AI is coming for your job. AI is taking over the world. If we compiled all the headlines about artificial intelligence from the last year, we'd have a picture of a dystopian world where jobs are scarce and AI and automation rule everything we do. In this scenario, millions of people are impacted by AI and autonomous systems created with little regard for their consequences: They are deployed in unethical ways, riddled with errors and bias, and discriminatory. The obscurity of how AI works and where it's used result in fear and confusion.
As artificial intelligence becomes more sophisticated and its ability to perform human tasks accelerates exponentially, we're finally seeing some attempts to wrestle with what that means, not just for business, but for humanity as a whole. From the first stone ax to the printing press to the latest ERP solution, technology that reduces or even eliminates physical and mental effort is as old as the human race itself. However, that doesn't make each step forward any less uncomfortable for the people whose work is directly affected – and the rise of AI is qualitatively different from past developments. Until now, we developed technology to handle specific routine tasks. A human needed to break down complex processes into their component tasks, determine how to automate each of those tasks, and finally create and refine the automation process.
Project-based learning opportunities come in all forms at MIT, as Melanie Chen discovered during her internship at Lincoln Laboratory this year. A computer science major, she served as a teaching assistant, curriculum developer, and mentor to high school students participating in Cog*Works, part of the Beaver Works Summer Institute. Now, finishing up her fall sophomore semester, Chen is finding plenty of opportunities to apply the lessons she has learned from her hands-on experience teaching others. "One of the greatest skills I've learned is effective communication," she notes. "Whether it's students, peers, colleagues, or mentors, I've learned what it takes to be able to create a trusting relationship, so that we can effectively work on a project of this scale together."
Nick Schwartz, Olivia Zhao, and Liang Zhou -- have been named winners of the prestigious Marshall Scholarship. Funded by the British government, the Marshall Scholarship program supports one or two years of graduate study in any field at a U.K. institution. Up to 40 top American students are selected each year. This year's awards continue MIT's strong showing; last year, MIT had four Marshall Scholar winners, the largest number of any university. Scholars are selected on the basis of academic merit, leadership, and ambassadorial potential to strengthen U.S.-U.K. understanding.
I'm practicing explaining ML concepts, so, experts, please correct me if any of my points are incorrect or misleading. For example, I was reading an example of regression analysis where the factors such as cylinders, displacement, horsepower affect the mpg of a car. The topic would touch on how many of these factors, or neurons is too much. It seems like you may be conflating features, data about what you're observing that you give as inputs to your model, such as "cylinders, displacement, horsepower", and neurons, which are fine-tuned by training to make up the function you're trying to learn. Maybe I can help you by giving a few reasons people don't just keep increasing the number of neurons.
For Peter Cao, who has dedicated 16 years of his career to teaching chemistry in a high school in central China's Anhui province, in every teacher there lives a "doctor". He spends two to three hours a day grading assignments, a process the 38-year-old describes as "diagnosing". "By reviewing the homework of my pupils, I can have an overall picture about their understanding of the lessons I give," Cao said, adding that this "diagnosis" helps him draw up a teaching plan for the following day. But if the Chinese online education start-up Master Learner has its way, Cao and his 14 million fellow teachers in China will be able to hand this time-consuming review process to a "super teacher", a powerful "brain" capable of answering nearly 500 million of the most tested questions in China's middle schools as well as scoring high points in each Gaokao test, China's life-changing college entrance exam, for the past 30 years. If the super teacher sounds too smart to be human, that is because it is not.
In the 3rd grade, Henoch Argaw began tutoring his fellow students at Southeast Christian Academy Elementary School in Colorado. "He told me and Sehin [his mother] that he was writing a math instruction book," recalls Neway Argaw, his father. "By that time, he was already attending 5th grade [level] math and science courses." Their son continued tutoring all the way through high school and also took up a related pursuit, refereeing and coaching youth soccer for the Colorado Storm and other Colorado soccer clubs. He was also a competitive chess player and played the trumpet since 4th grade.
The workshop is intended for Software Developers with a strong background in fundamental Computer Science topics like data structures and algorithms. SPEAKER: The workshop was delivered by MENA Devs' member Islam El-Ashi who is a hacker and a software engineer. Based in Silicon Valley, Islam was an early member of a number of startups like TellApart and Wish, and also contributed at larger companies like Twitter, Google, and Evernote. Prior to moving to Silicon Valley, Islam was a social entrepreneur in Egypt and the founder of Diraya, an organization producing MOOCs and followed by thousands of high school students in Egypt.