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Three Issues to Consider Before Moving to the Cloud

first_imgNovember 28, 2011 Free Workshop | August 28: Get Better Engagement and Build Trust With Customers Now Opinions expressed by Entrepreneur contributors are their own. By letting a cloud services vendor store your company’s files and applications online, you can easily expand in-house storage capacity and processing muscle as your business grows. Let’s say you have a surge of new orders on your e-commerce system or you need to expand processing power for an application. Your vendor can boost your computing capabilities to satisfy those needs.But there can be some risks associated with moving to the cloud. What if your vendor goes bankrupt and closes? What if its technology fails, leaving you unable to access your sensitive business data?Migrating valuable business information to a technology vendor is a big move and can require a lot of consideration. Here, we look at three of the most important factors to consider when making the transition for your company.1. Research the vendors.Because it’s relatively easy to offer cloud services, your choices will range from larger, established companies to unknown startups. The small players may be competent, but be sure you feel comfortable being among the first to put them to the test.To assess vendors, ask for customer references, talk to other companies you trust and do some research online. The ideal vendor will have a strong track record for both performance and customer service.Related: How One Small Company Outsourced IT to a Cloud-Computing PlatformFor example, low latency — the delay in moving data from one point to another — is an important measure to check. Prompt and reliable service is critical because your cloud provider is essentially your new outside information technology department. If the vendor is slow to deal with your problems, your response time to customers also will likely suffer.2. Be aware of legal issues.Understand your potential legal liabilities and ensure that your cloud vendor abides by the same rules that govern your company.Your business may have internal audit rules that demand careful handling of sensitive information. If you’re in a regulated industry or do business with the government, you also may be subject to restrictions on handling data.Related: How to Move Your Business Data into the Cloud SafelyMake sure the vendor also understands the latest privacy laws affecting your business and that it states in writing its commitment to confidentiality and its methods for securing your data. “As your data is stored in the same storage space as your neighboring tenants, you need to know how your cloud vendor will ensure that your data isn’t illegally accessed,” says Ian Huynh, vice president of engineering at Hubspan, a cloud services integration company.3. Consider a back up to the cloud.Your data will likely be just fine in the servers of a cloud services vendor. I’ve been using online services for years and have never lost data. But if you would like an added measure of security, you might want to regularly back up data stored in the cloud.You could manually export your information from the cloud and back it up on-site. Or you could select a cloud backup service from such companies as Symantec, CloudBerry, KineticD and CTERA Networks, which typically allow users to keep copies of their cloud data on local servers or in other locations.Related: Still Foggy on Cloud Computing? Installing software on a server or local computer hard disk has been the traditional way of using computers for years. However, online software offers growing businesses a much faster, more productive and more cost effective way to create, manage and move information. Just be cautious about what software you can move securely to the cloud. Enroll Now for Free 3 min read This hands-on workshop will give you the tools to authentically connect with an increasingly skeptical online audience.last_img read more

Amazon Blasts FAA for Slowness on Drone Regulation

first_img Attend this free webinar and learn how you can maximize efficiency while getting the most critical things done right. E-commerce power Amazon.com blasted federal regulators on Tuesday for being slow to approve commercial drone testing, saying the United States is falling behind other countries in the potentially lucrative area of unmanned aviation technology.Less than a week after the Federal Aviation Administration gave Amazon.com the green light to test a delivery drone outdoors, the company told U.S. lawmakers that the prototype drone had already become obsolete while the company waited more than six months for the agency’s permission.”We don’t test it anymore. We’ve moved on to more advanced designs that we already are testing abroad,” said Paul Misener, Amazon.com’s vice president for global public policy.”Nowhere outside of the United States have we been required to wait more than one or two months to begin testing,” Misener said in written testimony submitted to the Senate Subcommittee on Aviation Operations, Safety and Security.Misener said Amazon had applied on Friday for permission to test a more advanced drone system and now hopes for quicker approval.The Amazon.com case illustrates the frustrations of many companies and industry lobbyists, who say the U.S. regulatory process is not keeping up with rapidly developing drone technology that could generate new revenues and cost savings for a range of industries.Misener, who was scheduled to join a witness panel at the subcommittee hearing, said European and other international authorities have more “reasonable” approaches that recognize the potential economic benefits of commercial drone operations.”This low level of government attention and slow pace are inadequate, especially compared to the regulatory efforts in other countries,” Misener said.”The (FAA) already has adequate statutory authority. What the FAA needs is impetus, lest the United States fall further behind,” he added.Seattle-based Amazon.com, the largest e-commerce company in the United States, wants to use drones to deliver packages to its customers over distances of 10 miles (16 km)or more, which would require drones to travel autonomously while equipped with technology to avoid collisions with other aircraft.The FAA recently proposed rules that would lift the current ban on most commercial drone flights, but several restrictions attached would make package delivery and other business applications unfeasible.Among other constraints, the proposed rules would limit commercial drones to an altitude of 500 feet (150 meters), allow flights only during daytime and require operators to keep the aircraft in sight at all times.The agency does not expect to finalize the rules until late 2016 or early 2017, according to government officials. During this period, the current ban will stay in place; companies can apply for exemptions to use drones for specific business applications.The FAA has been slow to grant exemptions, however, granting only 48 of several hundred requests.The Republican-led subcommittee called the hearing to examine the agency’s efforts to integrate unmanned aircraft systems, or UAS, safely into U.S. airspace. Industry forecasters say that drones would generate nearly $14 billion of U.S. economic activity in the first three years of integration and $82 billion over a decade.Meanwhile, Australia, Canada, France and the United Kingdom have progressed toward airspace integration and allow for commercial use, the Government Accountability Office (GAO) said in a report to the subcommittee.Australia has granted operating certificates to 185 businesses, while several European countries have granted licenses to more than 1,000 operators, according to the report.While the GAO said overseas restrictions are similar to those proposed by the FAA, it noted that France has begun to allow beyond-line-of-sight operations on a limited basis.(Additional reporting by Allison Lampert in Montreal Editing by Soyoung Kim and Jonathan Oatis) 4 min read Register Now » March 24, 2015 This story originally appeared on Reuters Free Webinar | Sept 5: Tips and Tools for Making Progress Toward Important Goalslast_img read more

Deep learning is not an optimum solution for every problem faced An

first_imgOver the past few years, we have seen some advanced technologies in artificial intelligence shaping human life. Deep learning (DL) has become the main driving force in bringing new innovations in almost every industry. We are sure to continue to see DL everywhere. Most of the companies including startups are already integrating deep learning into their own day-to-day process. Deep learning techniques and algorithms have made building advanced neural networks practically feasible, thanks to high-level open source libraries such as TensorFlow, Keras, PyTorch and more. We recently interviewed Valentino Zocca, a deep learning expert and the author of the book, Python Deep Learning. Valentino explains why deep learning is getting so much hype, and what’s the roadmap ahead in terms of new technologies and libraries. He will also talks about how major vendors and tech-savvy startups adopt deep learning within their organization. Being a consultant and an active developer he is expecting a better approach than back-propagation for carrying out various deep learning tasks. Author’s Bio Valentino Zocca graduated with a Ph.D. in mathematics from the University of Maryland, USA, with a dissertation in symplectic geometry, after having graduated with a laurel in mathematics from the University of Rome. He spent a semester at the University of Warwick. After a post-doc in Paris, Valentino started working on high-tech projects in the Washington, D.C. area and played a central role in the design, development, and realization of an advanced stereo 3D Earth visualization software with head tracking at Autometric, a company later bought by Boeing. At Boeing, he developed many mathematical algorithms and predictive models, and using Hadoop, he has also automated several satellite-imagery visualization programs. He has since become an expert on machine learning and deep learning and has worked at the U.S. Census Bureau and as an independent consultant both in the US and in Italy. He has also held seminars on the subject of machine learning and deep learning in Milan and New York. Currently, Valentino lives in New York and works as an independent consultant to a large financial company, where he develops econometric models and uses machine learning and deep learning to create predictive models. But he often travels back to Rome and Milan to visit his family and friends. Key Takeaways Deep learning is one of the most adopted techniques used in image and speech recognition and anomaly detection research and development areas. Deep learning is not the optimum solution for every problem faced. Based on the complexity of the challenge, the neural network building can be tricky. Open-source tools will continue to be in the race when compared to enterprise software. More and more features are expected to improve on providing efficient and powerful deep learning solutions. Deep learning is used as a tool rather than a solution across organizations. The tool usage can differ based on the problem faced. Emerging specialized chips expected to bring more developments in deep learning to mobile, IoT and security domain. Valentino Zocca states We have a quantity vs. quality problem. We will be requiring better paradigms and approaches in the future which can be improved through research driven innovative solutions instead of relying on hardware solutions. We can make faster machines, but our goal is really to make more intelligent machines for performing accelerated deep learning and distributed training. Full Interview Deep learning is as much infamous as it is famous in the machine learning community with camps supporting and opposing the use of DL passionately. Where do you fall on this spectrum? If you were given a chance to convince the rival camp with 5-10 points on your stand about DL, what would your pitch be like? The reality is that Deep Learning techniques have their own advantages and disadvantages. The areas where Deep Learning clearly outperforms most other machine learning techniques are in image and speech recognition and anomaly detection. One of the reasons why Deep Learning does so much better is that these problems can be decomposed into a hierarchical set of increasingly complex structures, and, in multi-layer neural nets, each layer learns these structures at different levels of complexity. For example, an image recognition, the first layers will learn about the lines and edges in the image. The subsequent layers will learn how these lines and edges get together to form more complex shapes, like the eyes of an animal, and finally the last layers will learn how these more complex shapes form the final image. However, not every problem can suitably be decomposed using this hierarchical approach. Another issue with Deep Learning is that it is not yet completely understood how it works, and some areas, for example, banking, that are heavily regulated, may not be able to easily justify their predictions. Finally, many neural nets may require a heavier computational load than other classical machine learning techniques. Therefore, the reality is that one still needs a proficient machine learning expert who deeply understands the functioning of each approach and can make the best decision depending on each problem. Deep Learning is not, at the moment, a complete solution to any problem, and, in general, there can be no definite side to pick, and it really depends on the problem at hand. Deep learning can conquer tough challenges, no doubt. However, there are many common myths and realities around deep learning. Would you like to give your supporting reasoning on whether the following statements are myth or fact? You need to be a machine learning expert or a math geek to build deep learning models We need powerful hardware resources to use deep learning Deep learning models are always learning, they improve with new data automagically Deep learning is a black box, so we should avoid using it in production environments or in real-world applications. Deep learning is doomed to fail. It will be replaced eventually by data sparse, resource economic learning methods like meta-learning or reinforcement learning. Deep learning is going to be central to the progress of AGI (artificial general intelligence) research Deep Learning has become almost a buzzword, therefore a lot of people are talking about it, sometimes misunderstanding how it works. People hear the word DL together with “it beats the best player at go”, “it can recognize things better than humans” etc., and people think that deep learning is a mature technology that can solve any problem. In actuality, deep learning is a mature technology only for some specific problems, you do not solve everything with deep learning and yet at times, whatever the problem, I hear people asking me “can’t you use deep learning for it?” The truth is that we have lots of libraries ready to use for deep learning. For example, you don’t need to be a machine learning expert or a math geek to build simple deep learning models for run-of-the-mill problems, but in order to solve for some of the challenges that less common issues may present, a good understanding of how a neural network works may indeed be very helpful. Like everything, you can find a grain of truth in each of those statements, but they should not be taken at face value. With MLaaS being provided by many vendors from Google to AWS to Microsoft, deep learning is gaining widespread adoption not just within large organizations but also by data-savvy startups. How do you view this trend? More specifically, is deep learning being used differently by these two types of organizations? If so, what could be some key reasons? Deep Learning is not a monolithic approach. We have different types of networks, ANNs, CNNs, LSTMs, RNNs, etc. Honestly, it makes little sense to ask if DL is being used differently by different organizations. Deep Learning is a tool, not a solution, and like all tools it should be used differently depending on the problem at hand, not depending on who is using it. There are many open source tools and enterprise software (especially the ones which claim you don’t need to code much) in the race. Do you think this can be the future where more and more people will opt for ready-to-use (MLaaS) enterprise backed cognitive tools like IBM Watson rather than open-source tools? This holds true for everything. At the beginning of the internet, people would write their own HTML code for their web pages, now we use tools who do most of the work for us. But if we want something to stand-out we need a professional designer. The more a technology matures, the more ready-to-use tools will be available, but that does not mean that we will never need professional experts to improve on those tools and provide specialized solutions. Deep learning is now making inroads to mobile, IoT and security domain as well. What makes DL great for these areas? What are some challenges you see while applying DL in these new domains? I do not have much experience with DL in mobiles, but that is clearly a direction that is becoming increasingly important. I believe we can address these new domains by building specialized chips. Deep learning is a deeply researched topic within machine learning and AI communities. Every year brings us new techniques from neural nets to GANs, to capsule networks that then get widely adopted both in research and in real-world applications. What are some cutting-edge techniques you foresee getting public attention in deep learning in 2018 and in the near future? And why? I am not sure we will see anything new in 2018, but I am a big supporter of the idea that we need a better paradigm that can excel more at inductive reasoning rather than just deductive reasoning. At the end of last year, even DL pioneer Geoff Hinton admitted that we need a better approach than back-propagation, however, I doubt we will see anything new coming out this year, it will take some time. We keep hearing noteworthy developments in AI and deep learning by DeepMind and OpenAI. Do you think they have the required armory to revolutionize how deep learning is performed? What are some key challenges for such deep learning innovators? As I mentioned before, we need a better paradigm, but what this paradigm is, nobody knows. Gary Marcus is a strong proponent of introducing more structure in our networks, and I do concur with him, however, it is not easy to define what that should be. Many people want to use the brain as a model, but computers are not biological structures, and if we had tried to build airplanes by mimicking how a bird flies we would not have gone very far. I think we need a clean break and a new approach, I do not think we can go very far by simply refining and improving what we have. Improvement in processing capabilities and the availability of custom hardware have propelled deep learning into production-ready environments in recent years. Can we expect more chips and other hardware improvements in the coming years for GPU accelerated deep learning and distributed training? What other supporting factors will facilitate the growth of deep learning? Once again, foreseeing the future is not easy, however, as these questions are related, I think only so much can be gained by improving chips and GPUs. We have a quantity vs. quality problem. We can improve quantity (of speed, memory, etc.) through hardware improvements, but the real problem is that we need a real quality improvement, better paradigms, and approaches, that needs to be achieved through research and not with hardware solutions. We can make faster machines, but our goal is really to make more intelligent machines. A child can learn by seeing just a few examples, we should be able to create an approach that allows a machine to also learn from few examples, not by cramming millions of examples in a short time. Would you like to add anything more to our readers? Deep Learning is a fascinating discipline, and I would encourage anyone who wanted to learn more about it to approach it as a research project, without underestimating his or her own creativity and intuition. We need new ideas. If you found this interview to be interesting, make sure you check out other insightful interviews on a range of topics: Blockchain can solve tech’s trust issues – Imran Bashir “Tableau is the most powerful and secure end-to-end analytics platform”: An interview with Joshua Milligan “Pandas is an effective tool to explore and analyze data”: An interview with Theodore Petroulast_img read more

Silversea Expedition Classic ships head to 1000 ports in 2018

first_img MONACO — The nine ships from across Silversea’s Classic and Expedition fleet will visit more than 1,000 destinations in 130 countries across the world in 2018, almost 600 via the Expedition fleet and over 400 with the Classic fleet.“At Silversea, we don’t believe that ‘bigger is better’, in fact we believe and deliver the complete opposite,” said Silversea Cruises CEO Roberto Martinoli. “However, in this instance, as we announce our new itineraries, enlarging the range of destinations, further enriching our guests with new cultural references, giving more opportunity to make discoveries, guaranteeing a bigger circle of friends and overloading on unforgettable memories can only be a positive thing.”Silver Cloud starts 2018 as an ice-class expedition ship following an extensive refurbishment and conversion. Silver Discoverer sails deeper into Asia, with many new destinations in Thailand, Malaysia, Vietnam, Cambodia, the Philippines and Japan.“As the company that introduced Expedition cruising to the luxury market, Silversea continues to innovate and over-deliver on destinations and experiences, and remains the pioneer in this regard,” said Martinoli.More news:  Windstar celebrates record-breaking bookings in JulyHere are a few highlights from Silversea 2018 schedule:. The Ultimate Mediterranean experience: 44 days in the heart of the Mediterranean season aboard Silversea’s flagship, Silver Muse. This Grand Voyage will call at 39 different ports in nine different countries, from the Greek Isles to the Balearics, from the French Riviera to Tuscany and the Amalfi Coast.. New Asian experience: Five voyages to the Philippines with three different ships (Silver Shadow, Silver Whisper, Silver Muse). A key highlight destination is the island of Palawan.. Africa & Indian Ocean: Several voyages in the heart of the Indian Ocean, exploring the Vanilla Islands, with multiple visits to Maldives, Seychelles, Madagascar, Mayotte and Reunion.. Explorer by Name and by Nature: Silver Explorer sails across the Pacific Ocean to Kiribati, the Cook Islands and French Polynesia, adding new ports of call along the way. She will also carry her guests to the North West Coast of America, to Costa Rica, the southernmost tip of Mexico’s Baja California peninsula (home of El Arco), visiting Monterey, Anacortes and Cypress Island. Share Travelweek Group Thursday, September 29, 2016 Silversea Expedition, Classic ships head to 1,000+ ports in 2018last_img read more