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One of the most contentious debates in technology is around the question of automation and jobs. At issue is whether advances in automation, specifically with regards to artificial intelligence and robotics, will spell trouble for today’s workers. This debate is played out in the media daily, and passions run deep on both sides of the issue. In the past, however, automation has created jobs and increased real wages.
A widespread concern with the current scenario is that the workers most likely to be displaced by technology lack the skills needed to do the new jobs that same technology will create.
Let’s look at this concern in detail. Those who fear automation will hurt workers start by pointing out that there is a wide range of jobs, from low-pay, low-skill to high-pay, high-skill ones. This can be represented as follows:
They then point out that technology primarily creates high-paying jobs, like geneticists, as shown in the diagram below.
Meanwhile, technology destroys low-wage, low-skill jobs like those in fast food restaurants, as shown below:
Then, those who are worried about this dynamic often pose the question, “Do you really think a fast-food worker is going to become a geneticist?”
They worry that we are about to face a huge amount of systemic permanent unemployment, as the unskilled displaced workers are ill-equipped to do the jobs of tomorrow.
It is important to note that both sides of the debate are in agreement at this point. Unquestionably, technology destroys low-skilled, low-paying jobs while creating high-skilled, high-paying ones.
So, is that the end of the story? As a society are we destined to bifurcate into two groups, those who have training and earn high salaries in the new jobs, and those with less training who see their jobs vanishing to machines? Is this latter group forever locked out of economic plenty because they lack training?
The question, “Can a fast food worker become a geneticist?” is where the error comes in. Fast food workers don’t become geneticists. What happens is that a college biology professor becomes a geneticist. Then a high-school biology teacher gets the college job. Then the substitute teacher gets hired on full-time to fill the high school teaching job. All the way down.
The question is not whether those in the lowest-skilled jobs can do the high-skilled work. Instead the question is, “Can everyone do a job just a little harder than the job they have today?” If so, and I believe very deeply that this is the case, then every time technology creates a new job “at the top,” everyone gets a promotion.
This isn’t just an academic theory—it’s 200 years of economic history in the west. For 200 years, with the exception of the Great Depression, unemployment in the US has been between 2 percent and 13 percent. Always. Europe’s range is a bit wider, but not much.
If I took 200 years of unemployment rates and graphed them, and asked you to find where the assembly line took over manufacturing, or where steam power rapidly replaced animal power, or the lightning-fast adoption of electricity by industry, you wouldn’t be able to find those spots. They aren’t even blips in the unemployment record.
You don’t even have to look back as far as the assembly line to see this happening. It has happened non-stop for 200 years. Every fifty years, we lose about half of all jobs, and this has been pretty steady since 1800.
How is it that for 200 years we have lost half of all jobs every half century, but never has this process caused unemployment? Not only has it not caused unemployment, but during that time, we have had full employment against the backdrop of rising wages.
How can wages rise while half of all jobs are constantly being destroyed? Simple. Because new technology always increases worker productivity. It creates new jobs, like web designer and programmer, while destroying low-wage backbreaking work. When this happens, everyone along the way gets a better job.
Our current situation isn’t any different than the past. The nature of technology has always been to create high-skilled jobs and increase worker productivity. This is good news for everyone.
People often ask me what their children should study to make sure they have a job in the future. I usually say it doesn’t really matter. If I knew everything I know now and went back to the mid 1980s, what could I have taken in high school to make me better prepared for today? There is only one class, and it wasn’t computer science. It was typing. Who would have guessed?
The great skill is to be able to learn new things, and luckily, we all have that. In fact, that is our singular ability as a species. What I do in my day-to-day job consists largely of skills I have learned as the years have passed. In my experience, if you ask people at all job levels,“Would you like a little more challenging job to make a little more money?” almost everyone says yes.
That’s all it has taken for us to collectively get here today, and that’s all we need going forward.
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Swarms of microrobots will scuttle along beneath our roads and pavements, finding and fixing leaky pipes and faulty cables. Thanks to their efforts, we can avoid costly road work that costs billions of dollars each year—not to mention frustrating traffic delays.
That is, if a new project sponsored by the U.K. government is a success. Recent developments in the space seem to point towards a bright future for microrobots.
Microrobots Saving Billions
Each year, around 1.5 million road excavations take place across the U.K. Many are due to leaky pipes and faulty cables that necessitate excavation of road surfaces in order to fix them. The resulting repairs, alongside disruptions to traffic and businesses, are estimated to cost a whopping £6.3 billion ($8 billion).
A consortium of scientists, led by University of Sheffield Professor Kirill Horoshenkov, are planning to use microrobots to negate most of these costs. The group has received a £7.2 million ($9.2 million) grant to develop and build their bots.
According to Horoshenkov, the microrobots will come in two versions. One is an inspection bot, which will navigate along underground infrastructure and examine its condition via sonar. The inspectors will be complemented by worker bots capable of carrying out repairs with cement and adhesives or cleaning out blockages with a high-powered jet. The inspector bots will be around one centimeter long and possibly autonomous, while the worker bots will be slightly larger and steered via remote control.
If successful, it is believed the bots could potentially save the U.K. economy around £5 billion ($6.4 billion) a year.
The U.K. government has set aside a further £19 million ($24 million) for research into robots for hazardous environments, such as nuclear decommissioning, drones for oil pipeline monitoring, and artificial intelligence software to detect the need for repairs on satellites in orbit.
The Lowest-Hanging Fruit
Microrobots like the ones now under development in the U.K. have many potential advantages and use cases. Thanks to their small size they can navigate tight spaces, for example in search and rescue operations, and robot swarm technology would allow them to collaborate to perform many different functions, including in construction projects.
To date, the number of microrobots in use is relatively limited, but that could be about to change, with bots closing in on other types of inspection jobs, which could be considered one of the lowest-hanging fruits.
Engineering firm Rolls-Royce (not the car company, but the one that builds aircraft engines) is looking to use microrobots to inspect some of the up to 25,000 individual parts that make up an engine. The microrobots use the cockroach as a model, and Rolls Royce believes they could save engineers time when performing the maintenance checks that can take over a month per engine.
Even Smaller Successes
Going further down in scale, recent years have seen a string of successes for nanobots. For example, a team of researchers at the Femto-ST Institute have used nanobots to build what is likely the world’s smallest house (if this isn’t a category at Guinness, someone needs to get on the phone with them), which stands a ‘towering’ 0.015 millimeters.
One of the areas where nanobots have shown great promise is in medicine. Several studies have shown how the minute bots are capable of delivering drugs directly into dense biological tissue, which can otherwise be highly challenging to target directly. Such delivery systems have a great potential for improving the treatment of a wide range of ailments and illnesses, including cancer.
There’s no question that the ecosystem of microrobots and nanobots is evolving. While still in their early days, the above successes point to a near-future boom in the bots we may soon refer to as our ‘littlest everyday helpers.’
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Facial recognition technology has progressed to point where it now interprets emotions in facial expressions. This type of analysis is increasingly used in daily life. For example, companies can use facial recognition software to help with hiring decisions. Other programs scan the faces in crowds to identify threats to public safety.
Unfortunately, this technology struggles to interpret the emotions of black faces. My new study, published last month, shows that emotional analysis technology assigns more negative emotions to black men’s faces than white men’s faces.
This isn’t the first time that facial recognition programs have been shown to be biased. Google labeled black faces as gorillas. Cameras identified Asian faces as blinking. Facial recognition programs struggled to correctly identify gender for people with darker skin.
My work contributes to a growing call to better understand the hidden bias in artificial intelligence software.
To examine the bias in the facial recognition systems that analyze people’s emotions, I used a data set of 400 NBA player photos from the 2016 to 2017 season, because players are similar in their clothing, athleticism, age and gender. Also, since these are professional portraits, the players look at the camera in the picture.
I ran the images through two well-known types of emotional recognition software. Both assigned black players more negative emotional scores on average, no matter how much they smiled.
For example, consider the official NBA pictures of Darren Collison and Gordon Hayward. Both players are smiling, and, according to the facial recognition and analysis program Face++, Darren Collison and Gordon Hayward have similar smile scores—48.7 and 48.1 out of 100, respectively.
Basketball players Darren Collision (left) and Gordon Hayward (right). basketball-reference.com
However, Face++ rates Hayward’s expression as 59.7 percent happy and 0.13 percent angry and Collison’s expression as 39.2 percent happy and 27 percent angry. Collison is viewed as nearly as angry as he is happy and far angrier than Hayward—despite the facial recognition program itself recognizing that both players are smiling.
In contrast, Microsoft’s Face API viewed both men as happy. Still, Collison is viewed as less happy than Hayward, with 98 and 93 percent happiness scores, respectively. Despite his smile, Collison is even scored with a small amount of contempt, whereas Hayward has none.
Across all the NBA pictures, the same pattern emerges. On average, Face++ rates black faces as twice as angry as white faces. Face API scores black faces as three times more contemptuous than white faces. After matching players based on their smiles, both facial analysis programs are still more likely to assign the negative emotions of anger or contempt to black faces.
Stereotyped by AI
My study shows that facial recognition programs exhibit two distinct types of bias.
First, black faces were consistently scored as angrier than white faces for every smile. Face++ showed this type of bias. Second, black faces were always scored as angrier if there was any ambiguity about their facial expression. Face API displayed this type of disparity. Even if black faces are partially smiling, my analysis showed that the systems assumed more negative emotions as compared to their white counterparts with similar expressions. The average emotional scores were much closer across races, but there were still noticeable differences for black and white faces.
This observation aligns with other research, which suggests that black professionals must amplify positive emotions to receive parity in their workplace performance evaluations. Studies show that people perceive black men as more physically threatening than white men, even when they are the same size.
Some researchers argue that facial recognition technology is more objective than humans. But my study suggests that facial recognition reflects the same biases that people have. Black men’s facial expressions are scored with emotions associated with threatening behaviors more often than white men, even when they are smiling. There is good reason to believe that the use of facial recognition could formalize preexisting stereotypes into algorithms, automatically embedding them into everyday life.
Until facial recognition assesses black and white faces similarly, black people may need to exaggerate their positive facial expressions—essentially smile more—to reduce ambiguity and potentially negative interpretations by the technology.
Although innovative, artificial intelligence can perpetrate and exacerbate existing power dynamics, leading to disparate impact across racial/ethnic groups. Some societal accountability is necessary to ensure fairness to all groups because facial recognition, like most artificial intelligence, is often invisible to the people most affected by its decisions.
Lauren Rhue, Assistant Professor of Information Systems and Analytics, Wake Forest University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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