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#434865 5 AI Breakthroughs We’ll Likely See in ...
Convergence is accelerating disruption… everywhere! Exponential technologies are colliding into each other, reinventing products, services, and industries.
As AI algorithms such as Siri and Alexa can process your voice and output helpful responses, other AIs like Face++ can recognize faces. And yet others create art from scribbles, or even diagnose medical conditions.
Let’s dive into AI and convergence.
Top 5 Predictions for AI Breakthroughs (2019-2024)
My friend Neil Jacobstein is my ‘go-to expert’ in AI, with over 25 years of technical consulting experience in the field. Currently the AI and Robotics chair at Singularity University, Jacobstein is also a Distinguished Visiting Scholar in Stanford’s MediaX Program, a Henry Crown Fellow, an Aspen Institute moderator, and serves on the National Academy of Sciences Earth and Life Studies Committee. Neil predicted five trends he expects to emerge over the next five years, by 2024.
AI gives rise to new non-human pattern recognition and intelligence results
AlphaGo Zero, a machine learning computer program trained to play the complex game of Go, defeated the Go world champion in 2016 by 100 games to zero. But instead of learning from human play, AlphaGo Zero trained by playing against itself—a method known as reinforcement learning.
Building its own knowledge from scratch, AlphaGo Zero demonstrates a novel form of creativity, free of human bias. Even more groundbreaking, this type of AI pattern recognition allows machines to accumulate thousands of years of knowledge in a matter of hours.
While these systems can’t answer the question “What is orange juice?” or compete with the intelligence of a fifth grader, they are growing more and more strategically complex, merging with other forms of narrow artificial intelligence. Within the next five years, who knows what successors of AlphaGo Zero will emerge, augmenting both your business functions and day-to-day life.
Doctors risk malpractice when not using machine learning for diagnosis and treatment planning
A group of Chinese and American researchers recently created an AI system that diagnoses common childhood illnesses, ranging from the flu to meningitis. Trained on electronic health records compiled from 1.3 million outpatient visits of almost 600,000 patients, the AI program produced diagnosis outcomes with unprecedented accuracy.
While the US health system does not tout the same level of accessible universal health data as some Chinese systems, we’ve made progress in implementing AI in medical diagnosis. Dr. Kang Zhang, chief of ophthalmic genetics at the University of California, San Diego, created his own system that detects signs of diabetic blindness, relying on both text and medical images.
With an eye to the future, Jacobstein has predicted that “we will soon see an inflection point where doctors will feel it’s a risk to not use machine learning and AI in their everyday practices because they don’t want to be called out for missing an important diagnostic signal.”
Quantum advantage will massively accelerate drug design and testing
Researchers estimate that there are 1060 possible drug-like molecules—more than the number of atoms in our solar system. But today, chemists must make drug predictions based on properties influenced by molecular structure, then synthesize numerous variants to test their hypotheses.
Quantum computing could transform this time-consuming, highly costly process into an efficient, not to mention life-changing, drug discovery protocol.
“Quantum computing is going to have a major industrial impact… not by breaking encryption,” said Jacobstein, “but by making inroads into design through massive parallel processing that can exploit superposition and quantum interference and entanglement, and that can wildly outperform classical computing.”
AI accelerates security systems’ vulnerability and defense
With the incorporation of AI into almost every aspect of our lives, cyberattacks have grown increasingly threatening. “Deep attacks” can use AI-generated content to avoid both human and AI controls.
Previous examples include fake videos of former President Obama speaking fabricated sentences, and an adversarial AI fooling another algorithm into categorizing a stop sign as a 45 mph speed limit sign. Without the appropriate protections, AI systems can be manipulated to conduct any number of destructive objectives, whether ruining reputations or diverting autonomous vehicles.
Jacobstein’s take: “We all have security systems on our buildings, in our homes, around the healthcare system, and in air traffic control, financial organizations, the military, and intelligence communities. But we all know that these systems have been hacked periodically and we’re going to see that accelerate. So, there are major business opportunities there and there are major opportunities for you to get ahead of that curve before it bites you.”
AI design systems drive breakthroughs in atomically precise manufacturing
Just as the modern computer transformed our relationship with bits and information, AI will redefine and revolutionize our relationship with molecules and materials. AI is currently being used to discover new materials for clean-tech innovations, such as solar panels, batteries, and devices that can now conduct artificial photosynthesis.
Today, it takes about 15 to 20 years to create a single new material, according to industry experts. But as AI design systems skyrocket in capacity, these will vastly accelerate the materials discovery process, allowing us to address pressing issues like climate change at record rates. Companies like Kebotix are already on their way to streamlining the creation of chemistries and materials at the click of a button.
Atomically precise manufacturing will enable us to produce the previously unimaginable.
Final Thoughts
Within just the past three years, countries across the globe have signed into existence national AI strategies and plans for ramping up innovation. Businesses and think tanks have leaped onto the scene, hiring AI engineers and tech consultants to leverage what computer scientist Andrew Ng has even called the new ‘electricity’ of the 21st century.
As AI plays an exceedingly vital role in everyday life, how will your business leverage it to keep up and build forward?
In the wake of burgeoning markets, new ventures will quickly arise, each taking advantage of untapped data sources or unmet security needs.
And as your company aims to ride the wave of AI’s exponential growth, consider the following pointers to leverage AI and disrupt yourself before it reaches you first:
Determine where and how you can begin collecting critical data to inform your AI algorithms
Identify time-intensive processes that can be automated and accelerated within your company
Discern which global challenges can be expedited by hyper-fast, all-knowing minds
Remember: good data is vital fuel. Well-defined problems are the best compass. And the time to start implementing AI is now.
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#434823 The Tangled Web of Turning Spider Silk ...
Spider-Man is one of the most popular superheroes of all time. It’s a bit surprising given that one of the more common phobias is arachnophobia—a debilitating fear of spiders.
Perhaps more fantastical is that young Peter Parker, a brainy high school science nerd, seemingly developed overnight the famous web-shooters and the synthetic spider silk that he uses to swing across the cityscape like Tarzan through the jungle.
That’s because scientists have been trying for decades to replicate spider silk, a material that is five times stronger than steel, among its many superpowers. In recent years, researchers have been untangling the protein-based fiber’s structure down to the molecular level, leading to new insights and new potential for eventual commercial uses.
The applications for such a material seem near endless. There’s the more futuristic visions, like enabling robotic “muscles” for human-like movement or ensnaring real-life villains with a Spider-Man-like web. Near-term applications could include the biomedical industry, such as bandages and adhesives, and as a replacement textile for everything from rope to seat belts to parachutes.
Spinning Synthetic Spider Silk
Randy Lewis has been studying the properties of spider silk and developing methods for producing it synthetically for more than three decades. In the 1990s, his research team was behind cloning the first spider silk gene, as well as the first to identify and sequence the proteins that make up the six different silks that web slingers make. Each has different mechanical properties.
“So our thought process was that you could take that information and begin to to understand what made them strong and what makes them stretchy, and why some are are very stretchy and some are not stretchy at all, and some are stronger and some are weaker,” explained Lewis, a biology professor at Utah State University and director of the Synthetic Spider Silk Lab, in an interview with Singularity Hub.
Spiders are naturally territorial and cannibalistic, so any intention to farm silk naturally would likely end in an orgy of arachnid violence. Instead, Lewis and company have genetically modified different organisms to produce spider silk synthetically, including inserting a couple of web-making genes into the genetic code of goats. The goats’ milk contains spider silk proteins.
The lab also produces synthetic spider silk through a fermentation process not entirely dissimilar to brewing beer, but using genetically modified bacteria to make the desired spider silk proteins. A similar technique has been used for years to make a key enzyme in cheese production. More recently, companies are using transgenic bacteria to make meat and milk proteins, entirely bypassing animals in the process.
The same fermentation technology is used by a chic startup called Bolt Threads outside of San Francisco that has raised more than $200 million for fashionable fibers made out of synthetic spider silk it calls Microsilk. (The company is also developing a second leather-like material, Mylo, using the underground root structure of mushrooms known as mycelium.)
Lewis’ lab also uses transgenic silkworms to produce a kind of composite material made up of the domesticated insect’s own silk proteins and those of spider silk. “Those have some fairly impressive properties,” Lewis said.
The researchers are even experimenting with genetically modified alfalfa. One of the big advantages there is that once the spider silk protein has been extracted, the remaining protein could be sold as livestock feed. “That would bring the cost of spider silk protein production down significantly,” Lewis said.
Building a Better Web
Producing synthetic spider silk isn’t the problem, according to Lewis, but the ability to do it at scale commercially remains a sticking point.
Another challenge is “weaving” the synthetic spider silk into usable products that can take advantage of the material’s marvelous properties.
“It is possible to make silk proteins synthetically, but it is very hard to assemble the individual proteins into a fiber or other material forms,” said Markus Buehler, head of the Department of Civil and Environmental Engineering at MIT, in an email to Singularity Hub. “The spider has a complex spinning duct in which silk proteins are exposed to physical forces, chemical gradients, the combination of which generates the assembly of molecules that leads to silk fibers.”
Buehler recently co-authored a paper in the journal Science Advances that found dragline spider silk exhibits different properties in response to changes in humidity that could eventually have applications in robotics.
Specifically, spider silk suddenly contracts and twists above a certain level of relative humidity, exerting enough force to “potentially be competitive with other materials being explored as actuators—devices that move to perform some activity such as controlling a valve,” according to a press release.
Studying Spider Silk Up Close
Recent studies at the molecular level are helping scientists learn more about the unique properties of spider silk, which may help researchers develop materials with extraordinary capabilities.
For example, scientists at Arizona State University used magnetic resonance tools and other instruments to image the abdomen of a black widow spider. They produced what they called the first molecular-level model of spider silk protein fiber formation, providing insights on the nanoparticle structure. The research was published last October in Proceedings of the National Academy of Sciences.
A cross section of the abdomen of a black widow (Latrodectus Hesperus) spider used in this study at Arizona State University. Image Credit: Samrat Amin.
Also in 2018, a study presented in Nature Communications described a sort of molecular clamp that binds the silk protein building blocks, which are called spidroins. The researchers observed for the first time that the clamp self-assembles in a two-step process, contributing to the extensibility, or stretchiness, of spider silk.
Another team put the spider silk of a brown recluse under an atomic force microscope, discovering that each strand, already 1,000 times thinner than a human hair, is made up of thousands of nanostrands. That helps explain its extraordinary tensile strength, though technique is also a factor, as the brown recluse uses a special looping method to reinforce its silk strands. The study also appeared last year in the journal ACS Macro Letters.
Making Spider Silk Stick
Buehler said his team is now trying to develop better and faster predictive methods to design silk proteins using artificial intelligence.
“These new methods allow us to generate new protein designs that do not naturally exist and which can be explored to optimize certain desirable properties like torsional actuation, strength, bioactivity—for example, tissue engineering—and others,” he said.
Meanwhile, Lewis’ lab has discovered a method that allows it to solubilize spider silk protein in what is essentially a water-based solution, eschewing acids or other toxic compounds that are normally used in the process.
That enables the researchers to develop materials beyond fiber, including adhesives that “are better than an awful lot of the current commercial adhesives,” Lewis said, as well as coatings that could be used to dampen vibrations, for example.
“We’re making gels for various kinds of of tissue regeneration, as well as drug delivery, and things like that,” he added. “So we’ve expanded the use profile from something beyond fibers to something that is a much more extensive portfolio of possible kinds of materials.”
And, yes, there’s even designs at the Synthetic Spider Silk Lab for developing a Spider-Man web-slinger material. The US Navy is interested in non-destructive ways of disabling an enemy vessel, such as fouling its propeller. The project also includes producing synthetic proteins from the hagfish, an eel-like critter that exudes a gelatinous slime when threatened.
Lewis said that while the potential for spider silk is certainly headline-grabbing, he cautioned that much of the hype is not focused on the unique mechanical properties that could lead to advances in healthcare and other industries.
“We want to see spider silk out there because it’s a unique material, not because it’s got marketing appeal,” he said.
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#434805 Get Ready for National Robotics Week
It's the tenth year of the most popular robotics celebration in the U.S. Continue reading
Posted in Human Robots
#434701 3 Practical Solutions to Offset ...
In recent years, the media has sounded the alarm about mass job loss to automation and robotics—some studies predict that up to 50 percent of current jobs or tasks could be automated in coming decades. While this topic has received significant attention, much of the press focuses on potential problems without proposing realistic solutions or considering new opportunities.
The economic impacts of AI, robotics, and automation are complex topics that require a more comprehensive perspective to understand. Is universal basic income, for example, the answer? Many believe so, and there are a number of experiments in progress. But it’s only one strategy, and without a sustainable funding source, universal basic income may not be practical.
As automation continues to accelerate, we’ll need a multi-pronged approach to ease the transition. In short, we need to update broad socioeconomic strategies for a new century of rapid progress. How, then, do we plan practical solutions to support these new strategies?
Take history as a rough guide to the future. Looking back, technology revolutions have three themes in common.
First, past revolutions each produced profound benefits to productivity, increasing human welfare. Second, technological innovation and technology diffusion have accelerated over time, each iteration placing more strain on the human ability to adapt. And third, machines have gradually replaced more elements of human work, with human societies adapting by moving into new forms of work—from agriculture to manufacturing to service, for example.
Public and private solutions, therefore, need to be developed to address each of these three components of change. Let’s explore some practical solutions for each in turn.
Figure 1. Technology’s structural impacts in the 21st century. Refer to Appendix I for quantitative charts and technological examples corresponding to the numbers (1-22) in each slice.
Solution 1: Capture New Opportunities Through Aggressive Investment
The rapid emergence of new technology promises a bounty of opportunity for the twenty-first century’s economic winners. This technological arms race is shaping up to be a global affair, and the winners will be determined in part by who is able to build the future economy fastest and most effectively. Both the private and public sectors have a role to play in stimulating growth.
At the country level, several nations have created competitive strategies to promote research and development investments as automation technologies become more mature.
Germany and China have two of the most notable growth strategies. Germany’s Industrie 4.0 plan targets a 50 percent increase in manufacturing productivity via digital initiatives, while halving the resources required. China’s Made in China 2025 national strategy sets ambitious targets and provides subsidies for domestic innovation and production. It also includes building new concept cities, investing in robotics capabilities, and subsidizing high-tech acquisitions abroad to become the leader in certain high-tech industries. For China, specifically, tech innovation is driven partially by a fear that technology will disrupt social structures and government control.
Such opportunities are not limited to existing economic powers. Estonia’s progress after the breakup of the Soviet Union is a good case study in transitioning to a digital economy. The nation rapidly implemented capitalistic reforms and transformed itself into a technology-centric economy in preparation for a massive tech disruption. Internet access was declared a right in 2000, and the country’s classrooms were outfitted for a digital economy, with coding as a core educational requirement starting at kindergarten. Internet broadband speeds in Estonia are among the fastest in the world. Accordingly, the World Bank now ranks Estonia as a high-income country.
Solution 2: Address Increased Rate of Change With More Nimble Education Systems
Education and training are currently not set for the speed of change in the modern economy. Schools are still based on a one-time education model, with school providing the foundation for a single lifelong career. With content becoming obsolete faster and rapidly escalating costs, this system may be unsustainable in the future. To help workers more smoothly transition from one job into another, for example, we need to make education a more nimble, lifelong endeavor.
Primary and university education may still have a role in training foundational thinking and general education, but it will be necessary to curtail rising price of tuition and increase accessibility. Massive open online courses (MooCs) and open-enrollment platforms are early demonstrations of what the future of general education may look like: cheap, effective, and flexible.
Georgia Tech’s online Engineering Master’s program (a fraction of the cost of residential tuition) is an early example in making university education more broadly available. Similarly, nanodegrees or microcredentials provided by online education platforms such as Udacity and Coursera can be used for mid-career adjustments at low cost. AI itself may be deployed to supplement the learning process, with applications such as AI-enhanced tutorials or personalized content recommendations backed by machine learning. Recent developments in neuroscience research could optimize this experience by perfectly tailoring content and delivery to the learner’s brain to maximize retention.
Finally, companies looking for more customized skills may take a larger role in education, providing on-the-job training for specific capabilities. One potential model involves partnering with community colleges to create apprenticeship-style learning, where students work part-time in parallel with their education. Siemens has pioneered such a model in four states and is developing a playbook for other companies to do the same.
Solution 3: Enhance Social Safety Nets to Smooth Automation Impacts
If predicted job losses to automation come to fruition, modernizing existing social safety nets will increasingly become a priority. While the issue of safety nets can become quickly politicized, it is worth noting that each prior technological revolution has come with corresponding changes to the social contract (see below).
The evolving social contract (U.S. examples)
– 1842 | Right to strike
– 1924 | Abolish child labor
– 1935 | Right to unionize
– 1938 | 40-hour work week
– 1962, 1974 | Trade adjustment assistance
– 1964 | Pay discrimination prohibited
– 1970 | Health and safety laws
– 21st century | AI and automation adjustment assistance?
Figure 2. Labor laws have historically adjusted as technology and society progressed
Solutions like universal basic income (no-strings-attached monthly payout to all citizens) are appealing in concept, but somewhat difficult to implement as a first measure in countries such as the US or Japan that already have high debt. Additionally, universal basic income may create dis-incentives to stay in the labor force. A similar cautionary tale in program design was the Trade Adjustment Assistance (TAA), which was designed to protect industries and workers from import competition shocks from globalization, but is viewed as a missed opportunity due to insufficient coverage.
A near-term solution could come in the form of graduated wage insurance (compensation for those forced to take a lower-paying job), including health insurance subsidies to individuals directly impacted by automation, with incentives to return to the workforce quickly. Another topic to tackle is geographic mismatch between workers and jobs, which can be addressed by mobility assistance. Lastly, a training stipend can be issued to individuals as means to upskill.
Policymakers can intervene to reverse recent historical trends that have shifted incomes from labor to capital owners. The balance could be shifted back to labor by placing higher taxes on capital—an example is the recently proposed “robot tax” where the taxation would be on the work rather than the individual executing it. That is, if a self-driving car performs the task that formerly was done by a human, the rideshare company will still pay the tax as if a human was driving.
Other solutions may involve distribution of work. Some countries, such as France and Sweden, have experimented with redistributing working hours. The idea is to cap weekly hours, with the goal of having more people employed and work more evenly spread. So far these programs have had mixed results, with lower unemployment but high costs to taxpayers, but are potential models that can continue to be tested.
We cannot stop growth, nor should we. With the roles in response to this evolution shifting, so should the social contract between the stakeholders. Government will continue to play a critical role as a stabilizing “thumb” in the invisible hand of capitalism, regulating and cushioning against extreme volatility, particularly in labor markets.
However, we already see business leaders taking on some of the role traditionally played by government—thinking about measures to remedy risks of climate change or economic proposals to combat unemployment—in part because of greater agility in adapting to change. Cross-disciplinary collaboration and creative solutions from all parties will be critical in crafting the future economy.
Note: The full paper this article is based on is available here.
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