San Francisco is known as a hub of tech innovation, making USF an ideal place to study computer and data science. The location gives students the opportunity to connect professionally with companies everyone knows: Google, Twitter, Facebook – the list goes on. But what opportunities does USF offer students to participate in peer reviewed scholarship, a place where current students and faculty can connect over tech R&D on campus? As of Fall 2018, the answer comes in the form of the weekly MAGICS Lab meetings, a way to gain valuable mentorship and learn about emerging technologies, a place where undergraduate, graduate students, and faculty all have the opportunity to learn, research, and publish together. This group welcomes all skill-levels, from novice to seasoned researchers alike.
In the rest of this blog, we'll use an example to provide more detail into how to build a forecasting model using the above workflow. Machine learning is all about running experiments. The faster you can run experiments, the more quickly you can get feedback, and thus the faster you can get to a Minimum Viable Model (MVM). Let's build a model to forecast the median housing price week-by-week for New York City. We spun up a Deep Learning VM on Cloud AI Platform and loaded our data from nyc.gov into BigQuery.
Bellevue, Wash., located in the Seattle metro area, is undergoing a citywide review of near-miss incidents involving pedestrians, cyclists and other cars. Using images from its closed circuit video network, as well as high-level analytics and machine learning, the city wants to understand which streets and intersections are the most dangerous, and how they might be made safer. Bellevue is partnering with the group Together for Safer Roads (TSR), which represents a coalition of private-sector companies, including Brisk Synergies, to conduct a comprehensive near-miss study from August to September where roughly half of the city's network of 80 public video cameras will be used to gather some 34,000 hours of footage representing about 21 terabytes of data. The data will be processed by Brisk using artificial intelligence and machine learning to gain insights into "near-miss" incidents. "This is the first network-wide traffic safety monitoring assessment of its kind," said Franz Loewenherz, principal transportation planner for Bellevue.
How much does someone's living room tell about how they live? Peeking into another person's life might be just part of natural human curiosity, but the answer to this question may provide insights in a wide range of aspects of human behavior. A new study published in EPJ Data Science uses the power of machine learning to explore patterns of home decors--and what they could tell about their owners--in popular accommodation website Airbnb. The Internet has provided the world with more images than can be viewed in a lifetime. Some sites, like Craigslist, Zillow, and Airbnb, specifically let us see the interiors of peoples' homes, nests of revealing human creativity, design, style and culture.
With all the excitement and hype about AI that's "just around the corner"--self-driving cars, instant machine translation, etc.--it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you're already using--right now? In the process of navigating to these words on your screen, you almost certainly used AI. You've also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases. We distinguish between AI and machine learning (ML) throughout this article when appropriate. At Emerj, we've developed concrete definitions of both artificial intelligence and machine learning based on a panel of expert feedback. To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI.
SAN FRANCISCO--Guiding Oracle's development of its latest generation of cloud applications are three main business imperatives: help customers innovate rapidly, create nimble processes, and make the most of their mobile, social, and other communications channels. Speaking at Oracle OpenWorld, Steve Miranda, executive vice president of applications development, emphasized the considerable work the company has done incorporating machine learning algorithms into its comprehensive, tightly integrated suites of cloud applications. "We're ready to run your business in the cloud," Miranda said. At Oracle OpenWorld, Steve Miranda, Oracle's executive vice president for applications development, outlines machine learning capabilities in the company's cloud applications. An intuitive, easy-to-use, voice-enabled user interface that runs on various computing platforms but is especially suited to mobile devices.
U.S. Secretary of Energy Rick Perry announced the establishment of the DOE Artificial Intelligence and Technology Office (AITO). The Secretary has established the office to serve as the coordinating hub for the work being done across the DOE enterprise in Artificial Intelligence. This action has been taken as part of the President's call for a national AI strategy to ensure AI technologies are developed to positively impact the lives of Americans. DOE-fueled AI is already being used to strengthen our national security and cybersecurity, improve grid resilience, increase environmental sustainability, enable smarter cities, improve water resource management, as well as speed the discovery of new materials and compounds, and further the understanding, prediction, and treatment of disease. DOE's National Labs are home to four of the top ten fastest supercomputers in the world, and we're currently building three next-generation, exascale machines, which will be even faster and more AI-capable computers.
The phrase "artificial intelligence" in pop culture often conjures up dystopian images such as the sentient computer Hal 9000 from the 1968 film "2001: A Space Odyssey" that killed people for its self preservation; or the cyborg assassin with a metal endoskeleton in director James Cameron's "The Terminator." In recent years, our fascination with the potential of AI has taken a more starry-eyed turn, as shown in the 2013 sci-fi drama "Her," where the main character falls in love with a virtual assistant. In reality, artificial intelligence (AI) technology is quickly permeating every aspect of our lives. From Amazon's voice-activated Alexa to writing technology that helps managers craft job postings, AI is in our hearts, homes and workplaces. And it's only going to become a bigger part of our lives: Experts call the rise of AI the driving force behind the fourth industrial revolution.
In an attempt to prevent artificial intelligence-generated fake news from spreading across the internet, a team of scientists built an AI algorithm that creates what might be the most believable bot-written fake news to date -- based on nothing more than a lurid headline. The system, GROVER, can create fake and misleading news articles that are more believable than those written by humans, according to research shared to the preprint server ArXiv on Wednesday -- and also detect them. "We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data," the researchers wrote in the paper. "Counterintuitively, the best defense against Grover turns out to be Grover itself, with 92% accuracy." In other words, the algorithm is apparently able to detect AI-written fake news better than any other tool out there.
In the last blog, I reflected on how we promote and nurture an open innovation culture at Jade, including the organizational and business benefits that have resulted from this initiative. Now, I'd like to share with you one of the ideas that was born from open innovation. Let me give you some brief background first. It's no news that digital transformation has taken center stage in enterprises these days. Nearly eight out of 10 companies in the US are in the process of doing so, but fail to scale and sustain their digital transformation initiatives.1