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AI-Defined Infrastructure | @CloudExpo #AI #DX #IoT #ArtificialIntelligence
The Foundation for New Generation Business Models and Applications

In 2016, artificial intelligence (AI) reached its climax. Research and advisory firm Tractica predicted that the annual worldwide AI revenue will grow from $643.7 million in 2016 to $38.8 billion by 2025. The revenue for enterprise AI applications will increase from $358 million in 2016 to $31.2 billion by 2025, representing a compound annual growth rate (CAGR) of 64.3%. Thus, IT and business decision makers must face up to the potentials of AI already today. For each kind of organization this leads to the question, which type of technologies or infrastructure they can leverage to operate an AI-ready enterprise stack.

What Is Artificial Intelligence (AI)?
In 1955, Prof. John McCarthy defined AI as "The goal of AI is to develop machines that behave as though they were intelligent." Discussing "intelligent" in this context, we are talking about a vigorous system. A system that must be considered as a raw IQ container, a system that needs unstructured input to train its senses, a system that needs a semantic understanding of the world to be able to take further actions. A system that needs a detailed map of its context to act independently and transfer experience from one context to another, a system that is equipped with all the necessities to develop, foster and maintain knowledge.

It is our responsibility to share our knowledge with these machines as we would share it with our children, spouses or colleagues. This is the only way to transform these machines, made of hard- and software, into a status we would describe as "smart", helping them to become more intelligent by learning on a daily basis, building the groundwork to create a self-learning system. In doing so, research distinguishes three types of AIs:

  • Strong AI: A strong AI (or superintelligence) is a self-aware machine with ideal thoughts, feelings, consciousness and all the necessary links. For all those who are already looking forward to a reality á la "Her" or "Ex_Machina" still need to wait. Large neural networks have millions of neurons. Brains have billions of neurons. Neural networks only simulate the electrical system in a brain, the brain also has a chemical, potentially a quantum mechanics based system. The layer based modelling of deep learning networks is to simplify training, the brain has no such restrictions. Neural networks are about as far away from a brain that thinks as a snail is from a supersonic jet. Thus, a strong AI doesn't exist yet and is very far away.
  • Narrow AI: Most business cases in AI focus on solving particular very pointed challenges. These narrow AIs are great at optimizing specific tasks like recommending songs on Pandora ormanaging analyses to improve tomato growth in a greenhouse.
  • General AI: A general AI can handle tasks from different areas and origins with the ability to shorten training time from one area to the next by applying experience gathered in one area and applied in a different area. This knowledge transfer is only possible if there is a semantic connection between these areas. The stronger and denser this connection, the faster and easier knowledge transition is achieved. In comparison to a narrow AI, a general AI has all the necessary knowledge and abilities to improve not only tomato growth in a greenhouse but cucumber, eggplant, peppers, radishes and kohlrabi as well. Thus, a general AI is a system, that can handle more than just one specific task.

However, one thing is obvious. Without technologies, such as cloud computing, AI wouldn't have achieved its boom particularly today. Both cloud services and progress in machine intelligence have made it easier for organizations to apply AI-based functionalities to interact closer with its customers. More and more companies like Airbnb, Netflix, Uber or Expedia are already using cloud-based systems to process AI relevant tasks that draw on an intensive utilization of CPU/ GPU as well as services for comprehensive computing and analysis tasks.

In the context of their AI strategy, companies should evaluate AI services from different cloud providers. Another part of their strategy should contain an AI-defined infrastructure. The foundation for this kind of infrastructure is a general AI that unifies three typical human characteristics, which empower an organization to autonomously operate its IT and business processes.

  • Learning: The general AI receives best practices and reasoning from experts based on ongoing learning units. For this purpose, the knowledge is taught in granular pieces that consist of discrete parts of a process. In the context of a greenhouse, the experts teach the AI any process step by step, e.g., how to grow cucumbers, eggplants or paprika. In doing so, they share their context-based knowledge with the AI that includes among others "what has to be done" and "why this has to be done".
  • Understanding: By creating a semantic data graph the general AI gets an understanding of the world in which the organization is acting with its IT and business objectives. Thus, the semantic data graph of a greenhouse would consolidate different contexts (e.g., information, characteristics and specifics of the greenhouse, cucumber culture, eggplant culture and paprika culture) and enrich (compare learning) it on an ongoing basis. The IT of an organization plays an important role, since all data are running together here.
  • Solving: With the concept of machine reasoning, problems are solved in ambiguous and changing environments. The general AI dynamically reacts to the ever-changing context, selecting the best course of action. Based on the trained knowledge (learning) and the creation of the semantic graph (understanding) the general AI can grow more than one single type of vegetable in a greenhouse. This is ensured with the growing amount of trained knowledge pieces that lead to a knowledge pool that is further optimized by machine selection of best knowledge combinations for problem resolution. This type of collaborative learning improves process time task by task. However, the number of possible permutations grows exponentially with added knowledge. Connected to a knowledge core, the General AI continuously optimizes performance by eliminating unnecessary steps and even changing routes based on other contextual learning. Thus, the bigger the semantic data graph gets, the better and more dynamically further types of vegetables can be cultured.

What Requirements Concerning Infrastructure Environments Does an AI Have?
Right now, AI is the technology that has the potential not only to improve existing infrastructure like cloud environments but expedite a new generation of infrastructure technologies as well. As an important technology trend, AI has influenced a new generation of development frameworks as well as a new generation of hardware technologies to run scalable AI applications.

Mobile and IoT applications have only minor requirements concerning runtime environments to an infrastructure. However, it is critical to provide appropriate services to build a backend for those types of applications. By contrast, AI applications do not only expect sophisticated backend services but also optimized runtime environments that are adapted for GPU intensive requirements of AI solutions. AI applications challenge the infrastructure with regards to the simultaneous task processing in very short time cycles. For accelerating deep learning applications in particular GPU processors are employed. GPU optimized applications distribute CPU-intensive areas of an application to the GPU and let the ordinary computations handle by the CPU. In doing so, the execution of the entire application is accelerated. The advantage of a GPU towards a CPU is reflected in the respective architectures. A CPU is exclusively designed for serial data processing and only has a few cores. A GPU, however, is composed of a parallel architecture with a vast number of small cores that process the tasks simultaneously. According to NVIDIA, the application throughput of a GPU is 10 to 100 times higher in comparison to a CPU. Thus, an infrastructure should be able to provide a deep learning framework such as TensorFlow and Torch over hundreds or thousands of nodes on a demand basis that immediately are deployed with the optimal CPU configuration.

The following list (in a partial state) deals with the requirements for infrastructure to support AI applications:

  • Support of current frameworks: Infrastructure must be able to support AI application based on AI frameworks like TensorFlow, Caffe, Theano and Torch the same way as web applications and backend processes. Thus, an infrastructure should not exclusively focus on AI frameworks but design the portfolio in the interests of a developer.
  • GPU optimized environment: An infrastructure has to make sure that every AI process can be processed. Thus, it must support GPU environments in order to provide fast computational power. Microsoft was the frontrunner in this area by offering its N-series GPU instances.
  • Management environment and tools: One of the biggest challenges of current infrastructure environments is the drawback of management tools for running AI frameworks. Here, in particular, the direct interaction between AI frameworks and the infrastructure is necessary to ensure the best balance and thus deliver the best performance.
  • AI-integrated infrastructure services: Infrastructure provider must and will not only support AI functionalities but integrate AI as a central part of their infrastructure and service stacks. This type of an AI-defined Infrastructure won't only increase the intelligence of cloud services and applications but also simplify the setup and operations of the infrastructure by the customer.
  • Machine Reasoning: Infrastructure providers who provide their customers with technologies for machine reasoning are helping them to solve problems in ambiguous and changing environments. Based on machine reasoning the AI environment is able to dynamically react to the ever-changing context, selecting the best course of action. This is ensured by selecting the best knowledge combinations for problem resolution. In the end, the results are optimized with machine learning algorithms.

Infrastructure Environments and Technologies for AI
In the course of years, cloud platform provider made enormous investments into AI functionalities and services. The leading public cloud provider in particular Amazon, Microsoft and Google are in the lead. But also several PaaS providers extended their offerings with AI services. The current AI technology landscape consists of the following three main categories:

  • Cloud machine learning (ML) platforms:Technologies like AWS Machine Learning or Google Machine Learning make it possible to use machine learning models based on proprietary technologies. Because even if Google Cloud ML sets on TensorFlow, most of the other cloud based ML services do not allow to execute AI applications that e.g. have been written in Theano, Torch, TensorFlow or Caffe.
  • AI cloud services:Technologies like Microsoft Cognitive Services, Google Cloud Vision or Natural Language APIs enable the use of complex AI abilities based on a simple API call. This allows organizations to develop applications with AI capabilities without investing into and owning the necessary AI infrastructure.
  • Technologies for private and public cloud environments: Technologies like HIRO are designed to run on top of public cloud environments like Amazon Web Services as well as private clouds such as OpenStack or VMware. They enable organizations to develop and operate transcontextual AI-based business models based on a general AI.

Further AI relevant categories and vendors are:

  • Machine Learning: Rapidminer, Context Relevant, H20, Datarpm, LiftIngniter, Spark Beyond, Yhat, Wise.io, Sense, GraphLab, Alpine, Nutonian
  • Conversational AI/ Bots: Mindfield, SemanticMachines, Maluuba, Mobvoi, KITT AI, Clara, Automat, Wit.ai, Cortical.io, Idibon, Luminoso
  • Vision: Clarifai, Chronocam, Orbital Insight, Pilot.ai, Captricity, Crokstyle
  • Auto: NuTonomy, Drive.ai, AI Motive, Nauto, Nexar, Zoox
  • Robotics: Ubtech, Anki, Rokid, Dispatch
  • Cybersecurity: Cyclance, Sift Science, Spark Cognition, Deep Instict, Shift Technology, Dark Trace
  • BI & Analytics: DataRobot, Trifaca, Tamr, Esigopt, Paxata, Dataminr, CrowdFlower, Logz.io
  • Ad, Sales and CRM: TalkIQ, Deepgram, Persado, Appier, Chors, InsideSales.com, Drawbridge, DigitalGenius, Resci
  • Healthcare: Freenome, Cloud Medx, Zebra, Enlitic, Two AR, iCarbonX, Atomwise, Deep Genomics, Babylon, Lunit
  • Text Analysis: Textio, Fido.ai, Narrative
  • IoT: Nanit, Konux, Verdigris, Sight Machine
  • Commerce: Bloomreach, Mode.ai
  • Fintech & Insurance: Cape Analytics, Kensho, Numerai, Alphasense, Kasisto

At the end of the day, the progressive developments of AI technologies are going to influence infrastructure environments and let them shift from a supporting mode towards a model where AI applications get the equal support like today's web applications and services.

The Future Is an AI-enabled Enterprise
An AI-enabled Infrastructure is an essential part of today's enterprise stack and builds the foundation for the AI-enabled enterprise. Because one thing is obvious. There are multiple challenges established companies are facing nowadays. Like the often-quoted war for talent or the inability of many large corporates to change effectively. But there is still an underestimated threat called competition - not from their own peers - but from high-tech companies like Amazon, Google, Facebook, etc., that are unstoppably marching into their markets. These high-tech companies invade the well-known competitive space of established companies with unimaginable financial resources and by hijacking the consumer life cycle.

Amazon is just one example who already has started to cut out the middleman within the own supply chain. We can be sure that business models of companies like DHL, UPS or FedEx are going to look different in the future - hint: Amazon Prime Air. Furthermore, Amazon has arranged everything to become a complete end-to-end provider of goods - digital as well as non-digital. It's likely that it won't be long until Facebook gets its banking license. Access to potential customers, enough information about its users and the necessary financial resources already exist. Consequently, established companies need to have powerful answers if they still want to exist tomorrow.

AI is one of these answers in the corporate toolkit to help overcome these competitive threats. However, time is running out for established companies. High-tech companies have already become uncatchable.

About Rene Buest
Rene Buest is Director of Market Research & Technology Evangelism at Arago. Prior to that he was Senior Analyst and Cloud Practice Lead at Crisp Research, Principal Analyst at New Age Disruption and member of the worldwide Gigaom Research Analyst Network. At this time he was considered a top cloud computing analyst in Germany and one of the worldwide top analysts in this area. In addition, he was one of the world’s top cloud computing influencers and belongs to the top 100 cloud computing experts on Twitter and Google+. Since the mid-90s he is focused on the strategic use of information technology in businesses and the IT impact on our society as well as disruptive technologies.

Rene Buest is the author of numerous professional technology articles. He regularly writes for well-known IT publications like Computerwoche, CIO Magazin, LANline as well as Silicon.de and is cited in German and international media – including New York Times, Forbes Magazin, Handelsblatt, Frankfurter Allgemeine Zeitung, Wirtschaftswoche, Computerwoche, CIO, Manager Magazin and Harvard Business Manager. Furthermore he is speaker and participant of experts rounds. He is founder of CloudUser.de and writes about cloud computing, IT infrastructure, technologies, management and strategies. He holds a diploma in computer engineering from the Hochschule Bremen (Dipl.-Informatiker (FH)) as well as a M.Sc. in IT-Management and Information Systems from the FHDW Paderborn.

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Cloud Expo 2013 East Opening Keynote by IBM
In this Cloud Expo Keynote, Danny Sabbah, CTO & General Manager, Next Generation Platform, will detail the critical architectural considerations and success factors organizations must internalize to successfully implement, optimize and innovate using next generation architectures.
Lisa Larson, Vice President of Enterprise Cloud Solutions of Rackspace Hosting Live From New York City
In the old world of IT, if you didn't have hardware capacity or the budget to buy more, your project was dead in the water. Budget constraints can leave some of the best, most creative and most ingenious innovations on the cutting room floor. It's a true dilemma for developers and innovators – why spend the time creating, when a project could be abandoned in a blink? That was the old world. In the new world of IT, developers rule. They have access to resources they can spin up instantly. A hybrid cloud ignites innovation and empowers developers to focus on what they need. A hybrid cloud blends the best of all worlds, public cloud, private cloud and dedicated servers to fit the needs of developers and offer the ideal environment for each app and workload without the constraints of a one-size-fits-all cloud.

Keynote: Driving Cloud Innovation: SSDs Change Cloud Storage Paradigm
Cloud is a transformational shift in computing that can have a powerful effect on enterprise IT when designed correctly and used to its full potential. Join Citrix in a discussion that centers on building, connecting and empowering users with cloud services and hear examples of how enterprises are solving real-world business challenges with an architecture and solution purpose-built for the cloud.

Go Beyond IaaS to Deliver "Anything As a Service"
Many organizations want to expand upon the IaaS foundation to deliver cloud services in all forms—software, mobility, infrastructure and IT. Understanding the strategy, planning process and tools for this transformation will help catalyze changes in the way the business operates and deliver real value. Join us to learn about the new ITaaS model and how to begin the transformation.


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Digital transformation is about embracing digital technologies into a company's culture to better connect with its customers, automate processes, create better tools, enter new markets, etc. Such a transformation requires continuous orchestration across teams and an environment based on open collaboration and daily experiments. In his session at 21st Cloud Expo, Alex Casalboni, Technical (Cloud) Evangelist at Cloud Academy, explored and discussed the most urgent unsolved challenges to achieve full cloud literacy in the enterprise world.
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The best way to leverage your Cloud Expo presence as a sponsor and exhibitor is to plan your news announcements around our events. The press covering Cloud Expo and @ThingsExpo will have access to these releases and will amplify your news announcements. More than two dozen Cloud companies either set deals at our shows or have announced their mergers and acquisitions at Cloud Expo. Product announcements during our show provide your company with the most reach through our targeted audiences.
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When applications are hosted on servers, they produce immense quantities of logging data. Quality engineers should verify that apps are producing log data that is existent, correct, consumable, and complete. Otherwise, apps in production are not easily monitored, have issues that are difficult to detect, and cannot be corrected quickly. Tom Chavez presents the four steps that quality engineers should include in every test plan for apps that produce log output or other machine data. Learn the steps so your team's apps not only function but also can be monitored and understood from their machine data when running in production.
Best Recent Articles on Cloud Computing & Big Data Topics
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IT organizations face a growing demand for faster innovation and new applications to support emerging opportunities in social, mobile, growth markets, Big Data analytics, mergers and acquisitions, strategic partnerships, and more. This is great news because it shows that IT continues to be a key stakeholder in delivering business service innovation. However, it also means that IT must deliver new innovation despite flat budgets, while maintaining existing services that grow more complex every day.
Cloud computing is transforming the way businesses think about and leverage technology. As a result, the general understanding of cloud computing has come a long way in a short time. However, there are still many misconceptions about what cloud computing is and what it can do for businesses that adopt this game-changing computing model. In this exclusive Q&A with Cloud Expo Conference Chair Jeremy Geelan, Rex Wang, Vice President of Product Marketing at Oracle, discusses and dispels some of the common myths about cloud computing that still exist today.
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This week I had the pleasure of delivering the opening keynote at Cloud Expo New York. It was amazing to be back in the great city of New York with thousands of cloud enthusiasts eager to learn about the next step on their journey to embracing a cloud-first worldl."
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One of the best conferences we have attended in a while. Great job, Cloud Expo team! Keep it going."

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Digital Transformation Blogs
Digital transformation is about embracing digital technologies into a company's culture to better connect with its customers, automate processes, create better tools, enter new markets, etc. Such a transformation requires continuous orchestration across teams and an environment based on open collaboration and daily experiments. In his session at 21st Cloud Expo, Alex Casalboni, Technical (Cloud) Evangelist at Cloud Academy, explored and discussed the most urgent unsolved challenges to achieve full cloud literacy in the enterprise world.
The best way to leverage your Cloud Expo presence as a sponsor and exhibitor is to plan your news announcements around our events. The press covering Cloud Expo and @ThingsExpo will have access to these releases and will amplify your news announcements. More than two dozen Cloud companies either set deals at our shows or have announced their mergers and acquisitions at Cloud Expo. Product announcements during our show provide your company with the most reach through our targeted audiences.
Having been in the web hosting industry since 2002, dhosting has gained a great deal of experience while working on a wide range of projects. This experience has enabled the company to develop our amazing new product, which they are now excited to present! Among dHosting's greatest achievements, they can include the development of their own hosting panel, the building of their fully redundant server system, and the creation of dhHosting's unique product, Dynamic Edge.