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Posts Tagged ‘machine learning

Artificial Intelligence, or AI, is everywhere these days. From once being a futuristic concept in Hollywood movies, to now touching our daily lives. Artificial intelligence applications, projects and platforms are being developed in every part of the world today. More and more of them successfully escape lab life and strike mainstream trends, appearing in mass products, online tools and open-source APIs. The market for AI is ripe and research estimates put it at around $5 billion by 2020.

But did you know that artificial intelligence actually debuted in 1956? Some people believed robots and AI machines would be doing the work of humans by the mid-1970s. Of course, that didn’t happen. What happened instead was that funding dried up and a period called “The AI Winter” began. That ostensibly lasted into the 2000s, when IBM’s Watson showed a lot of interest in artificial intelligence again.

And now in 2017 you may find AI examples everywhere — in robotics, healthcare, business and everyday life, in the cloud and on your mobile device.
 

 
And one of the most promising areas for AI is in mobile. The growth of artificial intelligence is driving a whole new class of mobile app possibilities.
 
What makes mobile an ideal platform for AI?

AI has transformed how we interact with our smartphones. Thanks to the advances in the fields of Natural Language Processing, Deep Learning and Machine Learning, we have been able to make chat-bot interfaces, which are much more natural and convenient.

AI capabilities are being built into mobile apps of all kinds, making them contextually aware of user behavior and making each app session more valuable than the last, increasing overall retention rates. With the ability to quickly analyze massive amount of consumer behavior and data, mobile devices with artificial intelligence applications can recognize a person the way humans recognize other people — by individual characteristics.

It’s impossible to enumerate all of the applications we will see for mobile devices capable of performing sophisticated perceptual tasks involving vision, speech, or other sensory input. But they are likely to be found in every industry. Please find a few well-known examples below.

SIRI is one of the most famous AI applications. It’s personal assistant software for Apple devices, which works as an intelligent knowledge guide to recommend, answer questions and delegate requests to other connected web services.

GOOGLE NOW is another intelligent personal assistant that goes as a part of the Google app available for Android and iOS. The app allows Google to pull all the synced information from all Google services you use and your location history for making you recommendations and alerts in the form of different Google Now Cards: Activity summary, Boarding pass, Events, Flights, Location reminders, Parking location, TV and many other.

CORTANA is the Microsoft’s intelligent personal assistant initially designed for Windows Phone. Cortana software reacts to a user’s voice and accomplishes limited commands, answers questions using the information from the browser installed, works as a secretary by scheduling events, locating necessary files and opening the apps needed.

ALEXA is the voice service created by Amazon for Amazon Echo intelligent speaker. Alexa uses natural language processing algorithms to adapt to natural voice of the user. The more a user interacts with Alexa the more it evolves and gets smarter, delivering higher quality answers to a user’s questions.

KINECT is an AI-based motion controller and a motion sensing technology by Microsoft that is used in Xbox One and Xbox 360 game consoles. Kinect analyzes natural user interface and reacts to voice commands and gestures. Kinect technology for non-gaming purposes including healthcare, retail industry, military and robotics.

 
How Will Mobile AI Impact Businesses?

There are three ways AI can help your business: virtual assistance, insights generation and manual process automation.

Virtual assistance is something a small business can start using right away. You already use Siri on a daily basis. A virtual assistant can assist with customer service tasks like scheduling meetings or answering simple and repetitive customer questions.

AI can be helpful with generation of insights. We are collecting massive amounts of data on customers, but it is pointless if it is not in a usable form. AI can transform that data into practical insights and learn from it, allowing AI to adapt to market behavior changes.

Automation of manual process is taking place very much like the industrial revolution when machines replaced people. AI is using smart algorithms replacing routine and often time-consuming tasks such as compiling reports and researching topics.

Major players in the technology industry already proved the success of AI mobile apps. With new advancements in technology and shifting consumer demands, AI mobile app development is the next big thing for enterprises:

  • Bank of America, for instance, is currently developing Erica, a “virtual assistant” that can give financial advice based on a customer’s spending patterns through the bank’s app.
  • Facebook, for example, has integrated chat-bots into its Messenger app for seamless interactions for businesses.
  • Uber uses this technology to provide the best route to its driver by learning from previous trips along the same route taken by their drivers.
  • It’s also used by YouTube to recommend you similar music.
  • Retail giants such as eBay and Amazon use it for product recommendations.
  • Starbucks announced a new AI-powered mobile app called “My Starbucks Barista.” Users simply tell the app what they want, and it places the order for them.
  • Similarly, Taco Bell released the TacoBot, which doesn’t just take orders, but also recommends menu items and answers questions.

The benefits of AI technology across the enterprise are far from being fully realized, so it stands to reason that there’s huge interest in AI among businesses at the moment. By 2018 the world’s top 200 companies will be exploiting what they call “intelligent apps” — it’s only a matter of time.

And if you still think AI is out of your apps’ reach, consider that you might not be aware that you’re already using AI in your company.

Thanks for reading! If you have any questions or comments, you are welcome with them!

 

Victoria Sazonchik

Victoria Sazonchik

Business Development Manager

E-mail: victoria.sazonchik@altabel.com
Skype: victoria_sazonchik
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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

What is the hottest trend in artificial intelligence right now? Machine Learning is the right answer! Thanks to technological advances and emerging frameworks, Machine Learning may soon hit the mainstream. Because of new computing technologies, Machine Learning today is not like Machine Learning of the past. While many Machine Learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Every single day it’s become clear that Machine Learning is already forcing massive changes in the way companies operate. Every Fortune 500 company is already running more efficiently — and making more money — because of Machine Learning. But how this “phenomenon” helps business bring money and attract new and new customers?

Problems that can be easily solved using ML

Every single business some time or other can face with definite problems. But there are some kinds of business problems Machine Learning can prevent if not handle at all:

Email spam filters
Some spam filtering can be done by rules (IE: by overtly blocking IP addresses known explicitly for spam), but much of the filtering is contextual based on the inbox content relevant for each specific user. Lots of email volume and lots of user’s marking “spam” (labeling the data) makes for a good supervised learning problem.

Speech recognition
There is no single combination of sounds to specifically signal human speech, and individual pronunciations differ widely – Machine Learning can identify patterns of speech and help to convert speech to text. Nuance Communications (maker of Dragon Dictation) is among the better known speech recognition companies today.

Face detection
It’s incredibly difficult to write a set of “rules” to allow machines to detect faces (consider all the different skin colors, angles of view, hair / facial hair, etc), but an algorithm can be trained to detect faces, like those used at Facebook. Many tools for facial detection and recognition are open source.

Credit card purchase fraud detection
Like email spam filters, only a small portion of fraud detection can be done using concrete rules. New fraud methods are constantly being used, and systems must adapt to detect these patterns in real time, coaxing out the common signals associated with fraud.

Product / music / movie recommendation
Each person’s preferences are different, and preferences change over time. Companies like Amazon, Netflix and Spotify use ratings and engagement from a huge volume of items (products, songs, etc) to predict what any given user might want to buy, watch, or listen to next.

Here is enumerated not all but just a few problems that can be solved. And with the course of time this list will only expand.

Industries that already use ML in action

Most industries working with large amounts of data have recognized the value of Machine Learning technology. The adoption of Machine Learning is likely to be diverse and across a range of industries, including retail, automotive, financial services, health care, and etc. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors. In some cases, it will help transform the way companies interact with customers.

Retail industry
Machine Learning could completely reshape the retail customer experience. The improved ability to use facial recognition as a customer identification tool is being applied in new ways by companies such as Amazon at its Amazon Go stores or through its Alexa platform. Amazon Go removes the need for checkouts through the use of computer vision, sensor fusion, and deep or Machine Learning, and it’s expected that many shopping centers and retailers will start to explore similar options this year.

Financial services
Banks and other businesses in the financial industry use Machine Learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.

Health care
Machine Learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment. Machine Learning can be used to understand risk factors for disease in large populations. For instance, Medecision company developed an algorithm that is able to identify eight variables to predict avoidable hospitalizations in diabetes patients.

Oil and gas
Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective, and many others thing that you can do using ML. For example ExxonMobil, the largest publicly traded international oil and gas company, uses technology and innovation to help meet the world’s growing energy needs. Exxon Mobil’s Corporate Strategic Research (CSR) laboratory is a powerhouse in energy research focusing on fundamental science that can lead to technologies having a direct impact on solving our biggest energy challenges.

Government
Government agencies such as public safety and utilities have a particular need for Machine Learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine Learning can also help detect fraud and minimize identity theft. Chicago’s Department of Public Health is early adopter. It used to identify children with dangerous levels of lead in their bodies through blood tests and then cleanse their homes of lead paint. Now it tries to spot vulnerable youngsters before they are poisoned.

Marketing and sales
Websites recommending items you might like based on previous purchases are using Machine Learning to analyze your buying history – and promote other items you’d be interested in. This ability to capture data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail. PayPal, for example, is using Machine Learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

Transportation
Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of Machine Learning are important tools to delivery companies, public transportation and other transportation organizations. In some cases, mathematical models are used to optimize shipping routes. By honing in on excessive driving routes, drivers can see a reduction of nearly one mile of driving every day. For a company like UPS, a reduction of one mile per day per driver would equal a savings of as much as $50 million a year in fuel.

Have you ever worked with ML? Was it useful for your business? Or maybe you are still thinking about whether it costs to implement Machine Learning in your business? Will it be relevant and defensibly? If you have an answer on at least one question – share with me your experience. We will be happy to discuss it in comments. But if you don’t have an answer, always remember – Big companies are investing in Machine Learning not because it’s a fad or because it makes them seem cutting edge. They invest because they’ve seen positive ROI. And that’s why innovation will continue.

 

Yuliya Poshva

Business Development Manager

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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

Artificial Intelligence, Machine Learning are new buzzwords that are actively discussed in the tech world. Do you remember how our future was described in the movies some time ago: Terminator, Skynet, AI rules the world? General AI machines have remained in the movies and science fiction novels however narrow AI technologies are gradually evolving from the science fiction era to the reality and are already around us. Google uses Machine Learning to filter out spam messages from Gmail. Facebook trained computers to identify specific human faces nearly as accurately as humans do. Deep Learning is used by Netflix and Amazon to decide what you want to watch or buy next.

AI, machine learning, and deep learning are not quite the same thing but these terms are often used haphazardly and interchangeably, and that sometimes leads to some confusion. So let`s see what is the difference between each type of technology.
 
Artificial Intelligence (AI)

Artificial intelligence, which has been around since the 1950s, has seen ebbs and flows in popularity over the last 60+ years. But today, with the recent explosion of big data, high-powered parallel processing, and advanced neural algorithms, we are seeing a renaissance in AI—and companies from Amazon to Facebook to Google are scrambling to take the lead.

AI is the broadest way to think about advanced, computer intelligence. It can refer to anything from a computer program playing a game of chess, to a voice-recognition system like Amazon’s Alexa interpreting and responding to speech. The technology can broadly be categorized into three groups: Narrow AI (that is focused on one narrow task), artificial general intelligence or AGI (a machine with the ability to apply intelligence to any problem, rather than just one specific problem), and superintelligent AI (when its equal to humans or even surpasses them).

Pardoe believes that “we’ve just entered the “Fourth Industrial Revolution”, and while the adoption of AI has just started, the next few years will transform many sectors.
 
Machine learning

Machine learning is one subfield of AI. Or let`s say it`s the field of AI which today is showing the most promise at providing tools that industry and society can use to drive change. The core principle here is that machines take data and “learn” for themselves. Unlike hand-coding a software program with specific instructions to complete a task, ML allows a system to learn to recognize patterns on its own and make predictions.
 

 
Here are some of the popular machine learning methods:

-supervised learning: the “trainer” will present the computer with certain rules that connect an input (an object’s feature, like “smooth,” for example) with an output (the object itself, like a marble), and the algorithm learns by comparing its actual output with correct outputs to find errors. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.

-unsupervised learning: the computer is given inputs and is left alone to discover patterns. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns.

-reinforcement learning: the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker, for instance, the driverless car), the environment (everything the agent interacts with, for instance the road) and actions (what the agent can do).
 
Deep Learning

Deep learning is a brunch of Machine Learning, let`s see it as the cutting-edge of the cutting-edge. It uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making.

Deep Learning involves feeding a computer system with a lot of data, which it can use to make decisions about other data. This data is fed through neural networks. These networks are logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received.

Text-based searches, fraud detection, spam detection, handwriting recognition, image search, speech recognition, Street View detection, and translation are all tasks that can be performed through deep learning. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future.

The machine revolution has certainly started and the AI revolution is sure to pave the way for some significant changes in our lives. Machines will gradually improve, slowly replacing jobs that require repetitious behavior. But what happens when one day the machines become smarter than us?

 

Anna Kozik

Business Development Manager

E-mail: Anna.Kozik@altabel.com
Skype: kozik_anna
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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

As computers (and sensors) get smaller, smarter and connected, our everyday objects, from clothing to lavatories to cars, get more intelligent. By so doing embedded software is essential to the operation of today’s smart devices.
 

Embedded systems control many devices in common use today. Ninety-eight percent of all microprocessors are manufactured as components of embedded systems. Manufacturers ‘build in’ embedded software in the electronics of e.g. cars, telephones, modems, robots, appliances, toys, security systems, pacemakers, televisions and set-top boxes, and digital watches, for example.

Embedded systems are not always standalone devices. Many embedded systems consist of small parts within a larger device that serves a more general purpose.

 
Specifics of embedded development:

  • The development of embedded systems requires a good combination of industry knowledge, up-to-date technology expertise and excellent quality and project management skills.
  • Code is typically written in C or C++, but various high-level programming languages, such as Python, JavaScript and even the Go programming language, are now also in common use to target microcontrollers and embedded systems. However the complexity is not in the lines of code, most of the times, since embedded software is more focused towards controlling and managing the system (or hardware).
  • Programmers spend nearly all of their time using their embedded software development environment, which is an integrated collection of software development tools that manage the entire embedded software development process: analyzing, designing, documenting, writing, compiling, debugging, testing, optimizing, and verifying software. The choice of an embedded software development environment is the most important determinant of the productivity and effectiveness of programmers.
  • Today’s embedded systems development spans sensor, device, gateway, and cloud. This dramatically increases the complexity of development, troubleshooting, and fault isolation.
  • Unlike smartphones and personal computers, which sells in millions, most embedded products such as ECG machines, PoS machines, Laboratory and Test equipment, Ticket vending machines, etc. have low sales volume.
  • Furthermore, the product life of embedded devices ranges to 7+ years in contrast to the 15-18 months life for smartphones and to 4-6 years life for laptops. Due to this limited sales volume and long product life, custom or chip-based development of embedded devices adds significant overheads in terms of supply chain inefficiencies, platform obsolescence, non-optimal cost structure, and barriers to adopt latest technologies.

 
Embedded vs. application software development
 

Embedded software development

Application software development

Embedded software is physically part of a device, loaded by the manufacturer, and cannot be changed or removed by the user.

Application software is an optional program that the user chooses, installs and can remove.

It’s important to consider not only algorithm performance, but also the overall system robustness, reliability, and cost in the architecture and design. It’s closely associated with hardware manufacturing. You can’t write embedded software in your bedroom and unleash it on the world. Either you make a device yourself, or you work for someone who does.

Application software is similar and different. You can do it for yourself or for The Man, with the difference that no manufacturing is involved so there is much less capital outlay.

Embedded software however is often less visible, but no less complicated. Unlike application software, embedded software has fixed hardware requirements and capabilities, addition of third-party hardware or software is strictly controlled. To manage quality risk, as well as to meet tighter standards for software certification, embedded software engineers need to leverage software simulation tools and certified code generators.

Application software is usually less complex than embedded devices. It has more flexible requirements and solutions.

Embedded systems often reside in machines that are expected to run continuously for years without errors and in some cases recover by themselves if an error occurs. Unreliable mechanical moving parts such as disk drives, switches or buttons are avoided.

Therefore the application software for personal computers is usually developed and tested less scrupulously.

Embedded software may use no operating system, or when they do use, a wide variety of operating systems can be chosen from, typically a real-time operating system. This runs from small one-person operations consisting of a run loop and a timer, to LynxOS, VxWorks, BeRTOS, ThreadX, to Windows CE or Linux (with patched kernel).

Standard computers generally use operating systems such as OS X, Windows or GNU/Linux.

 

Hot trends for Embedded s/w development: Big Data, Internet of Things, Connected Cars and Homes

The amount of data that’s being created and stored on a global level is almost inconceivable, and it just keeps growing, yet only a small percentage of data is actually analyzed.

The importance of BD doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable cost and time reductions, new product development and optimized offerings, and smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:

  • Determining root causes of failures, issues and defects in near-real time.
  • Generating coupons at the point of sale based on the customer’s buying habits.
  • Recalculating entire risk portfolios in minutes.
  • Detecting fraudulent behavior before it affects your organization.

Big data affects organizations across practically every industry, from Banking, Education and Government to Health Care and Retail industry, etc.

The Internet of Things is yet another ubiquitous word in the world of embedded technologies. The core of IoT is the availability of the application or thing and its data to be a connectable ecosystem.

– For example, the Connected Home also known as the Smart Home, uses modern automation systems to provide a practical way of controlling electronic devices in the home. Connected Homes technology can include but is not limited to the scheduling and automatic operation of heating, security systems and lighting. This advanced technology allows these vital home functions to be controlled remotely from anywhere in the world using an internet connected device.

– The race to build the fully Connected Car, and ultimately the completely Autonomous vehicle, is also under way. Drivers around the world are getting used to the increasing amount of digital technology in their cars. Many of the normal features of the car such as monitors of performance data like speed, fuel efficiency, and gas tank levels; heating and air conditioning; and the audio system — all have been digitized in hopes of providing the driver with easier operation and better information. And the car, including smartphones and other devices carried onboard by drivers and passengers now reaches out to the surrounding world for music streamed from the cloud, real-time traffic information, and personalized roadside assistance. Recent innovations allow automobiles to monitor and adjust their position on the highway, alerting drivers if they are drifting out of their lane, and slowing down if they get too close to the car in front of them.

Naturally, smart homes, smart cars, and other connected products won’t just be aimed at home and private life. They’ll also have a major impact on business.

 
Conclusion

We’re just beginning to imagine the possibilities of embedded systems. Innovations in sensors, big data, and machine learning now make it possible for engineering teams to develop smarter and more autonomous systems that have the potential to dramatically improve designs and create new categories of products and services previously unimaginable.

Embedded software engineers develop embedded hardware and software solutions, custom-made for applications in various target markets. With capabilities that span the complete system and software lifecycle, Altabel Group is placed to manage entire projects from start to finish, working closely with customers to understand their needs and deliver excellent results. For more information on our work in the industry, please click here.

Thank you! And you’re always welcome with your questions.

 

Victoria Sazonchik

Victoria Sazonchik

Business Development Manager

E-mail: victoria.sazonchik@altabel.com
Skype: victoria_sazonchik
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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

 
Machine learning

A breakthrough in the technology of artificial intelligence and its active use in practice is the trend of the last two-three years. If earlier the creation of a high-quality machine translation system required a decade, now startups can offer consumers quite a competitive product in this area within one year.

Machine learning is a new approach to information processing, it turns the machine into an intelligent device very fast. In many ways, the development boom based on machine learning programs happened due to the fact that almost everything you need can be found among free software. It is enough to download the development environment, a number of libraries and read the manual. For a week or two, you can write, for example, a program recognizing wine labels or even individuals.

AI opened a completely new universe that humanity will explore for centuries. This means that robots are getting smarter and can learn independently. They are even capable of transmitting their knowledge to each other. To do this, of course, communication infrastructure is necessary. With its help, the program, which has recently invented a new universal language, could teach the other machines.

By the way, people did not expect artificial intelligence to create a new language, it was a by-product performed while teaching machines to translate from different languages. The program has learned how to translate from the languages it hadn’t been asked to by itself. Hence, the researchers concluded that a computer system uses meta-level language for communication, a new sort of Esperanto, a universal language.

 
Robots and VR
 

Analytical agencies called 2016 the year of virtual reality technologies. According to the Digi Capital forecast, by 2020 the virtual reality market will come up to $ 30 billion. Today we have every reason to believe that in 2017 VR-technology will finally become mass.

This trend has affected robotics as well. Complex machine control via VR-helmets and screens shows that augmented reality is gaining popularity. At MWC in Barcelona 2016, all visitors were offered to try themselves as excavator operators, controlling real excavators via Oculus Rift helmet.

This is one of the main scenarios of applying VR in industry and business, which will be used in a variety of situations: unmanned vehicles control (trailers, drones, trucks), surgical operations, exploring out of reach places (the ocean bottom, mines, permafrost). However, the automation trend of the last decade is increasing in order to completely avoid people’s participation in these processes.

 
Artificial Intelligence
 

The idea of intelligent robots has been exciting minds for a long time. We are used to different fiction anthropomorphic golems, androids, perfect voice assistants. Moreover, the success of HBO Westworld recent show demonstrates that the interest in artificial intelligence is rapidly increasing.

Meanwhile, the representatives of different professions were asked to imagine AI as a professional assistant at work or even in the role of a leader. Intelligent Apps have the potential to transform the workplace by making everyday tasks easier and its users more effective. The prospect of getting help from the robot frightens 25% of people, 40% are against the robot leader. However, the majority of people can easily imagine robots among their colleagues- 35% want to see a robot as a personal assistant. Every fourth looks positive on robots to take a leading position.

 
Internet of Things

The internet of Things has been labeled as “the next Industrial Revolution” because of the way it will change the way people live, work, have fun and travel, as well as how governments and businesses interact with the world.

Most of us are used to applications, which allow us to switch tracks on the audio system, to open our cars, turn on the lights, change the temperature in the room. According to Ericsson ConsumerLab research, two out of five people expect applications to remember users’ preferences and configure home appliances in the nearest future. It is as a good way to save personal time that can be spent on tasks that are more important.

 
Unmanned vehicles
 

They can either be remote controlled or remote guided, or they can be autonomous vehicles which are capable of exploring the environment and navigating on their own. With the right technology, multiple cars could “talk” to one another and reduce the chance for crashes.

Every fourth interviewee said he would feel safer if all the cars would be driven by robots. Meanwhile, 65% said they would prefer to have an autonomous vehicle rather than drive themselves.

Self-driven cars – futuristic, comfortable and safe. However, at the moment none of the existing systems can completely take over driving. Even the most sophisticated systems can fail.

 
Augmented reality
 

Approximately four out of five users believe that a complete blending of real and virtual worlds will happen just within three years. Half of the respondents are already interested in buying special gloves or shoes that would control VR-objects (for example, for playing virtual instruments).

A well-known game Pokemon GO is a good example to demonstrate the real potential of augmented reality. Many people want to use similar possibilities not only in the games but in real life as well. More than half of users would like to have AR-glasses to see better in the dark and, for example, to be able to observe criminals. One out of three would like to use augmented reality to get rid of unpleasant elements of their landscape, such as graffiti and litter. Many people dream of not seeing street signs, uninteresting shop windows and billboards.

 
Security Paradox of “smart” devices
 

More than half of the respondents use applications and trackers that transmit alarm and danger warnings. Using such apps people expect to increase their personal safety level. The paradox is that 60% of those who feel more secure with a smartphone admit that would try to avoid those situations while not having a phone in the pocket. People rely on their smartphones capabilities too much. Meanwhile, they won’t know what to do if they lose the device or the battery dies. Three out of five people, who believe that the smartphone makes their lives safer, are in a bigger danger.

 
Social fragmentation
 

For every third respondent social networks have become a main source of information. However, social networks do not connect people from all around the world, on the contrary, they form small groups and communities. There is a chance that this fragmentation will only increase: every week, every day individuals exclude each other from friends or refuse to accept connection requests based on the opinions of other people.
 
We all know that making predictions about the course of technology’s future is challenging. Surprises can appear in any direction. Now we can only imagine those amazing opportunities we are going to explore in the nearest future.

Feel free to share your thoughts about technology prospects for the near future in comments below!

 

Darya Bertosh

Darya Bertosh

Business Development Manager

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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

Technology is winning its everyday challenges at a pace faster than ever before. As compared to the previous year, tech trends have become embedded to practically every sphere of digital business. There is constant growth of software spending on technologies because technology is now rooted in every sphere of digital business. For entrepreneurs and self-starters it is necessary to leverage strategic technologies to reach target audiences next year.
 

What is to become mainstream in 2017?

AI & Advanced Machine Learning

Artificial intelligence (AI) and advanced machine learning (ML) are represented by many technologies and techniques such as deep learning, neural networks, natural-language processing. They have a potential to create more advanced systems that are able to adapt. Such systems will be able to change future behavior, leading to the creation of more intelligent devices and programs. But the trend is to develop ML and AI to autonomously operated systems in long-term perspective. These techniques are likely to be introduced into almost every sphere of digital business as inborn components within a decade.

Virtual & Augmented Reality

The world is now ready for augmented reality (AR) and virtual reality (VR) technology while early-stage devices are springing up in different spheres. Much work is done to transform interaction of human beings to the next level by moving them to immerse environment with the help of VR. It allows undergoing training in remote places or creating certain scenarios under pre-established criteria. As for AR, it can blend the real and virtual worlds, which has great potential for application in lots of businesses. It is estimated by market researchers that worldwide revenues for the AR/VR market will grow from $5.2 billion in 2016 to more than $162 billion in 2020. That is why many observers claim that the year 2017 to be a starting point (or at least a transition period) of AR/VR versions of practically every application to emerge.

Intelligent Things

Robots, drones and vehicles-these intelligent things have spread tremendously through the current year. But what potential do they have for the coming year? It is predicted by Gartner agency and other research firms that the apps that control IoT devices will also use machine learning and AI. This means that all the ordinary elements of environment, from toothbrush to your car, may become interconnected and collaborate to make decisions in everyday practice. Major advancements are yet to come. Experts claim that solutions to tie every app which controls intelligent things together into a single, seamless user experience are to be made in the year 2017.
 

Digital Twins

Next year is predicted to be the time when digital twin’s idea will spread to most remote parts of the world. It is a software replica of a physical thing or system which uses sensor and physics data. The sphere of application of a digital twin will widen with the time and by the year 2020 they will likely to be used for improving operations and creating new things.

Conversational systems

Intelligent objects are predicted to have some form of conversational interface in the near future. And the coming year, in particular, is likely to produce a device mesh when there will be a merge of different interaction techniques resulting in innovative digital user experience. It is now represented by a trend in app development which lets users interact with apps through texting. The next year is likely to provide such solutions to other intelligent objects which surround us in everyday life.

Mesh App and Service Architecture

MASA- or “Mesh App and Service Architecture” is considered to be an IT-system which enables communication, collaboration and learning within some digital ecosystem. Such architecture will hold together and interconnect different services to enable users gain experience through shifting across different sections (e.g., desktop, smartphone, vehicles).

Adaptive Security Architecture

There is much room for new smart devices for better learning and protecting. It is especially necessary in the vulnerable system of IoT which can be brought down by DDoS attacks. The idea behind adaptive security architecture lies in recruiting AI smart solutions within security tools. IoT is now becoming a special frontier for security specialists. Will 2017 become a year when new remediation tools and processes will be embedded into IoT intelligent devices? The answer is to be given soon.

These are some of major tech trends we’re in store for in 2017. They seem strategic and have lots of potential to grow to autonomous systems, like in case with AI and advanced machine learning. Some of the abovementioned trends are likely to take off next year; others will boost their presence in the digital business in several years. But even ordinary people will soon be able to experience the world where boundaries between real and digital blur.

What’s your idea of the tech trends for 2017? Please feel free to share your thought in the comments below.

 

Yuliya Tolkach

Yuliya Tolkach

Business Development Manager

E-mail: Yulia.Tolkach@altabel.com
Skype: yuliya_tolkach
LI Profile: Yuliya Tolkach

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com


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