All of these recent discoveries are examples of landscape archaeology. They’re also examples of how artificial intelligence is helping scientists hunt for new archaeological digs on a scale and at a pace unimaginable even a decade ago.

“AI in archaeology has been increasing substantially over the past few years,” said Dylan Davis, a PhD candidate in the Department of Anthropology at Penn State University. “One of the major uses of AI in archaeology is for the detection of new archaeological sites.”

The near-ubiquitous availability of satellite data and other types of aerial imagery for many parts of the world has been both a boon and a bane to archaeologists. They can cover far more ground, but the job of manually mowing their way across digitized landscapes is still time-consuming and laborious. Machine learning algorithms offer a way to parse through complex data far more quickly.

AI Gives Archaeologists a Bird’s Eye View

Davis developed an automated algorithm for identifying large earthen and shell mounds built by native populations long before Europeans arrived with far-off visions of skyscrapers and superhighways in their eyes. The sites still hidden in places like the South Carolina wilderness contain a wealth of information about how people lived, even what they ate, and the ways they interacted with the local environment and other cultures.

In this particular case, the imagery comes from LiDAR, which uses light pulses that can penetrate tree canopies to map forest floors. The team taught the computer the shape, size, and texture characteristics of the mounds so it could identify potential sites from the digital 3D datasets that it analyzed.

“The process resulted in several thousand possible features that my colleagues and I checked by hand,” Davis told Singularity Hub. “While not entirely automated, this saved the equivalent of years of manual labor that would have been required for analyzing the whole LiDAR image by hand.”

In Madagascar—where Davis is studying human settlement history across the world’s fourth largest island over a timescale of millennia—he developed a predictive algorithm to help locate archaeological sites using freely available satellite imagery. His team was able to survey and identify more than 70 new archaeological sites—and potentially hundreds more—across an area of more than 1,000 square kilometers during the course of about a year.

Machines Learning From the Past Prepare Us for the Future

One impetus behind the rapid identification of archaeological sites is that many are under threat from climate change, such as coastal erosion from sea level rise, or other human impacts. Meanwhile, traditional archaeological approaches are expensive and laborious—serious handicaps in a race against time.

“It is imperative to record as many archaeological sites as we can in a short period of time. That is why AI and machine learning are useful for my research,” Davis said.

Studying the rise and fall of past civilizations can also teach modern humans a thing or two about how to grapple with these current challenges.

Researchers at the Institut Català d’Arqueologia Clàssica (ICAC) turned to machine-learning algorithms to reconstruct more than 20,000 kilometers of paleo-rivers along the Indus Valley civilization of what is now part of modern Pakistan and India. Such AI-powered mapping techniques wouldn’t be possible using satellite images alone.

That effort helped locate many previously unknown archaeological sites and unlocked new insights into those Bronze Age cultures. However, the analytics can also assist governments with important water resource management today, according to Hèctor A. Orengo Romeu, co-director of the Landscape Archaeology Research Group at ICAC.

“Our analyses can contribute to the forecasts of the evolution of aquifers in the area and provide valuable information on aspects such as the variability of agricultural productivity or the influence of climate change on the expansion of the Thar desert, in addition to providing cultural management tools to the government,” he said.

Leveraging AI for Language and Lots More

While landscape archaeology is one major application of AI in archaeology, it’s far from the only one. In 2000, only about a half-dozen scientific papers referred to the use of AI, according to the Web of Science, reputedly the world’s largest global citation database. Last year, more than 65 papers were published concerning the use of machine intelligence technologies in archaeology, with a significant uptick beginning in 2015.

AI methods, for instance, are being used to understand the chemical makeup of artifacts like pottery and ceramics, according to Davis. “This can help identify where these materials were made and how far they were transported. It can also help us to understand the extent of past trading networks.”

Linguistic anthropologists have also used machine intelligence methods to trace the evolution of different languages, Davis said. “Using AI, we can learn when and where languages emerged around the world.”

In other cases, AI has helped reconstruct or decipher ancient texts. Last year, researchers at Google’s DeepMind used a deep neural network called PYTHIA to recreate missing inscriptions in ancient Greek from damaged surfaces of objects made of stone or ceramics.

Named after the Oracle at Delphi, PYTHIA “takes a sequence of damaged text as input, and is trained to predict character sequences comprising hypothesised restorations of ancient Greek inscriptions,” the researchers reported.

In a similar fashion, Chinese scientists applied a convolutional neural network (CNN) to untangle another ancient tongue once found on turtle shells and ox bones. The CNN managed to classify oracle bone morphology in order to piece together fragments of these divination objects, some with inscriptions that represent the earliest evidence of China’s recorded history.

“Differentiating the materials of oracle bones is one of the most basic steps for oracle bone morphology—we need to first make sure we don’t assemble pieces of ox bones with tortoise shells,” lead author of the study, associate professor Shanxiong Chen at China’s Southwest University, told Synced, an online tech publication in China.

AI Helps Archaeologists Get the Scoop…

And then there are applications of AI in archaeology that are simply … interesting. Just last month, researchers published a paper about a machine learning method trained to differentiate between human and canine paleofeces.

The algorithm, dubbed CoproID, compares the gut microbiome DNA found in the ancient material with DNA found in modern feces, enabling it to get the scoop on the origin of the poop.

Also known as coprolites, paleo-feces from humans and dogs are often found in the same archaeological sites. Scientists need to know which is which if they’re trying to understand something like past diets or disease.

“CoproID is the first line of identification in coprolite analysis to confirm that what we’re looking for is actually human, or a dog if we’re interested in dogs,” Maxime Borry, a bioinformatics PhD student at the Max Planck Institute for the Science of Human History, told Vice.

…But Machine Intelligence Is Just Another Tool

There is obviously quite a bit of work that can be automated through AI. But there’s no reason for archaeologists to hit the unemployment line any time soon. There are also plenty of instances where machines can’t yet match humans in identifying objects or patterns. At other times, it’s just faster doing the analysis yourself, Davis noted.

“For ‘big data’ tasks like detecting archaeological materials over a continental scale, AI is useful,” he said. “But for some tasks, it is sometimes more time-consuming to train an entire computer algorithm to complete a task that you can do on your own in an hour.”

Still, there’s no telling what the future will hold for studying the past using artificial intelligence.

“We have already started to see real improvements in the accuracy and reliability of these approaches, but there is a lot more to do,” Davis said. “Hopefully, we start to see these methods being directly applied to a variety of interesting questions around the world, as these methods can produce datasets that would have been impossible a few decades ago.”

Image Credit: James Wheeler from Pixabay

What do all these disparate efforts have in common? They’re some of the solutions that the world’s most promising artificial intelligence startups are pursuing.

Data research firm CB Insights released its much-anticipated fourth annual list of the top 100 AI startups earlier this month. The New York-based company has become one of the go-to sources for emerging technology trends, especially in the startup scene.

About 10 years ago, it developed its own algorithm to assess the health of private companies using publicly-available information and non-traditional signals (think social media sentiment, for example) thanks to more than $1 million in grants from the National Science Foundation.

It uses that algorithm-generated data from what it calls a company’s Mosaic score—pulling together information on market trends, money, and momentum—along with other details ranging from patent activity to the latest news analysis to identify the best of the best.

“Our final list of companies is a mix of startups at various stages of R&D and product commercialization,” said Deepashri Varadharajanis, a lead analyst at CB Insights, during a recent presentation on the most prominent trends among the 2020 AI 100 startups.

About 10 companies on the list are among the world’s most valuable AI startups. For instance, there’s San Francisco-based Faire, which has raised at least $266 million since it was founded just three years ago. The company offers a wholesale marketplace that uses machine learning to match local retailers with goods that are predicted to sell well in their specific location.

Funding for AI in Healthcare

Another startup valued at more than $1 billion, referred to as a unicorn in venture capital speak, is Butterfly Network, a company on the East Coast that has figured out a way to turn a smartphone phone into an ultrasound machine. Backed by $350 million in private investments, Butterfly Network uses AI to power the platform’s diagnostics. A more modestly funded San Francisco startup called Eko is doing something similar for stethoscopes.

In fact, there are more than a dozen AI healthcare startups on this year’s AI 100 list, representing the most companies of any industry on the list. In total, investors poured about $4 billion into AI healthcare startups last year, according to CB Insights, out of a record $26.6 billion raised by all private AI companies in 2019. Since 2014, more than 4,300 AI startups in 80 countries have raised about $83 billion.

One of the most intensive areas remains drug discovery, where companies unleash algorithms to screen potential drug candidates at an unprecedented speed and breadth that was impossible just a few years ago. It has led to the discovery of a new antibiotic to fight superbugs. There’s even a chance AI could help fight the coronavirus pandemic.

There are several AI drug discovery startups among the AI 100: San Francisco-based Atomwise claims its deep convolutional neural network, AtomNet, screens more than 100 million compounds each day. Cyclica is an AI drug discovery company in Toronto that just announced it would apply its platform to identify and develop novel cannabinoid-inspired drugs for neuropsychiatric conditions such as bipolar disorder and anxiety.

And then there’s OWKIN out of New York City, a startup that uses a type of machine learning called federated learning. Backed by Google, the company’s AI platform helps train algorithms without sharing the necessary patient data required to provide the sort of valuable insights researchers need for designing new drugs or even selecting the right populations for clinical trials.

Keeping Cyber Networks Healthy

Privacy and data security are the focus of a number of AI cybersecurity startups, as hackers attempt to leverage artificial intelligence to launch sophisticated attacks while also trying to fool the AI-powered systems rapidly coming online.

“I think this is an interesting field because it’s a bit of a cat and mouse game,” noted Varadharajanis. “As your cyber defenses get smarter, your cyber attacks get even smarter, and so it’s a constant game of who’s going to match the other in terms of tech capabilities.”

Few AI cybersecurity startups match Silicon Valley-based SentinelOne in terms of private capital. The company has raised more than $400 million, with a valuation of $1.1 billion following a $200 million Series E earlier this year. The company’s platform automates what’s called endpoint security, referring to laptops, phones, and other devices at the “end” of a centralized network.

Fellow AI 100 cybersecurity companies include Blue Hexagon, which protects the “edge” of the network against malware, and Abnormal Security, which stops targeted email attacks, both out of San Francisco. Just down the coast in Los Angeles is Obsidian Security, a startup offering cybersecurity for cloud services.

Deepfakes Get a Friendly Makeover

Deepfakes of videos and other types of AI-manipulated media where faces or voices are synthesized in order to fool viewers or listeners has been a different type of ongoing cybersecurity risk. However, some firms are swapping malicious intent for benign marketing and entertainment purposes.

Now anyone can be a supermodel thanks to Superpersonal, a London-based AI startup that has figured out a way to seamlessly swap a user’s face onto a fashionista modeling the latest threads on the catwalk. The most obvious use case is for shoppers to see how they will look in a particular outfit before taking the plunge on a plunging neckline.

Another British company called Synthesia helps users create videos where a talking head will deliver a customized speech or even talk in a different language. The startup’s claim to fame was releasing a campaign video for the NGO Malaria Must Die showing soccer star David Becham speak in nine different languages.

There’s also a Seattle-based company, Wellsaid Labs, which uses AI to produce voice-over narration where users can choose from a library of digital voices with human pitch, emphasis, and intonation. Because every narrator sounds just a little bit smarter with a British accent.

AI Helps Make Smart Cities Smarter

Speaking of smarter: A handful of AI 100 startups are helping create the smart city of the future, where a digital web of sensors, devices, and cloud-based analytics ensure that nobody is ever stuck in traffic again or without an umbrella at the wrong time. At least that’s the dream.

A couple of them are directly connected to Google subsidiary Sidewalk Labs, which focuses on tech solutions to improve urban design. A company called Replica was spun out just last year. It’s sort of SimCity for urban planning. The San Francisco startup uses location data from mobile phones to understand how people behave and travel throughout a typical day in the city. Those insights can then help city governments, for example, make better decisions about infrastructure development.

Denver-area startup AMP Robotics gets into the nitty gritty details of recycling by training robots on how to recycle trash, since humans have largely failed to do the job. The U.S. Environmental Protection Agency estimates that only about 30 percent of waste is recycled.

Some people might complain that weather forecasters don’t even do that well when trying to predict the weather. An Israeli AI startup, ClimaCell, claims it can forecast rain block by block. While the company taps the usual satellite and ground-based sources to create weather models, it has developed algorithms to analyze how precipitation and other conditions affect signals in cellular networks. By analyzing changes in microwave signals between cellular towers, the platform can predict the type and intensity of the precipitation down to street level.

And those are just some of the highlights of what some of the world’s most promising AI startups are doing.

“You have companies optimizing mining operations, warehouse logistics, insurance, workflows, and even working on bringing AI solutions to designing printed circuit boards,” Varadharajanis said. “So a lot of creative ways in which companies are applying AI to solve different issues in different industries.”

Image Credit: Butterfly Network

Of course, the computers and data centers that support AI’s complex algorithms are very much dependent on electricity. While that may seem pretty obvious, it may be surprising to learn that AI can be extremely power-hungry, especially when it comes to training the models that enable machines to recognize your face in a photo or for Alexa to understand a voice command.

The scale of the problem is difficult to measure, but there have been some attempts to put hard numbers on the environmental cost.

For instance, one paper published on the open-access repository arXiv claimed that the carbon emissions for training a basic natural language processing (NLP) model—algorithms that process and understand language-based data—are equal to the CO2 produced by the average American lifestyle over two years. A more robust model required the equivalent of about 17 years’ worth of emissions.

The authors noted that about a decade ago, NLP models could do the job on a regular commercial laptop. Today, much more sophisticated AI models use specialized hardware like graphics processing units, or GPUs, a chip technology popularized by Nvidia for gaming that also proved capable of supporting computing tasks for AI.

OpenAI, a nonprofit research organization co-founded by tech prophet and profiteer Elon Musk, said that the computing power “used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time” since 2012. That’s about the time that GPUs started making their way into AI computing systems.

Getting Smarter About AI Chip Design

While GPUs from Nvidia remain the gold standard in AI hardware today, a number of startups have emerged to challenge the company’s industry dominance. Many are building chipsets designed to work more like the human brain, an area that’s been dubbed neuromorphic computing.

One of the leading companies in this arena is Graphcore, a UK startup that has raised more than $450 million and boasts a valuation of $1.95 billion. The company’s version of the GPU is an IPU, which stands for intelligence processing unit.

To build a computer brain more akin to a human one, the big brains at Graphcore are bypassing the precise but time-consuming number-crunching typical of a conventional microprocessor with one that’s content to get by on less precise arithmetic.

The results are essentially the same, but IPUs get the job done much quicker. Graphcore claimed it was able to train the popular BERT NLP model in just 56 hours, while tripling throughput and reducing latency by 20 percent.

An article in Bloomberg compared the approach to the “human brain shifting from calculating the exact GPS coordinates of a restaurant to just remembering its name and neighborhood.”

Graphcore’s hardware architecture also features more built-in memory processing, boosting efficiency because there’s less need to send as much data back and forth between chips. That’s similar to an approach adopted by a team of researchers in Italy that recently published a paper about a new computing circuit.

The novel circuit uses a device called a memristor that can execute a mathematical function known as a regression in just one operation. The approach attempts to mimic the human brain by processing data directly within the memory.

Daniele Ielmini at Politecnico di Milano, co-author of the Science Advances paper, told Singularity Hub that the main advantage of in-memory computing is the lack of any data movement, which is the main bottleneck of conventional digital computers, as well as the parallel processing of data that enables the intimate interactions among various currents and voltages within the memory array.

Ielmini explained that in-memory computing can have a “tremendous impact on energy efficiency of AI, as it can accelerate very advanced tasks by physical computation within the memory circuit.” He added that such “radical ideas” in hardware design will be needed in order to make a quantum leap in energy efficiency and time.

It’s Not Just a Hardware Problem

The emphasis on designing more efficient chip architecture might suggest that AI’s power hunger is essentially a hardware problem. That’s not the case, Ielmini noted.

“We believe that significant progress could be made by similar breakthroughs at the algorithm and dataset levels,” he said.

He’s not the only one.

One of the key research areas at Qualcomm’s AI research lab is energy efficiency. Max Welling, vice president of Qualcomm Technology R&D division, has written about the need for more power-efficient algorithms. He has gone so far as to suggest that AI algorithms will be measured by the amount of intelligence they provide per joule.

One emerging area being studied, Welling wrote, is the use of Bayesian deep learning for deep neural networks.

It’s all pretty heady stuff and easily the subject of a PhD thesis. The main thing to understand in this context is that Bayesian deep learning is another attempt to mimic how the brain processes information by introducing random values into the neural network. A benefit of Bayesian deep learning is that it compresses and quantifies data in order to reduce the complexity of a neural network. In turn, that reduces the number of “steps” required to recognize a dog as a dog—and the energy required to get the right result.

A team at Oak Ridge National Laboratory has previously demonstrated another way to improve AI energy efficiency by converting deep learning neural networks into what’s called a spiking neural network. The researchers spiked their deep spiking neural network (DSNN) by introducing a stochastic process that adds random values like Bayesian deep learning.

The DSNN actually imitates the way neurons interact with synapses, which send signals between brain cells. Individual “spikes” in the network indicate where to perform computations, lowering energy consumption because it disregards unnecessary computations.

The system is being used by cancer researchers to scan millions of clinical reports to unearth insights on causes and treatments of the disease.

Helping battle cancer is only one of many rewards we may reap from artificial intelligence in the future, as long as the benefits of those algorithms outweigh the costs of using them.

“Making AI more energy-efficient is an overarching objective that spans the fields of algorithms, systems, architecture, circuits, and devices,” Ielmini said.

Image Credit: analogicus from Pixabay

Last month, scientists announced they had taken one more step toward that vision. A paper in the journal PLOS Pathogens described how they synthetically engineered mosquitoes to stop the spread of dengue fever, a viral tropical disease that sickens as many as 100 million people each year.

Now imagine genetically tweaking an invasive species of mosquito to save native Hawaiian birds from extinction, or transferring genes from one species of endangered chestnut tree to another to help the latter resist blight. Employing the same sort of genetic engineering used to make a plant-based burger bleed, scientists are beginning to explore the ways synthetic biology could help protect biodiversity and conserve species.

Synbio Meets Conservation

Synthetic biology, or synbio, employs the latest and greatest gene-editing tools, such as the “cut-and-paste” technology known as CRISPR-Cas9. Combined with new techniques to digitize and automate the design and modeling of various genetic elements, scientists can now engineer organisms to produce novel food ingredients or to rewire the switches that express genes that control certain functions.

In the case of those dengue-carrying mosquitoes, scientists genetically tweaked members of the Aedes aegypti species by transferring genes from the human immune system that create an antibody to suppress dengue fever into the blood-sucking insect. The antibody is activated and expressed once the female mosquito draws blood. In effect, the mosquito is “cured” of dengue fever before it can transmit the disease.

The next step would be to propagate the new genetic element to confer dengue immunity through a population. That’s where a gene drive comes in. Gene drive systems, which can be natural or synthetically engineered, skew inheritance of a certain genetic element so that it will spread more quickly through generations.

The idea is to bypass normal inheritance rules—that classic Darwinian concept that inheritance is driven by genetic variations that improve an organism’s ability to compete in a dog-eat-dog world—so that re-engineered traits become dominant.

In terms of conservation, synbio could potentially address several areas of concern, such as curbing invasive species, reducing pressures from wildlife trade, improving resistance to disease, and even bringing a species back from the brink of extinction.

Invasive Species

Biologists at the University of California San Diego (UCSD), who also led the team that wrote the PLOS Pathogens paper on mosquitoes, developed a novel gene drive system for manipulating genetic inheritance in Drosophila suzukii, a fruit fly with the common name spotted-wing drosophila.

This particular pest, native to Japan and first discovered in the US in 2008, injects its eggs into soft ripening fruit like berries. Current practices to defend against spotted-wing drosophila rely on either heavy insecticide use or early harvesting. It’s estimated the pest costs the US economy as much as $700 million each year in losses.

The engineered gene drive from UCSD, dubbed Medea after the character in Greek mythology that killed her offspring, uses a synthetic “toxin” and a corresponding “antidote” function to achieve 100 percent inheritance bias in less than 20 generations.

This genetic Trojan Horse could then be used to spread elements that confer susceptibility to certain environmental factors, such as triggering the death of the modified fruit flies at a certain temperature.

UC San Diego associate professor Omar Akbari told Singularity Hub that his team is “getting close to field testing some of our technologies. The furthest along for our group would be the use of [precision guided sterile insect technique] to control wild populations of D. Suzuki.”

Wildlife Trade

A number of companies are turning to synbio to create ingredients where the natural product is expensive, rare, or threatened. Take the well-known example of vanilla. Most products on the market use a synthetic version of vanilla’s main ingredient, vanillin, made from petrochemicals.

Swiss company Evolva has developed a genetically modified yeast to produce vanillin in a manner similar to brewing beer. Modern Meadow also uses DNA editing tools to engineer specialized collagen-producing yeast cells for making leather products.

In a case more directly related to wildlife conservation, Singaporean scientists engineered a synthetic replacement for horseshoe crab blood cells, which have been used in biomedical applications for decades. All four species of horseshoe crabs are considered imperiled by the International Union for Conservation of Nature (IUCN).

However, while a replacement product for horseshoe crab blood has been commercially available for more than 15 years, it has yet to be broadly adopted for various reasons. That’s finally changing, as new studies have confirmed that available synthetics are just as reliable as horseshoe crab blood for detecting endotoxins in biomedical manufacturing.

Disease Resistance

The long-lived American chestnut was once one of the dominant tree species of forests in the eastern US. A blight from Asia introduced in the late 1800s has all but wiped them out. Efforts to breed American chestnuts with disease-resistant chestnut trees in China have had limited success, as it’s not easy to propagate the desired traits from several genes through succeeding generations.

A project led by the College of Environmental Science and Forestry in Syracuse, New York is using synbio to produce a blight-resistant American chestnut without even harming the fungus.

The researchers have copied a single gene from wheat and transferred it into American chestnuts. The gene produces an enzyme called oxalate oxidase that doesn’t kill the fungus. Instead, it breaks down the fungus toxin that attacks the tree’s tissue properties.

The bonus is that the fungus itself is left untouched, so the blight remains dormant and doesn’t evolve resistance over time.

Species Preservation

While bringing the dead back to life is one trick that will likely elude scientists in our lifetime, synbio researchers have been actively working to resurrect the woolly mammoth and other extinct species such as the passenger pigeon, which disappeared for good more than a century ago.

These projects aren’t strictly creating pure examples of these long-gone species. Rather, scientists are inserting sections of ancient DNA code into modern relatives. In the case of the woolly mammoth, researchers are attempting to create a mammoth-elephant hybrid using the Asian elephant.

Proponents of this sort of resurrection science say it’s less about trying to revive extinct species than about saving those that are currently at risk of disappearing. The Asian elephant (Elephas maximus) is on the IUCN Red List of Threatened Species.

A team led by George Church out of Harvard University hopes that by transferring genes in the mammoth genome to the Asian elephant it will be able to survive in the Arctic; relevant genes might include those that code for extra fat and dense hair. That would extend the animal’s range into regions that are already changing due to a warming climate.

Should We Do It?

Like geoengineering—manipulating the environment to stave off the effects of climate change—bioengineering has its critics and detractors. Some react viscerally to the idea of altering natural systems in any way.

One of the main arguments revolves around the concern that introducing a genetically modified species could have unintended consequences. While no one expects a Jurassic Park scenario where genetically enhanced monsters chase Jeff Goldblum through the jungle, there is a chance that genetically tweaked traits could jump species or otherwise go off script.

Kent Redford believes fostering a conversation about the possible advantages and disadvantages of the role of synbio in conservation is important regardless of where one stands on the divide.

“My mission is to make sure that the conservation community knows about these technologies and has taken a considered and informed opinion on them, and tried to influence [these] technologies for the good of biodiversity—to minimize harm and to increase positive outcomes,” he told Singularity Hub during a phone interview.

A conservation expert who has served at the The Nature Conservancy and Wildlife Conservation Society, Redford is the chair of an IUCN task force on synthetic biology and biodiversity conservation. He was the lead editor on an assessment report, Genetic Frontiers for Conservation, which will be presented this summer at the IUCN World Conservation Congress in France.

The opinion of the IUCN matters. Its 1,300 member organizations include governments, non-governmental organizations, business associations, and scientific and academic institutions.

Redford declined to speculate as to what sort of recommendations may come out of the IUCN meeting. He did note that the intersection of synbio and conservation remains on the periphery for many in the conservation community.

“Most of my colleagues don’t see why they should be paying much attention to this,” he said. Some of those who are aware of these emerging technologies consider them to be relevant tools to help solve some of the intractable problems in conservation. Others believe these genetic techniques have the “potential to completely ruin the natural world and the lives of poor people.”

Akbari agreed that the biggest challenge for synbio in conservation isn’t the technology but securing regulatory approvals and public support. “I think we need time,” he said. “As more technologies are developed and tested with positive outcomes—I believe the resistance will lessen.”

While the scientific community debates the potential and the pitfalls of synbio, biodiversity will continue to decline.

A report last year by the United Nations’ Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services issued a number of disturbing statistics. For example, the average abundance of native species in most major land-based habitats has fallen by at least 20 percent, mainly since 1900. And nearly 10 percent of all domesticated breeds of mammals humans have used for food and agriculture throughout history were extinct by 2016, with at least 1,000 more breeds still threatened.

“I think the natural world is in serious trouble,” Redford said. Whether synbio can be part of the answer to that problem remains a big question.

Image Credit: Image by RayNight from Pixabay

Electric Airplanes Ready for Take Off?

It’s been nearly 50 years since the first battery-powered aircraft, the Militky MB-E1, lifted off using a standard Bosch 10-kilowatt electric motor. There have been plenty of one-off stunts and experimental airplanes showcasing the potential of electrified aircraft in the ensuing decades, from the German e-Genius to Boeing’s hybrid E-Fan X.

But 2019 could prove to be the year that aviation historians mark as the first step toward commercial electric aircraft. Earlier this month, an electric motor developed by an Australian startup headquartered in Seattle, magniX, powered a retrofitted de Havilland Canada DHC-2 Beaver around the harbor outside of Vancouver.

Six months before that, Los Angeles-based startup Ampaire tested its electric propulsion and lightweight battery system aboard a reconfigured Cessna 337 Skymaster in the Californian summer skies. A battery-powered electric motor replaced one of the aircraft’s original two engines, creating what the company calls a parallel hybrid where the internal combustion engine and electric motor work in concert as the plane flies.

“What we’ve created is an electrification package for existing planes,” Cory Combs, co-founder and chief technology officer for Ampaire, told Singularity Hub.

Ampaire isn’t building its own planes—at least not yet—but instead is developing the technology and system integration required to retrofit existing aircraft as electric-fuel hybrids. “Think of us kind of like a flying Chevy Volt: if the front wheels were powered all electric and the back wheels were powered gas and you could shut down either one,” he explained.

It’s been more than 20 years since Toyota introduced the hybrid Prius and more than 10 years since Tesla made all-electric vehicles cool with the Roadster. The advent of electric airplanes has lagged due to three things, according to Combs: weight, weight, and weight.

“It’s all about the weight,” he said. “An electric car can be heavier than a typical car, and that’s fine because the ground’s holding you up. You don’t really lose much efficiency.” That’s thanks to regenerative braking that helps the vehicle recover lost energy. An airplane has no such mechanism, so it must cut weight wherever possible, relying on battery technology developed for the electric vehicle industry.

“When batteries are light enough, we will go all-electric, and planes of increasing size and speed and range will go all-electric over time,” Combs predicted.

Reducing greenhouse gas emissions is one of the key drivers behind the push for electric airplanes from companies like Ampaire and magniX, as well as other competitors such as Zunum Aero (currently in financial straits), Wright Electric, Faradair, and Eviation, among others.

While air travel currently only accounts for about 2.4 percent of global carbon dioxide emissions, the United Nations’ International Civil Aviation Organization predicts aviation could represent 25 percent of the global carbon budget by 2050 based on the need to keep global temperature rise to within 1.5 degrees Celsius above pre-industrial levels. In addition, almost all small planes today still burn leaded fuel, Combs noted, and few if any aircraft carry emissions control systems like a catalytic converter on a car.

“By going electric and hybrid-electric, we’re reducing fuel burn, and we’re starting to get that lead out,” said Combs, estimating that Ampaire’s aircraft will reduce emissions by 50 to 70 percent to start.

The Need for Speed

Electric airplanes will be restricted to doing short hops for regional air travel, with a range of about 100 miles for most of the commercial models currently being tested. It’s likely many decades will pass before commercial e-planes with significant passenger capacity will be able to cross continents or oceans.

Supersonic jets like the retired Concorde, however, once made the leap from New York to Paris in about 3.5 hours. Supersonic jets fly faster than the speed of sound, referred to as Mach 1, which is about 760 miles per hour (though it varies based on air temperature and other factors).

There are dozens of military and experimental aircraft—not to mention weapons, some of which can travel at hypersonic speeds of Mach 5 and beyond—capable of traveling faster than the speed of sound.

However, commercial supersonic travel ended with the last flight of the Concorde on October 24, 2003. While economic and safety factors certainly played a role in retiring the innovative aircraft, inefficient fuel use and excessive noise pollution from the sonic boom created by traveling faster than the speed of sound also led to its demise.

Yet it’s possible we’ll see the return of supersonic commercial travel in years rather than decades, as engineers solve the sonic boom problem and improve fuel efficiency.

Major aerospace companies Lockheed Martin and Boeing are both developing new supersonic jets. The former has a nearly $250 million contract with NASA to prove a new design that will turn the boom into a thump. The space agency just announced that final assembly of the X-59 Quiet SuperSonic Technology (QueSST) was under way.

A number of startups are also jumping into the supersonic cockpit, including Aerion Corporation, Boom, Spike Aerospace, and Hermeus. Only Denver-based Boom appears to be working on a supersonic airplane, dubbed Overture, that is not targeted for the private jet market.

The startup, which has raised about $150 million in funding, says Overture will initially carry 55 passengers at speeds of Mach 2.2. Among the technological advancements are thermal-tolerant composite materials that enable better aerodynamics, longer airframe life with minimal maintenance, and greater fuel efficiency.

Boom also uses advanced computer simulations to test thousands of design iterations rather than costly and time-consuming physical models and wind tunnel testing.

“The result of newer technologies is a much higher level of aerodynamic optimization and efficiency. Overture will be faster, quieter, more affordable to build, and more fuel efficient than the engineers of the 1960s could have imagined,” the company wrote in a recent blog post.

The Overture could enter service by the mid-2020s, according to Boom.

The Sky’s Not the Limit

Meanwhile, companies like Blue Origin, Virgin Galactic, and SpaceX may one day take passengers to the edge of space en route from New York to Paris in less than an hour.

Space tourism has always been on the radar of these giants of the New Space industry, but city-to-city rocket travel could prove to be even more lucrative in the long term.

SpaceX CEO Elon Musk first floated the idea a couple of years ago, saying the company’s reusable rocket would transport passengers from floating landing pads near major cities. Earlier this year, Swiss investment bank UBS predicted long-distance suborbital travel could turn into a $20 billion business.

UBS didn’t provide an exact timeline for when suborbital rocket travel will take off, but it’s safe to say that it probably won’t happen in the next decade. The technology will need to go through a long and stringent safety process before regulatory agencies will allow companies to strap hundreds of people onto a highly explosive rocket.

The infrastructure to support city-to-city rocket travel will also take years to build, though the boom in spaceports that has accompanied the rapidly growing rocket launch industry represents the first step toward that goal.

The future of aviation isn’t reliant on just one technology or breakthrough. It’s a process that builds on the successes and failures of the past, whether it’s building lighter batteries to power electric airplanes or advances in engineering than enable aircraft to travel faster than the speed of sound more efficiently.

The future of aviation is greener, faster, and higher—and coming to a galaxy near you.

Image Credit: Boom

At the heart of the discussion with Singularity Hub is the idea that artificial intelligence will be the engine that drives breakthroughs in how we approach healthcare and healthy aging—a concept with little traction even just five years ago.

“At that time, it was considered too futuristic that artificial intelligence and data science … might be more accurate compared to any hypothesis of human doctors,” said Kaminskiy, co-founder and managing partner at Deep Knowledge Ventures, an investment firm that is betting big on AI and longevity.

How times have changed. Artificial intelligence in healthcare is attracting more investments and deals than just about any sector of the economy, according to data research firm CB Insights. In the most recent third quarter, AI healthcare startups raised nearly $1.6 billion, buoyed by a $550 million mega-round from London-based Babylon Health, which uses AI to collect data from patients, analyze the information, find comparable matches, then make recommendations.

Even without the big bump from Babylon Health, AI healthcare startups raised more than $1 billion last quarter, including two companies focused on longevity therapeutics: Juvenescence and Insilico Medicine.

The latter has risen to prominence for its novel use of reinforcement learning and general adversarial networks (GANs) to accelerate the drug discovery process. Insilico Medicine recently published a seminal paper that demonstrated how such an AI system could generate a drug candidate in just 46 days. Co-founder and CEO Alex Zhavoronkov said he believes there is no greater goal in healthcare today—or, really, any venture—than extending the healthy years of the human lifespan.

“I don’t think that there is anything more important than that,” he told Singularity Hub, explaining that an unhealthy society is detrimental to a healthy economy. “I think that it’s very, very important to extend healthy, productive lifespan just to fix the economy.”

An Aging Crisis

The surge of interest in longevity is coming at a time when life expectancy in the US is actually dropping, despite the fact that we spend more money on healthcare than any other nation.

A new paper in the Journal of the American Medical Association found that after six decades of gains, life expectancy for Americans has decreased since 2014, particularly among young and middle-aged adults. While some of the causes are societal, such as drug overdoses and suicide, others are health-related.

While average life expectancy in the US is 78, Kaminskiy noted that healthy life expectancy is about ten years less.

To Zhavoronkov’s point about the economy (a topic of great interest to Kaminskiy as well), the US spent $1.1 trillion on chronic diseases in 2016, according to a report from the Milken Institute, with diabetes, cardiovascular conditions, and Alzheimer’s among the most costly expenses to the healthcare system. When the indirect costs of lost economic productivity are included, the total price tag of chronic diseases in the US is $3.7 trillion, nearly 20 percent of GDP.

“So this is the major negative feedback on the national economy and creating a lot of negative social [and] financial issues,” Kaminskiy said.

Investing in Longevity

That has convinced Kaminskiy that an economy focused on extending healthy human lifespans—including the financial instruments and institutions required to support a long-lived population—is the best way forward.

He has co-authored a book on the topic with Margaretta Colangelo, another managing partner at Deep Knowledge Ventures, which has launched a specialized investment fund, Longevity.Capital, focused on the longevity industry. Kaminskiy estimates that there are now about 20 such investment funds dedicated to funding life extension companies.

In November at the inaugural AI for Longevity Summit in London, he and his collaborators also introduced the Longevity AI Consortium, an academic-industry initiative at King’s College London. Eventually, the research center will include an AI Longevity Accelerator program to serve as a bridge between startups and UK investors.

Deep Knowledge Ventures has committed about £7 million ($9 million) over the next three years to the accelerator program, as well as establishing similar consortiums in other regions of the world, according to Franco Cortese, a partner at Longevity.Capital and director of the Aging Analytics Agency, which has produced a series of reports on longevity.

A Cure for What Ages You

One of the most recent is an overview of Biomarkers for Longevity. A biomarker, in the case of longevity, is a measurable component of health that can indicate a disease state or a more general decline in health associated with aging. Examples range from something as simple as BMI as an indicator of obesity, which is associated with a number of chronic diseases, to sophisticated measurements of telomeres, the protective ends of chromosomes that shorten as we age.

While some researchers are working on moonshot therapies to reverse or slow aging—with a few even arguing we could expand human life on the order of centuries—Kaminskiy said he believes understanding biomarkers of aging could make more radical interventions unnecessary.

In this vision of healthcare, people would be able to monitor their health 24-7, with sensors attuned to various biomarkers that could indicate the onset of everything from the flu to diabetes. AI would be instrumental in not just ingesting the billions of data points required to develop such a system, but also what therapies, treatments, or micro-doses of a drug or supplement would be required to maintain homeostasis.

“Consider it like Tesla with many, many detectors, analyzing the behavior of the car in real time, and a cloud computing system monitoring those signals in real time with high frequency,” Kaminskiy explained. “So the same shall be applied for humans.”

And only sophisticated algorithms, Kaminskiy argued, can make longevity healthcare work on a mass scale but at the individual level. Precision medicine becomes preventive medicine. Healthcare truly becomes a system to support health rather than a way to fight disease.

Image Credit: Photo by h heyerlein on Unsplash

You’re probably familiar with the oft-quoted statistics from the Food and Agriculture Organization (FAO) of the United Nations by now: Globally, about one-third of food is lost or wasted each year from the farm to the refrigerator, representing about 1.3 billion tons. The economic price tag is estimated at nearly $1 trillion annually.

The refrain from the FAO goes even further: If we could reverse this trend, we would have enough food to feed the world’s undernourished population, as well as help meet the nutritional needs of a planet estimated to reach nearly 10 billion people by 2050.

Technology has long been helping to hack world hunger. These days most conversations about tech’s impact on any sector of the economy inevitably involves artificial intelligence—sophisticated software that allows machines to make decisions and even predictions in ways similar to humans. Food waste tech is no different.

A report from the Ellen MacArthur Foundation and Google estimates that technologies employing AI to “design out food waste” could help generate up to $127 billion a year by 2030. These technologies range from machine vision that can spot when fruit is ready to be picked to algorithms that forecast demand in order to ensure retailers don’t overstock certain foods.

Weighing the Value of Food Waste

One London-based startup that has been generating headlines by reducing food waste is Winnow Solutions. The company took in $20 million in October from equity investments and loans to scale its AI platform, Winnow Vision, which identifies and weighs food waste for commercial kitchens. It then automatically assigns a dollar value to each scraped plate of fettuccine Alfredo or bowl of carrots dumped into its smart waste bin.

Winnow Vision can identify waste foods correctly more than 80 percent of the time and is improving as it learns, Peter Krebs, managing director of Winnow in North America, told Singularity Hub by email. That’s better than the busy kitchen staff, which correctly categorize food waste between 70 and 75 percent of the time.

More complexity comes when food has significantly changed appearance, he added, such as when food has been burnt.

It’s then up to people to turn those insights into action. “At Winnow we use the old adage: What gets measured gets managed,” Krebs said. “Once chefs have the insight into what is being wasted on a daily basis, kitchens can reduce food waste quickly. An average kitchen that uses Winnow reduces food waste by over 50 percent in the first year.”

The startup has partnered with Swedish retailer IKEA, which has installed Winnow Vision in all 23 of its stores throughout the UK and Ireland. IKEA says it has cut food waste in half at those outlets and saved 1.2 million meals in 2018. Berg says his company hopes to take a $1 billion bite out of food waste in commercial kitchen waste per year by 2025.

Dynamic Pricing and Inventory Tracking

On the retail side, an Israeli startup founded three years ago called Wasteless has developed an algorithm for dynamically pricing perishable products. The software tracks an item’s price in real time and adjusts the cost based on its expiration date, so that products with a shorter shelf life are automatically discounted. The software also plugs into a store’s inventory management and other parts of the operation.

In one 12-week pilot test, a store retailer reduced food waste by 39 percent while boosting revenues by 110 percent and still maintaining a positive net margin.

Other AI startups are focused on the inventory management side of the retail business. Brooklyn-based Crisp, for instance, just emerged from stealth mode with a $14 million payday in September. The company claims its algorithms ingest every kind of variable that affects business, from production shortages to holiday traffic, in order to deliver automated forecasts so stores can order the right amount of inventory.

Another AI startup operating a similar business model that took in a $6.1 million chunk of funding in September is San Francisco-based Afresh Technologies. Its algorithms also factor in the intangibles like weather and peak freshness of produce to help predict demand and manage inventory. It claims its technology has reduced in-store food waste by as much as 45 percent.

Keeping an Eye on Agriculture

Meanwhile, back on the farm, companies like Centaur Analytics out of California are using advanced sensor networks and artificial intelligence for crop analytics. The five-year-old startup uses military-grade sensors to collect data on a variety of conditions on post-harvest storage facilities like grain bins.

The data are crunched in the cloud by algorithms that have learned to detect what conditions can lead to early spoilage or deterioration of the product—or even if there’s a threat of a pest infestation. The company can reportedly go one step further than other Internet of Things platforms by predicting the quality of the grain into the future based on past and current conditions.

It’s not just startups that are coming up with AI-centric solutions to food waste and spoilage on the farm. Microsoft has been touting how its AI applications like Azure Cognitive Services helped ensure a dairy farm in Australia produces high-quality milk safely and efficiently.

A sophisticated temperature-monitoring system that uses sensors and Microsoft’s AI technologies, for instance, can detect temperature fluctuations in storage tanks and trucks. The machine then sends an alert to prevent milk from spoiling if a serious problem occurs, such as an electrical failure to the refrigeration system.

Bringing More Money to the Table

The nonprofit ReFed, with a mission to reduce food waste, has estimated it would require $18 billion in funding over a 10-year period to reduce food waste by 20 percent in the US, which spends $218 billion a year growing, processing, transporting, and disposing of food that is never eaten.

In a 2018 report, the organization said about $125 million was invested in food waste startups but estimated that the private sector would need to account for about a third of the total $18 billion in investments to reach the 20 percent reduction goal.

The payoff is not just economic—though ReFed claims its math adds up to $100 billion in business profit and consumer savings—as reducing food waste by 20 percent over the next decade will save 16 trillion gallons of water and reduce greenhouse gas emissions by 180 million tons.

The appetite for reducing food waste is growing. It’s just a matter of whether we can stomach the initial cost in developing the technology to make it happen.

Image Credit: Image by Manfred Richter from Pixabay

The Age of Animal Protein—with its environmental, ethical, and health baggage—seems to be giving way to the Age of Alternative Protein.

There are companies going well beyond plant-based burgers or creamy milks made from nuts. Startups are trying to coax animal cells into steaks and bypass the slaughterhouse altogether or tweak the genetics of microbes to produce proteins found in milk and eggs.

If the idea of chowing down on a lab-grown burger someday still sounds a little fantastical, then the following efforts to reimagine and redesign animal-free protein will seem out of this world.

Microbes as Meat and Machine

Let’s start small: Extremophiles are terran-based organisms that can exist in the most hostile conditions on Earth, like sunless caves or freezing oceans, but could be equally comfortable in the cold vacuum of space. In other words, they’re tough.

A particularly hardy species of microbes that inhabit the volcanic springs of Yellowstone National Park may also turn out to be edible and highly nutritious. A startup spun out of the University of Chicago called Sustainable Bioproducts is attempting to create single-cell protein using extremophile bacteria, leveraging years of fundamental research on the microorganism and a proprietary fermentation technology.

Sustainable Bioproducts reproduces the microbes in the lab, feeding them a starchy solution that produces a protein with all nine essential amino acids that humans need in their diet. The resulting product is truly flexitarian in that it can be made savory or sweet, meat-like or dairy-like.

The tech drew investments from some of the world’s biggest food companies, like Archer Daniels Midland and Danone, along with the save-the-world investment fund known as Breakthrough Energy Ventures, which is backed by $1 billion and a who’s who of philanthropist billionaires such as Bill Gates, Richard Branson, and Jeff Bezos.

A number of other startups are turning microbes into protein by feeding them everything from methane and carbon dioxide to wastewater from food production. For example, Calysta Energy out of Silicon Valley employs microbes that metabolize methane, while Hong Kong-based iCell Sustainable Nutrition feeds its protein-rich bugs wastewater from sugar production. Many of these companies are targeting animal feed markets like aquaculture.

In other cases, the microbes are not the meat of the product but a means to a really alternative protein that employs the same fermentation science behind brewing beer—but using genetically modified yeast or bacteria. Founded just this year and already backed by more than $117 million in capital, Motif FoodWorks isn’t stopping at replicating the usual sorts of dairy and egg proteins pursued by companies like Perfect Day or Clara Foods.

Instead, the company—spun out of Ginkgo Bioworks, a startup unicorn that engineers microbes for food ingredients, flavors, and fragrances—plans to serve as an R&D lab for companies seeking to create even more exotic proteins, from platypus milk to sturgeon eggs.

It’s not just about brewing weird food for the next Silicon Valley party of longevity-seeking billionaires: Platypus milk, for instance, has shown antibacterial properties that could help fight the rise of antibiotic-resistant superbugs.

Lab-Grown Bugs for Breakfast?

Speaking of bugs: Insects have been touted as a more sustainable and nutritious protein than beef for years now. That’s led to cricket protein bars and cricket protein flour. But, for many, there’s still the ick factor.

So what about lab-grown insect protein?

Scientists at Tufts, led by Natalie Rubio, recently investigated the feasibility of growing insect muscle cells on a mushroom-based scaffolding, which is the structural support for cell attachment and tissue growth. Not only do the cells grow like crazy, but it’s cheaper and easier to culture insect tissues versus mammalian ones.

The research grew out of Rubio’s work on using insect cells as bioactuators for soft robotic muscle applications. Some of the same characteristics that make insect-derived biobots superior to those constructed from mammalian tissues or cells, such as tolerance to a wide range of environmental conditions, make lab-grown insect protein an attractive alternative, according to Rubio’s team.

“The robustness of established techniques for culturing insect cells … make them ideal candidates for incorporation into cultured meat and other innovative food products,” they concluded.

3D Printing Plant-Based Beef

Even more straightforward plant-based food, like that coming from companies such as Beyond Meat and Impossible Foods, may soon get a new technological twist: 3D printing. A few startups have emerged in the last year or so that are developing 3D printing technologies for improving the texture, flavor, and appearance of plant-based meats.

Israeli startup Redefine Meat out of Tel Aviv, for instance, just came on the food tech scene last year but promises to begin selling its 3D printers directly to supermarkets and food manufacturers by 2020.

“We are developing a new 3D printing process that controls texturization and product placement, and a new printer to support it,” chief business officer Adam Lahav told Singularity Hub by email. “Our printer is a semi-industrial printer designed to produce large amounts of meat in a short space of time.”

Lahav explained that existing food technology is very good at mimicking individual aspects of meat, such as good flavor, texture, or appearance, but scoring the trifecta is extremely challenging.

“3D printing enables a new level of control on texture, flavor, appearance, and their combination, resulting in products that ‘behave’ much more like meat in your mouth,” he said.

Barcelona-based startup Novameat, also founded last year, is reconfiguring 3D printing technology that its founder, Giuseppe Sconti, worked on for bioprinting human tissue.

“I realized that if 3D printers could imitate human tissue that well, then I could generate a meat substitute that had the same texture as animal tissue,” Sconti said in a recent interview.

Making the Switch to Alt Protein

Obviously, all of these innovations require money, and cash is certainly flowing to many of the startups that are attempting to commercialize these alternative proteins.

The venture capital firm itself, AgFunder, announced a new Alt Protein Fund with a tentative target size of $20 million. The fund will invest in alternative animal proteins, including plant-based products, cellular agriculture, and other technologies required to support the emerging industry, according to Rob Leclerc, a founding partner at AgFunder.

In the announcement, Leclerc noted that new technologies are surpassing the animal-based ones that first helped build human civilization.

“We anticipate that many consumers will shift to animal-free food products if those products begin to meet or exceed their animal-based alternatives on key areas like cost, taste, functionality, convenience, and health,” he wrote. “Factor in a more conscious consumer concerned about the impact of animal agriculture on our environment and sustainability, and that switch may happen even faster.”

Analysts at Barclays seem to believe the shift to alternative protein will happen quickly. They project the alt protein market will be worth $140 billion within the next decade, capturing 10 percent of the $1.4 trillion meat industry.

The big brains at global management consulting firm A.T. Kearney are even more bullish, writing that one-third of the global meat supply will be provided by alternative meat technologies within 10 years, seriously disrupting traditional agriculture and creating a new industry around alternative meat proteins.

“At the current stage, it is hard to tell how fast the disruption will come about,” they wrote. “However, one can already observe how wholesalers, retailers, and consumer goods companies are trying to find a lucrative starting position by purchasing exclusive distribution rights or through acquisition of startups.”

Apparently, in such a dog-eat-dog world, the vegetarians will end up winning after all.

Image Credit: Stijn te Strake / Unsplash

But sneakers and the red carpet? The definitively low-tech fashion industry would seem to be one of the last to turn over its creative direction to data scientists and machine learning algorithms.

However, big brands, e-commerce giants, and numerous startups are betting that AI can ingest data and spit out Chanel. Maybe it’s not surprising, given that fashion is partly about buzz and trends—and there’s nothing more buzzy and trendy in the world of tech today than AI.

In its annual survey of the $3 trillion fashion industry, consulting firm McKinsey predicted that while AI didn’t hit a “critical mass” in 2018, it would increasingly influence the business of everything from design to manufacturing.

“Fashion as an industry really has been so slow to understand its potential roles interwoven with technology. And, to be perfectly honest, the technology doesn’t take fashion seriously.” This comment comes from Zowie Broach, head of fashion at London’s Royal College of Arts, who as a self-described “old fashioned” designer has embraced the disruptive nature of technology—with some caveats.

Co-founder in the late 1990s of the avant-garde fashion label Boudicca, Broach has always seen tech as a tool for designers, even setting up a website for the company circa 1998, way before an online presence became, well, fashionable.

Broach told Singularity Hub that while she is generally optimistic about the future of technology in fashion—the designer has avidly been consuming old sci-fi novels over the last few years—there are still a lot of difficult questions to answer about the interface of algorithms, art, and apparel.

For instance, can AI do what the great designers of the past have done? Fashion was “about designing, it was about a narrative, it was about meaning, it was about expression,” according to Broach.

AI that designs products based on data gleaned from human behavior can potentially tap into the Pavlovian response in consumers in order to make money, Broach noted. But is that channeling creativity, or just digitally dabbling in basic human brain chemistry?

She is concerned about people retaining control of the process, whether we’re talking about their data or their designs. But being empowered with the insights machines could provide into, for example, the geographical nuances of fashion between Dubai, Moscow, and Toronto is thrilling.

“What is it that we want the future to be from a fashion, an identity, and design perspective?” she asked.

Off on the Right Foot

Silicon Valley and some of the biggest brands in the industry offer a few answers about where AI and fashion are headed (though not at the sort of depths that address Broach’s broader questions of aesthetics and ethics).

Take what is arguably the biggest brand in fashion, at least by market cap but probably not by the measure of appearances on Oscar night: Nike. The $100 billion shoe company just gobbled up an AI startup called Celect to bolster its data analytics and optimize its inventory. In other words, Nike hopes it will be able to figure out what’s hot and what’s not in a particular location to stock its stores more efficiently.

The company is going even further with Nike Fit, a foot-scanning platform using a smartphone camera that applies AI techniques from fields like computer vision and machine learning to find the best fit for each person’s foot. The algorithms then identify and recommend the appropriately sized and shaped shoe in different styles.

No doubt the next step will be to 3D print personalized and on-demand sneakers at any store.

San Francisco-based startup ThirdLove is trying to bring a similar approach to bra sizes. Its 20-member data team, Fortune reported, has developed the Fit Finder quiz that uses machine learning algorithms to help pick just the right garment for every body type.

Data scientists are also a big part of the team at Stitch Fix, a former San Francisco startup that went public in 2017 and today sports a market cap of more than $2 billion. The online “personal styling” company uses hundreds of algorithms to not only make recommendations to customers, but to help design new styles and even manage the subscription-based supply chain.

Future of Fashion

E-commerce giant Amazon has thrown its own considerable resources into developing AI applications for retail fashion—with mixed results.

One notable attempt involved a “styling assistant” that came with the company’s Echo Look camera that helped people catalog and manage their wardrobes, evening helping pick out each day’s attire. The company more recently revisited the direct consumer side of AI with an app called StyleSnap, which matches clothes and accessories uploaded to the site with the retailer’s vast inventory and recommends similar styles.

Behind the curtains, Amazon is going even further. A team of researchers in Israel have developed algorithms that can deduce whether a particular look is stylish based on a few labeled images. Another group at the company’s San Francisco research center was working on tech that could generate new designs of items based on images of a particular style the algorithms trained on.

“I will say that the accumulation of many new technologies across the industry could manifest in a highly specialized style assistant, far better than the examples we’ve seen today. However, the most likely thing is that the least sexy of the machine learning work will become the most impactful, and the public may never hear about it.”

That prediction is from an online interview with Leanne Luce, a fashion technology blogger and product manager at Google who recently wrote a book called, succinctly enough, Artificial Intelligence and Fashion.

Data Meets Design

Academics are also sticking their beakers into AI and fashion. Researchers at the University of California, San Diego, and Adobe Research have previously demonstrated that neural networks, a type of AI designed to mimic some aspects of the human brain, can be trained to generate (i.e., design) new product images to match a buyer’s preference, much like the team at Amazon.

Meanwhile, scientists at Hong Kong Polytechnic University are working with China’s answer to Amazon, Alibaba, on developing a FashionAI Dataset to help machines better understand fashion. The effort will focus on how algorithms approach certain building blocks of design, what are called “key points” such as neckline and waistline, and “fashion attributes” like collar types and skirt styles.

The man largely behind the university’s research team is Calvin Wong, a professor and associate head of Hong Kong Polytechnic University’s Institute of Textiles and Clothing. His group has also developed an “intelligent fabric defect detection system” called WiseEye for quality control, reducing the chance of producing substandard fabric by 90 percent.

Wong and company also recently inked an agreement with RCA to establish an AI-powered design laboratory, though the details of that venture have yet to be worked out, according to Broach.

One hope is that such collaborations will not just get at the technological challenges of using machines in creative endeavors like fashion, but will also address the more personal relationships humans have with their machines.

“I think who we are, and how we use AI in fashion, as our identity, is not a superficial skin. It’s very, very important for how we define our future,” Broach said.

Image Credit: Inspirationfeed / Unsplash

Our biggest undeclared war today is not being fought by drones in the mountains of Afghanistan or even through the less-lethal barrage of threats over the nuclear programs in North Korea and Iran. In this particular war, it is the US that is under attack and on the defensive.

This is cyberwarfare.

The definition of what constitutes a cyber attack is a broad one, according to Greg White, executive director of the Center for Infrastructure Assurance and Security (CIAS) at The University of Texas at San Antonio (UTSA).

At the level of nation-state attacks, cyberwarfare could involve “attacking systems during peacetime—such as our power grid or election systems—or it could be during war time in which case the attacks may be designed to cause destruction, damage, deception, or death,” he told Singularity Hub.

For the US, the Pearl Harbor of cyberwarfare occurred during 2016 with the Russian interference in the presidential election. However, according to White, an Air Force veteran who has been involved in computer and network security since 1986, the history of cyber war can be traced back much further, to at least the first Gulf War of the early 1990s.

“We started experimenting with cyber attacks during the first Gulf War, so this has been going on a long time,” he said. “Espionage was the prime reason before that. After the war, the possibility of expanding the types of targets utilized expanded somewhat. What is really interesting is the use of social media and things like websites for [psychological operation] purposes during a conflict.”

The 2008 conflict between Russia and the Republic of Georgia is often cited as a cyberwarfare case study due to the large scale and overt nature of the cyber attacks. Russian hackers managed to bring down more than 50 news, government, and financial websites through denial-of-service attacks. In addition, about 35 percent of Georgia’s internet networks suffered decreased functionality during the attacks, coinciding with the Russian invasion of South Ossetia.

The cyberwar also offers lessons for today on Russia’s approach to cyberspace as a tool for “holistic psychological manipulation and information warfare,” according to a 2018 report called Understanding Cyberwarfare from the Modern War Institute at West Point.

US Fights Back

News in recent years has highlighted how Russian hackers have attacked various US government entities and critical infrastructure such as energy and manufacturing. In particular, a shadowy group known as Unit 26165 within the country’s military intelligence directorate is believed to be behind the 2016 US election interference campaign.

However, the US hasn’t been standing idly by. Since at least 2012, the US has put reconnaissance probes into the control systems of the Russian electric grid, The New York Times reported. More recently, we learned that the US military has gone on the offensive, putting “crippling malware” inside the Russian power grid as the U.S. Cyber Command flexes its online muscles thanks to new authority granted to it last year.

“Access to the power grid that is obtained now could be used to shut something important down in the future when we are in a war,” White noted. “Espionage is part of the whole program. It is important to remember that cyber has just provided a new domain in which to conduct the types of activities we have been doing in the real world for years.”

The US is also beginning to pour more money into cybersecurity. The 2020 fiscal budget calls for spending $17.4 billion throughout the government on cyber-related activities, with the Department of Defense (DoD) alone earmarked for $9.6 billion.

Despite the growing emphasis on cybersecurity in the US and around the world, the demand for skilled security professionals is well outpacing the supply, with a projected shortfall of nearly three million open or unfilled positions according to the non-profit IT security organization (ISC)².

UTSA is rare among US educational institutions in that security courses and research are being conducted across three different colleges, according to White. About 10 percent of the school’s 30,000-plus students are enrolled in a cyber-related program, he added, and UTSA is one of only 21 schools that has received the Cyber Operations Center of Excellence designation from the National Security Agency.

“This track in the computer science program is specifically designed to prepare students for the type of jobs they might be involved in if they went to work for the DoD,” White said.

However, White is extremely doubtful there will ever be enough cyber security professionals to meet demand. “I’ve been preaching that we’ve got to worry about cybersecurity in the workforce, not just the cybersecurity workforce, not just cybersecurity professionals. Everybody has a responsibility for cybersecurity.”

Artificial Intelligence in Cybersecurity

Indeed, humans are often seen as the weak link in cybersecurity. That point was driven home at a cybersecurity roundtable discussion during this year’s Brainstorm Tech conference in Aspen, Colorado.

Participant Dorian Daley, general counsel at Oracle, said insider threats are at the top of the list when it comes to cybersecurity. “Sadly, I think some of the biggest challenges are people, and I mean that in a number of ways. A lot of the breaches really come from insiders. So the more that you can automate things and you can eliminate human malicious conduct, the better.”

White noted that automation is already the norm in cybersecurity. “Humans can’t react as fast as systems can launch attacks, so we need to rely on automated defenses as well,” he said. “This doesn’t mean that humans are not in the loop, but much of what is done these days is ‘scripted’.”

The use of artificial intelligence, machine learning, and other advanced automation techniques have been part of the cybersecurity conversation for quite some time, according to White, such as pattern analysis to look for specific behaviors that might indicate an attack is underway.

“What we are seeing quite a bit of today falls under the heading of big data and data analytics,” he explained.

But there are signs that AI is going off-script when it comes to cyber attacks. In the hands of threat groups, AI applications could lead to an increase in the number of cyberattacks, wrote Michelle Cantos, a strategic intelligence analyst at cybersecurity firm FireEye.

“Current AI technology used by businesses to analyze consumer behavior and find new customer bases can be appropriated to help attackers find better targets,” she said. “Adversaries can use AI to analyze datasets and generate recommendations for high-value targets they think the adversary should hit.”

In fact, security researchers have already demonstrated how a machine learning system could be used for malicious purposes. The Social Network Automated Phishing with Reconnaissance system, or SNAP_R, generated more than four times as many spear-phishing tweets on Twitter than a human—and was just as successful at targeting victims in order to steal sensitive information.

Cyber war is upon us. And like the current war on terrorism, there are many battlefields from which the enemy can attack and then disappear. While total victory is highly unlikely in the traditional sense, innovations through AI and other technologies can help keep the lights on against the next cyber attack.

Image Credit: pinkeyes / Shutterstock.com