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Posts Tagged ‘ML

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 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.

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

Skype: juliaposhva
LI Profile: Yuliya Poshva



Altabel Group

Professional Software Development


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

Skype: kozik_anna
LI Profile: Anna Kozik



Altabel Group

Professional Software Development


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

Skype: yuliya_tolkach
LI Profile: Yuliya Tolkach



Altabel Group

Professional Software Development


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