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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

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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

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altabel

Altabel Group

Professional Software Development

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www.altabel.com

Every web designer, design agency or somebody who works with websites must keep themselves up to date with the latest goings on in the world of web design, because the web is a unique environment which is constantly changing and evolving. Every year some new trends are born, some fade and some just continue to prosper.

We’re in a golden era of tools for designers with new products coming out every month. Innovative tools are popping up in every part of the workflow. From brand/asset management (Lingo and Bynder), to prototyping and collaboration (Marvel, Zeplin, InVision, Flinto, Justinmind), to website creation (Webydo, Blocs, Webflow), to tools for the amateur or marketer (Canva, Stencil, PicMonkey), and of course, to professional creation tools (Sketch, Affinity). And those are just the larger, more successful ones.


In this article we will take a look at new web design predictions for 2017, which hopefully will help you design better websites.

Let’s start!

There is no doubt that 2017 is definitely the year for super-rich gradient colors. Gradients and bright colors are already being implemented much more than in the past. We will also see many companies rebranded their own brand with bright bold colors. Instagram, Stripe and Asana are some good examples that already started.

We can all notice that today every young adult is an expert web user. And even the amateurs are acting like pros: using multiple tabs, and swiping to go back a page.

The result is that everything is faster. And we’ve all learned to become impatient. If you want to make a mild mannered person explode with annoyance, just make their Internet really slow for a minute.

Now websites are forced not just to become faster, but to become faster to understand. Designs which slow the user down have the same impact on their audience as these websites which don’t load at all.

Simpler designs are easier to scan, which means they’re faster to appreciate.

This is the biggest reason for the death of skeuomorphic design: users are more perceptive, less patient, and clutter only slows them down.

Apps put most websites to shame with super-minimal, beautiful interfaces. And they’re doing this because minimal interfaces perform better.

What about animation?

If you want to make a website look dated, cover it with animated “Under Construction” GIFs and Flash animation. But several things are coming together to make animation a rising star in modern web design.

Flat design can end up looking too consistent, boring even. Animation helps a website to stand out and to pack more information into less space.

Mobile apps have redefined what a user expects. Mobile apps use motion to convey meaning, and websites are just starting to do the same.

Typography trends emerge every year. Everyone is aware of the importance of typography in UX design. Much more than just arranging pretty fonts on a nice background, typography is an essential part of every design, it can make or break a whole project. It enhances your story, emotions you want to drive. It helps you to communicate the message to your users. This year we will see an increase in bold fonts.

If you haven’t dived into flexbox yet, you’re in for a treat. This relatively “new” CSS layout module offers both incredible responsive-friendliness in its functionality, but also makes a lot of sense to visual designers used to manipulating objects on the canvas with the align and distribute tools offered in the likes of Sketch and Illustrator.

Coming up hot on the heels of flexbox in the race for newer, better layout modules is CSS grid. While flexbox helps us solve some seriously aggravating and long-standing web design problems like vertical centering, it really wasn’t intended for use in full-page layouts. Grid, on the other hand, was built for full-page layouts. And like flexbox, it allows you to easily rearrange content order for different media queries.

There will be more focus on conversation. You might call 2016 the year of the bot. 2017’s going to see a lot more bots popping up across your life.

What this might mean, exactly, we’ll have to wait and see. But possible impacts include:

  • An even greater interest in “human” language (more good news for content strategists!)
  • Increased capacities for writers and content strategists to act as UX designers and bot developers
  • More conversational/natural-language forms
  • Attempts to transform the comment section from the internet’s sewer into fonts of “engagement” and new content — an effort already kicked off by the Coral Project

These are the main trends we believe will be trending for web design in 2017, but we want to hear from all of you! What are your predictions? Waiting for your comments.

 

Kate Kviatkovskaya

Kate Kviatkovskaya

Business Development Manager

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altabel

Altabel Group

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During the annual Health Information and Management Systems Society conference, IBM CEO Ginni Rometty declared that the era of cognitive computing in healthcare is upon us.

“It actually is an era that will play out in front of us, which is what we call the cognitive era,” Rometty said. “I hope to persuade you … that this idea of cognitive healthcare, systems that learn, that this is real and it’s mainstream and it is here and it can change almost everything about healthcare.”

The official IBM website says that IBM Watson Healthcare mission is to empower leaders, advocates and influencers in health through support that helps them achieve remarkable outcomes, accelerate discovery, make essential connections and gain confidence on their path to solving the world’s biggest health challenges.

Let’s look into what IBM Watson is and what exactly it will bring us.

IBM Watson is an advanced artificial intelligence program that is transforming healthcare into a quantifiable service where every bit of information is available and physicians only have to go through their personalized reports instead of reading through dozens of papers for every patient’s case.

Here are just some upgrades that IBM Watson will bring to healthcare.

Your doctor will be well-informed

At the moment one of the most significant challenges in healthcare is the huge amount of information available. Your doctor can not be aware of all the information that has been published recently. Watson however is able to search all the information, so doctors don’t have to spend hours and hours on reading and investigating.

It’s currently being used in genome analysis research at a hospital in the US where it found a third of patients were affected by information published in articles since their treatments began.

You’ll be recommended better treatments

If, for example, you’re diagnosed with cancer, you might benefit from the platform, Watson for Oncology. Usually the doctor meets with cancer patients and spends time reviewing their notes – which would be presented in paper format or in a list of emails. It turns out that A doctor’s decision will be made basing on his individual experience and the information available in front of him.

IBM Watson takes all those unstructured notes and restructures it in a way that the doctor can check easily, with treatment recommendations of which drug to give, which radiation or dosage.

You will be prescribed better medication

A very important aspect of IBM Watson is medication. Generally it takes about 12 years to produce a pill, but recent tests at the Baylor College of medicine in Houston, Texas, has reduced significant parts of the research process to weeks, months, and days. IBM Watson is able to accelerate the discovery of new treatment by streamlining research processes. As a patient, you will benefit from having more appropriate treatments available for you when you need it.

It’s clear that IBM Watson is already transforming healthcare, but much progress still lies ahead.

“We’re just at the beginning of something that will be very big and very transformative over the next 50 years,” said Watson Healthcare Executive Lead, Thomas Balkizas.

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

 

yana-khaidukova

Yana Khaidukova

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altabel

Altabel Group

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Digital health is dramatically reshaping and redefining how healthcare is delivered. And here are some new trends that we can observe now and which are expected to change the future of eHealth.
 
Distributed Healthcare

New technological aids has changed the relationship between patient and doctor. Patients can now google information about illnesses and treatments, read their digital patient journal online, learn of their doctor’s findings and take responsibility for their own care in a completely different way than in the past.

The use of digital and mobile IT solutions in healthcare means that care is no longer available only in a specific location. Nowadays, patients have the right to choose where they wish to be treated and, in the future, this will not only include choosing which hospital to visit, but also whether to hold their appointments via video link or to treat their depression using online therapy.
 
Smart Devices

Apps and mobile technology are already a natural part of our everyday life.
There is a number of eHealth applications now available and one of them is the digital diary which allows patients to record measurement data and appraisals or to note down their general physical and mental states during the day. As a next step they forward this information to their doctor.

Apps like this also give patients a simple means by which to take greater control over their own well-being, whether related to blood-sugar levels, blood pressure, or mood.
At the moment, healthcare do not use all the rich data that this type of smart device can provide. However, through projects such as the Swedish eHealth Agency’s Health for Me and other platforms that allow patients to collect their health data, an attempt is being made to both understand and find ways to utilize this digital “treasure” for the benefit of both patients and providers.
 
Interoperability

One major feature of eHealth is large IT systems. These are designed to suit a broad user base, however, which invariably makes it difficult for them to cater specifically to any one user. The future lies in creating smaller, customized systems that can communicate with one another through their interoperability. Custom-designed digital solutions entail opening up the market to small-scale actors and utilizing the entire ecosystem during development.
 
Big Data

Big Data has changed the way we manage, analyze and operate data in any industry. Healthcare is obviously one of the most promising areas where Big Data can be applied to make a change. In future perspective healthcare analytics can reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. Treatment delivery methods face new challenges today: average human lifespan is increasing together with the world population. Healthcare professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for best strategies to use these numbers.

Even if healthcare services is not something that exсites you, still you are a potential patient, and just like everyone of us you should be aware about new healthcare analytics applications and how they can help you.
 
Artificial Intelligence

Anytime a new technology enters healthcare, there are a number of challenges it faces. Common setbacks of artificial intelligence in healthcare include a lack of data exchange, regulatory compliance requirements and patient and provider adoption. AI has come across all of these issues, narrowing down the areas in which it can succeed.
The most popular use of artificial intelligence in healthcare is in IBM’s smart cloud, where Watson lives. The Watson platform has been used in a number of disciplines within healthcare including with payers, oncology and patient risk assessment.
 
To know more about the way IBM Watson works and its perspectives for the future please check out my new article “IBM Watson. Future is closer than you think” next week.

 

yana-khaidukova

Yana Khaidukova

Business Development Manager

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altabel

Altabel Group

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As The Internet of Things continues to grow, huge amount of data is going to be generated. How huge is the “huge”? Really huge. I do mean that.

Physical devices across the globe are consuming and creating data to drive a continuously connected world. David Booth, CEO at BackOffice Associates believes that currently we are at the tipping point of the Internet of Things. He says, “It was not a big leap for the industry to realize that an IoT global network of continuously connected devices would mean that data would not only be created at geometric rates, but that it would become one of the most valuable commodities in the world.”

Alongside the fact that year 2016 was declared to be the year of the first Zettabyte in internet traffic, Cisco report says the number will reach 2.3 ZB by 2020. Before long we will be transferring this much data annually.

If it does not say anything to you, imagine a byte equals 1 character of text – a zettabyte would cover War And Peace by Leo Tolstoy(which is about 1,250 pages) at least 325 trillion times. Or if 1 gigabyte can store 960 minutes of music – technically a zettabyte would be able to store just over 2 billion years of music. If that still isn’t illustrative enough, let’s measure in cups of coffee. Cisco states that if the 11oz coffee on your desk equals to one gigabyte, a zettabyte would have the same volume as the Great Wall of China. This amount of information is mind-blowing. Zettabyte transformed Big Data into enormously Big Data.
 

The Internet of Things (IoT) is expanding rapidly and relentlessly. And as IoT grows, so do the volumes of data it generates. Ignoring this fact is not an option, and companies will do so at their own peril and risk.

Though there are many new start-up companies storing, analyzing and integrating massive amounts of big data created from the IoT, not many of them have actually considered how the IoT can and will transform organization thinking by implementing data quality and information governance.

With so much data being created, companies must understand what they want to do with it, what are their data requirements and ensure that they have access to the right data. Unless a company can find a way to accumulate, manage and, most important, monetize their data storage, data hoarding can be a real issue for them. Put simply, while the value IoT brings is in the information it creates, innovation gold lies in the filtered data an organization has extracted from the intermediate layer between the devices and the cloud (so called “fog”).

Obviously, data provides powerful potential for boosting analytics efforts. And analyzing the amount of data that is going to be created by the Internet of Things requires new, advanced analytic techniques. The good news is, artificial intelligence and cognitive computing are maturing at a fast pace.

When used properly analytics can help organizations translate IoT’s digital data into knowledge that will contribute to developing new products, offerings, and business models. IoT can provide useful insights into the world outside company walls, and help strategists and decision-makers understand their customers, products, and markets more clearly. It can drive so much more — including opportunities to integrate and automate business processes in ways never imagined before.

Rowan Trollope, Senior Vice President and General Manager of Cisco’s Internet of Things (IoT) and Applications, told participants at the Cisco Live conference, “One of the biggest mistakes you could make now is to underestimate the Internet of Things. This is a life or death issue for most of our customers. They have seen what has happened with Uber and taxi companies and with Netflix and Blockbuster”.

The bottom line is that IoT and Big Data can either disrupt your business or help you become more competitive compared to other businesses that are about to be disrupted.

 

alexandra-presniatsova

Alexandra Presniatsova

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altabel

Altabel Group

Professional Software Development

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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
LI Profile: Victoria Sazonchik

 

altabel

Altabel Group

Professional Software Development

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

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