It's easy for students to search the web when working on assignments, but Microsoft now wants to teach those students how to spot misinformation and otherwise think critically. In addition to an existing Search Coach, Microsoft is introducing a Search Progress feature in Teams for Education that helps teachers foster healthy online research habits through practice work. Educators can not only require a certain number of sources for an assignment, but check to see that students are searching responsibly — they'll know if pupils are only clicking the first site in the results, or using filters like NewsGuard to check source quality. Students can show their reasoning and work before turning in a project, too.
The Progress tool bolsters Search Coach (shown below), which encourages students using Teams to both search more precisely and watch out for falsehoods. They can limit searches to certain domains (such as .gov or .edu), date ranges and file types. They can even pass queries through fact checking sites to learn if a claim holds up under scrutiny. Bing's safe search is enabled by default, and the results are ad-free. Teachers can also use search trends to refine their lessons.
Search Progress will be available in preview form later in the year. Search Coach is already available in Teams worldwide. Both features will work with over 50 languages, Microsoft says.
Microsoft also wants to improve students' overall reading skills. The company's Reading Coach will be available in the Immersive Readers for Word Online, OneNote, Teams Assignments, Minecraft Education and other platforms, giving students more reading fluency experience both online and in the apps they use. Reading Progress, meanwhile, will add comprehension questions to be sure kids truly understand what they read. Both upgrades will be available later this year.
There's plenty of concern that OpenAI's ChatGPT could help students cheat on tests, but just how well would the chatbot fare if you asked it to write a graduate-level exam? It would pass — if only just. In a newly published study, University of Minnesota law professors had ChatGPT produce answers for graduate exams at four courses in their school. The AI passed all four, but with an average grade of C+. In another recent paper, Wharton School of Business professor Christian Terwiesch found that ChatGPT passed a business management exam with a B to B- grade. You wouldn't want to use the technology to impress academics, then.
The research teams found the AI to be inconsistent, to put it mildly. The University of Minnesota group noted that ChatGPT was good at addressing "basic legal rules" and summarizing doctrines, but floundered when trying to pinpoint issues relevant to a case. Terwiesch said the generator was "amazing" with simple operations management and process analysis questions, but couldn't handle advanced process questions. It even made mistakes with 6th grade-level math.
There's room for improvement. The Minnesota professors said they didn't adapt text generation prompts to specific courses or questions, and believed students could get better results with customization. At Wharton, Terwiesch said the bot was adept at changing answers in response to human coaching. ChatGPT might not ace an exam or essay by itself, but a cheater could have the system generate rough answers and refine them.
Both camps warned that schools should limit the use of technology to prevent ChatGPT-based cheating. They also recommended altering the questions to either discourage AI use (such as focusing on analysis rather than reciting rules) or increase the challenge for those people leaning on AI. Students still need to learn "fundamental skills" rather than leaning on a bot for help, the University of Minnesota said.
The study groups still believed that ChatGPT could have a place in the classroom. Professors could teach pupils how to rely on AI in the workplace, or even use it to write and grade exams. The tech could ultimately save time that could be spent on the students, Terwiesch explains, such as more student meetings and new course material.
It's arriving several months later than expected, but Arkane's next big game is (relatively) close at hand. The studio has confirmed that Redfall will be available on May 2nd for Xbox Series X/S and Windows PCs, with launch day access through Game Pass. As before, the vampire-slaying shooter is an expansion of the formulas behind Dishonored and Prey — it's a stealth-friendly game built with multiplayer and unpredictability in mind.
Like other Arkane titles, Redfall hinges on player choice. You can directly fight the vampires taking over the game's namesake town, but you'll also be rewarded if you take the stealthy approach or make clever uses of your gear. The open world concept isn't strictly new (Prey had it), but the choice of characters and online play are. You can choose a hero that reflects your play style, and up to four people can take down the villains in co-op mode. Certain tactics only work when you're playing as a team.
The free-ranging gameplay adds some variety, but you can also expect weapons with randomized stats and customization. Some mechanics will be familiar. You'll find activities beyond the main story missions (such as rescuing survivors), skill trees and other systems that encourage doing more than the essentials.
It's too soon to say if Redfall will maintain Arkane's reputation. However, it's evident the developer is happy to build on its better-known gameplay mechanics rather than take some chances like it did with Deathloop. If you tried Prey and wished your friends could join in, though, you might be happy.
Researchers say they have determined a way to make agrivoltaics — the process of growing crops underneath solar panels — more efficient. They found that red wavelengths are more efficient for growing plants, while the blue part of the spectrum is better for producing solar energy. Solar panels that only allow red wavelengths of light to pass through could enable farmers to grow food more productively while generating power at the same time.
Previous studies have found that agrivoltaics can reduce the amount of water required for crops, since they're shaded from direct sunlight. Researchers at Michigan Technological University determined in 2015 that shading can reduce water usage by up to 29 percent. Majdi Abou Najm, an associate professor at University of California, Davis' department of land, air and water resources, told Modern Farmer that by splitting the light spectrum, crops can get the same amount of carbon dioxide with less water while shielding them from heat.
The researchers put the idea to the test by growing tomatoes under blue and red filters, as well as a control crop without any coverings. Although the yield for the covered plots was about a third less than the control, the latter had around twice the amount of rotten tomatoes. Abou Najm noted that the filters helped to reduce heat stress and crop wastage.
Majdi Abou Najm/UC Davis
For this approach to work in practice, though, manufacturers would need to develop translucent solar panels that capture blue light and allow red light to pass through. Matteo Camporese, an associate professor at the University of Padova in Italy and lead author of a paper on the topic, suggested that translucent, carbon-based organic solar cells could work. These cells could be applied onto surfaces such as glass.
There are other issues, including the fact wavelength-selective agrivoltaic systems may need to account for different crop types. Harvesting those crops efficiently might require some out-of-the-box thinking too. Still, the research seems promising and, with a growing global population, it's important to consider different approaches to using our resources more productively.
“We cannot feed 2 billion more people in 30 years by being just a little more water-efficient and continuing as we do,” Abou Najm said. “We need something transformative, not incremental. If we treat the sun as a resource, we can work with shade and generate electricity while producing crops underneath. Kilowatt hours become a secondary crop you can harvest.”
On Tuesday, New York City public schools banned ChatGPT from school devices and WiFi networks. The artificial intelligence-powered chatbot, released by OpenAI in November, quickly gained a foothold with the public — and drew the ire of concerned organizations. In this case, the worry is that students will stunt their learning by cheating on tests and turning in essays they didn’t write.
ChatGPT (short for “generative pre-trained transformer”) is a startlingly impressive application, a sneak preview of the light and dark sides of AI’s incredible power. Like a text-producing version of AI art (OpenAI is the same company behind DALL-E 2), it can answer fact-based questions and write essays and articles that are often difficult to discern from human-written content. And it will only get harder to tell the difference as the AI improves.
“While the tool may be able to provide quick and easy answers to questions, it does not build critical-thinking and problem-solving skills, which are essential for academic and lifelong success,” Jenna Lyle, a spokesperson for New York City public schools, wrote in an email to NBC News. Still, the organization may have difficulty enforcing the ban. Blocking the chatbot over the school’s internet network and on lent-out devices is easy enough, but that won’t stop students from using it on their own devices with cellular networks or non-school WiFi.
OpenAI is developing “mitigations” it claims will help anyone identify ChatGPT-generated text. Although that’s a welcome move by the Elon Musk-founded startup, recent history isn’t exactly rife with examples of big business putting what’s best for society over the bottom line. (Relying on AI powerhouses to self-regulate sounds as foolproof as trusting the fossil-fuel industry to prioritize the environment over profits.) And artificial intelligence is big business: OpenAI has reportedly been in talks to sell shares at a $29 billion valuation, making it one of the most valuable US startups.
NurPhoto via Getty Images
Not everyone in the education community is against the AI chatbot. Adam Stevens, a teacher at Brooklyn Tech who spent years teaching history at NYC’s Paul Robeson High School, compares ChatGPT to the world’s most famous search engine. “People said the same thing about Google 15 or 20 years ago when students could ‘find answers online,’” he toldChalkbeat. He argues that the bot could be an ally for teachers, who could use it as a baseline essay response, which the class could work together to improve upon.
Stevens believes the key is to invite students to “explore things worth knowing” while moving away from standardized metrics. “We’ve trained a whole generation of kids to pursue rubric points and not knowledge,” he said, “and of course, if what matters is the point at the end of the semester, then ChatGPT is a threat.”
No matter how schools handle AI bots, the genie is out of the bottle. Barring government regulation (unlikely in the near future, given the US Congress’ current trajectory), AI-powered answers, essays and art are here to stay. The next part, dealing with the potential societal fallout — including the automation of more and more jobs — will be where the real challenges begin.
The holiday travel season is once again upon us! It's the magical time of the year that combines standing in airport security lines with incrementally losing your mind as the hands of your watch perpetually tick closer to a boarding time that magically moved up 45 minutes since you left the house and the goober in front of you is in the year of our lord 2022 still somehow confused about why we have to take our shoes off in security and goddamit dude stop arguing with the TSA and untie your laces already these tickets are nonrefundable.
Ai can help fix this. It can perhaps even give regular folks a taste of the effortless airport experience that more well-heeled travelers enjoy — the private jet set who don't ever have to worry about departure times or security lines like the rest of us schmucks stuck flying Spirit.
In their latest book POWER AND PREDICTION: The Disruptive Economics of Artificial Intelligence, University of Toronto economists and professors Ajay Agrawal, Joshua Gans, and Avi Goldfarb examine the foundational impact that AI/ML systems have on human decision making as we increasingly rely on automation and big data predictions. In the excerpt below, they posit what the airports of tomorrow might look like if AI eliminates traffic congestion and security delays.
Before considering the threat AI prediction may pose to airports, as with everything, there is an alternative system that can show us what the other side looks like. One example is the alternative universe of the very, very wealthy. They don’t fly commercial and so have no occasion to deal with either the old or newly designed public airport terminals. Instead, they fly privately and go through private terminals. Normally, glitz, glamour, nice restaurants, and art galleries are going to be where the very rich are. But in the world of airports, private terminals are positively spartan.
The reason there is no investment in making private terminals better places is that the very uncertainty that plagues the rest of us doesn’t plague the rich. With a commercial plane, you are tied to a schedule, and those planes will leave late passengers behind. With a private plane, the schedule is more flexible or even nonexistent. If the passengers aren’t there, the plane doesn’t leave until they arrive. If the passengers are there earlier, the plane leaves then. The whole system is designed so there is no waiting—at least, on the part of the passengers. No waiting means no need to invest in making waiting more pleasant. At the same time, the rich don’t have rules about when they need to leave for the airport. They leave when they want. If more people could have that experience, then surely the optimal terminal would be more spartan than cathedral.
You don’t have to be rich, however, to see this alternative universe. Instead, just compare the world on the other side of the arrival gates to those at departure. When arrival areas are separated from departure areas, they are spartan. You might find some light food outlets, but everything else is designed to get you out of the airport. The critical issue is how close the taxi and parking facilities are, even though you may not be in a stressful rush. Do you even remember any details of arrivals at your regular airport, other than how best to get out?
The AI Airport Threat
Airports are no strangers to AI. Air traffic control has adopted AI-based systems to better predict aircraft arrivals and congestion. At Eindhoven Airport, a new AI baggage-handling system is being piloted whereby passengers simply photograph their bags, drop them off, and pick them up at their destination—no labels required. Subject to privacy requirements, it hopes to do the same with people. All this will help you get to your flight more quickly.
None of these things, however, hit at the key drivers of uncertainty in your travel to your flight — traffic and security. Change, however, is already here with regard to traffic. Navigational apps such as Waze account for traffic conditions and can reasonably estimate how long it takes to get to any airport based on the time of day. The apps aren’t perfect, but they keep getting better.
The apps free passengers from having rules that tell them how early they need to leave for the airport. Instead, they can add that flight time to their calendar, and an app tells them the best time to depart and schedule their time accordingly. Even better, in the near future, the uncertainty in the actual time a flight leaves will be taken into account. Rather than just telling you when you need to leave based on a scheduled departure, the app will tell you when to leave depending on the flight’s predicted actual departure. Again, there is residual uncertainty, but the leap from having no information to having more precise information could save hours of waiting time. Similarly, many Uber riders who previously thought they wouldn’t care about knowing the predicted arrival time of their taxi now cite that information as one of the most valuable features of the service. Uber uses AI to make that prediction. AI could also predict security line wait times. Put it all together, and you can use the AI to decide when to leave for the airport rather than rely on rules. As with everything, there will be some who leap at this possibility ahead of others. At Incheon and many other airports, waiting isn’t bad anymore, so maybe you don’t need to make an informed decision.
Those developing an AI-driven navigation app or flight departure predictor have no direct interest in the earnings of in-terminal airport activities. However, the value of their AI applications depends critically on how many people do not want to wait at airports. Thus, if airports are currently less costly to wait in, the value of those apps is diminished. The security line prediction is another matter. Airports claim that they want to improve security times and reduce uncertainty. But as economists, we don’t think their incentives are aligned with passengers. Yes, improving security times leaves more time to spend at the facilities past security. But, at the same time, it will reduce uncertainty and cause people to tighten their airport arrival times. Combined with AI that solves the other uncertainty for passengers in getting to the terminal, will the airports want to eliminate the uncertainty under their own control?
Accommodating Rules
Our broader point is not about airports but about rules. Rules arise because it is costly to embrace uncertainty, but they create their own set of problems. The so-called Shirky Principle, put forth by technology writer Clay Shirky, states that “institutions will try to preserve the problem to which they are the solution.” The same can be said of businesses. If your business is to provide a way to help people when they wait for a plane, what’s the chance you are going to ensure they don’t have to wait for planes?
If you want to find opportunities by creating new AI-enabled decisions, you need to look beyond the guardrails that protect rules from the consequences of uncertainty and target activities that make bearing those costs easier or to reduce the likelihood of bad outcomes that the rules would otherwise have to tolerate.
We can see this in the long-standing protection farmers employ in England — building hedgerows. A hedgerow is a carefully planned set of robust trees and plants that serve as a wall between fields. It is extremely useful if your field is full of farm animals, and you do not want to employ a person to ensure they do not wander off. It is also useful if you do not want heavy rainfall to erode soil too quickly or if you want to protect crops from strong winds. Given all this protection against risky events, we are not surprised that this practice was the origin of the term “hedging,” which evolved to have a broader insurance meaning.
But hedgerows come at a cost. By dividing farmland, they make it impossible to use certain farming techniques — including mechanization — that are only efficient for large swathes of land. After World War II, the British government actually subsidized the removal of hedgerows, although in some cases, that removal was excessive, given their role in risk management. Today, there is a movement to restore hedgerows, led most prominently by the Prince of Wales. In many situations, costly investments are made to cover or shelter a would-be decision-maker from risk. Miles of highways are cocooned with guardrails to prevent cars from going down embankments, hills, or into oncoming traffic. Most are, fortunately, never used, but each allows a road to be built in a way that might have otherwise not been sufficiently safe, given the fallibility of human drivers.
More generally, building codes precisely specify various measures to protect those inside buildings from uncertain events. These include fire, but also damage from weather, weak building foundations, and other natural phenomena like earthquakes.
What these protection measures have in common is that they typically generate what looks like over-engineered solutions. They are designed for a certain set of events — the once-in-a-lifetime storm or the once-in-a-century flood. When those events occur, the engineering seems worthwhile. But, in their absence, there is cause to wonder. For many years, Freakonomics authors Steven Levitt and Stephen Dubner pointed out how life vests and rafts on aircraft — not to mention the safety demonstrations of each — appeared wasteful, given that no aircraft had successfully landed on water. Then, in 2009, Captain Sullenberger landed a US Airways plane with no working engines on the Hudson River. Does that one example of a low-probability event make the precautionary life vests worth it? It is hard to know. But we cannot conclude that the absence of a possible outcome causes us to assess the probability of that outcome at zero.
Levitt and Dubner’s main point, however, is that while it is often possible when protection measures are employed to assess the likelihood or change in the likelihood of underlying uncertainty over time, it is not possible to measure whether the investments made to reduce the probability of a consequence are excessive, as the very risk management strategy employed takes away that information. It is entirely possible that too much is wasted on something that, for other reasons, is no longer high risk at all.
Machine learning systems have been mopping the floor with their human opponents for well over a decade now (seriously, that first Watson Jeopardy win was all the way back in 2011), though the types of games they excel at are rather limited. Typically competitive board or video games using a limited play field, sequential moves and at least one clearly-defined opponent, any game that requires the crunching of numbers is to their advantage. Diplomacy, however, requires very little computation, instead demanding players negotiate directly with their opponents and make respective plays simultaneously — things modern ML systems are generally not built to do. But that hasn't stopped Meta researchers from designing an AI agent that can negotiate global policy positions as well as any UN ambassador.
Diplomacy was first released in 1959 and works like a more refined version of RISK where between two and seven players assume the roles of a European power and attempt to win the game by conquering their opponents' territories. Unlike RISK where the outcome of conflicts are decided by a simple the roll of the dice, Diplomacy demands players first negotiate with one another — setting up alliances, backstabbing, all that good stuff — before everybody moves their pieces simultaneously during the following game phase. The abilities to read and manipulate opponents, convince players to form alliances and plan complex strategies, navigate delicate partnerships and know when to switch sides, are all a huge part of the game — and all skills that machine learning systems generally lack.
On Wednesday, Meta AI researchers announced that they had surmounted those machine learning shortcomings with CICERO, the first AI to display human-level performance in Diplomacy. The team trained Cicero on 2.7 billion parameters over the course of 50,000 rounds at webDiplomacy.net, an online version of the game, where it ended up in second place (out of 19 participants) in a 5-game league tournament, all while doubling up the average score of its opponents.
The AI agent proved so adept "at using natural language to negotiate with people in Diplomacy that they often favored working with CICERO over other human participants," the Meta team noted in a press release Wednesday. "Diplomacy is a game about people rather than pieces. If an agent can't recognize that someone is likely bluffing or that another player would see a certain move as aggressive, it will quickly lose the game. Likewise, if it doesn't talk like a real person — showing empathy, building relationships, and speaking knowledgeably about the game — it won't find other players willing to work with it."
Meta
Essentially, Cicero combines the strategic mindset from Pluribot or AlphaGO with the natural language processing (NLP) abilities of Blenderbot or GPT-3. The agent is even capable of forethought. "Cicero can deduce, for example, that later in the game it will need the support of one particular player, and then craft a strategy to win that person’s favor – and even recognize the risks and opportunities that that player sees from their particular point of view," the research team noted.
The agent does not train through a standard reinforcement learning scheme as similar systems do. The Meta team explains that doing so would lead to suboptimal performance as, "relying purely on supervised learning to choose actions based on past dialogue results in an agent that is relatively weak and highly exploitable."
Instead Cicero uses "iterative planning algorithm that balances dialogue consistency with rationality." It will first predict its opponents' plays based on what happened during the negotiation round, as well as what play it thinks its opponents think it will make before "iteratively improving these predictions by trying to choose new policies that have higher expected value given the other players' predicted policies, while also trying to keep the new predictions close to the original policy predictions." Easy, right?
The system is not yet fool-proof, as the agent will occasionally get too clever and wind up playing itself by taking contradictory negotiating positions. Still, its performance in these early trials is superior to that of many human politicians. Meta plans to continue developing the system to "serve as a safe sandbox to advance research in human-AI interaction."
The former New York Post employee who hijacked the outlet’s content management system and Twitter account to post a series of racist and sexist headlines last week has apologized for his actions. “I deserved to get fired for a very volatile, irresponsible, and disgusting action and an utmost betrayal of the New York Post,” Miguel Gonzalez told the Daily Beast, revealing his identity to the outlet and public.
The 25-year-old claims he went on his publishing spree after suffering an “emotional tantrum,” further claiming his actions weren’t politically motivated. Among the things Gonzalez posted on October 27th were fake headlines calling for the murder of President Joe Biden and House Representative Alexandria Ocasio-Cortez. “I let my own stupidity get the best of me,” he told the outlet.
Gonzalez began working for The Post in 2019. It was his first job out of journalism school. When the incident first occured, The Post said it was the victim of a hacking attack, before later sharing that an employee had been the one to gain access to its systems. As a digital producer at the outlet, Gonzalez used his credentials to access The Post’s publishing tools with “relative ease,” and did so from his home in New Jersey. Gonzalez says he hopes to stay in journalism and he has started applying for jobs at outlets like Gothamist.
Women have faced gender-based discrimination in the workforce throughout history, denied employment in all but a handful of subservient roles, regularly ignored for promotions and pay raises — and rarely ever compensated at the same rates as their male peers. This long and storied socioeconomic tradition of financially screwing over half the population continues largely unabated into the 21st century where women still make 84 cents on the dollar that men do. In her new book, The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future, Professor of Law and founding member of the Center for Intellectual Property Law and Markets at the University of San Diego, Dr. Orly Lobel, explores how digital technologies, often maligned for their roles in exacerbating societal ills, can be harnessed to undo the damage they've caused.
For years, the double standard was glaring: employers demanded secrecy about salaries while asking prospective employees for their salary histories. Now, we can tackle both ends of this asymmetry. Just as digitization is helping to reverse information flows to foster more transparency in the market about employees’ worth, new laws are also directing employers to not rely as much on past pay levels, which can be tainted by systemic inequality. In 2016, Massachusetts became the first state to pass a law prohibiting employers from asking job candidates about their salary histories. Since then, more than a dozen states have followed suit.
Barring employers from asking prospective job candidates about their salary histories has two goals. The first is breaking the vicious pay gap cycle, which emerges when women are paid less at a previous job and that gap is then replicated by the next employer. The second is addressing gender differences in the negotiation process Salary figures are plagued by gender disparity, and they can perpetuate and further exacerbate existing market disparities. When a woman discloses that she currently earns less than a man, she could be harming her salary trajectory — both in the applied-for position and for the rest of her career. Each time she discloses her current salary to a potential employer, that gap is likely to grow, as recruitment efforts and promotions are often offered as a percentage increase in relation to current base salary. Rather than relying on biased figures, bans on salary history inquiry induce employers to use other ways to determine a potential employee’s worth, including a shift to automated computation. Employers using market and internal data can consider merit-related characteristics when determining pay, such as experience, training, education, skill, and past performance.
And yet, as we have seen, human bias can creep into our algorithms, and an algorithm that is fed data tainted by salary bias is likely to perpetuate that bias itself. Feedback loops are digital vicious cycles that can result in self-fulfilling outcomes. Once again: bias in, bias out. The risk is that an algorithm will learn that certain types or categories of employees are on average underpaid, and then calculate that into salary offers. This is the wrong that recent policy has been designed to eliminate — and that we can program AI to avoid. Removing the anchored numerical figure encourages employers to proactively assess pay based on the company’s needs and the candidate’s fit rather than on a tainted number. At the same time, having pay scale information for a job but not having a salary history on the table can embolden women to ask for more.
What’s more, AI can also help in the future — maybe not even the distant future — by replacing some of the negotiation that takes place in unequal settings. Empirical studies on negotiation differences between men and women have repeatedly shown that women on average negotiate less, and that when they do, employers react negatively. Women don’t ask for higher salaries, better terms, promotions, or opportunities nearly as frequently as men do. In my research, I’ve called this the negotiation deficit. In one study at Carnegie Mellon University, 93 percent of female MBA students accepted an initial salary offer, while only 43 percent of men did. In another study, female participants simulating salary negotiations asked for an average of $7,000 less than male participants. Economists Andreas Leibbrandt and John List have also found that while women are much less likely to negotiate with employers over salary, this difference disappears when all job seekers are explicitly told that pay is negotiable, mitigating the pay gap. My own experimental research with behavioral psychologist and law professor Yuval Feldman, my longtime collaborator, has found that women in some work environments act less as “homo economicus” — that is, as rational economic actors — and more as altruistic social actors, such that women do not demand for themselves as much as men, and are more likely to value non-monetary benefits, such as good corporate culture.
Can these research insights offer us clues for developing new software tools that will spur women to negotiate? Digital platforms can serve employees by providing advice and information on asking for a raise or preparing for an interview. Information on pay—and especially an explicit expectation that pay can and should be negotiated—can empower applicants to negotiate higher salaries before accepting job offers. The digital platform PayScale conducts annual surveys asking thousands of job seekers whether they disclosed their pay at previous jobs during the interview process. PayScale’s 2018 survey found that women who were asked about their salary histories and refused to disclose were offered positions 1.8 percent less often than women who were asked and disclosed. By contrast, men who refused to disclose when asked about salary history received offers 1.2 percent more often than men who did disclose.
Even when women do negotiate, they are treated differently. In my research, I call this phenomenon the negotiation penalty. Women are told to “lean in” and make demands, but the reality is that for centuries, women have been universally viewed as weaker negotiators than their male counterparts. In one series of experiments, participants evaluated written accounts of candidates who did or did not initiate negotiations for higher salaries. The results in each experiment showed that participants penalized female candidates more than male candidates for initiating negotiations, deeming women who asked for more not “nice” or too “demanding.” While qualities such as assertiveness, strength, and competitiveness culturally benefit male negotiators, women who display such characteristics are often considered too aggressive. Another study looked at data from a group of Swedish job seekers and found not only that women ended up with lower salaries than equally qualified male peers, but also that they were often penalized for negotiating like them. Nick Yee and Jeremy Bailenson have shown that attractive avatars lead to more intimate behavior with a confederate in terms of self-disclosure and interpersonal distance. In a second study, they also observed that tall avatars lead to more confident behavior than short avatars in a negotiation task. They term it the Proteus Effect (the Greek god Proteus was known to have the ability to take on many self-representations). The Proteus Effect suggests that the visual characteristics and traits of an avatar are associated with correlating behavioral stereotypes and expectations, including those that affect the way we negotiate.
The eleventh annual competition for artificial intelligence that has been trained to negotiate — the Hagglebot Olympics, as it’s been termed in the popular media — was held in January 2021. Universities from Turkey and Japan won this time. In some experiments involving negotiations with bots, most people did not even realize they were talking to a bot rather than another person — the bots had learned to hold fluent conversations that completely mimicked humans. Using game theory, researchers are increasingly improving the ways bots can negotiate on behalf of humans, eliminating some of the aspects in which we humans are fallible, like trying to factor in and weigh many different aspects of the deal. AI can now predict the other side’s preferences quite fast. For example, an AI listening by microphone to the first five minutes of negotiation is learning to predict much of the eventual deal just from the negotiators’ voices. Following these speech patterns through machine learning, it turns out that when the voice of a negotiator varies a lot in volume and pitch, they are being a weak player at the negotiation table. When the negotiating sides mirror each other, it means they are closer to reaching an agreement. Using AI also has helped uncover the ways in which women are penalized at the negotiation table. A new study out of the University of Southern California used a chatbot that didn’t know the gender identities of participants to evaluate negotiation skills. The study showed that most of us — both men and women — do quite badly at negotiating salaries. Over 40 percent of participants didn’t negotiate at all, and most people left money on the table they could have received. Women valued stock options less than men did as part of their compensation package, affecting women’s likelihood to accumulate wealth over time. These advances can also help with negotiation disparities across different identities. A group of Israeli and American researchers looked at how a smart computer can negotiate with humans from different cultural backgrounds. Without telling the machine anything about the characteristics of people from three countries — Israel, Lebanon, and the United States — they let the AI learn about the patterns of cultural negotiation differences by engaging in negotiation games. They found that the computer was able to outperform people in all countries. These developments are promising. We can envision bots learning about negotiation differences and ultimately countering such differences to create more equitable exchanges, level the playing field, and achieve fair outcomes. They can be designed to tackle the specific distributive goals we have.
As the need for cleaner, more sustainable transport becomes ever more urgent, I’ve noticed a familiar pattern in conversations on the topic. Someone will point out that bikes are a lot more efficient and environmentally friendly, reduce congestion and are often faster than cars in cities. Others respond saying that bikes can’t possibly replace cars for a multitude of reasons: Riding on roads is dangerous, it requires a fit body, it makes you get all sweaty, it’s not ideal for trips into the office and bikes can’t protect you from the rain. The other objection is that a standard bike can only carry one person, making it useless for the times when you need to carry multiple people, or lots of stuff. Bikes can’t be used to ferry kids on the school run or haul a week’s worth of groceries, and so it’s pointless to look at them.
Except, of course, bikes have always been able to do those things, sometimes more efficiently than a car, SUV or truck. Cargo bikes offer the capacity to carry multiple people at once and / or haul sizable loads of stuff with very little trouble. It’s this form of cycling that may provide the easiest win for both individuals and cities to help solve the climate crisis. The argument that you need to be physically fit to ride – if that’s even true – doesn’t really apply any more given the benefit of electrification. It means that modern cargo bikes can rid dense city streets of delivery vans cluttering up our roads, and SUVs doing little more than the school run. And this isn’t a dispatch from some far-flung utopia, but something that might become massively popular as a looming fuel crisis causes the price of fuel to skyrocket.
The Bakfiets
RUBEN RAMOS via Getty Images
It’s worth saying that cargo bikes are nothing new – in the days before the car was king, cargo bikes were used by many. In Europe, before the second world war, cargo bikes were a common sight on the streets, used by grocers, tradespeople and families to carry goods and people. In the post-war era, and the age of car-centric reconstruction that followed, cargo bikes were left a curiosity in many countries, save, of course, their use to sell ice cream or other food at funfairs, festivals and markets.
There are roughly four types of cargo bike in common use today, although none of these terms are official and there’s plenty of blurring on the edges. Cargo Bikes, for instance, are stretch limousine versions of regular two-wheeled bikes, with a small cargo section behind the front wheel and in front of the rider. Then there are Box Trikes, with two wheels up front and a much larger box between them, while the rider steers from behind. Now, both of these can be described as Bakfiets, from the Dutch “box bike,” but there’s a world between the two and three-wheeled versions.
A more nebulous category is the Longtail, a regular bicycle with a longer, load-bearing frame behind the rider. Instead of a pannier rack, the frame can hold a small cargo box, or a bench seat that can hold an adult or two children. Bikes like Tern’s GSD or Yuba’s Spicy Curry are examples of the type of bike I’m talking about here. Finally there are Cargo Trikes and Cargo Quad Cycles, where the rider sits up front and there’s a hefty box mounted on the two rear wheels. EAV’s 2Cubed, for instance, is already being adopted by some major logistics companies. (Obviously three-wheeled Bakfiets can also be called Cargo Trikes but I’m trying to keep the definitions clear here.)
The Babboe
Daniel Cooper
The Netherlands already underwent its dramatic transition into a cycling-first society, and is the nominal home of the cargo bike. Its bikes are designed not just for one or two people, but families of up to five, and I felt compelled to try one before lecturing people on the future of transport. Raleigh, the British distributors of several Dutch bicycles, leant me a Babboe Curve-E, which is arguably the SUV of the cycling world.
The Curve-E is big, beefy and relatively expensive – in Europe it retails for €3,449 ($3,441). The Curve-E’s box has a volume of around 275 liters (72 gallons) and a load capacity of 100kg (220 pounds), with two benches running along the front and back sides. On each side are two three-point harnesses, and the bike is designed to carry up to four small children comfortably.
(In the US, you can buy a more powerful mid-drive version of the Curve-E I rode from Going Dutch Bicycles in New York for $6,250. It’s worth saying, of course, that the cost of importing a model like this is significant, and there are domestic alternatives available for less. For instance, Bunch Bikes – which previously featured on Shark Tank – will sell you a four-seater model for $3,999.)
I’ve been using the Curve-E as much as I can in place of the family car, trying to see which parts of our lives it can fit into. My wife wasn’t enthused about being a participant in this story, and so I used the bike for various adventures with my two kids. Of particular interest to me was if the Babboe would revolutionize the school run, enabling me to save time at the start and end of each day.
Cleaning up our roads
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If you read Engadget, then you already know how bad cars and trucks are for climate change, air quality and congestion. The rise of e-commerce, supercharged by COVID, has seen a massive surge in fossil fuel-powered delivery vehicles on city streets. And that’s not good for congestion, air quality or emissions. But cargo cycling has already been found to be something of a silver bullet for all of the problems caused by this surge in heavy goods vehicles on our streets.
Last year, Dr. Ersilia Verlingheri at the University of Westminster found that a cargo bike is 1.61 times faster than a van to make deliveries. Using GPS data strapped to both bike and truck couriers, she found that the bikes had a faster average speed and reduced carbon emissions by 90 percent compared to a diesel vehicle, and 33 percent compared to an electric van. The study focused on London, and found that there are more than 213,100 vans working in the city, occupying 2,557,200 square meters of road space. Dr. Verlingheri’s study found that more than half of all motorized freight could be completed by a bike instead of a van. And that the benefits of doing so are staggering – including tens of thousands of hours lost to traffic jams, and several hundred thousand tonnes of CO2 not being released into the atmosphere.
A smaller 2019 study that focused on Seattle, found that electric-assisted cargo bikes were more cost-effective than vans in densely populated areas, such as the hearts of major cities. And that benefits of bikes were magnified when you added in the extra effort needed to find parking, and the second-order costs of owning a truck. Not to mention, of course, the cost of buying the truck, keeping it fueled, maintained, as well as the necessary insurances and permits to ensure it’s road legal.
Zedify
Daniel Cooper
One company already well ahead of this argument is Zedify, a British courier business making “last mile” deliveries in major cities. It exclusively uses low-and-zero emission vehicles, with the bulk of its fleet made up with a number of cargo trikes. The managing director of the Norwich branch of the company, Richard Jennings, talked me through the benefits of a bike-first delivery fleet. The first being the cost, the second being the relative speed compared to deliveries made by a light truck.
Jennings explained that most major freight companies operate large depots at business parks far outside a population center. Each van is loaded full with parcels before being sent in to cover a planned route that will take the bulk of the day to complete. Zedify’s model, by contrast, uses a smaller hub in the center of a city, where parcels in bulk are dropped off and then loaded on a smaller fleet of cargo trikes. These trikes will then do multiple routes each day, with riders able to choose their own routing in order to avoid cyclist-unfriendly roads and dodge traffic jams.
On paper, that sounds less efficient, but in practice Jennings said that it was significantly better, and Zedify deliveries are often a lot faster than expected. It’s also significantly cheaper, since all of the major capital costs associated with maintaining a fleet of vans are eliminated. The local setup, at least, uses cargo trikes from specialist provider Iceni Cycles, based in Wiltshire. It sells its heavy-duty delivery trike for £11,705 (around $13,486), or leases them for periods of up to five years for £61.47 ($71) a week.
While many fleet companies have to spend enormous sums on regular maintenance, Jennings can employ a single bike mechanic to run the entire fleet. Zedify doesn’t charge a premium for its services either, meaning that any cost savings can be passed on to employees. Jennings said that he’s able to “take better care of [his] people.” Zedify also made (local) headlines for being able to maintain deliveries during one of the UK’s several recent fuel crises.
There are limits, of course – a standard Iceni trike has a maximum weight limit of around 550 pounds, but Jennings says that the safe operating weight is just under 400. After that point, and hauling goods around just gets a lot harder to deal with. That means bicycle couriers won’t be delivering heavy goods, like home appliances or beds, any time soon. But the bulk of smaller goods could easily be carried by bike, removing a big reason for why city streets are full of vans. If companies like Zedify can corner the market in shipping and grocery delivery, then we should see significant benefits fairly quickly.
Jennings also showed me his latest purchase, a Maderna Tractor, a four-wheeled monster capable of taking pallet-sized loads. It’s equipped with a Bafang mid-drive motor that gives it extraordinary power and speed for a bike – as I learned when I rode it. It’s the sort of bike that you could imagine riding for a day without ever feeling fatigued, and certainly one you could have a lot of fun tearing around town on.
Our first trips
My adventures with the Babboe Curve-E involved me taking the kids out and about around the city. They were (and still are) delirious with excitement whenever we go out on the bike. Part of this, I suspect, is because it offers them a substantially better view of the trip compared to sitting in the back seat of a car. They like waving to people as we pass them by, and shouting hello to cyclists when they, in turn, pass us. They sit side-by-side on the forward facing bench, preferring the view (and a little bit of a squeeze) to one facing the other.
At a standing start, the bike requires a decent amount of push, but I found I didn’t need the electric assist at all. As soon as you start moving, the bike’s weight and inertia seem to do a lot of the work for you, to the point where I was riding the brakes more than the pedals. It’s also pretty quick, quicker than I was comfortable riding (especially with my kids in the front box) and so I never felt the need to switch up the gears to go faster.
More often than not, cars would give me a fairly generous berth – I think the uniqueness of the Babboe’s design on British roads meant there was some degree of curiosity. Especially on the main road close to my home, where cyclists are often given short shrift by motorists, it was a striking change. I suspect, too, the fact that the bike is wide enough, and my ride position high enough, that almost by default, I was taking a more aggressive pose on the road than I would ordinarily. That’s important, given the lack of segregated cycle infrastructure, although cargo bikes are often forced onto the roads by default, as most cycle lanes that do exist are designed for the two-wheeled variety.
The cargo boom
It’s clear that some of the factors that have boosted interest in cargo bikes relate to the energy crisis. COVID and Russia’s invasion of Ukraine have caused prices to spike, and Europeans are looking for ways to cut their energy consumption across the board. Back in August, Cycling Industry News reported that while e-bike sales – which had spiked for much of 2020 – were starting to slow, eCargo Bikes were still growing. In fact, the uptake of cargo bikes has increased by 37 percent compared to the previous year, while manufacturer Urban Arrow said that it expected to see sales jump by 50 percent across 2022.
The school run
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The kids enjoy the Babboe so much that they ask, whenever we go out, if we’re taking it or the car. I was, therefore, expecting this bike to totally revolutionize the school run each day and make everyone’s life a lot easier. It didn’t, but there’s one very good reason that I struggled in this instance, and I want to be clear that it is actually worth doing. You just need to really make sure that you know what bike you’re buying, and what your home terrain is like.
My home city is relatively flat, but it does have a handful of utterly murderous hills, and my kids’ school is at the top of one of the worst. According to local maps, the gentlest gradient to get up the hill is around 11 percent, which is a very significant slope. (The road on the other end has a maximum gradient of 22.4 percent, which I wouldn’t attempt to walk, let alone ride up.)
Now, I’ve tested my cycling output to be around 200W, and the motor on the Babboe can output 250W. But it turns out that it’s not enough, given the weight of the bike, to get up that 11 degree gradient without a lot of sweating. In fact, it’s so hard to get up there, especially with kids in the front, that no matter what gear I rode in, or what strategies I tried, with the electric assistance on full, I was still a hyperventilating puddle by the time I got to the top.
This, I should admit, is something that Babboe (if you check) does say in its marketing materials, as its bikes are designed for flat Dutch roads. If a buyer expects to cover a lot of hilly ground, then they should opt for the specialist Mountain version of its bike with a far more powerful mid-drive motor. At my child’s school, another parent bought the same model of Babboe that I had – but said that he would be trading it in for a Mountain version at the earliest opportunity.
Cost
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The elephant in the room is price. You can expect to pay upwards of $3,000 for a standard cargo cycle, and some of the fancier brands start at $5,000. The common response from cyclists is that people think nothing of dropping tens of thousands of dollars on a car, nor the hidden costs of fuel, tax, insurance, servicing and depreciation. On a total cost of ownership basis, the price difference between a car and a cargo bike is stark, and bikes win out nine times out of every 10 when picking the ideal form of transportation. But I can see, and share, the mental barriers to spending thousands on a bike for all of the obvious reasons.
For a start, the comfort level is far less than that of a car, you’re exposed to the elements and you’re limited by range. Then there’s the unspoken truth that in many countries in North America and Europe, bicycle theft is effectively legal. After all, with law enforcement resources stretched thin and the prevalence of bicycle crime, it’s difficult to enforce. Even in situations where people can show the location of their bike with built-in GPS, officers are reluctant to engage in recovery action.
Interesting (!) afternoon while filming, tracking my stolen bike which has an internal tracker & can’t be ridden without a code being ferried presumably in a van …from being swiped in London Bridge….To Stratford in half an hour and now finding a new resting place in East Ham… pic.twitter.com/4SBsatjvA8
I took plenty of extra precautions, and rarely let my Babboe out of my sight knowing that if I’d left it in the street, even with a chorus of locks, it was at risk. That dilemma is doubled for people who have spent upwards of $3,000 on an e-cargo bike as their primary mode of transportation. Sadly, a lack of infrastructure to keep these bikes safe and secure means that they’re a prime target for thieves, and so you can’t always trust that they’ll be where you left them.
The solution to this problem, surely, would be for a manufacturer to grasp this market for itself. Is it possible for someone to mass-produce a low-spec, but solid, cargo bike “for the people?” And, when I say that, I mean at the sort of prices where it’d be affordable for utility, rather than sport and leisure, cyclists.
Certainly, this isn’t likely to come in the form of a cargo trike. Ben Johnson is the founder of The Cargo Bike Company, a former engineer who got into cargo biking when his kids were born and he “couldn’t afford a European one.” He produces custom cargo bikes and trikes from his workshop in Derbyshire, UK, with a focus on commercial bikes as well as custom bikes adapted to assist people with mobility issues. He said that the rise in cargo cycling is tied to the falling cost and greater access of electric motors, which “enables people to shift loads around town.” He, however, has resisted the trend in his own bikes, saying that the reliability issues are too risky for a small business like his to take on.
Johnson added that there are several factors that mean that cargo bikes will remain a more costly purchase for many. That includes the fact that major manufacturers are “very happy to use unusual or high-end engineering” on its bikes, including drum brakes, geared hubs and stub axles. But as well as the equipment hung on the frame, a major difference between a regular bike and a cargo bike is the time taken to build the frame itself. For instance, Taiwanese maker Giant says that it can produce a bike frame in under two hours, whereas it takes Johnson a full day to weld a frame, and a further day to build the bike that sits on it – in between it’s sent off to a third-party for painting.
That’s not to say that there aren’t affordable cargo bikes available, but the segment that’s ripest for lower prices is the longtail. RadPower’s RadWagon 4 can take a 350lb payload on its long rear rack, or that space could be used to carry two passengers for just $2,000. Similarly, Richard Andrews, who works in local government on cycling strategy in the UK said that an even more disruptive bike is hiding in plain sight. He pointed to (French sports retailer) Decathlon’s R500 electric longtail as a bike that could be mass-produced by the sort of manufacturer who could afford the initial outlay. There are only two downsides to the R500 – it uses a rear hub motor, and it’s presently out of stock.
Farewell
It’s now time to send the Babboe back to the company for someone else to test it. I didn’t expect to feel as sad sending it back as I presently do, mostly because of how engaged it made my kids. It was fun to cycle – except up and down hills – and I think they enjoyed having a front-row seat on the journey, taking in the city around them. I think that, with a model better suited to the terrain, a cargo e-bike could remove the need for us to have a car for any trips into the city. The only thing I would need is a place to securely store it when I’m out and about, or the reassurance that it wouldn’t go missing.
I should, at least, have some hope there – here in the UK, the previous administration published Gear Change: A Bold Vision for Cycling and Walking. The paper committed to improving road design to ensure segregated cycleways – with a physical barrier between cars and bikes – would be built as standard. It also, more crucially, pledged to back the construction of high-quality, theft-deterrent bicycle parking in towns and cities, as well as bike hangers for residential areas. This should benefit folks who might want to switch to cargo cycling but don’t have the space to store a bike in their own home.
Fundamentally, I’m a convert, even if I still don’t consider myself a cyclist by any means. Riding a cargo bike feels natural, fun and easy, and is something I want to do on a regular basis, especially since I’d like to think my kids will still appreciate the help getting to and from places for the next five years or more. I think I learned two things over the last couple of months: Cargo cycling really is for everyone, and don’t buy a bike with a hub motor if you live anywhere close to a huge hill.
