If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
A prized attribute among law enforcement specialists, the expert ability to visually identify human faces can inform forensic investigations and help maintain safe border crossings, airports, and public spaces around the world. The field of forensic facial recognition depends on highly refined traits such as visual acuity, cognitive discrimination, memory recall, and elimination of bias. Humans, as well as computers running machine learning (ML) algorithms, possess these abilities. And it is the combination of the two--a human facial recognition expert teamed with a computer running ML analyses of facial image data--that provides the most accurate facial identification, according to a recent 2018 study in which Rama Chellappa, Distinguished University Professor and Minta Martin Professor of Engineering, and his team collaborated with researchers at the National Institute of Standards and Technology and the University of Texas at Dallas. Chellappa, who holds appointments in UMD's Departments of Electrical and Computer Engineering and Computer Science and Institute for Advanced Computer Studies, is not surprised by the study results.
A novel technique developed by MIT researchers rethinks hardware data compression to free up more memory used by computers and mobile devices, allowing them to run faster and perform more tasks simultaneously. Data compression leverages redundant data to free up storage capacity, boost computing speeds, and provide other perks. In current computer systems, accessing main memory is very expensive compared to actual computation. Because of this, using data compression in the memory helps improve performance, as it reduces the frequency and amount of data programs need to fetch from main memory. Memory in modern computers manages and transfers data in fixed-size chunks, on which traditional compression techniques must operate.
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. The 2019 IEEE International Conference on Soft Robotics (RoboSoft) takes place in Seoul, South Korea, next week, and the organizers put together this preview video stuffed full of--what else?--soft robots. Single-stream recycling is currently an extremely labor intensive process due to the need for manual object sorting.
Two MIT alumnae and three current MIT doctoral students are among this year's 30 recipients of The Paul and Daisy Soros Fellowships for New Americans. The five students -- Joseph Maalouf, Indira Puri, Grace Zhang, Helen Zhou, and Jonathan Zong -- will each receive up to $90,000 to fund their doctoral educations. The newest fellows were selected from a pool of 1,767 applications based on their potential to make significant contributions to U.S. society, culture, or their academic fields. The P.D. Soros Fellowships are open to all American immigrants and children of immigrants, including DACA recipients, refugees, and asylum seekers. In the past nine years, 34 MIT students and alumni have been awarded this fellowship.
As a little girl Khadijah Ismail would spend hours watching aeroplanes through the window of the attic bedroom she shared with her sister near Manchester Airport. She even wrote the airport a letter "on fancy paper and everything", giving her address and asking them to send more planes past her house. The eldest of four children, Khadijah loved maths and got a scholarship to a highly academic private day school. Her mum and dad hoped she would be the first in the family with a university degree. At 16 she won a prestigious Arkwright Engineering Scholarship and put the award, of several hundred pounds, towards buying a robot for her school.
IBM's Chair, CEO and President Ginni Rometty has a powerful message for workers and employers in all strata of society: The Fourth Industrial Revolution is underway and it is shaping up to be one of the most significant challenges and opportunities of our lifetime. We are already seeing jobs, policies, industries and entire economies shifting as our digital and physical worlds merge. According to the World Economic Forum, the value of digital transformations in the Fourth Industrial Revolution is estimated at $100 trillion in the next 10 years alone, across all sectors, industries and geographies. "As a result, we face an imminent and profound transformation of the workforce over the next five to 10 years as analytics and artificial intelligence change job roles at companies in all industries," Rometty said while giving a keynote address at the CNBC's At Work Talent & HR: Building the Workforce of the Future Conference in New York on Tuesday, April 2. In February, the executive was appointed to Trump's American Workforce Policy Advisory Board along with 24 other leaders. While only a minority of jobs will disappear, the majority of roles that remain will require people to work with the aid of analytics and some form of AI and this will require skills training on a large scale, Rometty said.
In debates about the future of work, technology is often portrayed as the villain. One recent study calculated that 38 percent of jobs in the United States were at a "high risk" of being automated during the next decade. In the construction industry, predictions are especially dire: estimates of robot-fueled joblessness range from 24 percent in Britain to 41percent in Germany. Borja García de Soto is an Assistant Professor of Civil Engineering at New York University Abu Dhabi (NYUAD), a Global Network Assistant Professor of Civil and Urban Engineering at the NYU Tandon School of Engineering, and Director of NYUAD's S.M.A.R.T. Construction Research Group. There is no question that automation will change the way people work, but for some sectors of the economy, change is long overdue.
After being involved in two serious car accidents, Chien-Chih "Ernie" Ho dedicated himself to a lifelong goal: Develop self-driving car technology that could save millions of lives. Ho had learned from watching a TED Talk that self-driving car technology could prevent accidents -- potentially saving 3 million lives each year -- furthering his interest in the burgeoning area. However, as a student at National Chengchi University, a top business school in Taiwan, he had limited access to resources in technology education. Though he taught himself programming, winning several international software competitions in the process, it was difficult to find opportunities in the self-driving car industries. "In Taiwan, few people and schools are involved in the autonomous vehicle industry" he says.
The science fiction writer Douglas Adams imagined the greatest computer ever built, Deep Thought, programmed to answer the deepest question ever asked: the Great Question of Life, the Universe, and Everything. After 7.5 million years of processing, Deep Thought revealed its answer: Forty-two (1). As artificial intelligence (AI) systems enter every sector of human endeavor--including science, engineering, and health--humanity is confronted by the same conundrum that Adams encapsulated so succinctly: What good is knowing the answer when it is unclear why it is the answer? What good is a black box? In an informal survey of my colleagues in the physical sciences and engineering, the top reason for not using AI methods such as deep learning, voiced by a substantial majority, was that they did not know how to interpret the results.
Memories of middle school likely conjure up all sorts of thoughts and emotions. "Productive STEM learning" is probably low on the list. But on Tuesday, Lego is introducing a new coding and robotics set called Spike Prime that it hopes will break through with a notoriously distracted audience. Lego has already dabbled in this world with its Lego Mindstorms line. But those kits can potentially intimidate at the 11- to 14-year-old level, both in complexity and design.