Tag Archives: Resistance
#436261 AI and the future of work: The prospects ...
AI experts gathered at MIT last week, with the aim of predicting the role artificial intelligence will play in the future of work. Will it be the enemy of the human worker? Will it prove to be a savior? Or will it be just another innovation—like electricity or the internet?
As IEEE Spectrum previously reported, this conference (“AI and the Future of Work Congress”), held at MIT’s Kresge Auditorium, offered sometimes pessimistic outlooks on the job- and industry-destroying path that AI and automation seems to be taking: Self-driving technology will put truck drivers out of work; smart law clerk algorithms will put paralegals out of work; robots will (continue to) put factory and warehouse workers out of work.
Andrew McAfee, co-director of MIT’s Initiative on the Digital Economy, said even just in the past couple years, he’s noticed a shift in the public’s perception of AI. “I remember from previous versions of this conference, it felt like we had to make the case that we’re living in a period of accelerating change and that AI’s going to have a big impact,” he said. “Nobody had to make that case today.”
Elisabeth Reynolds, executive director of MIT’s Task Force on the Work of the Future, noted that following the path of least resistance is not a viable way forward. “If we do nothing, we’re in trouble,” she said. “The future will not take care of itself. We have to do something about it.”
Panelists and speakers spoke about championing productive uses of AI in the workplace, which ultimately benefit both employees and customers.
As one example, Zeynep Ton, professor at MIT Sloan School of Management, highlighted retailer Sam’s Club’s recent rollout of a program called Sam’s Garage. Previously customers shopping for tires for their car spent somewhere between 30 and 45 minutes with a Sam’s Club associate paging through manuals and looking up specs on websites.
But with an AI algorithm, they were able to cut that spec hunting time down to 2.2 minutes. “Now instead of wasting their time trying to figure out the different tires, they can field the different options and talk about which one would work best [for the customer],” she said. “This is a great example of solving a real problem, including [enhancing] the experience of the associate as well as the customer.”
“We think of it as an AI-first world that’s coming,” said Scott Prevost, VP of engineering at Adobe. Prevost said AI agents in Adobe’s software will behave something like a creative assistant or intern who will take care of more mundane tasks for you.
“We need a mindset change. That it is not just about minimizing costs or maximizing tax benefits, but really worrying about what kind of society we’re creating and what kind of environment we’re creating if we keep on just automating and [eliminating] good jobs.”
—Daron Acemoglu, MIT Institute Professor of Economics
Prevost cited an internal survey of Adobe customers that found 74 percent of respondents’ time was spent doing repetitive work—the kind that might be automated by an AI script or smart agent.
“It used to be you’d have the resources to work on three ideas [for a creative pitch or presentation],” Prevost said. “But if the AI can do a lot of the production work, then you can have 10 or 100. Which means you can actually explore some of the further out ideas. It’s also lowering the bar for everyday people to create really compelling output.”
In addition to changing the nature of work, noted a number of speakers at the event, AI is also directly transforming the workforce.
Jacob Hsu, CEO of the recruitment company Catalyte spoke about using AI as a job placement tool. The company seeks to fill myriad positions including auto mechanics, baristas, and office workers—with its sights on candidates including young people and mid-career job changers. To find them, it advertises on Craigslist, social media, and traditional media.
The prospects who sign up with Catalyte take a battery of tests. The company’s AI algorithms then match each prospect’s skills with the field best suited for their talents.
“We want to be like the Harry Potter Sorting Hat,” Hsu said.
Guillermo Miranda, IBM’s global head of corporate social responsibility, said IBM has increasingly been hiring based not on credentials but on skills. For instance, he said, as much as 50 per cent of the company’s new hires in some divisions do not have a traditional four-year college degree. “As a company, we need to be much more clear about hiring by skills,” he said. “It takes discipline. It takes conviction. It takes a little bit of enforcing with H.R. by the business leaders. But if you hire by skills, it works.”
Ardine Williams, Amazon’s VP of workforce development, said the e-commerce giant has been experimenting with developing skills of the employees at its warehouses (a.k.a. fulfillment centers) with an eye toward putting them in a position to get higher-paying work with other companies.
She described an agreement Amazon had made in its Dallas fulfillment center with aircraft maker Sikorsky, which had been experiencing a shortage of skilled workers for its nearby factory. So Amazon offered to its employees a free certification training to seek higher-paying work at Sikorsky.
“I do that because now I have an attraction mechanism—like a G.I. Bill,” Williams said. The program is also only available for employees who have worked at least a year with Amazon. So their program offers medium-term job retention, while ultimately moving workers up the wage ladder.
Radha Basu, CEO of AI data company iMerit, said her firm aggressively hires from the pool of women and under-resourced minority communities in the U.S. and India. The company specializes in turning unstructured data (e.g. video or audio feeds) into tagged and annotated data for machine learning, natural language processing, or computer vision applications.
“There is a motivation with these young people to learn these things,” she said. “It comes with no baggage.”
Alastair Fitzpayne, executive director of The Aspen Institute’s Future of Work Initiative, said the future of work ultimately means, in bottom-line terms, the future of human capital. “We have an R&D tax credit,” he said. “We’ve had it for decades. It provides credit for companies that make new investment in research and development. But we have nothing on the human capital side that’s analogous.”
So a company that’s making a big investment in worker training does it on their own dime, without any of the tax benefits that they might accrue if they, say, spent it on new equipment or new technology. Fitzpayne said a simple tweak to the R&D tax credit could make a big difference by incentivizing new investment programs in worker training. Which still means Amazon’s pre-existing worker training programs—for a company that already famously pays no taxes—would not count.
“We need a different way of developing new technologies,” said Daron Acemoglu, MIT Institute Professor of Economics. He pointed to the clean energy sector as an example. First a consensus around the problem needs to emerge. Then a broadly agreed-upon set of goals and measurements needs to be developed (e.g., that AI and automation would, for instance, create at least X new jobs for every Y jobs that it eliminates).
Then it just needs to be implemented.
“We need to build a consensus that, along the path we’re following at the moment, there are going to be increasing problems for labor,” Acemoglu said. “We need a mindset change. That it is not just about minimizing costs or maximizing tax benefits, but really worrying about what kind of society we’re creating and what kind of environment we’re creating if we keep on just automating and [eliminating] good jobs.” Continue reading
Posted in Human Robots
#436021 AI Faces Speed Bumps and Potholes on Its ...
Implementing machine learning in the real world isn’t easy. The tools are available and the road is well-marked—but the speed bumps are many.
That was the conclusion of panelists wrapping up a day of discussions at the IEEE AI Symposium 2019, held at Cisco’s San Jose, Calif., campus last week.
The toughest problem, says Ben Irving, senior manager of Cisco’s strategy innovations group, is people.
It’s tough to find data scientist expertise, he indicated, so companies are looking into non-traditional sources of personnel, like political science. “There are some untapped areas with a lot of untapped data science expertise,” Irving says.
Lazard’s artificial intelligence manager Trevor Mottl agreed that would-be data scientists don’t need formal training or experience to break into the field. “This field is changing really rapidly,” he says. “There are new language models coming out every month, and new tools, so [anyone should] expect to not know everything. Experiment, try out new tools and techniques, read, study, spend time; there aren’t any true experts at this point because the foundational elements are shifting so rapidly.”
“It is a wonderful time to get into a field,” he reasons, noting that it doesn’t take long to catch up because there aren’t 20 years of history.”
Confusion about what different kinds of machine learning specialists do doesn’t help the personnel situation. An audience member asked panelists to explain the difference between data scientist, data analyst, and data engineer. Darrin Johnson, Nvidia global director of technical marketing for enterprise, admitted it’s hard to sort out, and any two companies could define the positions differently. “Sometimes,” he says, particularly at smaller companies, “a data scientist plays all three roles. But as companies grow, there are different groups that ingest data, clean data, and use data. At some companies, training and inference are separate. It really depends, which is a challenge when you are trying to hire someone.”
Mitigating the risks of a hot job market
The competition to hire data scientists, analysts, engineers, or whatever companies call them requires that managers make sure any work being done is structured and comprehensible at all times, the panelists cautioned.
“We need to remember that our data scientists go home every day and sometimes they don’t come back because they go home and then go to a different company,” says Lazard’s Mottl. “That’s a fact of life. If you give people choice on [how they do development], and have a successful person who gets poached by competitor, you have to either hire a team to unwrap what that person built or jettison their work and rebuild it.”
By contrast, he says, “places that have structured coding and structured commits and organized constructions of software have done very well.”
But keeping all of a company’s engineers working with the same languages and on the same development paths is not easy to do in a field that moves as fast as machine learning. Zongjie Diao, Cisco director of product management for machine learning, quipped: “I have a data scientist friend who says the speed at which he changes girlfriends is less than speed at which he changes languages.”
The data scientist/IT manager clash
Once a company finds the data engineers and scientists they need and get them started on the task of applying machine learning to that company’s operations, one of the first obstacles they face just might be the company’s IT department, the panelists suggested.
“IT is process oriented,” Mottl says. The IT team “knows how to keep data secure, to set up servers. But when you bring in a data science team, they want sandboxes, they want freedom, they want to explore and play.”
Also, Nvidia’s Johnson pointed out, “There is a language barrier.” The AI world, he says, is very different from networking or storage, and data scientists find it hard to articulate their requirements to IT.
On the ground or in the cloud?
And then there is the decision of where exactly machine learning should happen—on site, or in the cloud? At Lazard, Mottl says, the deep learning engineers do their experimentation on premises; that’s their sandbox. “But when we deploy, we deploy in the cloud,” he says.
Nvidia, Johnson says, thinks the opposite approach is better. We see the cloud as “the sandbox,” he says. “So you can run as many experiments as possible, fail fast, and learn faster.”
For Cisco’s Irving, the “where” of machine learning depends on the confidentiality of the data.
Mottl, who says rolling machine learning technology into operation can hit resistance from all across the company, had one last word of caution for those aiming to implement AI:
Data scientists are building things that might change the ways other people in the organization work, like sales and even knowledge workers. [You need to] think about the internal stakeholders and prepare them, because the last thing you want to do is to create a valuable new thing that nobody likes and people take potshots against.
The AI Symposium was organized by the Silicon Valley chapters of the IEEE Young Professionals, the IEEE Consultants’ Network, and IEEE Women in Engineering and supported by Cisco. Continue reading
Posted in Human Robots
#435716 Watch This Drone Explode Into Maple Seed ...
As useful as conventional fixed-wing and quadrotor drones have become, they still tend to be relatively complicated, expensive machines that you really want to be able to use more than once. When a one-way trip is all that you have in mind, you want something simple, reliable, and cheap, and we’ve seen a bunch of different designs for drone gliders that more or less fulfill those criteria.
For an even simpler gliding design, you want to minimize both airframe mass and control surfaces, and the maple tree provides some inspiration in the form of samara, those distinctive seed pods that whirl to the ground in the fall. Samara are essentially just an unbalanced wing that spins, and while the natural ones don’t steer, adding an actuated flap to the robotic version and moving it at just the right time results in enough controllability to aim for a specific point on the ground.
Roboticists at the Singapore University of Technology and Design (SUTD) have been experimenting with samara-inspired drones, and in a new paper in IEEE Robotics and Automation Letters they explore what happens if you attach five of the drones together and then separate them in mid air.
Image: Singapore University of Technology and Design
The drone with all five wings attached (top left), and details of the individual wings: (a) smaller 44.9-gram wing for semi-indoor testing; (b) larger 83.4-gram wing able to carry a Pixracer, GPS, and magnetometer for directional control experiments.
Fundamentally, a samara design acts as a decelerator for an aerial payload. You can think of it like a parachute: It makes sure that whatever you toss out of an airplane gets to the ground intact rather than just smashing itself to bits on impact. Steering is possible, but you don’t get a lot of stability or precision control. The RA-L paper describes one solution to this, which is to collaboratively use five drones at once in a configuration that looks a bit like a helicopter rotor.
And once the multi-drone is right where you want it, the five individual samara drones can split off all at once, heading out on their own missions. It's quite a sight:
The concept features a collaborative autorotation in the initial stage of drop whereby several wings are attached to each other to form a rotor hub. The combined form achieves higher rotational energy and a collaborative control strategy is possible. Once closer to the ground, they can exit the collaborative form and continue to descend to unique destinations. A section of each wing forms a flap and a small actuator changes its pitch cyclically. Since all wing-flaps can actuate simultaneously in collaborative mode, better maneuverability is possible, hence higher resistance against environmental conditions. The vertical and horizontal speeds can be controlled to a certain extent, allowing it to navigate towards a target location and land softly.
The samara autorotating wing drones themselves could conceivably carry small payloads like sensors or emergency medical supplies, with these small-scale versions in the video able to handle an extra 30 grams of payload. While they might not have as much capacity as a traditional fixed-wing glider, they have the advantage of being able to descent vertically, and can perform better than a parachute due to their ability to steer. The researchers plan on improving the design of their little drones, with the goal of increasing the rotation speed and improving the control performance of both the individual drones and the multi-wing collaborative version.
“Dynamics and Control of a Collaborative and Separating Descent of Samara Autorotating Wings,” by Shane Kyi Hla Win, Luke Soe Thura Win, Danial Sufiyan, Gim Song Soh, and Shaohui Foong from Singapore University of Technology and Design, appears in the current issue of IEEE Robotics and Automation Letters.
[ SUTD ]
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Posted in Human Robots
#435370 The Rise of the Robots. Soon!
Are we the masters of our own eventual demise at the…hand of our robot creations? From where I’m standing, it sure looks like it!
Posted in Human Robots