It's used to train algorithms for things like sentiment analysis, predictive typing, automatic proofreading, and so on. In an analysis of 150 years of British periodicals, researchers were able to accurately detect changes in society: When electricity replaced steam; when trains replaced horses; epidemics; wars; and so on. Here's a chart of 50 jobs analyzed in the study, showing how strongly that job is associated with the female gender, and what the actual gender representation is: It makes technically good predictions that are morally bad. But at the same time, this gender bias is actually an accurate representation of the data; analyzing it over time, we could even predict a trendline of changes.
Within the next five to ten years, the onset of automation and artificial intelligence (AI) will lead to a revolution of the legal industry that will likely transform that model completely. It is widely acknowledged that AI will eventually change the legal industry, and automation will over time replace certain functions: lawyers will be able to perform their current tasks far more accurately and effectively. Indeed, if and when AI progresses to a very high level of intelligence, a large part of a lawyer's work will shift from providing legal advice to instead marketing: trying to retain clients and attract new ones and working closely with them to understand their needs. From a client perspective, if the work which lawyers currently carry out shifts towards spending more time working on their relationships with them -- including how to more efficiently and innovatively invoice them -- then, ultimately, that will lead to greater value for clients.
Although people of both genders struggle with age discrimination, research has shown women begin to experience age discrimination in hiring practices before they reach 50, whereas men don't experience it until several years later. Just as technology is causing barriers inside the workplace for older employees, online applications and search engines could be hurting older workers looking for jobs. Many applications have required fields asking for date of birth and high school graduation, something many older employees choose to leave off their resumes. Furthermore, McCann said, some search engines allow people to filter their search based on high school graduation date, thereby allowing employers and employees to screen people and positions out of the running.
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%.