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#433872 Breaking Out of the Corporate Bubble ...

For big companies, success is a blessing and a curse. You don’t get big without doing something (or many things) very right. It might start with an invention or service the world didn’t know it needed. Your product takes off, and growth brings a whole new set of logistical challenges. Delivering consistent quality, hiring the right team, establishing a strong culture, tapping into new markets, satisfying shareholders. The list goes on.

Eventually, however, what made you successful also makes you resistant to change.

You’ve built a machine for one purpose, and it’s running smoothly, but what about retooling that machine to make something new? Not so easy. Leaders of big companies know there is no future for their organizations without change. And yet, they struggle to drive it.

In their new book, Leading Transformation: How to Take Charge of Your Company’s Future, Kyle Nel, Nathan Furr, and Thomas Ramsøy aim to deliver a roadmap for corporate transformation.

The book focuses on practical tools that have worked in big companies to break down behavioral and cognitive biases, envision radical futures, and run experiments. These include using science fiction and narrative to see ahead and adopting better measures of success for new endeavors.

A thread throughout is how to envision a new future and move into that future.

We’re limited by the bubbles in which we spend the most time—the corporate bubble, the startup bubble, the nonprofit bubble. The mutually beneficial convergence of complementary bubbles, then, can be a powerful tool for kickstarting transformation. The views and experiences of one partner can challenge the accepted wisdom of the other; resources can flow into newly co-created visions and projects; and connections can be made that wouldn’t otherwise exist.

The authors call such alliances uncommon partners. In the following excerpt from the book, Made In Space, a startup building 3D printers for space, helps Lowe’s explore an in-store 3D printing system, and Lowe’s helps Made In Space expand its vision and focus.

Uncommon Partners
In a dingy conference room at NASA, five prototypical nerds, smelling of Thai food, laid out the path to printing satellites in space and buildings on distant planets. At the end of their four-day marathon, they emerged with an artifact trail that began with early prototypes for the first 3D printer on the International Space Station and ended in the additive-manufacturing future—a future much bigger than 3D printing.

In the additive-manufacturing future, we will view everything as transient, or capable of being repurposed into new things. Rather than throwing away a soda bottle or a bent nail, we will simply reprocess these things into a new hinge for the fence we are building or a light switch plate for the tool shed. Indeed, we might not even go buy bricks for the tool shed, but instead might print them from impurities pulled from the air and the dirt beneath our feet. Such a process would both capture carbon in the air to make the bricks and avoid all the carbon involved in making and then transporting traditional bricks to your house.

If it all sounds a little too science fiction, think again. Lowe’s has already been honored as a Champion of Change by the US government for its prototype system to recycle plastic (e.g., plastic bags and bottles). The future may be closer than you have imagined. But to get there, Lowe’s didn’t work alone. It had to work with uncommon partners to create the future.

Uncommon partners are the types of organizations you might not normally work with, but which can greatly help you create radical new futures. Increasingly, as new technologies emerge and old industries converge, companies are finding that working independently to create all the necessary capabilities to enter new industries or create new technologies is costly, risky, and even counterproductive. Instead, organizations are finding that they need to collaborate with uncommon partners as an ecosystem to cocreate the future together. Nathan [Furr] and his colleague at INSEAD, Andrew Shipilov, call this arrangement an adaptive ecosystem strategy and described how companies such as Lowe’s, Samsung, Mastercard, and others are learning to work differently with partners and to work with different kinds of partners to more effectively discover new opportunities. For Lowe’s, an adaptive ecosystem strategy working with uncommon partners forms the foundation of capturing new opportunities and transforming the company. Despite its increased agility, Lowe’s can’t be (and shouldn’t become) an independent additive-manufacturing, robotics-using, exosuit-building, AR-promoting, fill-in-the-blank-what’s-next-ing company in addition to being a home improvement company. Instead, Lowe’s applies an adaptive ecosystem strategy to find the uncommon partners with which it can collaborate in new territory.

To apply the adaptive ecosystem strategy with uncommon partners, start by identifying the technical or operational components required for a particular focus area (e.g., exosuits) and then sort these components into three groups. First, there are the components that are emerging organically without any assistance from the orchestrator—the leader who tries to bring together the adaptive ecosystem. Second, there are the elements that might emerge, with encouragement and support. Third are the elements that won’t happen unless you do something about it. In an adaptive ecosystem strategy, you can create regular partnerships for the first two elements—those already emerging or that might emerge—if needed. But you have to create the elements in the final category (those that won’t emerge) either with an uncommon partner or by yourself.

For example, when Lowe’s wanted to explore the additive-manufacturing space, it began a search for an uncommon partner to provide the missing but needed capabilities. Unfortunately, initial discussions with major 3D printing companies proved disappointing. The major manufacturers kept trying to sell Lowe’s 3D printers. But the vision our group had created with science fiction was not for vendors to sell Lowe’s a printer, but for partners to help the company build a system—something that would allow customers to scan, manipulate, print, and eventually recycle additive-manufacturing objects. Every time we discussed 3D printing systems with these major companies, they responded that they could do it and then tried to sell printers. When Carin Watson, one of the leading lights at Singularity University, introduced us to Made In Space (a company being incubated in Singularity University’s futuristic accelerator), we discovered an uncommon partner that understood what it meant to cocreate a system.

Initially, Made In Space had been focused on simply getting 3D printing to work in space, where you can’t rely on gravity, you can’t send up a technician if the machine breaks, and you can’t release noxious fumes into cramped spacecraft quarters. But after the four days in the conference room going over the comic for additive manufacturing, Made In Space and Lowe’s emerged with a bigger vision. The company helped lay out an artifact trail that included not only the first printer on the International Space Station but also printing system services in Lowe’s stores.

Of course, the vision for an additive-manufacturing future didn’t end there. It also reshaped Made In Space’s trajectory, encouraging the startup, during those four days in a NASA conference room, to design a bolder future. Today, some of its bold projects include the Archinaut, a system that enables satellites to build themselves while in space, a direction that emerged partly from the science fiction narrative we created around additive manufacturing.

In summary, uncommon partners help you succeed by providing you with the capabilities you shouldn’t be building yourself, as well as with fresh insights. You also help uncommon partners succeed by creating new opportunities from which they can prosper.

Helping Uncommon Partners Prosper
Working most effectively with uncommon partners can require a shift from more familiar outsourcing or partnership relationships. When working with uncommon partners, you are trying to cocreate the future, which entails a great deal more uncertainty. Because you can’t specify outcomes precisely, agreements are typically less formal than in other types of relationships, and they operate under the provisions of shared vision and trust more than binding agreement clauses. Moreover, your goal isn’t to extract all the value from the relationship. Rather, you need to find a way to share the value.

Ideally, your uncommon partners should be transformed for the better by the work you do. For example, Lowe’s uncommon partner developing the robotics narrative was a small startup called Fellow Robots. Through their work with Lowe’s, Fellow Robots transformed from a small team focused on a narrow application of robotics (which was arguably the wrong problem) to a growing company developing a very different and valuable set of capabilities: putting cutting-edge technology on top of the old legacy systems embedded at the core of most companies. Working with Lowe’s allowed Fellow Robots to discover new opportunities, and today Fellow Robots works with retailers around the world, including BevMo! and Yamada. Ultimately, working with uncommon partners should be transformative for both of you, so focus more on creating a bigger pie than on how you are going to slice up a smaller pie.

The above excerpt appears in the new book Leading Transformation: How to Take Charge of Your Company’s Future by Kyle Nel, Nathan Furr, and Thomas Ramsøy, published by Harvard Business Review Press.

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Posted in Human Robots

#431839 The Hidden Human Workforce Powering ...

The tech industry touts its ability to automate tasks and remove slow and expensive humans from the equation. But in the background, a lot of the legwork training machine learning systems, solving problems software can’t, and cleaning up its mistakes is still done by people.
This was highlighted recently when Expensify, which promises to automatically scan photos of receipts to extract data for expense reports, was criticized for sending customers’ personally identifiable receipts to workers on Amazon’s Mechanical Turk (MTurk) crowdsourcing platform.
The company uses text analysis software to read the receipts, but if the automated system falls down then the images are passed to a human for review. While entrusting this job to random workers on MTurk was maybe not so wise—and the company quickly stopped after the furor—the incident brought to light that this kind of human safety net behind AI-powered services is actually very common.
As Wired notes, similar services like Ibotta and Receipt Hog that collect receipt information for marketing purposes also use crowdsourced workers. In a similar vein, while most users might assume their Facebook newsfeed is governed by faceless algorithms, the company has been ramping up the number of human moderators it employs to catch objectionable content that slips through the net, as has YouTube. Twitter also has thousands of human overseers.
Humans aren’t always witting contributors either. The old text-based reCAPTCHA problems Google used to use to distinguish humans from machines was actually simultaneously helping the company digitize books by getting humans to interpret hard-to-read text.
“Every product that uses AI also uses people,” Jeffrey Bigham, a crowdsourcing expert at Carnegie Mellon University, told Wired. “I wouldn’t even say it’s a backstop so much as a core part of the process.”
Some companies are not shy about their use of crowdsourced workers. Startup Eloquent Labs wants to insert them between customer service chatbots and human agents who step in when the machines fail. Many times the AI is pretty certain what particular work means, and an MTurk worker can step in and quickly classify them faster and cheaper than a service agent.
Fashion retailer Gilt provides “pre-emptive shipping,” which uses data analytics to predict what people will buy to get products to them faster. The company uses MTurk workers to provide subjective critiques of clothing that feed into their models.
MTurk isn’t the only player. Companies like Cloudfactory and Crowdflower provide crowdsourced human manpower tailored to particular niches, and some companies prefer to maintain their own communities of workers. Unlabel uses an army of 50,000 humans to check and edit the translations its artificial intelligence system produces for customers.
Most of the time these human workers aren’t just filling in the gaps, they’re also helping to train the machine learning component of these companies’ services by providing new examples of how to solve problems. Other times humans aren’t used “in-the-loop” with AI systems, but to prepare data sets they can learn from by labeling images, text, or audio.
It’s even possible to use crowdsourced workers to carry out tasks typically tackled by machine learning, such as large-scale image analysis and forecasting.
Zooniverse gets citizen scientists to classify images of distant galaxies or videos of animals to help academics analyze large data sets too complex for computers. Almanis creates forecasts on everything from economics to politics with impressive accuracy by giving those who sign up to the website incentives for backing the correct answer to a question. Researchers have used MTurkers to power a chatbot, and there’s even a toolkit for building algorithms to control this human intelligence called TurKit.
So what does this prominent role for humans in AI services mean? Firstly, it suggests that many tools people assume are powered by AI may in fact be relying on humans. This has obvious privacy implications, as the Expensify story highlighted, but should also raise concerns about whether customers are really getting what they pay for.
One example of this is IBM’s Watson for oncology, which is marketed as a data-driven AI system for providing cancer treatment recommendations. But an investigation by STAT highlighted that it’s actually largely driven by recommendations from a handful of (admittedly highly skilled) doctors at Memorial Sloan Kettering Cancer Center in New York.
Secondly, humans intervening in AI-run processes also suggests AI is still largely helpless without us, which is somewhat comforting to know among all the doomsday predictions of AI destroying jobs. At the same time, though, much of this crowdsourced work is monotonous, poorly paid, and isolating.
As machines trained by human workers get better at all kinds of tasks, this kind of piecemeal work filling in the increasingly small gaps in their capabilities may get more common. While tech companies often talk about AI augmenting human intelligence, for many it may actually end up being the other way around.
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#431368 This Week’s Awesome Stories From ...

INTERNET OF THINGSAmazon Key Is a New Service That Lets Couriers Unlock Your Front DoorBen Popper | The Verge“When a courier arrives with a package for in-home delivery, they scan the barcode, sending a request to Amazon’s cloud. If everything checks out, the cloud grants permission by sending a message back to the camera, which starts recording. The courier then gets a prompt on their app, swipes the screen, and voilà, your door unlocks.”
ROBOTICSWatch Yamaha’s Humanoid Robot Ride a Motorcycle Around a RacetrackPhilip E. Ross | IEEE Spectrum“What’s striking is that the bike is unmodified: the robot is a hunched-over form on top. It senses the environment, calculates what to do, keeps the bike stable, manages acceleration and deceleration—all while factoring in road conditions, air resistance, and engine braking.”
ARTIFICIAL INTELLIGENCETech Giants Are Paying Huge Salaries for Scarce A.I. TalentCade Metz | The New York Times“Typical A.I. specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them. All of them requested anonymity because they did not want to damage their professional prospects.”
HEALTH This Doctor Diagnosed His Own Cancer With an iPhone UltrasoundAntonio Regalado | MIT Technology Review“The device he used, called the Butterfly IQ, is the first solid-state ultrasound machine to reach the market in the U.S. Ultrasound works by shooting sound into the body and capturing the echoes. Usually, the sound waves are generated by a vibrating crystal. But Butterfly’s machine instead uses 9,000 tiny drums etched onto a semiconductor chip.”
ENTREPRENEURSHIPWeWork: A $20 Billion Startup Fueled by Silicon Valley Pixie DustEliot Brown | Wall Street Journal“WeWork’s strategy carries the costs and risks associated with traditional real estate. Its client list is heavily weighted toward startups that may or may not be around for long. WeWork is on the hook for long-term leases, and it doesn’t own its own buildings. Vacancy rates have risen recently, and the company is increasing incentives to draw tenants… The model has proved popular, with 150,000 individuals renting space in more than 170 locations globally.”
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Posted in Human Robots

#431189 Researchers Develop New Tech to Predict ...

It is one of the top 10 deadliest diseases in the United States, and it cannot be cured or prevented. But new studies are finding ways to diagnose Alzheimer’s disease in its earliest stages, while some of the latest research says technologies like artificial intelligence can detect dementia years before the first symptoms occur.
These advances, in turn, will help bolster clinical trials seeking a cure or therapies to slow or prevent the disease. Catching Alzheimer’s disease or other forms of dementia early in their progression can help ease symptoms in some cases.
“Often neurodegeneration is diagnosed late when massive brain damage has already occurred,” says professor Francis L Martin at the University of Central Lancashire in the UK, in an email to Singularity Hub. “As we know more about the molecular basis of the disease, there is the possibility of clinical interventions that might slow or halt the progress of the disease, i.e., before brain damage. Extending cognitive ability for even a number of years would have huge benefit.”
Blood Diamond
Martin is the principal investigator on a project that has developed a technique to analyze blood samples to diagnose Alzheimer’s disease and distinguish between other forms of dementia.
The researchers used sensor-based technology with a diamond core to analyze about 550 blood samples. They identified specific chemical bonds within the blood after passing light through the diamond core and recording its interaction with the sample. The results were then compared against blood samples from cases of Alzheimer’s disease and other neurodegenerative diseases, along with those from healthy individuals.
“From a small drop of blood, we derive a fingerprint spectrum. That fingerprint spectrum contains numerical data, which can be inputted into a computational algorithm we have developed,” Martin explains. “This algorithm is validated for prediction of unknown samples. From this we determine sensitivity and specificity. Although not perfect, my clinical colleagues reliably tell me our results are far better than anything else they have seen.”
Martin says the breakthrough is the result of more than 10 years developing sensor-based technologies for routine screening, monitoring, or diagnosing neurodegenerative diseases and cancers.
“My vision was to develop something low-cost that could be readily applied in a typical clinical setting to handle thousands of samples potentially per day or per week,” he says, adding that the technology also has applications in environmental science and food security.
The new test can also distinguish accurately between Alzheimer’s disease and other forms of neurodegeneration, such as Lewy body dementia, which is one of the most common causes of dementia after Alzheimer’s.
“To this point, other than at post-mortem, there has been no single approach towards classifying these pathologies,” Martin notes. “MRI scanning is often used but is labor-intensive, costly, difficult to apply to dementia patients, and not a routine point-of-care test.”
Crystal Ball
Canadian researchers at McGill University believe they can predict Alzheimer’s disease up to two years before its onset using big data and artificial intelligence. They developed an algorithm capable of recognizing the signatures of dementia using a single amyloid PET scan of the brain of patients at risk of developing the disease.
Alzheimer’s is caused by the accumulation of two proteins—amyloid beta and tau. The latest research suggests that amyloid beta leads to the buildup of tau, which is responsible for damaging nerve cells and connections between cells called synapses.
The work was recently published in the journal Neurobiology of Aging.
“Despite the availability of biomarkers capable of identifying the proteins causative of Alzheimer’s disease in living individuals, the current technologies cannot predict whether carriers of AD pathology in the brain will progress to dementia,” Sulantha Mathotaarachchi, lead author on the paper and an expert in artificial neural networks, tells Singularity Hub by email.
The algorithm, trained on a population with amnestic mild cognitive impairment observed over 24 months, proved accurate 84.5 percent of the time. Mathotaarachchi says the algorithm can be trained on different populations for different observational periods, meaning the system can grow more comprehensive with more data.
“The more biomarkers we incorporate, the more accurate the prediction could be,” Mathotaarachchi adds. “However, right now, acquiring [the] required amount of training data is the biggest challenge. … In Alzheimer’s disease, it is known that the amyloid protein deposition occurs decades before symptoms onset.”
Unfortunately, the same process occurs in normal aging as well. “The challenge is to identify the abnormal patterns of deposition that lead to the disease later on,” he says
One of the key goals of the project is to improve the research in Alzheimer’s disease by ensuring those patients with the highest probability to develop dementia are enrolled in clinical trials. That will increase the efficiency of clinical programs, according to Mathotaarachchi.
“One of the most important outcomes from our study was the pilot, online, real-time prediction tool,” he says. “This can be used as a framework for patient screening before recruiting for clinical trials. … If a disease-modifying therapy becomes available for patients, a predictive tool might have clinical applications as well, by providing to the physician information regarding clinical progression.”
Pixel by Pixel Prediction
Private industry is also working toward improving science’s predictive powers when it comes to detecting dementia early. One startup called Darmiyan out of San Francisco claims its proprietary software can pick up signals before the onset of Alzheimer’s disease by up to 15 years.
Darmiyan didn’t respond to a request for comment for this article. Venture Beat reported that the company’s MRI-analyzing software “detects cell abnormalities at a microscopic level to reveal what a standard MRI scan cannot” and that the “software measures and highlights subtle microscopic changes in the brain tissue represented in every pixel of the MRI image long before any symptoms arise.”
Darmiyan claims to have a 90 percent accuracy rate and says its software has been vetted by top academic institutions like New York University, Rockefeller University, and Stanford, according to Venture Beat. The startup is awaiting FDA approval to proceed further but is reportedly working with pharmaceutical companies like Amgen, Johnson & Johnson, and Pfizer on pilot programs.
“Our technology enables smarter drug selection in preclinical animal studies, better patient selection for clinical trials, and much better drug-effect monitoring,” Darmiyan cofounder and CEO Padideh Kamali-Zare told Venture Beat.
Conclusions
An estimated 5.5 million Americans have Alzheimer’s, and one in 10 people over age 65 have been diagnosed with the disease. By mid-century, the number of Alzheimer’s patients could rise to 16 million. Health care costs in 2017 alone are estimated to be $259 billion, and by 2050 the annual price tag could be more than $1 trillion.
In sum, it’s a disease that cripples people and the economy.
Researchers are always after more data as they look to improve outcomes, with the hope of one day developing a cure or preventing the onset of neurodegeneration altogether. If interested in seeing this medical research progress, you can help by signing up on the Brain Health Registry to improve the quality of clinical trials.
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