STUDENT SPOTLIGHT Wilmingtons Jared Marquard Named To Deans List At Coastal Carolina University

first_imgCONWAY, SC — More than 2,000 Coastal Carolina University students were named to the Dean’s List for the Spring 2019 semester. That’s nearly 20 percent of the student body with a grade point average between 3.5-3.99 for the spring semester.Jared W. Marquard, a Recreation and Sport Management major from Wilmington, made the cut.About Coastal Carolina UniversityCoastal Carolina University is a dynamic, public comprehensive liberal arts institution located in Conway, just minutes from the resort area of Myrtle Beach, S.C.Coastal Carolina University offers baccalaureate degrees in 73 major fields of study. Among CCU’s 25 graduate-level programs are 21 master’s degrees, two educational specialist degrees, and the doctorates in education and marine science: coastal and marine systems science. The most popular undergraduate majors are marine science, management, exercise and sport science, communication and psychology. CCU boasts a growing array of internship, research and international opportunities for students, as well as numerous online programs through Coastal Online.More than 10,600 students from across the country and around the world interact with a world-class faculty, and enjoy a nationally competitive NCAA I athletic program, an inspiring cultural calendar, and a tradition of community interaction that is fueled by more than 160 student clubs and organizations.Coastal Carolina University was founded in 1954 as Coastal Carolina Junior College and became an independent state university in 1993.(NOTE: The above announcement is from Coastal Carolina University.)Like Wilmington Apple on Facebook. Follow Wilmington Apple on Twitter. Follow Wilmington Apple on Instagram. Subscribe to Wilmington Apple’s daily email newsletter HERE. Got a comment, question, photo, press release, or news tip? Email wilmingtonapple@gmail.com.Share this:TwitterFacebookLike this:Like Loading… RelatedSTUDENT SPOTLIGHT: Wilmington’s Patrick Dunfey Named To Dean’s List At Coastal Carolina UniversityIn “Education”STUDENT SPOTLIGHT: Wilmington’s Scott Falzano Named To Dean’s List At Coastal Carolina UniversityIn “Education”STUDENT SPOTLIGHT: WIlmington’s Dylan Masiello Wins Student Involvement Award At Coastal Carolina UniversityIn “Education”last_img read more

Mobile Sharing Economy Internet of Things the Coming Economic Boom

first_img The rise of the sharing economy over the past few years has shifted mindsets and traditional business models. Consumers are much more open to renting items and services from individuals instead of established businesses and organizations. This is shaking up engrained business models and allowing for new possibilities in the global marketplace. The peer-to-peer sharing models, like Spinlister (where I work) and Lyft, offer new and unique options for transportation at your fingertips.Related: 8 Ways the ‘Internet of Things’ Will Impact Your Everyday LifeSharing has gone mobile. A decade ago the sharing economy was fragmented and limited in its practical use. First, people had to accept the idea of sharing. People had to be willing to trust the community and take a risk on another person actually delivering that good or service.Second, when people began sharing they were unable to do so on a global level. That made it difficult to sustain any real income. Before mobile devices were widely available, sharing was limited to informal personal networks or websites offering limited services via the Internet. AirBNB probably would have succeeded regardless of mobile technology, but what about the other major players that help drive the sharing economy as a whole?Advances in mobile technology have propelled an entirely new marketplace with people sharing everything. More importantly, it has made the fulfillment of immediate or impulsive needs possible and convenient. It helps complete transactions that start online, coordinate multiple parties and make the entire experience frictionless.Sharing would struggle if it weren’t convenient and it would never be convenient without mobile technology. People are constantly on the go and busy. If an item they’re trying to share is on their person, they need a way to update the location of those goods to be truly useful and frictionless for both sides. Mobile technologies have opened the doors for people to effortlessly share goods and make money.The realization that idle goods can generate significant income, and mobile technology makes sharing those goods easy, has transformed the sharing economy into a multi-billion dollar industry. A recent internal study commissioned by us at Spinlister found that only 4 percent of Americans have used AirBNB or Uber. Imagine how big the sharing economy will be once it hits 20, 30, 40, or 50 percent saturation.Related: The Internet of Things May See Huge Growth, So Companies Want in NowSharing in an interconnected era. I was recently discussing a concept called The Internet of Things (IoT) with a brilliant young engineer working within an exclusive technology development department at a major electronics company. There is a race to develop hard goods that both serve a function and connect directly to the Internet, other goods and devices. That information can be relayed into third party applications.For Instance, imagine you need a eight-foot step ladder. With IoT, you could locate the ladder nearest to you. Add that data to a sharing economy platform and you could share almost every object you own! This is an extreme example but it illustrates how mobilization technology will expand the sharing economy in the future.While practical use of this technology is likely five to 10 years out for major product lines, I expect  these applications will trickle down to everyday goods over the next decade or two. Within that time frame I also expect the sharing economy to mature, more major players will emerge and a critical mass of people will regularly use a sharing economy platform.Once IoT is added into the equation, people will start thinking of mobility in terms outside of their mobile phones. This “mobility of things” will open the door to the sharing of almost everything you can think of. It will be easy, fluid, cheap and revenue generating.The sharing economy movement is the gold rush of our generation. The advances in mobile technology will strengthen the marketplace while making it easier and more convenient for all parties involved to participate.Related: What’s the Right Path for Startups Entering the ‘Internet of Things’? 4 min read September 22, 2014 Free Webinar | Sept. 9: The Entrepreneur’s Playbook for Going Global Growing a business sometimes requires thinking outside the box. Opinions expressed by Entrepreneur contributors are their own. Register Now »last_img read more

Have You Considered Using Blockchain as Part of Your Marketing

first_img Register Now » Growing a business sometimes requires thinking outside the box. The digital age is upon us, and it is unsparing in its reach. Every aspect of a company needs innovating in order for the organization as a whole to stay ahead of the competition.Related: 4 Ways Entrepreneurs Can Embrace the Next Phase of Digital Maturity, ‘Cognitive Transformation’In this world of demanding consumers, then, catching their attention via effective messaging is vital to begin any relationship, and only continued — and excellent — marketing will sustain a loyal customer base in a time of constant disruption.Here are five ways to build a better marketing strategy using innovative technology, and to stay ahead of the game in the digital age.1. Use blockchain as a tool to reach consumers.Today’s web ecosystem is one of interconnected subcommunities. The vast scope and nuances of these communities can make it hard to segment them into distinct, targetable groups. It also makes it harder to reach these distinct communities without an intermediary. However, technology like the blockchain is making it easier than ever to reach out directly to these types of groups.Related: When It Comes to Innovation, Go Big or Go HomeThe potential of a marketing tool to be able to reach a global community in a hyper-targeted, peer-to-peer fashion is on the horizon. If the disruption blockchain is capable of is any indication, this tech should be keeping status quo web marketers up at night.2. Target connected channels.Web channels and forums are based on interests, but are completely sustained by the free flow of information, relationships and exchange among their members. While some tend to be static or exclusive, many are constantly growing, sharing and adapting organisms. Finding and targeting these communities should be a key plank in any effective marketing strategy.For example, blockchain content platforms like DAC are global, decentralized and absolutely dependent on the constant communication and exchange of information among their members. For DAC, this means using blockchain technology to incentivize audio content creators and their fans to build and develop a robust content community..The blockchain-based audio-content website model aims to create and foster a sense of community among music fans across the world. In today’s web, messaging to one of these members is a way to message to his or her entire network — which should have any savvy CMO champing at the bit.3. Incentivize a community of supporters.The digital age has created many new channels for companies to reach their target audience, but many established digital marketing techniques have become less effective in recent years. Instead of solely advertising a message to your audience, you can achieve more impactful outcomes by incentivizing that audience with benefits that turn its members into active supporters of your company.Blockchain startup Agora, which offers end-to-end verifiable voting technology, has incentivized an audience through its token bounty program as well as the company’s core product. Agora has engaged its community by offering rewards to those who share its message on social media and other channels.Agora has also built reward mechanisms into its own voting platform. Through one such mechanism, governments can reward voters, using Agora’s tokens, for casting a ballot, which may ultimately serve to increase global political participation.4. AI has already arrived, and it’s smarter than you.Once the stuff of science fiction, artificial intelligence has recently exploded in scale and practicality. It is showing tremendous promise as a tool for marketing and messaging. As companies like Google develop efficient and effective marketing AI, incorporating this tech into future marketing plans will be crucial for any company that wishes to survive.Self-taught AI will be able to create more efficient algorithms than any human is capable of programming, meaning that customers will be reached more precisely and effectively than ever before.AI could even be responsible for creating marketing content, instantly adjusting its messaging according to user response. Anyone expecting to keep up with the growing number of platforms and segmentations that will define the digital age must begin to understand and embrace artificial intelligence as the uber-tool it will become.5. Big data is only getting bigger — so, use it.The current juggernaut technology in online advertising is the aptly named Big Data. Collected from the web habits of billions of users, this data is used by advertisers to classify and predict the habits of its consumers. Big Data will only get more important as the internet continues to grow and influence more aspects of our lives.Related: How Blockchain Is Creating a New Future for Digital MarketingThe internet of things (IoT) will be the interconnected network of all appliances and machines in one’s life. For example, sensors detecting the temperature will be able to communicate and adjust every appliance in the household to the ideal setting. This expanded network will create increased amounts of data, all of it useful for marketing purposes and targeting. The company that successfully models this IoT data will be able to conquer the future of marketing.How do you think technology can influence the future of marketing? March 15, 2018 5 min read Opinions expressed by Entrepreneur contributors are their own. Free Webinar | Sept. 9: The Entrepreneur’s Playbook for Going Globallast_img read more

Why TensorFlow always tops machine learning and artificial intelligence tool surveys

first_imgTensorFlow is an open source machine learning framework for carrying out high-performance numerical computations. It provides excellent architecture support which allows easy deployment of computations across a variety of platforms ranging from desktops to clusters of servers, mobiles, and edge devices. Have you ever thought, why TensorFlow has become so popular in such a short span of time? What made TensorFlow so special, that we seeing a huge surge of developers and researchers opting for the TensorFlow framework? Interestingly, when it comes to artificial intelligence frameworks showdown, you will find TensorFlow emerging as a clear winner most of the time. The major credit goes to the soaring popularity and contributions across various forums such as GitHub, Stack Overflow, and Quora. The fact is, TensorFlow is being used in over 6000 open source repositories showing their roots in many real-world research and applications. How TensorFlow came to be The library was developed by a group of researchers and engineers from the Google Brain team within Google AI organization. They wanted a library that provides strong support for machine learning and deep learning and advanced numerical computations across different scientific domains. Since the time Google open sourced its machine learning framework in 2015, TensorFlow has grown in popularity with more than 1500 projects mentions on GitHub. The constant updates made to the TensorFlow ecosystem is the real cherry on the cake. This has ensured all the new challenges developers and researchers face are addressed, thus easing the complex computations and providing newer features, promises, and performance improvements with the support of high-level APIs. By open sourcing the library, the Google research team have received all the benefits from a huge set of contributors outside their existing core team. Their idea was to make TensorFlow popular by open sourcing it, thus making sure all new research ideas are implemented in TensorFlow first allowing Google to productize those ideas. Read Also: 6 reasons why Google open sourced TensorFlow What makes TensorFlow different from the rest? With more and more research and real-life use cases going mainstream, we can see a big trend among programmers, and developers flocking towards the tool called TensorFlow. The popularity for TensorFlow is quite evident, with big names adopting TensorFlow for carrying out artificial intelligence tasks. Many popular companies such as NVIDIA, Twitter, Snapchat, Uber and more are using TensorFlow for all their major operations and research areas. On one hand, someone can make a case that TensorFlow’s popularity is based on its origins/legacy. TensorFlow being developed under the house of “Google” enjoys the reputation of the household name. There’s no doubt, TensorFlow has been better marketed than some of its competitors. Source: The Data Incubator However that’s not the full story. There are many other compelling reasons why small scale to large scale companies prefer using TensorFlow over other machine learning tools TensorFlow key functionalities TensorFlow provides an accessible and readable syntax which is essential for making these programming resources easier to use. The complex syntax is the last thing developers need to know given machine learning’s advanced nature. TensorFlow provides excellent functionalities and services when compared to other popular deep learning frameworks. These high-level operations are essential for carrying out complex parallel computations and for building advanced neural network models. TensorFlow is a low-level library which provides more flexibility. Thus you can define your own functionalities or services for your models. This is a very important parameter for researchers because it allows them to change the model based on changing user requirements. TensorFlow provides more network control. Thus allowing developers and researchers to understand how operations are implemented across the network. They can always keep track of new changes done over time. Distributed training The trend of distributed deep learning began in 2017, when Facebook released a paper showing a set of methods to reduce the training time of a convolutional neural network model. The test was done on RESNET-50 model on ImageNet dataset which took one hour to train instead of two weeks. 256 GPUs spread over 32 servers were used. This revolutionary test has open the gates for many research work which have massively reduced the experimentation time by running many tasks in parallel on multiple GPUs. Google’s distributed TensorFlow has allowed all the researchers and developers to scale out complex distributed training using in-built methods and operations that optimizes distributed deep learning among servers. . Google’s distributed TensorFlow engine which is part of the regular TensorFlow repo, works exceptionally well with the existing TensorFlow’s operations and functionalities. It has allowed exploring two of the most important distributed methods: Distribute the training time of a neural network model over many servers to reduce the training time. Searching for good hyperparameters by running parallel experiments over multiple servers. Google has given distributed TensorFlow engine the required power to steal the share of the market acquired by other distributed projects such as Microsoft’s CNTK, AMPLab’s SparkNet, and CaffeOnSpark. Even though the competition is tough, Google has still managed to become more popular when compared to the other alternatives in the market. From research to production Google has, in some ways, democratized deep learning., The key reason is TensorFlow’s high-level APIs making deep learning accessible to everyone. TensorFlow provides pre-built functions and advanced operations to ease the task of building different neural network models. It provides the required infrastructure and hardware which makes them one of the leading libraries used extensively by researchers and students in the deep learning domain. In addition to research tools, TensorFlow extends the services by bringing the model in production using TensorFlow Serving. It is specifically designed for production environments, which provides a flexible, high-performance serving system for machine learning models. It provides all the functionalities and operations which makes it easy to deploy new algorithms and experiments as per changing requirements and preferences. It provides an excellent feature of out-of-the-box integration with TensorFlow models which can be easily extended to serve other types of models and data. TensorFlow’s API is a complete package which is easier to use and read, plus provides helpful operators, debugging and monitoring tools, and deployment features. This has led to growing use of TensorFlow library as a complete package within the ecosystem by the emerging body of students, researchers, developers, production engineers from various fields who are gravitating towards artificial intelligence. There is a TensorFlow for web, mobile, edge, embedded and more TensorFlow provides a range of services and modules within their existing ecosystem making them as one of the ground-breaking end-to-end tools to provide state-of-the-art deep learning. TensorFlow.js for machine learning on the web JavaScript library for training and deploying machine learning models in the browser. This library provides flexible and intuitive APIs to build and train new and pre-existing models from scratch right in the browser or under Node.js. TensorFlow Lite for mobile and embedded ML It is a TensorFlow lightweight solution used for mobile and embedded devices. It is fast since it enables on-device machine learning inference with low latency. It supports hardware acceleration with the Android Neural Networks API. The future releases of TensorFlow Lite will bring more built-in operators, performance improvements, and will support more models to simplify the developer’s experience of bringing machine learning services within mobile devices. TensorFlow Hub for reusable machine learning A library which is used extensively to reuse machine learning models. Thus you can transfer learning by reusing parts of machine learning models. TensorBoard for visual debugging While training a complex neural network model, the computations you use in TensorFlow can be very confusing. TensorBoard makes it very easy to understand and debug your TensorFlow programs in the form of visualizations. It allows you to easily inspect and understand your TensorFlow runs and graphs. Sonnet Sonnet is a DeepMind library which is built on top of TensorFlow extensively used to build complex neural network models. All of this factors have made the TensorFlow library immensely appealing for building a wide spectrum of machine learning and deep learning projects. This tool has become a preferred choice for everyone from space research giant NASA and other confidential government agencies, to an impressive roster of private sector giants. Road Ahead for TensorFlow TensorFlow no doubt is better marketed compared to the other deep learning frameworks. The community appears to be moving very fast. In any given hour, there are approximately 10 people around the world contributing or improving the TensorFlow project on GitHub. TensorFlow dominates the field with the largest active community. It will be interesting to see what new advances TensorFlow and other utilities make possible for the future of our digital world. Continuing the recent trend of rapid updates, the TensorFlow team is making sure they address all the current and active challenges faced by the contributors and the developers while building machine learning and deep learning models. TensorFlow 2.0 will be a major update, we can expect the release candidate by next year early March. The preview version of this major milestone is expected to hit later this year. The major focus will be on ease of use, additional support for more platforms and languages, and eager execution will be the central feature of TensorFlow 2.0. This breakthrough version will add more functionalities and operations to handle current research areas such as reinforcement learning, GANs, building advanced neural network models more efficiently. Google will continue to invest and upgrade their existing TensorFlow ecosystem. According to Google’s CEO, Sundar Pichai “artificial intelligence is more important than electricity or fire.” TensorFlow is the solution they have come up with to bring artificial intelligence into reality and provide a stepping stone to revolutionize humankind. Read more The 5 biggest announcements from TensorFlow Developer Summit 2018 The Deep Learning Framework Showdown: TensorFlow vs CNTK Tensor Processing Unit (TPU) 3.0: Google’s answer to cloud-ready Artificial Intelligencelast_img read more