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.

Natural Language Processing (NLP) is a massive space within artificial intelligence (AI), which enterprises are integrating into their existing platforms more each day. As petabytes of textual data become available each day, companies can leverage NLP to retrieve deeper insights. Aspects such as entities, sentiment, emotion, and keywords can be extracted from textual data and enterprises can leverage this information to pivot, understand customer sentiment, and improve internal efficiency. Watson Natural Language Understanding (NLU) and Watson Natural Language Classifier (NLC) are cutting-edge NLP technologies that provide deep insight into textual data. Watson NLU provides insight such as entities, emotion, keywords, sentiment, and categories, while Watson NLC allows users to train a classification model in under 15 minutes and classify text.

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.

As brick-and-mortar retailers continue to struggle against online competitors, some are seeking out services that leverage big data and personalization to increase e-commerce sales. "During the rise of big data, it was said that data was the new oil," Brian Solis, principal analyst at Altimeter, told TechRepublic. "In an era of AI and machine learning however, personalized data is the new competitive advantage and will only become standard CX on the horizon." Indeed, 72% of retailers reported that AI will be a "competitive necessity" in the next five years, according to a recent Oxford Economics survey. One such tech option for retailers looking to fight off the competition is uSizy, a recommendation technology for fashion apparel and footwear businesses, which unveiled its latest product, uSizy Smart Business, on Wednesday.

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.

Digital experience technology is at its best when it disappears. When it melts into the customer's day. Its history has been one of incrementally removing friction from experiences until you forget the digital interface is there. Consider the humble checkout: from a multi-field form-fill for every purchase, to auto-populating upon login, to one-click, to a passing comment at a virtual assistant. The e-shopping example is an embodiment of the pursuit of the digital experience holy trinity: convenience, speed and usability.

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.

As the AI technology marches ahead with significant innovations and transformation, people do raise concern what its future beholds. The futuristic implications of AI swings between two aspects – first the anticipated positive impact of AI on economies and societies and the second negative impact of its potential over humankind. Specifically, the governments in Asia and the civil society residing in the region are concerned about structuring regulatory frameworks to guard against the possible threats. However, business leaders in Asia are quite optimistic about AI's positive impact on businesses, societies, and the welfare of humanity. Some do believe that AI will be the major growth driver for the region in the coming years.