Tag Archives: apply

#435642 Drone X Challenge 2020

Posted on September 14, 2019 by Android

Krypto Labs opens applications for Drone X Challenge 2020 Phase II, a US$1.5+ Million Global Challenge (US$1 Million Final Prize and US$500,000+ in R&D Grants)

In its most rewarding initiative to date, Krypto Labs, the global innovation hub with a unique ecosystem for funding ground-breaking startups, has announced the opening of Phase II of Drone X Challenge (DXC) 2020, the global multimillion-dollar challenge that is pushing the frontiers of innovation in drone technologies focusing on high payload capacity and high flight endurance.

Drone X Challenge 2020 is open to entrepreneurs, start-ups, researchers, university students and established companies. Teams that want to apply for Drone X Challenge 2020 Phase II will have to develop a drone system capable of achieving the minimum endurance and payload as per the category they are applying to.

Categories:

Fixed-wing drones battery powered
Fixed-wing drones hybrid/hydrocarbon powered
Multi-rotor drones battery powered
Multi-rotor drones hybrid/hydrocarbon powered

Drone X Challenge 2020 is divided in 3 phases and a final event, providing US$1 Million Final Prize. The outstanding applications that meet the requirements of Phase II will collectively receive US$300,000 in R&D grants.

The shortlisted teams of Phase I received US$320,000 in R&D grants, which required applicants to provide a technical proposal detailing the design of a drone capable of meeting the minimum requirements of payload and endurance.

The shortlisted teams of Drone X Challenge 2020 Phase I are:

RigiTech from Switzerland
Forward Robotics from Canada
Industrial Technology Research Institute (ITRI) from Taiwan
KopterKraft from Germany
DV8 Tech from USA
Richen Power from China
Industrial Technology Research Institute (ITRI) from Taiwan
Vulcan UAV Ltd from UK

Dr. Saleh Al Hashemi, Managing Director of Krypto Labs said: “This competition aligns with our efforts in contributing to the development of drone technology globally. We aim to redefine the way drone technologies are impacting our lives, and Krypto Labs is proud to be leading the way in the region by supporting startups, established companies, and industries involved in the field of drone development. By catalyzing and supporting these cutting-edge solutions, we aim to continue leveraging disruptive technologies that can create value and make an impact.”

For more information about Drone X Challenge 2020, please visit https://dronexchallenge2020.com. Continue reading →

Posted in Human Robots

#435628 Soft Exosuit Makes Walking and Running ...

Posted on September 13, 2019 by Android

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 →

Posted in Human Robots

#435145 How Big Companies Can Simultaneously Run ...

Posted on May 28, 2019 by Android

We live in the age of entrepreneurs. New startups seem to appear out of nowhere and challenge not only established companies, but entire industries. Where startup unicorns were once mythical creatures, they now seem abundant, not only increasing in numbers but also in the speed with which they can gain the minimum one-billion-dollar valuations to achieve this status.

But no matter how well things go for innovative startups, how many new success stories we hear, and how much space they take up in the media, the story that they are the best or only source of innovation isn’t entirely accurate.

Established organizations, or legacy organizations, can be incredibly innovative too. And while innovation is much more difficult in established organizations than in startups because they have much more complex systems—nobody is more likely to succeed in their innovation efforts than established organizations.

Unlike startups, established organizations have all the resources. They have money, customers, data, suppliers, partners, and infrastructure, which put them in a far better position to transform new ideas into concrete, value-creating, successful offerings than startups.

However, for established organizations, becoming an innovation champion in these times of rapid change requires new rules of engagement.

Many organizations commit the mistake of engaging in innovation as if it were a homogeneous thing that should be approached in the same way every time, regardless of its purpose. In my book, Transforming Legacy Organizations, I argue that innovation in established organizations must actually be divided into three different tracks: optimizing, augmenting, and mutating innovation.

All three are important, and to complicate matters further, organizations must execute all three types of innovation at the same time.

Optimizing Innovation
The first track is optimizing innovation. This type of innovation is the majority of what legacy organizations already do today. It is, metaphorically speaking, the extra blade on the razor. A razor manufacturer might launch a new razor that has not just three, but four blades, to ensure an even better, closer, and more comfortable shave. Then one or two years later, they say they are now launching a razor that has not only four, but five blades for an even better, closer, and more comfortable shave. That is optimizing innovation.

Adding extra blades on the razor is where the established player reigns.

No startup with so much as a modicum of sense would even try to beat the established company in this type of innovation. And this continuous optimization, both on the operational and customer facing sides, is important. In the short term. It pays the rent. But it’s far from enough. There are limits to how many blades a razor needs, and optimizing innovation only improves upon the past.

Augmenting Innovation
Established players must also go beyond optimization and prepare for the future through augmenting innovation.

The digital transformation projects that many organizations are initiating can be characterized as augmenting innovation. In the first instance, it is about upgrading core offerings and processes from analog to digital. Or, if you’re born digital, you’ve probably had to augment the core to become mobile-first. Perhaps you have even entered the next augmentation phase, which involves implementing artificial intelligence. Becoming AI-first, like the Amazons, Microsofts, Baidus, and Googles of the world, requires great technological advancements. And it’s difficult. But technology may, in fact, be a minor part of the task.

The biggest challenge for augmenting innovation is probably culture.

Only legacy organizations that manage to transform their cultures from status quo cultures—cultures with a preference for things as they are—into cultures full of incremental innovators can thrive in constant change.

To create a strong innovation culture, an organization needs to thoroughly understand its immune systems. These are the mechanisms that protect the organization and operate around the clock to keep it healthy and stable, just as the body’s immune system operates to keep the body healthy and stable. But in a rapidly changing world, many of these defense mechanisms are no longer appropriate and risk weakening organizations’ innovation power.

When talking about organizational immune systems, there is a clear tendency to simply point to the individual immune system, people’s unwillingness to change.

But this is too simplistic.

Of course, there is human resistance to change, but the organizational immune system, consisting of a company’s key performance indicators (KPIs), rewards systems, legacy IT infrastructure and processes, and investor and shareholder demands, is far more important. So is the organization’s societal immune system, such as legislative barriers, legacy customers and providers, and economic climate.

Luckily, there are many culture hacks that organizations can apply to strengthen their innovation cultures by upgrading their physical and digital workspaces, transforming their top-down work processes into decentralized, agile ones, and empowering their employees.

Mutating Innovation
Upgrading your core and preparing for the future by augmenting innovation is crucial if you want success in the medium term. But to win in the long run and be as or more successful 20 to 30 years from now, you need to invent the future, and challenge your core, through mutating innovation.

This requires involving radical innovators who have a bold focus on experimenting with that which is not currently understood and for which a business case cannot be prepared.

Here you must also physically move away from the core organization when you initiate and run such initiatives. This is sometimes called “innovation on the edges” because the initiatives will not have a chance at succeeding within the core. It will be too noisy as they challenge what currently exists—precisely what the majority of the organization’s employees are working to optimize or augment.

Forward-looking organizations experiment to mutate their core through “X divisions,” sometimes called skunk works or innovation labs.

Lowe’s Innovation Labs, for instance, worked with startups to build in-store robot assistants and zero-gravity 3D printers to explore the future. Mutating innovation might include pursuing partnerships across all imaginable domains or establishing brand new companies, rather than traditional business units, as we see automakers such as Toyota now doing to build software for autonomous vehicles. Companies might also engage in radical open innovation by sponsoring others’ ingenuity. Japan’s top airline ANA is exploring a future of travel that does not involve flying people from point A to point B via the ANA Avatar XPRIZE competition.

Increasing technological opportunities challenge the core of any organization but also create unprecedented potential. No matter what product, service, or experience you create, you can’t rest on your laurels. You have to bring yourself to a position where you have a clear strategy for optimizing, augmenting, and mutating your core and thus transforming your organization.

It’s not an easy job. But, hey, if it were easy, everyone would be doing it. Those who make it, on the other hand, will be the innovation champions of the future.

Image Credit: rock-the-stock / Shutterstock.com

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Posted in Human Robots

#434837 In Defense of Black Box AI

Posted on April 17, 2019 by Android

Deep learning is powering some amazing new capabilities, but we find it hard to scrutinize the workings of these algorithms. Lack of interpretability in AI is a common concern and many are trying to fix it, but is it really always necessary to know what’s going on inside these “black boxes”?

In a recent perspective piece for Science, Elizabeth Holm, a professor of materials science and engineering at Carnegie Mellon University, argued in defense of the black box algorithm. I caught up with her last week to find out more.

Edd Gent: What’s your experience with black box algorithms?

Elizabeth Holm: I got a dual PhD in materials science and engineering and scientific computing. I came to academia about six years ago and part of what I wanted to do in making this career change was to refresh and revitalize my computer science side.

I realized that computer science had changed completely. It used to be about algorithms and making codes run fast, but now it’s about data and artificial intelligence. There are the interpretable methods like random forest algorithms, where we can tell how the machine is making its decisions. And then there are the black box methods, like convolutional neural networks.

Once in a while we can find some information about their inner workings, but most of the time we have to accept their answers and kind of probe around the edges to figure out the space in which we can use them and how reliable and accurate they are.

EG: What made you feel like you had to mount a defense of these black box algorithms?

EH: When I started talking with my colleagues, I found that the black box nature of many of these algorithms was a real problem for them. I could understand that because we’re scientists, we always want to know why and how.

It got me thinking as a bit of a contrarian, “Are black boxes all bad? Must we reject them?” Surely not, because human thought processes are fairly black box. We often rely on human thought processes that the thinker can’t necessarily explain.

It’s looking like we’re going to be stuck with these methods for a while, because they’re really helpful. They do amazing things. And so there’s a very pragmatic realization that these are the best methods we’ve got to do some really important problems, and we’re not right now seeing alternatives that are interpretable. We’re going to have to use them, so we better figure out how.

EG: In what situations do you think we should be using black box algorithms?

EH: I came up with three rules. The simplest rule is: when the cost of a bad decision is small and the value of a good decision is high, it’s worth it. The example I gave in the paper is targeted advertising. If you send an ad no one wants it doesn’t cost a lot. If you’re the receiver it doesn’t cost a lot to get rid of it.

There are cases where the cost is high, and that’s then we choose the black box if it’s the best option to do the job. Things get a little trickier here because we have to ask “what are the costs of bad decisions, and do we really have them fully characterized?” We also have to be very careful knowing that our systems may have biases, they may have limitations in where you can apply them, they may be breakable.

But at the same time, there are certainly domains where we’re going to test these systems so extensively that we know their performance in virtually every situation. And if their performance is better than the other methods, we need to do it. Self driving vehicles are a significant example—it’s almost certain they’re going to have to use black box methods, and that they’re going to end up being better drivers than humans.

The third rule is the more fun one for me as a scientist, and that’s the case where the black box really enlightens us as to a new way to look at something. We have trained a black box to recognize the fracture energy of breaking a piece of metal from a picture of the broken surface. It did a really good job, and humans can’t do this and we don’t know why.

What the computer seems to be seeing is noise. There’s a signal in that noise, and finding it is very difficult, but if we do we may find something significant to the fracture process, and that would be an awesome scientific discovery.

EG: Do you think there’s been too much emphasis on interpretability?

EH: I think the interpretability problem is a fundamental, fascinating computer science grand challenge and there are significant issues where we need to have an interpretable model. But how I would frame it is not that there’s too much emphasis on interpretability, but rather that there’s too much dismissiveness of uninterpretable models.

I think that some of the current social and political issues surrounding some very bad black box outcomes have convinced people that all machine learning and AI should be interpretable because that will somehow solve those problems.

Asking humans to explain their rationale has not eliminated bias, or stereotyping, or bad decision-making in humans. Relying too much on interpreted ability perhaps puts the responsibility in the wrong place for getting better results. I can make a better black box without knowing exactly in what way the first one was bad.

EG: Looking further into the future, do you think there will be situations where humans will have to rely on black box algorithms to solve problems we can’t get our heads around?

EH: I do think so, and it’s not as much of a stretch as we think it is. For example, humans don’t design the circuit map of computer chips anymore. We haven’t for years. It’s not a black box algorithm that designs those circuit boards, but we’ve long since given up trying to understand a particular computer chip’s design.

With the billions of circuits in every computer chip, the human mind can’t encompass it, either in scope or just the pure time that it would take to trace every circuit. There are going to be cases where we want a system so complex that only the patience that computers have and their ability to work in very high-dimensional spaces is going to be able to do it.

So we can continue to argue about interpretability, but we need to acknowledge that we’re going to need to use black boxes. And this is our opportunity to do our due diligence to understand how to use them responsibly, ethically, and with benefits rather than harm. And that’s going to be a social conversation as well as as a scientific one.

*Responses have been edited for length and style

Image Credit: Chingraph / Shutterstock.com Continue reading →

Posted in Human Robots

#434648 The Pediatric AI That Outperformed ...

Posted on February 20, 2019 by Android

Training a doctor takes years of grueling work in universities and hospitals. Building a doctor may be as easy as teaching an AI how to read.

Artificial intelligence has taken another step towards becoming an integral part of 21st-century medicine. New research out of Guangzhou, China, published February 11th in Nature Medicine Letters, has demonstrated a natural-language processing AI that is capable of out-performing rookie pediatricians in diagnosing common childhood ailments.

The massive study examined the electronic health records (EHR) from nearly 600,000 patients over an 18-month period at the Guangzhou Women and Children’s Medical Center and then compared AI-generated diagnoses against new assessments from physicians with a range of experience.

The verdict? On average, the AI was noticeably more accurate than junior physicians and nearly as reliable as the more senior ones. These results are the latest demonstration that artificial intelligence is on the cusp of becoming a healthcare staple on a global scale.

Less Like a Computer, More Like a Person
To outshine human doctors, the AI first had to become more human. Like IBM’s Watson, the pediatric AI leverages natural language processing, in essence “reading” written notes from EHRs not unlike how a human doctor would review those same records. But the similarities to human doctors don’t end there. The AI is a machine learning classifier (MLC), capable of placing the information learned from the EHRs into categories to improve performance.

Like traditionally-trained pediatricians, the AI broke cases down into major organ groups and infection areas (upper/lower respiratory, gastrointestinal, etc.) before breaking them down even further into subcategories. It could then develop associations between various symptoms and organ groups and use those associations to improve its diagnoses. This hierarchical approach mimics the deductive reasoning human doctors employ.

Another key strength of the AI developed for this study was the enormous size of the dataset collected to teach it: 1,362,559 outpatient visits from 567,498 patients yielded some 101.6 million data points for the MLC to devour on its quest for pediatric dominance. This allowed the AI the depth of learning needed to distinguish and accurately select from the 55 different diagnosis codes across the various organ groups and subcategories.

When comparing against the human doctors, the study used 11,926 records from an unrelated group of children, giving both the MLC and the 20 humans it was compared against an even playing field. The results were clear: while cohorts of senior pediatricians performed better than the AI, junior pediatricians (those with 3-15 years of experience) were outclassed.

Helping, Not Replacing
While the research used a competitive analysis to measure the success of the AI, the results should be seen as anything but hostile to human doctors. The near future of artificial intelligence in medicine will see these machine learning programs augment, not replace, human physicians. The authors of the study specifically call out augmentation as the key short-term application of their work. Triaging incoming patients via intake forms, performing massive metastudies using EHRs, providing rapid ‘second opinions’—the applications for an AI doctor that is better-but-not-the-best are as varied as the healthcare industry itself.

That’s only considering how artificial intelligence could make a positive impact immediately upon implementation. It’s easy to see how long-term use of a diagnostic assistant could reshape the way modern medical institutions approach their work.

Look at how the MLC results fit snugly between the junior and senior physician groups. Essentially, it took nearly 15 years before a physician could consistently out-diagnose the machine. That’s a decade and a half wherein an AI diagnostic assistant would be an invaluable partner—both as a training tool and a safety measure. Likewise, on the other side of the experience curve you have physicians whose performance could be continuously leveraged to improve the AI’s effectiveness. This is a clear opportunity for a symbiotic relationship, with humans and machines each assisting the other as they mature.

Closer to Us, But Still Dependent on Us
No matter the ultimate application, the AI doctors of the future are drawing nearer to us step by step. This latest research is a demonstration that artificial intelligence can mimic the results of human deductive reasoning even in some of the most complex and important decision-making processes. True, the MLC required input from humans to function; both the initial data points and the cases used to evaluate the AI depended on EHRs written by physicians. While every effort was made to design a test schema that removed any indication of the eventual diagnosis, some “data leakage” is bound to occur.

In other words, when AIs use human-created data, they inherit human insight to some degree. Yet the progress made in machine imaging, chatbots, sensors, and other fields all suggest that this dependence on human input is more about where we are right now than where we could be in the near future.

Data, and More Data
That near future may also have some clear winners and losers. For now, those winners seem to be the institutions that can capture and apply the largest sets of data. With a rapidly digitized society gathering incredible amounts of data, China has a clear advantage. Combined with their relatively relaxed approach to privacy, they are likely to continue as one of the driving forces behind machine learning and its applications. So too will Google/Alphabet with their massive medical studies. Data is the uranium in this AI arms race, and everyone seems to be scrambling to collect more.

In a global community that seems increasingly aware of the potential problems arising from this need for and reliance on data, it’s nice to know there’ll be an upside as well. The technology behind AI medical assistants is looking more and more mature—even if we are still struggling to find exactly where, when, and how that technology should first become universal.

Yet wherever we see the next push to make AI a standard tool in a real-world medical setting, I have little doubt it will greatly improve the lives of human patients. Today Doctor AI is performing as well as a human colleague with more than 10 years of experience. By next year or so, it may take twice as long for humans to be competitive. And in a decade, the combined medical knowledge of all human history may be a tool as common as a stethoscope in your doctor’s hands.

Image Credit: Nadia Snopek / Shutterstock.com Continue reading →

Posted in Human Robots