In an enlightening discussion last week at the Chicago Humanities Festival, two thought leaders posited an unexpected response: it’s not so much that we’ve messed things up—it’s that we’re in the midst of an unprecedented democratic effort, and there are bound to be some bumps in the road. Moreover, if we want the future to look bright instead of dim, we need to start working harder to bridge societal divides.

Yascha Mounk is a German-American political scientist, professor of International Affairs at Johns Hopkins University, senior fellow at the Council on Foreign Relations, and the author of four books, the most recent being The Great Experiment: Why Diverse Democracies Fall Apart and How They Can Endure.

Dr. Eboo Patel is the founder and president of Interfaith America, a Chicago-based international nonprofit that aims to promote interfaith cooperation. He’s also the former faith advisor to President Obama and author of four books, most recently We Need to Build: Field Notes for Diverse Democracy.

According to Freedom House’s 2021 ‘Freedom in the World’ report, the world has entered the 16^th year of a democratic recession, and the international balance has shifted in favor of tyranny. What has caused democracy to become so fragile, and are we at some kind of precarious turning point?

Delicate Democracy

We’ve come to take it for granted that in relatively affluent countries like the US, Australia, Germany, or Japan, democracy will always be the chosen system of government and is unlikely to come under serious threat. “I started worrying about whether that was really true, because I saw all these signs of fading democratic values,” Mounk said. “People participating less in civil society, the extremes rising, people being more open to populist leaders.” Take Trump in the US, Bolsonaro in Brazil, Modhi in India, or López Obrador in Mexico, to name a few.

This shift, in Mounk’s opinion, has structural reasons, like a stagnation of living standards for middle- and working-class citizens, as well as a rise in internet and social media use, which pulls parties and issues to the extremes. “But it also has to do with the fact that we’re trying to do something unprecedented right now,” he added. “We’re trying to build religiously and ethnically diverse democracies that treat their members as equals.”

When democracies like Germany and the US were founded, they were to a large degree religiously and ethnically homogeneous. The US has become more diverse, but it doesn’t have a history of treating different groups of citizens equally; one group got the power and influence while other groups were excluded.

There’s a widespread pessimism in the US about the state of society—but, Mounk pointed out, building diverse democracies is extremely hard, and it’s gone wrong multiple times in history. “If you understand that, you can look at the changes in US society over the last decade and have optimism,” he said. “Maybe not at a political level, but in the changes you see at the heart of our society. We are actually making real progress towards building these diverse democracies, and we’ll continue to build on that in the coming decades.”

How’s that for a breath of fresh air?

Crucial Civil Society

Patel’s focus is on building civil society: athletic leagues, religious organizations or houses of worship, and other special interest or hobby groups where we spend time outside of our families. “Civil society is the place where people from diverse identities and divergent ideologies come together to engage in common aims and cooperative relationships,” Patel said. “This is the real genius of American society—you have a critical mass of institutions and spaces that bring people together for common aims, and the nature of the activity shapes cooperative relationships.”

Bringing people from different groups together to deepen trust and understanding is key. Groupishness, Mounk pointed out, is part of human nature, and that will never change—but we must manage it in a way that inspires cooperation and friendship rather than hatred, resentment, or violence.

He cited India as a thriving diverse democracy, but with periodic outbursts of violence between Hindus and Muslims. Studies have found that in villages and cities where there’s less violence, there are more civic associations that unite people; Hindus and Muslims are both members of literature clubs, athletic clubs, volunteer organizations, etc. In places where violence more frequently breaks out, these associations still exist, but they keep Hindus and Muslims separate. It’s no big surprise, then, that in moments when tensions are running high, the people in the first group of cities trust each other, while in the second, they don’t feel like they know each other.

This second group, though, has been the rule throughout history; what we’re trying to do now is the exception. “The rule of human history is that identity communities build institutions for their own identity communities to serve, grow, and reproduce them,” Patel said—and, when it comes down to it, to fight other communities.

A brilliance of diverse democracies, he added, is that groups can start institutions that are an expression of their identity—say, a Jesuit university or a Jewish volunteer organization—but serve people from any group. Patel’s own father, an Indian Muslim, came to the US to attend the MBA program at Notre Dame—a private Catholic university—and that’s why Patel is here today.

“That’s the secret sauce of America’s democracy, and I think it’s in danger,” he said. “We have to continue to build spaces out of our own identity expression that connect to other identities and have a stake in them thriving.”

Demography’s Not Destiny

The US Census Bureau has predicted the US will be a “majority minority” country by 2045. If that’s true, Mounk and Patel both said, we’d better start working harder to get away from the polarized, divisive political culture we’re stuck in now.

“We’re at a moment in America where liberals and conservatives don’t agree on anything,” Mounk said. “But there’s one thing they agree on, and it’s wrong and dangerous: that’s the idea that demography is destiny.” In his opinion this is far too simplistic, because the way different demographics vote can change over time.

Catholics and Irish Americans were key for Democrats in the 1960s, Mounk said, but today are one of the most reliable voter bases for Republicans. What made Trump competitive in the 2020 election was that he significantly increased his share of voters among every non-white demographic, from African-American to Asian-American to Hispanic. Biden ultimately won because he increased his share of white voters relative to Hillary Clinton’s share from 2016. “We simply cannot predict who’s going to be winning elections by running those numbers out into the future,” Mounk said. “And that’s a good thing for our society, because I don’t want to be able to look into this audience and know who you voted for by the color of your skin.”

Fixing the Future

Technology has done some damage to democracy, mainly through social media algorithms that amplify the most extreme voices at the expense of moderate, rational ones. What can tech do now to reverse this harm—and go beyond that to revitalize democracy, build meaningful connections between groups, and dial down political polarization?

Patel spoke of the importance of solution-minded entrepreneurs in helping heal social wounds. “A huge part of what makes our society healthy and vibrant is people standing up and saying ‘I’ll solve that,’” he said. “Local solutions to local problems can have massive national implications.”

The democratization of technology, information, and knowledge means there’s a greater proportion of people than ever before with access to tools that can catalyze positive change. We see people using tech to solve issues like homelessness, pollution, climate change, and even making existing digital technologies more ethical.

How can this spirit of community-oriented innovation be applied to fixing and preserving our ailing political system and bridging the many divides between citizens?

A line from Mounk’s book reads, “Never in history has a democracy succeeded in being both diverse and equal, treating members of many different ethnic or religious groups fairly, and yet achieving that goal is now central to the democratic project in countries around the world.”

We’ve got our work cut out for us.

Image Credit: Breaking The Walls / Shutterstock.com

Not surprisingly, more and more investors are now jumping on what can still seem like a techy, trendy bandwagon. In an economy where governments are printing money hand over fist, people want a more secure place to put their assets. In addition to prevailing economic uncertainty, many institutional investors are dipping their toes into the cryptocurrency, and even PayPal began offering customers the ability to buy Bitcoin late last year. Elon Musk’s repeated endorsement of the cryptocurrency hasn’t hurt, either. Some even believe digital currencies like Bitcoin are the future of money.

But intertwined with Bitcoin’s more speculative potential (as an asset or currency) is an important feature many investors may miss: its power to protect human rights and stand against tyranny.

In a new video for Reason magazine, Alex Gladstein, chief strategy officer at the Human Rights Foundation, explains why the cryptocurrency is an inalienable tool for preserving freedom, and how it’s being used by people in different parts of the world to do so.

Money makes the world go ’round, and as such, it’s a perfect tool for surveillance and control. The decline of cash in many societies and its replacement with digital payment methods means we’ve all but kissed financial privacy goodbye; all of our digital transactions are logged and kept on record for years.

In most democratic countries this doesn’t tend to come with consequences much more intrusive than targeted ads. But for the more than four billion people living under authoritarian regimes, it’s a different story.

Their governments can—and do—freeze peoples’ bank accounts, shut down ATMs, decide who gets cut off from financial services, and even seize private funds. Actions like these are often targeted at individuals labeled as problematic: activists, dissidents, union leaders, critics of the ruling party, intellectuals, and the like. Cutting off access to money is a quick-and-dirty way to immobilize people, not to mention wreak havoc when it’s done on a large scale.

If only there was a monetary system not controlled by a central bank, untouchable by governments, where value could be transmitted without corruption or interference and unaffected by international borders.

Enter Bitcoin.

Image Credit: Aleksi Räisä on Unsplash

One of these fields, and perhaps the most significant, is artificial intelligence. The Chinese government announced a plan in 2017 to become the world leader in AI by 2030, and has since poured billions of dollars into AI projects and research across academia, government, and private industry. The government’s venture capital fund is investing over $30 billion in AI; the northeastern city of Tianjin budgeted $16 billion for advancing AI; and a $2 billion AI research park is being built in Beijing.

On top of these huge investments, the government and private companies in China have access to an unprecedented quantity of data, on everything from citizens’ health to their smartphone use. WeChat, a multi-functional app where people can chat, date, send payments, hail rides, read news, and more, gives the CCP full access to user data upon request; as one BBC journalist put it, WeChat “was ahead of the game on the global stage and it has found its way into all corners of people’s existence. It could deliver to the Communist Party a life map of pretty much everybody in this country, citizens and foreigners alike.” And that’s just one (albeit big) source of data.

Many believe these factors are giving China a serious leg up in AI development, even providing enough of a boost that its progress will surpass that of the US.

But there’s more to AI than data, and there’s more to progress than investing billions of dollars. Analyzing China’s potential to become a world leader in AI—or in any technology that requires consistent innovation—from multiple angles provides a more nuanced picture of its strengths and limitations. In a June 2020 article in Foreign Affairs, Oxford fellows Carl Benedikt Frey and Michael Osborne argued that China’s big advantages may not actually be that advantageous in the long run—and its limitations may be very limiting.

Moving the AI Needle

To get an idea of who’s likely to take the lead in AI, it could help to first consider how the technology will advance beyond its current state.

To put it plainly, AI is somewhat stuck at the moment. Algorithms and neural networks continue to achieve new and impressive feats—like DeepMind’s AlphaFold accurately predicting protein structures or OpenAI’s GPT-3 writing convincing articles based on short prompts—but for the most part these systems’ capabilities are still defined as narrow intelligence: completing a specific task for which the system was painstakingly trained on loads of data.

(It’s worth noting here that some have speculated OpenAI’s GPT-3 may be an exception, the first example of machine intelligence that, while not “general,” has surpassed the definition of “narrow”; the algorithm was trained to write text, but ended up being able to translate between languages, write code, autocomplete images, do math, and perform other language-related tasks it wasn’t specifically trained for. However, all of GPT-3’s capabilities are limited to skills it learned in the language domain, whether spoken, written, or programming language).

Both AlphaFold’s and GPT-3’s success was due largely to the massive datasets they were trained on; no revolutionary new training methods or architectures were involved. If all it was going to take to advance AI was a continuation or scaling-up of this paradigm—more input data yields increased capability—China could well have an advantage.

But one of the biggest hurdles AI needs to clear to advance in leaps and bounds rather than baby steps is precisely this reliance on extensive, task-specific data. Other significant challenges include the technology’s fast approach to the limits of current computing power and its immense energy consumption.

Thus, while China’s trove of data may give it an advantage now, it may not be much of a long-term foothold on the climb to AI dominance. It’s useful for building products that incorporate or rely on today’s AI, but not for pushing the needle on how artificially intelligent systems learn. WeChat data on users’ spending habits, for example, would be valuable in building an AI that helps people save money or suggests items they might want to purchase. It will enable (and already has enabled) highly tailored products that will earn their creators and the companies that use them a lot of money.

But data quantity isn’t what’s going to advance AI. As Frey and Osborne put it, “Data efficiency is the holy grail of further progress in artificial intelligence.”

To that end, research teams in academia and private industry are working on ways to make AI less data-hungry. New training methods like one-shot learning and less-than-one-shot learning have begun to emerge, along with myriad efforts to make AI that learns more like the human brain.

While not insignificant, these advancements still fall into the “baby steps” category. No one knows how AI is going to progress beyond these small steps—and that uncertainty, in Frey and Osborne’s opinion, is a major speed bump on China’s fast-track to AI dominance.

How Innovation Happens

A lot of great inventions have happened by accident, and some of the world’s most successful companies started in garages, dorm rooms, or similarly low-budget, nondescript circumstances (including Google, Facebook, Amazon, and Apple, to name a few). Innovation, the authors point out, often happens “through serendipity and recombination, as inventors and entrepreneurs interact and exchange ideas.”

Frey and Osborne argue that although China has great reserves of talent and a history of building on technologies conceived elsewhere, it doesn’t yet have a glowing track record in terms of innovation. They note that of the 100 most-cited patents from 2003 to present, none came from China. Giants Tencent, Alibaba, and Baidu are all wildly successful in the Chinese market, but they’re rooted in technologies or business models that came out of the US and were tweaked for the Chinese population.

“The most innovative societies have always been those that allowed people to pursue controversial ideas,” Frey and Osborne write. China’s heavy censorship of the internet and surveillance of citizens don’t quite encourage the pursuit of controversial ideas. The country’s social credit system rewards people who follow the rules and punishes those who step out of line. Frey adds that top-down execution of problem-solving is effective when the problem at hand is clearly defined—and the next big leaps in AI are not.

It’s debatable how strongly a culture of social conformism can impact technological innovation, and of course there can be exceptions. But a relevant historical example is the Soviet Union, which, despite heavy investment in science and technology that briefly rivaled the US in fields like nuclear energy and space exploration, ended up lagging far behind primarily due to political and cultural factors.

Similarly, China’s focus on computer science in its education system could give it an edge—but, as Frey told me in an email, “The best students are not necessarily the best researchers. Being a good researcher also requires coming up with new ideas.”

Winner Take All?

Beyond the question of whether China will achieve AI dominance is the issue of how it will use the powerful technology. Several of the ways China has already implemented AI could be considered morally questionable, from facial recognition systems used aggressively against ethnic minorities to smart glasses for policemen that can pull up information about whoever the wearer looks at.

This isn’t to say the US would use AI for purely ethical purposes. The military’s Project Maven, for example, used artificially intelligent algorithms to identify insurgent targets in Iraq and Syria, and American law enforcement agencies are also using (mostly unregulated) facial recognition systems.

It’s conceivable that “dominance” in AI won’t go to one country; each nation could meet milestones in different ways, or meet different milestones. Researchers from both countries, at least in the academic sphere, could (and likely will) continue to collaborate and share their work, as they’ve done on many projects to date.

If one country does take the lead, it will certainly see some major advantages as a result. Brookings Institute fellow Indermit Gill goes so far as to say that whoever leads in AI in 2030 will “rule the world” until 2100. But Gill points out that in addition to considering each country’s strengths, we should consider how willing they are to improve upon their weaknesses.

While China leads in investment and the US in innovation, both nations are grappling with huge economic inequalities that could negatively impact technological uptake. “Attitudes toward the social change that accompanies new technologies matter as much as the technologies, pointing to the need for complementary policies that shape the economy and society,” Gill writes.

Will China’s leadership be willing to relax its grip to foster innovation? Will the US business environment be enough to compete with China’s data, investment, and education advantages? And can both countries find a way to distribute technology’s economic benefits more equitably?

Time will tell, but it seems we’ve got our work cut out for us—and China does too.

Image Credit: Adam Birkett on Unsplash

Dramatic political polarization. Rising anxiety and depression. An uptick in teen suicide rates. Misinformation that spreads like wildfire.

The common denominator of all these phenomena is that they’re fueled in part by our seemingly innocuous participation in digital social networking. But how can simple acts like sharing photos and articles, reading the news, and connecting with friends have such destructive consequences?

These are the questions explored in the new Netflix docu-drama The Social Dilemma. Directed by Jeff Orlowski, it features several former Big Tech employees speaking out against the products they once upon a time helped build. Their reflections are interspersed with scenes from a family whose two youngest children are struggling with social media addiction and its side effects. There are also news clips from the last several years where reporters decry the technology and report on some of its nefarious impacts.

Tristan Harris, a former Google design ethicist who co-founded the Center for Humane Technology (CHT) and has become a crusader for ethical tech, is a central figure in the movie. “When you look around you it feels like the world is going crazy,” he says near the beginning. “You have to ask yourself, is this normal? Or have we all fallen under some kind of spell?”

Also featured are Aza Raskin, who co-founded CHT with Harris, Justin Rosenstein, who co-founded Asana and is credited with having created Facebook’s “like” button, former Pinterest president Tim Kendall, and writer and virtual reality pioneer Jaron Lanier. They and other experts talk about the way social media gets people “hooked” by exploiting the brain’s dopamine response and using machine learning algorithms to serve up the customized content most likely to keep each person scrolling/watching/clicking.

The movie veers into territory explored by its 2019 predecessor The Great Hack—which dove into the Cambridge Analytica scandal and detailed how psychometric profiles of Facebook users helped manipulate their political leanings—by having its experts talk about the billions of data points that tech companies are constantly collecting about us. “Every single action you take is carefully monitored and recorded,” says Jeff Siebert, a former exec at Twitter. The intelligence gleaned from those actions is then used in conjunction with our own psychological weaknesses to get us to watch more videos, share more content, see more ads, and continue driving Big Tech’s money-making engine.

“It’s the gradual, slight, imperceptible change in your own behavior and perception that is the product,” says Lanier. “That’s the only thing there is for them to make money from: changing what you do, how you think, who you are.” The elusive “they” that Lanier and other ex-techies refer to is personified in the film by three t-shirt clad engineers working tirelessly in a control room to keep peoples’ attention on their phones at all costs.

Computer processing power, a former Nvidia product manager points out, has increased exponentially just in the last 20 years; but meanwhile, the human brain hasn’t evolved beyond the same capacity it’s had for hundreds of years. The point of this comparison seems to be that if we’re in a humans vs. computers showdown, we humans haven’t got a fighting chance.

But are we in a humans vs. computers showdown? Are the companies behind our screens really so insidious as the evil control room engineers imply, aiming to turn us all into mindless robots who are slaves to our lizard-brain impulses? Even if our brain chemistry is being exploited by the design of tools like Facebook and YouTube, doesn’t personal responsibility kick in at some point?

The Social Dilemma is a powerful, well-made film that exposes social media’s ills in a raw and immediate way. It’s a much-needed call for government regulation and for an actionable ethical reckoning within the tech industry itself.

But it overdramatizes Big Tech’s intent—these are, after all, for-profit companies who have created demand-driven products—and under-credits social media users. Yes, we fall prey to our innate need for connection and approval, and we’ll always have a propensity to become addicted to things that make us feel good. But we’re still responsible for and in control of our own choices.

What we’re seeing with social media right now is a cycle that’s common with new technologies. For the first few years of social media’s existence, we thought it was the best thing since sliced bread. Now it’s on a nosedive to the other end of the spectrum—we’re condemning it and focusing on its ills and unintended consequences. The next phase is to find some kind of balance, most likely through adjustments in design and, possibly, regulation.

“The way the technology works is not a law of physics. It’s not set in stone. These are choices that human beings like myself have been making, and human beings can change those technologies,” says Rosenstein.

The issue with social media is that it’s going to be a lot trickier to fix than, say, adding seatbelts and air bags to cars. The sheer size and reach of these tools, and the way in which they overlap with issues of freedom of speech and privacy—not to mention how they’ve changed the way humans interact—means it will likely take a lot of trial and error to come out with tools that feel good for us to use without being addicting, give us only true, unbiased information in a way that’s engaging without preying on our emotions, and allow us to share content and experiences while preventing misinformation and hate speech.

In the most recent episode of his podcast Making Sense, Sam Harris talks to Tristan Harris about the movie and its implications. Tristan says, “While we’ve all been looking out for the moment when AI would overwhelm human strengths—when would we get the Singularity, when would AI take our jobs, when would it be smarter than humans—we missed this much much earlier point when technology didn’t overwhelm human strengths, but it undermined human weaknesses.”

It’s up to tech companies to re-design their products in more ethical ways to stop exploiting our weaknesses. But it’s up to us to demand that they do so, be aware of these weaknesses, and resist becoming cogs in the machine.

Image Credit: Rob Hampson on Unsplash

A couple months ago, the deep-sea servers were brought back up to the surface so engineers could inspect them and evaluate how they’d performed while under water.

But wait—why were they there in the first place?

As bizarre as it seems to sink hundreds of servers into the ocean, there are actually several very good reasons to do so. According to the UN, about 40 percent of the world’s population lives within 60 miles of an ocean. As internet connectivity expands to cover most of the globe in the next few years, millions more people will come online, and a lot more servers will be needed to manage the increased demand and data they’ll generate.

In densely-populated cities real estate is expensive and can be hard to find. But know where there’s lots of cheap, empty space? At the bottom of the ocean. This locale also carries the added benefit of being really cold (depending where we’re talking, that is; if you’re looking off the coast of, say, Mumbai or Abu Dhabi, the waters are warmer).

Servers generate a lot of heat, and datacenters use most of their electricity for cooling. Keeping not just the temperature but also the humidity level constant is important for optimal functioning of the servers; neither of these vary much 100 feet under water.

Finally, installing data centers on the ocean floor is, surprisingly, much faster than building them on land. Microsoft claims its server-holding cylinders will take less than 90 days to go from factory ship to operation, as compared to the average two years it takes to get a terrestrial data center up and running.

Microsoft’s Special Projects team operated the underwater data center for two years, and it took a full day to dredge it up and bring it to the surface. One of the first things researchers did was to insert test tubes into the container to take samples of the air inside; they’ll use it to try to determine how gases released from the equipment may have impacted the servers’ operating environment.

The container was filled with dry nitrogen upon deployment, which seems to have made for a much better environment than the oxygen that land-bound servers are normally surrounded by; the failure rate of the servers in the water was just one-eighth that of Microsoft’s typical rate for its servers on land. The team thinks the nitrogen atmosphere was helpful because it’s less corrosive than oxygen. The fact that no humans entered the container for the entirety of its operations helped, too (no moving around of components or having to turn on lights or adjust the temperature).

Ben Cutler, a project manager in Microsoft’s Special Projects research group who leads Project Natick, believes the results of this phase of the project are sufficient to show that underwater data centers are worth pursuing. “We are now at the point of trying to harness what we have done as opposed to feeling the need to go and prove out some more,” he said.

Cutler envisions putting underwater datacenters near offshore wind farms to power them sustainably. The data centers of the future will require less human involvement, instead being managed and run primarily by technologies like robotics and AI. In this kind of “lights-out” datacenter, the servers would be swapped out about once every five years, with any that fail before then being taken offline.

The final step in this phase of Project Natick is to recycle all the components used for the underwater data center, including the steel pressure vessel, heat exchangers, and the servers themselves—and restoring the sea bed where the cylinder rested back to its original condition.

If Cutler’s optimism is a portent of things to come, it may not be long before the ocean floor is dotted with sustainable datacenters to feed our ever-increasing reliance on our phones and the internet.

Image Credit: Microsoft

Eating fish, then, seems preferable to meat. In fact, according to the Global Aquaculture Alliance, 3.1 billion people around the world now rely on fish and seafood for a fifth of their daily animal protein intake. Fish are also one of our only sources of long-chain omega-3 fatty acids.

But—spoiler—it turns out there are some serious issues with the seafood industry, too.

In Hot Water

Water temperatures are rising, throwing off marine ecosystems’ natural balance and kicking off negative ripple effects throughout their food chains. Overfishing—when we take fish out of their natural habitat at a rate too fast for them to keep up with in terms of replenishing the supply—has depleted wild populations of halibut, monkfish, tuna, and salmon, among others.

Salmon in particular are one of the most crucial species for the ecosystems they inhabit. Since they’re born in freshwater streams but then migrate to the sea to mature, salmon serve as a link between saltwater and freshwater ecosystems, bringing nutrients from the oceans inland and vice versa. They’re also a key food source for bigger animals like bears and whales.

But mass-producing salmon for human consumption has hurt the species’ wild population. Between the 1970s and today, for example, the number of wild Atlantic salmon out there has been cut in half or maybe more, going from 8 to 10 million to just 3 to 4 million. And salmon farms have a recurring problem with parasites called sea lice (I know—gross), which flourish in densely-packed pens and spread to wild fish when farmed fish escape.

Fish Without Fish

A San Francisco-based startup called Wildtype is developing a product that could one day help alleviate the problems caused by fish farming: the company is devoted to producing lab-grown salmon.

Similar to cultured meat like that made by Memphis Meats, Wildtype’s salmon starts with real animal cells and adds a mixture of nutrients, sugars, salts, amino acids, and growth factor to coax the cells to grow as they naturally would inside an animal’s body. Done right, the process can yield animal tissue that contains muscle, blood, and fat, just like you’d get from a farmed fish. Except in some ways it’s even better, because what you don’t get is mercury, microplastics, and the other contaminants that farmed fish are becoming rife with.

Wildtype created its own technology for the “scaffolds” where tissue grows. “This is applicable to other species than the salmon that we have worked on,” Wildtype co-founder Arye Elfenbein told Tech Crunch. “We basically create a scaffold that provides the right guidance…for cells to take up fats in different places or become more striated.”

The company just opened up a pre-order waiting list for its product to chefs around the country, despite commercial production being up to five years away.

Challenges Upstream

Last year the company did a taste test for employees, investors, and a group of chefs and restaurateurs. While the texture of the fish was apparently realistic, its taste was described as “lacking.” And that’s not the only challenge Wildtype will have to overcome; the company estimated that each spicy salmon roll served at the tasting cost $200 to produce.

This is the biggest issue with lab-grown meat, whether beef, pork, or fish—it’s difficult and costly to scale up its production. Wildtype is aiming to lower its costs to seven to eight dollars a pound within a few years. “The dream vision is the cleanest, purest, freshest salmon, without contaminants or antibiotics, for a price lower than farmed Atlantic salmon,” said cofounder Justin Kolbeck.

It’s likely that one day in the future—maybe distant, maybe not so much—we’ll look back in disbelief at the way we used to raise and slaughter entire animals just to get a few cuts of their flesh. It will seem wasteful and barbaric compared to growing exactly the cuts of meat that we want, with no death or pollution involved. It’s still a ways off, but if companies like Wildtype can make their vision a reality, people, animals, and the planet will all be better off for it.

Image Credit: Wildtype

One of these technologies is drones. Though the regulations around them are still somewhat piecemeal, drones have seen a steady uptick in practical use cases over the last couple years. Then along came a virus that made people want to stay at home, avoid interaction with strangers, and buy a lot more of the products they need for day-to-day life online (Amazon’s stock has gone up by more than 50 percent this year, and the company has had to hire 175,000 new employees to keep up with the huge demand spike in online purchases).

As America’s biggest retailer (as of 2019 it still far outpaced Amazon in terms of revenue), it’s only logical that Walmart is rushing to keep up—and looking to cutting-edge tech to help. This week the company launched a pilot drone delivery program in Fayetteville, North Carolina. In partnership with drone company Flytrex, the mega-retailer is initially planning to use drones to deliver grocery and household items.

Flytrex has been around since 2013, and has aimed to cater to people living in suburban areas; the company’s website emphasizes that urbanites have access to plenty of shopping and delivery options, while suburb-dwellers tend to be at least a few-mile drive away from stores and often don’t have access to a lot of home delivery services.

Flytrex drones go 32 miles per hour, have a cruising height of 230 feet, and can carry up to 6.6 pounds (“6-8 hamburgers” is the somewhat odd example their website gives for this weight). The drones don’t have any onboard cameras, navigating with GPS and sensors only, and they can fly about seven miles before needing a recharge. The company has been working with Icelandic retailer AHA since 2018, delivering groceries to peoples’ backyards in Reykjavik.

Controlled via a cloud-based dashboard, the Walmart delivery drones will hover 80 feet above customers’ yards and lower their orders down using a tether. This seems more complication-prone than other final-delivery options, like having the drone land and the customer pull their order from it, but hey, we’re in a pandemic and contact-free everything is what’s in style.

Walmart and Flytrex haven’t yet said how long the pilot program will last nor when or whether it will be expanded to other areas. But drone delivery is set to be a trend that only grows; a 2019 study estimated that the number of delivery drones in the global e-commerce industry will reach 2.2 million units by 2025, in the process creating a slew of jobs to repair and maintain the drones. Amazon was just granted FAA approval for drone delivery at the end of August, and will soon start its own pilot program in the US.

It will be a while still before we can look up and regularly see drones whizzing across the sky, or have one drop our packages in the yard for us—but the wheels (and propellers) have definitely been set in motion.

Image Credit: Walmart

In keeping with the trend, ridesharing market leader Uber announced on Tuesday that it will transition to a 100 percent electric car fleet by 2030. Lyft, its main competitor, made a similar announcement in June. Are the ride-hailing companies’ commitments to greening linked to the pandemic? It’s unclear; they likely would have implemented this switch at some point in the near future anyway, and the pandemic may simply have accelerated it (as it did for other technologies and trends, like automation and remote work).

The pandemic hasn’t been kind to Uber; for starters, no one really went anywhere for months on end. When people did venture out of their homes—anxious and restless and clad in the same sweats they’d been wearing all week—they opted for transportation methods that minimized contact with other people and with potentially germ-covered surfaces; walking, biking, and driving one’s own car all saw a resurgence. Uber implemented safety protocols including requiring drivers and passengers to wear masks at all times, but their business has still taken a big hit. I mean, come on—where’s the last place you went other than your living room, kitchen, or the nearest park (which you can most likely walk to)?

The company is focused on a brighter future, though, and one not dependent on fossil fuels. It’s a good thing, because ridesharing actually causes more pollution than driving one’s own car. When you drive yourself somewhere, you get there, turn the car off, and go do whatever you’re there to do; in other words, the car is only running when you’re using it to get from point A to point B. But Ubers and Lyfts run pretty much constantly—they drop you off then circle around for a while until they can pick up another rider, or they sit idling waiting for a notification to come in. All that in-between time adds up; a study from February of this year found that ride-hailing trips cause up to 69 percent more climate pollution than the trips they displace.

That’s not only not great for the environment, but not great for the future of ridesharing companies. So here’s Uber’s fix: it plans to be a zero-emissions platform by 2040, have 100 percent of its US, Canadian, and European rides take place in electric cars by 2030, and reach net-zero emissions from its corporate operations by 2030.

These goals are well and good, but not without complications. For starters, it’s not Uber that owns its cars—its drivers do. That means anyone who wants to pick up some cash on the side driving for Uber—and those who drive as a full-time job—would have to buy their own electric cars. The cost of electric cars is predicted to drop below that of gas cars by 2022, and they’re cheaper to own and operate over a long term, but for now, they still require a significantly higher up-front cash outlay.

For a company that doesn’t have a shining record of treating its employees well (it has fought tooth and nail to keep from having to give drivers employee benefits rather than hiring them as independent contractors), it might be a lot to ask to have drivers cough up an extra few thousand dollars to help meet a goal they may not particularly care about. Anticipating this, Uber says it has earmarked $800 million to help its drivers transition to electric vehicles. In a deal with General Motors, Uber drivers can get employee pricing on new Chevy Bolts (on top of an $8,500 rebate offered to all buyers). Uber is also launching additional incentives for drivers to electrify, such as getting paid an extra dollar for each ride they give in an electric car (in Canada and the US only).

Even if these incentives are enough to convince drivers to go out and buy an electric car, though (and if it were me, it would take a lot more than what’s being offered to get me to trade in my trusty, beloved Saab I’ve had for years), the added challenges don’t end there. Drivers will have to find places to charge their vehicles, which is trickier than fueling up at a gas station, especially if you don’t live in a place where you can just run an extension cord from your house out to your car.

Given how ubiquitous Uber and other ride-sharing services have become, it’s funny to think they weren’t even around ten years ago. Remember stepping into the street and throwing an arm into the air to hail a cab? The idea of being able to summon a custom ride anytime, anywhere using a powerful but tiny handheld computer was nothing short of inconceivable back then.

Uber’s commitment to go all-electric by 2030 is a lofty goal. But also, 10 years is a long way away, and a lot could happen during that time. Maybe drivers will be more willing than we think to spend some extra money for a plug-in vehicle. Or maybe, ten years down the road, we’ll have an entirely new method of transportation that has nothing to do with Uber—one that, as of 2020, is perhaps… nothing short of inconceivable.

Image Credit: Uber

So the last thing we need right now is yet another wrench in the spokes of democracy, in the form of disinformation; we all saw how that played out in 2016, and it wasn’t pretty. For the record, disinformation purposely misleads people, while misinformation is simply inaccurate, but without malicious intent. While there’s not a ton tech can do to make people feel safe at crowded polling stations or up the Postal Service’s budget, tech can help with disinformation, and Microsoft is trying to do so.

On Tuesday the company released two new tools designed to combat disinformation, described in a blog post by VP of Customer Security and Trust Tom Burt and Chief Scientific Officer Eric Horvitz.

The first is Microsoft Video Authenticator, which is made to detect deepfakes. In case you’re not familiar with this wicked byproduct of AI progress, “deepfakes” refers to audio or visual files made using artificial intelligence that can manipulate peoples’ voices or likenesses to make it look like they said things they didn’t. Editing a video to string together words and form a sentence someone didn’t say doesn’t count as a deepfake; though there’s manipulation involved, you don’t need a neural network and you’re not generating any original content or footage.

The Authenticator analyzes videos or images and tells users the percentage chance that they’ve been artificially manipulated. For videos, the tool can even analyze individual frames in real time.

Deepfake videos are made by feeding hundreds of hours of video of someone into a neural network, “teaching” the network the minutiae of the person’s voice, pronunciation, mannerisms, gestures, etc. It’s like when you do an imitation of your annoying coworker from accounting, complete with mimicking the way he makes every sentence sound like a question and his eyes widen when he talks about complex spreadsheets. You’ve spent hours—no, months—in his presence and have his personality quirks down pat. An AI algorithm that produces deepfakes needs to learn those same quirks, and more, about whoever the creator’s target is.

Given enough real information and examples, the algorithm can then generate its own fake footage, with deepfake creators using computer graphics and manually tweaking the output to make it as realistic as possible.

The scariest part? To make a deepfake, you don’t need a fancy computer or even a ton of knowledge about software. There are open-source programs people can access for free online, and as far as finding video footage of famous people—well, we’ve got YouTube to thank for how easy that is.

Microsoft’s Video Authenticator can detect the blending boundary of a deepfake and subtle fading or greyscale elements that the human eye may not be able to see.

In the blog post, Burt and Horvitz point out that as time goes by, deepfakes are only going to get better and become harder to detect; after all, they’re generated by neural networks that are continuously learning from and improving themselves.

Microsoft’s counter-tactic is to come in from the opposite angle, that is, being able to confirm beyond doubt that a video, image, or piece of news is real (I mean, can McDonald’s fries cure baldness? Did a seal slap a kayaker in the face with an octopus? Never has it been so imperative that the world know the truth).

A tool built into Microsoft Azure, the company’s cloud computing service, lets content producers add digital hashes and certificates to their content, and a reader (which can be used as a browser extension) checks the certificates and matches the hashes to indicate the content is authentic.

Finally, Microsoft also launched an interactive “Spot the Deepfake” quiz it developed in collaboration with the University of Washington’s Center for an Informed Public, deepfake detection company Sensity, and USA Today. The quiz is intended to help people “learn about synthetic media, develop critical media literacy skills, and gain awareness of the impact of synthetic media on democracy.”

The impact Microsoft’s new tools will have remains to be seen—but hey, we’re glad they’re trying. And they’re not alone; Facebook, Twitter, and YouTube have all taken steps to ban and remove deepfakes from their sites. The AI Foundation’s Reality Defender uses synthetic media detection algorithms to identify fake content. There’s even a coalition of big tech companies teaming up to try to fight election interference.

One thing is for sure: between a global pandemic, widespread protests and riots, mass unemployment, a hobbled economy, and the disinformation that’s remained rife through it all, we’re going to need all the help we can get to make it through not just the election, but the rest of the conga-line-of-catastrophes year that is 2020.

Image Credit: Darius Bashar on Unsplash

“Hey Alexa, play soothing music at volume three.”

“Hey Alexa, tell me where to find my keys.”

You can ask an Alexa or Google home assistant questions about facts, news, or the weather, and make commands for whatever you’ve synced them to (lights, alarms, TVs, etc.). But helping you find things is a capability that hasn’t quite come to pass yet; smart home assistants are essentially very rudimentary, auditory-only “brains” with limited functions.

But what if home assistants had a “body” too? How much more would they be able to do for us? (And what if the answer is “more than we want”?)

If Facebook’s AI research objectives are successful, it may not be long before home assistants take on a whole new range of capabilities. Last week the company announced new work focused on advancing what it calls “embodied AI”: basically, a smart robot that will be able to move around your house to help you remember things, find things, and maybe even do things.

Robots That Hear, Home Assistants That See

In Facebook’s blog post about audio-visual navigation for embodied AI, the authors point out that most of today’s robots are “deaf”; they move through spaces based purely on visual perception. The company’s new research aims to train AI using both visual and audio data, letting smart robots detect and follow objects that make noise as well as use sounds to understand a physical space.

The company is using a dataset called SoundSpaces to train AI. SoundSpaces simulates sounds you might hear in an indoor environment, like doors opening and closing, water running, a TV show playing, or a phone ringing. What’s more, the nature of these sounds varies based on where they’re coming from; the center of a room versus a corner of it, or a large, open room versus a small, enclosed one. SoundSpaces incorporates geometric details of spaces so that its AI can learn to navigate based on audio.

This means, the paper explains, that an AI “can now act upon ‘go find the ringing phone’ rather than ‘go to the phone that is 25 feet southwest of your current position.’ It can discover the goal position on its own using multimodal sensing.”

The company also introduced SemanticMapnet, a mapping tool that creates pixel-level maps of indoor spaces to help robots understand and navigate them. You can easily answer questions about your home or office space like “How many pieces of furniture are in the living room?” or “Which wall of the kitchen is the stove against?” The goal with SemanticMapnet is for smart robots to be able to do the same—and help us find and remember things in the process.

Semantic MapNet, our new framework for building top-down semantic maps and spatio-semantic memories (“mental maps”) from egocentric observations. This enables remembering where objects are and reasoning about spatial tasks (e.g. find the table). https://t.co/3JpXpK0YCY pic.twitter.com/uxduRnzoOM

— Facebook AI (@facebookai) August 24, 2020

These tools expand on Facebook’s Replica dataset and Habitat simulator platform, released in mid-2019.

The company envisions its new tools eventually being integrated into augmented reality glasses, which would take in all kinds of details about the wearer’s environment and be able to remember those details and recall them on demand. Facebook’s chief technology officer, Mike Schroepfer, told CNN Business, “If you can build these systems, they can help you remember the important parts of your life.”

Smart Assistants, Dumb People?

But before embracing these tools, we should consider their deeper implications. Don’t we want to be able to remember the important parts of our lives without help from digital assistants?

Take GPS. Before it came along, we were perfectly capable of getting from point A to point B using paper maps, written instructions, and good old-fashioned brain power (and maybe occasionally stopping to ask another human for directions). But now we blindly rely on our phones to guide us through every block of our journeys. Ever notice how much harder it seems to learn your way around a new place or remember the way to a new part of town than it used to?

The seemingly all-encompassing wisdom of digital tools can lead us to trust them unquestioningly, sometimes to our detriment (both in indirect ways—using our brains less—and direct ways, like driving a car into the ocean or nearly off a cliff because the GPS said to).

It seems like the more of our thinking we outsource to machines, the less we’re able to think on our own. Is that a trend we’d be wise to continue? Do we really need or want smart robots to tell us where our keys are or whether we forgot to add the salt while we’re cooking?

While allowing AI to take on more of our cognitive tasks and functions—to become our memory, which is essentially what Facebook’s new tech is building towards—will make our lives easier in some ways, it will also come with hidden costs or unintended consequences, as most technologies do. We must not only be aware of these consequences, but carefully weigh them against a technology’s benefits before integrating it into our lives—and our homes.

Image Credit: snake3d / Shutterstock.com