That means increasingly, businesses are in the market for a new kind of human worker: part tech nerd, part machinist. The new robotics lab at Bay de Noc Community College was funded by a grant from the U.S. Department of Labor. "It's not like GM [General Motors] where they're having entire assembly lines full of cars that are being built by robots, but even these smaller industries or smaller companies in our area are starting to use the same robotic arms," says Mark Kinney, Bay de Noc's Dean for Business and Technology. He says he'd like to put those to use building factory floor systems that are faster, safer and more precise, designing even slicker ways for robots and humans to share their work.
Exultantly titled books such as Triumphs and Wonders of the 19th Century, The Marvels of Modern Mechanism, Our Wonderful Progress, and Modern Wonder Workers were common. Larry Summers wrote in the Financial Times that "it is widely feared that half the jobs in the economy might be eliminated by innovations such as self-driving vehicles, automatic checkout machines and expert systems that trade securities more effectively than humans can." Klaus Schwab, head of the World Economic Forum, predicts that robotics and artificial intelligence will destroy 5 million jobs by 2020. According to the Bureau of Labor Statistics, truck drivers had a workplace fatal-injury rate seven times as high as the overall workplace average, and a non-fatal-injury rate three times as high.
This test measured the strength of associations between words as represented by GloVe, much as the IAT measures the strength of word associations in the human brain. The machine-learning tool reproduced human associations between flowers and pleasant words; insects and unpleasant words; musical instruments and pleasant words; and weapons and unpleasant words. Comparing the GloVe word-embedding results with real U.S. Bureau of Labor Statistics data on the percentage of women in occupations, Caliskan found a 90 percent correlation between professions that the GloVe saw as "female" and the actual percentage of women in those professions. In humans, implicit attitudes actually don't correlate very strongly with explicit attitudes about social groups.
In the new study, computer scientists replicated many of those biases while training an off-the-shelf machine learning AI on a "Common Crawl" body of text--2.2 million different words--collected from the Internet. To reveal the biases that can arise in natural language learning, Narayanan and his colleagues created new statistical tests based on the Implicit Association Test (IAT) used by psychologists to reveal human biases. Their work detailed in the 14 April 2017 issue of the journal Science is the first to show such human biases in "word embedding"--a statistical modeling technique commonly used in machine learning and natural language processing. Narayanan and his colleagues at Princeton University and University of Bath in the U.K. first developed a Word-Embedding Association Test (WEAT) to replicate the earlier examples of race and gender bias found in past psychology studies.
In the past year, dozens of companies, including GM, Japan Airlines, Hilton, and Pfizer, have launched initiatives using IBM's intelligence. Watson owes its ubiquity to the dozens of new AI tools, including emotional analysis and image recognition, that it offers developers. Retail outlets such as Macy's and the Mall of America are employing Watson's language-processing tools to help shoppers navigate their stores. The labor rights group Our Walmart recently created a chat app called WorkIt that uses Watson to digest the retailer's employment issues and policies (including leave-of-absence and sick-leave procedures) and answer questions that employees may not want to pose to managers.
As of now, LawGeex has trained its algorithm to deliver a review of up to 20 contract types including mainly business contracts, purchase orders and software licenses. "It is very different to how a lawyer would work," Shmuli Goldberg states. A robot lawyer works more accurately, Goldberg claims. Although few in numbers, some major law schools have already started to include technology into their curriculum.
The brief also states that globally, 655 million fewer women are economically active than men. In a previous report, MGI revealed that advancing women's equality could add $12 trillion to the global GDP by 2025. Why More People Aren't Working The MGI brief offers several reasons, including the fact that education hasn't keep pace with the skills needed for a changing workforce. MGI research on the potential for automation across 54 countries and more than 2,000 work activities indicated that the number of jobs that can be fully automated by adapting currently demonstrated technology is less than 5%.
"We are just starting to discover the countless ways we can apply cognitive computing to healthcare," said Ryan Pellet, senior vice president of consulting and services for Welltok. "We are excited to have addressed a costly and cumbersome issue with our proprietary technology and IBM Watson, and will continue to explore opportunities to simplify the consumer's experience and drive new, more effective ways to engage with and satisfy them."
Technology (the invention of steel) changed lives during the Industrial revolution, but it wasn't until mass production of the automobile nearly 100 years later that lives changed that much again. That's nearly a century for everyone to get used to change, and for legislation to catch up… just think: child labor laws didn't pass until after the turn of the 20th century, well over 100 years past the start of the Industrial Revolution.
Now imagine that time shoots forward fifteen years and a bank lends you money to buy one of the newfangled motorized trucks that are coming onto the market. But it still conveys an essential fact about the modern economy, which Karl Marx and other nineteenth-century critics of capitalism originally denied: over the long haul, technological progress raises productivity, which, in turn, generates higher wages and living standards. Between 1947 and 1973, output per hour (the standard measure of labor productivity) rose at an annual rate of about three per cent. According to the new figures, in the twelve months that ended in June, the growth rate of output per hour was negative 0.5 per cent.