Beacon is unlike any other member of staff at Staffordshire University. It is available 24/7 to answer students' questions, and deals with a number of queries every day – mostly the same ones over and over again – but always stays incredibly patient. That patience is perhaps what gives it away: Beacon is an artificial intelligence (AI) education tool, and the first digital assistant of its kind to be operating at a UK university. Staffordshire developed Beacon with cloud service provider ANS and launched it in January this year. The chatbot, which can be downloaded in a mobile app, enhances the student experience by answering timetable questions and suggesting societies to join.
One of the landmark events in the course of evolution of technology has been the advent of Artificial Intelligence, which has subsequently impacted different sectors of the society profoundly. Its multifaceted benefits have successfully initiated a complete paradigm shift even in our education sector. Education is one of the primary tools which is inextricably linked with the growth of human resources in the country. Artificial intelligence immensely helps to accentuate the growth and development index. It utilizes data models (as part of primary and secondary data sources) and makes decisions based on the input data whose success rate improves with further iterations.
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. It only takes 10 Spotpower (SP) to haul a truck across the Boston Dynamics parking lot ( 1 degree uphill, truck in neutral). These Spot robots are coming off the production line now and will be available for a range of applications soon.
When Katie O'Nell's high school biology teacher showed a NOVA video on epigenetics after the AP exam, he was mostly trying to fill time. But for O'Nell, the video sparked a whole new area of curiosity. She was fascinated by the idea that certain genes could be turned on and off, controlling what traits or processes were expressed without actually editing the genetic code itself. She was further excited about what this process could mean for the human mind. But upon starting at MIT, she realized that she was less interested in the cellular level of neuroscience and more fascinated by bigger questions, such as, what makes certain people generous toward certain others?
In the advent of artificial intelligence, robots, and automation, today's K-12 educators around the world are asking the question: "What skills do our students need to be ready for the future?" The "Freshman Technology Experience" -- a recent two-day event at Cambridge Rindge and Latin School (CRLS) in Cambridge, Massachusetts -- brought MIT researchers into the classroom to explore just that. As their 10th grader schoolmates underwent Massachusetts Comprehensive Assessment System (MCAS) testing in late March, 9th grade students put technologies developed by MIT to the test, rotating through sessions playing Shadowspect, a 3-D geometry puzzle game designed to assess learning, and MIT App Inventor, an intuitive, visual programming environment that makes coding easy and fun. Organized by instructional technologists at CRLS with MIT's Office of Government and Community Relations, the event sought to inspire a diverse array of students to build future-ready skills by seeking educational opportunities in fields like computer science. As students down the hall worked through problems with multiple choice answers on the MCAS, the freshman class tried out a new means of assessing their math skills.
For the majority of newcomers, machine learning algorithms may seem too boring and complicated subject to be mastered. Well, to some extent, this is true. In most cases, you stumble upon a few-page description for each algorithm and yes, it's hard to find time and energy to deal with each and every detail. However, if you truly, madly, deeply want to be an ML-expert, you have to brush up your knowledge regarding it and there is no other way to be. But relax, today I will try to simplify this task and explain core principles of 10 most common algorithms in simple words (each includes a brief description, guides, and useful links).
Examine the problem of maintaining the quality of big data and discover novel solutions. You will learn the four V's of big data, including veracity, and study the problem from various angles. The solutions discussed are drawn from diverse areas of engineering and math, including machine learning, statistics, formal methods, and the Blockchain technology. Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain. Using examples, the math behind the techniques is explained in easy-to-understand language.
It's common knowledge that Barack Obama met the woman who eventually became his wife, Michelle Robinson, when he came to work at her law firm as a summer associate. George W. Bush met the future Mrs. Bush, who was Laura Welch back then, at a barbecue and took her mini-golfing the next day. And we all remember that Bill and Hillary Clinton were law school sweethearts. The historical record is full of these president-and-first-lady origin stories: Harry Truman was just 6 when he met the woman he would go on to marry, in church. So it's only natural to ask how the current crop of presidential candidates' how-they-met stories stack up.
Before computers, no sane person would have set out to count gender pronouns in 4,000 novels, but the results can be revealing, as MIT's new digital humanities program recently discovered. Launched with a $1.3 million grant from the Andrew W. Mellon Foundation, the Program in Digital Humanities brings computation together with humanities research, with the goal of building a community "fluent in both languages," says Michael Scott Cuthbert, associate professor of music, Music21 inventor, and director of digital humanities at MIT. "In the past, it has been somewhat rare, and extremely rare beyond MIT, for humanists to be fully equipped to frame questions in ways that are easy to put in computer science terms, and equally rare for computer scientists to be deeply educated in humanities research. There has been a communications gap," Cuthbert says. While traditional digital humanities programs attempt to provide humanities scholars with some computational skills, the situation at MIT is different: Most MIT students already have or are learning basic programming skills, and all MIT undergraduates also take some humanities classes. Cuthbert believes this difference will make MIT's program a great success.
The work of a science writer, including this one, includes reading journal papers filled with specialized technical terminology, and figuring out how to explain their contents in language that readers without a scientific background can understand. Now, a team of scientists at MIT and elsewhere has developed a neural network, a form of artificial intelligence (AI), that can do much the same thing, at least to a limited extent: It can read scientific papers and render a plain-English summary in a sentence or two. Even in this limited form, such a neural network could be useful for helping editors, writers, and scientists scan a large number of papers to get a preliminary sense of what they're about. But the approach the team developed could also find applications in a variety of other areas besides language processing, including machine translation and speech recognition. The work is described in the journal Transactions of the Association for Computational Linguistics, in a paper by Rumen Dangovski and Li Jing, both MIT graduate students; Marin Soljačić, a professor of physics at MIT; Preslav Nakov, a senior scientist at the Qatar Computing Research Institute, HBKU; and Mićo Tatalović, a former Knight Science Journalism fellow at MIT and a former editor at New Scientist magazine.