radical reshaping of US economic policy is achievable in today’s political climate remains to be seen, but the authors conclude with a call to arms.
“The failure of the US labor market to deliver broadly shared prosperity despite rising productivity is not an inevitable byproduct of current technologies or free markets,” they write. “We can and should do better.”
Image Credit: Simon Abrams / Unsplash/a> Continue reading
Posted in Human Robots
#435614 3 Easy Ways to Evaluate AI Claims
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
Posted in Human Robots