The Present and Future Trends of Machine Learning on Devices

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.

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 2019

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 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.

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.

Current Trend in Artificial Intelligence | Machine Learning Future Trends

Artificial Intelligence 2018. Are you Ready for AI ? See Whats Happening in 2018 and 2019 

2045 is the year of the invention of a full-fledged artificial intelligence that imitates the human

An inexperienced Internet user will be surprised to learn that artificial intelligence (AI) is still the technology of the future: the concept of AI has become so firmly established in the information agenda. Almost every day the largest technology corporations report about the achievements of their own "intellects". "Intellect" can already process the photo, with it you can fight in the game (by the way, in Guo he already beat all the champions) or just chat in Twitter and ask through the voice interface to turn off the lights or turn on the music. But in fact, the developers have so far made progress only in what the National Council for Science and Technology (NSTC) defines as a "narrow" AI. "General" AI, which will become a full-fledged imitation of human thinking, has yet to be invented by mankind, and it is impossible to predict how long this process will take,

An advanced prototype robot child named David is programmed to show unconditional love. When his human family abandons him, David embarks on a dangerous quest to become a real boy.

Artificial Intelligence in Everyday Life | AI Revolution

The research process, and behind it the emerging market, is now moving forward primarily machine learning. This subsection of AI works through algorithms of artificial neural networks . Neural networks function according to the principle of the human brain, that is, they draw conclusions based on analysis of large data sets.For example, a group of researchers from the Massachusetts Institute of Technology (MIT) in December 2016 taught artificial intelligence to supplement static images with dynamic ones. To do this, scientists "fed" AI 2 million videos with a total duration of about a year and programmed the development for predicting a static image. So, when the "intellect" from MIT received a photo of the beach, it "enlivened" it with the help of propulsion of sea waves. And according to the image of the railway station, the AI ​​"directed" a short film (so far only 32-second timekeeping) about the departure of the train. Practical application of the technology is useful, for example, unmanned vehicles, which in the case of obstacles appearing on the road, one must be able to instantly make a decision about a sharp maneuver or continuation of traffic in order to avoid tragic consequences (today up to 90% of accidents occur due to the driver's fault).

Machine Learning Future Trends

Similarly, he trains his development of Google. The corporation, for example, (with the support of Oxford University) has taught AI to read lips more effectively than professional linguists. After studying the "intellect" 5 thousand hours of video, he was able to consider 47% of words from a random sample of 200 fragments, while a man - only 12%. Also the company in an entertaining form offered users to teach AI better to recognize images. To do this, in November 2016, a game experiment was launched, Quick, Draw !, under which AI must guess in 20 seconds what the person is drawing on the screen.

The principle of image recognition, many companies already use and for commercial purposes. So, the Russian startup Prisma has trained its neural network to process photos of users in the style of different artists. The output was a service, downloaded, according to the Techcrunch portal, more than 70 million times around the world and recognized by Apple as the "application of the year" for the iPhone.

By 2020, the AI ​​market will grow tens of times and reach $ 153 billion, analysts at Bank of America Merrill Lynch predict. According to their calculations, most of the market - more than $ 80 billion in monetary terms - will capture the developers of solutions for robotics . In addition to the mentioned use of AI in laying out drone routes , the technology will be needed, for example, to improve the concept of "smart houses" and develop the logistics of commercial drones (in December Amazon already made the first commercial delivery of a drone in Britain).

$153 billion of

such value will achieve market development of artificial intelligence by 2020

The most ambitious players of the technological industry are looking at the AI ​​market and ai business ideas. In the spring of 2016, the founder of Tesla and SpaceX Ilon Mask together with partners created a non-profit company OpenAI to develop "friendly" AI. Entrepreneurs set the task not only to save humanity from enslaving cars, but also to make technology accessible to all developers. OpenAI will receive $ 1 billion of investment. The company has already introduced its debut product - software for "training with reinforcement." This type of machine learning enables the object to develop, interacting independently with the environment. Technology allows AI, for example, to control robots or play board games. Sometimes this leads to incidents: Twitter-bot from Microsoft, which in May went to learn "with reinforcement" in the network of microblogging,

In the footsteps of OpenAI followed by other companies. In December 2016, Apple announced plans to lay out what the AI ​​had in the public domain. Earlier, Microsoft, IBM, Amazon, Google and Facebook announced the merger of efforts for the synergy of research "capacities". The world's largest social network is particularly interested in the rapid development of AI technologies to combat fake news in the tapes of user subscriptions. At the finish of the presidential campaign in the United States, false informational occasions became a serious problem for the service: from February to November, users "jumped" or "reposted" the 20 most popular "fakes" 8.7 million times, while the top 20 real news caused only 7.4 million reactions.

Algorithms of machine learning are used almost in all "fashion" today technological directions of research - from unmanned vehicles to smart home systems. AI technologies can potentially change any branch of the economy and almost any business. So, in the near future - 2017-2018, according to the forecast of the analytical company McKinsey, machine training will turn the recruitment market (artificial intelligence more accurately than professional "bounty hunters" will be able to search for optimal candidates for employers) and some segments of the IT market (for example, stormy the development of chat bots allows the business to build new communication strategies, including in social media).

In the future, AI should help states and businesses deal with cyberthreats. So, AI-Squared, a joint project of the Massachusetts Institute of Technology (MIT) and the PetternEx start-up, is a platform that processes large amounts of user data using a neural network algorithm to detect cyber attacks. Based on the results of a year and a half of platform tests (during this time it analyzed 3.6 billion files), its developers announced the detection and prevention of 85% of the attacks.

76 million

jobs can DESTROY THE ARTIFICIAL INTELLIGENCE IN THE NEXT 20 YEARS IN THE US

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Neural networks have long been used in cybersecurity technologies.

 For example, Kaspersky Lab, about a decade ago, created a special module capable of recognizing digital passwords in the form of pictures that malicious programs used to distribute in e-mail. Another example is image analysis technologies on websites for identification by means of parental controls or an adjacent technology for recognizing spam on an image. Neural networks are widely used to analyze network traffic: with their help, cybersecurity specialists are looking for anomalies that may indicate suspicious activity.

But apart from the positive consequences, the development of artificial intelligence carries risks as well. In the coming years, AI technologies will continue to attack users, the report of the analytical company ArkInvest says. According to experts, in the next twenty years, artificial intelligence can destroy only 76 million jobs in the US - ten times more than it was created during the years of Barack Obama's presidency. And in the perspective of thirty years AI can bring the global unemployment rate (with the current structure of the labor market) to 50%, The Guardian wrote, citing Professor Rice Moshe Vardi.
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how does technology affect business today are the questions now a days required workable solutions.

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Business Survival with AI BI in Digital Data Distribution

Distribution of information to users of BI 2018

Modern business dynamics and trends in data, pushed by emerging Internet technologies, led to a significant increase in the amount of data stored in corporate systems.

Many enterprises are faced with the problem of excess information as per big data predictions by researchers like forbes big data. Transaction processing systems generate daily reports. In hundreds of ways, using data warehouses and analytical tools, longitudinal and lateral slices of information (slice and dice) are performed. Tools for enterprise resource planning ( E Nterprise the R esource P Lanning, of ERP) collect all the data of the company. Tools for assessing performance and information received through the Internet further increase information congestion.

The situation is further complicated by the fact that modern enterprises operate on the world market, which means that their data and applications are geographically distributed. each branch has its own program and its own repository. Resources planning tools are implemented at all levels: applications for human resource management, financial systems and production-oriented software are created. The information used to work with these applications is stored in disparate sources (in different relational databases, as well as in special format files and in multidimensional samples).

BIG Data BI and AI

Today, many companies lose money and time, manually distributing information resources. According to some studies, about 15% of revenues are spent on the creation and distribution of paper documents on average. Thus, according to the Gardner Group, corporate employees spend 10% of their time looking for information necessary for a certain task or for making decisions, from 20% to 40% of the time - for processing already ready documents and 30% for tasks related with documents, but not adding value to the final service (product) of the company.

A new concept called "information broadcasting" is a technology that makes possible the transmission of personalized messages needed to support decision making via the Web, e-mail, fax, pager, mobile phone to hundreds of thousands of addressees.

Automating the process of sending information can provide users with timely data, expand the company's capabilities and prevent the accumulation of unnecessary data.

Moreover, the intellectual resources of the enterprise, created with the help of BI tools and analytical packages, should be automatically transferred also to the applications used in the operational departments to support decision making.

The most important factors for success | New inventions in science 2018

The task of correctly providing important business information using AI & business intelligence technologies to the appropriate consumer is clearly not an easy one. At the same time, there are a number of factors that determine its successful solution:

• Automatic saving of information in various working formats (HTML, PDF, Excel, etc.), which will satisfy the needs of specific users and allow performing the analysis.
• Distribution of significant material necessary for decision-making, including materials of "third parties" (drawings, Word documents, Power Point presentations, reports created both with the help of software purchased from various BI suppliers, and legacy reporting applications). Delivery of information via various channels, such as e-mail, Web, fax, network printer, etc .; transfer of information to mobile devices.
• Intelligent information dissemination functions, such as scheduled distribution or distribution, performed under certain business conditions;
• Providing only the necessary reports, which frees users from tedious viewing of all materials in the search for necessary sections.
• An intuitive system for cataloging and storing information, especially important in connection with the adoption of many legislative measures concerning the storage and management of documents.
• Support for any number of users, thanks to which the software for distribution of information will be able to scale successfully as the organization grows.

Examples of the effective use of applications that distribute information in this big data and ai trends world.

Electronic reporting

According to Gardner Group, on average, each document is copied 9-11 times. Mailing and delivery of hard copies is a rather laborious procedure, including sending faxes and emails, or manually delivering. This problem is almost completely eliminated with the use of electronic reports: not only paper consumption is reduced, but also other expenses, and, most importantly, the execution time of this work is significantly reduced.

The software intended for dissemination of information automatically sends data to those who are interested in them. This intelligent method of distribution further reduces the labor costs associated with manual processing of documents. The user no longer needs to search for information, it is automatically sent to his mailbox.

Scheduled reports

Applications for the delivery of information provide the execution of requests through e-mail. In accordance with some schedule (daily, weekly, etc.), certain information material is formed and sent to the subscriber. And now you do not need to make the same reports for hours. (For example, sales managers can assign a specific day in which they will receive a monthly report on their departments' work in their mailbox). To regulate the distribution plan, you can either only the administrator or several authorized users. The content, time, frequency, format and even method of dissemination of information can vary according to individual requirements.

Event-driven notifications

The administrator can schedule special notifications that will be sent to users if certain conditions are met. Such a dispatch will shorten the working time spent by the administrator on sending daily messages, as well as on the execution of reports by users and search for information.

The notifications work as follows:

• The administrator sets the notification condition, during which certain information is sent.
• Planned tasks stored in the repository are checked with a user-friendly frequency.
• If a scheduled notification check is detected, then the application tests all conditions (business rules).
• If the conditions are not met, the information is not sent, and the notification check is postponed for the next scheduled time.
• If the result of the test is positive, then the corresponding information is generated and sent as a notification. The notification is activated according to the settings (automatically, permanently, once, with a delay).

Notifications serve to inform employees about important events and increase the efficiency of the company. For example, they can help:

• leader, if the amount of the budget is exceeded;
• Investor if the stock price falls below a certain level;
• Trade representative, if a good deal is expected;
• manager for working with corporate clients, if a large customer receives a complaint.

Splitting reports

Reporting automates the manual process of "paper pushing", as a result, it is possible to significantly reduce administrative costs and processing time. For example, we will present a report that contains information on spent and missed days for each employee. The pages of this report can be automatically broken down and sent electronically to each employee, while many and many hours of HR department work will be saved.

New Technologies for Information or Data Distribution 

In recent years, the main means of disseminating information is the Web, because when it is used:

• Improves access to business information and increases the number of users who can work with it;
• provides a convenient interface for searching and navigating;
• it becomes possible to access different types of data located in different sources (both inside and outside the organization);
• due to the thin client technology, the hardware and software requirements are reduced and the costs for supporting the client seat are reduced;
• it is possible to use platform-independent software (Java) and a data representation system (XML);
• Information for employees, partners and customers can be distributed through Intranet / Extranet and the Internet. Due to this, the space of access to information expands and new ways of doing business are opened.

As a result, the information needed to make decisions can be transferred to users and applications using two main technologies:

• Web-based tools Business Intelligence (tools for creating reports, performing OLAP analysis and data mining), as well as packages of analytical applications for the Web;
• corporate information portals and distribution servers.

It is important to note that these technologies are not interchangeable: for maximum effect they should be used together.

Web-oriented BI-tools and analytical software packages distribute information and results of analytical operations through standard graphical and Web-interfaces. Many of these products also support scheduled and event-driven delivery of information to Web servers and mail systems, thereby leveraging all the capabilities of the underlying network tools.

As the volume of business information processed in the decision-making system grows, it becomes more likely that data will be distributed within a series of information stores located on different servers. This is true, for example, in cases where organizations obtain business information from external sources, internal offices, shared software and Web servers.

To help users apply this wide range of business data (both for unregulated access and for offline delivery), the second technology is used - corporate information portals .

When publishing corporate information on the portal, metadata about the location of particular information is used, as well as methods of extracting them. When the employee makes the requested request, the portal reads the relevant metadata and, using the means of access and delivery, selects the necessary information.

Effective work is achieved only if the information is properly shared, otherwise employees duplicate each other's activities and do not achieve mutual understanding. Thus, the portal becomes the basis that guarantees free data sharing and solution to technology problems in business

Conclusion

Rapid development and high competition in the market do not forgive mistakes. In this environment, it is necessary to make decisions correctly and in a timely manner. However, the task of finding the information necessary for this, seemingly simple at first glance, causes a number of difficulties, since the data are accumulated and stored throughout the organization. To solve it, companies need to develop infrastructures that ensure the timely dissemination and sharing of information.

Emerging trends , Data analyst trends, business intelligence future trends are the queries frequently asked to us.The software for dissemination of information should ensure the transfer of data to a huge number of users inside and outside the enterprise and significantly improve the efficiency of work by:

• rapid delivery of critical information;
• minimizing the time of the work cycle as a result of stopping the dissemination of information manually, more rational processing of documents and improving the productivity of employees;
• distribution of any content, including files of different formats (for example, reports from applications of other suppliers or old systems, drawings, diagrams, documents and presentations);
• optimal storage of files and the realization of a simple and quick extraction of the necessary information.

Future Development of IoT Security | IoT Application and Product Trends

Internet of things

By 2032-2035 IoT-infrastructure will spread to 1 trillion devices and 100 million applications, according to the forecast of the analytical company SMA

$ 11 trillion - in this fantastic amount, McKinsey analysts in June 2015 estimated the potential contribution to the global economy by 2025 of a new technology industry - the Internet of Things (IoT).

Future Development of IoT

Under IoT, experts understand a set of networks consisting of objects that can communicate with each other via IP connection without human intervention. According to McKinsey estimates, the term "Internet of things" is applicable to at least 150 object systems, so the economic effect of it can be so great already in the perspective of the next decade.

IoT claims to become a technological shell for almost all markets - from transport, consumer electronics and trade to agriculture, construction and insurance. According to the forecast of BI Intelligence, by 2021 the number of IoT-devices will grow to 22.5 billion from 6.6 billion in 2016. Investments in the Internet of things in this five-year period will reach $ 4.8 trillion, analysts expect. According to estimates of experts of the World Economic Forum, by 2022 the total number of devices connected to the Internet will reach the level of 1 trillion. This will entail both the formation of new markets, and a radical change in a number of traditional businesses, experts warn Davos.

22.5 billion 

THIS NUMBER OF IOT-UTILITIES WILL BE IN 2021

An example of the market, formed due to the development of IoT, is the technology of the "smart house". In 2016, the number of consumer "things" included in this "network of networks" has grown to 4 billion, and by 2020 it will reach 13.5 billion, the analytical company Gartner has calculated. Potentially IoT can capture all the "material" environment of the person, experts noted Deloitte: for example, a TV a few years ago no one was associated with online services, and today to present a modern device without the function of Smart TV and access to streaming services like Netflix is ​​almost impossible .The largest technology corporations are busy developing the IoT infrastructure for the "smart house": Apple introduced the HomeKit system in 2014, Google bought the manufacturer of "smart" Nest thermostats for $ 3.2 billion and Amazon developed the ecosystem of the home "manager" Alexa for the third year . According to the estimates of Markets & Markets, the technology market for the "smart house" will increase to $ 122 billion by 2022 from $ 47 billion in 2015.

In more traditional industries, IoT will form whole new segments. In medicine, for example, its size could reach $ 117 billion by 2020, predicted the analytical company MarketResearch. So, on the new technology "electronic" intensive care chambers are based. With the help of high-resolution cameras, the doctor can remotely monitor patients and provide real-time voice and voice advice to colleagues on-site, if emergency measures are required. Telemedicine can accelerate the penetration of quality medical services in developing countries, for example, India (there until 2019 the leading player in the emerging market, Philips plans to equip up to 30,000 hospital beds with cameras).Simpler versions are portable devices that allow monitoring in real time, transfer to a doctor and analyze data on the level of glucose in the blood, remove cardiograms, etc.

IoT mit technology review:

The future of IoT does not look cloudless, analysts are in solidarity: eliminating a person from the communication process with devices carries not only advantages, but also risks. So, IoT-botnet from the devices infected with malicious Mirai, became the reason of serial and powerful attacks in 2016. The goal of Mirai is to constantly scan the Internet for open IoT devices, access to which can be obtained through simple for hacking or default accounts. By compromising the devices, hackers connect them to the botnet for subsequent DDoS attacks.

For the past few years, researchers have been constantly raising the issue of protecting connected devices. Last year, Charlie Miller and Chris Valashek demonstrated how to get wireless access to the critical Jeep Cherokee systems , successfully taking control and forcing it to go out of the way! Vasilios Hiureas from Kaspersky Lab and Thomas Kinsey of Exigent Systems conducted a study of potential weaknesses in the protection of video surveillance systems . Later, one of the manufacturers of medical equipment reported a vulnerability in his insulin pump , which allows attackers to turn off the device or change the dosage of the medicine. There were fears about the items that we use every day, such aschildren's toys , baby monitors and doorbells .

To protect the data with which IoT gadgets work, manufacturers will spend more and more: by 2020, Gartner analysts expect the growth of expenses for providing IoT infrastructure security to $ 30 billion. It's especially important for businesses to treat the spheres in which the Internet of things interacts with sensitive information, for example, data on the state of human health. In the case of access by attackers to these data, even the doctor can not protect the patient: the hacker needs to make adjustments to the software shell of "things" responsible for making decisions, analysts say. As of 2014, up to 70% of IoT solutions were vulnerable, according to a study by Hewlett Packard.

$30 BILLION TO INCREASE IOT-INFRASTRUCTURE SECURITY COSTS 

IN 2020

As per Digital Technology Reviews The key problem in IoT is the youth of the industry - the lack of industry standards both in terms of system compatibility, and in terms of providing IS even at the level of development. Plus the illusion that once the device has a small processing power, then it is not interesting to any hackers (but this illusion was very revealingly disposed of by Mirai).

However, according to mit technology review 2018 the prospects for IoT business are not so gloomy, the technology just has not yet passed the "cycle of maturity" according to the methodology of Gartner and has enough time to "fill the bumps", analysts concluded.

The long-term perspective of IoT reviews is a transformation into the "Comprehensive Internet", IDC company noted in a study in 2016. This term hides the system of "close interaction of networks, people, processes, data and objects for the implementation of intellectual interaction and justified actions," experts explained. This transformation will take dozens of years.

Please let us know what are you forcasting about future development of iot or future of iot

, future of iot security , internet of things future applications and internet of things future trends future iot products.

2018 technology predictions

The Development of Cloud Technologies | Latest TechReview Updates 2018

In 2030, 52% of the world's data will be stored in a public cloud, according to the forecast of VMware

More than 50% of IT budget spending in the world last year for the first time in history had to cloud resources, according to Gartner.

Senior Vice President of Google Diane Green proclaimed 2016 the beginning of the era of "Cloud 2.0". The largest deal of the year in IT is the acquisition for $ 67 billion by Dell Corporation of EMC, the most profitable asset of which is the world's largest developer of virtualization software for VMware.Cloud data center networks technologies trends and challenges By 2018, according to the forecast of Cisco, cloud data centers will handle 78% of all workloads, and 59% of the total cloud load will be the SaaS segment (software as a service). Analysts of Deloitte and TechRepublic regularly point out "cloud" technologies among the directions of the IT market, in which the largest companies will invest most actively in the coming years.

78% ALL WORKING LOADS WILL PROCESS DAY CENTERS BY 2018

Gartner regularly conducts an audit of emerging technologies to determine which of the popular trends may never reach the "maturity cycle", and which - have already overcome all the growing ills and enter a development phase that involves the formation of a new market. "Cloud" technology Gartner attributed to the second group already in 2015: although computing in the cloud has long been not exotic, their age is just beginning.

"We constantly have to revise the estimates upwards. The market is growing faster than we expected back in 2014, "said Dave Bartoletti, an analyst at Forrester Research . According to the forecast of this company, in 2017, the world market of cloud technologies will grow by 22%, to $ 146 billion. For comparison: in 2015, the estimate of Forrester was $ 87 billion, and the forecast for 2020 was $ 236 billion. Segment IaaS (lease infrastructure) in 2017 will grow by 35%, to $ 32 billion. The revenue of the largest player in the segment of the Amazon Web Services division will reach $ 13 billion. It will grow by 2-3 times less on the cloud computing Microsoft Azure, to $ 1 billion - Google Cloud Platform, Forrester believes.

Cloud Big Data Technologies Reviews

The Russian market is still at an early stage of development, but is growing rapidly: the average annual increase to 2020 will be 34.3% and 20% for IaaS and SaaS, respectively, noted the research company iKS-Consulting. According to analysts, in 2015, the volume of the cloud technology market in the country grew by 39.6% compared to 2014, to 27.6 billion rubles, while 22.2 billion rubles. had on SaaS, 4.4 billion rubles. - on IaaS. Bloomberg calls the key trend, the "new norm" of the market in 2017 and the next few years, the transition of companies to a hybrid cloud: the business will no longer be locked into one provider by growing confidence in the clouds and will begin to diversify sources of resources for processing and storing data. Companies will build the process of obtaining additional resources from the cloud in a convenient and fastest way for them, while in what cloud these resources will be "allocated" at this particular moment in time, it does not matter. Gartner expects that by the end of 2017 up to 50% of cloud customers will prefer hybrids.

Why Cloud Technology is Needed ?

Cloud technology will be needed for a number of research areas where huge computing power is required. For example, for developments in the field of machine learning and artificial intelligence . So, Google last year hired Professor Fay-Fei Lee, who headed the Artificial Intelligence Laboratory at Stanford, into a division dedicated to the development of the cloud platform. One of the directions of "democratization" of research in the field of artificial intelligence, Lee, was just the creation of a cloud platform for machine learning.

Another direction is the provision of infrastructure with new robotics . The term "cloud robotics" as early as the beginning of 2010 was born in the depths of Google: it assumes that the "inherent function" of robots, which are increasingly used commercially, should be the ability to constantly access the processing power of data centers for decision-making. For example, an unmanned vehicle, delivering goods on a given route, while traveling on the road remotely reads the data of cartographic, geolocation and other services of the company-developer. The main problem of transition to the cloud infrastructure and, in general, the decentralization of IT functions in companies is security. 57% of companies say that the result of decentralization is the use of unsafe solutions, and 58% - that data protection control systems are insufficient, according to a survey of 3,300 specialists in large and medium-sized companies in 20 countries, conducted in 2016 by order of VMware.

The Development of Cloud Technologies

the construction of new data centers will inevitably require increased attention to cybersecurity, said a free-lance expert at IBM and Dell Kevin Jackson in an interview with Cloud Endure. According to his forecast, the companies will toughen the measures against cyber threats, increase the volume of security tests, improve technologies for secure access , including data encryption. According to a poll by Clutch, 64% of market players consider the cloud a more secure infrastructure. But the majority of respondents still develop a protective arsenal with the help of encryption (61% of respondents), additional identification technologies (52%) and additional tests of cloud systems (48%).

VMware last year agreed on cooperation in the field of data protection in software-defined data centers with Kaspersky Lab. According to Gartner, the budget for cloud cybersecurity in 2017 is ready to increase 80% of market players. At the same time, analysts advise companies to be especially sensitive to training of personnel responsible for communication with the cloud: more than half of the data leakage occurs not because of the vulnerabilities of the provider, but because of customers and their internal problems, states Gartner.

13% COMPANIES IN RUSSIA ALREADY FOLLOWED WITH INCIDENTS ASSOCIATED WITH CLOUD INFRASTRUCTURE SAFETY

In Russia, 13% of companies have already experienced incidents related to the security of the cloud infrastructure, at least once a year, according to a poll by Kaspersky Lab. At the same time, about a third of companies (32%) lost data as a result of these incidents. But business does not take this threat seriously: only 27% of Russian companies believe that the security of their corporate network depends on the security of their virtual systems and cloud infrastructures. At the same time, more than 80% of cyberinchicants in the clouds will occur due to actions, or rather, the inactivity of the virtual machine owner in the cloud - incorrect configuration of information security solutions inside cloud computing machines, too simple passwords and the work of insiders.

But Russian companies are still more concerned about protecting external cloud services. They worry that incidents can occur with suppliers outsourced to business processes, third-party cloud services, or in the IT infrastructure where the company leases processing power. At the same time, only 15% of companies monitor compliance with security requirements for third parties.

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