For some time now, Near Field Communication has taken mobile device connectivity to a whole new level, and this is one of the most promising advancements in digital technology. The NFC developed by the combined effort of Phillips and Sony has risen dramatically in popularity and is widely becoming accepted as recent smartphones, and other mobile devices are equipped with the feature.

Many people would prefer to buy an NFC-enabled smartphone in recent time, but many others may not know what this NFC fuzz is all about. Let’s dig a little deeper into what it is as well as see how I have deployed it personally.

What is NFC?

NFC (meaning Near Field Communication) is a wireless technology that makes use of the magnetic field of every device to connect to other NFC- enabled devices and share data. We observe the practical application of NFC while making payments at pay points where you can swipe your smartphone or card over a machine, and that’s it. Even with our smartphones, we find it easy to connect with other mobile devices by only bringing them a few centimeters close to themselves so that they can share data across the magnetic field.

What’s Special about NFC?

NFC has made it possible for you to make cashless payments via your mobile devices without having to carry your cards or wallet. Your mobile device serves as a virtual wallet and then by bringing it a few centimeters close to the NFC-enabled payment machine at the payment terminal, your transaction is completed. That simple! This saves a lot of time trying to deal with cards and pins and also relieves you of too many cash transactions. It is super-fast.

Just like Bluetooth and Wi-Fi, the NFC connects to devices wirelessly, but it is different from these technologies in the sense that, it can only operate at very close proximity (within the magnetic field of the devices). Its proximity is synonymous to the Radio Frequency Identity, but NFC allows data to transfer faster compared to RFID and Bluetooth, and may not handle huge files.

Soon mobile devices that are not NFC-enabled may find it challenging to compete in the market because most phone users transact regularly and will be very comfortable making payments via their smartphones and at a swift pace. Although this technology is not yet universally adopted as the primary method for payments, it is an auspicious tech advancement that can change the face of cashless transactions entirely.

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NFC for makes it easier to do business with you.

So, this technology piqued my interest about 3 years ago and I did some digging. A lot of technical specs and documentation was found. So, I got creative and decided to think about how I could leverage this for myself. What I found was amazing. I got a hold of business cards with and RFID tag embedded into the paper. The provider offered a website that allowed me to control the actions of NFC enabled phones once my card was scanned. I could do things like pull up my contact information to be saved as a contact directly to the user’s phone. Imaging that? No typing by the user, just scan my card and click save. Nice. But what else? I also realized that using this technology I could launch a website once the card was scanned. So, I deployed that as well. Yup, now with just the scan of my business card, users get taken to the purchase page for my book Meta Leadership on Amazon.com.

See the video here:
N FC Business Card

What this means is that technologies that seem next level or extremely cost intensive are really not and can be put to use by a variety of businesses in a variety of industries with little cost.

So, I ask you again, If I was able to deploy this technology making it orders of magnitude easier for users to purchase my book, why haven’t you made it easier for your customers to give you their money?

I want to thank the brilliant people at Moo.com for providing this service and making it easy to implement. You are a credit to your professional and your customers are provided a uniquely wonderful product well worth the price.

Don’t forget to pick up a copy of my book Meta Leadership available on Amazon and check out Moo.com for more information on NFC enabled business cards.

Up until now, many companies who build apps continue to simply rely on manual processes. While this as a good option in the past, companies just can’t rely on manual processes anymore. The reality is that there is always the human error facet making an app more prone to errors. However, these aren’t the only disadvantages.

In case you have been building apps in the last few years using manual processes, you probably already noticed that they simply take too long to be built. While this wasn’t a problem 3 or 5 years ago, the business world now demands that you need to be faster. If you’re not able to keep up, you can be sure that your competitors will and you’ll lose the train for profitability. So, what’s the solution?

Using Automated Website Deployment

As you decide to move from a manual process to an automated process to build your applications, you need to understand that you can take different approaches. These include:

#1: Deployment With CI Systems:
When you are looking to start using automated website deployment, you should consider deployment with CI systems.
This automated process includes the integration of servers such as Jenkins. However, you should notice that the deployment with CI systems is not an approach in itself. In fact, it is seen and used as an extension of the scripting approach.

#2: Scripting:
One of the best approaches that you can adopt when you are trying to implement automated website deployment is the use of scripts. After all, you have probably already seen (and used) many different scripts that allow an app to respond automatically in a specific context and on specific environments.
While scripting tends to be seen as one of the main automated website deployment approaches, the reality is that they need regular maintenance. After all, just one simple change can affect it making the script break. Ultimately, the deployment will fail.

#3: Model-Driven Deployment:
When you are looking for the last generation of automated website deployment tools, then you need a model-driven deployment.
The reality is that when you opt for this automated website deployment approach, you will be able to use different servers and software components to run a more complex software system. In return, you will have the ability to see what is actually happening within your company. After all, this automated website deployment solution allows you to audit and track all the deployments that have been made and that are being made within your company.

#4: Simple Deployment Tools:
While you may want to move from manual processes to automated processes when building your apps, you may not want to take a step that is bigger than your legs. So, instead of opting for a model-driven deployment, you may choose to use simple deployment tools.
These basic deployment tools allow you to deploy apps automatically without writing and maintaining scripts.
This automated website deployment approach includes a con: it can only be used for simple apps that don’t include multi-component or multi-step installations.

#5: Continuous Deployment:
This is the most extreme automated website deployment approach and we don’t advise moving from a manual approach to this approach immediately. We believe that this change should only be made when you have IT professionals that are clearly on top of automated website deployment approaches. After all, any changes that you make in the development stage will be immediately tested and deployed to production.
But why should you make the change? Why should you bother moving from manual processes to automatic processes when you are building your applications? Find out more “Next Practices” in Meta Leadership
Visit Michael Stattelman  for even more….

Main Benefits Of Automated Website Deployment

#1: Save Time:
When you are using a manual process to develop and build your apps, you need to be constantly updating. The reality is that even if you use scripts for certain situations, these also need to have a regular checkup.
But let’s talk about developing the app for the first time. When you’re at the developing stage, you know that doing things manually can take a lot of time. After all, you need to follow specific steps in exact order. So, when you miss one step, you have got to start the process all over again.

On the other hand, when you are using automated website deployment, you will only need to define things at once and your app will continue to work as on the first day.
While you may think this isn’t a great advantage, you are missing a key point — things are changing all the time and at a faster pace than ever. You just can’t keep wasting time updating scripts and manual checking all the processes. Even if you do, human errors are likely to occur.
So, when it is time for deploying, your IT team only need to press a button and wait a couple of minutes for the process to be complete.

#2: Improve Accuracy:
As we already mentioned above, manual website deployment is subjected to human errors. The reality is that it includes a lot of different steps that need to be done in a precise order. So, when the IT person responsible misses just one step, the problem may only be detected at a very late stage which turns everything more complicated. You won’t have any idea about what the problem is, what is causing it, and where it is coming from.
However, when you are using automated website deployment, this won’t happen.

#3: Everyone Can Do It:
While automated website deployment may seem confusing and daunting when taken at a glance for the first time, the reality is that everyone can do it.
When you are using a manual deployment process, you need to ensure that your IT team is well aware of all the steps they need to take and that they execute them meticulously to prevent future problems. Let’s say that you need a new server and that you added to the other ones you already have. Your team can’t forget to deploy it as well. As you can easily see, it is kind of easy to lose track of all that you need to do and the order that you need to follow. While you can keep a document where you specify all the single steps with detailed instructions, you may not even remember that you have it in the first place and you may also not be accurate enough when you are writing.

On the other hand, when you are using automated website deployment, the language is pretty simple and straightforward. All you need is a deployment training and you are all set not only to understand but also to change the process if needed.


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What Are Dark Factories?

In case you never heard the term “dark factories” before, then you need to know that this is attracting a lot of people. The term dark factory is based on the event that you have programmed robots or machines working for you on a 24/7 uninterrupted production. So, there won’t be any delays, no labor disputes, not heating or lighting bills, noting. There will only be programming robots working for you.

The Model Of The Future

Matthew Carr, the Emerging Markets Strategist at The Oxford Club, recently illustrated how dark factories are a reality that is approaching soon. The reason why he made this statement was based on what is happening in a company in China — the Changying Precision Technology Company. According to Matthew, this company may well be the first manufacturing company we are seeing that using the dark factory model.

This mobile phone company used to employ 650 workers and now it only needs 60. What the company did was that it replaced all these workers by robots. Overall, they put 60 robot arms to work and they eliminated 90% of the human workforce. While this is clearly a step into the dark factory definition, we have to say that no one was expecting it to work so well.

Overall, the results were incredible. Not only productivity increased by 250% as the product defects decreased by 80%.

According to Matthew, “The robots run 10 production lines, going 24/7. The only human workers left are there simply to ensure everything runs smoothly. Changying Precision Technology believes the number of humans can be reduced to as few as 20.”

Find out more “Next Practices” in Meta Leadership

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Amazon’s Is On Its Way

While you may be thinking that this is only happening in China, the truth is that Amazon is trying to do the same (or similar). The biggest online retailer decided to increase its robot workforce by 50%. They already represent 45,000 that are spread over 20 different order fulfillment centers. In case you don’t find these numbers impressive enough, 45,000 are more robots than the Netherlands army.

Amazon continues to have a lot of employees — 200,000 full-time and seasonal.

Are Dark Factories A Drastic Change?

While numbers don’t lie and we just showed you two examples of companies that are evolving to dark factories, the reality is that there are people who don’t think this is such a big change. One of these people is Yan Vermeulen, Partner at Odgers Berndtson Singapore and Head of the Southeast Asia Industrial Practice. According to Yan,“In principle, I don’t think dark factories are a drastic change. Factories have been getting more automated for many years. The only difference is turning the lights off, but this is more an economic decision than anything else. However, the shift to dark factories will take out manufacturing employment in commodity segments that don’t need either high manpower involvement or high technology involvement. So, there will always be industries that either require a lot of people to assemble or manufacture, or need high-end engineers on site for production.”

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In our world today, the computational devices we count on are actually getting extra smart day after day. In the quest to produce the personal computing device that is even more intelligent than its human user/maker, researchers as well as programmers have actually imbibed some exceptional algorithms into computing systems, machine learning is actually one of those offshoots of the artificial domain allowing for these abilities. 

Machine Learning or ML is a limb of Artificial Knowledge that is actually based on the idea that devices can easily gain from information, increase expertise, recognize trends, and make predictions on its own much like human beings have been. Through Machine Learning,  systems learn at rates that border on real-time, they also strengthen their knowledge from newer information compiled without being reconfigured. Basically, they program themselves automatically. 

Machine learning has actually expanded from its preliminary simple idea of usual pattern recognition to ending up being a method that is of significance to the global economy these days; helping make suggestions, and predicting events as they gain access to a continuous stream of information. It is actually a lot simpler to claim that Machine Learning has carried computing closer to human beings intellectually than ever before. 

Machines are actually resolving issues a lot quicker today as a result of their potential to process a vast array of records in a quick period, and this learning can easily happen with 4 techniques: supervised learning algorithm, without supervision discovering algorithm, semi-supervised learning algorithm, as well as Reinforcement Learning algorithm. 

Supervised Learning Algorithm: This Machine Learning method makes it possible for machines to learn under supervision from programmers by processing specifically labeled data which is the traditionally accepted type of data that computers processed. What this means is that they learn when data is inputted where and how they are supposed to; e.g., adding + or — in columns where they request for a charge or stuff like that. Anything outside labeled data is as good as useless for machines learning with this algorithm. 

Unsupervised Learning Algorithm: This the opposite of supervised learning and in this method, the machine learns from roughly placed data by trying to make sense out of the data cluster. This method is best when a machine is meant to learn from humans of various kinds as humans communicate in unlabeled data. In this case, the machine has to learn from unstable human communication like sarcasm, ironies, and others making it difficult for the machine to predict future events accurately. 

Semi-supervised Learning Algorithm: This method is more effective as the machine has to learn from both labeled and unlabeled data (more from unlabeled because it has to relate more with random humans). This method allows for a more accurate prediction of events and a more natural classification of data. It is mostly adopted by e-commerce stores to recommend products to customers based on the customer’s shopping records (unlabeled) and the sizes of the customer’s previously purchased products (labeled). 

Reinforcement Learning Algorithm: This is more like a trial and error kind of learning where the machine interacts with its environment, learn from it, perform actions and produce results and when it doesn’t work out well, it continues to work on improving further outcomes. It learns as the trial and error process goes through environmental feedbacks. 

All in all, the majority of organizational leadership has at least heard on Machine Learning, and quite possible have experienced its applications without even knowing it. Amazon product recommendation engine and Netflix suggestions are all extremely advanced and evolving applications of the above mentioned techniques. So as an advisory, this is definitely a technology that requires your attention in efforts to gain any sort of competitive advantage by enhancing operations or customer "cross-Up selling" capabilities for your organization. 
 

Find out more “Next Practices” in Meta Leadership

Visit Michael Stattelman  for even more….



Applications of Machine Learning 

Machine Learning has grown therefore pertinent that it is actually taken into consideration an important asset in different markets of the economic climate consisting of: 

Financial Services: Financial Institutions as well as other financial services has employed machine learning to help stop scams through spam obstructing as the device learns to block e-mails, data, as well as software program that share a certain level of correlations to avoid deceptive actions. Likewise, the financial field makes use of ML to recognize necessary ideas on records which helps determine assets possibilities and also aid clients recognize when to trade. 

Healthcare: Machine Learning has created possibilities for improved medical prognosis by the creation of wearable tools and sensors to keep track of a patient's health progress. 

Authorities Agencies: As a result of the comprehensive quantity of records on call to the government, they have actually depended on Machine Learning to assist take care of these records and help strengthen services and also monitor modern modifications in autonomous nations. 

Oil as well as Gas: This market being one of one of the most notable sectors of the globe economic condition needs Machine Learning evaluate information about minerals in the ground and anticipating refinery sensor failings. 

Transportation: This is just one of the major sectors where Machine Learning has actually created a wave with the creation of self-driven Google vehicles which reaches gain from the way it is actually steered in time and after that it takes control of the steering coming from there as time go on. 

Retail: This is actually one more market where Machine Learning possesses a notable impact as our company may find it evidently in store recommender systems where the recommendation maker profits from previous knowledge to accurately forecast what a certain customer will like and encourage them. 

Machine Learning is actually an encouraging advancement in Artificial Knowledge as well as technological improvements generally. If you have any sort of wish for organizational lasting Feasibility, this is actually most definitely a technology domain that information in your company ought to assigned to examining further. 

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