It was in demand only among amateurs and designers. They created one-off prototypes out of plastic because other materials, such as metal, made printing an expensive and impossibly long process.

Nowadays, 3D printing makes it possible to easily and quickly produce objects from any material, including metal. This means businesses don't have to store mountains of parts in stock. Once an order has been received, it can be immediately produced and sent to the client. In the long term, factories will become more versatile. Manufacturers will be able to produce parts of varying complexity without additional equipment.

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Embryologists at the University of Cambridge were able to artificially create mouse embryos from stem cells. This achievement opens up new possibilities for understanding how life began.


We knew that stem cells had powerful potential, but we had no idea that they could organize themselves into such structures.

Magdalena Zernica-Götz, Professor of Stem Cell Biology and Molecular Biology

The next step, according to Magdalena, will be the creation of an artificial embryo from human stem cells. Scientists at the University of Michigan and Rockefeller University are working on this.

Artificial human embryos will help study the very concept of life. However, this case raises a number of ethical questions. What if they turn out to be indistinguishable from real embryos? How long can they be grown in a lab before they feel pain?


businessinantwerp.eu

The concept of a “smart city” is still from the realm of science fiction. All plans to create such an infrastructure still exist only on paper. However, the New York company Alphab's Sidewalk Labs, as part of the Quayside project, is going to rethink this idea and create an entire quarter in Toronto using the latest digital technologies.

Alphab's Sidewalk Labs plans to deploy a variety of sensors that will collect information about the city and its inhabitants. The project plan talks about automated vehicles and robots working in the subway. In addition, the company will make the software publicly available so that developers can create and implement their services.

Alphab's Sidewalk Labs intends to closely monitor public life. This decision is causing concern among city residents. They worry about . However, Sidewalk Labs employees believe that they can resolve this issue.

Other North American cities are already in line to join the Quayside project, according to government agency Waterfront Toronto.

I've already received calls from San Francisco, Denver, Los Angeles and Boston asking to implement the system.

Will Fleissig, general manager Waterfront Toronto


learnfly.com

(AI) was an expensive toy for large companies like Amazon, Baidu, Google and Microsoft, but for the rest it turned out to be an inaccessible and incomprehensible tool. However, industry giants plan to place their developments in cloud services so that others can use them.

Until now, this area has been dominated by AWS, a subsidiary of Amazon. Google did not stand aside and developed TensorFlow - an open-source AI library source code. It is used to develop programs with machine learning. The search giant recently announced Cloud AutoML. This is a set of systems that will make AI easier to use.

Microsoft teamed up with Amazon to create Gluon, an open-source machine learning library. It should help create neural networks, a key artificial intelligence technology that roughly imitates human learning.

It is not yet known which company will become the market leader. In any case, consumers will benefit.


fraunhofer.de

Artificial intelligence has an excellent understanding of subjects. Show him a million photographs, and he will determine with extraordinary accuracy where a pedestrian is shown crossing the road. However, AI has long been deprived of the ability to create on its own. If artificial intelligence had imagination, it could use it to learn. For example, a neural network in a self-driving car would learn to recognize people on the road without having to go outside.

University of Montreal graduate student Ian Goodfellow has proposed a solution to this problem. He described a method called a “generative adversarial network,” or GAN. The algorithm is built on the interaction of two neural networks - a generator and a discriminator. One of them creates images, and the other compares them with a database and determines authenticity.

Let's take the example of . At the beginning of training, images of a pedestrian will differ from reality. The generator may draw him as having three arms, a huge head, or not looking human at all. The discriminator will reject these images. Eventually, one neural network will draw such a realistic pedestrian that another will not be able to distinguish it from the real one.

GAN is rightfully considered a technological breakthrough. Some experts are confident that with the help of this algorithm, artificial intelligence will learn to better understand the world around it.


1843magazine.static-economist.com

This is a fictional creature from the Hitchhiker's Guide to the Galaxy book series by Douglas Adams. A kind of organic implant with which the wearer can understand any language. The fish translates alien speech in real time and transmits signals directly to the brain.

Our technologies are not that advanced yet, but they can also do something. Google announced Pixel Buds headphones, which, in addition to performing their main tasks, can translate foreign speech in real time using a voice assistant. The headphones are currently under development. However, anyone can access basic voice translation technology on their smartphone.

Microsoft is also worth mentioning. The company implemented real-time translation through the Skype application. At this rate, humanity will invent its own Babel fish.

Natural gas is a cheap and accessible source of energy. It produces 30% of electricity in the United States and 22% worldwide. However, this pollutes the environment.

American startup NetPower has built an experimental power plant in Houston. The carbon dioxide produced by burning the gas will be processed or sold to other companies. By using new technology can not only be solved environmental problems, but also reduce the cost of electricity production.


lobnyamedia.ru

Zero-knowledge proof is a protocol that will protect personal data on the Internet. It gained great popularity thanks to the Zcash cryptocurrency, which was launched in 2016. The developers used a method called zk-SNARK to allow users to make anonymous transactions.

On most public blockchains, transactions are visible to everyone. In theory, they are anonymous, but by combining data from other sources, the user can be tracked. Vitalik Buterin, the creator of Etherium, the second most popular blockchain network, called zk-SNARK “a technology that is absolutely changing the game.”

Banks will be able to process payments without disclosing customer information. Last year, JPMorgan Chase added zk-SNARK to its proprietary blockchain-based payment system. Ordinary users will not be left out either. For example, they will be able to prove that they have enough money on the card without revealing bank details.

However, there is still a lot of work to be done. zk-SNARK is a complex and slow technology that requires additional configuration.

9. Genetic predictions


nationmagazine.ru

It turns out that the most common diseases, character and behavior traits, as well as intelligence depend not on one or several genes, but on their combinations. Using data from large genetic studies, scientists have developed so-called polygenic risk scores.

New DNA tests will help create more effective drugs. Pharmaceutical companies will be able to use test results in laboratory research. For example, recruit a group of volunteers who are at risk of developing it to test new drugs.

The problem with DNA tests is that, in addition to diseases, they can reveal character traits and even intelligence levels. On the one hand, this is good, on the other hand, it is unknown how teachers and parents will handle this information. How will raising children change if parents discover a low level of intelligence in a child?


geekinsight.ru

Chemists have long dreamed of effective drugs based on new proteins, powerful batteries and compounds that can turn sunlight into liquid fuel. We don't have all these things because it's very difficult to model molecules on modern computers. Not enough power.

Try to imitate the behavior of electrons even in a simple molecule, and you will encounter great difficulties. However, everything will change soon. IBM researchers recently simulated the molecule using a 7-qubit quantum computer. Over time, researchers will be able to simulate more complex molecules on machines with more qubits.

We made forecasts about what technologies will affect businesses and users in 2018 and how the finance, travel, telecommunications, retail, healthcare and media industries will change.

Bookmarks

Finance

  1. 2018 will be "the year of artificial intelligence" , with exponential growth in the use of artificial intelligence (AI). The AI ​​technology proficiency gap will become a war for talent, and conversations about AI as a job stealer will transform into discussions about AI as a job creator.

    2. The complexity and power of new AI programs will stimulate the development of cybersecurity for financial and personal data.

    Usage machine learning (machine learning, ML) for analyzing financial data will spread rapidly, especially in the area of ​​analyzing unstructured data, such as company and customer news. This will be next large region to manage investment risks.

    The development of AI technologies is driving a revolution in the regulation of financial markets. One of the regulatory benefits of AI is the potential to help avoid collapses of banking systems. Artificial intelligence is increasingly being used in the sector to risk assessments a domino effect occurs.

    Digitalization User experience will remain a key priority. Customers expect access to the same types of all-in-one solutions and consumer-centric interfaces that they use in other aspects of their lives. Companies that fail to do this while maintaining the security of financial and personal information will lose customers.

    Importance operational risk management (operational risk management, ORM) will increase as executives lose their jobs due to failures in operational risk management. Technology will play an increasingly significant role in company risk assessment, with management looking to improve data management practices to improve the accuracy of risk identification.

    Advantages distributed register (distributed ledger technologies, DLT), such as blockchain, will become understandable to more people, which will lead to their significant growth (not related to the growth of cryptocurrencies). Distributed ledger technology will be combined with other technologies such as the Internet of Things (IoT). Blockchain will be seen as a solution to problems of cybersecurity and personal data protection.

Trips

  1. Travel companies will continue to invest in personalization software (analyzing the user's personal preferences) to improve the user experience. Machine learning and artificial intelligence are also gaining momentum, and companies see promise in automating simple business processes.

    2. Speech technologies (voice activated technologies) may soon replace mobile applications. They perform better at recognizing spoken requests and providing more accurate answers for certain task sets.

    Virtual reality Virtual reality (VR) and augmented reality (AR) will change the way travelers shop online. Marriott, Best Western, Kayak, Carlson Rezidor and Airbnb are already using these technologies.

    Robots start working in hotels. Very soon they will register check-ins, be used for information support and entertainment of guests, and provide room service. Robotic Process Automation (RPA) can help businesses perform repetitive administrative tasks better and cheaper.

    Violations of the rules cybersecurity and risk management continue to be a concern for companies of all calibers. 2017 showed that even the world's largest, wealthiest and most resilient corporations can be undermined by weak approaches to security and privacy.

Telecom

  1. Telecommunications companies will continue to provide mobile services, including to maintain contacts with customers. At the same time, operators will introduce more and more new services to support and grow.

    2. Technologies 5G will enable the development and deployment of new types of digital services. The expected benefits of the service are revolutionary, one of the key ones is that it will greatly exceed the existing data transfer speed. But telecommunications companies are just beginning to announce planned timelines for network deployment. The first experiences of introducing 5G are expected in Russia at the FIFA World Cup and the Olympic Games in Korea this year. However, the full implementation of the technology should not be expected until 2020.

    By 2020, 25 billion remote devices will be created and internet of things (internet of things, IoT) will help connect 4.4 billion of them. Digital transformation will present new opportunities for the telecommunications industry, including the construction of platforms and applications for the transport, agriculture, healthcare, insurance and home sectors.

    Telcos will use their massive infrastructure to expand security offerings and mitigate growing cyber threats .

    Despite AT&T/Time Warner antitrust issues in the US, mergers and acquisitions in the sector will continue, with more than 2,400 telecom deals already announced since 2010.

Medicine

  1. Hospitals and pharmaceutical companies will take a keen interest in blockchain , using it to analyze patient data for research purposes. Patients, in turn, will be able to control access to personal data, which was previously impossible.
  2. The development of artificial intelligence (AI) technologies in telemedicine will accelerate as machine learning (ML) and natural language processing techniques develop. This will provide customers with a personalized experience and help improve efficiency and reduce costs in the healthcare system.

    3. Population health management - data aggregation and selection of best practices in health care - will lead to increased investment in wellness programs designed to keep healthy patients healthy and create best practices for preventing diseases in patients at risk.

Retail

    Retailers will continue digital transformation , driven by online sales from leading industry players. Thanks to the introduction of scalable Agile technologies, changes will occur both in the IT landscape of retailers and in their business models as a whole.

  1. Data analytics And machine learning will help retailers personalize their messages and use data to tailor algorithms for working with customers. Data analytics will play a significant role in inventory management and distribution.
  2. Retail-focused technology startups or robotics process automation solutions are expected as more and more retailers rely on automation to optimize costs.

Media and entertainment

  1. Technological changes in media and entertainment will make 2018 "year of voice" . More than 24.5 million Google Home and Amazon Echo devices are expected to be sold by the end of 2018.

    2. Virtual assistants (virtual assistants) will allow users to use voice search, and podcasts and social video will allow users to exchange voice messages.

    It is expected that in 2018 podcasting will grow from 21% to 24% along with the growth of visual social media platforms, which will allow users to create video content across platforms. Powerful storytelling in social video compels action, opening up a huge opportunity for branding, promotion and sales.

1. Smart apps

“Since each of us is registered on several social networks, I think in 2018 they will create several applications that will make it easy to repurpose content according to a specific platform. For example, turn a series of blog posts into e-book or present the key topics of the webinar in the form of clear infographics. Also, I look forward to the availability of user-friendly video editing apps on smartphones,” Syed Balkhi, OptinMonster.

2. Internet of Things

“Now there are IoT devices in almost every industry, all things are becoming “smart”. We encounter these technologies at home, in the car, in the office and in the shopping center. I think this trend will continue to spread in 2018, proving its worth,” Andy Carusa, FenSens.

3. Artificial Intelligence

"AI will continue to be main theme technology discussions and conferences, and huge amounts of money will continue to be invested in its development. Perhaps in 2018 will happen this year AI breakthrough that will revolutionize the relationship between businesses and customers." - Daniel Wesley, Quote.com.

“Our beliefs and feelings are the product of the unconscious systems of the brain”

Bitcoin Became the Third Largest Financial Bubble in History

Technologies

7. “Word of mouth”

“In my opinion, word of mouth will become the main driving force in 2018. Digital budgets will include funds for promotion through referral marketing, affiliate programs and opinion leaders,” Jeff Epstein, Ambassador.

8. Video and VR/AR/360-degree headsets

“The more interested we are in video content, the more money is invested in it. In 2018, the main trend will be VR/AR/360-degree headsets. Among other things, they can serve in a great way introduce clients to your business or clearly demonstrate all the services provided,” - Timothy Solomon, OneIMS.

9. Blockchain

“Today, blockchain is used in many industries, starting with payment systems, continuing with the real estate market and ending with brokerage operations. I think the trend will only increase in 2018, and many more companies will start using it for their needs,” Angela Root, Calendar.

AI helped find natural analogues of drugs against cancer and aging

10. Cybersecurity

“One of the main topics of 2017 was cybersecurity - or rather, the lack thereof. Remember, at least, the hacker one. Clients are only now beginning to fully understand the scale of the danger. After Facebook and Google testify at open congressional hearings, the discussion of the problem will move from public space to private space, where specific issues of countering cybercriminals will be addressed,” - Ashish Datta, Setfive Consulting.

11. Distribution of cloud computing

“In 2017, more and more companies began to move their production workloads to the cloud. Thanks to blockchain, this trend will continue to develop in 2018. With its help, they will be able to control supply chains and IDM,” Mike Schrade, Auptimal.

The first quantum computer using 53 qubits was created

12. Bots

“A variety of bots have become extremely widespread: from ordinary ones on social networks to advanced

In the coming year, artificial intelligence will turn from a job killer into a job creator, and travel companies and retailers will make their offers more personalized for each buyer. But one after another, even large and stable companies that cannot cope with the protection of user data will die. 2018 will be the year of voice recognition, machine learning and the transition to the masses. Here are the main directions in which technologies will develop in different industries.

Financial markets

2018 will be "the year of artificial intelligence", with exponential use of AI. The gap in the level of proficiency in AI technologies will lead to a war for talent, many specialties and jobs will appear that did not exist before: specialists with the skills of developing adaptive software, understanding the mechanism of facial and speech recognition, the operation of an artificial neural network, etc. will be in demand. .

The complexity and power of new AI programs will stimulate the development of cybersecurity of financial and personal data. While providing such programs with the necessary data to solve certain problems, it is difficult to protect all information from unauthorized use. Not only companies operating in the financial sector will have to implement multi-level data protection systems, but also everyone who works with personal and sensitive data: booking services, medical organizations, online stores, engineering organizations, etc.

Usage machine learning(machine learning, ML) for financial data analysis will explode, especially in the area of ​​unstructured data, such as company and customer news. Better analysis of news and analytical texts about companies, markets and internal communications of financial organizations will allow us to generate effective investment ideas and combat unfair behavior of market participants. And new large-scale data processing technologies will make it possible to use a wider range of data to manage investment risks.

The development of AI technologies is driving revolutionary changes in regulation of financial markets. One of the regulatory benefits of AI is the ability to help banking systems avoid collapses and assess the risks of a domino effect. The point is that AI can identify patterns and connections in data that humans cannot recognize, and compare data from different periods with greater accuracy and speed. This will allow us to predict possible options developments of events. In addition, AI allows you to track problems that arise with banks in real time.

Digitalization User experience will remain a key priority. People are accustomed to convenient solutions and interfaces (such as apps for calling a taxi), and they want banks to have similar services. A mobile bank should function both on a personal computer and on a mobile phone, and be intuitive - users will not waste time on low-quality solutions. Companies that fail to provide comfort while maintaining the security of financial and personal information risk losing customers.

The importance of operational risk management ( operational risk management, ORM) will increase, and the legacy systems that many financial institutions still use, combined with underdeveloped data management practices, make it difficult to measure business risk. With the help of AI, this problem can be solved without a large-scale reorganization.

More and more people will understand the benefits of other distributed ledger technologies ( distributed ledger technologies, DLT), which will lead to their significant spread (not related to the growth of cryptocurrencies). The emergence of new types of blockchain or its crossover with other technologies, such as IoT, looks promising. In 2018, DLT will be increasingly used in international transfers, supply chain management systems, as well as for storing personal data for KYC (know your customer) and electronic identification.

Travel and virtual reality

Travel companies will continue to invest in personalization software(analyzes the user's personal preferences) to improve the user experience. Machine learning and artificial intelligence are also gaining momentum: companies see promise in automating simple business processes. Until recently, orders, wishes, and preferences of customers were collected manually and formed into a profile. Already, most large travel agencies and hotels automatically record this data when creating new offers. The next step is to predict customer wishes. For example, accommodation options at ski resorts shortly before the new season will be offered to those who have previously repeatedly booked hotels for a holiday in the mountains.

Speech technologies(voice activated technologies) in the travel industry may soon eclipse the popularity of mobile applications. Voice assistants installed on mobile devices in users' homes and offices may soon begin to play the role of travel agents. Booking tickets, hotels, transfers, viewing the weather forecast, studying restaurant reviews and building routes to attractions can be entrusted to the voice assistant. He will not only advise when is the best time to travel, select suitable tickets, the most convenient hotels with the maximum rating and location near places that may be of interest to you, but will also offer to book a table in a restaurant that your friends liked. Companies have already begun to turn futuristic visions into reality. So, Expedia is considering a partnership with Amazon Alexa to order bookings using voice commands.

Virtual reality(virtual reality, VR) and augmented reality(augmented reality, AR) will change online shopping for travelers. With the help of these technologies, the choice of a place to go or a hotel becomes clearer - you can see a real picture and evaluate the cleanliness of the beach or the level of comfort in the room. Marriott, Best Western, Kayak, Carlson Rezidor and Airbnb are already using these technologies when booking rooms, and in the coming year there will be a noticeable increase in the number of such companies. Virtual reality technology will become more accessible, and its capabilities as a new marketing promotion channel will be used more often.

Robots will be used to register check-ins at hotels, provide information support and entertainment for guests, and provide room service. Robotic Process Automation(RPA) will help businesses perform repetitive administrative tasks better and cheaper.

Telecom and IoT

Technologies 5G will enable the development and deployment of new types of digital services. Thanks to low signal latency and improved throughput, the technology will allow the integration of several types of IoT devices. This means that communication services for autonomous driving, augmented and virtual reality and tactile Internet (a new type of communication that transmits not only information, but also tactile sensations) will be developed. Korea Telecom (KT) will showcase a trial 5G mobile platform at the Winter Olympics. But the full implementation of the technology will not occur earlier than 2020 - telecommunications companies are only setting the time frame for the deployment of networks.

By 2020, 25 billion remote devices will be created. ( internet of things, IoT) will help connect 4.4 billion of them. Digital transformation will present new opportunities for the telecommunications industry, including the construction of platforms and applications for the transport, agriculture, healthcare, insurance and home sectors.

Blockchain in medicine

Hospitals and pharmaceutical companies will be sensitive to blockchain, using it to analyze patient data for research purposes. Currently, the data is stored on hospital servers and the patient has no ability to manage it. If this is transferred to the blockchain, the patient will be able to control access and, if he wants, sell his data to pharmaceutical companies. Accordingly, companies will have more access to information that is valuable to them.

The development of artificial intelligence (AI) technologies in telemedicine will accelerate as it spreads machine learning(ML) and natural language processing methods(natural language processing). This will provide customers with a personalized experience, help increase efficiency and reduce costs in the healthcare system. DataArt created a prototype application to demonstrate the capabilities of AI and ML in medicine. It is a telemedicine platform connected to IBM Watson, a voice-to-text program. Watson listens to the conversation between the patient and the doctor and records it in a log, which significantly reduces the doctor's documentation burden and allows him to focus on the patient. Another AI element reads the conversation log using natural language processing and integrates with a set of medical tools to identify the chief complaint, encode it in the electronic health system and support the doctor in choosing the necessary medical measures. It is unlikely that AI will replace doctors, but it will definitely expand their capabilities, reduce bureaucratic burden and minimize errors.

Retail and distribution

Retailers will continue digital transformation, driven by online sales from leading industry players. Thanks to the introduction of scalable Agile technologies, changes will occur both in the IT landscape of retailers and in their business models as a whole. Among these changes are omni-channel strategies, which are not yet well used even by large networks. The store as a shopping destination will transform into a store as a showroom: customers will try/test/touch products before deciding to buy online.

Business digitalization is blurring the physical and virtual worlds, transforming business projects, industries, markets and organizations. The ongoing evolution of business uses new technologies to integrate the physical and virtual worlds, creating entirely new business models. The future will be defined by smart devices that provide ever-increasing penetration of digital services into all aspects of life. Gartner calls the interaction of people, devices, content and services the “intelligent digital group” ( intelligent digital mesh ). This is enabled by the digitalization of business platforms that provide a rich, intelligent set of services to support businesses.

Gartner identifies 10 main technological trends that can be grouped into three groups - artificial intelligence (AI), digitalization, construction mesh -networks (see Fig. 1).

Drawing 1. Top 10 Strategic Technology Trends for 2018

"Intellectual trend" explores how AI is permeating virtually every existing technology and creating entirely new directions. AI will be a major focus for technology providers through 2022. The use of AI will contribute to the emergence of increasingly flexible autonomous systems.

  1. Using AI
  2. Intelligent Applications and Analytics
  3. Smart things

"Digital trend"focuses on blending the physical and digital worlds. Due to the fact that the flow of data generated by things is growing exponentially, computing power is shifting to the edges of networks to process this flow of information and only summary data is sent to central nodes. Digital trends, along with the opportunities provided by AI, are the drivers of a new stage in business digitalization and the creation of a digital business ecosystem.

  1. Digital models
  2. Edge Cloud Computing
  3. Dialogue systems
  4. Immersion technologies(Immersive Experience)

"Trend in building mesh networks" refers to using connections between an ever-increasing number of people and companies, as well as devices, content and services, to achieve digital business results. Mesh topology requires the use of new capabilities that will provide deep security and be able to respond to emerging events in these connections.

  1. Blockchain
  2. Event-driven model
  3. Continuous Adaptive Risk and Trust (CARTA)

IN this list areas of development that have not yet become widespread, but have significant industry impact, are presented. By 2022, technologies related to these trends will reach a sufficient level of maturity.

Trend 1: Using AI

Creating systems that learn, adapt, and potentially act autonomously will be a major focus until at least 2020. The ability to use AI to improve decision-making, create new business models and ecosystems will lead to wins in digital initiatives until 2025. The development of AI builds on numerous technologies that have evolved over the years. This leads to:

  • More and more advanced machine learning algorithms are used - supervised, unsupervised and reinforcement learning algorithms;
  • Huge amounts of data are available for machine learning;
  • to process large amounts of data and complex algorithms, hardware is used that provides virtually unlimited computing power.

At the same time, today's tasks involve the use of “narrow AI” - see Fig. 2.

Drawing 2. Narrow AI's Place in the Long History of AI

“Narrow AI” consists of high-level machine learning programs focused on solving specific problems (for example, understanding human language or driving a vehicle in a controlled environment). The algorithms used are optimized for a specific given task. All existing examples of real-life implementations or developments of AI are examples of “narrow AI”. On the other hand, General Artificial Intelligence (General AI) uses machine learning to solve a wide range of problems. Such AI systems, if they existed, would successfully perform any intelligent task that a human could perform, and constantly learn, just as humans do. Such systems will probably not be created, but interest in them continues.

AI technologies are developing rapidly. Successful use of these technologies requires significant investment. The lack of development in data science will likely make AI difficult to apply in the short term. By 2020, 30% of new projects will develop AI with joint teams of scientists and programmers.

Applications of AI lead to a number of intelligent implementations. These include both physical devices (such as robots, autonomous vehicles and consumer electronics) and applications and services (virtual personal assistants and intelligent advisors). These AI implementations will be positioned as a new class of explicitly intelligent applications and things. They will provide built-in intelligence into a wide range of interconnected devices, as well as into existing software and service solutions. To create such systems, a complex scientific base is used. This means that many organizations will use AI mainly in ready-made intelligent applications and things, including models as a service (MaaS).

Trend 2. Intelligent applications and analytics

Companies are using AI techniques to create new categories of systems, such as virtual customer assistants, VCA, as well as to improve traditional applications (such as performance analytics systems, sales and marketing analytics systems, and security systems). Intelligent applications will transform the nature of work and the structure of the workplace. When exploring how and where AI can be used, it makes sense to focus on three target domains:

  • Analytics: AI can be used to create more predictive or prescriptive analytics. AI is also used for advanced analytics;
  • Process: AI can drive smarter app actions. For example, you can use AI to intelligently match invoices or analyze email documents to improve service quality;
  • User Experience : Human language interaction used to generate VPA, facial recognition or other AI applications to understand user emotion, context or intent and predict needs.

Over the next few years, virtually every application and service will incorporate AI in some capacity. Some of these applications will become intelligent applications explicitly and cannot exist without AI and machine learning. Others will use AI without the user noticing.

VPAs such as Google Now, Microsoft Cortana, Apple Siri, Alice from Yandex, chatbots (for example, Facebook Messenger) are developing rapidly and can work with AI (for example, Wit.ai). Applications can create a new intelligent middle layer for interactions between people and systems. For example, in healthcare, online consultants equipped with AI can improve doctors' understanding of the problem, allowing them to provide more personalized treatments.

Advanced analytics will allow you to spend more time on research

Augmented analytics is a next generation strategic data and analytics paradigm influenced by AI - see Figure. 3. AI uses machine learning to automate the process of data preparation and preliminary preparation of information. Advanced analytics will allow specialists to focus on solving specialized problems. Users will spend less time preparing data and more time analyzing the most important ideas.

Drawing 3. Augmented Analytics for Citizen and Professional Data Scientists

Both small startups and large companies are now offering applications with advanced analytics capabilities using AI. By 2020, advanced analytics will become the dominant driver of data analytics systems, and automation of computer science tasks will enable lay scientists to produce more advanced analysis than specialized data scientists can today.

Trend 3. Smart things

Intelligent things are systems that move beyond hard-coded software models and use AI to enhance behavior, resulting in more natural interactions with the environment and people. AI is driving the development of new intelligent solutions such as self-driving vehicles, robots and drones, as well as providing enhanced capabilities for a variety of existing IoT-connected platforms, consumer and industrial systems (see Figure 4).

Drawing 4. Intelligent Things Span Many Sectors

Smart things are either semi- or fully autonomous. The word "autonomous" when used to describe intelligent systems, needs to be interpreted. Gartner defines "autonomous" as meaning freedom from external control or human influence. What is meant is that these smart things can function without supervision for a certain period of time to complete a given task. Intelligent things can have different levels of autonomy, as illustrated by the following examples:

  • Robot vacuum cleaners that have limited autonomy and limited intelligence;
  • Drones that can autonomously avoid obstacles in flight;
  • Unmanned aircraft, which can fly in buildings, including through windows and doors.

Autonomous drones and robots will undergo significant technical evolution based on machine learning models and algorithms. Advances in one area will be available to applications in other areas.

Using autonomous vehicles in controlled environments (e.g. agriculture, mining or warehousing) is a growing area of ​​interest in smart things. In industrial settings, vehicles can be fully autonomous. At the same time, by 2022, according to Gartner, semi-autonomous scenarios that require driver participation will dominate and such autonomous vehicles will be used on roads in limited, clearly defined controlled areas (for example, the use of unmanned taxis within the Skolkovo technology park).

AI will increasingly be implemented in everyday things - smart home appliances, smart speakers, hospital equipment. This phenomenon is closely related to the emergence of conversational platforms, the expansion of the IoT and the trend towards developing digital models.

Other markets will have similar potential to realize embedded intelligence. For example, a modern digital stethoscope can record and store the sounds of your pulse and breathing. Collecting and storing such data, linking this data with diagnostic and treatment information, and creating applications using AI will allow doctors to receive assistance in diagnosing patients in real time. However, in more complex scenarios, important issues such as patient confidentiality and regulatory restrictions must be taken into account. Gartner believes these non-technical challenges and the difficulty of creating highly specialized assistants will slow the adoption of AI in industrial IoT and other business scenarios. Organizations that can remove these barriers will have significant competitive advantages.

A swarm of smart things will work together

As the number of smart systems proliferates, Gartner expects a shift from autonomous smart things to swarms of smart things. With this implementation, multiple devices will work together, independently of people or with control by one person. For example, if a drone inspects fields and finds that some of them are ready for harvesting, it can send an “autonomous harvester” to the right place. In the logistics market, the most effective solution may be the use of unmanned vehicles to transport goods to transshipment warehouses. Robots and drones aboard these self-driving cars can then make the final delivery of the goods to the buyer. The military is working in this area and is exploring the possibility of using drone swarms to attack or protect military targets.

Trend 4. Digital models

A digital model is a digital representation of a real entity or system - Fig. 5.

CAD = computer-aided design; FEA = finite element analysis; ML = machine learning

Drawing 5. Digital Twins Are Digital Representations of Real-World Objects

The implementation of a digital model is a software module that reflects a unique physical object. Data from multiple digital models can be aggregated to produce a composite view of multiple real-world objects. The concept of digital representation of real objects or systems is not new. At the same time, as part of the latest developments:

  • the reliability of the models is ensured;
  • communication between digital models and the real world is ensured, potentially in real time;
  • big data and AI are used;
  • provides the ability to interact between models and evaluate “what if” scenarios.

Building digital models within IoT projects is of particular interest today. Well-designed digital asset models can greatly simplify and speed up decision making in enterprises. Models are related to their real-life counterparts and are used to understand the state of things or a system, respond to changes, and improve operations. Organizations will initially implement simple digital models. They will evolve these models, improving their ability to collect and visualize the right data, apply the right analytics, and apply different sets of rules. After 2027, the use of digital models will no longer be limited to process engineers and research scientists.

Digital models can improve data understanding and decision making, and will ultimately help develop new business scenarios. Their use will bring many benefits over a variety of time frames, including:

  • Short term: Digital models will be used in monitoring, optimizing and improving user experience, which is important in almost all industries. The transition from preventative to predictive maintenance is the most valuable use of digital models of systems and mechanisms. Customer benefits include reduced downtime and lower operating costs.
  • Medium term: organizations will use digital models to manage companies and improve operational efficiency. Digital models will be used to plan equipment maintenance periods and predict failures based on the data obtained about the state of the systems, which will allow equipment to be repaired at the right moments (predictively) to prevent its failure. Organizations will also use digital models to improve the development process, using them to simulate the behavior of new products based on understanding the digital model of previous implementations, taking into account their cost, environmental impact and performance.
  • Long term period: Digital models will drive innovation by providing information on how to use and improve products and services. New business models can focus on proactive advice. For example, automotive engineers can use digital models in conjunction with an analytics tool to analyze how a particular vehicle will drive to suggest new features to reduce accidents. Engineers will also be able to propose new solutions for car maintenance from the driver's point of view.

Digital models will be linked to other digital objects

Digital models integrate vast amounts of information about individual assets and groups, often providing control over them. As they develop, the models will “talk to each other”, for example, to create a “digital factory” model from many connected digital models of individual workshops, assembly lines, etc. Digital asset models will be linked to other digital objects for people (digital personas), processes (law enforcement) and spaces (digital cities). Understanding these connections, highlighting individual elements where necessary, and tracking interactions will be important to maintaining a secure digital environment.

While digital asset models in the IoT space are getting a lot of attention today, more complex real-world digital models are having a much greater impact. Digital models are built on the concept that virtual asset models co-exist and are linked to real assets - they are twins. However, this concept is not limited to assets (or things). The creation of digital analogues of real elements is developing in various directions. Like digital models, these digital analogues of objects are often created from metadata structures and models of things with little or no connection to the real objects.

Trend 5: Edge cloud computing

Edge computing describes a computing topology in which the collection, processing, and delivery of content are located closer to the sources and consumers of information. Edge computing is based on the concepts of mesh networks and distributed computing. In this concept, data is tried to be processed locally in order to reduce network traffic and latency in content delivery. In fact, the concept of edge computing has been around for many years. The “where to process data” pendulum has swung between a centralized approach (such as the mainframe or centralized cloud) and more decentralized approaches (such as PCs and mobile devices). Connectivity and latency issues, the bandwidth limitations of standard networking approaches, and the greater functionality inherent in edge computing concepts favor the deployment of distributed models. So far, this topology, applications and network architectures have not been widely used. Systems and network management platforms will need to be expanded to include the features of edge computing technologies. These technologies include thinning, data compression and protection, and local analytics. Edge computing solves many pressing problems, such as the high cost of a WAN network and unacceptable latency. The topology of edge computing will make it possible in the near future to unambiguously determine the features of digital business and IT solutions.

Edge Computing brings distributed computing to the cloud

Most experts view cloud and edge computing as competing networking approaches. The deployment of public clouds is seen as a significant savings, centralizing data processing points, including performing calculations that would be more optimally performed at the network edge. But this is a misunderstanding of both concepts. Cloud computing is a style of computing in which highly scalable technological capabilities are delivered as a service using Internet technologies. Cloud computing does not require centralization. Edge computing brings aspects of distributed computing to the cloud model. Cloud and edge computing need to be viewed as complementary rather than competing concepts - Fig. 6.

Drawing 6. Cloud and Edge Computing Are Complementary Concepts

Some cloud implementations already take an approach that distributes functionality to the edge of the network (for example, Microsoft Office 365 and AWS Greengrass). Gartner expects this approach to be used more frequently as cloud vendors move further into the IoT market and IoT system vendors use cloud building to more effective management with your decisions. While IoT is a strong driver for the cloud-to-edge approach, this trend will also benefit mobile devices or desktop PCs. Most likely, other solutions similar to Office 365 will appear.

Trend 6. Dialogue systems

Conversational systems will lead to a major new paradigm shift in how people interact with the digital world. The difficulty of translating the user's intent (definition of task) will move from human to computer. The system will receive a question or command from a person in ordinary language. The system will respond to the person by performing a function, providing content, or asking for additional data.

The conversational system provides a high-level design model and execution engine in which human-machine interaction occurs. As the term "conversational" suggests, interaction interfaces are implemented primarily in the user's spoken or written language. Over time, other interaction mechanisms will be added - vision, taste, smell, touch. The use of expanded sensory channels will support advanced capabilities such as detecting emotions through facial expression analysis or human health through odor analysis.

Over the next few years, conversational systems based on natural (verbal or written) language will become the primary target for user interaction. Gartner predicts that by 2019, 20% of user interactions with smartphones will be through a VPA (virtual personal assistant). A Gartner study found that a quarter of smartphone users already use VPA daily or weekly.

Conversational platforms are most recognizable in the following formats:

  • VPAs such as Amazon Alexa, Apple Siri, Google Assistant and Microsoft Cortana;
  • VCA (virtual compute appliance), such as IPsoft's Amelia, Watson Virtual Agent, Artificial Solutions, Interactions, Next IT and Nuance;
  • Chatbot frameworks such as Amazon Lex, API.AI from Google, IBM Watson Conversation and Microsoft Bot Framework.

Interaction in conversational systems is typically informal and bidirectional. The interaction can be a simple request or question (such as “what’s the weather outside?” or “what time is it?”) with a simple answer. Otherwise, it may be a structured interaction, such as that required to reserve a table at a restaurant or a hotel room. As technology advances, it will be possible to implement extremely complex queries, resulting in quite complex results. For example, a dialogue system will be able to collect oral testimony from witnesses to a crime, and based on them, create an image of a suspect.

Drawing 7. Conversational Platforms Include New User Experience Design Elements

Trend 7. Immersive Experience

While conversational platforms are changing the way people interact with the digital world, virtual reality (VR), augmented reality (AR) and mixed reality (MR) are changing the way people experience the digital world. This combined shift in perception and interaction models will result in the realization of compelling user experiences.

VR and AR are separate but related technologies. MR extends both approaches to more securely connect the physical world. The visual aspect of interaction is important, but there are also other interaction models such as sensory (haptic feedback) and audio (spatial sound). This is more true for MR, in which the user will be able to interact with digital and real objects while maintaining a presence in the physical world.

VR provides a 3D computer-generated environment that surrounds the user and responds naturally to human actions. This usually happens using a virtual reality helmet (head-mounted display, HMD), which occupies the user’s entire field of view. Gesture controllers, or miniature controllers, track hand and body positions, allowing for touch feedback. Stationary controllers provide a deeper sense of immersion in virtual reality, with the ability to organize a 3D image for several participants at once.

AR is the use of real-time information in the form of text, graphics, video and other virtual additions integrated with real-world objects. Augmented reality is implemented through the use of a virtual reality helmet or mobile device. The overlay of virtual world elements on a real world background distinguishes augmented reality (AR) from virtual reality (VR). AR aims to enhance users' interaction with the real physical environment, rather than separating them from it. This definition also applies to mixed reality (MR), which further combines elements of many types of immersive technologies.

The VR and AR market is young and fragmented. However, investments in this area are not decreasing. In 2016, 2.09 billion US dollars were allocated, in 2017 it was planned to increase by 3% to 2.16 billion dollars. Most investments are intended to develop core technologies, or technologies that enable technological leaps in a given area. In 2017, Apple introduced ARKit 15, and Google introduced ARCore. These virtual reality technology platforms are designed for companies' mobile computing devices, and they indicate significant long-term interest from market leaders. ARCore and ARKit, Google Cardboard and Daydream, Samsung Gear VR use the smartphone as a computing platform for VR and AR.

VR and AR can improve productivity

Interest in the technology is high, leading to numerous new applications for virtual reality. Many of them provide no real business value beyond providing additional entertainment such as video games and 360-degree spherical videos. For companies, this means the market is chaotic. AR and VR are often used as a novelty to interact with customers. Typically, augmented reality is implemented through a smartphone (like Pokémon Go). Sometimes this is an option to use a virtual reality headset (for example, Everest VR on the HTC Vive, which allows viewers to enjoy watching themselves practically climb Mount Everest). However, 40% of organizations using or using AR believe the technology exceeds their expectations.

Through 2021, consumer and business content, as well as virtual reality applications, will evolve rapidly. In 2018, the virtual reality market will reach 67.2 million devices. Until 2021, head-mounted display (HMD) technology will continue to improve significantly, but AR technology will be most widely adopted on mobile devices.

A further development is mixed reality - Fig. 8. It implements technology that streamlines the interface to be more consistent with how people interact with their world. MR uses virtual reality headsets, smartphones and tablets, smart mirrors, car windshield display systems and projectors. Mixed reality goes beyond using only visual information, it also uses audio, haptic and other sensory input/output channels. MR also includes beacons and sensors embedded in the environment around the user.

Drawing 8. The Future of the User Experience (UX)

VR and AR integration with various systems(mobile, wearable, IoT, multiple sensors, conversational platforms) will expand the capabilities of applications. Rooms and the surrounding space will begin to interact with things and work together with virtual worlds. Imagine a warehouse that can not only detect the presence of workers, but also help them understand the condition of equipment being serviced and visually show parts that need replacement. However, while the potential of VR and AR is exciting, many challenges remain for widespread adoption and use.

Trend 8. Blockchain

Blockchain has evolved from a digital currency infrastructure to a platform for digital transformation. Blockchain and other distributed database technologies provide trust in untrusted environments, eliminating the need for a single authority for authentication. This Gartner study uses the term "blockchain" as an umbrella term for all distributed database technologies. Blockchain technologies offer a radical departure from current centralized transactions and record-keeping mechanisms.

At its core, blockchain is a shared, distributed, decentralized, and tokenized database. Blockchain is a powerful tool for digital business and provides:

  • Eliminating the complexities of interaction in business and technology;
  • The ability to create your own asset and distribute it;
  • Create a managed trust model.

Blockchain is gaining popularity as it offers opportunities to transform the industry's operating model. Funding for blockchain projects continues to grow, and one interesting development is the use of initial coin offerings (ICOs) as a source of funding. The increased interest in blockchain was initially found in the financial industry. But blockchain has many potential implementations beyond financial services, including government applications, healthcare, manufacturing, logistics, content distribution, authentication and patent law.

A critical aspect of blockchain technology is the unregulated creation and transfer of funds, of which Bitcoin is an example. This opportunity is funding much of blockchain development, but it is a concern for government regulators and governments. Discussions about permissioned, permissionless, hybrid, and private ecosystems and the governance of these systems will lead to more robust analysis of distributed databases. Working solutions will emerge in 2021 as this analysis is completed.

Blockchain potentially offers significant long-term benefits despite its challenges

The main potential benefits of blockchain include:

  • Improved Cash Flow
  • Reduced transaction costs
  • Reduced estimated time
  • Origin of assets
  • Creating your own asset
  • New models of trust

Using an open blockchain can eliminate the need for trusted authentication authorities in transaction records and arbitration disputes. This is because trust is built into the model through immutable records in a distributed database. The potential of this technology to radically transform economic interactions should raise a number of important questions for society, governments and companies. There are no clear answers to these questions yet.

Blockchain faces other important issues that will prevent reliable, scalable solutions from being realized before 2022. Blockchain technologies and concepts are immature, poorly understood, and unproven for mission-critical business operations.

Trend 9. Event-Driven Model

Businesses are always aware of and ready to embrace new aspects of digital technology. This is central to the digitalization of business. Business events reflect the beginning of certain states or changes in states. Some business events or combinations of events represent business moments—identified situations that require specific business actions. The most important business issues have implications for multiple parties (for example, individual applications, lines of business, or partners).

Larger business events can be detected faster and analyzed in more detail using event brokers, IoT, cloud computing, blockchain, in-memory data management and AI. But technology alone cannot provide the full value of an event-driven model. This requires a change in culture and leadership: IT leaders, planners and architects must embrace “event thinking.” By 2020, 80% of digital business decisions will require real-time situational awareness. And 80% of new business ecosystems will need support for event processing.

Event-driven architecture is optimized for flexibility, fault tolerance, extensibility, lower cost of change, open design. To achieve user goals in conversational platforms, it is necessary to provide a dynamic, event-based approach. The user interface with conversational platforms becomes more intelligent, responding to the dynamic and changing user context and integrating various system elements. Data streams from IoT systems are event streams. Real-time decision making and situational awareness require constant monitoring and evaluation of events.

Events will become more important in the smart digital mesh network

Query-driven and event-driven application design models are complementary - Fig. 10. Both models are useful, depending on the business process being performed. The request-driven model, with its team-based and structured approach, provides greater confidence and control over interactions between services. This model is relatively rigid, with limited concurrency and dependency creation. The event-based approach is more flexible, supporting event streams and real-time scaling. But this requires the introduction of an intermediate layer, an event broker. Process designers, architects, and programmers should treat both approaches as equals. The event-driven model will gradually become the preferred approach due to its flexibility.

Drawing 10. Event-Driven and Request-Driven Application Design Models Are Complementary

Trend 10: Continuous Adaptive Risk and Trust (CARTA)

The intelligent digital mesh network and its associated digital technology platforms and application architectures are creating an increasingly complex world for security systems. The continued evolution of the “hacking industry” and its use of increasingly sophisticated tools, including the same advanced technologies that are available to “honest” companies, significantly increases the threat potential. Relying on perimeter protection based on static rules is no longer correct and outdated. This is especially important as organizations increasingly use mobile devices, cloud services and open APIs to build business ecosystems for customers and partners. IT leaders must focus on detecting and responding to threats, and use traditional measures such as blocking to prevent attacks and other abuses. At the same time, digital businesses will require greater access security when systems and information reside in a digital mesh network. Security and risk leaders must take a strategic approach based on continuous adaptive risk and trust assessment (CARTA). This is vital for secure access to digital business initiatives in a world of advanced targeted attacks and will enable real-time decision making based on risk assessment and the use of a trust model.

Barriers between security and application development teams need to be removed

As part of the CARTA approach, organizations must remove barriers between development and security teams. An analogy for this situation is how DevOps tools and processes bridge the gap between development and operations. Security teams cannot afford to wait until the end of the build and release process of an application to conduct detailed vulnerability scans. Security requirements should be clearly defined and easily integrated into development processes, not the other way around. Information security architects, together with DevOps, must integrate the testing procedure at the necessary points in the workflows. The organization of work should be transparent to developers, allow collaboration and flexibility in the development environment. This will lead to the DevSecOps model shown in Fig. 11.

All information security platforms must provide full functionality via APIs. In this way, processes can be integrated into the DevOps process and automated into the developer's preferred tool chain.

Conclusions

Artificial intelligence (AI) is delivering value to every industry by enabling the creation of new business models, supporting core verticals such as customer engagement, digital manufacturing, smart cities, self-driving cars, risk management, computer vision and speech recognition.

As people, places, processes and “things” become increasingly digital, they will be represented by digital models. This will provide fertile ground for new event-driven business processes, business models and digital ecosystems.

The way we interact with digital technologies will undergo a radical transformation over the next five to ten years. Conversational platforms, augmented reality, virtual reality and mixed reality will enable more natural and immersive interactions with the digital world.

Digital businesses are event-driven, which means they must constantly adapt to new challenges. The same applies to the security infrastructure and risk assessments that support it.