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#434786 AI Performed Like a Human on a Gestalt ...

Dr. Been Kim wants to rip open the black box of deep learning.

A senior researcher at Google Brain, Kim specializes in a sort of AI psychology. Like cognitive psychologists before her, she develops various ways to probe the alien minds of artificial neural networks (ANNs), digging into their gory details to better understand the models and their responses to inputs.

The more interpretable ANNs are, the reasoning goes, the easier it is to reveal potential flaws in their reasoning. And if we understand when or why our systems choke, we’ll know when not to use them—a foundation for building responsible AI.

There are already several ways to tap into ANN reasoning, but Kim’s inspiration for unraveling the AI black box came from an entirely different field: cognitive psychology. The field aims to discover fundamental rules of how the human mind—essentially also a tantalizing black box—operates, Kim wrote with her colleagues.

In a new paper uploaded to the pre-publication server arXiv, the team described a way to essentially perform a human cognitive test on ANNs. The test probes how we automatically complete gaps in what we see, so that they form entire objects—for example, perceiving a circle from a bunch of loose dots arranged along a clock face. Psychologist dub this the “law of completion,” a highly influential idea that led to explanations of how our minds generalize data into concepts.

Because deep neural networks in machine vision loosely mimic the structure and connections of the visual cortex, the authors naturally asked: do ANNs also exhibit the law of completion? And what does that tell us about how an AI thinks?

Enter the Germans
The law of completion is part of a series of ideas from Gestalt psychology. Back in the 1920s, long before the advent of modern neuroscience, a group of German experimental psychologists asked: in this chaotic, flashy, unpredictable world, how do we piece together input in a way that leads to meaningful perceptions?

The result is a group of principles known together as the Gestalt effect: that the mind self-organizes to form a global whole. In the more famous words of Gestalt psychologist Kurt Koffka, our perception forms a whole that’s “something else than the sum of its parts.” Not greater than; just different.

Although the theory has its critics, subsequent studies in humans and animals suggest that the law of completion happens on both the cognitive and neuroanatomical level.

Take a look at the drawing below. You immediately “see” a shape that’s actually the negative: a triangle or a square (A and B). Or you further perceive a 3D ball (C), or a snake-like squiggle (D). Your mind fills in blank spots, so that the final perception is more than just the black shapes you’re explicitly given.

Image Credit: Wikimedia Commons contributors, the free media repository.
Neuroscientists now think that the effect comes from how our visual system processes information. Arranged in multiple layers and columns, lower-level neurons—those first to wrangle the data—tend to extract simpler features such as lines or angles. In Gestalt speak, they “see” the parts.

Then, layer by layer, perception becomes more abstract, until higher levels of the visual system directly interpret faces or objects—or things that don’t really exist. That is, the “whole” emerges.

The Experiment Setup
Inspired by these classical experiments, Kim and team developed a protocol to test the Gestalt effect on feed-forward ANNs: one simple, the other, dubbed the “Inception V3,” far more complex and widely used in the machine vision community.

The main idea is similar to the triangle drawings above. First, the team generated three datasets: one set shows complete, ordinary triangles. The second—the “Illusory” set, shows triangles with the edges removed but the corners intact. Thanks to the Gestalt effect, to us humans these generally still look like triangles. The third set also only shows incomplete triangle corners. But here, the corners are randomly rotated so that we can no longer imagine a line connecting them—hence, no more triangle.

To generate a dataset large enough to tease out small effects, the authors changed the background color, image rotation, and other aspects of the dataset. In all, they produced nearly 1,000 images to test their ANNs on.

“At a high level, we compare an ANN’s activation similarities between the three sets of stimuli,” the authors explained. The process is two steps: first, train the AI on complete triangles. Second, test them on the datasets. If the response is more similar between the illusory set and the complete triangle—rather than the randomly rotated set—it should suggest a sort of Gestalt closure effect in the network.

Machine Gestalt
Right off the bat, the team got their answer: yes, ANNs do seem to exhibit the law of closure.

When trained on natural images, the networks better classified the illusory set as triangles than those with randomized connection weights or networks trained on white noise.

When the team dug into the “why,” things got more interesting. The ability to complete an image correlated with the network’s ability to generalize.

Humans subconsciously do this constantly: anything with a handle made out of ceramic, regardless of shape, could easily be a mug. ANNs still struggle to grasp common features—clues that immediately tells us “hey, that’s a mug!” But when they do, it sometimes allows the networks to better generalize.

“What we observe here is that a network that is able to generalize exhibits…more of the closure effect [emphasis theirs], hinting that the closure effect reflects something beyond simply learning features,” the team wrote.

What’s more, remarkably similar to the visual cortex, “higher” levels of the ANNs showed more of the closure effect than lower layers, and—perhaps unsurprisingly—the more layers a network had, the more it exhibited the closure effect.

As the networks learned, their ability to map out objects from fragments also improved. When the team messed around with the brightness and contrast of the images, the AI still learned to see the forest from the trees.

“Our findings suggest that neural networks trained with natural images do exhibit closure,” the team concluded.

AI Psychology
That’s not to say that ANNs recapitulate the human brain. As Google’s Deep Dream, an effort to coax AIs into spilling what they’re perceiving, clearly demonstrates, machine vision sees some truly weird stuff.

In contrast, because they’re modeled after the human visual cortex, perhaps it’s not all that surprising that these networks also exhibit higher-level properties inherent to how we process information.

But to Kim and her colleagues, that’s exactly the point.

“The field of psychology has developed useful tools and insights to study human brains– tools that we may be able to borrow to analyze artificial neural networks,” they wrote.

By tweaking these tools to better analyze machine minds, the authors were able to gain insight on how similarly or differently they see the world from us. And that’s the crux: the point isn’t to say that ANNs perceive the world sort of, kind of, maybe similar to humans. It’s to tap into a wealth of cognitive psychology tools, established over decades using human minds, to probe that of ANNs.

“The work here is just one step along a much longer path,” the authors conclude.

“Understanding where humans and neural networks differ will be helpful for research on interpretability by enlightening the fundamental differences between the two interesting species.”

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#434781 What Would It Mean for AI to Become ...

As artificial intelligence systems take on more tasks and solve more problems, it’s hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that “By 2029, computers will have emotional intelligence and be convincing as people.”

We don’t know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they’re conscious, does that mean they actually are?

Perhaps a more relevant question at this juncture is: what is consciousness, and how do we replicate it if we don’t understand it?

In a panel discussion at South By Southwest titled “How AI Will Design the Human Future,” experts from academia and industry discussed these questions and more.

Wait, What Is AI?
Most of AI’s recent feats—diagnosing illnesses, participating in debate, writing realistic text—involve machine learning, which uses statistics to find patterns in large datasets then uses those patterns to make predictions. However, “AI” has been used to refer to everything from basic software automation and algorithms to advanced machine learning and deep learning.

“The term ‘artificial intelligence’ is thrown around constantly and often incorrectly,” said Jennifer Strong, a reporter at the Wall Street Journal and host of the podcast “The Future of Everything.” Indeed, one study found that 40 percent of European companies that claim to be working on or using AI don’t actually use it at all.

Dr. Peter Stone, associate chair of computer science at UT Austin, was the study panel chair on the 2016 One Hundred Year Study on Artificial Intelligence (or AI100) report. Based out of Stanford University, AI100 is studying and anticipating how AI will impact our work, our cities, and our lives.

“One of the first things we had to do was define AI,” Stone said. They defined it as a collection of different technologies inspired by the human brain to be able to perceive their surrounding environment and figure out what actions to take given these inputs.

Modeling on the Unknown
Here’s the crazy thing about that definition (and about AI itself): we’re essentially trying to re-create the abilities of the human brain without having anything close to a thorough understanding of how the human brain works.

“We’re starting to pair our brains with computers, but brains don’t understand computers and computers don’t understand brains,” Stone said. Dr. Heather Berlin, cognitive neuroscientist and professor of psychiatry at the Icahn School of Medicine at Mount Sinai, agreed. “It’s still one of the greatest mysteries how this three-pound piece of matter can give us all our subjective experiences, thoughts, and emotions,” she said.

This isn’t to say we’re not making progress; there have been significant neuroscience breakthroughs in recent years. “This has been the stuff of science fiction for a long time, but now there’s active work being done in this area,” said Amir Husain, CEO and founder of Austin-based AI company Spark Cognition.

Advances in brain-machine interfaces show just how much more we understand the brain now than we did even a few years ago. Neural implants are being used to restore communication or movement capabilities in people who’ve been impaired by injury or illness. Scientists have been able to transfer signals from the brain to prosthetic limbs and stimulate specific circuits in the brain to treat conditions like Parkinson’s, PTSD, and depression.

But much of the brain’s inner workings remain a deep, dark mystery—one that will have to be further solved if we’re ever to get from narrow AI, which refers to systems that can perform specific tasks and is where the technology stands today, to artificial general intelligence, or systems that possess the same intelligence level and learning capabilities as humans.

The biggest question that arises here, and one that’s become a popular theme across stories and films, is if machines achieve human-level general intelligence, does that also mean they’d be conscious?

Wait, What Is Consciousness?
As valuable as the knowledge we’ve accumulated about the brain is, it seems like nothing more than a collection of disparate facts when we try to put it all together to understand consciousness.

“If you can replace one neuron with a silicon chip that can do the same function, then replace another neuron, and another—at what point are you still you?” Berlin asked. “These systems will be able to pass the Turing test, so we’re going to need another concept of how to measure consciousness.”

Is consciousness a measurable phenomenon, though? Rather than progressing by degrees or moving through some gray area, isn’t it pretty black and white—a being is either conscious or it isn’t?

This may be an outmoded way of thinking, according to Berlin. “It used to be that only philosophers could study consciousness, but now we can study it from a scientific perspective,” she said. “We can measure changes in neural pathways. It’s subjective, but depends on reportability.”

She described three levels of consciousness: pure subjective experience (“Look, the sky is blue”), awareness of one’s own subjective experience (“Oh, it’s me that’s seeing the blue sky”), and relating one subjective experience to another (“The blue sky reminds me of a blue ocean”).

“These subjective states exist all the way down the animal kingdom. As humans we have a sense of self that gives us another depth to that experience, but it’s not necessary for pure sensation,” Berlin said.

Husain took this definition a few steps farther. “It’s this self-awareness, this idea that I exist separate from everything else and that I can model myself,” he said. “Human brains have a wonderful simulator. They can propose a course of action virtually, in their minds, and see how things play out. The ability to include yourself as an actor means you’re running a computation on the idea of yourself.”

Most of the decisions we make involve envisioning different outcomes, thinking about how each outcome would affect us, and choosing which outcome we’d most prefer.

“Complex tasks you want to achieve in the world are tied to your ability to foresee the future, at least based on some mental model,” Husain said. “With that view, I as an AI practitioner don’t see a problem implementing that type of consciousness.”

Moving Forward Cautiously (But Not too Cautiously)
To be clear, we’re nowhere near machines achieving artificial general intelligence or consciousness, and whether a “conscious machine” is possible—not to mention necessary or desirable—is still very much up for debate.

As machine intelligence continues to advance, though, we’ll need to walk the line between progress and risk management carefully.

Improving the transparency and explainability of AI systems is one crucial goal AI developers and researchers are zeroing in on. Especially in applications that could mean the difference between life and death, AI shouldn’t advance without people being able to trace how it’s making decisions and reaching conclusions.

Medicine is a prime example. “There are already advances that could save lives, but they’re not being used because they’re not trusted by doctors and nurses,” said Stone. “We need to make sure there’s transparency.” Demanding too much transparency would also be a mistake, though, because it will hinder the development of systems that could at best save lives and at worst improve efficiency and free up doctors to have more face time with patients.

Similarly, self-driving cars have great potential to reduce deaths from traffic fatalities. But even though humans cause thousands of deadly crashes every day, we’re terrified by the idea of self-driving cars that are anything less than perfect. “If we only accept autonomous cars when there’s zero probability of an accident, then we will never accept them,” Stone said. “Yet we give 16-year-olds the chance to take a road test with no idea what’s going on in their brains.”

This brings us back to the fact that, in building tech modeled after the human brain—which has evolved over millions of years—we’re working towards an end whose means we don’t fully comprehend, be it something as basic as choosing when to brake or accelerate or something as complex as measuring consciousness.

“We shouldn’t charge ahead and do things just because we can,” Stone said. “The technology can be very powerful, which is exciting, but we have to consider its implications.”

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#434753 Top Takeaways From The Economist ...

Over the past few years, the word ‘innovation’ has degenerated into something of a buzzword. In fact, according to Vijay Vaitheeswaran, US business editor at The Economist, it’s one of the most abused words in the English language.

The word is over-used precisely because we’re living in a great age of invention. But the pace at which those inventions are changing our lives is fast, new, and scary.

So what strategies do companies need to adopt to make sure technology leads to growth that’s not only profitable, but positive? How can business and government best collaborate? Can policymakers regulate the market without suppressing innovation? Which technologies will impact us most, and how soon?

At The Economist Innovation Summit in Chicago last week, entrepreneurs, thought leaders, policymakers, and academics shared their insights on the current state of exponential technologies, and the steps companies and individuals should be taking to ensure a tech-positive future. Here’s their expert take on the tech and trends shaping the future.

Blockchain
There’s been a lot of hype around blockchain; apparently it can be used for everything from distributing aid to refugees to voting. However, it’s too often conflated with cryptocurrencies like Bitcoin, and we haven’t heard of many use cases. Where does the technology currently stand?

Julie Sweet, chief executive of Accenture North America, emphasized that the technology is still in its infancy. “Everything we see today are pilots,” she said. The most promising of these pilots are taking place across three different areas: supply chain, identity, and financial services.

When you buy something from outside the US, Sweet explained, it goes through about 80 different parties. 70 percent of the relevant data is replicated and is prone to error, with paper-based documents often to blame. Blockchain is providing a secure way to eliminate paper in supply chains, upping accuracy and cutting costs in the process.

One of the most prominent use cases in the US is Walmart—the company has mandated that all suppliers in its leafy greens segment be on a blockchain, and its food safety has improved as a result.

Beth Devin, head of Citi Ventures’ innovation network, added “Blockchain is an infrastructure technology. It can be leveraged in a lot of ways. There’s so much opportunity to create new types of assets and securities that aren’t accessible to people today. But there’s a lot to figure out around governance.”

Open Source Technology
Are the days of proprietary technology numbered? More and more companies and individuals are making their source code publicly available, and its benefits are thus more widespread than ever before. But what are the limitations and challenges of open source tech, and where might it go in the near future?

Bob Lord, senior VP of cognitive applications at IBM, is a believer. “Open-sourcing technology helps innovation occur, and it’s a fundamental basis for creating great technology solutions for the world,” he said. However, the biggest challenge for open source right now is that companies are taking out more than they’re contributing back to the open-source world. Lord pointed out that IBM has a rule about how many lines of code employees take out relative to how many lines they put in.

Another challenge area is open governance; blockchain by its very nature should be transparent and decentralized, with multiple parties making decisions and being held accountable. “We have to embrace open governance at the same time that we’re contributing,” Lord said. He advocated for a hybrid-cloud environment where people can access public and private data and bring it together.

Augmented and Virtual Reality
Augmented and virtual reality aren’t just for fun and games anymore, and they’ll be even less so in the near future. According to Pearly Chen, vice president at HTC, they’ll also go from being two different things to being one and the same. “AR overlays digital information on top of the real world, and VR transports you to a different world,” she said. “In the near future we will not need to delineate between these two activities; AR and VR will come together naturally, and will change everything we do as we know it today.”

For that to happen, we’ll need a more ergonomically friendly device than we have today for interacting with this technology. “Whenever we use tech today, we’re multitasking,” said product designer and futurist Jody Medich. “When you’re using GPS, you’re trying to navigate in the real world and also manage this screen. Constant task-switching is killing our brain’s ability to think.” Augmented and virtual reality, she believes, will allow us to adapt technology to match our brain’s functionality.

This all sounds like a lot of fun for uses like gaming and entertainment, but what about practical applications? “Ultimately what we care about is how this technology will improve lives,” Chen said.

A few ways that could happen? Extended reality will be used to simulate hazardous real-life scenarios, reduce the time and resources needed to bring a product to market, train healthcare professionals (such as surgeons), or provide therapies for patients—not to mention education. “Think about the possibilities for children to learn about history, science, or math in ways they can’t today,” Chen said.

Quantum Computing
If there’s one technology that’s truly baffling, it’s quantum computing. Qubits, entanglement, quantum states—it’s hard to wrap our heads around these concepts, but they hold great promise. Where is the tech right now?

Mandy Birch, head of engineering strategy at Rigetti Computing, thinks quantum development is starting slowly but will accelerate quickly. “We’re at the innovation stage right now, trying to match this capability to useful applications,” she said. “Can we solve problems cheaper, better, and faster than classical computers can do?” She believes quantum’s first breakthrough will happen in two to five years, and that is highest potential is in applications like routing, supply chain, and risk optimization, followed by quantum chemistry (for materials science and medicine) and machine learning.

David Awschalom, director of the Chicago Quantum Exchange and senior scientist at Argonne National Laboratory, believes quantum communication and quantum sensing will become a reality in three to seven years. “We’ll use states of matter to encrypt information in ways that are completely secure,” he said. A quantum voting system, currently being prototyped, is one application.

Who should be driving quantum tech development? The panelists emphasized that no one entity will get very far alone. “Advancing quantum tech will require collaboration not only between business, academia, and government, but between nations,” said Linda Sapochak, division director of materials research at the National Science Foundation. She added that this doesn’t just go for the technology itself—setting up the infrastructure for quantum will be a big challenge as well.

Space
Space has always been the final frontier, and it still is—but it’s not quite as far-removed from our daily lives now as it was when Neil Armstrong walked on the moon in 1969.

The space industry has always been funded by governments and private defense contractors. But in 2009, SpaceX launched its first commercial satellite, and in subsequent years have drastically cut the cost of spaceflight. More importantly, they published their pricing, which brought transparency to a market that hadn’t seen it before.

Entrepreneurs around the world started putting together business plans, and there are now over 400 privately-funded space companies, many with consumer applications.

Chad Anderson, CEO of Space Angels and managing partner of Space Capital, pointed out that the technology floating around in space was, until recently, archaic. “A few NASA engineers saw they had more computing power in their phone than there was in satellites,” he said. “So they thought, ‘why don’t we just fly an iPhone?’” They did—and it worked.

Now companies have networks of satellites monitoring the whole planet, producing a huge amount of data that’s valuable for countless applications like agriculture, shipping, and observation. “A lot of people underestimate space,” Anderson said. “It’s already enabling our modern global marketplace.”

Next up in the space realm, he predicts, are mining and tourism.

Artificial Intelligence and the Future of Work
From the US to Europe to Asia, alarms are sounding about AI taking our jobs. What will be left for humans to do once machines can do everything—and do it better?

These fears may be unfounded, though, and are certainly exaggerated. It’s undeniable that AI and automation are changing the employment landscape (not to mention the way companies do business and the way we live our lives), but if we build these tools the right way, they’ll bring more good than harm, and more productivity than obsolescence.

Accenture’s Julie Sweet emphasized that AI alone is not what’s disrupting business and employment. Rather, it’s what she called the “triple A”: automation, analytics, and artificial intelligence. But even this fear-inducing trifecta of terms doesn’t spell doom, for workers or for companies. Accenture has automated 40,000 jobs—and hasn’t fired anyone in the process. Instead, they’ve trained and up-skilled people. The most important drivers to scale this, Sweet said, are a commitment by companies and government support (such as tax credits).

Imbuing AI with the best of human values will also be critical to its impact on our future. Tracy Frey, Google Cloud AI’s director of strategy, cited the company’s set of seven AI principles. “What’s important is the governance process that’s put in place to support those principles,” she said. “You can’t make macro decisions when you have technology that can be applied in many different ways.”

High Risks, High Stakes
This year, Vaitheeswaran said, 50 percent of the world’s population will have internet access (he added that he’s disappointed that percentage isn’t higher given the proliferation of smartphones). As technology becomes more widely available to people around the world and its influence grows even more, what are the biggest risks we should be monitoring and controlling?

Information integrity—being able to tell what’s real from what’s fake—is a crucial one. “We’re increasingly operating in siloed realities,” said Renee DiResta, director of research at New Knowledge and head of policy at Data for Democracy. “Inadvertent algorithmic amplification on social media elevates certain perspectives—what does that do to us as a society?”

Algorithms have also already been proven to perpetuate the bias of the people who create it—and those people are often wealthy, white, and male. Ensuring that technology doesn’t propagate unfair bias will be crucial to its ability to serve a diverse population, and to keep societies from becoming further polarized and inequitable. The polarization of experience that results from pronounced inequalities within countries, Vaitheeswaran pointed out, can end up undermining democracy.

We’ll also need to walk the line between privacy and utility very carefully. As Dan Wagner, founder of Civis Analytics put it, “We want to ensure privacy as much as possible, but open access to information helps us achieve important social good.” Medicine in the US has been hampered by privacy laws; if, for example, we had more data about biomarkers around cancer, we could provide more accurate predictions and ultimately better healthcare.

But going the Chinese way—a total lack of privacy—is likely not the answer, either. “We have to be very careful about the way we bake rights and freedom into our technology,” said Alex Gladstein, chief strategy officer at Human Rights Foundation.

Technology’s risks are clearly as fraught as its potential is promising. As Gary Shapiro, chief executive of the Consumer Technology Association, put it, “Everything we’ve talked about today is simply a tool, and can be used for good or bad.”

The decisions we’re making now, at every level—from the engineers writing algorithms, to the legislators writing laws, to the teenagers writing clever Instagram captions—will determine where on the spectrum we end up.

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#433807 The How, Why, and Whether of Custom ...

A digital afterlife may soon be within reach, but it might not be for your benefit.

The reams of data we’re creating could soon make it possible to create digital avatars that live on after we die, aimed at comforting our loved ones or sharing our experience with future generations.

That may seem like a disappointing downgrade from the vision promised by the more optimistic futurists, where we upload our consciousness to the cloud and live forever in machines. But it might be a realistic possibility in the not-too-distant future—and the first steps have already been taken.

After her friend died in a car crash, Eugenia Kuyda, co-founder of Russian AI startup Luka, trained a neural network-powered chatbot on their shared message history to mimic him. Journalist and amateur coder James Vlahos took a more involved approach, carrying out extensive interviews with his terminally ill father so that he could create a digital clone of him when he died.

For those of us without the time or expertise to build our own artificial intelligence-powered avatar, startup Eternime is offering to take your social media posts and interactions as well as basic personal information to build a copy of you that could then interact with relatives once you’re gone. The service is so far only running a private beta with a handful of people, but with 40,000 on its waiting list, it’s clear there’s a market.

Comforting—Or Creepy?
The whole idea may seem eerily similar to the Black Mirror episode Be Right Back, in which a woman pays a company to create a digital copy of her deceased husband and eventually a realistic robot replica. And given the show’s focus on the emotional turmoil she goes through, people might question whether the idea is a sensible one.

But it’s hard to say at this stage whether being able to interact with an approximation of a deceased loved one would be a help or a hindrance in the grieving process. The fear is that it could make it harder for people to “let go” or “move on,” but others think it could play a useful therapeutic role, reminding people that just because someone is dead it doesn’t mean they’re gone, and providing a novel way for them to express and come to terms with their feelings.

While at present most envisage these digital resurrections as a way to memorialize loved ones, there are also more ambitious plans to use the technology as a way to preserve expertise and experience. A project at MIT called Augmented Eternity is investigating whether we could use AI to trawl through someone’s digital footprints and extract both their knowledge and elements of their personality.

Project leader Hossein Rahnama says he’s already working with a CEO who wants to leave behind a digital avatar that future executives could consult with after he’s gone. And you wouldn’t necessarily have to wait until you’re dead—experts could create virtual clones of themselves that could dispense advice on demand to far more people. These clones could soon be more than simple chatbots, too. Hollywood has already started spending millions of dollars to create 3D scans of its most bankable stars so that they can keep acting beyond the grave.

It’s easy to see the appeal of the idea; imagine if we could bring back Stephen Hawking or Tim Cook to share their wisdom with us. And what if we could create a digital brain trust combining the experience and wisdom of all the world’s greatest thinkers, accessible on demand?

But there are still huge hurdles ahead before we could create truly accurate representations of people by simply trawling through their digital remains. The first problem is data. Most peoples’ digital footprints only started reaching significant proportions in the last decade or so, and cover a relatively small period of their lives. It could take many years before there’s enough data to create more than just a superficial imitation of someone.

And that’s assuming that the data we produce is truly representative of who we are. Carefully-crafted Instagram profiles and cautiously-worded work emails hardly capture the messy realities of most peoples’ lives.

Perhaps if the idea is simply to create a bank of someone’s knowledge and expertise, accurately capturing the essence of their character would be less important. But these clones would also be static. Real people continually learn and change, but a digital avatar is a snapshot of someone’s character and opinions at the point they died. An inability to adapt as the world around them changes could put a shelf life on the usefulness of these replicas.

Who’s Calling the (Digital) Shots?
It won’t stop people trying, though, and that raises a potentially more important question: Who gets to make the calls about our digital afterlife? The subjects, their families, or the companies that hold their data?

In most countries, the law is currently pretty hazy on this topic. Companies like Google and Facebook have processes to let you choose who should take control of your accounts in the event of your death. But if you’ve forgotten to do that, the fate of your virtual remains comes down to a tangle of federal law, local law, and tech company terms of service.

This lack of regulation could create incentives and opportunities for unscrupulous behavior. The voice of a deceased loved one could be a highly persuasive tool for exploitation, and digital replicas of respected experts could be powerful means of pushing a hidden agenda.

That means there’s a pressing need for clear and unambiguous rules. Researchers at Oxford University recently suggested ethical guidelines that would treat our digital remains the same way museums and archaeologists are required to treat mortal remains—with dignity and in the interest of society.

Whether those kinds of guidelines are ever enshrined in law remains to be seen, but ultimately they may decide whether the digital afterlife turns out to be heaven or hell.

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#433474 How to Feed Global Demand for ...

“You really can’t justify tuna in Chicago as a source of sustenance.” That’s according to Dr. Sylvia Earle, a National Geographic Society Explorer who was the first female chief scientist at NOAA. She came to the Good Food Institute’s Good Food Conference to deliver a call to action around global food security, agriculture, environmental protection, and the future of consumer choice.

It seems like all options should be on the table to feed an exploding population threatened by climate change. But Dr. Earle, who is faculty at Singularity University, drew a sharp distinction between seafood for sustenance versus seafood as a choice. “There is this widespread claim that we must take large numbers of wildlife from the sea in order to have food security.”

A few minutes later, Dr. Earle directly addressed those of us in the audience. “We know the value of a dead fish,” she said. That’s market price. “But what is the value of a live fish in the ocean?”

That’s when my mind blew open. What is the value—or put another way, the cost—of using the ocean as a major source of protein for humans? How do you put a number on that? Are we talking about dollars and cents, or about something far larger?

Dr. Liz Specht of the Good Food Institute drew the audience’s attention to a strange imbalance. Currently, about half of the yearly global catch of seafood comes from aquaculture. That means that the other half is wild caught. It’s hard to imagine half of your meat coming directly from the forests and the plains, isn’t it? And yet half of the world’s seafood comes from direct harvesting of the oceans, by way of massive overfishing, a terrible toll from bycatch, a widespread lack of regulation and enforcement, and even human rights violations such as slavery.

The search for solutions is on, from both within the fishing industry and from external agencies such as governments and philanthropists. Could there be another way?

Makers of plant-based seafood and clean seafood think they know how to feed the global demand for seafood without harming the ocean. These companies are part of a larger movement harnessing technology to reduce our reliance on wild and domesticated animals—and all the environmental, economic, and ethical issues that come with it.

Producers of plant-based seafood (20 or so currently) are working to capture the taste, texture, and nutrition of conventional seafood without the limitations of geography or the health of a local marine population. Like with plant-based meat, makers of plant-based seafood are harnessing food science and advances in chemistry, biology, and engineering to make great food. The industry’s strategy? Start with what the consumer wants, and then figure out how to achieve that great taste through technology.

So how does plant-based seafood taste? Pretty good, as it turns out. (The biggest benefit of a food-oriented conference is that your mouth is always full!)

I sampled “tuna” salad made from Good Catch Food’s fish-free tuna, which is sourced from legumes; the texture was nearly indistinguishable from that of flaked albacore tuna, and there was no lingering fishy taste to overpower my next bite. In a blind taste test, I probably wouldn’t have known that I was eating a plant-based seafood alternative. Next I reached for Ocean Hugger Food’s Ahimi, a tomato-based alternative to raw tuna. I adore Hawaiian poke, so I was pleasantly surprised when my Ahimi-based poke captured the bite of ahi tuna. It wasn’t quite as delightfully fatty as raw tuna, but with wild tuna populations struggling to recover from a 97% decline in numbers from 40 years ago, Ahimi is a giant stride in the right direction.

These plant-based alternatives aren’t the only game in town, however.

The clean meat industry, which has also been called “cultured meat” or “cellular agriculture,” isn’t seeking to lure consumers away from animal protein. Instead, cells are sampled from live animals and grown in bioreactors—meaning that no animal is slaughtered to produce real meat.

Clean seafood is poised to piggyback off platforms developed for clean meat; growing fish cells in the lab should rely on the same processes as growing meat cells. I know of four companies currently focusing on seafood (Finless Foods, Wild Type, BlueNalu, and Seafuture Sustainable Biotech), and a few more are likely to emerge from stealth mode soon.

Importantly, there’s likely not much difference between growing clean seafood from the top or the bottom of the food chain. Tuna, for example, are top predators that must grow for at least 10 years before they’re suitable as food. Each year, a tuna consumes thousands of pounds of other fish, shellfish, and plankton. That “long tail of groceries,” said Dr. Earle, “is a pretty expensive choice.” Excitingly, clean tuna would “level the trophic playing field,” as Dr. Specht pointed out.

All this is only the beginning of what might be possible.

Combining synthetic biology with clean meat and seafood means that future products could be personalized for individual taste preferences or health needs, by reprogramming the DNA of the cells in the lab. Industries such as bioremediation and biofuels likely have a lot to teach us about sourcing new ingredients and flavors from algae and marine plants. By harnessing rapid advances in automation, robotics, sensors, machine vision, and other big-data analytics, the manufacturing and supply chains for clean seafood could be remarkably safe and robust. Clean seafood would be just that: clean, without pathogens, parasites, or the plastic threatening to fill our oceans, meaning that you could enjoy it raw.

What about price? Dr. Mark Post, a pioneer in clean meat who is also faculty at Singularity University, estimated that 80% of clean-meat production costs come from the expensive medium in which cells are grown—and some ingredients in the medium are themselves sourced from animals, which misses the point of clean meat. Plus, to grow a whole cut of food, like a fish fillet, the cells need to be coaxed into a complex 3D structure with various cell types like muscle cells and fat cells. These two technical challenges must be solved before clean meat and seafood give consumers the experience they want, at the price they want.

In this respect clean seafood has an unusual edge. Most of what we know about growing animal cells in the lab comes from the research and biomedical industries (from tissue engineering, for example)—but growing cells to replace an organ has different constraints than growing cells for food. The link between clean seafood and biomedicine is less direct, empowering innovators to throw out dogma and find novel reagents, protocols, and equipment to grow seafood that captures the tastes, textures, smells, and overall experience of dining by the ocean.

Asked to predict when we’ll be seeing clean seafood in the grocery store, Lou Cooperhouse the CEO of BlueNalu, explained that the challenges aren’t only in the lab: marketing, sales, distribution, and communication with consumers are all critical. As Niya Gupta, the founder of Fork & Goode, said, “The question isn’t ‘can we do it’, but ‘can we sell it’?”

The good news is that the clean meat and seafood industry is highly collaborative; there are at least two dozen companies in the space, and they’re all talking to each other. “This is an ecosystem,” said Dr. Uma Valeti, the co-founder of Memphis Meats. “We’re not competing with each other.” It will likely be at least a decade before science, business, and regulation enable clean meat and seafood to routinely appear on restaurant menus, let alone market shelves.

Until then, think carefully about your food choices. Meditate on Dr. Earle’s question: “What is the real cost of that piece of halibut?” Or chew on this from Dr. Ricardo San Martin, of the Sutardja Center at the University of California, Berkeley: “Food is a system of meanings, not an object.” What are you saying when you choose your food, about your priorities and your values and how you want the future to look? Do you think about animal welfare? Most ethical regulations don’t extend to marine life, and if you don’t think that ocean creatures feel pain, consider the lobster.

Seafood is largely an acquired taste, since most of us don’t live near the water. Imagine a future in which children grow up loving the taste of delicious seafood but without hurting a living animal, the ocean, or the global environment.

Do more than imagine. As Dr. Earle urged us, “Convince the public at large that this is a really cool idea.”

Widely available
Medium availability
Emerging

Gardein
Ahimi (Ocean Hugger)
New Wave Foods

Sophie’s Kitchen
Cedar Lake
To-funa Fish

Quorn
SoFine Foods
Seamore

Vegetarian Plus
Akua
Good Catch

Heritage
Hungry Planet
Odontella

Loma Linda
Heritage Health Food
Terramino Foods

The Vegetarian Butcher
May Wah

VBites

Table based on Figure 5 of the report “An Ocean of Opportunity: Plant-based and clean seafood for sustainable oceans without sacrifice,” from The Good Food Institute.

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