Tag Archives: Digital

#432021 Unleashing Some of the Most Ambitious ...

At Singularity University, we are unleashing a generation of women who are smashing through barriers and starting some of the most ambitious technology companies on the planet.

Singularity University was founded in 2008 to empower leaders to use exponential technologies to solve our world’s biggest challenges. Our flagship program, the Global Solutions Program, has historically brought 80 entrepreneurs from around the world to Silicon Valley for 10 weeks to learn about exponential technologies and create moonshot startups that improve the lives of a billion people within a decade.

After nearly 10 years of running this program, we can say that about 70 percent of our successful startups have been founded or co-founded by female entrepreneurs (see below for inspiring examples of their work). This is in sharp contrast to the typical 10–20 percent of venture-backed tech companies that have a female founder, as reported by TechCrunch.

How are we so dramatically changing the game? While 100 percent of the credit goes to these courageous women, as both an alumna of the Global Solutions Program and our current vice chair of Global Grand Challenges, I want to share my reflections on what has worked.

At the most basic level, it is essential to deeply believe in the inherent worth, intellectual genius, and profound entrepreneurial caliber of women. While this may seem obvious, this is not the way our world currently thinks—we live in a world that sees women’s ideas, contributions, work, and existence as inherently less valuable than men’s.

For example, a 2017 Harvard Business Review article noted that even when women engage in the same behaviors and work as men, their work is considered less valuable simply because a woman did the job. An additional 2017 Harvard Business Review article showed that venture capitalists are significantly less likely to invest in female entrepreneurs and are more likely to ask men questions about the potential success of their companies while grilling women about the potential downfalls of their companies.

This doubt and lack of recognition of the genius and caliber of women is also why women are still paid less than men for completing identical work. Further, it’s why women’s work often gets buried in “number two” support roles of men in leadership roles and why women are expected to take on second shifts at home managing tedious household chores in addition to their careers. I would also argue these views as well as the rampant sexual harassment, assault, and violence against women that exists today stems from stubborn, historical, patriarchal views of women as living for the benefit of men, rather than for their own sovereignty and inherent value.

As with any other business, Singularity University has not been immune to these biases but is resolutely focused on helping women achieve intellectual genius and global entrepreneurial caliber by harnessing powerful exponential technologies.

We create an environment where women can physically and intellectually thrive free of harassment to reach their full potential, and we are building a broader ecosystem of alumni and partners around the world who not only support our female entrepreneurs throughout their entrepreneurial journeys, but who are also sparking and leading systemic change in their own countries and communities.

Respecting the Intellectual Genius and Entrepreneurial Caliber of Women
The entrepreneurial legends of our time—Steve Jobs, Elon Musk, Mark Zuckerberg, Bill Gates, Jeff Bezos, Larry Page, Sergey Brin—are men who have all built their empires using exponential technologies. Exponential technologies helped these men succeed faster and with greater impact due to Moore’s Law and the Law of Accelerating Returns which states that any digital technology (such as computing, software, artificial intelligence, robotics, quantum computing, biotechnology, nanotechnology, etc.) will become more sophisticated while dramatically falling in price, enabling rapid scaling.

Knowing this, an entrepreneur can plot her way to an ambitious global solution over time, releasing new applications just as the technology and market are ready. Furthermore, these rapidly advancing technologies often converge to create new tools and opportunities for innovators to come up with novel solutions to challenges that were previously impossible to solve in the past.

For various reasons, women have not pursued exponential technologies as aggressively as men (or were prevented or discouraged from doing so).

While more women are founding firms at a higher rate than ever in wealthy countries like the United States, the majority are small businesses in linear industries that have been around for hundreds of years, such as social assistance, health, education, administrative, or consulting services. In lower-income countries, international aid agencies and nonprofits often encourage women to pursue careers in traditional handicrafts, micro-enterprise, and micro-finance. While these jobs have historically helped women escape poverty and gain financial independence, they have done little to help women realize the enormous power, influence, wealth, and ability to transform the world for the better that comes from building companies, nonprofits, and solutions grounded in exponential technologies.

We need women to be working with exponential technologies today in order to be powerful leaders in the future.

Participants who enroll in our Global Solutions Program spend the first few weeks of the program learning about exponential technologies from the world’s experts and the final weeks launching new companies or nonprofits in their area of interest. We require that women (as well as men) utilize exponential technologies as a condition of the program.

In this sense, at Singularity University women start their endeavors with all of us believing and behaving in a way that assumes they can achieve global impact at the level of our world’s most legendary entrepreneurs.

Creating an Environment Where Woman Can Thrive
While challenging women to embrace exponential technologies is essential, it is also important to create an environment where women can thrive. In particular, this means ensuring women feel at home on our campus by ensuring gender diversity, aggressively addressing sexual harassment, and flipping the traditional culture from one that penalizes women, to one that values and supports them.

While women were initially only a small minority of our Global Solutions Program, in 2014, we achieved around 50% female attendance—a statistic that has since held over the years.

This is not due to a quota—every year we turn away extremely qualified women from our program (and are working on reformulating the program to allow more people to participate in the future.) While part of our recruiting success is due to the efforts of our marketing team, we also benefited from the efforts of some of our early female founders, staff, faculty, and alumnae including Susan Fonseca, Emeline Paat-Dahlstrom, Kathryn Myronuk, Lajuanda Asemota, Chiara Giovenzana, and Barbara Silva Tronseca.

As early champions of Singularity University these women not only launched diversity initiatives and personally reached out to women, but were crucial role models holding leadership roles in our community. In addition, Fonseca and Silva also both created multiple organizations and initiatives outside of (or in conjunction with) the university that produced additional pipelines of female candidates. In particular, Fonseca founded Women@TheFrontier as well as other organizations focusing on women, technology and innovation, and Silva founded BestInnovation (a woman’s accelerator in Latin America), as well as led Singularity University’s Chilean Chapter and founded the first SingularityU Summit in Latin America.

These women’s efforts in globally scaling Singularity University have been critical in ensuring woman around the world now see Singularity University as a place where they can lead and shape the future.

Also, thanks to Google (Alphabet) and many of our alumni and partners, we were able to provide full scholarships to any woman (or man) to attend our program regardless of their economic status. Google committed significant funding for full scholarships while our partners around the world also hosted numerous Global Impact Competitions, where entrepreneurs pitched their solutions to their local communities with the winners earning a full scholarship funded by our partners to attend the Global Solution Program as their prize.

Google and our partners’ support helped individuals attend our program and created a wider buzz around exponential technology and social change around the world in local communities. It led to the founding of 110 SU chapters in 55 countries.

Another vital aspect of our work in supporting women has been trying to create a harassment-free environment. Throughout the Silicon Valley, more than 60% of women convey that while they are trying to build their companies or get their work done, they are also dealing with physical and sexual harassment while being demeaned and excluded in other ways in the workplace. We have taken actions to educate and train our staff on how to deal with situations should they occur. All staff receives training on harassment when they join Singularity University, and all Global Solutions Program participants attend mandatory trainings on sexual harassment when they first arrive on campus. We also have male and female wellness counselors available that can offer support to both individuals and teams of entrepreneurs throughout the entire program.

While at a minimum our campus must be physically safe for women, we also strive to create a culture that values women and supports them in the additional challenges and expectations they face. For example, one of our 2016 female participants, Van Duesterberg, was pregnant during the program and said that instead of having people doubt her commitment to her startup or make her prove she could handle having a child and running a start-up at the same time, people went out of their way to help her.

“I was the epitome of a person not supposed to be doing a startup,” she said. “I was pregnant and would need to take care of my child. But Singularity University was supportive and encouraging. They made me feel super-included and that it was possible to do both. I continue to come back to campus even though the program is over because the network welcomes me and supports me rather than shuts me out because of my physical limitations. Rather than making me feel I had to prove myself, everyone just understood me and supported me, whether it was bringing me healthy food or recommending funders.”

Another strength that we have in supporting women is that after the Global Solutions Program, entrepreneurs have access to a much larger ecosystem.

Many entrepreneurs partake in SU Ventures, which can provide further support to startups as they develop, and we now have a larger community of over 200,000 people in almost every country. These members have often attended other Singularity University programs, events and are committed to our vision of the future. These women and men consist of business executives, Fortune 500 companies, investors, nonprofit and government leaders, technologists, members of the media, and other movers and shakers in the world. They have made introductions for our founders, collaborated with them on business ventures, invested in them and showcased their work at high profile events around the world.

Building for the Future
While our Global Solutions Program is making great strides in supporting female entrepreneurs, there is always more work to do. We are now focused on achieving the same degree of female participation across all of our programs and actively working to recruit and feature more female faculty and speakers on stage. As our community grows and scales around the world, we are also intent at how to best uphold our values and policies around sexual harassment across diverse locations and cultures. And like all businesses everywhere, we are focused on recruiting more women to serve at senior leadership levels within SU. As we make our way forward, we hope that you will join us in boldly leading this change and recognizing the genius and power of female entrepreneurs.

Meet Some of Our Female Moonshots
While we have many remarkable female entrepreneurs in the Singularity University community, the list below features a few of the women who have founded or co-founded companies at the Global Solutions Program that have launched new industries and are on their way to changing the way our world works for millions if not billions of people.

Jessica Scorpio co-founded Getaround in 2009. Getaround was one of the first car-sharing service platforms allowing anyone to rent out their car using a smartphone app. GetAround was a revolutionary idea in 2009, not only because smartphones and apps were still in their infancy, but because it was unthinkable that a technology startup could disrupt the major entrenched car, transport, and logistics companies. Scorpio’s early insights and pioneering entrepreneurial work brought to life new ways that humans relate to car sharing and the future self-driving car industry. Scorpio and Getaround have won numerous awards, and Getaround now serves over 200,000 members.

Paola Santana co-founded Matternet in 2011, which pioneered the commercial drone transport industry. In 2011, only military, hobbyists or the film industry used drones. Matternet demonstrated that drones could be used for commercial transport in short point-to-point deliveries for high-value goods laying the groundwork for drone transport around the world as well as some of the early thinking behind the future flying car industry. Santana was also instrumental in shaping regulations for the use of commercial drones around the world, making the industry possible.

Sara Naseri co-founded Qurasense in 2014, a life sciences start-up that analyzes women’s health through menstrual blood allowing women to track their health every month. Naseri is shifting our understanding of women’s menstrual blood as a waste product and something “not to be talked about,” to a rich, non-invasive, abundant source of information about women’s health.

Abi Ramanan co-founded ImpactVision in 2015, a software company that rapidly analyzes the quality and characteristics of food through hyperspectral images. Her long-term vision is to digitize food supply chains to reduce waste and fraud, given that one-third of all food is currently wasted before it reaches our plates. Ramanan is also helping the world understand that hyperspectral technology can be used in many industries to help us “see the unseen” and augment our ability to sense and understand what is happening around us in a much more sophisticated way.

Anita Schjøll Brede and Maria Ritola co-founded Iris AI in 2015, an artificial intelligence company that is building an AI research assistant that drastically improves the efficiency of R&D research and breaks down silos between different industries. Their long-term vision is for Iris AI to become smart enough that she will become a scientist herself. Fast Company named Iris AI one of the 10 most innovative artificial intelligence companies for 2017.

Hla Hla Win co-founded 360ed in 2016, a startup that conducts teacher training and student education through virtual reality and augmented reality in Myanmar. They have already connected teachers from 128 private schools in Myanmar with schools teaching 21st-century skills in Silicon Valley and around the world. Their moonshot is to build a platform where any teacher in the world can share best practices in teachers’ training. As they succeed, millions of children in some of the poorest parts of the world will have access to a 21st-century education.

Min FitzGerald and Van Duesterberg cofounded Nutrigene in 2017, a startup that ships freshly formulated, tailor-made supplement elixirs directly to consumers. Their long-term vision is to help people optimize their health using actionable data insights, so people can take a guided, tailored approaching to thriving into longevity.

Anna Skaya co-founded Basepaws in 2016, which created the first genetic test for cats and is building a community of citizen scientist pet owners. They are creating personalized pet products such as supplements, therapeutics, treats, and toys while also developing a database of genetic data for future research that will help both humans and pets over the long term.

Olivia Ramos co-founded Deep Blocks in 2016, a startup using artificial intelligence to integrate and streamline the processes of architecture, pre-construction, and real estate. As digital technologies, artificial intelligence, and robotics advance, it no longer makes sense for these industries to exist separately. Ramos recognized the tremendous value and efficiency that it is now possible to unlock with exponential technologies and creating an integrated industry in the future.

Please also visit our website to learn more about other female entrepreneurs, staff and faculty who are pioneering the future through exponential technologies. Continue reading

Posted in Human Robots | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

#431999 Brain-Like Chips Now Beat the Human ...

Move over, deep learning. Neuromorphic computing—the next big thing in artificial intelligence—is on fire.

Just last week, two studies individually unveiled computer chips modeled after information processing in the human brain.

The first, published in Nature Materials, found a perfect solution to deal with unpredictability at synapses—the gap between two neurons that transmit and store information. The second, published in Science Advances, further amped up the system’s computational power, filling synapses with nanoclusters of supermagnetic material to bolster information encoding.

The result? Brain-like hardware systems that compute faster—and more efficiently—than the human brain.

“Ultimately we want a chip as big as a fingernail to replace one big supercomputer,” said Dr. Jeehwan Kim, who led the first study at MIT in Cambridge, Massachusetts.

Experts are hopeful.

“The field’s full of hype, and it’s nice to see quality work presented in an objective way,” said Dr. Carver Mead, an engineer at the California Institute of Technology in Pasadena not involved in the work.

Software to Hardware
The human brain is the ultimate computational wizard. With roughly 100 billion neurons densely packed into the size of a small football, the brain can deftly handle complex computation at lightning speed using very little energy.

AI experts have taken note. The past few years saw brain-inspired algorithms that can identify faces, falsify voices, and play a variety of games at—and often above—human capability.

But software is only part of the equation. Our current computers, with their transistors and binary digital systems, aren’t equipped to run these powerful algorithms.

That’s where neuromorphic computing comes in. The idea is simple: fabricate a computer chip that mimics the brain at the hardware level. Here, data is both processed and stored within the chip in an analog manner. Each artificial synapse can accumulate and integrate small bits of information from multiple sources and fire only when it reaches a threshold—much like its biological counterpart.

Experts believe the speed and efficiency gains will be enormous.

For one, the chips will no longer have to transfer data between the central processing unit (CPU) and storage blocks, which wastes both time and energy. For another, like biological neural networks, neuromorphic devices can support neurons that run millions of streams of parallel computation.

A “Brain-on-a-chip”
Optimism aside, reproducing the biological synapse in hardware form hasn’t been as easy as anticipated.

Neuromorphic chips exist in many forms, but often look like a nanoscale metal sandwich. The “bread” pieces are generally made of conductive plates surrounding a switching medium—a conductive material of sorts that acts like the gap in a biological synapse.

When a voltage is applied, as in the case of data input, ions move within the switching medium, which then creates conductive streams to stimulate the downstream plate. This change in conductivity mimics the way biological neurons change their “weight,” or the strength of connectivity between two adjacent neurons.

But so far, neuromorphic synapses have been rather unpredictable. According to Kim, that’s because the switching medium is often comprised of material that can’t channel ions to exact locations on the downstream plate.

“Once you apply some voltage to represent some data with your artificial neuron, you have to erase and be able to write it again in the exact same way,” explains Kim. “But in an amorphous solid, when you write again, the ions go in different directions because there are lots of defects.”

In his new study, Kim and colleagues swapped the jelly-like switching medium for silicon, a material with only a single line of defects that acts like a channel to guide ions.

The chip starts with a thin wafer of silicon etched with a honeycomb-like pattern. On top is a layer of silicon germanium—something often present in transistors—in the same pattern. This creates a funnel-like dislocation, a kind of Grand Canal that perfectly shuttles ions across the artificial synapse.

The researchers then made a neuromorphic chip containing these synapses and shot an electrical zap through them. Incredibly, the synapses’ response varied by only four percent—much higher than any neuromorphic device made with an amorphous switching medium.

In a computer simulation, the team built a multi-layer artificial neural network using parameters measured from their device. After tens of thousands of training examples, their neural network correctly recognized samples 95 percent of the time, just 2 percent lower than state-of-the-art software algorithms.

The upside? The neuromorphic chip requires much less space than the hardware that runs deep learning algorithms. Forget supercomputers—these chips could one day run complex computations right on our handheld devices.

A Magnetic Boost
Meanwhile, in Boulder, Colorado, Dr. Michael Schneider at the National Institute of Standards and Technology also realized that the standard switching medium had to go.

“There must be a better way to do this, because nature has figured out a better way to do this,” he says.

His solution? Nanoclusters of magnetic manganese.

Schneider’s chip contained two slices of superconducting electrodes made out of niobium, which channel electricity with no resistance. When researchers applied different magnetic fields to the synapse, they could control the alignment of the manganese “filling.”

The switch gave the chip a double boost. For one, by aligning the switching medium, the team could predict the ion flow and boost uniformity. For another, the magnetic manganese itself adds computational power. The chip can now encode data in both the level of electrical input and the direction of the magnetisms without bulking up the synapse.

It seriously worked. At one billion times per second, the chips fired several orders of magnitude faster than human neurons. Plus, the chips required just one ten-thousandth of the energy used by their biological counterparts, all the while synthesizing input from nine different sources in an analog manner.

The Road Ahead
These studies show that we may be nearing a benchmark where artificial synapses match—or even outperform—their human inspiration.

But to Dr. Steven Furber, an expert in neuromorphic computing, we still have a ways before the chips go mainstream.

Many of the special materials used in these chips require specific temperatures, he says. Magnetic manganese chips, for example, require temperatures around absolute zero to operate, meaning they come with the need for giant cooling tanks filled with liquid helium—obviously not practical for everyday use.

Another is scalability. Millions of synapses are necessary before a neuromorphic device can be used to tackle everyday problems such as facial recognition. So far, no deal.

But these problems may in fact be a driving force for the entire field. Intense competition could push teams into exploring different ideas and solutions to similar problems, much like these two studies.

If so, future chips may come in diverse flavors. Similar to our vast array of deep learning algorithms and operating systems, the computer chips of the future may also vary depending on specific requirements and needs.

It is worth developing as many different technological approaches as possible, says Furber, especially as neuroscientists increasingly understand what makes our biological synapses—the ultimate inspiration—so amazingly efficient.

Image Credit: arakio / Shutterstock.com Continue reading

Posted in Human Robots | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

#431899 Darker Still: Black Mirror’s New ...

The key difference between science fiction and fantasy is that science fiction is entirely possible because of its grounding in scientific facts, while fantasy is not. This is where Black Mirror is both an entertaining and terrifying work of science fiction. Created by Charlie Brooker, the anthological series tells cautionary tales of emerging technology that could one day be an integral part of our everyday lives.
While watching the often alarming episodes, one can’t help but recognize the eerie similarities to some of the tech tools that are already abundant in our lives today. In fact, many previous Black Mirror predictions are already becoming reality.
The latest season of Black Mirror was arguably darker than ever. This time, Brooker seemed to focus on the ethical implications of one particular area: neurotechnology.
Emerging Neurotechnology
Warning: The remainder of this article may contain spoilers from Season 4 of Black Mirror.
Most of the storylines from season four revolve around neurotechnology and brain-machine interfaces. They are based in a world where people have the power to upload their consciousness onto machines, have fully immersive experiences in virtual reality, merge their minds with other minds, record others’ memories, and even track what others are thinking, feeling, and doing.
How can all this ever be possible? Well, these capabilities are already being developed by pioneers and researchers globally. Early last year, Elon Musk unveiled Neuralink, a company whose goal is to merge the human mind with AI through a neural lace. We’ve already connected two brains via the internet, allowing one brain to communicate with another. Various research teams have been able to develop mechanisms for “reading minds” or reconstructing memories of individuals via devices. The list goes on.
With many of the technologies we see in Black Mirror it’s not a question of if, but when. Futurist Ray Kurzweil has predicted that by the 2030s we will be able to upload our consciousness onto the cloud via nanobots that will “provide full-immersion virtual reality from within the nervous system, provide direct brain-to-brain communication over the internet, and otherwise greatly expand human intelligence.” While other experts continue to challenge Kurzweil on the exact year we’ll accomplish this feat, with the current exponential growth of our technological capabilities, we’re on track to get there eventually.
Ethical Questions
As always, technology is only half the conversation. Equally fascinating are the many ethical and moral questions this topic raises.
For instance, with the increasing convergence of artificial intelligence and virtual reality, we have to ask ourselves if our morality from the physical world transfers equally into the virtual world. The first episode of season four, USS Calister, tells the story of a VR pioneer, Robert Daley, who creates breakthrough AI and VR to satisfy his personal frustrations and sexual urges. He uses the DNA of his coworkers (and their children) to re-create them digitally in his virtual world, to which he escapes to torture them, while they continue to be indifferent in the “real” world.
Audiences are left asking themselves: should what happens in the digital world be considered any less “real” than the physical world? How do we know if the individuals in the virtual world (who are ultimately based on algorithms) have true feelings or sentiments? Have they been developed to exhibit characteristics associated with suffering, or can they really feel suffering? Fascinatingly, these questions point to the hard problem of consciousness—the question of if, why, and how a given physical process generates the specific experience it does—which remains a major mystery in neuroscience.
Towards the end of USS Calister, the hostages of Daley’s virtual world attempt to escape through suicide, by committing an act that will delete the code that allows them to exist. This raises yet another mind-boggling ethical question: if we “delete” code that signifies a digital being, should that be considered murder (or suicide, in this case)? Why shouldn’t it? When we murder someone we are, in essence, taking away their capacity to live and to be, without their consent. By unplugging a self-aware AI, wouldn’t we be violating its basic right to live in the same why? Does AI, as code, even have rights?
Brain implants can also have a radical impact on our self-identity and how we define the word “I”. In the episode Black Museum, instead of witnessing just one horror, we get a series of scares in little segments. One of those segments tells the story of a father who attempts to reincarnate the mother of his child by uploading her consciousness into his mind and allowing her to live in his head (essentially giving him multiple personality disorder). In this way, she can experience special moments with their son.
With “no privacy for him, and no agency for her” the good intention slowly goes very wrong. This story raises a critical question: should we be allowed to upload consciousness into limited bodies? Even more, if we are to upload our minds into “the cloud,” at what point do we lose our individuality to become one collective being?
These questions can form the basis of hours of debate, but we’re just getting started. There are no right or wrong answers with many of these moral dilemmas, but we need to start having such discussions.
The Downside of Dystopian Sci-Fi
Like last season’s San Junipero, one episode of the series, Hang the DJ, had an uplifting ending. Yet the overwhelming majority of the stories in Black Mirror continue to focus on the darkest side of human nature, feeding into the pre-existing paranoia of the general public. There is certainly some value in this; it’s important to be aware of the dangers of technology. After all, what better way to explore these dangers before they occur than through speculative fiction?
A big takeaway from every tale told in the series is that the greatest threat to humanity does not come from technology, but from ourselves. Technology itself is not inherently good or evil; it all comes down to how we choose to use it as a society. So for those of you who are techno-paranoid, beware, for it’s not the technology you should fear, but the humans who get their hands on it.
While we can paint negative visions for the future, though, it is also important to paint positive ones. The kind of visions we set for ourselves have the power to inspire and motivate generations. Many people are inherently pessimistic when thinking about the future, and that pessimism in turn can shape their contributions to humanity.
While utopia may not exist, the future of our species could and should be one of solving global challenges, abundance, prosperity, liberation, and cosmic transcendence. Now that would be a thrilling episode to watch.
Image Credit: Billion Photos / Shutterstock.com Continue reading

Posted in Human Robots | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

#431873 Why the World Is Still Getting ...

If you read or watch the news, you’ll likely think the world is falling to pieces. Trends like terrorism, climate change, and a growing population straining the planet’s finite resources can easily lead you to think our world is in crisis.
But there’s another story, a story the news doesn’t often report. This story is backed by data, and it says we’re actually living in the most peaceful, abundant time in history, and things are likely to continue getting better.
The News vs. the Data
The reality that’s often clouded by a constant stream of bad news is we’re actually seeing a massive drop in poverty, fewer deaths from violent crime and preventable diseases. On top of that, we’re the most educated populace to ever walk the planet.
“Violence has been in decline for thousands of years, and today we may be living in the most peaceful era in the existence of our species.” –Steven Pinker
In the last hundred years, we’ve seen the average human life expectancy nearly double, the global GDP per capita rise exponentially, and childhood mortality drop 10-fold.

That’s pretty good progress! Maybe the world isn’t all gloom and doom.If you’re still not convinced the world is getting better, check out the charts in this article from Vox and on Peter Diamandis’ website for a lot more data.
Abundance for All Is Possible
So now that you know the world isn’t so bad after all, here’s another thing to think about: it can get much better, very soon.
In their book Abundance: The Future Is Better Than You Think, Steven Kotler and Peter Diamandis suggest it may be possible for us to meet and even exceed the basic needs of all the people living on the planet today.
“In the hands of smart and driven innovators, science and technology take things which were once scarce and make them abundant and accessible to all.”
This means making sure every single person in the world has adequate food, water and shelter, as well as a good education, access to healthcare, and personal freedom.
This might seem unimaginable, especially if you tend to think the world is only getting worse. But given how much progress we’ve already made in the last few hundred years, coupled with the recent explosion of information sharing and new, powerful technologies, abundance for all is not as out of reach as you might believe.
Throughout history, we’ve seen that in the hands of smart and driven innovators, science and technology take things which were once scarce and make them abundant and accessible to all.
Napoleon III
In Abundance, Diamandis and Kotler tell the story of how aluminum went from being one of the rarest metals on the planet to being one of the most abundant…
In the 1800s, aluminum was more valuable than silver and gold because it was rarer. So when Napoleon III entertained the King of Siam, the king and his guests were honored by being given aluminum utensils, while the rest of the dinner party ate with gold.
But aluminum is not really rare.
In fact, aluminum is the third most abundant element in the Earth’s crust, making up 8.3% of the weight of our planet. But it wasn’t until chemists Charles Martin Hall and Paul Héroult discovered how to use electrolysis to cheaply separate aluminum from surrounding materials that the element became suddenly abundant.
The problems keeping us from achieving a world where everyone’s basic needs are met may seem like resource problems — when in reality, many are accessibility problems.
The Engine Driving Us Toward Abundance: Exponential Technology
History is full of examples like the aluminum story. The most powerful one of the last few decades is information technology. Think about all the things that computers and the internet made abundant that were previously far less accessible because of cost or availability … Here are just a few examples:

Easy access to the world’s information
Ability to share information freely with anyone and everyone
Free/cheap long-distance communication
Buying and selling goods/services regardless of location

Less than two decades ago, when someone reached a certain level of economic stability, they could spend somewhere around $10K on stereos, cameras, entertainment systems, etc — today, we have all that equipment in the palm of our hand.
Now, there is a new generation of technologies heavily dependant on information technology and, therefore, similarly riding the wave of exponential growth. When put to the right use, emerging technologies like artificial intelligence, robotics, digital manufacturing, nano-materials and digital biology make it possible for us to drastically raise the standard of living for every person on the planet.

These are just some of the innovations which are unlocking currently scarce resources:

IBM’s Watson Health is being trained and used in medical facilities like the Cleveland Clinic to help doctors diagnose disease. In the future, it’s likely we’ll trust AI just as much, if not more than humans to diagnose disease, allowing people all over the world to have access to great diagnostic tools regardless of whether there is a well-trained doctor near them.

Solar power is now cheaper than fossil fuels in some parts of the world, and with advances in new materials and storage, the cost may decrease further. This could eventually lead to nearly-free, clean energy for people across the world.

Google’s GMNT network can now translate languages as well as a human, unlocking the ability for people to communicate globally as we never have before.

Self-driving cars are already on the roads of several American cities and will be coming to a road near you in the next couple years. Considering the average American spends nearly two hours driving every day, not having to drive would free up an increasingly scarce resource: time.

The Change-Makers
Today’s innovators can create enormous change because they have these incredible tools—which would have once been available only to big organizations—at their fingertips. And, as a result of our hyper-connected world, there is an unprecedented ability for people across the planet to work together to create solutions to some of our most pressing problems today.
“In today’s hyperlinked world, solving problems anywhere, solves problems everywhere.” –Peter Diamandis and Steven Kotler, Abundance
According to Diamandis and Kotler, there are three groups of people accelerating positive change.

DIY InnovatorsIn the 1970s and 1980s, the Homebrew Computer Club was a meeting place of “do-it-yourself” computer enthusiasts who shared ideas and spare parts. By the 1990s and 2000s, that little club became known as an inception point for the personal computer industry — dozens of companies, including Apple Computer, can directly trace their origins back to Homebrew. Since then, we’ve seen the rise of the social entrepreneur, the Maker Movement and the DIY Bio movement, which have similar ambitions to democratize social reform, manufacturing, and biology, the way Homebrew democratized computers. These are the people who look for new opportunities and aren’t afraid to take risks to create something new that will change the status-quo.
Techno-PhilanthropistsUnlike the robber barons of the 19th and early 20th centuries, today’s “techno-philanthropists” are not just giving away some of their wealth for a new museum, they are using their wealth to solve global problems and investing in social entrepreneurs aiming to do the same. The Bill and Melinda Gates Foundation has given away at least $28 billion, with a strong focus on ending diseases like polio, malaria, and measles for good. Jeff Skoll, after cashing out of eBay with $2 billion in 1998, went on to create the Skoll Foundation, which funds social entrepreneurs across the world. And last year, Mark Zuckerberg and Priscilla Chan pledged to give away 99% of their $46 billion in Facebook stock during their lifetimes.
The Rising BillionCisco estimates that by 2020, there will be 4.1 billion people connected to the internet, up from 3 billion in 2015. This number might even be higher, given the efforts of companies like Facebook, Google, Virgin Group, and SpaceX to bring internet access to the world. That’s a billion new people in the next several years who will be connected to the global conversation, looking to learn, create and better their own lives and communities.In his book, Fortune at the Bottom of the Pyramid, C.K. Pahalad writes that finding co-creative ways to serve this rising market can help lift people out of poverty while creating viable businesses for inventive companies.

The Path to Abundance
Eager to create change, innovators armed with powerful technologies can accomplish incredible feats. Kotler and Diamandis imagine that the path to abundance occurs in three tiers:

Basic Needs (food, water, shelter)
Tools of Growth (energy, education, access to information)
Ideal Health and Freedom

Of course, progress doesn’t always happen in a straight, logical way, but having a framework to visualize the needs is helpful.
Many people don’t believe it’s possible to end the persistent global problems we’re facing. However, looking at history, we can see many examples where technological tools have unlocked resources that previously seemed scarce.
Technological solutions are not always the answer, and we need social change and policy solutions as much as we need technology solutions. But we have seen time and time again, that powerful tools in the hands of innovative, driven change-makers can make the seemingly impossible happen.

You can download the full “Path to Abundance” infographic here. It was created under a CC BY-NC-ND license. If you share, please attribute to Singularity University.
Image Credit: janez volmajer / Shutterstock.com Continue reading

Posted in Human Robots | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

#431872 AI Uses Titan Supercomputer to Create ...

You don’t have to dig too deeply into the archive of dystopian science fiction to uncover the horror that intelligent machines might unleash. The Matrix and The Terminator are probably the most well-known examples of self-replicating, intelligent machines attempting to enslave or destroy humanity in the process of building a brave new digital world.
The prospect of artificially intelligent machines creating other artificially intelligent machines took a big step forward in 2017. However, we’re far from the runaway technological singularity futurists are predicting by mid-century or earlier, let alone murderous cyborgs or AI avatar assassins.
The first big boost this year came from Google. The tech giant announced it was developing automated machine learning (AutoML), writing algorithms that can do some of the heavy lifting by identifying the right neural networks for a specific job. Now researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL), using the most powerful supercomputer in the US, have developed an AI system that can generate neural networks as good if not better than any developed by a human in less than a day.
It can take months for the brainiest, best-paid data scientists to develop deep learning software, which sends data through a complex web of mathematical algorithms. The system is modeled after the human brain and known as an artificial neural network. Even Google’s AutoML took weeks to design a superior image recognition system, one of the more standard operations for AI systems today.
Computing Power
Of course, Google Brain project engineers only had access to 800 graphic processing units (GPUs), a type of computer hardware that works especially well for deep learning. Nvidia, which pioneered the development of GPUs, is considered the gold standard in today’s AI hardware architecture. Titan, the supercomputer at ORNL, boasts more than 18,000 GPUs.
The ORNL research team’s algorithm, called MENNDL for Multinode Evolutionary Neural Networks for Deep Learning, isn’t designed to create AI systems that cull cute cat photos from the internet. Instead, MENNDL is a tool for testing and training thousands of potential neural networks to work on unique science problems.
That requires a different approach from the Google and Facebook AI platforms of the world, notes Steven Young, a postdoctoral research associate at ORNL who is on the team that designed MENNDL.
“We’ve discovered that those [neural networks] are very often not the optimal network for a lot of our problems, because our data, while it can be thought of as images, is different,” he explains to Singularity Hub. “These images, and the problems, have very different characteristics from object detection.”
AI for Science
One application of the technology involved a particle physics experiment at the Fermi National Accelerator Laboratory. Fermilab researchers are interested in understanding neutrinos, high-energy subatomic particles that rarely interact with normal matter but could be a key to understanding the early formation of the universe. One Fermilab experiment involves taking a sort of “snapshot” of neutrino interactions.
The team wanted the help of an AI system that could analyze and classify Fermilab’s detector data. MENNDL evaluated 500,000 neural networks in 24 hours. Its final solution proved superior to custom models developed by human scientists.
In another case involving a collaboration with St. Jude Children’s Research Hospital in Memphis, MENNDL improved the error rate of a human-designed algorithm for identifying mitochondria inside 3D electron microscopy images of brain tissue by 30 percent.
“We are able to do better than humans in a fraction of the time at designing networks for these sort of very different datasets that we’re interested in,” Young says.
What makes MENNDL particularly adept is its ability to define the best or most optimal hyperparameters—the key variables—to tackle a particular dataset.
“You don’t always need a big, huge deep network. Sometimes you just need a small network with the right hyperparameters,” Young says.
A Virtual Data Scientist
That’s not dissimilar to the approach of a company called H20.ai, a startup out of Silicon Valley that uses open source machine learning platforms to “democratize” AI. It applies machine learning to create business solutions for Fortune 500 companies, including some of the world’s biggest banks and healthcare companies.
“Our software is more [about] pattern detection, let’s say anti-money laundering or fraud detection or which customer is most likely to churn,” Dr. Arno Candel, chief technology officer at H2O.ai, tells Singularity Hub. “And that kind of insight-generating software is what we call AI here.”
The company’s latest product, Driverless AI, promises to deliver the data scientist equivalent of a chessmaster to its customers (the company claims several such grandmasters in its employ and advisory board). In other words, the system can analyze a raw dataset and, like MENNDL, automatically identify what features should be included in the computer model to make the most of the data based on the best “chess moves” of its grandmasters.
“So we’re using those algorithms, but we’re giving them the human insights from those data scientists, and we automate their thinking,” he explains. “So we created a virtual data scientist that is relentless at trying these ideas.”
Inside the Black Box
Not unlike how the human brain reaches a conclusion, it’s not always possible to understand how a machine, despite being designed by humans, reaches its own solutions. The lack of transparency is often referred to as the AI “black box.” Experts like Young say we can learn something about the evolutionary process of machine learning by generating millions of neural networks and seeing what works well and what doesn’t.
“You’re never going to be able to completely explain what happened, but maybe we can better explain it than we currently can today,” Young says.
Transparency is built into the “thought process” of each particular model generated by Driverless AI, according to Candel.
The computer even explains itself to the user in plain English at each decision point. There is also real-time feedback that allows users to prioritize features, or parameters, to see how the changes improve the accuracy of the model. For example, the system may include data from people in the same zip code as it creates a model to describe customer turnover.
“That’s one of the advantages of our automatic feature engineering: it’s basically mimicking human thinking,” Candel says. “It’s not just neural nets that magically come up with some kind of number, but we’re trying to make it statistically significant.”
Moving Forward
Much digital ink has been spilled over the dearth of skilled data scientists, so automating certain design aspects for developing artificial neural networks makes sense. Experts agree that automation alone won’t solve that particular problem. However, it will free computer scientists to tackle more difficult issues, such as parsing the inherent biases that exist within the data used by machine learning today.
“I think the world has an opportunity to focus more on the meaning of things and not on the laborious tasks of just fitting a model and finding the best features to make that model,” Candel notes. “By automating, we are pushing the burden back for the data scientists to actually do something more meaningful, which is think about the problem and see how you can address it differently to make an even bigger impact.”
The team at ORNL expects it can also make bigger impacts beginning next year when the lab’s next supercomputer, Summit, comes online. While Summit will boast only 4,600 nodes, it will sport the latest and greatest GPU technology from Nvidia and CPUs from IBM. That means it will deliver more than five times the computational performance of Titan, the world’s fifth-most powerful supercomputer today.
“We’ll be able to look at much larger problems on Summit than we were able to with Titan and hopefully get to a solution much faster,” Young says.
It’s all in a day’s work.
Image Credit: Gennady Danilkin / Shutterstock.com Continue reading

Posted in Human Robots | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment