Earlier this year, I visited US for a couple of weeks, and having a genuine interest in retail technology, I visited quite a few retail stores. I went to see classical stores, but also had the chance to have a preview of the future of retail: Autonomous and Frictionless Stores!

Customers in this digital world don’t want to spend too much time while shopping. They want everything to happen very fast. Customers are looking for a seamless shopping experience all the time. That’s how the concept of frictionless stores came to exist. Frictionless stores are one the biggest new thing in consumer shopping.

iot smart retail futuristic technology concept, happy girl try to use smart display with virtual or augmented reality in the shop or retail to choose select ,buy cloths and give a rating of products — Photo: Adobe Stock

What are Frictionless Stores

The concept of frictionless stores started a few years ago. When I talk to retailers this is one of the topics that always pops up. All major brands are looking for innovative ways to create better customer experience and frictionless stores is one way to make that happen. These store improves the shopping experience to the point where customers don’t have to wait at any point of shopping such as selecting, receiving and paying for the product. Initially frictionless stores only confined to ease and less hassle shopping. But as innovations such as mobile wallets, digital receipts, free and fast shipping, and one-click purchasing emerged and began to reshape the consumer shopping experience, the definition began to be reshaped as well. Today, a frictionless experience means more than just less hassle. It means greater speed, personalization, and wow experiences.

How Frictionless Stores work

Let’s try to understand ow frictionless stores work. In frictionless stores, Buyers and sellers are connected in a way that provides buyers the ability to instantly find, compare and buy products and services they need. In frictionless stores, customers should feel that they have full control. The concept and technology has evolved over time, and nowadays customers expect to have this experience through their smartphones. Retailers and brands are trying to find new ways modifying the definition of frictionless stores to provide customers the best possible shopping experience. They need that commitment to stay ahead of the competition. As a result of that, nowadays, frictionless shopping means eliminating anything that negatively impacts customer experience.

Importance of Frictionless Stores

How has frictionless shopping fared according to researches? Alliance Datacenter has done a study and found out that customers from all generations looking for a great service and an ideal shopping experience. This is true for all the areas in the world. If some brand fails to deliver what they want, customers will find a different one. According to the research, 76 percent of consumers said they give brands only two to three times before they stop shopping with them. Another 43 percent said their main reason to leave a brand is poor experience in shopping. What all these means is that if a customer encounters friction they will run away from that brand fast without probably giving a second chance.

Amazon Go Stores

Similar to frictionless stores, Amazon introduced Amazon Go stores. What is special about Amazon Go is you don’t have to wait for checkouts. That basically means you no longer have to wait in queues. First Amazon Go store was a grocery store of 1800 square feet. It spread fast, in fact, you can see a lot of Amazon Go stores now in the USA and Europe.

How is this even possible? What technologies have they used? Amazon was doing many types of research in the areas of computer vision, sensor fusion, and deep learning. Amazon Go is a fruitful result of that. You need Amazon Go application to do shopping with Amazon Go stores. All you have to do open your Go app, choose the product you want, buy it and the just leave. This application can detect when a product is purchased or returned to the shop. The application can remember what you bought and you can revisit these details at your virtual cart. When you finish shopping, you will be charged and you will receive a receipt for what you buy

Buy Awesome foods with Amazon Go stores

You may wonder now what you can buy there? What items are available on Amazon Go stores? I will just point out how one Amazon Go store had marketed their shop. “We offer all the delicious meals for breakfast, lunch or dinner. We have many fresh snack options made every day by our chefs at our local kitchens and bakeries. You can buy a range of grocery items from milk and locally made chocolates to staples like bread and artisan cheeses. Try us, you will find well-known brands you love in our shops.” by the way, don’t expect to go in there and buy books, tech or clothes or anything else that Amazon sells online. It’s basically quick-and-easy food and other groceries. It’s just that there’s no cashier.

So many people have been attracted to Amazon Go stores so it is quite evident that this concept will make a huge impact on the future of retail stores.

If you want to know more about frictionless Sstores, feel free to contact me at: bjorn.nostdahl@gunnebo.com or check out these related articles:

Artificial Intelligence (AI), Business Intellegence (BI), Machine Learning (ML), Microsoft Azure, Software Development Insights

Machine Learning and Cognitive Services

May 23, 2019May 24, 2019 Bjørn T. Nøstdahl

Machine learning is gradually becoming the driving force for every business. Business organizations, large or small trying to seek machine learning models to predict present and future demands and do innovation, production, marketing, and distribution for their products.

Business value concerns of all forms of value that decides the well-being of a business. It’s a much broader term than economic value encompassing many other factors such as customer satisfaction, employee satisfaction, social values etc. It’s the key measurement of the success of a business. AI helps you to Accelerate this business value in two ways. That’s through allowing to make correct decisions and innovation.

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

Remember the days when Yahoo was the major search engine and Internet Explorer was the Major web browser. One of the main reason for their downfall was their inability to make correct decisions. Wise decisions are made by analyzing data. More data you analyze, better decisions you make. Machine Learning greatly support in this cause.

There was a time, Customers accepted what companies were offering them. Things are different now. Demands of customers for new features are ever more increasing. Machine Learning has been the decisive factor behind almost every new innovation whether it be face recognition, personal assistants or autonomous vehicles.

Machine Learning in more details

First starts with learning what machine learning is. Machine learning enables systems to learn and make decisions without explicitly programming for it. Machine learning is applied in a broad range of fields. Nowadays, Almost every human activity getting automated with the help of machine learning. A particular area of study that machine learning largely exploited is data science.

Data science plays with data. Data must be extracted to make the best decisions for a business.

The amount of data that a business has to work with is enormous today. For example, social media producing billions of data every day. To stay ahead of your competitors, every business must make the best use of this data. That’s where you need machine learning.

Machine learning has invented many techniques to make better decisions out of large data sets. These include Neural networks, SVM, Reinforcement learning and many other algorithms.

Among them, Neural networks are leading the way. It improves consistently spawning child technologies such as convolutional and recurrent neural networks to provide better results in different scenarios.

Learning machine learning from the beginning, and trying to develop models from scratch is not a wise idea. That yields huge cost and demands a lot of expertise in the subject. That why someone should try to take the assistance of a machine learning vendor. Google, Amazon, Microsoft they all provides Machine learning services. Let’s take Microsoft as an example, and review what qualities we should look for when selecting a vendor.

Using cloud as a solution for machine learning

It simplifies and accelerates the building, training, and deployment of machine learning models. It provides with a set of APIs to interact with when creating models hiding all the complexity in devising machine learning algorithms. Azure has the capability to identify suitable algorithms and tune hyperparameters faster. Autoscale is a built-in feature of Azure cloud services which automatically scale applications. This autoscaling feature has many advantages. It allows your application to perform best while keeping the cost to a minimum. Azure machine learning APIs can be used with any major technologies such as C# and Java.

There are many other advantages you will have with cloud Machine Learning

Flexible pricing. You pay for what you use.
High user-friendliness. Easier to learn and less restrictive.
More accurate predictions based on a wide range of algorithms.
Fine tuning results are easier.
Ability to publish your data model as a web service Which is easy to consume.
The tool allows data streaming platforms like Azure Event Hubs to consume data from thousands of concurrently connected devices.
You can publish experiments for data models in just a few minutes whereas expert data scientists may take days to do the same.
Azure security measures manage the security of Azure Machine Learning that protects data in the cloud and offers security-health monitoring of the environment

Using Cognitive Services to power your business applications

We will go on to discuss how Azure cognitive service can be used power up a business application. Azure cognitive services are a combination of APIs, SDKs, and services which allows developers to build intelligent applications without having expertise in data science or AI. These applications can have the ability to see, hear, speak, understand or even to reason.

Azure cognitive services were introduced to extend the Microsoft existing portfolio of APIs.

New services provided by Azure cognitive services includes

Computer vision API which provides with advanced algorithms necessary to implement image processing
Face API to enable face detection and recognition
Emotion API gives options to recognize the emotion of a face
Speech service adds speech functionalities to applications
Text analytics can be used for natural language processings

Most of these APIs were built targeting business applications. Text analytics can be used to harvest user feedbacks thus allowing businesses to take necessary actions to accelerate their value. Speech services allow business organizations to provide better customer services to their clients. All these APIs have a free trial which can be used to evaluate them. You can use these cognitive services to build various types of AI applications that will solve a complex problem for you thus accelerating your business value.

If you want to talk more about ML and AI, feel free to contact me: bjorn.nostdahl@gunnebo.com 🙂

Artificial Intelligence (AI), Business Intellegence (BI), Machine Learning (ML), Microsoft Azure, Software Development Insights

Microsoft LEAP: Looking into the future

February 1, 2019March 18, 2019 Bjørn T. Nøstdahl

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.

Microsoft-navalgroup_Brest — 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.

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.

QPR18_Copenhagen_57022000x1108 — 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), Machine Learning (ML), Microsoft Azure, Software Development Insights

Microsoft LEAP: Adding Business Value and Intelligence

January 31, 2019March 7, 2019 Bjørn T. Nøstdahl

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.

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.

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.

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.

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.