Golf Needs Tiger Woods To Save A Sport That

Watched golf this weekend—no this was not a masochistic endeavor. It was sort of a one-man straw poll on the sport that once rose like a supernova that is now descending like a fallen star.What is watching golf like without Tiger Woods?It’s like having sweet tea without the sugar; like watching Training Day without Denzel; like a Beyonce video that’s too dark to see.Empty.It does not matter the event or the depth of the field. If Tiger Woods is not playing, the panache is absent.It once was considered an overstatement to say that golf lived and died with Tiger Woods. Not anymore. Golf is dying—professionally and recreationally—as Woods makes his descent.Watching Woods play badly is more interesting than seeing world No. 1 Rory McIlroy shoot 62.Who would have thought that a Black man would dominate the sport so significantly that at one point people wondered if it was good for the game that he won so much? Here’s the thing: Everyone won when Woods was at his apex.TV ratings were sky high. Prize money exponentially increased to induce Woods to play and because sponsors were at a premium, knowing Woods was going to fill the course with patrons in record numbers and generate unprecedented TV ratings. Many who never considered the game recreationally began playing, especially in the Black community.Woods made a lot of people a lot of money as a Black man reigning over a traditionally white sport, and doing it with flair.You had to watch. And you began to think you should at least try to play. Even if you weren’t very good—which describes everybody in the beginning.But the worst case scenario happened to the game: Tiger Woods is not playing as he tries to locate a game that has been missing for some time now. His absence and the decline of golf viewership run parallel. It is not a coincidence.Phil Mickelson’s a great American player who does not have the zest of Tiger. McIlroy is as interesting as Irish coffee. A host of young, talented players have no drawing power to the casual, would-be viewer.Television ratings have dipped to levels of the absurd without Woods. The final round of the 2014 Masters lured only 7.8 percent of television viewers. That weekend produced the unique major championship’s smallest TV audience since 1993.Sunday when Tiger does not probably could have been tallied by head count by Neilson.Unless Woods’ game has deteriorated to the point where he wouldn’t want to embarrass himself, he will tee it up next month in Augusta at the Masters. Watch the ratings skyrocket, whether he plays well or not.If, by some amazing reversal, Wood is in contention on Sunday, golf executives around the country would be kissing his spikes. He moves the interest meter like no one in sports has since Michael Jordan.Outside the PGA Tour, golf is losing, too. TaylorMade-Adidas Golf, the world’s biggest maker of golf clubs and clothes, had sales plummet 28 percent last year, its parent company Adidas said to The Washington Post.More stats: Sports & Fitness Industry Association data show those who said they played golf at least once last year has fallen to one of its lowest point in years. Young people—that coveted 18-to-30 demographic—playing golf has fallen an incredible 35 percent over the last decade.And more stats: More golf courses closed than opened in 2013 for the eighth straight year, according to the National Golf Foundation. And the number of course closures has sped up, averaging 137 closings every year since 2011, data from golf-industry researcher Pellucid show—right around the time Woods began to fall apart.“There’s nobody out there who’s going to save us,” Pellucid’s president Jim Koppenhaver said at a Professional Golfers Association of America gathering in January. “We have to save ourselves.”Tiger Woods gave golf a great gift. An identity. A reason to watch. Inspiration to play. All that’s fading now, and the numbers say the descent is in full throttle. As he works to get back to form, the game continues to diminish in stature and interest. Now, you can bet all those who were rooting against Woods are now praying for him to save the sport. read more

Season of gold

first_imgMMTC’s flagship event ‘Festival of Gold’ has returned this festive season to dazzle the city with handpicked collections of hallmarked gold and certified stone-studded jewellery, silver and gold medallions in 999 purity, and the latest range of Sanchi silverware in 92.5 per cent purity. The event was inaugurated by dignitaries from the Dept of Commerce, Ministry of Commerce & Industry and by the senior management of MMTC on the evening of October 27, and will continue until November 10 (excluding November 5 and 6)at Hotel Ashok, Chanakyapuri as well as MMTC’s sales outlets across the city.   Also Read – ‘Playing Jojo was emotionally exhausting’MMTC, the only government of India enterprise in the jewellery sector, has been holding its annual exhibition-cum-sale of hallmarked jewellery, the ‘Festival of Gold,’ since 1994. However, due to restrictions imposed on gold imports by the government of India over the last few years to check the fiscal deficit, availability of the precious metal had been sparse for the domestic industry and the popular event had to be put on hold for two years. With the recent relaxation in these conditions, the company has brought back the much-awaited exhibition-cum-sale on a grandiose level with collections from different states of India, medallions in various denominations and competitively-priced Sanchi silverware items.  Also Read – Leslie doing new comedy special with NetflixThis year’s edition has been attracting customers in droves since day one, and has registered record footfall with sales touching the 16 crores mark in the first week of the event. On every customer’s wish list here is the temple style jewellery from the southern states, handcrafted gold jewellery from West Bengal, and MMTC’s special bridal range of jewellery. There is in fact something for everyone here, whether one is looking for something traditional or contemporary, plain or studded, heavy or light, for gifting or to add glitter of gold at the event, noted cinestar Neha Dhupia, who visited the Festival on the evening of November 3 and expressed her delight at being able to visit the one-of-its-kind event.  Dhupia, who has been a part of MMTC’s gold event in the past also, took a leisurely round of the many counters at the exhibition and interacted with the MMTC management as well as the guests and the media.MMTC has been assigned the prestigious responsibility of marketing the India Gold Coin by the government of India, being regarded as the sovereign and national coin of India. The India Gold Coin is being minted in denominations of 5 gm and 10 gm and bears the Ashok Chakra. Once launched, the India gold coin will be made available at various outlets of MMTC alongwith the ‘Festival of Gold.’last_img read more

How to Get Funding Act Like You Dont Need It

first_img Free Webinar | Sept 5: Tips and Tools for Making Progress Toward Important Goals Attend this free webinar and learn how you can maximize efficiency while getting the most critical things done right. 3 min read This story appears in the October 2012 issue of . Subscribe » Register Now » Here’s one way to raise more money in a seed round: Say, “No thanks.” That’s what Zain Jaffer and Jack Smith, founders of the Vungle mobile ad platform, did when a bevy of heavyweight investors clamored to get a piece of their company as it exited the hot San Francisco incubator AngelPad.The two say they weren’t initially interested in securing funding; they didn’t want to deal with the pressure of fighting for control with investors. But as tech luminaries like Google Ventures, AOL Ventures, Crosslink Capital and angel investors SoftTech VC, SV Angel, 500 Startups and Tim Draper started knocking on their door, it was tough to turn down the cash. They closed on $2 million in January.”When we said we didn’t have any more room for investors, they thought it was a bluffing tactic, so they pushed slightly harder,” says Smith, who moved with Jaffer from London in 2011 to take part in AngelPad. They launched Vungle in beta this year.The investors wanted in on the duo’s ability to tap into the rapidly growing world of mobile advertising, a sector worth $1.45 billion in 2011 and expected to reach $2.61 billion this year, according to eMarketer. Vungle takes screen grabs and video from apps in action to build the equivalent of a movie trailer for its clients. Because the spots are structured like trailers, they “give you more of a flavor of the application’s features and appearance,” Jaffer says.Vungle’s app trailers are absurdly cheap and easy to make. Armed with experience in video production, the founders can turn around a finished production in as little as 24 hours–no need for pricey designs and programming. The cost: free (for now) with a commitment to buy a mobile ad campaign that starts at a few thousand dollars.Distribution comes from developers who’ve embedded a simple Vungle code into their apps so that the trailers can appear at natural breaks in the action, such as the completion of a game level or task. That’s how it works on faceBlocker, an app that allows users to blur certain faces or details on a photograph. As users make their way through the app, a 15-second Vungle trailer will play at a natural pause point.David Silverman, partner at San Francisco-based Crosslink Capital, loves Vungle’s take on in-app advertising. He says the ability to capture a segment of this market is “the fundamental piece” to what will be a profitable business.Smith and Jaffer are hard at work expanding Vungle’s client base beyond the 20 or so it has now and getting themselves on the radar of app developers whose business models depend on advertising. “Until now, if you wanted to run a video ad campaign for or in your app, good luck,” Jaffer says. “We figured out how to make it easy.” November 5, 2012last_img read more

4 Ways Personal Technology Outperforms Enterprise Software

first_img Growing a business sometimes requires thinking outside the box. 5 min read Opinions expressed by Entrepreneur contributors are their own. The freelance economy rapidly is growing in the United States. More than 15 million people are self-employed, and numerous experts predict we’ll soon see a steep increase in the number of individuals who leave corporate America to work for themselves. One study put the number at 60 million people by 2020 — nearly 40 percent of the workforce.Why are so many people making the switch, and which changing dynamics have made it possible? While no single answer explains the shift, we can identify a number of contributing factors. For example, we know employers see the benefit of hiring a contractor to perform specific functions for a limited period of time. It’s more cost-effective than hiring a full-time employee. We also know numerous platforms exist today to connect freelancers with available work. All this makes it easier than ever before to be self-employed.But other drivers are at play, too. Here’s one that might surprise you: There is substantial disparity in the technology capabilities of large corporations versus self-employed freelancers. According to enterprise software expert Sean Nolan, founder and CEO of Blink, personal technology currently has a substantial edge.“Enterprise software is far behind the standard being set by personal technology today,” Nolan says. “In fact, it is so bad that it is giving a competitive advantage to startups and freelance workers who are more productive, more satisfied with their work and able to operate their small businesses more cost effectively.”These are four ways personal technology is superior to present-day office technology:1. User friendliness.It might not be fair to compare personal and office technologies in this regard because they’re designed for completely different audiences. Still, that doesn’t justify how far behind the times office technology has fallen.Personal technology has the user in mind. Interfaces are clean and engaging, data is visualized in digestible ways, and many functions are gamified to encourage use. Taken as a whole, these systems make it more enjoyable to be in business for ourselves. We can hand-select the software that best suits our working preferences and style.“We are all conditioned by the technology we use at home, so when we go to the office and have a vastly inferior experience, that is hugely frustrating,” Nolan says. “It’s destroying employee productivity and retention.”Related: 5 Productivity Tools for Self-Employed Internet Entrepreneurs2. Mobile applications.Desktop computers are almost synonymous with “old technology.” But in many companies, it’s the only device an employee has to do his or her job. We’re confined by that workstation and the very limited software installed on our system. By contrast, home-office technology is typified by its flexibility.Freelance professionals work from their phones, tablets and laptops while they travel on planes and trains. They get things done in cities around the world. They’re able to do so because their work programs feature excellent applications. They can access the files they need from any device, and they all connect to the same information stored on the cloud.Apart from the frustration of working on an outdated console, employees who are limited by old technology are less effective in their roles. They cannot work unless they’re at their desks. The workforce — and millennials, in particular — demand a better work-life balance. Mobile applications allow employees to be effective anywhere they go.Related: 5 Ways to Be More Mobile-Friendly in 20173. Customization.Everyone has different needs depending on the job they perform. Naturally, this means they need access to different kinds of information and tools. Here again, personal technology outperforms enterprise technology.“At home you can change the settings on all of your applications and devices to help you do your job most effectively,” Nolan says. “But the average user of enterprise software is confined to limited, preset options. A large percentage of the workforce cannot even access job-critical information without the help of other employees.”Of course, enterprise software is designed to be big, which makes it difficult to be customizable on a user-by-user basis. Even so, a wealth of new technologies can assist with that problem. Bots, micro apps and chat functions are a few of the solutions for large-scale employers looking to improve user experience.Related: How Technology Rapidly Is Changing the Way Things Get Done Across Industries4. Artificial intelligence.It’s hard to write anything about artificial intelligence (AI) that hasn’t been covered elsewhere, but we can’t ignore it, either. Personal technology already incorporates AI into numerous software products freelancers use to do their work. From sales and marketing platforms to billing and accounting technologies, these systems all use AI to exponentially increase productivity.The corporate world has been slow to adopt AI for its own use. It needs to embrace the functionality that AI can deliver or risk falling further behind. According to an MIT study, only 38 percent of CEOs list technology renovation among their priorities.Related: The Growth of Artificial Intelligence in EcommerceWhat might push enterprise-technology enhancements higher on the agenda? An exodus of high-quality employees from the corporate world might get some attention. When the best professionals prefer to freelance their talent, executives must take stock of the fundamental differences between working for a company and working for oneself. Technology has to be a factor. Free Webinar | Sept. 9: The Entrepreneur’s Playbook for Going Global January 26, 2017 Register Now »last_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