Tuesday, November 5, 2019

The Present and Future Trends of Machine Learning on Devices [Update for 2020]

As you, of course, noticed, machine learning on devices is now developing more and more. Apple mentioned this about a hundred times during WWDC 2017. It's no surprise that developers want to add machine learning to their applications.APIs IoT big data and AI are driving forces for Machine Learning in 2020.

However, many of these learning models are used only to draw conclusions based on a limited set of knowledge. Despite the term "machine learning", no learning on the device occurs, knowledge is inside the model and does not improve with time.

Machine Learning Future Trends

The reason for this is that learning the model requires a lot of processing power, and mobile phones are not yet capable of it. It is much easier to train models offline on a server farm, and all improvements to the model include in the application update.

It is worth noting that training on the device makes sense for some applications, and I believe that with time, such training of models will become as familiar as using models for forecasting. In this text I want to explore the possibilities of this technology.

Machine Learning Future Trends
Machine Learning Future Trend

Machine Learning Today Deep Learning in Neural Networks an overview

The most common application of deep and machine learning in applications is now the computer vision for analyzing photos and videos. But machine learning is not limited to images, it is also used for audio, language, time sequences and other types of data. The modern phone has many different sensors and a fast connection to the Internet, which leads to a lot of data available for the models.

iOS uses several models of in-depth training on devices: face recognition in the photo, the phrase " Hello, Siri " and handwritten Chinese characters . But all these models do not learn anything from the user.

Almost all machine learning APIs (MPSCNN, TensorFlow Lite, Caffe2) can make predictions based on user data, but you can not get these models to learn new from these data.

Now the training takes place on a server with a large number of GPUs. This is a slow process that requires a lot of data. A convolutional neural network, for example, is trained on thousands or millions of images. Training such a network from scratch will take several days on a powerful server, a few weeks on the computer and for ages on the mobile device.

Learning on the server is a good strategy if the model is updated irregularly, and each user uses the same model. The application receives a model update every time you update the application in the App Store or periodically load new settings from the cloud.

Now training large models on the device is impossible, but it will not always be so. These models should not be large. And most importantly: one model for everyone may not be the best solution.

Why do I need training on the device? 

There are several advantages of learning on the device:

  • The application can learn from the data or behavior of the user.
  • The data will remain on the device.
  • Transferring any process to the device saves money.
  • The model will be trained and updated continuously.
  • This solution does not work for every situation, but there are applications for it. I think that its main advantage is the ability to customize the model to a specific user.

On iOS devices, this is already done by some applications:

The keyboard learns based on the texts that you type, and makes assumptions about the next word in the sentence. This model is trained specifically for you, not for other users. Since training takes place on the device, your messages are not sent to the cloud server .
The "Photos" application automatically organizes images into the "People" album. I'm not entirely sure how this works, but the program uses the Face Recognition API on the photo and places similar faces together. Perhaps this is simply uncontrolled clustering, but the learning should still occur, since the application allows you to correct its errors and is improved based on your feedback. Regardless of the type of algorithm, this application is a good example of customization of user experience based on their data.
Touch ID and Face ID learn based on your fingerprint or face. Face ID continues to learn over time, so if you grow a beard or start wearing glasses, it will still recognize your face.
Motion Detection. Apple Watch learns your habits, for example, changing the heartbeat during different activities. Again, I do not know how this works, but obviously training must occur.
Clarifai Mobile SDK allows users to create their own models for classifying images using photos of objects and their designations. Typically, the classification model requires thousands of images for training, but this SDK can learn only a few examples. The ability to create image classifiers from your own photos without being an expert in machine learning has many practical applications.
Some of these tasks are easier than others. Often "learning" is simply remembering the last action of the user. For many applications this is enough, and this does not require fancy machine learning algorithms.

The keyboard model is simple enough, and training can occur in real time. The "Photos" application learns more slowly and consumes a lot of energy, so training occurs when the device is on charge. Many practical applications of training on the device are between these two extremes.

Other existing examples include spam detection (your email client learns on the letters you define as spam), text correction (it learns your most common mistakes when typing and fixes them) and smart calendars, like Google Now , that study recognize your regular actions.AI and machine learning in 2018 2019 2020 are going to change alot.

How far can we go in Machine Learning ?

If the goal of learning on the device is to adapt the machine learning model to the needs or behavior of specific users, then what can we do about it?

Here's a funny example: a neural network turns the drawings into emoji. She asks you to draw some different shapes and learns the pattern to recognize them. This application is implemented on the Swift Playground, not the fastest platform. But even under such conditions, the neural network does not study for long - on the device it takes only a few seconds ( that's how this model works ).

If your model is not too complicated, like this two-layer neural network, you can already conduct training on the device.

Note: on iPhone X, developers have access to a 3D model of the user's face in low resolution. You can use this data to train a model that selects emoji or another action in the application based on the facial expressions of the users.

Here are a few other future opportunities:

  • Smart Reply is a model from Google that analyzes an incoming message or letter and offers a suitable answer. It is not yet trained on the device and recommends the same answers to all users, but (in theory) it can be trained on the user's texts, which will greatly improve the model.
  • Handwriting recognition, which will learn exactly on your handwriting. This is especially useful on the iPad Pro with Pencil. This is not a new feature, but if you have the same bad handwriting as mine, then the standard model will allow too many errors.
  • Recognition of speech, which will become more accurate and adjusted to your voice.
  • Tracking sleep / fitness applications. Before these applications will give you tips on how to improve your health, they need to know you. For security reasons, it's best to stay on the device.
  • Personalized models for dialogue. We still have to see the future of chat bots, but their advantage lies in the fact that the bot can adapt to you. When you talk to a chat-bot, your device will learn your speech and preferences and change the answers of the chat-bot to your personality and manner of communication (for example, Siri can learn to give fewer comments).
  • Improved advertising. No one likes advertising, but machine learning can make it less intrusive for users and more profitable for the advertiser. For example, an advertising SDK can learn how often you look and click on ads, and choose more suitable advertising for you. The application can train a local model that will only request advertisements that work for a particular user.
  • Recommendations are the widespread use of machine learning. The podcast player can be trained on the programs you listened to to give advice. Now applications are performing this operation in the cloud, but this can be done on the device.
  • For people with disabilities, applications can help navigate the space and better understand it. I do not understand this, but I can imagine that applications can help, for example, distinguish between different drugs using a camera.
  • These are just a few ideas. Since all people are different, machine learning models could adapt to our specific needs and desires. Training on the device allows you to create a unique model for a unique user.

Different scenarios for learning models

Before applying the model, you need to train it. Training should be continued to further improve the model.

There are several training options:

  • Lack of training on user data. Collect your own data or use publicly available data to create a single model. When you improve a model, you release an application update or simply load new settings into it. So do most of the existing applications with machine learning.
  • Centralized training. If your application or service already requires data from the user that is stored on your servers, and you have access to them, then you can conduct training based on this data on your server. User data can be used to train for a particular user or for all users. So do platforms like Facebook. This option raises questions related to privacy, security, scaling and many others. The question of privacy can be solved by the method of "selective privacy" of Apple, but it also has its consequences .
  • Collaborative training. This method moves training costs to the users themselves. Training takes place on the device, and each user teaches a small part of the model. Updates of the model are sent to other users, so that they can learn from your data, and you - on them. But this is still a single model, and all of them end up with the same parameters. The main advantage of such training is its decentralization . In theory, this is better for privacy, but, according to studies , this option may be worse.
  • Each user is trained in his own model. In this version, I am personally most interested. The model can be learned from scratch (as in the example with pictures and emoji) or it can be a trained model that is customized for your data. In any case, the model can be improved over time. For example, the keyboard starts with a model already taught in a specific language, but eventually learns to predict which sentence you want to write. The downside of this approach is that other users can not benefit from this. So this option works only for applications that use unique data.

How to Train on the Device to Learn?

It is worth remembering that training on user data is different from learning on a large amount of data. The initial model of the keyboard can be trained on a standard body of texts (for example, on all Wikipedia texts), but a text message or letter will be written in a language different from the typical Wikipedia article. And this style will differ from user to user. The model should provide for these types of variations.

The problem is also that our best methods of in-depth training are rather inefficient and rude. As I said, the training of the image classifier can take days or weeks. The learning process, stochastic gradient descent, passes through small stages. The data set can have a million images, each of which the neural network will scan about a hundred times.

Obviously, this method is not suitable for mobile devices. But often you do not need to train the model from scratch. Many people take an already trained model and then use transfer learning based on their data. But these small data sets still consist of thousands of images, and even so the learning is too slow.

With our current training methods, the adjustment of models on the device is still far away. But not all is lost. Simple models can already be trained on the device. Classical machine learning models such as logistic regression, decision trees, or naive Bayesian classifiers can be quickly trained, especially when using second-order optimization techniques such as L-BFGS or a conjugate gradient. Even the basic recurrent neural network should be available for implementation.

For the keyboard, the online learning method can work. You can conduct a training session after a certain number of words typed by the user. The same applies to models that use an accelerometer and traffic information, where the data comes in the form of a constant stream of numbers. Since these models are trained on a small part of the data, each update must occur quickly. Therefore, if your model is small and you do not have so much data, then training will take seconds. But if your model is larger or you have a lot of data, then you need to be creative. If the model studies people's faces in your photo gallery, it has too much data to process, and you need to find a balance between the speed and accuracy of the algorithm.

Here are a few more problems that you will encounter when learning on the device:

  • Large models. For deep learning networks, current learning methods are too slow and require too much data. Many studies are now devoted to learning models on a small amount of data (for example, in one photo) and for a small number of steps. I am sure that any progress will lead to the spread of learning on the device.
  • Multiple devices. You probably use more than one device. The problem of transferring data and models between the user's devices remains to be solved. For example, the application "Photos" in iOS 10 does not transmit information about people's faces between devices, so it learns on all devices separately.
  • Application updates. If your application includes a trained model that adapts to user behavior and data, what happens when you update the model with the application?

Training on the device is still at the beginning of its development, but it seems to me that this technology will inevitably become important in the creation of applications.

For more details you must check AI for Good. It is a United Nations platform, centered around annual Global Summits, that fosters the dialogue on the beneficial use of Artificial Intelligence, by developing concrete projects

Begins May 4, 2020
Ends May 8, 2020

Thursday, September 19, 2019

5 Best Virtual Phone Number Providers in 2019

vp 2019

You've reached the perfect location if you're taking a look at the virtual phone number providers for your company. Finding a small business phone service doesn't need to be complicated and doesn’t matter If you have a large or small business, you will find the most excellent virtual phone number providers for your specific needs here.
What's a Virtual Phone Number?
Providers of virtual cell telephone numbers allow business owners to decide on a name for their business that may be utilized with the cell phone or landline employed for individual calls.
For consumer convenience, the providers mentioned in this article have been evaluated based on advanced features, flexible options, the price of their services, and customer support are very best of the best are.
Why Get a Virtual Phone Number?
Cell phone numbers service functions and allow to hand a business phone number that plotted for their phone. Additionally, they enable a user to create and receive calls in their mobiles without even showing their number.
Moreover, since industry owners may choose their area code, businesses will give the appearance of being local where they're located to them.
Types of Virtual Phone Numbers:
  • Virtual numbers: Also known as DID numbers are VoIP numbers that allow you to any location worldwide. DID numbers are linked with a specific city or area at a country, giving your business a local presence inside city, no matter where you're located!
  • Toll-free digital numbers: Callers around the world to attach to companies free of charge. In most states, these virtual telephone numbers possess some version of this"800" dial-code
Essential Factors of Virtual Phone Number Providers
1. Price - The price tag of phone services and features.
2. Multi-line Management - The simplicity of keeping business and personal calls separate.
3. Quality of mobile calls - Customer satisfaction through the high quality of phone service offered by each provider.
4. Auto-Attendant - Accessibility of attendants to this point to their telephone to greet and route callers.
5. Ease of use: Each service should be evaluated for easy use, mobile application, and how easy the system is to set up.
6. Call direction: Phone management features, including voicemail call screening, call forwarding and call block capability.
7. Extensions: For routing calls via telephone extensions.
8. Customer service: Services for customer, including hours of availability.
Best Virtual phone numbers suppliers in 2019
1. Grasshopper
Grasshopper provides phone services to business owners who do not require equipment to be installed in their own office or even in their smartphone.
Grasshopper offers users an automated attendant for a meager bottom price, call forwarding, along with unlimited calling. The features provided by Grasshopper rivals are the features provided by conventional providers of business phone systems and services.
2. Google Voice
Google voice offers consultants, freelancers, individuals, or anybody needing to separate business from personal requirements a new contact number for outgoing and incoming calls with no monthly fee.
Google voice services arrive with voicemail, call screening, and also the capability to prevent callers. Some features aren't available with Google Voice. However the service is excellent for business owners who require essential functions.

3. Nextiva
Ranks as best small business voice-over-internet-protocol (VoIP) option, supplies cell telephone numbers, stronger business phone features, also offers three service plans: Basic, Pro, and Enterprise.
Nextiva can be a superb alternative for organizations and entrepreneurs needing an affordable number to use with a business phone method that is integrated and complete.
4. Call Hippo
Call Hippo offers virtual business phone amounts of over 50+ countries. The virtual phone numbers are also designed for individuals who don't wish to spend money on calling.
Call Hippo brings your loved ones and friends close to you. Moreover, using Hippo's phone systems for the real estate business, you can continue to keep your callers engaged with custom on-hold music!
5. Mighty Call
Mighty Call's service is dedicated to helping small-medium enterprises using their voice and talk customer support needs along with those thinking about what they have to provide can select out of SMB pricing plans and also an enterprise option.
The very robust quality of Mighty Call compresses it to a package and is the fact that it gets an entire telephone routing service, and users won’t need to update anything on their end, considering that the app is cloud-based, which makes the process simpler for your end-user.
It's simple to buy Local digital Numbers from a merchant and resell them that doesn't create them established providers. Another essential aspect is that a number of the worldwide digital number providers may not provide support may not assist depending on your time zone.

Wednesday, July 3, 2019

You Should Consider This Before Sharing Your Vacation Photos

Best way to share photos online privately

Summer is filled with memories for both young and old. These should not only be perpetuated on the mobile, many also choose to share freshly with the summer holidays on social media.

It can be fun, but there are also some things to consider before sharing pictures of children and young people on the internet. 

It is both about what is actually allowed - and about a good portion of common sense!

We have talked to social media expert Astrid Valen-Utvik in the communication agency Valen-Utvik about what she thinks you should keep in mind before posting the summer memories:

1. Think about who you want to share with

We as parents should think well that the picture we share with only a few hundred followers on, for example, Instagram, can quickly be shared and thus disappear outside our control.

Valen-Utvik recommends that you reflect on where the pictures are shared and which pictures you want to share.

- If you want even more control over your content, you can have a closed profile on Instagram or Facebook. On Facebook, you can also set your account settings so that photos are shared only with friends, or selected friends. Some make a messenger group or a separate Facebook group when they have children. It makes it possible to share photos and videos of the newborns with only the closest family and friends.

- The ability to have relatively good control is present, but we as parents must know about this - and be conscious enough to use the opportunities we have available.

2. Ask for permission

Although the children are not of age, they are entitled to their own privacy. If the children are old enough, you should ask permission to share pictures of them in their own channels. 

- I have three children myself, at 11, 13 and 15, and yes, I share pictures of them in social media. Now that they are so big, I always ask before I share pictures of them and respect their decision.

Valen-Utvik started a blog when the elders were born and shared pictures of everyday life along the way. This was around 2004, and her reach was not that great. As the consequences increased, she reduced the profiling of the children.

- I hardly mention them by name, and show more pictures of situations and moods. There are not necessarily pictures where they look straight into the camera. I continue to share some small glimpses here and there, but I don't want the amount of I parts of the kids to become their digital profile and digital footprint, she says.

- I want the children themselves to be able to decide and influence it.

3. Everything doesn't have to be shared

"It can't be that dangerous, then?" Still, images are used in completely different ways than intended.

- That's why I wouldn't share pictures of my kids in very light clothing, no matter how small and cute they have been. An image of children crying or angry, children who are naked, children who are vulnerable in some way - at least I will not share myself.

But it is not only in the darkest hooks of the internet that images can be abused, but also in commercial settings.

- We have seen examples of images uploaded by unsuspecting parents being used in advertising on Facebook. It may be for services or products the parents have never even heard of. We also have examples of images stolen from social media, and then used as advertising posters outdoors in foreign countries, says Valen-Utvik.

- Photos can certainly be misused in many other ways too, but the most important thing is this: Even if you have uploaded images to social media, it is still your intellectual work and only you decide how to use them. If you find that someone has used your image without approval, I recommend that you grab it.

Checklist before sharing photos:

The Data Inspectorate has prepared a checklist of things to consider before sharing photos of children online:

  1. Legality : Never share pictures of other people's children without the consent of their superiors.
  2. Image Type : Think about the content and use filter or inferior resolution whenever possible, making the pictures less interesting to others.
  3. Quantity : Share the fewest possible pictures.
  4. Channel usage : Be aware of how to share the pictures. Everything must not be open. Use privacy settings and create closed groups.
  5. Delete regularly : Take a spring cleaning and periodically previous photos you have published.
  6. Always ask the children : Use questions like "Do you think it's okay to share this picture with my family or friends?" Then you make it understandable to them. Respect the answer.

Friday, June 28, 2019

This Simple Grip Can Stop The Hackers | Secure Bitcoin Wallet

Two-step verification protects your accounts online, and is much easier than you think.

Are you afraid of being hacked online? Then the mobile you always carry with you can be the key to your security - literally.

How to prevent being hacked on your computer iphone and Android mobiles ?

The key word here is  two-step verification , a very simple approach that can greatly reduce the risk of others accessing the services you use the most online.

how to protect against criminal or unethical hackers

Huge damage

Whether it is e-mail, online banking, social media or other digital services, there are virtually no limits to how big the damage can be if wrong people get access to one (or more!) Of those accounts.

Just imagine if anyone should access your email. If they have control over this, they can effectively reset the passwords on any other service you use so they can effectively block you from your entire digital life.

Which includes everything from contacts, calendars, pictures stored in the cloud, Facebook networks, backups and so on

No longer just password

This can happen, for example, by using the same, weak passwords on many different websites, or that services are attacked so that users' private data ends up in the wrong hands.

And that's exactly where the two-step verification comes in: As the name suggests, this technique ensures that you need to take an extra step to verify your identity, beyond password entry (which  should be unique and strong anyway ).

Although someone else would get your password, they will not be able to log in without the other link, which in almost all cases involves your mobile.

Easy via mobile

Virtually the vast majority of major players you use online today, support two-step verification via the mobile phone. This can be done by sending you an SMS with a unique code, or that you download this code via a separate app - not unlike the process you probably already know from BankID.

Precisely because most of today's mobile phones are so well secured with personal codes, chances are that you are the one who confirms your identity via the mobile phone - and not someone unauthorized.

Several methods

This is also a process that over the years has become much easier, while an increasing number of online actors support and facilitate the extra security chain. Mostly, you also only need to double-check every 30 days so that it does not become a too cumbersome process on a daily basis.

Apple has its own system for this double verification. After activating the feature on your Apple ID (see fact box), each time you try to log in to a new device, you will need to enter a code served on the screen for an iPhone or iPad, for example.

Most of all, Google may have come. They have recently introduced a method where you can easily verify your identity at the touch of a button.

When you try to sign in to a Google service, just press the "yes" button that pops up on your mobile screen through the Google app.

This one is pre-installed on Android phones and can  be downloaded to the iPhone .

how to protect against unethical hackers

Own code app

At the same time, Google has a free app, Google Authenticator, that supports an open standard for security codes. 

With this app, you can serve unique and time-limited one-time codes from both Google and all other standard-supporting actors - including Facebook, Outlook, Skype, Dropbox, LastPass and many more.

Download Google Authenticator for Android or iPhone . There are also other options, such as Authy , which also allow you to back up all your cloud settings - which Google Authenticator does not do.

To enable two-step verification on your mobile:
Two-step verification is about adding an extra layer of protection to your online accounts beyond the classic password.

As a rule, this happens by having to enter a code that you get on your mobile screen. The process is also called 2-step verification, two-factor authentication and a variety of other variants.

To enable two-step verification on an iPhone with iOS 10.3 or later:

  • Go to  Settings> [your name]> Password and security
  • Select  Turn on two factor authentication
  • Press  Continue
  • You will then be asked to enter the mobile number you want to receive verification codes (possibly an automated call)
  • Press  Next and you will receive a code
  • Enter this code to verify your mobile number, and then turn on the  Factor Authentication
  • Read more at Apple

To enable two-step verification on Android:

  • Go to  Settings> Google
  • Touch  Login and Security (under "Security", if applicable, in the Google Account selection)
  • Select  2-step verification and follow the on-screen instructions. You may need to sign in to your Google Account again
  • Read more at Google
This is how to protect yourself from hackers 2019

Wednesday, June 19, 2019

Gyroscope Robot is on Sale now in Summer 2019


A miniature and very charming robotic gyrocomputer Loomo from Segway has got its own page on Indiegogo and promises to go on sale this spring - the shipment of the first parties is planned for May 2018.

Loomo is not an ordinary gyroscope, it's a full-fledged robot working on an AI platform. You can ride it, but it's not his only function. He is able to respond to commands, he can recognize faces, silhouettes and knows the command "for me", so if you're tired of driving, you can go on foot, having commanded Loomo to keep up and follow you on your own. The software developers collaborated with the creators of the autopilot for BMW, so the robogiroscourter can also "park" on its own.

Gyroscope Robot

Gyroscope Robot 2018

The kid can ride at speeds up to 10 kilometers per hour and will be useful for performing various tasks. For example, you can get him to work as a flyer or a video player.

Gyrobot reviews

Like most "smart" things, Loomo has its own application, which allows using a smartphone to move a robot, setting a route for it, watching the world with its eyes, voice phrases entered into the application, monitor people, and still shoot photos and videos using a robot. In addition, developers report that they will release a separate SDK for Android, so everyone can independently provide the robot with new features and tricks, and at the same time and tighten programming skills.

For now Loomo can be bought for 1299 dollars - so much will pay for it "early birds", who managed to buy robots from the first batch. For everyone else, the price will start at $ 1,799.

Here is how to get raspberry pi 3 projects ideas 2018 

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Wednesday, May 29, 2019

In what way did Google Surpass Amazon ? Google vs Amazon 2019

Amazon Echo vs Google Home 

Periodically, new categories of devices appear on the market. Some of them become incredibly popular. An example of such devices is smartphones. Others are less interesting to a wider audience. However, sometimes it takes more than one year before new devices get distributed. And over time, the leaders of the new market segment are determined. Recently announced the next version of the mobile OS developed by her, Android P, Google managed to outperform Amazon in what Amazon was leading from the very beginning.

 Google vs Amazon 2018
Google Home

Deliveries of Google Home and Mini have surpassed deliveries of Amazon Echo

 Google vs Amazon

Amazon offered the first Amazon Echo back in 2015. As the first product of the new category, Echo had the largest market share of devices already in use and continued to lead the sales quarterly. So it was before the first quarter of this year. Smart speakers are the most dynamically developing category of technological products that combine a column and virtual personal assistant software. Over the three-month period, from January to March 2018, according to Canalys, 9 million devices of this category were delivered to the world market. Growth in comparison with the corresponding indicator of the past year amounted to an impressive 210%. But Google managed to outperform Amazon in terms of supply in this market segment, as reported in a note by Alan Friedman (Alan Friedman), a published resourcephonearena.com.

During the first quarter of this year, 3.2 million Google Home and Google Home Mini were delivered to the world market. And this outperforms Amazon's intelligent Echo column volume of 2.5 million devices. It should be further stressed that Google for the first time managed to outperform Amazon in terms of delivering smart columns throughout the quarter.

 Google vs Amazon 2019
Google Home Mini

Growth of supplies of Google Home and Mini - 483%

Even more impressive is the increase in the supply of smart columns Google, accounting for 483%. For comparison: a similar indicator of Amazon - 8%. During the period under review, 4.1 million smart speakers were delivered to the United States. In China, 1.8 million digital devices of this product category were delivered.

Why Apple HomePod is not among the most popular smart speakers?

Amazon Echo
Amazon Echo 2018

The third supplier of smart speakers was the Chinese vendor Alibaba with a score of 1.1 million devices. Apple also offers consumers its smart speakers - HomePod. And the indicators of its supply are not reflected in the number of leaders, but entered the category "Others". Other vendors, in addition to the four leading suppliers of smart speakers, were delivered in aggregate 1.56 million devices. However, Apple began offering its smart column only on February 9, 2018. Thus, its product was introduced on the market only during two incomplete months during the first quarter of this year.

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Wednesday, May 22, 2019

Huawei Ban 2019 and Global Impact on GSMA Business

As you may have read in the news, an executive order has been issued in the US, prohibiting US companies from buying foreign-made telecommunications equipment deemed a national security risk. The Executive Order did not name China or Chinese companies specifically. However, separately, and soon after the order was signed, the US Department of Commerce’s Bureau of Industry and Security (BIS) added Huawei and many other affiliates to its “Entity List”, meaning that any US company, person or government agency is prohibited from exporting, re-exporting or transferring hardware, software and technology of US origin to Huawei without a license from BIS. As a consequence, American companies such as Google has carried out restrictions on Huawei to comply with the order.

Huawei 5g Ban

It is hard to predict the global impact of the latest developments in the US.However one thing is obvious that huawei 5G rollout will have a major blow after this recent development and also for those operators who are aggressively following up with huawei for 5G technology deployment.  The GSMA is currently working with relevant stakeholders in Washington to review the ramifications of the US government actions, and trying to identify ways to minimize the impact on mobile operators. Based on their understanding of the current situation, although similar to the restrictions in last year’s ZTE Denial Order, it does not prohibit, for example, the receipt of network hardware or software from Huawei or the receipt of parts and components to repair Huawei-origin equipment already owned by the operator. It also does not necessarily prevent Huawei from using its existing stockpile of equipment that was lawfully obtained prior to the Entity List Designation. As per Google statements, it is likely that end users of existing Huawei devices will continue to benefit from access to Google services (including Google Play and the security protections from Google Play Protect).

We don’t currently know the full impact of this for mobile operators across the glob. However entire telecom industry is monitoring the situation and seeking further clarification from vendors on potential implications on operations.Organizations are also requesting information both from Huawei and Google in terms of what this potentially could mean for customers with Huawei mobiles. As always, their priority is to continue to deliver the best products and services to their customers. 

We Digital Technology Review are seeking to be as transparent as possible with viewers who contact us with questions, but right now we have limited information to offer as we await clarifications. It is too early to provide firm details on how this will impact telcos at this point, and we will provide further updates as soon as we can.

Wednesday, April 10, 2019

2019 TechReview Updates on 5G to Power The Internet Of Things IOT | Future of 5G

In 2022, 550 million devices of the Internet of things will be connected to 5G networks

(Ericsson forecast)

10 years - just so much goes to the telecommunications industry to introduce a new generation of communication since the 1980s. According to this schedule, the 2020s promise to become a decade of the 5G (5th Generation) standard - networks with a bandwidth of up to 20 Gbit / s.

Its implementation will not only accelerate the mobile Internet and the quality of communication on our smartphones - 5G is intended to become an infrastructure for key technologies of the future: virtual and augmented reality , unmanned vehicles, Internet of things , etc., followed from explanations that the International Telecommunication Union (ITU) 2015.

The technological development of the standard should be completed by 2020. The data transfer rate will increase by 30-50 times in comparison with the previous generation. If the 3G standard reduces the signal delay to 100 milliseconds, 4G to 10 milliseconds, then the 5G is only 1 millisecond.

IoT and 5G



The new standard will have to work with a large amount of data: according to Cisco, in 2020 global mobile traffic will grow to 30.6 exabytes per month (1 exabyte - 1 million Tb) - this is 8.3 times more than in 2015 (3.7 exabytes). Also, from 7.9 billion to 11.6 billion, the number of mobile devices connected to the Internet will increase, not only smartphones and tablets, but also items of the Internet of things, for example, gadgets for the "smart" home. Ericsson expects that by 2022, 550 million of these devices will be connected to 5G.

A single network is necessary for the harmonious operation of the ecosystem of new gadgets, which today rely on different protocols. The same applies to unmanned vehicles: for each drone, every day it will be necessary to transfer and process terabytes of data (not only from the sensors of the car itself, but also from cameras, radar, etc.). According to the German automaker BMW, after 2020, the fifth generation network will link up to 70 million "autopilots". Finnish Nokia called 5G a chance to reduce the accident rate on the roads to zero. The technology should help in the development of agriculture (remote management of machinery, monitoring of farmland using drones), industry (management of robotic assembly production, industrial 3D printers), medicine (remote operation via the network in real modetime ) and the entertainment industry (computer games with virtual reality technology with a high level of graphics, high-definition video transmission without delay).

All key markets related to 5G, in the long-term 2020 will surely grow, analysts expect Gartner. Thus, the telecommunications industry will increase from $ 1.4 trillion in 2015 to $ 1.7 trillion in 2020 (the average annual growth rate is 3.9%). The market of wearable devices ("smart" glasses, watches, etc.) over these years will grow from $ 12.5 billion to $ 37.1 billion; the market of equipment for the construction of mobile networks - from $ 40 billion to $ 60 billion; the market of voice services - from $ 900 million to $ 1.1 billion; the mobile applications market - from $ 45.3 billion to $ 162.5 billion.

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 its application is already widely tested on a limited scale. The development of fifth-generation networks is carried out by all the leading telecommunications operators in the world - American Verizon and AT & T, British Vodafone, Scandinavian Telenor and Teliasonera, etc. The Chinese market for Huawei and ZTE, Korean Samsung, European Nokia and Ericsson and American Cisco and Qualcomm . Follow the development of technology and technology giants Silicon Valley. For example, Google in 2015 launched a secretive program for managing drones through a fifth-generation connection called SkyBender.

China has already established near Beijing, Huairou, the world's largest test station 5G: Ericsson, Huawei, Nokia, ZTE, DTT and Intel are participating in the network testing, Xinhua News Agency reported in March with reference to the Ministry of Industry and Information of the country.

Korean operator SK Telecom in November 2015 announced the achievement of a data transfer rate of 19.1 Gb / s. In 2017, the company hopes to launch the first commercial technology tests. At the same time, Verizon is counting on the commercialization of 5G. In 2018, the innovative communication promises to provide the participants and spectators of the Winter Olympics in Pyeongchang Korean operator KT.

MTS and Megafon announced similar plans for the 2018 World Cup in Russia (Megafon in June, and MTS in September 2016 already conducted the first tests of 5G, working on the development of technology and other participants of the Big Four - Vimpelcom and Tele2). The Tokyo Olympics in 2020 will be covered by the operator's 5G network of NTT DoCoMo. China Mobile in December 2016 announced the commercial launch of the standard in China in 2020.

$5 billion

All these plans will require large-scale investments: according to Markets Reports Hub, the consulting company, until 2020, research expenditures for 5G should be at least $ 5 billion per year.

Technology developers are left with many regulatory issues: 

under the auspices of ITU, governments and businesses will have to unify the 5G standards, allocate frequencies in higher bands (new base stations will need to be built), maintain and update the mobile infrastructure (5G networks will work in parallel with the already built networks of previous ones generations). Finally, developers have to believe that their hopes for a technological breakthrough of other new technologies that need the 5G properties will come true. This will entail a large-scale renewal of the mobile "iron" park: users who want to join the future through the fifth generation communication will have to acquire a new smartphone, laptop, fitness tracker , voice assistant, etc.

5g Security:

 As with other new technologies, the 5G carries an additional threat. 

 Intel analyst Matthew Rosenquist considers the most vulnerable industry for which the fifth generation connection increases the risks of data leaks, Internet of things (IoT). With increasing network speed, more physical objects will be connected to it. As a result, hackers will try to gain access to corporate and user information in such sectors as transportation (attacks on control systems of unmanned vehicles based on 5G), health (theft of health data of patients served remotely due to high-speed data transfer in new networks) and the transport of goods by drones (hacking of logistics systems and for the conduct of terrorist attacks using drones that quickly "seize" the signal in 5G networks), warned the expert.

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