Some software developers are now letting artificial intelligence help write their code. They’re finding that AI is just as flawed as humans.
Last June, GitHub, a subsidiary of Microsoft that provides tools for hosting and collaborating on code, released a beta version of a program that uses AI to assist programmers. Start typing a command, a database query, or a request to an API, and the program, called Copilot, will guess your intent and write the rest.
Alex Naka, a data scientist at a biotech firm who signed up to test Copilot, says the program can be very helpful, and it has changed the way he works. “It lets me spend less time jumping to the browser to look up API docs or examples on Stack Overflow,” he says. “It does feel a little like my work has shifted from being a generator of code to being a discriminator of it.”
But Naka has found that errors can creep into his code in different ways. “There have been times where I’ve missed some kind of subtle error when I accept one of its proposals,” he says. “And it can be really hard to track this down, perhaps because it seems like it makes errors that have a different flavor than the kind I would make.”
The risks of AI generating faulty code may be surprisingly high. Researchers at NYU recently analyzed code generated by Copilot and found that, for certain tasks where security is crucial, the code contains security flaws around 40 percent of the time.
The figure “is a little bit higher than I would have expected,” says Brendan Dolan-Gavitt, a professor at NYU involved with the analysis. “But the way Copilot was trained wasn’t actually to write good code—it was just to produce the kind of text that would follow a given prompt.”
Despite such flaws, Copilot and similar AI-powered tools may herald a sea change in the way software developers write code. There’s growing interest in using AI to help automate more mundane work. But Copilot also highlights some of the pitfalls of today’s AI techniques.
While analyzing the code made available for a Copilot plugin, Dolan-Gavitt found that it included a list of restricted phrases. These were apparently introduced to prevent the system from blurting out offensive messages or copying well-known code written by someone else.
Oege de Moor, vice president of research at GitHub and one of the developers of Copilot, says security has been a concern from the start. He says the percentage of flawed code cited by the NYU researchers is only relevant for a subset of code where security flaws are more likely.
De Moor invented CodeQL, a tool used by the NYU researchers that automatically identifies bugs in code. He says GitHub recommends that developers use Copilot together with CodeQL to ensure their work is safe.
The GitHub program is built on top of an AI model developed by OpenAI, a prominent AI company doing cutting-edge work in machine learning. That model, called Codex, consists of a large artificial neural network trained to predict the next characters in both text and computer code. The algorithm ingested billions of lines of code stored on GitHub—not all of it perfect—in order to learn how to write code.
OpenAI has built its own AI coding tool on top of Codex that can perform some stunning coding tricks. It can turn a typed instruction, such as “Create an array of random variables between 1 and 100 and then return the largest of them,” into working code in several programming languages.
Copilot and Codex have led some developers to wonder if AI might automate them out of work. In fact, as Naka’s experience shows, developers need considerable skill to use the program, as they often must vet or tweak its suggestions.
Hammond Pearce, a postdoctoral researcher at NYU involved with the analysis of Copilot code, says the program sometimes produces problematic code because it doesn’t fully understand what a piece of code is trying to do. “Vulnerabilities are often caused by a lack of context that a developer needs to know,” he says.
Some developers worry that AI is already picking up bad habits. “We have worked hard as an industry to get away from copy-pasting solutions, and now Copilot has created a supercharged version of that,” says Maxim Khailo, a software developer who has experimented with using AI to generate code but has not tried Copilot.
Khailo says it might be possible for hackers to mess with a program like Copilot. “If I was a bad actor, what I would do would be to create vulnerable code projects on GitHub, artificially boost their popularity by buying GitHub stars on the black market, and hope that it will become part of the corpus for the next training round.”
Both GitHub and OpenAI say that, on the contrary, their AI coding tools are only likely to become less error prone. OpenAI says it vets projects and code both manually and using automated tools.
De Moor at GitHub says recent updates to Copilot should have reduced the frequency of security vulnerabilities. But he adds that his team is exploring other ways of improving the output of Copilot. One is to remove bad examples that the underlying AI model learns from. Another may be to use reinforcement learning, an AI technique that has produced some impressive results in games and other areas, to automatically spot bad output, including previously unseen examples. “Enormous improvements are happening,” he says. “It’s almost unimaginable what it will look like in a year.”
Will Knight is a senior writer for WIRED, covering artificial intelligence. He was previously a senior editor at MIT Technology Review, where he wrote about fundamental advances in AI and China’s AI boom. Before that, he was an editor and writer at New Scientist. He studied anthropology and journalism in..
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The robot revolution is always allegedly just around the corner. In the utopian vision, technology emancipates human labor from repetitive, mundane tasks, freeing us to be more productive and take on more fulfilling work. In the dystopian vision, robots come for everyone’s jobs, put millions and millions of people out of work, and throw the economy into chaos.
Such a warning was at the crux of Andrew Yang’s ill-fated presidential campaign, helping propel his case for universal basic income that he argued would become necessary when automation left so many workers out. It’s the argument many corporate executives make whenever there’s a suggestion they might have to raise wages: $15 an hour will just mean machines taking your order at McDonald’s instead of people, they say. It’s an effective scare tactic for some workers.
But we often spend so much time talking about the potential for robots to take our jobs that we fail to look at how they are already changing them — sometimes for the better, but sometimes not. New technologies can give corporations tools for monitoring, managing, and motivating their workforces, sometimes in ways that are harmful. The technology itself might not be innately nefarious, but it makes it easier for companies to maintain tight control on workers and squeeze and exploit them to maximize profits.
“The basic incentives of the system have always been there: employers wanting to maximize the value they get out of their workers while minimizing the cost of labor, the incentive to want to control and monitor and surveil their workers,” said Brian Chen, staff attorney at the National Employment Law Project (NELP). “And if technology allows them to do that more cheaply or more efficiently, well then of course they’re going to use technology to do that.”
Automation hasn’t replaced all the workers in warehouses, but it has made work more intense, even dangerous, and changed how tightly workers are managed. Gig workers can find themselves at the whims of an app’s black-box algorithm that lets workers flood the app to compete with each other at a frantic pace for pay so low that how lucrative any given trip or job is can depend on the tip, leaving workers reliant on the generosity of an anonymous stranger. Worse, gig work means they’re doing their jobs without many typical labor protections.
In these circumstances, the robots aren’t taking jobs, they’re making jobs worse. Companies are automating away autonomy and putting profit-maximizing strategies on digital overdrive, turning work into a space with fewer carrots and more sticks.
A robot boss can do a whole lot more watching
In recent years, Amazon has become the corporate poster child for automation in the name of efficiency — often at the expense of workers. There have been countless reports of unsustainable conditions and expectations at Amazon’s fulfillment centers. Its drivers reportedly have to consent to being watched by artificial intelligence, and warehouse workers who don’t move fast enough can be fired.
Demands are so high that there have been reports of people urinating in bottles to avoid taking a break. The robots aren’t just watching, they’re also picking up some of the work. Sometimes, it’s for the better, but in other cases, they may actually be making work more dangerous as more automation leads to more pressure on workers. One report found that worker injuries were more prevalent in Amazon warehouses with robots than warehouses without them.
“It would have been prohibitively expensive to employ enough managers to time each worker’s every move to a fraction of a second or ride along in every truck, but now it takes maybe one,” Dzieza wrote. “This is why the companies that most aggressively pursue these tactics all take on a similar form: a large pool of poorly paid, easily replaced, often part-time or contract workers at the bottom; a small group of highly paid workers who design the software that manages them at the top.”
A 2018 Gartner survey found that half of large companies were already using some type of nontraditional techniques to keep an eye on their workers, including analyzing their communications, gathering biometric data, and examining how workers are using their workspace. They anticipated that by 2020, 80 percent of large companies would be using such methods. Amid the pandemic, the trend picked up pace as businesses sought more ways to keep tabs on the new waves of workers working from home.
This has all sorts of implications for workers, who lose privacy and autonomy when they’re constantly being watched and directed by technology. Daron Acemoglu, an economist at MIT, warned that they’re also losing money. “Some of these new digital technologies are not simply replacing workers or creating new tasks or changing other aspects of productivity, but they’re actually monitoring people much more effectively, and that means rents are being shared very differently because of digital technologies,” he said.
He offered up a hypothetical example of a delivery driver who is asked to deliver a certain number of packages in a day. Decades ago, the company might pay the driver more to incentivize them to work a little faster or harder or put in some extra time. But now, they’re constantly being monitored so that the company knows exactly what they’re doing and is looking for ways to save time. Instead of getting a bonus for hitting certain metrics, they’re dinged for spending a few seconds too long here or there.
The problem isn’t technology itself, it’s the managers and corporate structures behind it that look at workers as a cost to be cut instead of as a resource.
“A lot of this boom of Silicon Valley entrepreneurship where venture capital made it very easy for companies to create firms didn’t exactly prioritize the well-being of workers as one of their main considerations,” said Amy Bix, a historian at Iowa State University who focuses on technology. “A lot of what goes on in the structure of these corporations and the development of technology is invisible to most ordinary people, and it’s easy to take advantage of that.”
The future of Uber isn’t driverless cars, it’s drivers
In 2016, former CEO Travis Kalanick told Bloomberg making an autonomous vehicle was “basically existential” for the company. After a deadly accident with an autonomous Uber vehicle in 2018, current chief executive Dara Khosrowshahi reiterated that the company remained “absolutely committed” to the self-driving cause. But in December 2020 and after investing $1 billion, Uber sold off its self-driving unit. A little over four months later, its main competitor, Lyft, followed suit. Uber says it’s still not giving up on autonomous technology, but the writing on the wall is clear that driverless cars aren’t core to Uber’s business model, at least in the near future.
“Five or 10 years from now, drivers are still going to be a big piece of the mix on a percentage basis [of Uber’s business], and on an absolute basis, they may be an even bigger piece than they are today even with autonomous in the mix because the business should get bigger as both segments get bigger,” said Chris Frank, director of corporate ratings at S&P Global. “In addition, drivers will need to handle more complex conditions like poorly marked roads or inclement weather.”
In other words, they’re going to need workers to make money — workers they would very much like not to classify as such.
Gig economy companies such as Uber, Lyft, and DoorDash are fighting tooth and nail to make sure the people they enlist to make deliveries or drive people around are not considered their employees. In California last year, such companies dumped $200 million into lobbying to pass Proposition 22, which lets app-based transportation and delivery companies classify their workers as independent contractors and therefore avoid paying for benefits such as sick leave, employer-provided health care, and unemployment. After it passed, a spokesman for the campaign for the ballot measure said it “represents the future of work in an increasingly technologically-driven economy.”
It’s a future of work that might not be pleasant for gig workers. In California, some workers say they’re not getting the benefits companies promised after Prop 22’s passage, such as health care stipends. Companies said that workers would make at least 120 percent of California’s minimum wage, but that’s contemplating the time they spend driving only. Before the ballot initiative was passed, research from the UC Berkeley Labor Center estimated that it would guarantee a minimum wage of just $5.64 per hour.
Companies say they’ve been clear with drivers about how to qualify for the health care stipend, which is available to drivers with more than 15 engaged hours a week (in other words, if you don’t have a job and are waiting around, it doesn’t count). In a statement to Vox, Geoff Vetter, a spokesperson for the Protect App-Based Drivers + Services Coalition, the lobbying group that championed Prop 22, said that 80 percent of drivers work fewer than 20 hours per week and most work less than 10 hours per week, and that many have health insurance through other jobs.
Gig companies have sometimes been cagey about how much their workers make, and they’re often changing their formulas. In 2017, Uber agreed to pay the Federal Trade Commission $20 million over charges that it misled prospective drivers about how much they could make with the app. The FTC found that Uber claimed some of its drivers made $90,000 in New York and $74,000 in San Francisco, when in reality their median incomes were actually $61,000 and $53,000, respectively. DoorDash caused controversy over a decision to pocket tips and use them to pay delivery workers, which it has since reversed.
Even though Uber is charging customers more for rides in the wake of the pandemic, that’s not directly being passed onto their drivers. According to the Washington Post, Uber changed the way it paid drivers in California soon after Prop 22 passed so that they were no longer paid a proportion of the cost of the ride but instead by time and distance, with different bonuses and incentives based on market and surge pricing. (This is how Uber does it in most states, but it had changed things up during the push to get Prop 22 passed.) Uber’s CEO pushed back on the Post story in a series of tweets, arguing that decoupling driver pay from customer fares had not hurt California drivers and that some are now getting a higher cut from their rides.
In light of a driver shortage, Uber recently announced what it’s billing as a $250 million “driver stimulus” that promises higher earnings to try to get drivers back onto the road. The company acknowledges this initiative is likely temporary once the supply-demand imbalance works itself out. Still, it’s hard not to notice how quickly Uber and Lyft have been able to corner most of the ride-hailing app market and exert control over their drivers and customers.
“When a new thing like this comes on, there’s huge new consumer benefits, and then over time they are the market, they have less competition except one another, probably they’re a cartel at this point. And then they start doing stuff that’s much nastier,” said David Autor, an economist at MIT.
One of the gig economy’s main selling points to workers is that it offers flexibility and the ability to work when they want. It’s certainly true that an Uber or Lyft driver has much more autonomy on the job than, say, an Amazon warehouse worker. “People drive with Lyft because they prefer the freedom and flexibility to work when, where, and for however long they want,” a Lyft spokesperson said in a statement to Vox.
“They can choose to accept a ride or not, enjoy unlimited upward earning potential, and can decide to take time off from driving whenever they want, for however long they want, without needing to ask a ‘boss’ — all things they can’t do at most traditional jobs.” The spokesperson also noted that most of its drivers work outside of Lyft.
But flexibility doesn’t mean gig companies have no control over their drivers and delivery people. They use all sorts of tricks and incentives to try to push workers in certain directions and manage them, essentially, by algorithm. Uber drivers report being bothered by the constant surveillance, the lack of transparency from the company, and the dehumanization of working with the app. The algorithm doesn’t want to know how your day is, it just wants you to work as efficiently as possible to maximize its profits.
Carlos Ramos, a former Lyft driver in San Diego, described the feeling of being manipulated by the app. He noticed the company must have needed morning drivers because of the incentives structures, but he also often wondered if he was being “punished” if he didn’t do something right.
“Sometimes, if you cancel a bunch of rides in a row or if you don’t take certain rides to certain things, you won’t get any rides. They’ve shadow turned you off,” he said. The secret deprioritization of a worker is something many Lyft and Uber drivers speculate happens. “You also have no way of knowing what’s going on behind there. They have this proprietary knowledge, they have this black box of trade secrets, and those are your secrets you’re telling them,” said Ramos, now an organizer with Gig Workers Rising.
Companies deny that they secretly shut off drivers. “It is in Lyft’s best interests for drivers to have as positive an experience as possible, so we communicate often and work directly with drivers to help them improve their earnings,” a Lyft spokesperson said. “We never ‘shadow ban’ drivers, and actively coach them when they are in danger of being deactivated.”
The future of innovation isn’t inevitable
We often talk about technology and innovation with a language of inevitability. It’s as though whenever wages go up, companies will of course replace workers with robots. Now that the country is turned on to online delivery, it can be made to seem like the grocery industry is on an unavoidable path to gig work. After all, that’s what happened with Albertsons. But that’s not really the case — there’s plenty of human agency in the technological innovation story.
“Technology of course doesn’t have to exploit workers, it doesn’t have to mean robots are coming for all of our jobs,” Chen said. “These are not inevitable outcomes, they are human decisions, and they are almost always made by people who are driven by a profit motive that tends to exploit the poor and working class historically.”
Chase Copridge, a longtime California worker who’s done the gamut of gig jobs — Instacart, DoorDash, Amazon Flex, Uber, and Lyft — is one of the people stuck in that position, the victim of corporate tendencies on technological overdrive. He described seeing delivery offers that pay as little as $2. He turns those jobs down, knowing that it’s not economically worth it for him. But there might be someone else out there who picks it up. “We’re people who desperately need to make ends meet, who are willing to take the bare minimum that these companies are giving out to us,” he said. “People need to understand that these companies thrive off of exploitation.”
Not all decisions around automation are ones that increase productivity or improve really anything except corporate profits. Self-checkout stations may reduce the need for cashiers, but are they really making the shopping experience faster or better? Next time you go to the grocery store and inevitably screw up scanning one of your own items and waiting several minutes for a worker to appear, you tell me.
Despite technological advancements, productivity growth has been on the decline in recent years. “This is the paradox of the last several decades, and especially since 2000, that we had enormous technological changes as we perceive it but measured productivity growth is quite weak,” Autor said. “One reason may be that we’re automating a lot of trivial stuff rather than important stuff. If you compare antibiotics and indoor plumbing and electrification and air travel and telecommunications to DoorDash and smartphones or self-checkout, it may just not be as consequential.”
Acemoglu said that when firms focus so much on automation and monitoring technologies, they might not explore other areas that could be more productive, such as creating new tasks or building out new industries. “Those are the things that I worry have fallen by the wayside in the last several years,” he said. “If your employer is really set on monitoring you really tightly, that biases things against new tasks because those are things that are not easier to monitor.”
It matters what you automate, and not all automation is equally beneficial, not only to workers but also to customers, companies, and the broader economy.
Grappling with how to handle technological advancements and the ways they change people’s lives, including at work, is no easy task. While the robot revolution isn’t taking everyone’s jobs, automation is taking some of them, especially in areas such as manufacturing. And it’s just making work different: A machine may not eliminate a position entirely, but it may turn a more middle-skill job into a low-skill job, bringing lower pay with it. Package delivery jobs used to come with a union, benefits, and stable pay; with the rise of the gig economy, that’s declining. If and when self-driving trucks arrive, there will still be some low-quality jobs needed to complete tasks the robots can’t.
“The issue that we’ve faced in the US economy is that we’ve lost a lot of middle-skill jobs so people are being pushed down into lower categories,” Autor said. “Automation historically has tended to take the most dirty and dangerous and demeaning jobs and hand them over to machines, and that’s been great.
What’s happened in the last bunch of decades is that automation has affected the middle-skill jobs and left the hard, interesting, creative jobs and the hands-on jobs that require a lot of dexterity and flexibility but don’t require a lot of formal skills.”
But again, none of this is inevitable. Companies are able to leverage technology to get the most out of workers because workers often don’t have power to push back, enforce limits, or ask for more. Unionization has seen steep declines in recent decades. America’s labor laws and regulations are designed around full-time work, meaning gig companies don’t have to offer health insurance or help fund unemployment. But the laws could — and many would argue should — be modernized.
“The key thing is it’s not just technology, it’s a question of labor power, both collectively and individually,” Bix said. “There are a lot of possible outcomes, and in the end, technology is a human creation. It’s a product of social priorities and what gets developed and adopted.”
Maybe the robot apocalypse isn’t here yet. Or it is, and many of us aren’t quite recognizing it, in part because we got some of the story wrong. The problem isn’t really the robot, it’s what your boss wants the robot to do.
Concepts of artificial servants and companions date at least as far back as the ancient legends of Cadmus, who is said to have sown dragon teeth that turned into soldiers and Pygmalion whose statue of Galatea came to life. Many ancient mythologies included artificial people, such as the talking mechanical handmaidens (Ancient Greek: Κουραι Χρυσεαι (Kourai Khryseai); “Golden Maidens”) built by the Greek god Hephaestus (Vulcan to the Romans) out of gold.
Gaston Maspero (2009). Manual of Egyptian Archaeology: A Guide to the Studies of Antiquities in Egypt. BoD. p. 108. ISBN9783861950967.
Ronnie Littlejohn, Jeffrey Dippmann (2011). Riding the Wind with Liezi: New Perspectives on the Daoist Classic. SUNY Press. pp. 195–196. ISBN9781438434551.
Ronnie Littlejohn, Jeffrey Dippmann (2011). Riding the Wind with Liezi: New Perspectives on the Daoist Classic. SUNY Press. p. 196. ISBN9781438434551.
Ronnie Littlejohn, Jeffrey Dippmann (2011). Riding the Wind with Liezi: New Perspectives on the Daoist Classic. SUNY Press. p. 195. ISBN9781438434551.
Ronnie Littlejohn, Jeffrey Dippmann (2011). Riding the Wind with Liezi: New Perspectives on the Daoist Classic. SUNY Press. pp. 194–195. ISBN9781438434551.
Ronnie Littlejohn, Jeffrey Dippmann (2011). Riding the Wind with Liezi: New Perspectives on the Daoist Classic. SUNY Press. p. 197. ISBN9781438434551.
Adrienne Mayor (2018). Gods and Robots: Myths, Machines, and Ancient Dreams of Technology. Princeton University Press. pp. 205–206. ISBN9780691185446.
Haug, “Walewein as a postclassical literary experiment”, pp. 23–4; Roman van Walewein, ed. G.A. van Es, De Jeeste van Walewein en het Schaakbord van Penninc en Pieter Vostaert (Zwolle, 1957): 877 ff and 3526 ff.
See also P. Sullivan, “Medieval Automata: The ‘Chambre de beautés’ in Benoît‘s Roman de Troie.” Romance Studies 6 (1985): 1–20.
Hemal, Ashok K.; Menon, Mani (2018). Robotics in Genitourinary Surgery. Springer. p. 7. ISBN9783319206455.
Kevin LaGrandeur (2013). Androids and Intelligent Networks in Early Modern Literature and Culture: Artificial Slaves. Routledge. p. 24. ISBN9780415631211.
Kevin LaGrandeur (2013). Androids and Intelligent Networks in Early Modern Literature and Culture: Artificial Slaves. Routledge. p. 29. ISBN9780415631211.
Kevin LaGrandeur (2013). Androids and Intelligent Networks in Early Modern Literature and Culture: Artificial Slaves. Routledge. p. 30. ISBN9780415631211.
Rosheim, Mark E. (1994), Robot Evolution: The Development of Anthrobotics, John Wiley & Sons, p. 9, ISBN9780471026228
Kevin LaGrandeur (2013). Androids and Intelligent Networks in Early Modern Literature and Culture: Artificial Slaves. Routledge. p. 31. ISBN9780415631211.
Kevin LaGrandeur (2013). Androids and Intelligent Networks in Early Modern Literature and Culture: Artificial Slaves. Routledge. p. 33. ISBN9780415631211.
Ashok K. Hemal & Mani Menon (2018). Robotics in Genitourinary Surgery. Springer. p. 6. ISBN9783319206455.
Ashok K. Hemal & Mani Menon (2018). Robotics in Genitourinary Surgery. Springer. p. 5. ISBN9783319206455.
Armand Mattelart (1996). The Invention of Communication. University of Minnesota Press. p. 23. ISBN9780816626977.
T. N. Hornyak (2006). Loving the Machine: The Art and Science of Japanese Robots. Kodansha International.
Lisa Nocks (2006). The Robot: The Life Story of a Technology. Greenwood Publishing Group. p. 55]. ISBN9780313331688.
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Armin Krishnan (2016). Killer Robots: Legality and Ethicality of Autonomous Weapons. Routledge. p. 15. ISBN9781317109129.
Rob Dyrdek takes a measured approach to his daily activities. The serial entrepreneur and venture studio founder, who happens to also host MTV’s hit show Ridiculousness—a comedy show featuring famous guests like Kylie Jenner–says he schedules out nearly every minute of every day on his calendar, with the goal of maximizing his time and energy.
To wit, Dyrdek organizes his calendar by categories and subcategories, like time with his wife or kids, hitting the gym, brain training, and work. He also wakes up every day and rates from 0 to 10 how he slept, how motivated he feels, and how he felt about various aspects of the previous day, like his life, work, and health. All of this data gets scraped together and aggregated into dashboards, using a program that he paid someone to build.
With that insight, he says, you can move things out of your life you don’t like doing and focus on what makes you happy. “It’s all about how much can you automate and systematize in your existence in order to really live as light as possible,” he says.
What else helps? A little dome time. At 6:30 a.m. almost every day Dyrdek says he spends about 20 minutes time in a Somadome, a large meditation pod that uses colors and binaural beats that play through a headphone (essentially sound therapy) set to help you relax. You climb in, pull down the door, and then choose ambient noise or a specific meditation session like “love” or “heal.”
Dyrdek discovered the pod in January 2018, when a friend told him about it, and his children’s health specialist offered to connect him with the company’s CEO, Sarah Attia. At that time, Dyrdek was unsure of how to tackle a meditation practice, despite the long list of potential benefits. “It just was so ominous a mountain that I wasn’t ready to climb,” he says. “As soon as I wake up, I go. So it’shard for me to even think, how am I ever going to get myself into a meditative state.”
The Somadome, along with Dyrdek’s other life optimization techniques, he says, makes it easier–especially when meditation has become so useful for helping him reach his goals. In 2018, Dyrdek was negotiating a TV deal for Ridiculousness and was hoping to bolster an eventual sale of his production company, Superjacket Productions, by maximizing the number of episodes slated for the show. During the negotiations, he would sit in his Somadome andvisualize how it would feel to stand on stage and say, “Welcome to Season 30.”
He landed on a deal with an “unprecedented” 500-episode order that would mean he’d finish the show in season 30. “So I can’t tell you that the dome did it, but I had clarity,”he says, adding that entrepreneurs often underestimate the extent to which mental precision can help them both design their lives and evolve their businesses. In late 2019, Thrill One Sports & Entertainment acquired Dyrdek’s portfolio companies Superjacket Productions and Street League Skateboarding.
For Dyrdek, the best part about the Somadome is the various features that make difficult things, like remaining calm and clear about what you want out of life and meditating consistently, easy. He paid $25,000 for the device when he bought it and says he’s used it almost daily since. “It’s paid for itself a thousand fold,” he says. A smaller and less expensive version–about $4,000–will soon become available to consumers, according to the company.
What is the first thing you do when you launch a new smartphone ? Download all the apps you need, of course. After a few hours (or days) downloading applications, your entry menu ends up covered in colorful squares, giving you the satisfaction that you have everything: apps for social networks, transport, dating, online commerce, for video conferencing and fitness, for name the most popular.
However, recent research found that many of them are slowly killing your smartphone. The pCloud company, which offers cloud storage services, conducted a study to discover which applications are most demanding for our mobile devices.
The research looked at 100 of the most popular apps based on three criteria: the features each app uses (such as location or camera), the battery consumption, and whether dark mode is available. Thus they found which of these not only drain the battery of our phone, they also occupy the most memory and make it slower.
These are the apps classified as ‘smartphone killers’
According to the study, the Fitbit and Verizon apps turned out to be the biggest ‘smartphone killers. Both allow 14 of the 16 available functions to run in the background, including the four most demanding: camera, location, microphone and WiFi connection. This earned them the highest score in the study: 92.31%.
Of the 20 most demanding applications for mobile battery, 6 are social networks . Facebook , Instagram , Snapchat , Youtube , WhatsApp, and LinkedIn allow 11 functions to run in the background, such as photos, WiFi, location, and microphone. Of these, only IG allows dark mode to save up to 30% battery, just like Twitter , which did not enter the top 20.
Dating apps Tinder , Bumble and Grinder account for 15% of the top 20 most demanding apps. On average, they allow 11 functions to run in the background and none have a dark mode.
In terms of the amount of memory they require, travel and transportation apps dominated the list. The United Airlines app is the one that consumes the most storage on the phone, as it requires 437.8 MB of space. Lyft follows with 325.1 MB and then Uber , which occupies 299.6 MB.
Among the video conferencing apps, Microsoft Teams is the one that consumes the most memory, occupying 232.2 MB of space. In comparison, Zoom only requires 82.1 MB and Skype 111.2 MB.
The top 20 of the most demanding applications, based on the functions they execute and all the activity they generate, was as follows:
Fitbit – 92%
Verizon – 92%
Uber – 87%
Skype – 87%
Facebook – 82%
AirB & B – 82%
BIGO LIVE – 82%
Instagram – 79%
Tinder – 77%
Bumble – 77%
Snapchat – 77%
WhatsApp – 77%
Zoom – 77%
YouTube – 77%
Booking – 77%
Amazon – 77%
Telegram – 77%
Grinder – 72%
Likke – 72%
LinkedIn – 72%
Among the 50 applications that kill the battery and memory of the phone are also Twitter (no. 25), Shazam (30), Shein (31), Spotify (32), Pinterest (37), Amazon Prime (38), Netflix (40), TikTok (41), Duolingo (44) and Uber Eats (50).
If you are already considering doing a general cleaning of apps, you can consult the complete list here .
Our smartphones have become such an integral part of our lives that we can’t imagine life without it. Just like any object, phones are also subjected to wear and tear as well as our mishandling. Here are some things that you should stop if you want to prolong your phone’s life.
Draining your phone’s battery Most smartphones have lithium-ion batteries with limited life cycles. If you’re constantly draining your phone to 1% before charging, it reduces the battery’s life cycles.
Exposing your phone to drastic temperatures We understand that your phone can’t be left in your bag or pocket all the time. However, don’t leave it out in temperatures below 0 and above 35 degrees celsius as permanent damages may be done to the handset.
Maxing out your storage Your phone needs extra storage space in order for the operating system to continue functioning. Maxing out your storage causes your phone to lag or crash. Avoid this by backing up your phone’s content regularly to either your computer or cloud storage.
Leaving your phone in the shower Doesn’t a nice hot shower feels good at the end of the day? Not so much for your phone. Steam can seep into your phone and condense into water, which may short circuit the hardware.
Constantly dropping your phone No matter how good the protective casing your phone is in, dropping it constantly will affect its internal hardware. Be thankful if it’s just a cracked screen; more often than not, the damages are more serious than that.
Too many background apps Is it really necessary to keep Candy Crush, Facebook, Instagram, Calendar and Whatsapp all opened at the same time? This causes your phone to dedicate extra RAM to these apps and drains your battery.
Not turning your phone off Like humans, your phone also needs a break once in a while. Leaving it on 24/7 can shorten the lifespan of the battery and decrease its performance.
Overnight charging Most smartphones are clever enough to cut off the power supply to the battery once it’s fully charged. However, lithium-ion batteries don’t fare well against high heats. When you leave your phone plugged in overnight, especially with the casing on, overheating can occur and decrease the battery life.
Relying on cellular data If you’re only using 3G/4G for internet connectivity, think again. Connecting to Wi-Fi consumes less energy than data network which helps make your battery lasts longer.
Cleaning your phone with household products There’s a reason why cleaning agents exist specifically for phones. The chemicals in your household bleach or detergent can damage the protective layer often found on your phone’s screen.
In the US, a 2016 Gallup poll found that the majority of schools want to start teaching code, with 66 percent of K-12 school principals thinking that computer science learning should be incorporated into other subjects. Most countries in Europe have added coding classes and computer science to their school curricula, with France and Spain introducing theirs in 2015. This new generation of coders is expected to boost the worldwide developer population from 23.9 million in 2019 to 28.7 million in 2024.
Despite all this effort, there’s still some confusion on how to teach coding. Is it more like a language, or more like math? Some new research may have settled this question by watching the brain’s activity while subjects read Python code.
Two schools on schooling
Right now, there are two schools of thought. The prevailing one is that coding is a type of language, with its own grammar rules and syntax that must be followed. After all, they’re called coding languages for a reason, right? This idea even has its own snazzy acronym: Coding as Another Language, or CAL. Others think that it’s a bit like learning the logic found in math; formulas and algorithms to create output from input. There’s even a free online course to teach you both coding and math at the same time.
Which approach is more effective? The debate has been around since coding was first taught in schools, but it looks like the language argument is now winning. Laws in Texas, Oklahoma, and Georgia allow high school students to take computer science to fulfill their foreign language credits (the 2013 Texas law says this applies if the student has already taken a foreign language class and appears unlikely to advance).
The debate holds a special interest for neuroscientists; since computer programming has only been around for a few decades, the brain has not evolved any special region to handle it. It must be repurposing a region of the brain normally used for something else.
So late last year, neuroscientists in MIT tried to see what parts of the brain people use when dealing with computer programming. “The ability to interpret computer code is a remarkable cognitive skill that bears parallels to diverse cognitive domains, including general executive functions, math, logic, and language,” they wrote.
Since coding can be learned as an adult, they figured it must rely on some pre-existing cognitive system in our brains. Two brain systems seemed like likely candidates: either the brain’s language system, or the system that tackles complex cognitive tasks such as solving math problems or a crossword. The latter is known as the “multiple demand network.”
Coding on the brain
In their experiment, researchers asked participants already proficient at coding to lie in an fMRI machine to measure their brain activity. They were then asked to read a coding problem and asked to predict the output.The two coding languages used in the study are known for their “readability”—Python and ScratchJr. The latter was specifically developed for children and is symbol-based so that children who have not yet learned to read can still use it.
The main task involved giving participants a person’s height and weight and asking them to calculate a person’s BMI. This problem was either presented as Python-style code or as a normal sentence. The same method was done for ScratchJr, but participants were asked to track the position of a kitten as it walked and jumped.
Control tasks involved memorizing a sequence of squares on a grid (to activate participants’ multiple demand system) and reading one normal and one nonsense sentence (to activate their language system). Their results showed that the language part of the brain responded weakly when reading code (the paper’s authors think this might be because there was no speaking/listening involved). Instead, these tasks were mostly handled by the multiple demand network.
The multiple demand network is spread across the frontal and parietal (top) lobes of our brain, and it’s responsible for intense mental tasks—the parts of our lives that make us think hard. The network can be roughly split between the left part (responsible for logic) and the right (more suited to abstract thinking). The MIT researchers found that reading Python code appears to activate both the left and right sides of the multiple demand network, and ScratchJr activated the right side slightly more than the left.
“We found that the language system does not respond consistently during code comprehension in spite of numerous similarities between code and natural languages,” they write.Interestingly, code-solving activated parts of the multiple-demand network that are not activated when solving math problems. So the brain doesn’t tackle it as language or logic—it appears to be its own thing.
The distinct process involved in interpreting computer code was backed up by an experiment done by Japanese neuroscientists last year. This work showed snippets of code to novice, experienced, and expert programmers while they lay in an fMRI. The participants were asked to categorize them into one of four types of algorithms. As expected, the programmers with higher skills were better at categorizing the snippets. But the researchers also found that activity in brain regions associated with natural language processing, episodic memory retrieval, and attention control also strengthened with the skill level of the programmer.
So while coding may not be as similar to languages as we had thought, it looks like both benefit from starting young.
Fintan is a freelance science journalist based in Hamburg, Germany. He has also written for The Irish Times, Horizon Magazine, and SciDev.net and covers European science policy, biology, health and bioethics.
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