Artificial Intelligence (AI), Business Intellegence (BI), Gunnebo Business Solutions, Machine Learning (ML), Microsoft Azure

Microsoft LEAP: Looking into the future

Cloud Computing have become one of the most profitable industries in the world and cloud will remain a very hot topic for a foreseeable future. There is a huge competition among cloud service providers to win customers by providing the best services to their customers. Cloud service providers invest a lot of money on inventions. Thus, cloud services make most of the trends in the future IT industry. Microsoft Azure and Amazon AWS is one of the leaders in innovation in their respective fields.

Data centers around the world

As the demand for cloud services rapidly increasing in all parts of the world, establishing data centers around the globe becomes a necessity. Azure has understood this well and expecting to expand its service by constructing data center regions in many parts of the world.

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From news.microsoft.com article about Project Natick’s Northern Isles datacenter at a Naval Group facility in Brest, France. Photo by Frank Betermin

The world is divided into geographies defined by geopolitical boundaries or country borders. These geographies define the data residency boundaries for customer data. Azure geographies respect the requirements within geographical boundaries. It ensures data residency, compliance, sovereignty, and resiliency. Azure regions are organized into geographies. A region is defined by a bandwidth and latency envelope. Azure owns the greatest number of global regions among cloud providers. This is a great benefit for businesses who seek to bring their applications closer to users around the world while protecting data residency.

The Two Major Azure’s Global Expansion of Cloud Services

Two of the most expansion that Microsoft Azure has incorporated to improve its service updates includes the following:

Expansion of Virtual Networks and Virtual Machines Support.

With utility virtual machines like A8 and A9 that provides the advantages of operations like rapid processors and interconnection amidst more virtual cores, there can now be the seamless configuration of virtual networks for specific geographical locations and regions.

This feature gives more room for optimal operations, cloud services, complex engineering design video encoding and a lot more.

Incorporation of Azure Mobile Services, and its Expansion to Offline Features

Even with a disconnected service, this operation makes it possible for applications to operate effectively on offline features.  Furthermore, is that this extends the incorporation of Azure cloud services to apps on various platforms, including Android and iOS on mobile phones.

Then there are Availability Zones. It is the 3 rd level in the Azure network hierarchy.

Availability zones are physically separated locations. They exist inside regions. They are made up of one or more data centers. Constructing availability zones is not easier. They are not just data centers, they need advanced networking, independent power, cooling etc. The primary purpose of Availability zones is to helps customers to run mission-critical applications.

You will have following benefits with Azure availability zones

  • Better protection for your data – you won’t lose your data due to the destruction of a data center
  • High- availability, better performance, more resources for businesses to continuity.
  • 99% SLA on virtual machines

Open source technology

Microsoft took some time to understand the value of Open source technologies. But now they are doing really fine. With .Net Core and the .Net Standard, Microsoft has done a major commitment to open source. Looking at GitHub alone, Microsoft is one of the largest contributors to open source.

Redmond, Washington USA - 4th June 2018 Microsoft confirms its acquiring GitHub
“Microsoft is a developer-first company, and by joining forces with GitHub we strengthen our commitment to developer freedom, openness and innovation,” said Satya Nadella, CEO, Microsoft.

With  .Net core 3.0, Microsoft introduced many features that will enable developers to create high security fast productive web and cloud applications. .NET Core 3 is a major update which adds support for building Windows desktop applications using Windows Presentation Foundation (WPF), Windows Forms, and Entity Framework 6 (EF6). ASP.NET Core 3 enables client-side development with Razor Components. EF Core 3 will have support for Azure Cosmos DB. It will also include support for C# 8 and .NET Standard 2.1 and much more.

Mixed reality and AI perceptions

Mixed reality tries to reduce the gap between our imagination and reality. With AI, it is about to change the way how we see the world. It seems to become the primary source of entertainment. Although Mixed reality got popular in the Gaming industry, now you can see its applications in other industries as well. The global mixed reality market is booming. That’s why the biggest names in tech are battling it out to capture the MR market. All major tech products have introduced MR devices such as Meta2 handsets, GoogleGlass 2.0, Microsoft HoloLens.

Mixed reality and AI perception is a result of the cooperation of many advanced technologies. This technology stack includes Natural Language interaction, Object recognition, real-world perception, real-world visualization, Contextual data access, Cross-device collaboration, and cloud streaming.

Factory Chief Engineer Wearing VR Headset Designs Engine Turbine on the Holographic Projection Table. Futuristic Design of Virtual Mixed Reality Application

As I said earlier, Although the Gaming industry was the first to adopt mixed reality, now MR applications are more used in other industries. Let’s visit some of the industries and see how Mixed reality has transformed them and what benefits those industries get from mixed reality and AI perception.

You can see tech giants such as SAAB, NETSCAPE, DataMesh, using mixed reality in the manufacturing industry. According to research, mixed reality helps to increase worker productivity by 84%, improve collaboration among cross-functional teams by 80% and improve customer service interaction by 80%. You may wonder How mixed reality was able to achieve it? What it offers to the manufacturing industry. There are many applications of Mixed reality in manufacturing, following is a small list of them.

  • Enhanced Predictive Maintenance
  • Onsite Contextual Data Visualization
  • Intuitive IOT Digital Twin Monitoring
  • Remote collaboration and assistance
  • Accelerated 3D modeling and product design
  • Responsive Simulation training

Retail, Healthcare, Engineering, Architecture are some other industries that use mixed reality heavily.

Quantum revolution

Quantum computing could be the biggest thing in the future. It is a giant leap forward from today’s technology. It has the potential to alter our industrial, academic societal and economic landscapes forever.  You will see these massive implications nearly every industry including energy, healthcare, smart materials, and environmental system. Microsoft is taking a unique revolutionary approach to quantum with its Quantum Development Kit.

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Picture from cloudblogs.microsoft.com article about the potential of quantum computing

Microsoft can be considered as the only one who took quantum computing seriously in the commercial world. They have a quantum dream team which is formed by the greatest minds in physics, mathematics, computer science, and engineering to provide cutting-edge quantum innovation. Their quantum solution integrates seamlessly with Azure. They have taken a scalable topological approach towards quantum computing which helps to harness superior qubits. These superior qubits can perform complex computations with high accuracy at a lower cost.

There are three important features in Quantum development kit which makes it the go-to Quantum computing solution.

It introduces its own language, Q#. Q# created only for quantum programming. It has general programming features such as operators, native types and other abstractions.  Q# can easily integrate with Visual Studio and VS code which makes Q# feature rich. Q# is interoperable with the Python programming language. With the support of enterprise-grade tools, you can easily work on any OS windows, macOS, or Linux.

Quantum development kit provides a simulated environment which greatly supports optimizing the codes. This is very different from other quantum computing platforms which still exist in a kind of crude level. This simulation environment also helps you to debug your code, set breakpoints, estimates costs, and many other things.

As we discussed earlier, Microsoft has become the main contributor in the open source world. They provide Open source license for libraries and samples. They have tried a lot to make quantum computing easier. A lot of training materials are presented to attract developers to into quantum programming realm. The open source license is a great encouragement for developers to use the Quantum development kit in their applications while contributing to the Q# community.

Cloud services will shape the future of the IT industry. Quantum computing, Open source technologies, Mixed reality will play a great role in it.

This is my last day in Redmond, but I really look forward to coming again next year! If you have any questions, feel free to contact me at bjorn.nostdahl@gunnebo.com

Artificial Intelligence (AI), Gunnebo Business Solutions, Machine Learning (ML), Microsoft Azure

Microsoft LEAP: Adding Business Value and Intelligence

Adding Business Value and Intelligence

The concept of business value and intelligence is aimed at more productive measures through the utilization of various tech application and analytical tool for the assessment of raw data. Business intelligence makes use of activities like data mining, analytical processing, querying and reporting. Companies take advantage to improve their operationalization, as well as accelerate their decision making. Business intelligence is also useful in the aspect of reducing cost and expenses and also identifying new business opportunities.

Machine learning technologies. Millennial students teaching a robot to analyse data

A lot of experts have shared their ideas and spoken on various aspect of business values and intelligence relating to AI in Redmond. Notable speakers include Jennifer Marsman, Maxim Lukiyanov, Martin Wahl, and Noelle LaCharite. The aspects that they extensively spoke on is a machine and learning fundamentals, introduction to new azure machine learning service, using cognitive services to power your business applications, and how to solve business problems using AI, respectively.

Machine and Learning Fundamentals

The fundamentals of machine learning have to do with understanding both the theoretical and programming aspect. it is also important to be up to date with the latest algorithm and technology that is being implemented by the various programming tools for machine learning. The there simplest explanation of the term machine learning is that the operation of the machine in such a way that it would be able to perform various tasks.

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Algorithms can learn how to perform these tasks in various ways, and this brings us to the different types of machine learning. They include supervised learning which is carried out to enable the machine to identify and differentiate between various data. Unsupervised learning, on the other hand, does not have to do with a specific data or structure that the machine is supposed to produce. Another type of machine learning is reinforcement learning.

The importance of a machine model’s accuracy cannot be understated. The accuracy is what really determines how effective a machine can be for the operationalization of a company. machine models are estimated or measured mainly by prediction making and putting them to work in the real world sense. In the business world, a model cannot be accepted until it has been tested against the real world and the results are satisfactory. Measuring a machine model depends on the characteristics of such a particular model, and the circumstances the model is needed in the real world.

Two vital aspects of Machine learning are CNN and RNN. CNN is convolutional neural networks, while RNN is recurrent neural networks. For CNN mainly generate free size outputs, and are used for minimal amounts of reprocessing. RNN on the other hand functions on random inputs and outputs. They can also be sued for the processing of random sequences. So in basic terms, CNN is built such that they can be able to recognize images while RNN, on the other hand, recognizes sequences.

Presentation about machine learning technology, scientist touching screen, artificial intelligence-1

Furthermore, Jennifer Marsman helped in the description of various methods that are related to artificial intelligence, and they include the following.

  • Search and Optimization

The use of a search engine and search optimization helps to rank AI algorithms. Explaining the role of AI for search and optimization purposes on search engines could be very technical. Machines are also taught on how to work with these to rank algorithms.

  • Logic

Logic also plays a major role in AI. The application of Logic in Ai could be as an analytical tool, as a knowledge representation formalism, and also a method of reasoning. Logic can also be used in the aspect of programming language. With this, it can explore both the prospects and the problems of the success of AI.

  • Probabilistic Methods for Uncertain Reasoning

One of the most widely artificial methods for representing uncertainty is a probability. A lot of certainty factors have been utilized for quantifying uncertainty for alternative numerical schemes over the years.

  • Classifier and Statistical Learning Methods

Classifiers associated with AI includes Naive Bayes, Decision trees, perceptron, amidst a host of others. There are also various statistical learning methods and theories that are in used to evaluate the uncertainties of AI. However, there are limitations to these statistical models, and this is where logic comes.

  • Artificial Neural Networks

This is the impact of the earlier mentioned RNN and CNN on the concept of AI. A typical explanation of ANN in a natural language processing AI which can be used in the interpretation of human speech.

  • Evaluation Progress in AI

This is imperative in the estimation of the progress of the concept of AI across all sectors including business models. Three evaluation types include human discrimination, peer confrontation, and problem benchmarks.

An Introduction to New Azure Machine Learning Service

Maxim Lukiyanov spoke about the working principle of the new Azure machine learning service. The service helps to simplify and accelerate building, training, as well as the development of various machine learning models. Furthermore, the automated machine can be utilized in such a way that algorithms that are needed are easily identified, and the hyperparameters are tuned faster.

New Azure Machine Learning Service also helps to improve productivity and reduce costs with auto-scaling compute methods, as well as develops for the machine learning procedure. New Azure Machine Learning Service also have the advantage of storing the data easily on the cloud. Using the latest programming language is also a seamless operation with the New Azure Machine Learning Service, with open source frameworks like PyTorch, TensorFlow, and scikit-learn.

Maxim also spoke further on some benefits of the New Azure Machine Learning:

  • Easy and flexible pricing method, as you will have to pay a=for only the features that you use.
  • The machine learning is very easy to understand, and the tools that come with it are not in any way restrictive.
  • With the various data and algorithm of the tool, there will be more accurate predictions
  • The tools from the machine make it very easy to import data, and as well as fine-tune the results.
  • A lot of other devices can be connected easily to the platform with the aid of the tolls
  • Data models can be easily published as a web service
  • The time scale for the publish of experiments is only a matter of minutes. This is a very major upgrade when compared to expert data scientists that take days.
  • There is adequate security from the Azure security measures. And this is very useful for the storage of Data in the cloud.

Using Cognitive Services to Power your Business Applications: An Overview and Look at Different AI Use Cases

Martin Wahl explained that with Azure cognitive services, customers are set to benefit from AI with developers. With this, they will not even need the service of a data scientist, which is a major advantage to saving both time and costs. This is done by building this machine in such a way that the learning models, pipelines and infrastructure needed are packaged up on cognitive service for important activities such as vision, speech, search, processing of text, understanding languages, and many more operations. This means that anyone who is capable of writing a program at all can make use of the machine learning to improve the application.

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Customers who have patronized this service are already benefiting from cognitive services such as face container, text container, custom vision service support for logo detection, language detection, in-depth analysis and many more.

Martin Wahl finally explained that with Azure service, more value is added to the business, and the implementation of artificial intelligence is easier than ever.

How to Solve Complex Business Problems Using AI Without Needing a Data Scientist or Machine Learning Expert.

With the possession of basic skills like python coding, data visualization, Hadoop platform, apache spark etc. complex business problems can be solved, even without being a machine learning expert or a data scientist.  All of these are made possible through the help of AI and all that is needed is just dedication and willingness. Some procedure to go about this include:

  • Understanding the basics: This has to do with acquiring general knowledge on the basics, both theoretically and practically.
  • Learning Statistics: Statistics is core to solving business problems, and some of the aspect to be looked at include Sampling, data structures, variable, correlation, and regression etc.
  • Learning Python
  • Making attempts on an explanatory data analysis project
  • Creation of learning models
  • Understanding the technologies that are related to big data
  • Exploring deeper models
  • Completing a complex business problem.

Finally, Noelle LaCharite gave a vivid explanation of how a PoC was made and I did one myself in Delphi in 30 minutes with the aid of Azure AI.